Science topic

Data Science - Science topic

Data science combines the power of computer science and applications, modeling, statistics, engineering, economy and analytics. Whereas a traditional data analyst may look only at data from a single source - a single measurement result, data scientists will most likely explore and examine data from multiple disparate sources. According to IBM, "the data scientist will sift through all incoming data with the goal of discovering a previously hidden insight, which in turn can provide a competitive advantage or address a pressing business problem. A data scientist does not simply collect and report on data, but also looks at it from many angles, determines what it means, then recommends ways to apply the data." Data Science has grown importance with Big Data and will be used to extract value from the Cloud to business across domains.
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I have a B.Sc. Degree in Civil Engineering and M.Sc. Degree in Road and Transport Engineering and another M.Sc. Degree in Project Management Analysis and Evaluation. Since I am a Researcher and Lecturer in University, I have been doing researches focused on Pavement materials like concrete and and subgrade material stabilization, Driver behavior and risk related to construction project. Lately I became more interested in Sustainable infrastructure and environmental researches and I am planning to focus on getting a Ph.D. scholarship and for that I am learning the concept of Data Science, Machine Learning and AI with Python. Since the topic of sustainable infrastructure is the hottest topic now a days, I would be grateful if any one recommend me and assist me on topics focus on this specific area.
Thank you!
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A promising Ph.D. research title could be "AI-Driven Sustainable Infrastructure: Enhancing Resilience and Efficiency in Civil and Environmental Engineering."This study could explore how artificial intelligence, IoT, and data analytics can optimize sustainable infrastructure design, reduce carbon footprints, and improve resilience to climate change. Potential areas include smart water management systems, energy-efficient construction materials, and predictive maintenance for eco-friendly infrastructure. This research could combine digital technologies with sustainability principles to provide innovative solutions for greener, smarter, and more durable urban development.
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Modernizing civil engineering education involves incorporating new technologies, teaching methodologies, and industry practices to equip students with the necessary skills and knowledge to meet the challenges of the future.
Here are some key strategies to modernize civil engineering education:
  1. Update Curriculum: Regularly review and update the curriculum to include emerging technologies and trends in civil engineering. Introduce courses on topics like sustainable design, renewable energy, smart infrastructure, and digital construction.
  2. Incorporate Digital Tools: Integrate computer-aided design (CAD), Building Information Modeling (BIM), and other software tools into the curriculum to familiarize students with modern engineering workflows and industry standards.
  3. Hands-on Learning: Emphasize practical, hands-on experiences in addition to theoretical knowledge. Incorporate real-world projects and case studies to give students a taste of actual engineering challenges.
  4. Interdisciplinary Approach: Promote collaboration with other engineering disciplines and fields like architecture, environmental science, and data science. Encourage students to work in cross-functional teams to solve complex problems.
  5. Sustainability Focus: Highlight sustainable practices throughout the curriculum. Encourage students to think about environmental impact, life cycle assessments, and green infrastructure solutions.
  6. Industry Partnerships: Establish strong partnerships with industry professionals and companies. Invite guest speakers, organize workshops, and facilitate internships to expose students to the latest industry practices.
  7. Research and Innovation: Encourage faculty and students to engage in research and innovation. Support projects that address real-world challenges and have the potential for practical implementation.
  8. Online Learning: Utilize online platforms and digital resources to provide flexible learning options. This could include recorded lectures, virtual labs, and interactive simulations.
  9. Soft Skills Development: Emphasize the development of soft skills like communication, teamwork, leadership, and problem-solving, which are vital for success in the modern engineering workplace.
  10. Diversity and Inclusion: Foster an inclusive learning environment that welcomes individuals from diverse backgrounds, cultures, and perspectives. Encourage diversity in the engineering workforce.
  11. Ethics and Social Responsibility: Integrate ethical considerations and social responsibility principles into the curriculum, helping students understand the impact of engineering decisions on society and the environment.
  12. Continuing Education and Lifelong Learning: Encourage a culture of continuous learning among both students and faculty. Offer professional development opportunities for faculty to stay updated with the latest advancements.
  13. International Exposure: Promote international collaborations and exchange programs to expose students to global engineering challenges and diverse cultural perspectives.
  14. Entrepreneurship and Business Skills: Provide opportunities for students to learn about entrepreneurship and business aspects related to civil engineering projects, encouraging them to think beyond technical aspects.
By implementing these strategies, civil engineering education can better equip students with the skills and mindset required to tackle the challenges of a rapidly evolving world. It ensures that graduates are ready to make a positive impact on society and contribute to sustainable and innovative engineering practices.
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In other words, modernization in the education of civil engineering involves shifting institutions from traditional lecture-based teaching to interactive and problem-solving approaches, emulating on-site scenarios. Simulation tools, AR, and digital twins in understanding complex structure and material will make students understand better. Innovation will be encouraged with the multidisciplinary approach in combining civil engineering with environmental science, robotics, and geospatial technology. Equally, universities should encourage entrepreneurial thinking by calling on students to provide sustainable solutions for infrastructure and smart cities. In this way, by provoking adaptability and lifelong learning, the future civil engineers will be able to thrive in the constantly developing industry.
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Although the title of question asked seems weird. Its been a time I have stayed out of touch with research. I have been building on the basics of the above topics. I am looking for recent research topics to work or collaborate on any of the above topics.
Wishing you all a HAPPY NEW YEAR 2025.
Looking forward for the support and good wishes
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Thanks all for your kind suggestion and review
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Hi All,
I am actively seeking research assistant opportunities in molecular biology or bioinformatics. I recently completed my Master’s in Molecular Biology and Bioinformatics and have extensive experience analyzing NGS data and am proficient in Python, R, and Bash scripting. I'm keen on bioinformatics, data analysis, and data science opportunities where I can apply my skills. I'm open to both onsite and offsite opportunities. Any leads will be greatly appreciated. Thanks!
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A Research Assistant (RA) in Molecular Biology and Bioinformatics often plays a crucial role in conducting experiments, managing data, and supporting the research team. Here’s a breakdown of their typical responsibilities in these fields:
Roles in Molecular Biology
1. Laboratory Work
• Perform experiments like PCR, qPCR, cloning, gel electrophoresis, and Western blotting.
• Maintain and grow cell cultures, perform transfections, and prepare competent cells.
• Extract DNA, RNA, and proteins from various samples.
2. Experimental Design and Optimization
• Assist in designing experiments and troubleshooting protocols.
• Optimize reaction conditions (e.g., PCR, enzyme digestions) for improved efficiency.
3. Data Collection and Analysis
• Record experimental results accurately in lab notebooks or software.
• Use image analysis software (e.g., ImageJ) for Western blot or microscopy data.
4. Lab Maintenance
• Maintain lab stocks, order reagents, and ensure equipment is calibrated.
• Follow safety and compliance protocols (e.g., biosafety level requirements).
5. Documentation and Reporting
• Prepare reports and summaries for supervisors or publications.
• Participate in lab meetings and present findings.
Roles in Bioinformatics
1. Data Management
• Analyze genomic, transcriptomic, or proteomic data.
• Work with large datasets (e.g., RNA-Seq, ChIP-Seq) and databases (e.g., NCBI, Ensembl).
2. Programming and Tools
• Develop or use pipelines for data analysis (e.g., using Python, R, or Bash).
• Work with bioinformatics tools like BLAST, Clustal Omega, or Bioconductor.
3. Visualization
• Create visualizations of data using tools like ggplot2 (R) or Matplotlib (Python).
• Generate heatmaps, volcano plots, or phylogenetic trees.
4. Collaboration
• Work with biologists to interpret results and guide experiments.
• Assist in integrating experimental data with computational findings.
5. Documentation and Reporting
• Write detailed reports and summaries of computational analyses.
• Contribute to manuscripts or grant applications with bioinformatics insights.
Essential Skills
• Molecular Biology: Pipetting accuracy, protocol adherence, and troubleshooting.
• Bioinformatics: Coding skills (Python, R, Linux), data analysis, and visualization.
• Soft Skills: Communication, teamwork, and problem-solving.
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In what applications are AI and Big Data technologies, including Big Data Analytics and/or Data Science, combined?
In my opinion, AI and Big Data technologies are being combined in a number of areas where analysis of large data sets combined with intelligent algorithms allows for better results and automation of processes. One of the key applications is personalization of services and products, especially in the e-commerce and marketing sectors. By analyzing behavioral data and consumer preferences, AI systems can create personalized product recommendations, dynamic advertisements or tailored pricing strategies. The process is based on the analysis of huge datasets, which allow precise prediction of consumer behavior.
I described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:
OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT
I described the applications of Big Data technologies in sentiment analysis, business analytics and risk management in my co-authored article:
APPLICATION OF DATA BASE SYSTEMS BIG DATA AND BUSINESS INTELLIGENCE SOFTWARE IN INTEGRATED RISK MANAGEMENT IN ORGANIZATION
And what is your opinion on this topic?
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Best wishes,
Dariusz Prokopowicz
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My Opinion on AI + Big Data:
Totally agree with you, Dariusz! It's all about using HUGE amounts of data with smart AI to make things better and more automated. Personalization is a HUGE win – like, finally, ads that actually show me stuff I'm interested in!
Adding to Your Points:
You nailed it with e-commerce, but think about:
  • Doctors: AI looking at tons of medical info to give us better, faster diagnoses.
  • Finance: Catching fraudsters and giving better investment advice.
  • Self-Driving Cars: How cool is that? (also a little scary).
  • Factories: AI making sure everything runs smoothly and finds the odd wonky widget.
  • Cities: Using all that data to make traffic flow better, like magic!
Why it Works:
  • More data = smarter AI: Like giving a kid a HUGE book instead of a pamphlet.
  • AI does the boring stuff: So humans can focus on cool stuff.
  • Prediction Power!: AI can figure out what's gonna happen, which is amazing.
  • Personalization Explosion: Things get made JUST for you, and that's kinda awesome.
Your Articles:
I'm super interested to read what you wrote! AI and data are changing the world, and it's good to talk about the good and the not-so-good parts of it.
In a nutshell: I think AI and Big Data are like peanut butter and jelly – they're just better together! It's exciting (and maybe a little bit worrying) to see how it's all playing out.
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Hello, I am seeking opportunities to contribute as a co-author on research papers in the fields of data science and machine learning. If you are currently working on a relevant research topic, I would be delighted to collaborate and offer my expertise.
