Deep Learning - Science topic

2026 3rd International Conference on Machine Learning and Neural Networks (MLNN 2026) will be held in Chengdu, China, from April 10 to 12, 2026.

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

◕ Machine Learning Algorithms and Techniques

·Supervised and Unsupervised Learning: Methods and Applications

·Deep Learning Architectures and Their Applications

·Reinforcement Learning for Autonomous Systems and Robotics

·Transfer Learning and Domain Adaptation

·Evolutionary and Hybrid Algorithms for Machine Learning Optimization

·Semi-supervised Learning and Meta-learning Approaches

·Ensemble Learning and Online Learning for Dynamic Data

·Probabilistic Models: Bayesian Networks and Markov Models

·Computational Complexity and Optimization in Machine Learning

·Real-world Applications of Machine Learning in Industry and Society

◕ Neural Networks and Deep Learning

·Convolutional and Recurrent Neural Networks in Computer Vision and Time Series

·Generative Models: GANs and Autoencoders in Data Generation and Dimensionality Reduction

·Neural Networks in Natural Language Processing and Speech Recognition

·Deep Reinforcement Learning for Robotics, Automation, and Network Optimization

·Neural Networks in Healthcare: Medical Imaging, Diagnostics, and Anomaly Detection

·Neural Networks for Financial Forecasting and Cybersecurity

·Transfer Learning and Few-shot Learning in Deep Learning

·Deep Learning for Recommender Systems and Data Augmentation

◕Machine Learning and Neural Networks in Communication Systems

·Machine Learning for Wireless Communication and 5G Networks

·AI-based Optimization and Resource Allocation in Communication Networks

·Deep Learning for Signal Processing, Channel Estimation, and Spectrum Management

·Cognitive Radio Networks and Machine Learning for Dynamic Spectrum Access

·Neural Networks for MIMO Systems, Beamforming, and Network Security

·Machine Learning for Network Traffic Prediction and Intrusion Detection

·AI Techniques for Quality of Service (QoS) and Quality of Experience (QoE) in Networks

·Data-driven Approaches for IoT and Autonomous Communication Systems

·Deep Reinforcement Learning for Communication Network Optimization

All papers, both invited and contributed, will be reviewed by two or three expert reviewers from the conference committees. After a careful reviewing process, all accepted papers of MLNN 2026 will be published in ACM International Conference Proceedings Series, which will be archived in the ACM Digital Library, and indexed by EI Compendex, Scopus.

Full Paper Submission Date: February 6, 2026

Registration Deadline: March 27, 2026

Final Paper Submission Date: March 27, 2026

Conference Dates: April 10-12, 2026

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

"Hi,I am looking to conduct research on how Machine Learning /Deep Learning is evolving to secure modern computer networks. Are there any specific sub-fields (like Adversarial ML or Federated Learning for IDS) that you believe are currently under-researched? I would appreciate any pointers toward recent literature or unsolved problems in this area."

A maximum of about 5%. Is that a correct way to understand it?

Is there a significant performance difference between LLMs using a 'think mode' (like Chain-of-Thought) and those using a 'non-think mode' (direct prompting) for short text generation or short text classification tasks?

Can training solely with SFT (without RL) using TB-PB level (high-quality) data solve the hallucination problem in LLMs?

2026 International Conference on Condensed Matter Physics and Artificial Intelligence (CMPAI 2026) will be held on April 17-19, 2026 in San Francisco, USA/ Hybrid.

𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗪𝗲𝗯𝘀𝗶𝘁𝗲: https://www.aischolar.com/conference/cmpai2026?invite=researchgate

---𝗖𝗮𝗹𝗹 𝗙𝗼𝗿 𝗣𝗮𝗽𝗲𝗿𝘀---

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

◕ AI in Superconductivity and Magnetism

Design and optimization of superconducting materials

Prediction of magnetic properties and new phases

AI-assisted study of superconducting and magnetic mechanisms

Machine learning applications in magnetic quantum materials

Intelligent experimental data processing and analysis

Smart control for superconducting devices

◕ AI in Low-dimensional and Topological Physics

Identification and classification of topological materials

Electronic state analysis in low-dimensional systems

Topological quantum computing and information processing

Dynamics simulations of low-dimensional heterostructures

Detection of topological phases and experimental data analysis

◕ AI in Non-equilibrium and Statistical Physics

Simulations of non-equilibrium dynamics and phase transitions

Modeling of many-body system dynamics

Analysis of thermal transport and energy conversion

Theoretical modeling of non-equilibrium systems

Deep learning in complex dissipative systems

Integration of intelligent simulations with experimental data

◕ Intelligent Methods in Computational and Materials Physics

AI-assisted electronic structure calculations

Multiscale materials simulations with machine learning

High-throughput materials screening and design

Analysis of defects and interfaces in electronic structures

Machine-learned potentials and accelerated simulations

AI research in energy materials

Other ...

---𝗦𝗽𝗲𝗮𝗸𝗲𝗿𝘀 (𝗜𝗻𝘁𝗲𝗻𝗱 𝘁𝗼 𝗶𝗻𝘃𝗶𝘁𝗲)---

Prof. Daowen Qiu, Sun Yat-sen University, China

Assoc. Prof. Siow Kim Shyong, The National University of Malaysia, Malaysia

---𝗣𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻---

All papers, both invited and contributed, will be reviewed by two or three experts from the committees. After a careful reviewing process, all accepted full papers of CMPAI 2026 will be published in the conference proceedings and will be submitted to EI Compendex and Scopus for indexing.

---𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗗𝗮𝘁𝗲𝘀---

Full Paper Submission Date: March 2, 2026

Registration Deadline: March 31, 2026

Final Paper Submission Date: March 31, 2026

Conference Dates: April 17-19, 2026

--- 𝗣𝗮𝗽𝗲𝗿 𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻---

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

This question aims to identify which AI, ML, and DL models are most effective for detecting cyberattacks and anomalies in healthcare data and network environments. Healthcare systems generate sensitive data from sources such as EHRs, medical imaging systems, IoT-based medical devices, and cloud healthcare platforms, making them frequent targets of cyber threats.

Traditional security methods often fail to detect zero-day attacks, insider threats, and subtle data manipulation. Therefore, researchers increasingly rely on AI-driven detection models that can learn complex patterns from large and heterogeneous healthcare datasets.

