Natural Language Processing - Science topic

Hello,

I wish to share a few notes I made during a lecture I attended at TU/e, the lecture was about AGI, why it should be disregarded and why the scientific community should focus on specialized models in AI instead.

the discussion triggered in me an idea of an AGI system design I shared it on GitHub https://github.com/Samir-atra/AGI-Lecture-Notes

I hope you enjoy it.

I have developed a dataset for Swahili digraphs, and I need to publish it and its journal.

Hello everyone,

I am currently seeking research collaborations in Artificial Intelligence as part of a pre-doctoral project. My goal is to actively prepare for a future PhD by gaining hands-on experience in applied research.

My skills include:

• Strong foundation in artificial intelligence, machine learning, and deep learning.

• Proficiency in frameworks such as TensorFlow, PyTorch, and scikit-learn.

• Experience in software development using Python, C, C++, Go, Java, Node.js and other relevant technologies.

Interest in topics such as:

• Industrial applications of AI

• Natural Language Processing (NLP)

• Image processing

• Generative models (LLM, GAN, etc.)

• Trusted AI

• Optimization

• Algorithms

• I am not available full-time, but I can dedicate a significant amount of time to collaborative projects alongside my other professional commitments.

• I prefer remote or online collaborations with researchers or labs.

• Actively contribute to research projects (analysis, modeling, experimentation).

• Co-author and publish meaningful results in conferences or journals.

• Develop in-depth expertise in preparation for my PhD.

If you are working on an interesting project or are looking for a motivated collaborator for research, I would be happy to discuss it. Feel free to contact me directly here or via email for more details.

Thank you for your time, and I look forward to potential collaborations! 😊

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

I created a lecture series for this, please suggest any improvement.

https://youtu.be/6LS3zv9sQlY?si=uUMl2-1RfCGgbW6p

I am an honours student in the Linguistics Department. I am eager to conduct BNLP research and am interested in pursuing a higher education in this discipline (NLP) . How can I begin taking measures to work in this industry outside of my studies? Please help with information or guidance.

[CFP]2024 6th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI 2024) - December

2024 6th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI 2024) will be held in Nanjing, China during December 6-8, 2024. It aims at a key theme on Robotics, Intelligent Control, and Artificial Intelligence. It is a great opportunity to get together and share ideas and knowledge among the Industry experts, researchers and academics.

We invite you to submit your latest research results for presentation. You are also welcomed to suggest any topic, activity or scientific program you would like to see covered during the conference. Your active contribution will be crucial to the success of the conference and its impact on our global mission.

◕Robotics and Intelligent Control

· Robot Structure Design and Control

· Robot Navigation, Positioning and Autonomous Control

· Bio-machine-electric System and Integration

· Intelligent Unmanned System Perception and Control

· Human-machine-environment Natural Interaction and Interaction

......

◕Intelligent Communication In Robotics and Intelligent Control

· Intelligent Communication and Cooperative Control

· Robotics and Advanced Communication Technologies

· Perception and Cognitive Communication

· Self-Organizing Networks and Robotic Clusters

......

◕Intelligent IoT In Robotics and Intelligent Control

· Internet of Things and Intelligent Robotics

· Intelligent Home Systems and Service Robotics

· IoT Architecture and Robotic Operation Platforms

· Edge AI and Its Integration in Robotics

......

Full Paper Submission Date: October 31, 2024

Registration Deadline: November 8, 2024

Conference Dates: December 6-8, 2024

[CFP]2024 4th International Symposium on Artificial Intelligence and Big Data (AIBFD 2024) - December

AIBDF 2024 will be held in Ganzhou during December 27-29, 2024. The conference will focus on the artificial intelligence and big data, discuss the key challenges and research directions faced by the development of this field, in order to promote the development and application of theories and technologies in this field in universities and enterprises, and provide innovative scholars who focus on this research field, engineers and industry experts provide a favorable platform for exchanging new ideas and presenting research results.

◕Track 1：Artificial Intelligence

Natural language processing

Fuzzy logic

Signal and image processing

Speech and natural language processing

Learning computational theory

......

◕Track 2：Big data technology

Decision support system

Data mining

Data visualization

Sensor network

Analog and digital signal processing

......

Full Paper Submission Date: December 23, 2024

Registration Deadline: December 23, 2024

Conference Dates: December 27-29, 2024

Link: https://www.techscience.com/cmc/special_detail/AI_data_analysis

The rapid evolution of Artificial Intelligence (AI), data analysis techniques, and big data has significantly transformed various fields. AI technologies have shown tremendous potential in enhancing various applications, predicting trends, and automating complex tasks. Natural Language Processing (NLP) has also advanced, enabling better understanding and generation of human language. However, these advancements come with challenges such as data privacy concerns, algorithmic biases, and the constant need for adaptation to emerging trends. This special issue aims to explore both the advancements and challenges associated with AI, data analysis, and big data, highlighting their impact on various domains and ensuring robust implementations.

