Neural Networks - Science topic

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

like pi, pid, neural network etc.

[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

Hello,

I would like to know if a dataset exists that would contain lots of different 3D models of buildings along with annotations (church, house, castle, barn, bridge, etc). This dataset would be used to research automatic synthesis of buildings and cities from these exemplars using Deep Neural Networks (DNN) or other approaches.

A good example of this kind of dataset but not dedicated to buildings would be Princeton ShapeNet dataset:

If something similar would exist for buildings, it would be a great help for my research.

Regards,

Bruno

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I'm aware of the gradient descent and the back-propagation algorithm. What I don't get is: when is using a bias important and how do you use it?

Hello, I am a master's student studying in Yonsei Univeristy, Korea.

I am trying to estimate the state of satellite, using Neural Network.

Below is a simple flow of my study.

1. Train (t0 ~ t1)

Train neural network using known observation & true state data

2. Validation (t1 ~ t2)

Using observation data starting from t1, validate the network

3. Test (t3 ~ t4)

With new observation data, estimate the true ECI coordinate at different time.

[For all steps]

Input : observation data ( RADAR SEZ coordinate data or Orbital Element data )

Output : true data ( ECI coordinate data)

I know that the validation is already done while training,

but the validation part is for checking whether the network is well-trained.

I used "narxnet" from the deep learning toolbox, and it worked well until the validation part.

However, in order to use the network made with "narxnet" for the test part,

I had to retrain using data from just before.

(to estimate t3~t4, need tx ~ t3 data trained network)

So all my work have failed, and I am going to restart on doing this.

Here is what I want to ask.

Thank you very much for reading my questions.

Jee Hoon, Kim.

There is set_custom_radial_params function in sfparamgen.py file in tools/python/symfunc_paramgen/src/sfparamgen.py. But there is no such function included to generate angular or any other kind of symmetry function for a defined range of parameters. How we can generate those symmetry functions within desired range of values then?

Last year I studied a course on Computer Systems Architecture/Organization. During a lecture, I learned about data hazards and one of the common solutions to them: Reordering the instructions. Modern processors solve this using OOE, but since this is integrated into the processor, it increases chip size, power consumption, and thermal efficiency. So I thought "What if we had an AI-driven processor which does that for the CPU?"

Does anyone know if this has already been successfully researched or implemented? I would greatly appreciate any insightful comments.

Other than conventional radial and angular symmetry functions, I want to generate generate polynomial symmetry function. How to do that? Is there any code available?

Locating neural network fitting App in Matlab 2024a

Hi, I'm Prithiviraja. I'm currently building a deep learning model to color SAR image. I came across lot of resources only using ASPP for feature extraction from SAR Image. I'm planning to use both FPN and ASPP for that process, while FPN is mostly used for object detection. Kindly tell me your suggestion.

Is there a fixed code used in shape prediction in neural networks?

How do we design a neural network and decide on the number of hyper parameters like the depth and number of neurons in each layer. Also how do we decide on the activation function to be used in each layer precisely

How can reinforcement learning techniques be combined with neural networks to improve the accuracy and efficiency of decision-making in dynamic and changing environments?

In the field of materials science, the use of artificial intelligence (AI) opens up exciting new possibilities. Here are some important and relevant questions a researcher should consider:

General Questions

Questions Specific to Materials Science

Questions on Innovation and Development

These questions can help guide research and maximize the impact of AI in the field of materials.

I am reaching out to seek your valuable advice and recommendations regarding the best software tools to use for this research. Specifically, I am looking for software with a user-friendly interface that can facilitate the implementation of image reconstruction techniques and artificial neural networks (ANN) or convolutional neural networks (CNN).

If you have experience or knowledge in this area, I would greatly appreciate your insights on the following:

Your guidance and suggestions will be invaluable in helping me move forward with my research. Thank you in advance for your time and assistance.

Best regards,

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 want to apply neural network on kidney stones images (whether its CT images or ultrasound) to determine whether the kidney has stones or not.

Sign language is a visual language that uses hand shapes, facial expressions, and body movements to convey meaning. Each country or region typically has its own unique sign language, such as American Sign Language (ASL), British Sign Language (BSL), or Indian Sign Language (ISL). The use of AI models to understand and translate sign language is an emerging field that aims to bridge the communication gap between the deaf community and the hearing world. Here’s an overview of how these AI models work:

AI models for sign language recognition and translation use a combination of computer vision, natural language processing (NLP), and machine learning techniques. The primary goal is to develop systems that can accurately interpret sign language and convert it into spoken or written language, and vice versa.