Human intelligence is not based on generally accepted concepts, well-known laws\rules and logical connections. Instead of that, the intellect analyzes intuitive images via analogy, pursuing search of (!) alternatives, and more often among opposite still possible solutions and logically unrelated to generally accepted concepts, models, and rules.

Therefore, learning, reasoning, and clarifying parameters within well-known patterns based on well-known concepts, laws, rules, and logical connections cannot be a solution to a problem with elements of intelligence.

Language models are not acceptable as well to formalize the problem/task precisely because LLM imply by default a search of ready solutions among a huge array of data that are known LMs patterns for selection the solution.

Intelligence needs a different problem\task formal language that is not tied to well-known patterns, concepts and models, though only pursues the effective solving the problem\task.

Therefore, there is no need for large language models, neural network technologies, deep learning, giant databases, or the enormous energy of computers!

You can teach a parrot to speak and understand\even interpret human speech, and even solve a certain range of tasks within a template. But the parrot's intelligence is quite capable for solving elementary intellectual tasks and problems (!) of the parrot even without knowing the human language, and it does not require more energy than it needs to support the body, just like a human!

This means that the chosen areas of the AI's development are dead ends, diverting huge computational and human resources to useless demonstrations of the software product as impotent and limited in human cognition and intelligence.

We don't need AI as a navigator for databases of known data!

We don't need a chatterbox with databases and well-known solutions.

We don't need neural networks, we don't need deep learning, we don't need what is logical and lies on the open surface. Other criteria and parameters are needed to describe\formalize the components of the task, the conditions for their interaction, and the evaluation of the result. What is needed is not well-known statistics, but intellectual mathematics.

We need an "intelligent problem\task calculator" that can reveal an array of solutions (including those that were, could have been, and could not have been) while, after analyzing, propose by justifying, the most effective and unique solution.

And for this process of intellectual problem solving, it is not the human language that is needed, but the formal mathematical language of the computing intelligence.

For example: to divide 3 by 2, even with a calculator it is not necessary use a lot of energy, because it will use a mathematical language that is adequate to the task.

But then the task is to distribute three drivers/machinists into two trams/metro trains that must continuously and around the clock serve the movement of two vehicles, a simple arithmetic solution is not suitable.

On the other hand, an intelligent calculator should not rely on neither the multiplication table (as a built-in function) nor the use of routine addition operations and other well-known mathematical multiplication models that require learning a specific behavior pattern in performing sequential operations.

Otherwise, we spend huge resources with the "intelligence" index, and as a result, we end up with a talking browser based on databases and phrases, a graphomaniac and a buffoon like AI chatbot for everyday use.

is it possible to implement end to end handwritten recognizer based on classical time series and sequence modelling like Markov and probabilistic graphical modelling. this effort as a solution to set aside CNN deep learning based solution and to gain advantages of hardware requirements and also to benefit of faster processing time.

Generally, pre-training data is on the TB-PB scale, while post-training data is on the MB-GB scale.

For LLM pre-training + post-training, using 10T Chinese data vs using 10T Chinese-English data—will the former perform better in Chinese?

Is it possible to surpass Chat LLMs in a specific domain by doing this?

For LLM post-training, can the RL-loss be split into two SFT training-steps: one training-step to minimize the loss on positive data, and another training-step to maximize the loss on negative data?

2. Role of Deep Learning in Computer Vision | CNNs, Applications & Real-World Examples Explained

Watch Here - https://youtu.be/lt21C9blRsg

Dear Colleagues and Friends from RG,

This is a dilemma concerning the boundary between technology and power. Arguments in favor (Yes, it does) warn that if the AI model relied upon by the government is a product of a corporation, then the rules and optimizations embedded in that code become de facto new state regulations, even though they have not been voted on by parliament (so-called code as law). Tech corporations can thus shape the market to suit their own interests, which is contrary to sovereignty. Arguments against (No, it does not) claim that politicians retain ultimate control and can reject AI recommendations; AI is merely an analytical tool. Furthermore, the use of the best private models is necessary for efficiency, and this influence can be controlled through strict contracts and public audits of external algorithms.

What is your opinion on this topic?

Please reply

I invite you to discuss

Thank you very much. All the best!

I invite you to collaborate on research.

Best regards,

Dariusz Prokopowicz

Does the LLM generative model require negative samples of reinforcement learning? Does removing low-quality data equate to adding negative samples?

What performance can be achieved by training an LLM using a pure SFT approach with RL's positive reward data collection?

Including collecting RL data for SFT

Do generative tasks really need negative samples?

Is removing low-quality SFT data equivalent to adding RL negative samples?

Does RL post-training of an LLM equate to collecting more positive reward data for SFT post-training?

What are the key advantages and limitations of using traditional machine learning (e.g., SVM, KNN) compared to deep learning (CNNs) for bone fracture classification in X-ray images, especially when dealing with limited or imbalanced datasets?

This is a critical issue of information security and data trust. Arguments in favor (Can be protected) claim that there are advanced cryptographic and modeling techniques (e.g., adversarial AI, federated learning) that allow for data integrity verification and rapid detection of manipulation, which is key to combating corruption and defending against foreign influence. Arguments against (Difficult to protect) warn that the more complex the AI model, the more difficult it is to detect subtle, deliberate data distortions (or manipulation of market signals to elicit a specific AI response), creating a new geostrategic weapon. By relying on AI, the state becomes extremely vulnerable to silent digital attacks, the effects of which can be catastrophic, as erroneous decisions are automatically put into effect.

What is your opinion on this topic?

Please reply

I invite you to discuss

Thank you very much. All the best!

I invite you to collaborate on research.

Best regards,

Dariusz Prokopowicz

IEEE 2026 9th International Symposium on Big Data and Applied Statistics (ISBDAS 2026) will be held from March 6 to 8, 2026, in Guangzhou, China.