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yes! I am Looking for co-author
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I hope this message finds you well. I am reaching out to share a recent implementation of realtime image dataset creation using MATLAB, which I believe offers significant potential for practical applications in computer vision, machine learning, and data science. Our implementation focuses on capturing and processing images in real-time, which could be beneficial in areas such as object detection, surveillance, healthcare, and robotics.
Given the growing importance of real-time data processing in research and industry, I am exploring the possibility of publishing a research article that outlines the methodology, experimental setup, and potential applications of this implementation. Furthermore, I am actively seeking potential collaborators who may be interested in contributing to my extending this work.
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Key Tools and Functions:
  • Image Acquisition Toolbox: This toolbox allows you to capture images from various sources like cameras, video files, or image sequences. You can set up real-time image acquisition using functions like imaqhwinfo, videoinput, and imageAcquisition.
  • Image Processing Toolbox: This toolbox provides a rich set of functions for image processing tasks, including image filtering, noise reduction, feature extraction, and object detection. You can use these functions to preprocess and analyze the acquired images.
  • Deep Learning Toolbox: This toolbox provides tools for designing, training, and deploying deep learning models. You can use it to create custom deep learning models for image classification, object detection, and other tasks.
General Approach:
  1. Set up Image Acquisition:Use the Image Acquisition Toolbox to configure a camera or other image source. Define the image acquisition parameters, such as frame rate, image resolution, and color format. Start the image acquisition process.
  2. Real-Time Image Processing:Use functions from the Image Processing Toolbox to process the acquired images in real-time. This might involve tasks like noise reduction, image enhancement, feature extraction, or object detection. You can also apply deep learning models to analyze the images and extract relevant information.
  3. Data Storage and Labeling:Store the processed images and their corresponding labels in a suitable format. You can use MATLAB's data storage capabilities or external databases to store the data. Manually label the images or use automated labeling techniques, such as object detection algorithms.
Additional Considerations:
  • Performance: Consider the computational requirements of your image processing and deep learning tasks. You may need to optimize the code and hardware to achieve real-time performance.
  • Data Quality: Ensure that the acquired images are of good quality and that the labeling is accurate.
  • Data Privacy: If you are collecting and storing personal data, ensure that you comply with relevant data privacy regulations.
While there might not be a specific article on real-time image dataset creation using MATLAB, you can leverage the powerful tools and techniques provided by MATLAB to implement your own solution. By combining these tools and following the general approach outlined above, you can effectively create and manage real-time image datasets for various applications.
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In some cases, we need all the partial derivatives of a multi-variable function. If it is a scalar function (as usual), the collection of the first partial derivatives is called a Gradient. If it is a vector-valued multi-variable function, the collection of the first partial derivatives is called the Jacobian matrix.
In some other cases, we just need a partial derivative, just respect to one specific variable.
Here is where my problem starts:
In neural networks, the gradient of the loss function with respect to individual parameters (for example: ∂L/∂w11 where w11 represents the first weight​ of the first layer)
can theoretically be computed directly, using the chain rule without explicitly relying on Jacobians, In my opinion. By tracing the dependencies of a single weight through the network, it is possible to compute its gradient step by step. Because all the functions in the individual neurons, are scalar functions. Involving scalar relationships with individual parameters. Without the need to consider all the Linear Transformations across the layers.
An example chain rule representation for 1 layer network:
∂L/∂w11 = ∂L/∂a11 * ∂a/∂z11 * ∂z/∂w11 It can be applied to multiple-layer networks.
However, it is noted that Jacobians are necessary when propagating gradients through entire layers or networks because they compactly represent the relationship between inputs and outputs in vector-valued functions. But this requires all the partial derivatives, instead of one.
This raises a question: if it is possible to compute gradients directly for individual weights, why are Jacobians necessary in the chain rule of the backpropagation? Why do we need to compute all the partial derivatives at once?
I am waiting for your response. #DeepLearning #NeuralNetworks #MachineLearning #MachineLearningMathematics #DataScience #Mathematics
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While it is theoretically possible to compute the gradient for each weight separately without explicitly using the Jacobian, doing so would be inefficient and complex in practice, especially for large networks. The Jacobian matrix provides a powerful and efficient way to handle the complexity of deep learning models, enabling fast training and efficient gradient propagation across layers. This is why it is a key component of the backpropagation algorithm.
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Hi
I am looking to contribute in research/review papers in the field of AI/ML
If anyone out there is researching on any topic I am willing to contribute as co-author, I want to gain experience so that I can conduct my own research.
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Yashraj Dudhe I am interested
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How do you think artificial intelligence can affect medicine in real world. There are many science-fiction dreams in this regard!
but how about real-life in the next 2-3 decades!?
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AI could pose pandemic-scale biosecurity risks. Here’s how to make it safer
"Advances in artificial intelligence (AI) promise to transform biomedical research, but could pose significant biosafety and biosecurity risks, argue three public health researchers and two policy researchers. They urge governments and AI developers to work with safety and security experts to mitigate harms that could result in the greatest loss of life and disruption to society, such as outbreaks of transmissible pathogens. That means building a scientific consensus through processes that engage diverse, independent experts..."
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Publisher:
Emerald Publishing
Book Title:
Data Science for Decision Makers: Leveraging Business Analytics, Intelligence, and AI for Organizational Success
Editors:
· Dr. Miltiadis D. Lytras, The American College of Greece, Greece
· Dr. Lily Popova Zhuhadar, Western Kentucky University, USA
Book Description
As the digital landscape evolves, the integration of Business Analytics (BA), Business Intelligence (BI), and Artificial Intelligence (AI) is revolutionizing Decision-Making processes across industries. Data Science for Decision Makers serves as a comprehensive resource, exploring these fields' convergence to optimize organizational success. With the continuous advancements in AI and data science, this book is both timely and essential for business leaders, managers, and academics looking to harness these technologies for enhanced Decision-Making and strategic growth. This book combines theoretical insights with practical applications, addressing current and future challenges and providing actionable guidance. It aims to bridge the gap between advanced analytical theories and their applications in real-world business scenarios, featuring contributions from global experts and detailed case studies from various industries.
Book Sections and Chapter Topics
Section 1: Foundations of Business Analytics and Intelligence
· The evolution of business analytics and intelligence
· Key concepts and definitions in BA and BI
· Data management and governance
· Analytical methods and tools
· The role of descriptive, predictive, and prescriptive analytics
Section 2: Artificial Intelligence in Business
· Overview of AI technologies in business
· AI for data mining and pattern recognition
· Machine learning algorithms for predictive analytics
· Natural language processing for business intelligence
· AI-driven decision support systems
Section 3: Integrating AI with Business Analytics and Intelligence
· Strategic integration of AI in business systems
· Case studies on AI and BI synergies
· Overcoming challenges in AI adoption
· The impact of AI on business reporting and visualization
· Best practices for AI and BI integration
Section 4: Advanced Analytics Techniques
· Advanced statistical models for business analytics
· Deep learning applications in BI
· Sentiment analysis and consumer behavior
· Realtime analytics and streaming data
· Predictive and prescriptive analytics case studies
Section 5: Ethical, Legal, and Social Implications
· Data privacy and security in AI and BI
· Ethical considerations in data use
· Regulatory compliance and standards
· Social implications of AI in business
· Building trust and transparency in analytics
Section 6: Future Trends and Directions
· The future of AI in business analytics
· Emerging technologies and their potential impact
· Evolving business models driven by AI and analytics
· The role of AI in sustainable business practices
· Preparing for the next wave of digital transformation
Objectives of the Book
· Provide a deep understanding of AI’s role in transforming business analytics and intelligence.
· Present strategies for integrating AI to enhance Decision-Making and operational efficiency.
· Address ethical and regulatory considerations in data analytics.
· Serve as a practical guide for executives, data scientists, and academics in a data-driven economy.
Important Dates
· Chapter Proposal Submission Deadline: 25 November 2024
· Full Chapter Submission Deadline: 31 January 2025
· Revisions Due: 4 April 2025
· Submission to Publisher: 1 May 2025
· Anticipated Publication: Winter 2025
Target Audience
· Business Professionals and Executives: Seeking insights to improve Decision-Making.
· Data Scientists and Business Analysts: Expanding their toolkit with AI and analytics techniques.
· Academic Researchers and Educators: Using it as a resource for teaching and research.
· IT and MIS Professionals: Enhancing their understanding of BI systems and data management.
· Policy Makers and Regulatory Bodies: Understanding the social and regulatory impacts of AI and analytics.
Keywords
· Artificial Intelligence
· Business Analytics
· Business Intelligence
· Data Science
· Decision-Making
Submission Guidelines
We invite chapter proposals that align with the outlined sections and objectives. Proposals should include:
· Title
· Authors and affiliations
· Abstract (200-250 words)
· Keywords
Contact Information
Dr. Miltiadis D. Lytras: miltiadis.lytras@gmail.com
Dr. Lily Popova Zhuhadar: lily.popova.zhuhadar@wku.edu
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I’m interested in section 5
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We are excited to invite researchers and practitioners to submit their work to the upcoming Workshop on Combating Illicit Trade, organized by Working Group 4 of the EU COST Action GLITSS. This workshop will focus on leveraging data science, artificial intelligence (AI), machine learning, and blockchain to address the global challenge of illicit trade.
Scope:
Illicit trade spans a wide range of domains, from trafficking of historical artifacts, human and wildlife trafficking, to environmental crimes. In this workshop, we aim to:
  • Address challenges in collecting reliable datasets and developing robust performance measures.
  • Explore the use of advanced technologies such as remote sensing, deep learning, network analysis, and blockchain to combat illicit trade.
  • Foster collaboration across academia, industry, and policy to innovate and share methodologies for the detection and prevention of illicit trade.
Topics of Interest:
  • Machine Learning, Deep Learning, and Reinforcement Learning
  • Explainable AI and Computer Vision
  • Remote Sensing and Spatial Data Analysis
  • Pattern Recognition and Predictive Analytics
  • Illicit Trade: Human and Wildlife Trafficking, Artefacts, Cultural Property
  • Environmental and Endangered Species Crimes
  • Financial and Cyber Crimes
  • Drugs, Arms, and Counterfeits
  • Blockchain and Cryptography
Important Dates:
  • Paper Submission: November 15, 2024
  • Authors Notification: January 6, 2025
  • Camera Ready and Registration: January 22, 2025
This workshop offers a unique opportunity to contribute to the global fight against illicit trade using cutting-edge technologies. We encourage authors to submit their research and join us in advancing this important field.