1. Data Collection and Preprocessing:

• Video Data: Collecting large datasets of sign language videos is crucial. These datasets should include diverse signers, variations in signing speed, and different signing environments.

• Annotation: Annotating the data with corresponding words or phrases to train the model.

2. Feature Extraction:

• Hand and Body Tracking: Using computer vision techniques to detect and track hand shapes, movements, and body posture.

• Facial Expression Recognition: Identifying facial expressions that are integral to conveying meaning in sign language.

3. Model Architecture:

• Convolutional Neural Networks (CNNs): Often used for processing video frames to recognize hand shapes and movements.

• Recurrent Neural Networks (RNNs) / Long Short-Term Memory (LSTM): Useful for capturing temporal dependencies in the sequence of signs.

• Transformer Models: Increasingly popular due to their ability to handle long-range dependencies and parallel processing capabilities.

4. Training:

• Training the AI model on the annotated dataset to recognize and interpret sign language accurately.

• Fine-tuning the model using validation data to improve its performance.

5. Translation and Synthesis:

• Sign-to-Text/Speech: Converting recognized signs into written or spoken language.

• Text/Speech-to-Sign: Generating sign language from spoken or written input using avatars or video synthesis.

• Variability in Signing: Different individuals may sign differently, and the same sign can have variations based on context.

• Complexity of Sign Language: Sign language involves complex grammar, facial expressions, and body movements that are challenging to capture and interpret.

• Data Scarcity: There is a limited amount of annotated sign language data available for training AI models.

• Communication Tools: Development of real-time sign language translation apps and devices to assist deaf individuals in communicating with non-signers.

• Education: Providing educational tools for learning sign language, improving accessibility in classrooms.

• Customer Service: Implementing sign language interpretation in customer service to enhance accessibility.

• Improved Accuracy: Enhancing the accuracy of sign language recognition and translation through better models and larger, more diverse datasets.

• Multilingual Support: Developing models that can handle multiple sign languages and dialects.

• Integration with AR/VR: Leveraging augmented reality (AR) and virtual reality (VR) to create more immersive and interactive sign language learning and communication tools.

The development of AI models for sign language holds great promise for improving accessibility and communication for the deaf and hard-of-hearing communities, fostering inclusivity and understanding in a diverse society.

1. DeepASL

• Description: DeepASL is a deep learning-based system for translating American Sign Language (ASL) into text or speech. It uses Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to process video frames and capture the temporal dynamics of sign language.

• Notable Feature: DeepASL incorporates a sign language dictionary to improve translation accuracy and can handle continuous sign language sequences.

2. Google AI - Hand Tracking

• Description: Google has developed a hand-tracking technology that can detect and track 21 key points on a hand in real-time. While not specifically designed for sign language, this technology can be used as a foundation for sign language recognition systems.

• Notable Feature: It offers real-time hand tracking using a single camera, which can be integrated into mobile devices and web applications.

3. SignAll

• Description: SignAll is a comprehensive sign language translation system that uses multiple cameras to capture hand movements and body posture. It translates ASL into English text and can be used for various applications, including education and customer service.

• Notable Feature: SignAll uses a combination of computer vision, machine learning, and NLP to achieve high accuracy in sign language translation.

4. Microsoft Azure Kinect

• Description: Microsoft’s Azure Kinect is a depth-sensing camera that can be used to capture detailed hand and body movements. It provides an SDK for developers to build applications that include sign language recognition capabilities.

• Notable Feature: The depth-sensing capability of Azure Kinect allows for precise tracking of 3D movements, which is essential for accurate sign language interpretation.

5. Sighthound

• Description: Sighthound is a company that develops computer vision software, including models for gesture and sign language recognition. Their software can detect and interpret hand gestures in real-time.

• Notable Feature: Sighthound’s software is highly customizable and can be integrated into various platforms and devices.

6. Kinect Sign Language Translator

• Description: This was an early project by Microsoft Research that used the Kinect sensor to capture and translate ASL. The project demonstrated the feasibility of using depth-sensing technology for sign language recognition.

• Notable Feature: It was one of the first systems to use depth sensors for sign language translation, paving the way for future developments.