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

1. Big Data Algorithms

Intelligent computing applications

Models and calculations

Intelligent computing algorithms

Evolutionary computation

Data Mining

Ternary decision making and machine learning

Combinatorial algorithms

Data and text mining

Knowledge reasoning

Deep Learning

2. The applied mathematics theory

Game Theory

Cognitive modeling and computation

Probability and statistics

Differential equation and their applications

Discrete mathematics and control

Linear algebra and its applications

Numerical analysis

Operations Research and optimization

Approximate theory

Combinatorial mathematics

The theory of computability

Discrete geometry

Matrix calculations

All papers will be reviewed by two or three expert reviewers from the conference committees. After a careful reviewing process, all accepted papers will be published by IEEE (ISBN: 979-8-3315-7218-1) and submit to EI Compendex and Scopus for indexing.

Full Paper Submission Date: January 31, 2026

Registration Deadline: February 17, 2026

Final Paper Submission Date: February 4 , 2026

Conference Dates: March 6-8, 2026

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

Not through show-case prompts.

Learn More: https://ais.cn/u/2meMFr

IEEE - 2026 9th International Symposium on Big Data and Applied Statistics (ISBDAS 2026) will be held from March 6 to 8, 2026, in Guangzhou, China.

---𝗖𝗮𝗹𝗹 𝗳𝗼𝗿 𝗽𝗮𝗽𝗲𝗿𝘀---

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

Intelligent computing applications

Models and calculations

Intelligent computing algorithms

Evolutionary computation

Data Mining

Ternary decision making and machine learning

Combinatorial algorithms

Data and text mining

Knowledge reasoning

Deep Learning

...

Game Theory

Cognitive modeling and computation

Probability and statistics

Differential equation and their applications

Discrete mathematics and control

Linear algebra and its applications

Numerical analysis

Operations Research and optimization

Approximate theory

Combinatorial mathematics

The theory of computability

Discrete geometry

Matrix calculations

...

---𝗦𝗽𝗲𝗮𝗸𝗲𝗿𝘀---

Prof. Nianyin Zeng,Xiamen University, China

Prof. Han Huang,South China University of Technology, China

Prof.Hongliang Dai,Guangzhou University,China

---𝗣𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻---

All papers will be reviewed by two or three expert reviewers from the conference committees. After a careful reviewing process, all accepted papers will be published by IEEE (ISBN: 979-8-3315-7218-1) and submit to EI Compendex and Scopus for indexing.

---𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗗𝗮𝘁𝗲𝘀---

Full Paper Submission Date: January 31, 2026

Registration Deadline: February 17, 2026

Final Paper Submission Date: February 4 , 2026

Conference Dates: March 6-8, 2026

--- 𝗣𝗮𝗽𝗲𝗿 𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻---

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

2026 3rd International Conference on Remote Sensing Technology and Survey Mapping (RSTSM 2026) will be held in Wuhan, Hubei Province, China, on March 6-8, 2026.

Learn More: https://ais.cn/u/eUrUNv

---𝗖𝗮𝗹𝗹 𝗳𝗼𝗿 𝗽𝗮𝗽𝗲𝗿𝘀---

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

◕Track 1: Remote Sensing Technology and Applications

1. Application of optical remote sensing technology in surface feature extraction and classification

2. Optical texture analysis and application of high-resolution remote sensing images

3. Improvement and enhancement of spatial and temporal resolution of optical remote sensing images

4. computer vision-based aerial image processing and feature recognition technology

5. Application of Optical Remote Sensing Technology in Environmental Monitoring and Resource Management

◕Track 2: Laser Scanning Measurement Technology and Applications

6. Application of laser scanning measurement technology in 3D mapping and building surveying

7. Performance and accuracy of laser measurement instruments and sensors

8. Laser Scanning Data for Underground Target Detection and Underground Object Imaging

9. Laser scanning based data analysis

◕Track 3: Application of Computer Vision and Machine Learning in Remote Sensing and Surveying and Mapping

10. Innovative Methods of Computer Vision Technology in Remote Sensing Image Interpretation and Analysis

11. Computer algorithms and models based on remote sensing data monitoring

12. deep learning and artificial intelligence applications in remote sensing and survey mapping

13. Optical Imaging and Computer Algorithms for Defect Detection and Recognition

◕Track 4: Remote Sensing Data Processing and Applications

14. Remote Sensing Data Preprocessing and Correction Methods

15. Remote Sensing Data Fusion and Feature Extraction

16. Remote sensing data-based change detection and monitoring techniques

17. Application of remote sensing data in natural disaster monitoring and prediction

---𝗣𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻---

After 2-3 experts of the organizing committee strictly audited the contribution of the conference, all the final papers will be officially published by SPIE - The International Society for Optical Engineering (ISSN: 0277-786X) in the Conference Proceedings. The published papers will then be submitted to EI Compendex, Scopus for indexing.

---𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗗𝗮𝘁𝗲𝘀---

Full Paper Submission Date:January 31, 2026

Registration Deadline:February 28, 2026

Final Paper Submission Date: February 27 , 2026

Conference Dates: March 6-8, 2026

--- 𝗣𝗮𝗽𝗲𝗿 𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻---

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

2026 5th International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2026) will be held in Kuala Lumpur, Malaysia during January 30- February 1, 2026.

𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗪𝗲𝗯𝘀𝗶𝘁𝗲: https://www.aischolar.com/conference/icbbem2026?invite=researchgate

---𝗖𝗮𝗹𝗹 𝗳𝗼𝗿 𝗽𝗮𝗽𝗲𝗿𝘀---

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

◕ Track 1: Big Data and Intelligent Analytics

Big data algorithms

Big data applications

Big data services

Intelligent data analytics

Knowledge discovery

Data quality and integration

Data preprocessing

Scalable data storage

Cloud-based big data systems

High-performance data processing

Infrastructure for big data and blockchain

Machine learning for big data

Deep learning for big data

Data-driven decision support

◕Track 2: Intelligent Economy and Digital Management

Digital economy

AI-driven innovation

Information technology in economic management

Intelligent modeling and prediction

Predictive analytics for economic systems

Data mining in digital economy

Time series forecasting

Algorithmic decision-making

Smart finance

Financial technology (FinTech)

Big data in financial regulation

Risk control systems

Intelligent e-commerce

Digital business

Market analytics

Blockchain in economic governance

◕ Track 3: Blockchain Systems and Applications

Blockchain architectures

Blockchain platforms

Distributed ledger technologies

Smart contracts

Blockchain-based IoT systems

Cyber-physical blockchain systems

Data provenance

Blockchain data management

Blockchain security

Data integrity and authentication

Privacy and trust management

Attack detection and defense

Decentralized applications (DApps)

Digital assets

Blockchain in digital economy

Supply chain management

Cross-chain interoperability

Transaction management

---𝗣𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻---

All papers, both invited and contributed, will be reviewed by two or three experts from the committees. After a careful reviewing process, all accepted papers of ICBBEM 2026 will be published in IET Conference Proceedings (ISSN: 2732-4494) and submitted to EI Compendex, Scopus for indexing.

---𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗗𝗮𝘁𝗲𝘀---

Full Paper Submission Date: January 15, 2026

Registration Date: January 23, 2026

Final Paper Submission Date: January 23, 2026

Conference Dates: January 30-February 1, 2026

--- 𝗣𝗮𝗽𝗲𝗿 𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻---

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

AI systems increasingly speak with empathy, confidence, and social awareness. This human like attitude can improve engagement and trust. It can also blur the line between tool and agent. Users may overestimate understanding, intent, or responsibility where none exists. This raises a tension between usability and honesty. Are we designing AI to support users, or to simulate humanity in ways that quietly mislead them?

Dear Colleagues and Friends from RG,

This issue shifts the discussion from the national to the global level. The arguments in favor (Yes) claim that if countries fully automate their economic strategies, they will act selfishly and competitively (e.g., optimizing their exports at the expense of their neighbors), leading to a new kind of trade and currency war based on the speed of algorithms. The establishment of a global body (e.g., a strengthened IMF or AI-managed G20) could harmonize policies and prevent systemic crises, acting for the global good. Arguments against (No) focus on national sovereignty and fear that centralized AI-based economic management would create an uncontrollable superpower that could impose unfavorable economic conditions on weaker countries. Furthermore, the lack of competition and diversity of economic strategies could hinder the evolution and testing of new, potentially better policy models.

What do you think about this?

Please reply

I invite you to discuss

Thank you very much. All the best!

I invite you to collaborate on research.

Best regards,

Dariusz Prokopowicz

Dear Colleagues and Friends from RG,

This is a question of risk and fragility resulting from complexity. The arguments in favor (Yes) focus on the speed and accuracy of AI responses, which are crucial in modern, ultra-fast markets; these systems can monitor an infinite number of variables and respond in milliseconds, which is essential to prevent panic and the spread of risk in a world of global financial interconnections. Human response is too slow. Arguments against (No) emphasize that these systems are black boxes, and their complexity is directly proportional to the risk of a catastrophe whose causes cannot be determined (explainability problem). If a complex AI system fails in the face of a “black swan” event, human decision-makers will be completely unprepared to take control, leading to a deeper and more uncontrollable collapse than would be the case in a system based on simpler, transparent rules.

What is your opinion on this topic?

Please reply

I invite you to join the discussion

Thank you very much. All the best!

I invite you to collaborate on research.

Best regards

Dariusz Prokopowicz

Hello, I'm currently working on a DL-CNN project. If you want to know more about the details please message me. Hoping to find people to work with. Thank you!

I have worked on encrypted application traffic detection using a deep learning model in my master's thesis, and I have also written a paper on that, but now I need a sponsor that can help me publish my paper in a well-known journal. I have tried working with my university professor and have been waiting for a while for proper guidance, but I haven't been able to get any. I am open to suggestions. Thanks

Dear Colleagues and Friends from RG,

This is a dilemma concerning purposefulness and measurability. The arguments in favor (Yes) assume that AI can take into account hundreds of factors (mental health, environmental quality, equal opportunities) and propose a more holistic and true measure of well-being, which is ethically desirable; focusing solely on GDP leads to environmentally and socially destructive policies. AI allows us to target the real needs of citizens. The arguments against (No) warn that traditional indicators (such as GDP) are crucial for transparency, international comparison, and democratic control; replacing them with complex, ever-changing AI targets would make policy incomprehensible and not subject to democratic accountability, creating the illusion of success by constantly changing the rules of the game and ultimately undermining the credibility of the state. Researchers need to define how to introduce the complexity of AI without losing democratic transparency.

What do you think about this?

Please reply

I invite you to discuss

Thank you very much. All the best!

I invite you to collaborate on research.

Warm regards,

Dariusz Prokopowicz

Hi everyone, I’m an MSCS student in the U.S. preparing a MICCAI 2026 submission on histopathology self-supervised learning and label efficiency (benchmarking performance under 1–10% labeled regimes with strong baselines).

Due to international travel constraints, I may not be able to attend in person in Abu Dhabi. I’m looking for a collaborator who can contribute meaningfully before submission (experiments/analysis/writing/reproducibility) and, if accepted, potentially present on-site as an author.

If interested, please DM me with your background (medical imaging / SSL / PyTorch) and availability. Thanks!

How can Deep Learning be utilized to improve the accuracy of early diagnosis for [specific disease] using radiological images, while minimizing the rate of false positives?

Hello Dear Everyone,

This is a clash between the state's economic security and the individual's privacy. Arguments for (Yes) focus on common welfare and fiscal justice; by analyzing transactional data, AI can detect large-scale fraud mechanisms and the shadow economy, leading to greater budget revenues and fairer tax burdens, which benefits everyone. Proponents argue that privacy can be technically secured (e.g., through anonymization or homomorphic techniques). Arguments against (No) warn against the economic surveillance state; the ability to track every transaction and consumer decision in real-time gives the authorities immense control over citizens, posing a threat to political and economic freedoms – even a minimal data leak or misuse could lead to discrimination or persecution. Researchers should focus on defining the minimum, essential scope of data and establishing mechanisms for external audit of the algorithms used by the state.

What do you think about this?

Please reply

I invite you to the discussion

Thank you very much. All the best!

I invite you to scientific collaboration,

Best regards,

Dariusz Prokopowicz

Dear Researchers,

Before, knowledge was rare. People feared sharing it, but it had value. Today, knowledge is open to everyone. Yet it often lacks value because there are too much information and too little understanding.

I'm an undergraduate doing a Software Engineering degree. I'm looking for a research topic for my final year project. If anyone has any ideas or research topics or any advice on how or where to find one please post them.