This special issue seeks to provide a comprehensive overview of the latest advancements in AI, data analysis, and big data technologies and their applications across different fields. It aims to present innovative solutions, evaluate their effectiveness, and discuss the challenges faced in implementing these technologies in real-world scenarios

The scope includes but is not limited to :

·  AI-driven prediction and automation systems

·  Advanced data analysis techniques

·  Big data processing and Social Media analysis

·  Natural Language Processing (NLP) advancements and applications

·  Ethical and privacy considerations in AI and data analysis

·  Case studies and real-world applications

·  Challenges in integrating AI with existing systems

·  Future trends and emerging technologies in the field

·  AI and Machine Learning for Predictive Analytics

·  Behavioral Analytics an big data

·  Automated Systems and Process Optimization

·  Big Data Analytics and Processing Techniques

·  Natural Language Processing (NLP) for Various Applications

·  Privacy and Ethical Issues in AI Solutions

·  Data Analysis Techniques for Predictive Modelling

·  Challenges in AI-Enhanced Systems

We would be honored if you could consider contributing to our special issue . I will assist you throughout the submission process to ensure everything proceeds smoothly. You can reach me at elaine.lu@techscience.com for any further information.

We look forward to your valuable contribution to this topic.

Hi, I’m Kazi Redwan, Lead of Team Tech Wing. We’re a research group working on AI, Machine Learning, algorithms, networks, and IoT, focusing on developing innovative solutions for various challenges. Currently, we have two key projects in progress, with more exciting works coming soon!

We’re always open to collaborating with passionate individuals and teams. If you’re interested in working together on cutting-edge technologies, feel free to connect. Let’s innovate and make an impact together!

#AI #MachineLearning #Algorithms #Networks #IoT #TeamTechWing #ResearchCollaboration

[CFP]2024 2nd International Conference on Artificial Intelligence, Systems and Network Security (AISNS 2024) - December

AISNS 2024 is to bring together innovative academics and industrial experts in the field of Artificial Intelligence, Systems and Cyber Security to a common forum. The primary goal of the conference is to promote research and developmental activities in computer information science and application technology and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in computer information science and application technology and related areas.

◕Artificial Intelligence

· AI Algorithms

· Natural Language Processing

· Fuzzy Logic

· Computer Vision and Image Understanding

· Signal and Image Processing

......

◕Network Security

· Active Defense Systems

· Adaptive Defense Systems

· Analysis, Benchmark of Security Systems

· Applied Cryptography

· Authentication

· Biometric Security

......

◕Computer Systems

· Operating Systems

· Distributed Systems

· Database Systems

Full Paper Submission Date: October 10, 2024

Registration Deadline: November 29, 2024

Conference Dates: December 20-22, 2024

Hi, I have been working on some Natural Language Processing research, and my dataset has several duplicate records. I wonder should I delete those duplicate records to increase the performance of the algorithms on test data?

I'm not sure whether duplication has a positive or negative impact on test or train data. I found some controversial answers online regarding this, which make me confused!

For reference, I'm using ML algorithms such as Decision Tree, KNN, Random Forest, Logistic Regression, MNB etc. On the other hand, DL algorithms such as CNN and RNN.

[CFP]2024 4th International Conference on Digital Society and Intelligent Systems (DSInS 2024) - November

DSInS 2024 will be held in Sydney, Australia during November 20-22, 2024. The conference will focus on the application of Intelligent systems in digital society, discuss the key challenges and research directions faced by the development of this field, in order to promote the development and application of theories and technologies in this field in universities and enterprises, and provide innovative scholars who focus on this research field, engineers and industry experts provide a favorable platform for exchanging new ideas and presenting research results.

Internet of Things Planned highlights of DSInS 2024 include:

● Addresses and presentations by some of the most respected researchers in the Intelligent Systems and Digital Society

● Panel discussions

● Presentations of accepted academic and practitioner research papers; a poster paper session

◕Intelligent Systems

Pattern recognition

Machine learning

Neural networks

Natural language processing

Deep learning

Knowledge graph

......

◕Digital Society

Digital manufacturing

Digital communication

Digital transportation

Digital community

Digital government

......

◕Application of Intelligent systems on Digital Society

Character recognition

Video surveillance

Factory automation

Assistive robotics

Intelligent Fault Diagnosis

......

Final Paper Submission Date: October 25, 2024

Conference Dates: November 20-22, 2024

Introduction to Natural Language Processing (NLP)

Watch now: https://youtu.be/7qJB2WYvr5I

Are you curious about how machines understand and process human language? Dive into the fascinating world of Natural Language Processing (NLP) with this video!