For more details on submission guidelines and registration, please visit https://icpram.scitevents.org/DSAIB-IllicitTrade.aspx.
Looking forward to your submissions!
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I am very ineterested.
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The exponential development of quantum computing presents both enhanced opportunities and significant challenges in the field of cybersecurity. Quantum computing has the potential to revolutionize areas such as cryptography, data science, and artificial intelligence due to its ability to process information exponentially faster than classical computers. However, this power also introduces new vulnerabilities that could compromise the security of existing encryption methods.
Emerging Cybersecurity Threats from Quantum Computing:
  1. Breaking Classical Cryptographic Protocols: Classical cryptographic algorithms like RSA, Diffie-Hellman, and ECC (Elliptic Curve Cryptography) are foundational to modern cybersecurity, protecting everything from personal data to financial transactions. These methods rely on the complexity of certain mathematical problems (e.g., factoring large numbers or solving discrete logarithms), which are computationally difficult for classical computers to solve. However, Shor’s algorithm, a quantum algorithm, can solve these problems in polynomial time, making many classical encryption schemes vulnerable to decryption by sufficiently powerful quantum computers. This poses a serious threat to sensitive data stored or transmitted today.
  2. Quantum Key Distribution (QKD) Vulnerabilities: Quantum Key Distribution is a quantum encryption method that leverages the principles of quantum mechanics to securely exchange cryptographic keys. However, despite its potential, QKD is still in the experimental stage and faces scalability and technical challenges. A widespread, practical implementation could introduce new vulnerabilities, especially in the transmission of quantum keys over large-scale networks.
  3. Post-Quantum Cryptography Threats: Quantum computers may also disrupt the development and deployment of post-quantum cryptography (PQC) algorithms designed to be resistant to quantum attacks. As governments and organizations transition to quantum-safe encryption, the timeline for safe adoption may leave systems exposed to quantum-enabled attacks before quantum-resistant cryptographic systems are widely implemented.
Adapting Classical Encryption Techniques to Quantum Computing:
To mitigate the risks posed by quantum computing, there is a growing push towards developing and implementing quantum-resistant encryption methods. This includes adapting classical encryption techniques to maintain security in a quantum world.
  1. Post-Quantum Cryptography (PQC): PQC algorithms are being developed to be resistant to quantum computing’s ability to break traditional encryption schemes. These algorithms rely on problems that are believed to be hard for quantum computers to solve, such as: Lattice-based Lattice LatticeLattice-based cryptography: Uses the complexity of lattice problems to create encryption systems that are hard for quantum computers to break. Code-based cryptography: Utilizes error-correcting codes to form cryptographic systems that quantum computers are less likely to break. Hash-based cryptography: Uses cryptographic hash functions to create digital signatures that are resistant to quantum attacks. Multivariate polynomial cryptography: Relies on the difficulty of solving systems of multivariate polynomial equations over finite fields.
  2. Hybrid Encryption Models: A more immediate approach to secure systems in the quantum era is the use of hybrid encryption models that combine both classical and quantum-safe cryptographic methods. For example, an encrypted communication could use both RSA for the immediate security and a PQC algorithm for future-proofing, ensuring the data remains protected even after quantum computers become more powerful.
  3. Quantum-Safe Key Exchange Protocols: Traditional key exchange protocols, like Diffie-Hellman, need to be adapted to withstand quantum decryption capabilities. Researchers are investigating new key exchange mechanisms, such as lattice-based or code-based protocols, that can resist quantum algorithms. This would ensure secure key generation and distribution even in the presence of quantum threats.
  4. Quantum Cryptography and Quantum Key Distribution (QKD): As quantum computing advances, QKD techniques are being explored for their ability to provide theoretically unbreakable encryption. QKD relies on the principles of quantum mechanics, such as the no-cloning theorem and quantum superposition, to ensure secure key exchanges. However, practical, large-scale deployment is still in development, and integrating QKD into global systems will require overcoming significant technical and scalability challenges.
Conclusion:
The emergence of quantum computing is a transformative development in the field of technology, but it poses serious threats to traditional cybersecurity protocols. To safeguard sensitive data, researchers and industry experts are focusing on the development of quantum-resistant encryption algorithms, along with hybrid encryption systems that combine classical and post-quantum techniques. Adapting to the quantum era will require a collaborative, multi-disciplinary approach that spans cryptography, quantum physics, and cybersecurity. This research is crucial to preparing our global digital infrastructure for the future and ensuring that systems remain secure in the face of powerful quantum capabilities.
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The rise of quantum computing threatens current encryption, particularly public-key cryptography (e.g., RSA, ECC), which could be easily broken by quantum algorithms like Shor’s. Symmetric encryption, such as AES, is also affected, as quantum computers could reduce its security by half. To address this, **post-quantum cryptography (PQC)** is emerging, focusing on algorithms resistant to quantum attacks. Strategies include **larger key sizes** for symmetric encryption, **hybrid cryptosystems** that combine current and quantum-resistant algorithms, and **quantum key distribution (QKD)** for secure key sharing. Organizations and standards bodies are working on new protocols to secure systems as quantum computing develops.
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A postgraduate student in pedodontics in India aimed to evaluate the diagnostic accuracy of the Diagnodent device for detecting early caries in school children. With a sample of 100 children aged 6 to 12 years, the student conducted a study at a local school. Each child underwent a clinical examination followed by a Diagnodent assessment, which uses laser fluorescence to identify carious lesions.
Suggest statistical solution for the above scenario &
Appraise how this study underscores the potential of integrating data science methods in clinical settings, paving the way for evidence-based dental practices?
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Çürük lezyonları tespit etmek, lazer floresanın gücüne bağlı. Bir de lezyonların nasıl yerleştirildiğine bağlı. Buradan elde edilen veriler kaydedilir.
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How can distinguish the difference between data science and data analysis.
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Data science and data analysis are closely related but distinct fields:
*Data Analysis*
Focuses on:
1. Examining existing data to answer specific questions.
2. Identifying trends, patterns, and correlations.
3. Summarizing and visualizing data insights.
4. Informing business decisions or solving problems.
Typical tasks:
1. Data visualization
2. Statistical modeling
3. Hypothesis testing
4. Data mining
*Data Science*
Encompasses:
1. Extracting insights from complex, diverse data sources.
2. Developing predictive models and machine learning algorithms.
3. Designing and implementing data-driven solutions.
4. Integrating data insights into business strategy.
Typical tasks:
1. Data engineering
2. Machine learning
3. Natural Language Processing (NLP)
4. Deep learning
Key differences:
1. Scope: Data analysis focuses on specific questions, while data science explores broader business problems.
2. Complexity: Data science involves more complex data sources and advanced analytics techniques.
3. Goals: Data analysis informs decisions, while data science drives business innovation.
4. Skills: Data scientists require programming skills (e.g., Python, R), while data analysts may focus on statistical software (e.g., Excel, SPSS).
To illustrate the difference:
Data Analysis: "What were our sales last quarter?"
Data Science: "How can we predict future sales trends using customer behavior, market data, and weather ??
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A public health researcher conducted a longitudinal study to evaluate the effectiveness of three preventive dental procedures—topical fluoride application, pit and fissure sealants, and atraumatic restorative treatment (ART)—in reducing dental caries among school children. A sample of 60 students aged 6-12 years was randomly selected from three primary schools. Each child received the three treatments in a random order at different intervals over a 12-month period, with caries measurements taken at six time points: baseline, and at 1, 3, 6, 9, and 12 months post-treatment. The main goal was to assess the effectiveness of each procedure in preventing the progression of dental caries.
Suggest relevant statistical analysis for the above scenario with relevant justifications (Add online resources and citations from scientific portals)?
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This sounds like an exam question.
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Hi,
I am a new Master's student in Data Science, looking for a remote research assistant position or collaboration opportunities. I want to deepen my applied knowledge in data science and explore its applications in various fields.
I am familiar with quantitative trading and recommendation systems but am also eager to learn how data science is applied in areas such as climate change, environmental studies, and pandemics.
If there are any available opportunities, I would love to contribute and expand my expertise.
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Samira Akter Tumpa
Thanks a bunch! I really appreciate these tips! 😊
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Understanding Data in Machine Learning
In this video, I break down the essentials of data in machine learning—covering everything from data collection, cleaning, and preprocessing to its pivotal role in training accurate models. Whether you're a beginner or looking to strengthen your understanding of how data powers machine learning algorithms, this video will guide you through the core concepts!
Key Topics Covered:
Types of data used in machine learning
How to handle missing and inconsistent data
Data normalization and transformation techniques
Best practices for preparing data for model training
Perfect for anyone eager to dive deeper into AI and data science!
Don't forget to like, share, and subscribe for more AI-based insights!
#MachineLearning #DataScience #AI #DataPreparation #MLBasics #DataCleaning #DeepLearning #AIForBeginners #ML2024
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Dear Rahul Jain ,
Data Processing is the task of converting data from a given form to a much more usable and desired form i.e. making it more meaningful and informative. Using Machine Learning algorithms, mathematical modeling, and statistical knowledge, this entire process can be automated. The output of this complete process can be in any desired form like graphs, videos, charts, tables, images, and many more, depending on the task we are performing and the requirements of the machine. This might seem to be simple but when it comes to massive organizations like Twitter, Facebook, Administrative bodies like Parliament, UNESCO, and health sector organizations, this entire process needs to be performed in a very structured manner. So, the steps to perform are as follows:
Regards,
Shafagat
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I am currently in the process of selecting a topic for my dissertation in Data Science. Given the rapid advancements and the increasing number of studies in this field, I want to ensure that my research is both original and impactful.
I would greatly appreciate your insights on which topics or areas within Data Science you feel have been overdone or are generally met with fatigue by the academic community. Are there any specific themes, methods, or applications that you think should be avoided due to their oversaturation in recent dissertations?
Your guidance would be invaluable in helping me choose a research direction that is both fresh and relevant.
Thank you in advance for your assistance!
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In Data Science, topics like basic machine learning, generic deep learning applications, hyperparameter tuning, benchmarking on standard datasets, and overused themes like Big Data and sentiment analysis have become oversaturated. To avoid fatigue in the academic community, researchers should focus on emerging, interdisciplinary areas and develop novel methodologies for greater impact.