Thanks in advance ✌

Topics covered include Machine learning，Deep learning，Transformer，GAN, CNN,RNN, LSTM，Big data，Evolutionary algorithms，Particle swarm optimisation，Engineering design optimisation，Applications associated with intelligent computation

2026 2nd International Conference on Artificial Intelligence, Digital Media Technology and Social Computing (ICAIDS 2026) will be held in Chicago, USA on January 30-February 1, 2026.

More Details: https://www.aischolar.com/conference/ICAIDS2026?invite=researchgate

ICAIDS Location(Hybrid): Chicago, United States / Virtual

Conference papers will be submitted to these databases: EI Compendex, Scopus

Full Paper Submission Deadline: January 20, 2026

Early Bird Price Deadline: December 10, 2025

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

◕ Multimodal Data and Social Computing

Multimodal Analysis of Online Communities

Multimodal Fusion Techniques

Multi-Source Data Verification

Cross-Modal Content Generation

Web Mining

Soft Computing

Mobile Computing

Web 3.0

Semantic Web

Opinion Mining

Modelling of Social Behaviour

Modeling Complex Social Systems

...

◕ Digital Media Technology and Application

Visual Information Processing

Visual Image System

Virtual, Augmented, and Mixed Reality Production

Speech Recognition and Intelligent Interaction

Scene Modeling

Multimodal Image Fusion

Immersive and Interactive Media Experiences

Image Processing

Audio and Video Content Understanding and Analysis

Adaptive Video Streaming

3D Reconstruction and Rendering

3D Audio and Spatial Sound

...

◕ Artificial Intelligence in Digital Media

Generative Artificial Intelligence

Graph Convolutional Networks

Time Series Forecasting for Web Data

Text Categorization

Image Generation and Manipulation

Human-AI Collaboration

Deepfake Detection and Prevention

Deep Learning

Voice and Speech Processing LLMs

Trend Tracking

Trend Identification

Topic Recognition

...

All papers, both invited and contributed, will be reviewed by two or three experts from the committees. After a careful reviewing process, all accepted papers of ICAIDS 2026 will be published in Conference Proceedings and submitted to EI Compendex, Scopus and ACM Digital Library for indexing.

Submission Deadline: January 1, 2026

Registration Deadline: January 15, 2026

Conference Dates: January 30- February 1, 2026

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

Ontolgies and knowledge graphs can be included as representative of symbolic paradigm in AI. While Topological IA is a very recent term (July current year), its components are not: topological deep learning, topological data analysis, etc. Can you imagine a term representing a concept including all those fields, subfields mentioned above?

Three years ago my first book got published as a co editor in last three years I have coordinated with editors and writers to proof and complete 55 books

1. Hassan, A., Dutta, P. K., Gupta, S., Mattar, E., & Singh, S. (Eds.). (2024). Human-centered approaches in Industry 5.0: Human–machine interaction, virtual reality training, and customer sentiment analysis (369 pp.). IGI Global. https://www.igi-global.com/book/human-centered-approaches-industry/333621

Thematic overview: Explores Industry 5.0 from a human-centric lens, focusing on ergonomics of human–machine collaboration, VR/AR-based training ecosystems, and AI-driven sentiment analysis to personalize industrial services and experiences.

2. Hassan, A., Prasad, V. K., Bhattacharya, P., Dutta, P., & Damaševičius, R. (Eds.). (2024). Federated learning and AI for Healthcare 5.0 (391 pp.). IGI Global. https://www.igi-global.com/book/federated-learning-healthcare/326124

Thematic overview: Covers federated learning pipelines and privacy-preserving AI architectures for Healthcare 5.0, highlighting real-world use cases in distributed hospitals, edge–cloud integration, and secure clinical decision support.

3. Hassan, A., Dutta, P. K., Gupta, S., Mattar, E., & Singh, S. (Eds.). (2024). Emerging technologies in digital manufacturing and smart factories (291 pp.). IGI Global. https://www.igi-global.com/book/emerging-technologies-digital-manufacturing-smart/325239

Thematic overview: Examines digital manufacturing building blocks—IIoT, CPS, digital twins, and robotics—showing how smart factories leverage data, automation, and AI to achieve resilient and adaptive production.

4. Hassan, A., Prasad, V. K., Bhattacharya, P., Dutta, P. K., & Damaševičius, R. (Eds.). (2024). Pioneering smart Healthcare 5.0 with IoT, federated learning, and cloud security (352 pp.). IGI Global. https://www.igi-global.com/book/pioneering-smart-healthcare-iot-federated/333616

Thematic overview: Integrates IoT sensing, federated analytics, and secure cloud infrastructures to architect smart Healthcare 5.0 systems with robust data governance and patient-centric intelligence.

5. Raj, P., Dutta, P. K., Chong, P. H. J., Song, H. H., & Zaitsev, D. A. (Eds.). (2025). Applied graph data science: Graph algorithms and platforms, knowledge graphs, neural networks, and applied use cases. Elsevier. https://shop.elsevier.com/books/applied-graph-data-science/raj/978-0-443-29654-3

Thematic overview: Surveys the full stack of graph data science—from classical algorithms and graph databases to GNNs and knowledge graphs—with applied case studies in telecom, finance, cybersecurity, and recommendation.

6. Bhardwaj, R., Dutta, P. K., Raj, P., Pandey, A. K., Saini, K., González Briones, A., & Kaabar, M. K. A. (Eds.). (2024). Hybrid information systems: Non-linear optimization strategies with artificial intelligence. De Gruyter. https://www.degruyter.com/document/doi/10.1515/9783111331133/html

Thematic overview: Focuses on AI-powered, non-linear optimization methods for hybrid information systems, addressing scheduling, resource allocation, and decision-making in complex cyber–physical environments.

7. Thaichon, P., Dutta, P. K., Raj, P., & Gupta, S. (Eds.). (2024). Technology and luxury hospitality (1st ed., 334 pp.). Routledge. https://www.taylorfrancis.com/books/edit/10.4324/9781003488248/

Thematic overview: Analyses the intersection of luxury hospitality and emerging technologies, covering digital guest journeys, data-driven personalization, revenue optimization, and brand positioning.