Key topics covered:

What is NLP?

Applications of NLP in real-world scenarios

Key techniques like tokenization, sentiment analysis, and more

NLP in AI and Machine Learning

This video is perfect for beginners who want to explore the basics of NLP and understand its impact on today's AI-driven world.

If you're keen to explore more about AI, Machine Learning, and cutting-edge technologies, don't forget to subscribe to my channel! 💡

#NLP #MachineLearning #ArtificialIntelligence #DataScience #AI #TechEducation #ProfessorRahulJain #YouTubeLearning #NaturalLanguageProcessing

🔒SCI: Call for Papers-Artificial Intelligence Algorithms and Applications

🔗SI URL: https://www.techscience.com/cmc/special_detail/AI_algorithms_applications

📅 Submission Deadline: 28 February 2025

🌟 Guest Editors:

Dr. Antonio Sarasa-Cabezuelo, Complutense University of Madrid, Madrid, 28040, Spain

🔍 Summary:

Artificial Intelligence (AI) has become a transformative force in technology, driving innovation across diverse sectors. AI algorithms, which form the backbone of intelligent systems, are increasingly applied in areas such as healthcare, robotics, and beyond. The continuous evolution of these algorithms has enabled more accurate predictions, efficient data processing, and the development of autonomous systems, making AI a critical research area. Understanding and advancing AI algorithms is essential for addressing complex real-world challenges, fostering technological growth, and enhancing human-machine collaboration.

This Special Issue aims to explore the latest advancements in AI algorithms and their wide-ranging applications. The focus is on cutting-edge research that contributes to the development, optimization, and practical deployment of AI algorithms. By gathering contributions from experts in the field, this issue seeks to highlight innovative approaches and emerging trends that can drive future developments in AI. The scope includes both theoretical explorations and real-world applications, providing a comprehensive view of the current state and potential of AI technologies.

Suggested Themes:

· Machine learning and deep learning algorithms

· AI in healthcare and medical diagnostics

· Robotics and autonomous systems

· Natural language processing and understanding

· AI-driven cybersecurity solutions

· Reinforcement learning and decision-making systems

· Computer vision and image recognition

· Explainable AI and transparency in algorithms

· AI for smart cities and urban planning

· Human-computer interaction and AI

· AI in supply chain management and logistics

· AI in entertainment and media content creation

· Evolutionary algorithms and optimization techniques

· AI for predictive maintenance and industrial automation

· AI in agriculture and food security

🎈Keywords

Artificial Intelligence, Machine Learning, Deep Learning, Autonomous Systems, Natural Language Processing, Robotics, AI Applications

01. What is Machine Learning

02. Types of Machine Learning

03. Understanding Data in Machine Learning

04. Master Data Preprocessing for Machine Learning Clean & Prepare Your Data

05. What is Supervised Learning ?

06. Regression Algorithms | Linear Regression

07.Classification Algorithms Explained | Logistic Regression, Decision Trees, Support Vector Machine

08. What is Unsupervised Learning? Basic Introduction

09. Clustering Techniques | K Means Clustering

10. Simplifying Data with PCA | Principal Component Analysis | Dimensionality Reduction

11. Neural Networks Overview | Understanding layers, Neurons & Activations

12. Training Neural Networks | Weights, Biases, Backpropogation & Gradient Descent

13. What is Reinforcement Learning?

14. Q-Learning & Markov Decision Processes

15. Model Evaluation in Supervised Learning Accuracy, Precision, Recall, F1 Score & Confusion Matrix

16. Overfitting vs Underfitting | How to Recognize & Avoid Them + The Bias Variance Trade off

17. Build a Simple Linear Regression Model | Step by Step Guide Using Real World Data

18. Classification Model using Decision Trees

19. Clustering with K Means Algorithm | Hands on Example

20. Introduction to Deep Learning | How It Differs from Traditional ML + Real World Use Cases

21. Introduction to Natural Language Processing (NLP)

22. Machine Learning Recap, Suggestions for Advanced Topics and Continued Learning

#MachineLearning #AIForBeginners #FreeCourse #LearnAI #ArtificialIntelligence #MLBasics #ProfessorRahulJain #FutureOfLearning #AIEducation #ProfessorRahulJain

How does CFG contribute to the practical applications of natural language processing , particularly in parsing and language recognition? Provide a how CFG facilitates these processes in a real-world scenario.

i have joined as research scholar in computer science. i would like to do my PhD work in NLP domain. Please suggest some of the research topic related to NLP

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!