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I'm seeking co-authors for a research paper on enhancing malware detection using Generative Adversarial Networks (GANs). The paper aims to present innovative approaches to improving cybersecurity frameworks by leveraging GANs for synthetic data generation. We are targeting submission to a Scopus-indexed journal.
If you have expertise in cybersecurity, machine learning (especially GANs), or data science and are interested in contributing to this paper, please reach out to me.
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I am particular interestef in your research as it aligns with my current project on cyber security @Elshan Baghirov
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I'm currently seeking postdoctoral research opportunities in multidisciplinary areas within Computer Science, with an interest in both academic and industry settings. My research interests include advanced cloud-based data management for smart buildings, NLP for low-resource languages like Amharic, AI and machine learning, data science and big data, human-computer interaction, and robotics. I'm open to discussing potential opportunities and collaborations in these fields. Please feel free to contact me if you are aware of any suitable positions.
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Dear Dagim Sileshi Dill,
I would recommend the use of Artificial Intelligence in the Internet of Things as a postdoc research area in computer science with multidisciplinary applications.
For this purpose, I would analyze the use of Digital Twinning for the realization of various Intelligent Services.
See my presentation:
Here, Fig. 11 shows the most important areas of application of Digital Twins.
The article "Intelligent IoT - Replicating human cognition in the Internet of Things" can also help you:
Best regards and much success
Anatol Badach
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AI has started increasing Unemplyment in the market already. isnt it ?
AI is taking jobs of - POETS, Musical fretarnity, IT people, DATA SCience people and more to come.
please do write your views.
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Thank you , Wasswa Shafik , Geetha Baskaran , Konstantinos Karampelas , for your answers, that has added value to the discussion.
best regards
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Why specifically does Wolfram Matematica theorize reality is discrete in nature?
Maybe because reality is too unpredictable to be continuous. Plus, discrete data suggests either every entity is unique or simply too different for perfect predictions.
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You may find huge raw data for it at www.rawdatalibrary.net
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Hello All,
I am looking for researchers currently in academia who are interested in research in AI and machine learning applications for the telecom industry to collaborate and write research papers. I am a data scientist with 7 years of experience in the industry and with major telecom clients in the US. My research interests are Network Optimization, Network Operations, Data science for Telecom, Machine Learning, and AI.
Best,
Dileesh.
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The six papers in this special section address the application of artificial intelligence, machine learning, and data analytics at different layers and different applications of different types of communications networks. The objective of using these tools is the optimal design and improved operation of networks. These articles feature new opportunities to develop and advance various areas of communications through the use and applications of AI/ML/ deep learning technologies.
Regards,
Shafagat
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Please if anyone could help me find a proper research question, because I am in a pickle of finding one in my data science field, I have no idea what to go with my research question. please help.
Thank you.
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You could do a work about how to create neural networks with high acceptance rate. Mostly chatgpt is useless because it pretend to describe a bunch of data into one overfitted model. Instead create a neural network to detect food and show when (this is the important) it can make succesful decisions with an ordered database and a simple neural network. Most commercial neural networks fails on this process.
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Chalmers in his book: What is this thing called Science? mentions that Science is Knowledge obtained from information. The most important endeavors of science are : Prediction and Explanation of Phenomenon. The emergence of Big (massive) Data leads us to the field of Data Science (DS) with the main focus on prediction. Indeed, data belong to a specific field of knowledge or science (physics, economy, ....).
If DS is able to realize prediction for the field of sociology (for example), to whom the merit is given: Data Scientist or Sociologist?
10.1007/s11229-022-03933-2
#DataScience #ArtificialIntelligence #Naturallanguageprocessing #DeepLearning #Machinelearning #Science #Datamining
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Yes, data science is considered a science because it involves systematic methods, processes, and algorithms to extract knowledge and insights from structured and unstructured data, grounded in principles of statistics, mathematics, and computer science.
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Definitely yes
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My Awesomest Network, As You may know, I am during a process of continuous learning and upskilling. Now I attend Data Science course (SQL, Python et cetera) and I have to do some projects. Could You help me With them, please?
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yes, you may elaborate here or can mail me at 6shivam98@gmail.com , we can talk over those. At the same time if you need help regarding qualitative or quantitative analysis of data you can find a well define codes on my GitHub link to which can be found at my personal webpage , URL to which is given on my research gate profile.
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I'm currently running my Master's in Data Science and I'm at a point where in writing my research proposal. I will writing my dissertation on THE ROLE OF DATA SCIENCE AND DECISION-MAKING IN ACHIEVING A SUCCESSFUL PROJECT DELIVERY,
I really need any materials, support i can get in making this a success.
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Onipe Adabenege Yahaya
, thank you for this it will help a lot, I appreciate
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I'm currently in a research project on wavelet transform denoising. Due to lack of statistical knowledge, I'm not able to do research on thresholding method, so I'm curious if there are any other research directions(more prefer an engineering project), thank you for your answer.
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The most modern approaches in images' denoising are based on machine learning methodologies.
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Combining Data Science & Physics in BSc Physics syllabus is possible and beneficial. It will increase employment opportunities for Physics graduates, enhance their education and make the curriculum more innovative. As a result, enrollment in the BSc Physics course is likely to increase.
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  1. Introduction to Data Science:Content: Basics of data analysis, statistical methods, and machine learning. Skills: Programming in Python/R, using libraries like NumPy, pandas, matplotlib, scikit-learn.
  2. Computational Physics:Content: Numerical methods, simulations, and computational techniques used in physics. Skills: Programming for scientific computation, using tools like MATLAB, Python, or C++.
  3. Statistical Methods for Physicists:Content: Probability theory, statistical inference, hypothesis testing, regression analysis. Skills: Applying statistical methods to physical data, using statistical software.
  4. Machine Learning in Physics:Content: Machine learning algorithms, supervised and unsupervised learning, applications in physics. Skills: Implementing machine learning models, analyzing large datasets, applying ML to solve physics problems.
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I am currently exploring research opportunities in data science, C++ string manipulation, algorithm hybrid approaches, and healthcare-related machine learning. Are there any ongoing projects or research initiatives in these domains where my skills in data analysis using R and Python, coupled with expertise in algorithmic string manipulation, could be of value? Additionally, I am eager to contribute to collaborative efforts or co-authorship opportunities in these areas. If you have any relevant projects or suggestions, I would greatly appreciate your insights and potential for collaboration.
Thank you for your consideration.
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You can participate in biomedical DREAM challenges:
Create your own team, or ask to join one. Best performing teams will be invited as community authors for a scientific publication.
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Evaluation Metrics | L-01 | Basic Overview
Welcome to our playlist on "Evaluation Matrices in Machine Learning"! In this series, we dive deep into the key metrics used to assess the performance and effectiveness of machine learning models. Whether you're a beginner or an experienced data scientist, understanding these evaluation metrics is crucial for building robust and reliable ML systems.
📷 Check out our comprehensive guide to Evaluation Matrices in Machine Learning, covering topics such as:
Accuracy
Precision and Recall
F1 Score
Confusion Matrix
ROC Curve and AUC
MSE (Mean Squared Error)
RMSE (Root Mean Squared Error)
MAE (Mean Absolute Error)
Stay tuned as we explore each metric in detail, discussing their importance, calculation methods, and real-world applications. Whether you're working on classification, regression, or another ML task, these evaluation matrices are fundamental to measuring model performance accurately.
Don't forget to subscribe for more insightful content on machine learning and data science! 📷
#MachineLearning #DataScience #EvaluationMetrics #ModelPerformance #DataAnalysis #AI #MLAlgorithms #Precision #Recall #Accuracy
LinkedIn link for professional queries: https://www.linkedin.com/in/professorrahuljain/
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Watch Videos: Professor Rahul Jain Link: https://www.youtube.com/@professorrahuljain
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The neighborhood theory is devoted to description and solving follows problems:
- Embedding of graph systems or systems with quasi-distance in family of Euclidian spaces;
- Partition the system into intersecting subsystems upon the principle of proximity of thepoints;
- Optimal structurization of the system through the neighborhood criterion;- Strength of connection and mutual influence between the neighboring points;
- Internal and boundary points;
- Quasi-metric of neighborhood as minimal length of the broken line (geodesic) goingthrough the neighboring points;
- Curvature, difference (differential) operators, Voronoi regions, the neighboring sphericallayers, density of the geodesics;
- The Bayesian probabilistic model interpreting the a priori measure as a geometric spaceand the a posteriori one as a set of events in time;
- Dimension, volume and measure for the a priori geometric space;
- Entropy for the Bayesian probabilistic model as functional of the system;
- The problems of regression and classification;
- The local macroscopic region that define the neighborhood structure for the select pointwith acceptable accuracy;
- Distribution of density, number of the neighboring points and dimension;- Diffusion equation;
- Clustering problem on the basis of connectivity coefficient (internal clustering);
- Clustering problem on the basis of the extent to which the points are internal or boundary(external clustering);
- Parameterization of distances in the systems;
- The models of multisets and strings;
- Generative model;- Probability and time;
- The complex Markov chains and influence graph;
- Geometries on the systems with quasi-metric. (PDF) Neighborhood Theory. Available from: https://www.researchgate.net/publication/377731066_Neighborhood_Theory
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I have observed a notable trend wherein individuals from diverse fields are transitioning towards domains such as data science, data analytics, and machine learning (ML). Concurrently, there is a growing interest in exploring the synergies between these fields and ML to augment productivity. However, beyond mere application knowledge, there arises an important question: How can one effectively impart a deeper understanding of the conceptual frameworks and fundamental principles underlying these ML algorithms, while abstracting away from the technical details
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There are several effective ways to ensure clarity when discussing ML algorithms with non-technical individuals:
  1. Use analogies and metaphors to simplify complex concepts. For example, compare a neural network to the human brain, or a database to a library. Analogies can help illustrate the core ideas in an accessible way.
  2. Avoid technical jargon and acronyms. Instead, use plain language to explain the key elements, such as the data, the methods, and the outcomes. This makes the information more understandable for a non-technical audience.
  3. Focus on the problem being solved and the solution, rather than the technical details. Explain what business or research question the algorithm was trying to address, what data was used, and what the final results or impact were. This provides context and relevance.
  4. Use visual aids like graphs, images, and diagrams to complement the verbal explanations. The visual elements can help grab the audience's attention and clarify the concepts.
  5. Encourage questions and feedback from non-technical individuals. Engaging them in a collaborative dialogue can help ensure they understand the explanations.