7. AI4Bharat - Indian Sign Language

• Description: AI4Bharat, an initiative by IIT Madras, has developed models for recognizing Indian Sign Language (ISL). They aim to create an accessible communication platform for the deaf community in India.

• Notable Feature: Focuses on regional sign languages, which are often underrepresented in AI research.

• IBM Research: IBM has been involved in developing AI models for sign language recognition and translation, often publishing their findings in academic journals and conferences.

• University of Surrey - SLR Dataset: The University of Surrey has created large datasets for Sign Language Recognition (SLR) and developed models that are trained on these datasets.

• SignAll Browser Extension: A browser extension that translates ASL into text in real-time.

• ASL Fingerspelling Game: An online game that helps users learn ASL fingerspelling through AI-driven recognition and feedback.

These models and systems demonstrate the progress being made in the field of sign language recognition and translation, and they provide valuable tools for enhancing communication and accessibility for the deaf and hard-of-hearing communities.

In CNN(convolution neural network), can the feature map obtained determinately by a random initialization convolution kernel? if not, how to decide the weights in convolution kernel to obtain the feature maps we need? By trial and rerror, are we shotting if our eyes closed?

Can SHAPELY (SHAP) values be used to explain the importance of different features being fed to a Neural Network ? I know they are used in Traditional ML on Tabular data

How can attention mechanisms be integrated with convolutional neural networks to enhance performance in image classification tasks?

2024 5th International Conference on Computer Vision and Data Mining(ICCVDM 2024) will be held on July 19-21, 2024 in Changchun, China.

---Call For Papers---

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

◕ Computational Science and Algorithms

· Algorithms

· Automated Software Engineering

· Computer Science and Engineering

......

◕ Vision Science and Engineering

· Image/video analysis

· Feature extraction, grouping and division

· Scene analysis

......

◕ Software Process and Data Mining

· Software Engineering Practice

· Web Engineering

· Multimedia and Visual Software Engineering

......

◕ Robotics Science and Engineering

Image/video analysis

Feature extraction, grouping and division

Scene analysis

......

All accepted papers will be published by SPIE - The International Society for Optical Engineering (ISSN: 0277-786X), and submitted to EI Compendex, Scopus for indexing.

Full Paper Submission Date: June 19, 2024

Registration Deadline: June 30, 2024

Final Paper Submission Date: June 30, 2024

Conference Dates: July 19-21, 2024

How can we choose the weight matrix in the Convolutional Neural Network? And how this matrix is related to the kernels used in the algorithm?

IEEE 2024 4th International Symposium on Computer Technology and Information Science(ISCTIS 2024) will be held during July 12-14, 2024 in Xi’an, China.

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

1. Computer Engineering and Technology

Computer Vision & VR

Multimedia & Human-computer Interaction

Image Processing & Understanding

PDE for Image Processing

Video compression & Streaming

Statistic Learning & Pattern Recognition

......

2. Information Science

Digital Signal Processing (DSP)

Advanced Adaptive Signal Processing

Optical communication technology

Communication and information system

Physical Electronics and Nanotechnology

Wireless communication technology·

......

All accepted papers of ISCTIS 2024 will be published in conference proceedings by IEEE, which will be submitted to IEEE Xplore,EI Compendex, Scopus for indexing.

Full Paper Submission Date: June 20, 2024

Registration Deadline: June 25, 2024

Final Paper Submission Date: June 26, 2024

Conference Dates: July 12-14, 2024

I am using a biphasic electrical stimulation on neural networks to attempt to induce electrical kindling for epilepsy on neural networks. In addition, I will be attempting to modulate said networks to attenuate seizure-like events also using electrical stimulation (neuromodulation).

Should the pulse itself be negative (cathodic) followed by positive (anodic) phase? Why is this the case from an electrophysiological point of view? Does it better induce depolarizations? If so, how and why?