Title：Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

Journal：Computers, Materials & Continua (SCI IF2.0 CITESCORE5.3)

Link：https://www.techscience.com/cmc/v80n2/57626

Since the 1950s, when the Turing Test was introduced, there has been notable progress in machine language intelligence. Language modeling, crucial for AI development, has evolved from statistical to neural models over the last two decades. Recently, transformer-based Pre-trained Language Models (PLM) have excelled in Natural Language Processing (NLP) tasks by leveraging large-scale training corpora. Increasing the scale of these models enhances performance significantly, introducing abilities like context learning that smaller models lack. The advancement in Large Language Models, exemplified by the development of ChatGPT, has made significant impacts both academically and industrially, capturing widespread societal interest. This survey provides an overview of the development and prospects from Large Language Models (LLM) to Large Multimodal Models (LMM). It first discusses the contributions and technological advancements of LLMs in the field of natural language processing, especially in text generation and language understanding. Then, it turns to the discussion of LMMs, which integrates various data modalities such as text, images, and sound, demonstrating advanced capabilities in understanding and generating cross-modal content, paving new pathways for the adaptability and flexibility of AI systems. Finally, the survey highlights the prospects of LMMs in terms of technological development and application potential, while also pointing out challenges in data integration, cross-modal understanding accuracy, providing a comprehensive perspective on the latest developments in this field.

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.

Hello!

I am currently trying to reproduce the results described in "Inferring psycholinguistic properties of words" (https://www.aclweb.org/anthology/N16-1050.pdf), by implementing the authors' bootstrapping algorithm. However, for some unknown reason I keep getting correlations with the actual ratings in the .4-.6 range, rather than the .8-.9 range. If you have a software implementation that reaches the performance levels from the paper, could you please share it with me?

Many thanks,

Armand

会议征稿：【中国算力大会主办】2024算法、高性能计算与人工智能国际学术会议（AHPCAI 2024）定于2024年6月21-23日在中国郑州举行。会议主要围绕算法、高性能计算与人工智能等研究领域展开讨论。

大会官网（投稿网址）：https://ais.cn/u/AvAzUf​

会议时间：2024年6月21-23日

会议地点：中国·郑州

接受/拒稿通知：投稿后1周左右

收录检索：EI Compendex，Scopus

1、算法（算法分析/近似算法/可计算性理论/进化算法/遗传算法/数值分析/在线算法/量子算法/随机化算法/排序算法/算法图论和组合/计算几何/计算技术和应等）

2、人工智能（自然语言处理/知识表现/智能搜索/机器学习/感知问题/模式识别/逻辑程序设计软计算/不精确和不确定的管理/人工生命/神经网络/复杂系统/遗传算法/计算机视觉等）

3、高性能计算（网络计算技术/高性能计算软件与工具开发/计算机系统评测技术/云计算系统/移动计算系统/点对点计算/网格和集群计算/服务和互联网计算/效用计算等）

4、图像处理 （图像识别/图片检测网络/机器人视觉/聚类/图像数字化/图像增强和复原/图像数据编码/图像分割/模拟图像处理/数字图像处理/图像描述/光学图像处理/数字信号处理/图像传输等）

5、其他相关主题

1、 本会议投稿经过2-3位组委会专家严格审核之后，最终所录用的论文将由SPIE - The International Society for Optical Engineering (ISSN: 0277-786X)出版，出版后提交EI Compendex, Scopus检索。

*AHPCAI前三届已完成EI检索！稳定且快速！

2、本届会议作为2024算力大会分会进行公开征稿，高质量论文将遴选至2024算力大会，由IEEE出版，见刊后由期刊社提交至 EI Compendex和Scopus检索。

1、作者参会：一篇录用文章允许一名作者免费参会；

2、主讲嘉宾：申请主题演讲，由组委会审核；

3、口头演讲：申请口头报告，时间为10分钟；

4、海报展示：申请海报展示，A1尺寸，彩色打印；

5、听众参会：不投稿仅参会，也可申请演讲及展示。

6、报名参会：https://ais.cn/u/AvAzUf

Abstract submission deadline: 18th June, 2024

Submission link: https://www.nextgenaiconference.com/submission.php

The International Conference on Generative AI and its Applications (ICGAIA) is a prestigious event that brings together researchers, practitioners, and experts from academia, industry, and government organizations to discuss the latest advancements, trends, and challenges in the field of generative artificial intelligence (AI) and its various applications. ICGAIA serves as a platform for presenting cutting-edge research papers, sharing insights, exchanging ideas, and fostering collaborations among professionals working in areas such as machine learning, deep learning, natural language processing, computer vision, robotics, and more.

IEEE 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE 2024) will be held on September 20-22, 2024 in Wenzhou, China.