  6. Be transparent about the limitations and potential biases of the algorithms. Acknowledge that ML models can operate as "black boxes" with limited explainability, and discuss steps taken to address this, such as external testing and oversight.
The key is to translate the technical details into plain language, use relatable examples, and focus on the practical applications and implications rather than the inner workings of the algorithms. With the right approach, the non-technical audience can gain a clearer understanding of how ML systems work and their real-world impacts. Israel Musa Israel Musa
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InfoScience Trends offers comprehensive scientific content that delves into various facets of information science research. This includes but is not limited to topics such as information retrieval, data management, information behavior ethics and policy, human-computer interaction, information visualization, information literacy, digital libraries, information technology, information systems, social informatics, data science, and more.
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yes
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PhD student
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Lutsenko E.V., Golovin N.S. The revolution of the beginning of the XXI century in artificial intelligence: deep mechanisms and prospects // February 2024, DOI: 10.13140/RG.2.2.17056.56321, License CC BY 4.0, https://www.researchgate.net/publication/378138050
Lutsenko E.V., Golovin N.S. Systems // April 2024, DOI: 10.13140/RG.2.2.22863.09123, License CC BY 4.0, https://www.researchgate.net/publication/379654902
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need a. Suggestion for best article related to management studies in data science
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An article is a written piece of content that provides information, analysis, or commentary on a specific topic. Articles can be found in various forms, including newspapers, magazines, journals, blogs, and academic publications. They typically present facts, opinions, or research findings in a structured format, often including sections such as introduction, methods, results, discussion, and conclusion.
As for a suggestion for the best article related to management studies in data science, it would depend on your specific interests and focus within this field. Here's a recommendation for a notable article that provides valuable insights into the intersection of management and data science:
Title: "Data Science and Its Relationship to Big Data and Data-Driven Decision Making" Authors: Foster Provost and Tom Fawcett Published in: Journal of Management Information Systems, 2013: Article Link
This article offers a comprehensive overview of the role of data science in informing management decisions, particularly in the context of big data analytics and data-driven decision-making processes. It discusses key concepts, methods, and challenges associated with leveraging data science techniques to extract insights, drive innovation, and enhance organizational performance.
Please follow me if it's helpful. All the very best. Regards, Safiul
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Can I have some good, relevant topics related to data science for my dissertation, please. The topic should yield good results.
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I would like it very much if someone would study further on my new regression method!
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For Msc Data Science Final Project
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There are two factors related to this question. 1. Is the research question topical, of importance, not previously investigated, shedding light on a new angle to a problem and hence relevant to society, researchers & politicians. 2. Who is funding the project? is there funding? is there adequate funding? Some examples are; Technology information overload, information overload and technology integration, data privacy problems, confidentiality, systems integration, functionality of systems, timelines, costs, artificial intelligence. These are all topical issues - there maybe factors to explore/ investigate. So probably an area where there are opportunities for jobs/ research. I think also in the future the jobs in this area will continue to increase / expand. Another example which desperately needs investigation and alteration is the church, lack of science and religiosity. A very urgent issue that must be addressed, for rules, laws & policies regarding their outdated practices & teachings of prayer for this, that & the other. A very difficult & challenging project in that they are set in concrete in their antiquated ways of working & no one seems to care or want to change them to integrate science with religion. A very urgent & topical issue that is in fact causing many people/countries much distress & upset, but if you do a project in that area, it will be important, vital & necessary, but I doubt it will get you a job. Noone wants to fund research like that, & that probably won't give you a lot of scope to move in the future unfortunately. Just two examples to ponder.
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how AI,ML and data science used in core industry?
what type of tools you should be equipped with?
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AI (Artificial Intelligence), ML (Machine Learning), and data science are extensively used in various core industries to enhance efficiency, make data-driven decisions, and unlock new opportunities. Here are examples of how these technologies are applied in different sectors and the types of tools commonly used:
  1. Healthcare:Applications: Diagnosis and treatment optimization, predictive analytics for patient outcomes. Tools: TensorFlow, PyTorch, Scikit-Learn for ML; Python, R for data analysis; IBM Watson Health for healthcare analytics.
  2. Finance:Applications: Fraud detection, risk management, algorithmic trading, customer service automation. Tools: SAS, H2O.ai, Alteryx for data preparation; Apache Kafka for streaming data; Jupyter Notebooks, Tableau for visualization.
  3. Manufacturing:Applications: Predictive maintenance, quality control, supply chain optimization. Tools: Azure Machine Learning, RapidMiner for ML; Apache Spark for big data processing; Power BI for visualization.
  4. Retail:Applications: Demand forecasting, personalized marketing, inventory management. Tools: BigML, AWS SageMaker for ML; Google Analytics, Adobe Analytics for data analysis; KNIME for workflow automation.
  5. Energy:Applications: Predictive maintenance of equipment, energy consumption optimization. Tools: MATLAB, Weka for ML; Apache Hadoop for distributed computing; Splunk for log analysis.
  6. Telecommunications:Applications: Network optimization, fraud detection, customer churn prediction. Tools: Orange, RapidMiner for ML; Apache Flink for stream processing; D3.js for data visualization.
  7. Agriculture:Applications: Crop yield prediction, pest detection, precision farming. Tools: R, Python for data analysis; TensorFlow, Keras for ML; ArcGIS for spatial analysis.
  8. Transportation:Applications: Route optimization, predictive maintenance for vehicles, traffic management. Tools: Caffe, TensorFlow for ML; Apache Kafka for real-time data streaming; Power BI, Tableau for visualization.
  9. Education:Applications: Personalized learning, student performance prediction, adaptive assessments. Tools: Moodle, Open edX for learning management; scikit-learn, TensorFlow for ML.
  10. Entertainment:Applications: Content recommendation, user behavior analysis, personalized gaming experiences. Tools: Mahout, Unity ML-Agents for gaming AI; Apache Flink for stream processing.
Common Tools for AI, ML, and Data Science:
  1. Programming Languages:Python (NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch) R
  2. Data Visualization:Tableau Power BI Matplotlib Seaborn
  3. Big Data Processing:Apache Spark Hadoop
  4. Machine Learning Platforms:TensorFlow PyTorch Scikit-Learn H2O.ai
  5. Data Analysis and Manipulation:Jupyter Notebooks RStudio Alteryx
  6. Database Systems:SQL (for structured data) MongoDB (for unstructured data)
  7. Cloud Platforms:AWS (Amazon Web Services) Azure (Microsoft) Google Cloud Platform
  8. Workflow Automation:KNIME Apache Airflow
  9. Text Processing and NLP:NLTK (Natural Language Toolkit) spaCy
  10. Version Control:Git
It's important to note that the specific tools used can vary based on the industry, the nature of the data, and the goals of the applications. Staying updated on the latest developments in AI, ML, and data science is crucial for professionals in these fields.
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Hello,
I have the following problem. I have made three measurements of the same event under the same measurement conditions.
Each measurement has a unique probability distribution. I have already calculated the mean and standard deviation for each measurement.
My goal is to combine my three measurements to get a general result of my experiment.
I know how to calculate the combined mean: (x_comb = (x1_mean+x2_mean+x3_mean)/3)
I don't know how to calculate the combined standard deviation.
Please let me know if you can help me. If you have any other questions, don't hesitate to ask me.
Thank you very much! :)
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What is the pooled standard deviation?
The pooled standard deviation is a method for estimating a single standard deviation to represent all independent samples or groups in your study when they are assumed to come from populations with a common standard deviation. The pooled standard deviation is the average spread of all data points about their group mean (not the overall mean). It is a weighted average of each group's standard deviation.
Attached is the formula.
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What aspects of working with data are the most time-consuming in your research activities?
  1. Data collection
  2. Data processing and cleaning
  3. Data analysis
  4. Data visualization
What functional capabilities would you like to see in an ideal data work platform?
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Yes, I don't mind, and I am interested in everything related to statistics because it is my specialty.
Glad to inform me of the details
Thank You.
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Colleagues, good day!
We would like to reach out to you for assistance in verifying the results we have obtained.
We employ our own method for performing deduplication, clustering, and data matching tasks. This method allows us to obtain a numerical value of the similarity between text excerpts (including data table rows) without the need for model training. Based on this similarity score, we can determine whether records match or not, and perform deduplication and clustering accordingly.
This is a direct-action algorithm, relatively fast and resource-efficient, requiring no specific configuration (it is versatile). It can be used for quickly assessing previously unexplored data or in environments where data formats change rapidly (but not the core data content), and retraining models is too costly. It can serve as the foundation for creating personalized desktop data processing systems on consumer-grade computers.
We would like to evaluate the quality of this algorithm in quantitative terms, but we cannot find widely accepted methods for such an assessment. Additionally, we lack well-annotated datasets for evaluating the quality of matching.
If anyone is willing and able to contribute to the development of this topic, please step forward.
Sincerely, The KnoDL Team
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Dear teammates,
I am high experienced in clustering by optimization algorithms such as genetic algorithm, SA, particle swarm optimization algorithm and etc. So, I think I'm skilled to join your group. Please let me know if think so.
Thank you
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I am working with a time series dataset using the `fable` package (R). I have fitted several models (e.g., ARIMA) and generated forecasts. The accuracy calculation is resulting in NaN values, and the warning suggests incomplete out-of-sample data.
I am seeking guidance on how to handle this incomplete out-of-sample data issue and successfully calculate accuracy metrics for my time series forecasts. If anyone has encountered a similar problem or has expertise in time series analysis with R, your insights would be greatly appreciated.
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Dear Sachin. You should review every step in such a computation. For all those fcst evaluation criteria to produce NaN's, you should have NaN's generated in the fcst errors, which is really basic stuff.
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Graph Labeling, Graph Data Science, applications
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Graph labeling is a versatile process that adapts to the specific characteristics and goals of the graph data science task at hand. It helps transform raw graph data into a format suitable for various analytical and predictive tasks.
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How can the development of artificial intelligence technologies and applications help the development of science, the conduct of scientific research, the processing of results obtained from scientific research?