8. Hassan, A., Bhattacharya, P., Tikadar, S., Dutta, P. K., & Sagayam, M. (Eds.). (2024). Lightweight digital trust architectures in the Internet of Medical Things (IoMT) (448 pp.). IGI Global. https://www.igi-global.com/book/lightweight-digital-trust-architectures-internet/331762

Thematic overview: Proposes cryptographic and protocol-level solutions for lightweight trust, identity, and privacy in constrained IoMT devices and heterogeneous medical networks.

9. Khang, A., Dutta, P. K., Gupta, S., Ayedee, N., & Chatterjee, S. (Eds.). (2024). Revolutionizing the AI-digital landscape: A guide to sustainable emerging technologies for marketing professionals (366 pp.). Productivity Press/Routledge. https://www.routledge.com/Revolutionizing-the-AI-Digital-Landscape-A-Guide-to-Sustainable-Emerging/Khang-Dutta-Gupta-Ayedee-Chatterjee/p/book/9781032688299

Thematic overview: Bridges AI, martech, and sustainability, demonstrating how responsible data, automation, and analytics reshape marketing strategy, consumer engagement, and brand equity.

10. Ben Youssef, A., Dutta, P. K., Doshi, R., & Sajnani, M. (Eds.). (2024). AI, blockchain, and metaverse in hospitality and tourism Industry 4.0: Case studies and analysis (344 pp.). Chapman & Hall/CRC. https://www.taylorfrancis.com/books/edit/10.1201/9781032706474/

Thematic overview: Presents global case studies showing how AI, blockchain, and metaverse environments transform hospitality and tourism operations, experience design, and ecosystem partnerships.

11. Hassan, A., Bhattacharya, P., Dutta, P. K., Verma, J. P., & Kundu, N. K. (Eds.). (2024). Quantum computing and supply chain management: A new era of optimization (310 pp.). IGI Global. https://www.igi-global.com/book/quantum-computing-supply-chain-management/338306

Thematic overview: Explores quantum-inspired and quantum-native optimization for logistics, routing, inventory, and risk, with a strong focus on algorithmic design and real-world supply chain scenarios.

12. Misra, S., Dutta, P. K., Jadeja, R., & Mittal, M. (Eds.). (2024). Practical approaches to agile project management (467 pp.). IGI Global. https://www.igi-global.com/book/practical-approaches-agile-project-management/335468

Thematic overview: Provides actionable frameworks, methods, and case evidence for scaling agile and hybrid project management across software, engineering, and organizational transformation contexts.

13. Dutta, P. K., Raj, P., Sundaravadivazhagan, B., & Pon Selvan, C. (Eds.). (2024). Artificial intelligence solutions for cyber-physical systems (232 pp.). Auerbach/CRC Press. https://www.taylorfrancis.com/books/edit/10.1201/9781032694375/

Thematic overview: Focuses on AI-driven perception, control, and anomaly detection in CPS, spanning smart grids, transport, manufacturing, and mission-critical infrastructure.

14. Rabie, K., Karthik, C., Chowdhury, S., & Dutta, P. K. (Eds.). (2023). Deep learning in medical image processing and analysis (376 pp.). IET. https://shop.theiet.org/deep-learning-in-medical-image-processing-and-analysis

Thematic overview: Surveys CNNs, transformers, and hybrid models for segmentation, classification, prognosis, and multi-modal analysis in medical imaging workflows.

15. Patnaik, S., Hamad, A. M., Paul, D., Dutta, P. K., & Shafiq, M. (Eds.). (2024). Nutrition controversies and advances in autoimmune disease (2 vols., 855 pp.). IGI Global. https://www.igi-global.com/book/nutrition-controversies-advances-autoimmune-disease/340698

Thematic overview: Synthesizes clinical, biochemical, and nutritional perspectives to unpack contentious issues and emerging interventions in autoimmune disorders.

16. Raj, P., Rocha, Á., Singh, S. P., Dutta, P. K., & Sundaravadivazhagan, B. (Eds.). (2024). Building embodied AI systems: The agents, the architecture principles, challenges, and application domains. Springer. https://link.springer.com/book/9783031682551

Thematic overview: Addresses architectures, sensing, control, and deployment of embodied AI agents across robotics, autonomous systems, and interactive environments.

17. Dutta, S., Rocha, Á., Dutta, P. K., Bhattacharya, P., & Singh, R. (Eds.). (2024). Advances in data analytics for influencer marketing: An interdisciplinary approach. Springer. https://link.springer.com/book/10.1007/978-3-031-65727-6

Thematic overview: Explores analytics pipelines, social graph modeling, and metrics for understanding and optimizing influencer-driven campaigns.

18. Raj, P., Rocha, Á., Dutta, P. K., Fiorini, M., & Prakash, C. (Eds.). (2024). Illustrating digital innovations towards intelligent fashion: Leveraging information system engineering and digital twins for efficient design of next-generation fashion. Springer. https://link.springer.com/book/9783031710513

Thematic overview: Marries digital twins, information systems, and AI to reimagine fashion design, production planning, and sustainability in fashion ecosystems.

19. Bhattacharya, P., Liu, H., Dutta, P. K., Rodrigues, J. J. P. C., & Sethi, G. (Eds.). (2025). Revolutionizing Healthcare 5.0: The power of generative AI – Advancements in patient care through generative AI algorithms. Springer. https://link.springer.com/book/9783031757709

Thematic overview: Focuses on generative models for clinical documentation, imaging augmentation, decision support, and personalized patient interaction in Healthcare 5.0.

20. Dutta, P. K., Hamad, A., Haghi, A. K., & Prabhakar, P. K. (Eds.). (2025). Food and Industry 5.0: Transforming the food system for a sustainable future. Springer. https://link.springer.com/book/9783031767579

Thematic overview: Connects Industry 5.0 paradigms with food production, traceability, safety, and circular economy strategies in global food systems.

21. Dutta, P. K., Goyal, S. B., Verma, C., & Kannan, H. (Eds.). (n.d.). DeFi adoption: Digital transformation in the energy and utilities industry. Apple Academic Press. https://www.appleacademicpress.com/defi-adoption-in-the-energy-and-utilities-industry-toward-sustainable-digital-transformation/9781779642394

Thematic overview: Explores how DeFi, tokenization, and blockchain-based platforms can transform energy markets, billing, and sustainable utility services.