The topics of interest include, but are not limited to:

· Big Data Analysis

· Deep Learning、Machine Learning

· Artificial Intelligence

· Pattern Recognition

· Data Mining

· Cloud Computing Technologies

· Internet of Things

· AI Applied to the IoT

· Clustering and Classificatio

· Soft Computing

· Natural Language Processing

· E-commerce and E-learning

· Wireless Networking

· Network Security

· Big Data Networking Technologies

· Graph-based Data Analysis

· Signal Processing

· Online Data Analysis

· Sequential Data Processing

All papers, both invited and contributed, the accepted papers, will be published and submitted for inclusion into IEEE Xplore subject to meeting IEEE Xplore’s scope and quality requirements, and also submitted to EI Compendex and Scopus for indexing.

Full Paper Submission Date: July 10,2024

Registration Deadline: August 5, 2024

Final Paper Submission Date: August 20, 2024

Conference Dates: September 20-22, 2024

Please send the full paper(word+pdf) to Submission System:

Dear All,

*Call for Papers:*

The *Annual International Conference on Recent Trends in Healthcare Innovation*, often abbreviated as AICRTHI, stands as a pivotal event on the global healthcare calendar. This conference is organizing by *Department of Information Science & Engineering, Vidyavardhaka College of Engineering*, Mysuru, Karnataka, India.

We invite submissions of original research, case studies, and innovative ideas for presentation at the *AICRTHI-2024.* Selected papers presented at the conference will have the opportunity to be included in the conference proceedings of *CRC Press, Taylor & Francis Group (Scopus Indexed).*

The conference aims to cover a wide range of topics related to machine learning and computing systems, including but not limited to :

• Pharmaceutical Research and Drug Discovery

• Patient-centric care and Personalized Medicine

• Healthcare Analytics and Population Health Management

• Case Studies and Real-World Applications

• AI in Healthcare Operations and Management

• Support Systems (CDSS)

• Electronic Health Records (EHR) and Data Integration

• Natural Language Processing (NLP) in Healthcare

• Telemedicine and Remote Patient Monitoring

• Healthcare Robotics and Automation

*Important Dates:*

*Submission Deadline: 30th July 2024*

*Notification of Acceptance: 15th September 2024*

*Conference Dates: 24th and 25th October 2024*

*Submission Link*: https://cmt3.research.microsoft.com/AICRTHI2024

For inquiries or assistance regarding paper submissions, please contact

Dr. Prema N S, at premans@vvce.ac.in.

We look forward to receiving your submissions and welcoming you to *AICRTHI-2024!*

Call for papers: 2024 International Conference on Intelligent Computing and Data Mining (ICDM 2024) will be held on September 20-22, 2024 in Chaozhou, China.

大会官网(投稿网址）：https://ais.cn/u/AFBBfq

大会时间：2024年9月20-22日

大会地点：中国-潮州

收录检索：EI Compendex，Scopus

智能计算与数据挖掘是当今信息技术领域的研究热点，并在众多领域都有着广泛的应用，如金融、医疗、教育、交通等。随着大数据时代数据量爆炸式增长，如何从海量数据中提取有价值的信息，一直是需要迭代解决的问题。2024年智能计算与数据挖掘国际学术会议（ICDM 2024）为探讨相关问题提供一个平台，各位专家学者将深入探讨最新研究成果，通过对数据的分析和处理，提供智能化的决策支持，讨论在面对复杂问题时，如何运用数据驱动的方法，通过分析数据背后的规律和关联，找到问题的本质和解决方案，欢迎广大学者踊跃参会交流。

智能计算：遗传算法、进化计算与学习、群智能与优化、独立成分分析、自然计算、量子计算、神经网络、模糊理论与算法、普适计算、机器学习、深度学习、自然语言处理、智能控制与自动化、智能数据融合、智能数据分析与预测等。

数据挖掘：网络挖掘、数据流挖掘、并行和分布式算法、图和子图挖掘、大规模数据挖掘方法、文本、视频和多媒体数据挖掘、可扩展数据预处理、高性能数据挖掘算法、数据安全和隐私、电子商务的数据挖掘系统等。

ICDM 2024所征稿件会经由2-3位组委会专家审稿，最终所录用的论文将以IEEE出版，收录进IEEE Xplore数据库，见刊后由期刊社提交至EI Compendex和Scopus检索。

ICDM 2024的参会设有口头演讲/海报展示/听众三种形式，可点击以下链接报名参会，在会后领取参会证书：https://ais.cn/u/AFBBfq

1、口头演讲：申请口头报告，时间为10-15分钟左右

2、海报展示：制作A1尺寸彩色海报，线上/线下展示

3、听众参会：不投稿仅参会，可与现场嘉宾/学者进行交流互动

4、汇报PPT和海报，请于会议前一周提交至大会邮箱 （icicdm@163.com）

5、论文录用后可享一名作者免费参会名额

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

I'm part of a team developing a healthcare web application that includes features like scheduling appointments, storing medical reports, maintaining detailed medical history, and predicting potential diseases using advanced machine learning algorithms and natural language processing (NLP). We're using technologies such as Django, Node.js, React.js, OpenCV, Google Vision API, spaCy/NLTK, and BERT.