In recent discussions on the ongoing rapid development of artificial intelligence technologies, including generative artificial intelligence and general artificial intelligence, and their rapidly growing applications, a number of both positive determinants of this development are emerging but also a number of potential risks and threats are being identified. Recently, the key risks associated with the development of artificial intelligence technologies include not only the possibility of using AI technologies by cyber criminals and in hacking activities; the use of open-access tools based on generative artificial intelligence on the Internet to create crafted texts, photos, graphics and videos and their posting on social media sites to create fake news and generate disinformation; the use of "creations" created with applications based on intelligent chatbots in the field of marketing communications; the potential threat to many jobs being replaced by AI technology but also in the development of increasingly superior generative artificial intelligence technology, which may soon be creating new, even more superior AI technologies that could escape human control. Currently, all leading technology and Internet companies are developing their intelligent chatbots and AI-based tools, including generative AI and/or general AI, which they are already making available on the Internet or will soon do so. In this way, a kind of technological arms race is currently being realized between major technology companies at the forefront of ICT, Internet and Industry 4.0/5.0 information technologies. The technological progress that is currently taking place is accelerating as part of the transition from Industry 4.0 to Industry 5.0 technologies. In the context of the emerging threats mentioned above, many companies, enterprises, banks are already implementing and developing certain tools, applications based on AI in order to increase the efficiency of certain processes carried out within the framework of their business, logistics, financial activities, etc. In addition, in the ongoing discussions on the possibility of applying AI technologies in aspects interpreted positively, in solving various problems of the current development of civilization, including to support ongoing scientific research, to support the development of science in various disciplines of science. Accordingly, an important area of positive applications of AI technology is the use of this technology to improve the efficiency of reliably and ethically conducted scientific research. Thus, the development of science could be supported by the implementation of AI technology into the realm of science.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
How can the development of artificial intelligence technologies and applications help the development of science, the conduct of scientific research, the processing of results obtained from scientific research?
How can the development of artificial intelligence help the development of science and scientific research?
And what is your opinion on this topic?
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Best regards,
Dariusz Prokopowicz
The above text is entirely my own work written by me on the basis of my research. In writing this text I did not use other sources or automatic text generation systems.
Copyright by Dariusz Prokopowicz
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The current AI helps to retrieve the best results from the currently available human knowledge.
in the future, AI will create knowledge from data collected using instruments like LC-MSMS and images of ultrasound and CT.
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What are the possibilities of applying AI-based tools, including ChatGPT and other AI applications in the field of predictive analytics in the context of forecasting economic processes, trends, phenomena?
The ongoing technological advances in ICT and Industry 4.0/5.0, including Big Data Analytics, Data Science, cloud computing, generative artificial intelligence, Internet of Things, multi-criteria simulation models, digital twins, Blockchain, etc., make it possible to carry out advanced data processing on increasingly large volumes of data and information. The aforementioned technologies contribute to the improvement of analytical processes concerning the operation of business entities, including, among others, in the field of Business Intelligence, economic analysis as well as in the field of predictive analytics in the context of forecasting processes, trends, economic phenomena. In connection with the dynamic development of generative artificial intelligence technology over the past few quarters and the simultaneous successive increase in the computing power of constantly improved microprocessors, the possibilities of improving predictive analytics in the context of forecasting economic processes may also grow.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
What are the possibilities of applying AI-based tools, including ChatGPT and other AI applications for predictive analytics in the context of forecasting economic processes, trends, phenomena?
What are the possibilities of applying AI-based tools in the field of predictive analytics in the context of forecasting economic processes?
And what is your opinion on this topic?
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Best regards,
Dariusz Prokopowicz
The above text is entirely my own work written by me on the basis of my research.
In writing this text I did not use other sources or automatic text generation systems.
Copyright by Dariusz Prokopowicz
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Artificial Intelligence (AI) has revolutionized numerous industries, and its potential in the field of predictive analytics for forecasting economic processes is immense. AI-based tools, including ChatGPT and other AI applications, have the capability to transform the way we predict economic trends, phenomena, and processes.
One of the possibilities of applying AI-based tools in predictive analytics is their ability to analyze vast amounts of data quickly and efficiently. Traditional methods often struggle with handling large datasets, leading to delayed insights and inaccurate predictions. However, AI algorithms can process massive amounts of information within seconds, enabling economists to make more informed decisions based on real-time data.
Furthermore, AI-based tools can identify patterns and correlations that are not easily recognizable by humans. By analyzing historical economic data alongside various external factors such as social media sentiment or global events, these tools can uncover hidden relationships that contribute to accurate forecasts. This level of analysis provides invaluable insights for policymakers, businesses, and investors alike.
Another possibility lies in the ability of AI-based tools to continuously learn and adapt. As they process more data over time, these algorithms become smarter and more accurate in predicting economic trends. This iterative learning process ensures that forecasts remain up-to-date and relevant even in rapidly changing economic landscapes.
Moreover, implementing AI-based predictive analytics can significantly reduce human bias in forecasting economic processes. Human judgment is often influenced by personal beliefs or emotions which can lead to biased predictions. However, AI algorithms are driven purely by data-driven analysis without any subjective biases.
In conclusion, the possibilities of applying AI-based tools for predictive analytics in forecasting economic processes are vast. These technologies offer unparalleled speed in processing large datasets while uncovering hidden patterns that humans may overlook. Additionally, their continuous learning capabilities ensure accurate predictions even amidst dynamic environments. By embracing these advancements in technology assertively today, we can unlock a future where our understanding of economics is enhanced through precise forecasting techniques powered by artificial intelligence.
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Good morning everyone! I've just finished reading Shyon Baumann's paper on "Intellectualization and Art World Development: Film in the United States." This excellent paper includes a substantial section of textual analysis where various film reviews are examined. These reviews are considered a fundamental space for the artistic legitimation of films, which, during the 1960s, increasingly gained artistic value. To achieve this, Baumann focuses on two dimensions: critical devices and lexical enrichment. The paper is a bit dated, and the methodologies used can be traced back to a time when text analysis tools were not as widespread or advanced. On the other hand, they are not as advanced yet. The question is: are you aware of literature/methodologies that could provide insights to extend Baumann's work using modern text analysis technologies?
In particular, following the dimensions analyzed by Baumann:
a) CHANGING LANGUAGE
  • Techniques for the formation of artistic dictionaries that can replace the manual construction of dictionaries for artistic vocabulary (Baumann reviews a series of artistic writings and extracts terms, which are then searched in film reviews). Is it possible to do this automatically?
b) CHANGING CRITICAL DEVICES
  1. Positive and negative commentary -> I believe tools capable of performing sentiment analysis can be successfully applied to this dimension. Are you aware of any similar work?
  2. Director is named -> forming a giant dictionary of directors might work. But what about the rest of the crew who worked on the film? Is there a way to automate the collection of information on people involved in films?
  3. Comparison of directors -> Once point 2, which is more feasible, is done, how to recognize when specific individuals are being discussed? Does any tool exist?
  4. Comparison of films -> Similar to point 3.
  5. Film is interpreted -> How to understand when a film is being interpreted? What dimensions of the text could provide information in this regard? The problem is similar for all the following dimensions:
  6. Merit in failure
  7. Art vs. entertainment
  8. Too easy to enjoy
Expanding methods in the direction of automation would allow observing changes in larger samples of textual sources, deepening our understanding of certain historical events. The data could go more in-depth, providing a significant advantage for those who want to view certain artistic phenomena in the context of collective action.
Thank you in advance!
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I appreciate you raising this insightful question on how to leverage modern text analysis methods to build on Baumann's foundational work examining artistic legitimation. Automated techniques can certainly help scale such textual analysis to larger corpora. However, care must be taken to ensure computational approaches do not lose the nuance of manual qualitative interpretation.
Regarding building artistic dictionaries, word embedding models like word2vec can help uncover semantic relationships and suggest terms related to a seed vocabulary. However, human validation is still important before applying these dictionaries to make inferences.
For sentiment analysis, deep learning approaches like BERT have shown promise, but domain-specific tuning and qualitative checks are key to account for the complex expressions in artistic reviews. Models pre-trained on social media may not transfer well.
To identify creators, named entity recognition using dictionaries, rules, and ML approaches can help. However disambiguation remains challenging, so human-in-the-loop verification is recommended before making claims about individuals.
Overall, I believe the best approach is applying computational methods as a starting point, but having experts qualitatively analyze a sample of results to catch subtleties these tools may miss. If used prudently and in collaboration with scholars like yourself, text mining can uncover exciting new insights at scale. Please feel free to reach out to discuss further.
Wishing you the very best,
#textanalysis #digitalhumanities #mixedmethods
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The topic of my master's thesis is "The use of Big Data and Data Science technologies to assess the investment attractiveness of companies." I plan to design and implement a machine for market analysis, using graphs. I will be grateful to you for links to scientific articles on this topic.
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I don't have direct access to external databases or the internet to provide specific sources. However, I can guide you on how to find reliable sources for your master's dissertation:
  1. Academic Databases:Use databases like PubMed, IEEE Xplore, ScienceDirect, JSTOR, and Google Scholar to search for academic articles and papers related to your topic.
  2. University Library:Explore your university's library resources, both online and offline. Librarians can help you access journals, books, and other materials.
  3. Citations in Existing Literature:Examine the reference lists of relevant articles and books to discover other works related to your research. This can lead you to valuable sources.
  4. Contact Experts:Reach out to professors, researchers, or professionals in your field of study. They may suggest key publications or provide insights into recent research.
  5. Online Repositories:Check repositories such as arXiv.org, ResearchGate, and institutional repositories for preprints, theses, and open-access publications.
  6. Government Publications:Look for reports and publications from government agencies, as they often provide valuable data and research findings.
  7. Professional Organizations:Explore publications from relevant professional organizations and associations related to your field.
  8. Conferences and Proceedings:Review conference proceedings in your field, as they often contain the latest research. Websites like IEEE Conference Proceedings and ACM Digital Library are good starting points.
  9. Books:Search for books related to your topic through online bookstores, your university library, or platforms like Google Books.
  10. Theses and Dissertations:Explore the theses and dissertations database of your university or other institutions. This can provide in-depth studies related to your research.
Remember to critically evaluate each source for relevance, reliability, and academic rigor. Additionally, check the specific requirements of your institution or department for citation styles and guidelines for including sources in your dissertation.
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One of the most essential foundations of artificial intelligence and several other fields is linear algebra. For the first time, the new version of Sheldon Axler's essential book Linear Algebra Done Right, one of the most trusted linear algebra textbooks, has been made accessible to everyone for free:
Have fun reading it.
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Yes, it is one of the best textbooks for understanding concepts of algebra mostly used for computer science as well as almost all other applied sciences.