22. Aguilar-Zárate, P., Dutta, P. K., Gupta, S., & Haghi, A. K. (Eds.). (n.d.). Energy smart farming for sustainable management of natural resources. Apple Academic Press. https://appleacademicpress.com/energy-smart-farming-for-sustainable-management-of-natural-resources-/1613

Thematic overview: Examines digital and energy-smart farming practices to optimize water, soil, and energy use while boosting resilience and productivity.

23. Youssef, A. B., Raj, P., Bhattacharya, P., & Dutta, P. K. (Eds.). (n.d.). Green energies: Synergetic approaches to energy, technology, and sustainability. Apple Academic Press. https://www.appleacademicpress.com/green-energies-synergetic-approaches-to-energy-technology-and-sustainability/9781998511167

Thematic overview: Provides a multi-disciplinary view of green energy technologies, policy enablers, and socio-technical pathways to sustainability.

24. Bhattacharya, P., Prasad, V. K., Hemanth, D. J., Dutta, P. K., Kathait, A., & Dănciulescu, D. (Eds.). (2024). Secure big-data analytics for emerging healthcare in 5G and beyond: Concepts, paradigms, and solutions. IET. https://doi.org/10.1049/PBHE063E

Thematic overview: Focuses on secure, scalable big data frameworks that exploit 5G/6G capabilities for real-time analytics in healthcare.

25. Damaševičius, R., Dutta, P. K., Bacanin Dzakula, N., Vajjhala, N. R., & Desai, D. (Eds.). (2024). Customer insights into innovation strategies for distributed market needs. Cambridge Scholars Publishing. https://www.cambridgescholars.com/product/978-1-0364-1852-6

Thematic overview: Examines how data-driven customer insights guide innovation and product–service strategies in distributed and diverse markets.

26. Thaichon, P., Dutta, P. K., Haghi, A. K., & Dutta, S. (Eds.). (2024). Digital disruption in hospitality, AI and emerging technologies: A roadmap to personalized experiences, enhanced operations, and revenue growth. Emerald Publishing. https://bookstore.emerald.com/digital-disruption-in-hospitality-ai-and-emerging-technologies-hb-9781836087977.html

Thematic overview: Provides a roadmap for leveraging AI and emerging technologies to achieve hyper-personalization, operational efficiency, and new revenue models in hospitality.

27. Thaichon, P., Dutta, P. K., Haghi, A. K., & Dutta, S. (Eds.). (2025). Digital disruption in hospitality, sustainable hospitality: A global perspective on people, planet, and profit. Emerald Publishing. https://bookstore.emerald.com/digital-disruption-in-hospitality-sustainable-hospitality-hb-9781836623618.html

Thematic overview: Frames hospitality transformation through sustainability, showcasing ESG-aligned business models, green operations, and responsible tourism.

28. Chelliah, P. R., Dutta, P. K., Kumar, A., Santibanez Gonzalez, E. D. R., Mittal, M., & Gupta, S. (Eds.). (2025). Generative artificial intelligence in finance: Large language models, interfaces, and industry use cases to transform accounting and finance processes. Scrivener Publishing. https://doi.org/10.1002/9781394271078

Thematic overview: Explores generative AI and LLM-driven tools for financial reporting, audit, risk, and compliance, emphasizing explainability and governance.

29. Dutta, P. K., Gupta, S., Kashyap, S., Gehlot, A., Karmakar, R., & Bhattacharya, P. (Eds.). (2025). Emotional intelligence in the digital era: Concepts, frameworks, and applications. Routledge. https://www.routledge.com/Emotional-Intelligence-in-the-Digital-Era-Concepts-Frameworks-and-Applications/Dutta-Gupta-Kashyap-Gehlot-Karmakar-Bhattacharya/p/book/9781032703695

Thematic overview: Connects EI theory with digital workplaces, AI interfaces, and remote collaboration, outlining tools to cultivate emotionally intelligent organizations.

30. Dutta, P. K., Ricciuti, M., Bogrekci, I., & Suseelan, S. (Eds.). (2025). Digital transformation in aviation industry operations: Innovations and sustainable solutions. Routledge. https://www.routledge.com/Digital-Transformation-in-Aviation-Industry-Operations-Innovations-and-Sustainable-Solutions/Dutta-Ricciuti-Bogrekci-Suseelan/p/book/9781032799568

Thematic overview: Discusses digital technologies for optimizing aviation operations, passenger services, safety, and sustainability in airlines and airports.

31. Dutta, P. K., Bhattacharya, P., Verma, J. P., Chopra, A., Kundu, N. K., & Aurangzeb, K. (Eds.). (2025). Quantum computing and artificial intelligence in logistics and supply chain management (Chapman & Hall/CRC). https://www.taylorfrancis.com/books/edit/10.1201/9781003498292/

Thematic overview: Combines AI and quantum methods to tackle complex logistics problems, from multi-echelon planning to resilient, data-driven supply networks.

32. Dutta, P. K., Singh, B., Kaunert, C., & Sciacovelli, A. L. (Eds.). (2025). Security intelligence in the age of AI: Navigating legal and ethical frameworks [Kindle ed.]. Emerald Publishing. https://bookstore.emerald.com/security-intelligence-in-the-age-of-ai-hb-9781836081579.html

Thematic overview: Examines AI-enabled security intelligence alongside legal, ethical, and policy challenges in national security, law enforcement, and corporate risk.

33. Dutta, P. K., Ricciuti, M., Bogrekci, I., & Suseelan, S. (Eds.). (2025). Airline customer experience: Digitalization in passenger services (1st ed.). Routledge. https://www.taylorfrancis.com/books/edit/10.4324/9781003625100/

Thematic overview: Focuses on digital touchpoints, CX metrics, and service innovation in airline passenger experience management.

34. Yu, P., Dutta, P. K., Padhi, A., Das Mitra, S., & Sharma, V. K. (Eds.). (2025). AI and innovation in HRM: The future of strategic HR in the service economy (1st ed.). Routledge. https://www.routledge.com/AI-and-Innovation-in-HRM-The-Future-of-Strategic-HR-in-the-Service-Economy/Dutta-Padhi-Das-Sharma-Yu/p/book/9781032862750

Thematic overview: Details AI-driven talent analytics, HR automation, and strategic workforce management for service-centric organizations.