I am working on a project to integrate data from Campus Management Systems and Learning Management Systems into a predictive AI model that forecasts students' academic performance. I want to use Microsoft Copilot for natural language processing and user query handling. What is the best approach to achieve this integration? Should I use open-source predictive AI models (like Scikit-learn, TensorFlow, or PyTorch) and then feed the results into Microsoft Copilot, or should I develop a custom Copilot in Copilot Studio to handle both predictive and generative tasks? Do you have any insights or recommendations on handling the integration?

based on GPT-3

The primary problem that large language models solved is small sample learning, right?

Or they are only complement to each other

CHATGPT4

Advances in Natural Language Processing has shown that research questionnaires can handle by CHATGPT4.

Where should results from CHATGPT4! Primary source or Secondary source?

In the field of natural language processing, what research direction can we choose to quickly publish sci papers?

In the case where the instructor does not have a lot of scientific research experience, the laboratory has only a few gpu.

As a student in Bachelor degree program in computer science field we have a project in course of "Language Theory", our project related with Natural Language Processing:

- First phase talks about giving a dictionary of "Physical Objects Name's" and give it a "Text" (all this in input) after that it gives us a list of "Physical Objects Name's" in our "Text" (this is the output as a file).

- Second phase is to use the last list to as input and implement a code that can classify words by topics and the result will be the general topic or idea of our text.

In this project I did the first phase but in the second one I don't understand how can I implement my code.

P.S: I try to add a python file but I can't, so for all those who wanna help me I can send them my work.

I am searching for a new transformer that can be used in the NLP

Dear colleagues, our team is working on development of a cloud-based AI powered research assistant to be trained on the literature in your specific field and to become a companion and SME aiding your literature review, gap identification, formulation of research problems, questions and hypotheses, and terminology and term definition management. Using a MS Word plugin, it will also be capable to assist with the academic writing process (form & style). The application will learn from interactions with the user and will proactively search the online sources for relevant new literature to boost your research by doing more, discovering more, achieving more by augmenting research capabilities and by removing tedious manual tasks. We also work on integrating it with Zotero, GoogleScholar, and online library repositories.

We will start a crowdsourcing campaign to help finance development of this application designed exclusively for academic researchers and students after completing a feasibility study and development of a working prototype.

Would this be of interest to you personally and what recommendations would you have for the development team?

Thank you for your help!

2024 IEEE 6th International Conference on Internet of Things, Automation and Artificial Intelligence(IoTAAI 2024) will be held in Guangzhou, China from July 26 to 28, 2024.

The conference aims to provide a large platform for researchers in the field of modern machinery manufacturing and materials engineering to communicate and provide the participants with the most cutting-edge scientific and technological information. The conference invites experts and scholars from universities and research institutions, business people and other related personnel from home and abroad to attend and exchange ideas.

The topics of interest for submission include, but are not limited to:

1. Internet of Things

IoT Electronics

IoT Enabling Technologies

IoT Networks

IoT Applications

IoT Architecture

......

2. Automation

Electrical Automation

Circuits and Systems

Control Engineering

Robotics and Automation Systems

Automatic control and Information Technology

......

3. Artificial Intelligence

Intelligent Systems

Intelligent Optimized Design

Virtual Manufacturing and Network Manufacturing

System Optimization

......

All accepted full papers will be published and submitted for inclusion into IEEE Xplore subject to meeting IEEE Xplore's scope and quality requirements, and also submitted to EI Compendex and Scopus for indexing.

Full Paper Submission Date: May 11, 2024

Registration Deadline: July 24, 2024

Final Paper Submission Date: July 22, 2024

Conference Dates: July 26-28, 2024

Invitation code: AISCONF

*Using the invitation code on submission system/registration can get priority review and feedback

I've recently released a software package that combines my research interests (history of science and statistics) and my day job (machine learning and statistical modelling) It is called timeline_ai (see https://github.com/coppeliaMLA/timeline_ai) It extracts and then visualises timelines from the text of pdfs. It works particularly well on history books and biographies. Here are two examples:

The extraction is done using a large language model so there are occasional inaccuracies and “hallucinations". To counter that I've made the output checkable. You can click on each event and it will take you the page the event was extracted from. So far it has performed very well. I would love some feedback on whether people think it would be useful for research and education.

For word segmentation. Thank you very much!