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I have a netCDF4 (.nc) file having ocean SST data, with coordinates (lat, lon, time). I want to predict and plot maps for the future. How can I do this using python?
Please recommend a python code for time series forecasting based on this approach.
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This is not a programming question. It's a time-series question. Imagine measuring temperature in your back yard every hour for a day. You could use that to make predictions, but they might not be very useful, because weather changes from day to day. So, you need more than a day. Maybe you measure for 3 days. That would be better. But maybe those were 3 warm days, which are followed by 3 cool days. Etc.
Depending on your background, you might start by reading books on time-series analysis. Then move on to books about ocean physics. And then climate physics. You will soon see that statistical prediction is a weak approach, and that dynamical models are required. That takes you from the domain of reading and plotting with python to the domain of building PhD-level scientific and computing skills. The latter go way beyond plotting with python; you'll need to deploy supercomputers to run models that were took many person-decades to develop and take person-years to learn to run. Oh, and the end result will be a model prediction that will not agree with other model predictions to within the error bars we want for climate prediction.
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I have a monthly netCDF4 file containing chlorophyll-a values, and I aim to forecast these values using time series analysis.
My approach involves computing monthly spatial averages for this entire region and then forecasting these averages. Is this methodology valid?
Additionally, could you recommend a Python code for time series forecasting based on this approach?
Is it feasible to predict values for individual grid points without considering spatial averaging?
My study area encompasses an oceanic region of approximately 45,000 sq km near the southern coast of Sri Lanka.
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Forecasting the quantity of chlorophyll-a based on time series (historical data) is not considered scientifically reliable due to the variable nature of chlorophyll-a, as it serves as the sole reference source for predicting the past behavior of the study area. Instead, it is recommended to estimate the amount of chlorophyll-a based on the variability of SST and SSS. To initiate this process, it is appropriate to utilize the simple mlp artificial network algorithm, which can be tailored to suit your specific requirements. Please refer to this article for further information: https://doi.org/10.1016/j.jenvman.2022.115636
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I am currently studying in Nepal
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Canada: Canadian universities, particularly those in Toronto, Vancouver, and Montreal, offer strong data science programs. The University of Toronto, University of British Columbia, and McGill University are well-regarded for their data science courses. Canada also has welcoming immigration policies for international students. When choosing a country for your master's degree in data science, consider factors like program quality, costs, language of instruction, opportunities for internships and networking, and potential for post-graduation employment in your field of interest.
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I have an exciting opportunity for you to contribute your expertise and help with the case study as part of my doctoral research. 🔬 About the Research: I'm on a mission to uncover innovative approaches for enhancing cybersecurity through the power of machine learning, with a case study in a format of a quantitative survey. I'm looking for any specialist and expert with background in Cybersecurity, Machine Learning and Data Science. I understand your time is precious, which is why the case study is designed to be concise, requiring just a short 10-15 minute commitment. You can contribute by clicking on the following link: https://forms.gle/HWhH7dvJEpBU3rMTA
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Hi Divya,
You may refer to the following references for more insights on the topic:
Good luck!
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I am conducting a research project involving the use of the MACD (Moving Average Convergence Divergence) signal indicator for analyzing multivariate time series data, possibly for trading purposes.
I've defined some initial parameters such as ema_short_period, ema_long_period, and signal_period. However, I'm interested in insights and best practices for parameter selection in such analyses.
I used these values to calculate and implement this indicator.
ema_short_period = 12
ema_long_period = 26
signal_period = 9
What parameters should I consider when dealing with multivariate data, and how can I optimize these parameters for my specific analysis goals?
Additionally, if anyone has experience with using the MACD in multivariate time series analysis, I'd appreciate any advice or insights you can provide.
I'm implementing this using python.
Thank you!
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Selecting optimal parameters for MACD in multivariate time series analysis, especially in financial trading algorithms, is crucial to designing effective trading strategies. Here are some steps and considerations:
1. Understand the MACD Parameters:
  • ema_short_period: Short-term Exponential Moving Average.
  • ema_long_period: Long-term Exponential Moving Average.
  • signal_period: Signal line, which is an EMA of the MACD values.
2. Parameter Optimization Strategies:
  • Grid Search: Systematically work through multiple combinations of parameter tunes, cross-validating as it goes to determine which tune gives the best performance.
  • Random Search: Try random combinations of parameters and keep track of the best ones.
3. Cross-Validation:
  • Ensure to implement cross-validation during parameter tuning to avoid overfitting. Time series cross-validation is a reliable method.
4. Objective Function:
  • Define a clear objective function. It could be maximizing the strategy returns, the Sharpe ratio, or another relevant metric. Your optimization procedure should aim to optimize this function.
5. Be Aware of Overfitting:
  • The risk of overfitting is particularly high in trading algorithms. A strategy that is too finely tuned to past data may not perform well in the future.
6. Trading Considerations:
  • Risk Management: Ensure that your parameters and strategies are in line with acceptable risk levels.
  • Transaction Costs: Always account for transaction costs in your strategy.
7. Consider Multivariate Aspects:
  • Correlation: Ensure to check how the different variables in the multivariate time series are correlated with each other.
  • Cointegration: If using for pairs trading or similar strategies, testing for cointegration between the pairs might be useful.
8. Backtesting:
  • It’s vital to backtest your strategy with the chosen parameters on out-of-sample data to ensure its robustness.
9. Sensitivity Analysis:
  • Conduct sensitivity analysis for your parameters to ensure that your strategy is robust and not overly sensitive to the parameter choices.
10. Technology and Tools:
Since you’re using Python, utilize libraries like pandas for data manipulation, matplotlib and seaborn for data visualization, and statsmodels for statistical models and tests.
Python Implementation:
For the MACD calculation and visualization:
{
import pandas as pd
import matplotlib.pyplot as plt
def compute_macd(data, short_window, long_window, signal_window):
short_ema = data['Close'].ewm(span=short_window, adjust=False).mean()
long_ema = data['Close'].ewm(span=long_window, adjust=False).mean()
data['MACD'] = short_ema - long_ema
data['Signal_Line'] = data['MACD'].ewm(span=signal_window, adjust=False).mean()
return data
# Example
# data = pd.read_csv("your_data.csv")
# data = compute_macd(data, 12, 26, 9)
# plt.plot(data['MACD'], label='MACD')
# plt.plot(data['Signal_Line'], label='Signal Line')
# plt.legend(loc='upper left')
}
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Let's find the most essential and reliable no-code data science tools to speed up the elaboration of the research results. Thanks to Avi Chawla (source: LinkedIn post), I have some suggestions for you here. Let us know your tips.
Gigasheet
  • Browser-based no-code tool to analyze data at scale
  • Use AI to conduct data analysis
  • It's like a combination of Excel + Pandas with no scale limitations
  • Analyze up to 1B rows
Mito
  • Create a spreadsheet interface in Jupyter Notebook
  • Yse Mito AI to conduct data analysis
  • Automatically generates Python code for each analysis
PivotTableJS
  • Create Pivot tables, aggregations, and charts using drag-and-drop
  • Add heatmaps to tables
  • Works within Jupyter notebook
Drawdata
  • Draw any 2D scatter dataset by dragging the mouse
  • Export the data as DataFrame, CSV, or JSON
  • Create a histogram and line plot by dragging the mouse
PyGWalker
  • Open a tableau-style interface in Jupyter notebook
  • Analyze a DataFrame as you would in Tableau
Visual Python
  • A GUI-based Python code generator
  • Import libraries, perform data I/O, create plots, and write code for ML models by clicking buttons
Tensorflow Playground
  • Provides an elegant UI to build, train, and visualize neural networks
  • Browser-based tool
  • Change data, model architecture, hyperparameters, etc. by clicking buttons
ydata-profiling
  • Generate a standardized EDA report for your dataset
  • Works in a Jupyter notebook
  • Covers info about missing values, data statistics, correlation, and data interactions
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I can certainly tell you about some popular no-code data science tools that people often use:
1. **Tableau**: It's known for its data visualization capabilities, making it easy to create interactive charts and dashboards.
2. **Google Data Studio**: Great for creating custom, shareable reports and dashboards using data from various sources.
3. **IBM Watson Studio**: Offers a wide range of data science and machine learning tools with a user-friendly interface.
4. **RapidMiner**: Known for its powerful data preparation and machine learning features without the need for coding.
5. **KNIME**: A visual platform for data analytics, reporting, and integration.
6. **DataRobot**: Focuses on automated machine learning, making it easier to build predictive models.
7. **Alteryx**: Combines data preparation, data blending, and analytics into a single platform.
The choice of tool depends on your specific needs and preferences. These tools can be valuable for those who want to perform data science tasks without extensive coding knowledge.
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If i have got a matrix of 16x12 and i want to create 3 classes.Is there any machine learning technique which can identify the lower and upper boundary levels for each of the classes.
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The answer of Qamar Ul Islam is obviously AI generated.
I would recommend you to use one of the many clustering algorithms available in literature, k-means for example. However, if you already know which samples belong to which classes, what you want to find is a treshold between them, for that you can use a PCA approach, there are several algorithms such as confidence ellipses or Voronoi... Depends on what you exactly want.
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This part seems extremely difficult to optimize.
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I was once asked to re catalogue and/ or re-number a filing cabinet of paper references of which there was around 1000, but there were errors, some duplicate numbers, mis filing. I tried to do this in endnote. It is time consuming. I don't mind the paper reference system and manual coding/ manual numbering as what frightens me is the electronic way of extracting, copying and saving copious references, that is very unsettling in fact.
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How can data science and statistical analysis be used to improve the shipping and logistics industry?
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Just like any industry there should be national or state-based reports, as per hospital industry has hospital admissions data in the AIHW reports, there should be a like document for shipping related to statistics collected ie no of ships docked, containers unloaded, weight of shipping containers, products dumped due to contamination etc.
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Data augmentation creates something from nothing?
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There have been several substantial and reliable advancements in data augmentation techniques. Some of them include:
  1. Cutout and CutMix: Cutout randomly masks out square regions of input images, while CutMix combines two or more images by randomly selecting patches and their corresponding labels. Both techniques improve robustness and generalization.
  2. Mixup: Mixup blends pairs of images and their corresponding labels to generate new training samples. It helps regularize the model and reduces the impact of noisy labels.
  3. AutoAugment: AutoAugment uses reinforcement learning to search for the optimal augmentation policies for a given dataset. It finds augmentations that provide the most performance gain and improves model accuracy.