Why do Long Short-Term Memory (LSTM) networks generally exhibit lower Mean Squared Error (MSE) compared to traditional Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) in certain applications?

https://youtu.be/VQDB6uyd_5E In this video, we explore why Long Short-Term Memory (LSTM) networks often achieve lower Mean Squared Error (MSE) compared to traditional Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) in specific applications. We delve into the unique architecture of LSTMs, their ability to handle long-range dependencies, and how they mitigate issues like the vanishing gradient problem, leading to improved performance in tasks such as sequence modeling and time series prediction. Topics Covered: 1. Understanding the architecture and mechanisms of LSTMs 2. Comparison of LSTM, RNN, and CNN in terms of MSE performance 3. Handling long-range dependencies and vanishing gradients 4. Applications where LSTMs excel and outperform traditional neural networks Watch this video to discover why LSTMs are favored for certain applications and how they contribute to lower MSE in neural network models! #LSTM #RNN #CNN #NeuralNetworks #DeepLearning #MachineLearning #MeanSquaredError #SequenceModeling #TimeSeriesPrediction #VanishingGradient #AI Don't forget to like, comment, and subscribe for more content on neural networks, deep learning, and machine learning concepts! Let's dive into the world of LSTMs and their impact on model performance. Feedback link: https://maps.app.goo.gl/UBkzhNi7864c9BB1A LinkedIn link for professional queries: https://www.linkedin.com/in/professorrahuljain/ Join my Telegram link for Free PDFs: https://t.me/+xWxqVU1VRRwwMWU9 Connect with me on Facebook: https://www.facebook.com/professorrahuljain/ Watch Videos: Professor Rahul Jain  Link: https://www.youtube.com/@professorrahuljain

2024 4th International Conference on Computer, Remote Sensing and Aerospace (CRSA 2024) will be held at Osaka, Japan on July 5-7, 2024.

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

1. Algorithms

Image Processing

Data processing

Data Mining

Computer Vision

Computer Aided Design

......

2. Remote Sensing

Optical Remote Sensing

Microwave Remote Sensing

Remote Sensing Information Engineering

Geographic Information System

Global Navigation Satellite System

......

3. Aeroacoustics

Aeroelasticity and structural dynamics

Aerothermodynamics

Airworthiness

Autonomy

Mechanisms

......

All accepted papers will be published in the Conference Proceedings, and submitted to EI Compendex, Scopus for indexing.

Full Paper Submission Date: May 31, 2024

Registration Deadline: May 31, 2024

Conference Date: July 5-7, 2024

Invitation code: AISCONF

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

Given a multi-layer (say 10-12) neural network, are there standard techniques to compress it to a single layer or 2 layer NN ?

What are the most effective techniques for mitigating overfitting in neural networks, especially when dealing with limited training data?

memorize-ability > generalize-ability

Hello everyone and thank you for reading my question.

I have a data set that have around 2000 data point. It have 5 inputs (4 wells rate and the 5th is the time) and 2 ouputs ( oil cumulative and water cumulative). See the attached image.

I want to build a Proxy model to simualte the cumulative oil & water.

I have made 5 models ( ANN, Extrem Gradient Boost, Gradient Boost, Randam forest, SVM) and i have used GridSearch to tune the hyper parameters and the results for training the models are good. Of course I have spilited the training data set to training, test and validation sets.

So I have another data that I haven't include in either of the train,test and validation sets and when I use the models to predict the output for this data set the models results are bad ( failed to predict).

I think the problem lies in the data itself because the only input parameter that changes are the (days) parameter while the other remains constant.

But the problem is I can't remove the well rate or join them into a single variable because after the Proxy model has been made I want to optimize the well rates to maximize oil and minimize water cumulative respectively.

Is there a solution to suchlike issue?

How do you become a Machine Learning(ML) and Artificial Intelligence(AI) Engineer? or start research in AI/ML, Neural Networks, and Deep Learning?

Should I pursue a "Master of Science thesis in Computer Science." with a major in AI to become an AI Engineer?

I am researching on automatic modulation classification (AMC). I used the "RADIOML 2018.01A" dataset to simulate AMC and used the convolutional long-short term deep neural network (CLDNN) method to model the neural network. But now I want to generate the dataset myself in MATLAB.

My question is, do you know a good sources (papers or codes) that have produced a dataset for AMC in MATLAB (or Python)? In fact, have they produced the In-phase and Quadrature components for different modulations (preferably APSK and PSK)?

How does the addition of XAI techniques such as SHAP or LIME impact model interpretability in complex machine learning models like deep neural networks?