Hi, I'm Yusuke Mikami, a master's student doing LLM for embodied control

I'm personally making a list of LLM-related papers here

[Github] https://github.com/shure-dev/Awesome-LLM-Papers-Toward-AGI

[Notion table] https://potent-twister-29f.notion.site/b0fc32542854456cbde923e0adb48845?v=e2d14d2ef0c848f5a1d5b71f9977d7c5

However, I am a very new person in this field, so I want to have help from you.

Please post interesting papers and keywords at

[Github Issue] https://github.com/shure-dev/Awesome-LLM-Papers-Toward-AGI/discussions/8

What approaches can be used to enhance the interpretability of deep neural networks for better understanding of their decision-making process ?

Hello! I am working on a proposal for creating an electronic health record phenotyping classification algorithm (mental health focus). I am having a hard time finding solid guidance re:cohort identification. Specifically, is there a gold standard ratio of patients with the identified phenotype:healthy controls that should be gathered? I would be very appreciative of any guidance toward gold-standard studies or systematic reviews on this topic. Thanks in advance for taking the time to answer this question.

In envisioning the future of natural language processing, what innovations do you believe could surpass or significantly enhance the transformative impact of current transformer models?

Introduction:

The advancements in artificial intelligence (AI) have the potential to revolutionize healthcare practices, including nursing care. As AI technologies continue to evolve, there is growing interest in integrating these tools into nursing education. This inquiry aims to explore the prospects of teaching nursing students about the utilization of AI tools in healthcare and when

In recent years, the healthcare industry has witnessed significant developments in AI applications, such as machine learning, natural language processing, and computer vision. These technologies have shown promise in improving patient care, facilitating accurate diagnoses, and enhancing treatment outcomes. Consequently, it becomes crucial to equip future nurses with the knowledge and skills to harness the potential of AI in their practice.

1. When will nursing education incorporate AI tools?

- What are the current efforts and initiatives to introduce AI in nursing curricula?

- Are there any institutions or programs already integrating AI tools into nursing education?

- What are the potential benefits and challenges associated with teaching AI in nursing?

2. Permissibility and Ethical Considerations:

- Are there any legal or regulatory barriers that prevent the integration of AI in nursing education?

- What ethical considerations should be addressed before incorporating AI tools in nursing practice?

- What are the potential risks and limitations of relying on AI technologies in healthcare?

3. Feasibility and Implementation:

- What resources and infrastructure are required to teach AI concepts in nursing programs?

- Are there any limitations in terms of faculty expertise and readiness for AI integration?

- How can nursing educators effectively incorporate AI tools into the existing nursing curriculum?

4. Future Implications:

- What impact might AI integration have on nursing practice and patient outcomes?

- How can AI tools enhance the efficiency and accuracy of healthcare delivery?

- What are the potential roles of nurses in leveraging AI technologies in various healthcare settings?

I know That its too long Question , but Iam sure NOT ONLY me interested..

Looking for some project ideas using deep/shallow learning for my Masters. Would like to know if there any research gaps, could be taken on medical imaging or etc.

In the rapidly evolving landscape of the Internet of Things (IoT), the integration of blockchain, machine learning, and natural language processing (NLP) holds promise for strengthening cybersecurity measures. This question explores the potential synergies among these technologies in detecting anomalies, ensuring data integrity, and fortifying the security of interconnected devices.

Imagine machines that can think and learn like humans! That's what AI is all about. It's like teaching computers to be smart and think for themselves. They can learn from mistakes, understand what we say, and even figure things out without being told exactly what to do.

Just like a smart friend helps you, AI helps machines be smart too. It lets them use their brains to understand what's going on, adjust to new situations, and even solve problems on their own. This means robots can do all sorts of cool things, like helping us at home, driving cars, or even playing games!

There's so much happening in Artificial Intelligence (AI), with all sorts of amazing things being developed for different areas. So, let's discuss all the cool stuff AI is being used for and the different ways it's impacting our lives. From robots and healthcare to art and entertainment, anything and everything AI is up to is on the table!

Machine Learning: Computers can learn from data and improve their performance over time, like a student studying for a test.

Natural Language Processing (NLP): AI can understand and generate human language, like a translator who speaks multiple languages.

Computer Vision: Machines can interpret and make decisions based on visual data, like a doctor looking at an X-ray.

Robotics: AI helps robots perceive their environment and make decisions, like a self-driving car navigating a busy street.

Neural Networks: Artificial neural networks are inspired by the human brain and are used in many AI applications, like a chess computer that learns to make winning moves.

Ethical AI: We need to use AI responsibly and address issues like bias, privacy, and job displacement, like making sure a hiring algorithm doesn't discriminate against certain groups of people.

Autonomous Vehicles: AI-powered cars can drive themselves, like a cruise control system that can take over on long highway drives.

AI in Healthcare: AI can help doctors diagnose diseases, plan treatments, and discover new drugs, like a virtual assistant that can remind patients to take their medication.