  4. RandAugment: RandAugment applies a sequence of augmentation operations randomly sampled from a predefined policy, such as rotations, translations, and color transformations. It shows strong performance across various vision tasks.
  5. Style Transfer Augmentation: Style transfer techniques like CycleGAN and MUNIT can be used for augmentation by transferring styles or textures from one image to another while preserving the semantic content. It can generate augmented samples with diverse styles.
  6. Domain-Specific Augmentations: In addition to general-purpose augmentations, domain-specific augmentations have been developed. For example, CutoutInSIdeDetection is a technique for augmenting data in object detection tasks.
These advancements have proven to be reliable and effective in improving model training, generalization, and robustness, leading to better performance in various domains and tasks.
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Data augmentation creates something from nothing?
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Data augmentation is a technique used in machine learning and computer vision to generate additional training data by applying various transformations or modifications to the original dataset. It can include operations like rotating, scaling, flipping, cropping, or adding noise to the data.
While data augmentation can assist in improving the performance of machine learning models, it is not about creating something from nothing. Instead, it enhances the existing dataset by introducing variations that can help the model generalize better.
The reliability of data augmentation depends on several factors:
Application and domain: The effectiveness of data augmentation techniques can vary based on the specific application and domain. Some transformations might be more suitable for certain tasks, while others may have limited impact.
Quality of original data: Data augmentation cannot compensate for poor or insufficient original data. If the initial dataset is limited in size or lacks diversity, data augmentation alone may not yield reliable results.
Appropriate augmentation techniques: Choosing appropriate augmentation techniques is crucial. Some transformations, if not properly applied, might introduce unrealistic or misleading data. Careful consideration and domain knowledge are necessary to ensure reliable augmentation.
Validation and evaluation: It is essential to evaluate the performance of the machine learning model using proper validation techniques. Augmented data should be included in the validation process to understand its impact and ensure reliable model evaluation.
In summary, while data augmentation can be a valuable technique, its reliability depends on various factors such as the application, domain, quality of original data, appropriate techniques, and thorough validation. When used correctly, data augmentation can enhance model performance and generalization abilities.
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Hello people, I have a dataset of inhibitors as binary labels ( Zeros - Inactive , Ones - Active ). I have my ML/AI model working, now I would like to know how many are the best inhibitors out of these. Could anyone help me what should I do and what can be done to resolve my problems?
TIA
#DrugDesign #ML #AI #DataScience #DrugDiscovery
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It sounds like you're working on a binary classification task with a focus on identifying the best inhibitors. Here's a step-by-step approach to help you assess and further refine your model to get the results you need:
1. Model Diagnostic Assessment:
  • Confusion Matrix: Construct a confusion matrix to elucidate true positive, true negative, false positive, and false negative categorizations from model predictions.
  • Performance Metrics: Determine precision, recall, F1-score, and AUC-ROC to critically assess model accuracy and effectiveness.
  • ROC Curve Analysis: This graphical representation delineates the compromise between sensitivity and specificity.
  • Threshold Refinement: Many algorithms conventionally employ a 0.5 threshold for classification. An adjusted threshold may be imperative to either augment recall or precision. Such adjustments might be pivotal for precise inhibitor identification.
2. Examination of Feature Significance:
  • Should the model possess inherent capabilities (e.g., tree-based methodologies), it's pertinent to scrutinize feature importance scores, offering insights into the most influential features for active inhibitor prediction.
  • For models devoid of direct feature significance outputs, one might consider employing techniques such as Permutation Importance or SHAP values.
3. Model Refinement Strategies:
  • Resampling: In the presence of a class imbalance in the dataset, methodologies such as oversampling, undersampling, or the Synthetic Minority Over-sampling Technique (SMOTE) should be explored.
  • Hyperparameter Optimization: Techniques encompassing grid search or random search should be invoked for optimal hyperparameter tuning tailored to the task at hand.
  • Cross-Validation Strategy: Implementation of k-fold cross-validation is advised to yield a comprehensive model performance assessment.
4. Inhibitor Ranking Framework:
  • Probabilistic Outputs: Rather than binary outcomes, it is beneficial to procure probability scores from the model. Inhibitors manifesting elevated probabilities of activity might be deemed as the most potent.
  • Subsequent Analysis: Following the demarcation of paramount inhibitors, a deeper analysis is advocated, potentially emphasizing their molecular characteristics or mechanistic pathways.
Best regards,
Samawel JABALLI
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Is it possible to build a highly effective forecasting system for future financial and economic crises based on artificial intelligence technology in combination with Data Science analytics, Big Data Analytics, Business Intelligence and/or other Industry 4.0 technologies?
Is it possible to build a highly effective, multi-faceted, intelligent forecasting system for future financial and economic crises based on artificial intelligence technology in combination with Data Science analytics, Big Data Analytics, Business Intelligence and/or other Industry 4.0 technologies as part of a forecasting system for complex, multi-faceted economic processes in such a way as to reduce the scale of the impact of the paradox of a self-fulfilling prediction and to increase the scale of the paradox of not allowing a predicted crisis to occur due to pre-emptive anti-crisis measures applied?
What do you think about the involvement of artificial intelligence in combination with Data Science, Big Data Analytics, Business Intelligence and/or other Industry 4.0 technologies for the development of sophisticated, complex predictive models for estimating current and forward-looking levels of systemic financial, economic risks, debt of the state's public finance system, systemic credit risks of commercially operating financial institutions and economic entities, forecasting trends in economic developments and predicting future financial and economic crises?
Research and development work is already underway to teach artificial intelligence to 'think', i.e. the conscious thought process realised in the human brain. The aforementioned thinking process, awareness of one's own existence, the ability to think abstractly and critically, and to separate knowledge acquired in the learning process from its processing in the abstract thinking process in the conscious thinking process are just some of the abilities attributed exclusively to humans. However, as part of technological progress and improvements in artificial intelligence technology, attempts are being made to create "thinking" computers or androids, and in the future there may be attempts to create an artificial consciousness that is a digital creation, but which functions in a similar way to human consciousness. At the same time, as part of improving artificial intelligence technology, creating its next generation, teaching artificial intelligence to perform work requiring creativity, systems are being developed to process the ever-increasing amount of data and information stored on Big Data Analytics platform servers and taken, for example, from selected websites. In this way, it may be possible in the future to create "thinking" computers, which, based on online access to the Internet and data downloaded according to the needs of the tasks performed and processing downloaded data and information in real time, will be able to develop predictive models and specific forecasts of future processes and phenomena based on developed models composed of algorithms resulting from previously applied machine learning processes. When such technological solutions become possible, the following question arises, i.e. the question of taking into account in the built intelligent, multifaceted forecasting models known for years paradoxes concerning forecasted phenomena, which are to appear only in the future and there is no 100% certainty that they will appear. Well, among the various paradoxes of this kind, two particular ones can be pointed out. One is the paradox of a self-fulfilling prophecy and the other is the paradox of not allowing a predicted crisis to occur due to pre-emptive anti-crisis measures applied. If these two paradoxes were taken into account within the framework of the intelligent, multi-faceted forecasting models being built, their effect could be correlated asymmetrically and inversely proportional. In view of the above, in the future, once artificial intelligence has been appropriately improved by teaching it to "think" and to process huge amounts of data and information in real time in a multi-criteria, creative manner, it may be possible to build a highly effective, multi-faceted, intelligent forecasting system for future financial and economic crises based on artificial intelligence technology, a system for forecasting complex, multi-faceted economic processes in such a way as to reduce the scale of the impact of the paradox of a self-fulfilling prophecy and increase the scale of the paradox of not allowing a predicted crisis to occur due to pre-emptive anti-crisis measures applied. In terms of multi-criteria processing of large data sets conducted with the involvement of artificial intelligence, Data Science, Big Data Analytics, Business Intelligence and/or other Industry 4. 0 technologies, which make it possible to effectively and increasingly automatically operate on large sets of data and information, thus increasing the possibility of developing advanced, complex forecasting models for estimating current and future levels of systemic financial and economic risks, indebtedness of the state's public finance system, systemic credit risks of commercially operating financial institutions and economic entities, forecasting economic trends and predicting future financial and economic crises.
In view of the above, I address the following questions to the esteemed community of scientists and researchers:
Is it possible to build a highly effective, multi-faceted, intelligent forecasting system for future financial and economic crises based on artificial intelligence technology in combination with Data Science, Big Data Analytics, Business Intelligence and/or other Industry 4.0 technologies in a forecasting system for complex, multi-faceted economic processes in such a way as to reduce the scale of the impact of the paradox of the self-fulfilling prophecy and to increase the scale of the paradox of not allowing a forecasted crisis to occur due to pre-emptive anti-crisis measures applied?
What do you think about the involvement of artificial intelligence in combination with Data Science, Big Data Analytics, Business Intelligence and/or other Industry 4.0 technologies to develop advanced, complex predictive models for estimating current and forward-looking levels of systemic financial risks, economic risks, debt of the state's public finance system, systemic credit risks of commercially operating financial institutions and economic entities, forecasting trends in economic developments and predicting future financial and economic crises?
What do you think about this topic?
What is your opinion on this subject?
Please respond,
I invite you all to discuss,
Thank you very much,
Warm regards,
Dariusz Prokopowicz
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In my opinion, in order to determine the question of the possibility of building a highly effective forecasting system for future financial and economic crises based on artificial intelligence technology in combination with Data Science analytics, Big Data Analytics, Business Intelligence and/or other Industry 4.0/5.0 technologies, it is first necessary to precisely define the essence of forecasting specific risk factors, i.e. factors that in the past were the sources of the occurrence of certain types of economic, financial and other crises and that may be such factors in the future. But will such a structured forecasting system based on a combination of Big Data Analytics and Artificial Intelligence be able to forecast events that appear as unusual, generating new types of risks, referred to as so-called "black swans", such as forecasting the appearance of another but generated by a difficult to predict new type of risk, an unusual event leading to the occurrence of another e.g. something similar to the 2008 global financial crisis, the 2020 pandemic, or something completely new that has not yet appeared.
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Warm regards,
Dariusz Prokopowicz
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hey guys, I'm working on a new project where I should transfer Facebook ads campaigns data to visualize in tableau or Microsoft power BI, and this job should be done automatically daily, weekly or monthly, I'm planning to use python to build a data pipeline for this, do you have any suggestions or any Resources I can read or any projects similar I can get inspired from ? thank you .
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