Perhaps in the future - as a result of the rapid technological advances currently taking place and the rivalry of leading technology companies developing AI technologies - a general artificial intelligence (AGI) will emerge. At present, there are unresolved deliberations on the question of new opportunities and threats that may occur as a result of the construction and development of general artificial intelligence in the future. The rapid technological progress currently taking place in the field of generative artificial intelligence in connection with the already high level of competition among technology companies developing these technologies may lead to the emergence of a super artificial intelligence, a strong general artificial intelligence that can achieve the capabilities of self-development, self-improvement and perhaps also autonomy, independence from humans. This kind of scenario may lead to a situation where this kind of strong, super AI or general artificial intelligence is out of human control. Perhaps this kind of strong, super, general artificial intelligence will be able, as a result of self-improvement, to reach a state that can be called artificial consciousness. On the one hand, new possibilities can be associated with the emergence of this kind of strong, super, general artificial intelligence, including perhaps new possibilities for solving the key problems of the development of human civilization. However, on the other hand, one should not forget about the potential dangers if this kind of strong, super, general artificial intelligence in its autonomous development and self-improvement independent of man were to get completely out of the control of man. Probably, whether this will involve mainly new opportunities or rather new dangers for mankind will mainly be determined by how man will direct this development of AI technology while he still has control over this development.

I described the key issues of opportunities and threats to the development of artificial intelligence technology in my article below:

In view of the above, I address the following question to the esteemed community of scientists and researchers:

What do you think about 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

In other words, why have improvements to neural networks led to an increase in hyperparameters? Are hyperparameters related to some fundamental flaw of neural networks?

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

I am looking for a Q1 journal with a publication cost of 0 USD and a very short publishing period, specifically in the field of Hybrid Neural Networks. Can anyone suggest some?

Thank you.

I would like to know that prophet time series model is under the category of neural network or machine learning or deep learning? I want to forecast the price of product depending on other influential factors( 7 indicators) and all the data is monthly data with 15 years period.How can I implement with prophet model to get better accuracy? And i also want to compare the result with other time series model.Please suggest me how should I do about my work.thank you.

The future of AI holds boundless potential across various domains, poised to transform industries, societies, and everyday lives. Advancements in machine learning, deep learning, and neural networks continue to push the boundaries of what AI can achieve.

We anticipate AI systems becoming increasingly integrated into our daily routines, facilitating more personalized experiences in healthcare, education, entertainment, and beyond.

Collaborative efforts between technologists, policymakers, and ethicists will be essential to ensure AI development remains aligned with human values and societal well-being.

As AI algorithms become more sophisticated, they will enhance decision-making processes, optimize resource allocation, and drive innovation across sectors.

However, the future of AI also raises ethical, privacy, and employment concerns that necessitate careful consideration and regulation.

As AI evolves, fostering transparency, accountability, and inclusivity will be imperative to harness its transformative potential responsibly and equitably, shaping a future where AI serves as a powerful tool for positive change.

Does Neural Networks handle a multicollinearity?

I heard about ARTIFICIAL NEURAL NETWORK (ANN) and I watched a video of a researcher talked about this revolution.. However, is ANN will be the next solution to predict the adsorption behaviour and do the adsorption calculations based on the properties of the adsorbent materials?

My paper "Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks" has been published in nature communication since the 20th November. But on google scholar, only the pre-print from research square is available...

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Data is part of the code.

Neural network is actually code for fuzzy match.

If an activation function has a jump discontinuity, then in the training process, can we implement backpropagation to compute the derivatives and update the parameters?

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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.

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Imagine training a neural network on data like weather patterns, notoriously chaotic and unpredictable. Can the network, without any hints or constraints, learn to identify and repeat hidden periodicities within this randomness? This question explores the possibility of neural networks spontaneously discovering order in chaos, potentially revealing new insights into complex systems and their modeling through AI.

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.

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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.

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---𝐂𝐚𝐥𝐥 𝐅𝐨𝐫 𝐏𝐚𝐩𝐞𝐫𝐬---

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

- Neural Networks

- Signal and information processing

- Integrated Circuit Engineering

- Electronic and Communication Engineering

- Communication and Information System

All accepted papers will be published in IEEE（ISBN:979-8-3503-9437-5）, which will be submitted for indexing by IEEE Xplore, Ei Compendex, Scopus.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐃𝐚𝐭𝐞𝐬:

Full Paper Submission Date: November 12, 2023

Registration Deadline: November 28, 2023

Final Paper Submission Date: December 22, 2023

Conference Dates: January 19-21, 2024

𝐅𝐨𝐫 𝐌𝐨𝐫𝐞 𝐃𝐞𝐭𝐚𝐢𝐥𝐬 𝐩𝐥𝐞𝐚𝐬𝐞 𝐯𝐢𝐬𝐢𝐭:

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