Virtual Assistants: AI-powered virtual assistants like Siri, Alexa, and Google Assistant can understand and respond to human voice commands, like setting an alarm or playing music.

Game AI: AI is used in games to create intelligent and challenging enemies and make the game more fun, like a boss battle in a video game that gets harder as you play.

Deep Learning: Deep learning is a powerful type of machine learning used for complex tasks like image and speech recognition, like a self-driving car that can recognize stop signs and traffic lights.

Explainable AI (XAI): As AI gets more complex, we need to understand how it makes decisions to make sure it's fair and unbiased, like being able to explain why a loan application was rejected.

Generative AI: AI can create new content like images, music, and even code, like a program that can write poetry or compose music.

AI in Finance: AI is used in the financial industry for things like algorithmic trading, fraud detection, and customer service, like a system that can spot suspicious activity on a credit card.

Smart Cities: AI can help make cities more efficient and sustainable, like using traffic cameras to reduce congestion.

Facial Recognition: AI can be used to recognize people's faces, but there are concerns about privacy and misuse, like using facial recognition to track people without their consent.

AI in Education: AI can be used to personalize learning, automate tasks, and provide educational support, like a program that can tutor students in math or English.

This question blends various emerging technologies to spark discussion. It asks if sophisticated image recognition AI, trained on leaked bioinformatics data (e.g., genetic profiles), could identify vulnerabilities in medical devices connected to the Internet of Things (IoT). These vulnerabilities could then be exploited through "quantum-resistant backdoors" – hidden flaws that remain secure even against potential future advances in quantum computing. This scenario raises concerns for cybersecurity, ethical hacking practices, and the responsible development of both AI and medical technology.

Seeking insights on leveraging self-supervised learning techniques to address data scarcity issues in natural language processing. Explore methods and benefits in enhancing model performance without extensive labeled data.

How can advancements in natural language processing contribute to more effective human-computer interactions, and what potential challenges might arise in ensuring ethical and responsible AI use in communication and decision-making processes ?

I do not think that any approach to AI can ignore the massive data provided by the internet, part of which is nothing more than the digitalization of pre-internet or non-internet material. There is of course the problem of the enormously varying quality and reliability of this material, the presence of redundancy and its sheer vastness, which could lead one to wonder whether processing such raw data via rudimentary algorithms is really worth the energetic and environmental costs or the use of the expensive infrastructure involved.

I believe that the correct approach to AI must be based on formal logic and the logical-algebraic frameworks of theoretical computer science, as well as other kinds of mathematics beyond the the ones commonly employed in machine learning.

The Semantic Web project seemed a good approach along these lines. It involves a logical and formal semantic analysis of natural language. It calls for a far more sophisticated way of producing internet content and (re)presenting human knowledge on the internet. No Data without Metadata. We need a machine-human logical-semantic interlingua so that internet data can become machine readable in a logical and semantic sense (rather than mere statistical data chunked by a machine learning algorithm).

We should be able to effect complex structured queries to intelligent evolving self-correcting interlinked data bases according to varying degrees of precision which will be able to output the source and a measure of reliability of the data presented.

Machine learning will come into play for example at the level of automatic theorem proving, of the massively difficult task of the processing of logical queries.

Our ethical principles can be given formal logical formulation than can be understood by machines.

It seems that this approach (even if demanding more time and work and being filled with challenges) is far more desirable than internet-based Large Language Models. This kind of 'intelligent' AI seems to be in the long run a better ethical , environmental and human choice.

For details on the current OpenAI leadership situation see e.g.

https://www.theguardian.com/technology/2023/nov/19/openai-investors-push-for-return-of-ousted-ceo-sam-altman-chatgpt

The ChatGPT-3 (Generative Pretrained Transformer 3) is the third iteration of OpenAI’s popular language model. It was released in 2020 and is considered one of the most advanced large language models (LLM). It is being trained to retrieve massive amounts of text data from the Internet, making it capable of generating human-like text and performing various Natural Language Processing (NLP) tasks such as text completion, summarization, translation, and more. Whereas ChatGPT-3 is a conversational AI language model based on OpenAI’s ChatGPT-3 model and recently released on November 30, 2022. NLT-based ChatGPT-3 has been widely used in various industries, including health and medical sciences

Are LLMs relatively unsuitable for high-precision tasks?

I would like to know , what are the possible ways to extend conference paper into journal paper? My work is in the area of natural language processing

Generative AI has a wide range of applications across various fields. Here are some notable applications of Generative AI:

Generative AI continues to advance rapidly, opening up new possibilities in various domains and offering innovative solutions to complex problems. Its ability to generate human-like content has made it a valuable tool in many industries.

As a starting topic for undergraduate research