35. Raj, P., Sharma, D. P., Dutta, P. K., Siva Prasad, B. V. V., & Soundarabai, P. B. (Eds.). (2025). Artificial intelligence (AI) for IT energy efficiency and green AI for environment sustainability (1st ed.). Springer. https://link.springer.com/book/9783031894190

Thematic overview: Links AI with energy-aware IT operations, model efficiency, and environmentally sustainable computing practices.

36. Sbai, A., Khan, M., Dutta, P. K., Davim, J. P., & Edeh, M. O. (2025). Transformative role of transformer models in healthcare. IGI Global.

Thematic overview: Surveys transformer-based architectures for clinical NLP, imaging, multimodal fusion, and decision support across healthcare pathways.

37. Kumar, A., Ortiz-Rodriguez, F., Braga de Vasconcelos, J., Dutta, P. K., Saini, H. K., & Rathore, P. S. (Eds.). (2025). Adversarial deep generative techniques for early diagnosis of neurological conditions and mental health practices: Theoretical insights with practical applications. Springer. https://link.springer.com/book/10.1007/978-3-031-91147-7

Thematic overview: Explores adversarial and generative models for early detection, prognosis, and intervention in neurology and mental health.

38. Mohanty, S. N., Rocha, Á., & Dutta, P. K. (Eds.). (2025). Artificial intelligence in oncology: Cancer diagnosis and treatment, medical imaging, and personalized medicine. Springer Nature. https://link.springer.com/book/10.1007/978-3-031-94302-7

Thematic overview: Integrates AI tools for cancer screening, imaging, molecular profiling, and personalized therapeutic decision-making.

39. Singh, S. K., Jadeja, R. B., Khanna, A., Dutta, P. K., & Virdee, B. (Eds.). (2025). Security paradigms in 6G smart cities and IoT ecosystems: Navigating the future. CRC Press. https://www.routledge.com/Security-Paradigms-in-6G-Smart-Cities-and-IoT-Ecosystems-Navigating-the-Future/KumarSingh-Jadeja-Khanna-Dutta-Virdee/p/book/9781032913599

Thematic overview: Addresses security architectures, privacy, and trust frameworks for 6G-enabled smart cities and dense IoT environments.

40. Shafik, W., Ben Youssef, A., Pon Selvan, C., & Dutta, P. K. (Eds.). (2025). Sustainable healthcare systems in Africa: Technologies, practices, and management. CRC Press. https://www.routledge.com/Sustainable-Healthcare-Systems-in-Africa-Technologies-Practices-and-Management/Shafik-Youssef-Selvan-Dutta/p/book/9781032870205

Thematic overview: Examines socio-technical, managerial, and policy aspects of building sustainable, technology-enabled healthcare systems in Africa.

41. Dutta, P. K., Singh, B., Jain, V., Kaunert, C., & Vig, K. (Eds.). (2025). Artificial intelligence and human rights, democracy, and the rule of law. CRC Press. https://www.routledge.com/Artificial-Intelligence-and-Human-Rights-Democracy-and-the-Rule-of-Law/KumarDutta-Singh-Jain-Kaunert-Vig/p/book/9781032796635

Thematic overview: Explores how AI systems intersect with human rights, democratic institutions, surveillance, and rule-of-law safeguards.

42. Dutta, P. K., & Trivedi, S. (Eds.). (2026). Rural futures: Innovation, resilience, and sustainable development. Jenny Stanford Publishing/Routledge. https://www.routledge.com/Rural-Futures-Innovation-Resilience-and-Sustainable-Development/Dutta-Trivedi/p/book/9789815129939

Thematic overview: Presents innovation and resilience pathways for rural communities, spanning agriculture, livelihoods, digital inclusion, and governance.

43. Dutta, P. K., Singh, R., Haghi, A. K., & Hamad, A. A. (Eds.). (2026). Harnessing AI to reshape the future of agriculture. Springer Nature Switzerland.

Thematic overview: Focuses on AI-driven agritech solutions—remote sensing, decision support, robotics, and climate-smart applications—for next-generation agriculture.

44. Shafik, W., Dutta, P. K., & Pattanaik, P. (Eds.). (2025). The convergence of federated learning and Healthcare 5.0 and beyond: A new era of intelligent health systems. Springer.

Thematic overview: Explores federated learning architectures and protocols powering distributed, privacy-aware Healthcare 5.0 and future health ecosystems.

45. Dutta, P. K., & Trivedi, S. (Eds.). (2025). Agripreneurship, digital inclusion and sustainability: Rural innovation and growth in the Global South. CABI Publishing.

Thematic overview: Examines agripreneurship models, digital platforms, and sustainability practices driving rural economic growth and social inclusion.

46. Garg, H., Dutta, P. K., Gnanasankaran, N., & Chowdhury, S. (Eds.). (2026). Harnessing artificial intelligence to ensure diverse global teams. Academic Press.

Thematic overview: Investigates AI-enabled tools and frameworks to support, manage, and enhance diversity, equity, and inclusion in global workforces.

47. Dutta, P. K., Raj, P., Bhattacharya, P., Budhiraja, I., & Kaplun, D. (Eds.). (2026). Signal processing roadmap: Technologies, applications, and future directions. Morgan Kaufmann.

Thematic overview: Provides a forward-looking map of signal processing theory and applications, from classical domains to AI-native and quantum-era challenges.

48. Agerstam, M., Dutta, P. K., Sundaravadivazhagan, B., Nagasubramanian, G., & Raj, P. (Eds.). (2026). Cloud-native architecture (CNA) and artificial intelligence (AI) for the future of software engineering: The principles, patterns, platforms and practices (Vol. 141). Elsevier. https://shop.elsevier.com/books/cloud-native-architecture-cna-and-artificial-intelligence-ai-for-the-future-of-software-engineering-the-principles-patterns-platforms-and-practices/agerstam/978-0-443-22401-0

Thematic overview: Bridges CNA patterns with AI-driven development, covering microservices, DevOps, MLOps, and scalable intelligent software systems.

49. Dutta, P. K., Krishnan, S., Bhattacharya, P., Amesho, K. T. T., Rocha, Á., & Haghi, A. K. (Eds.). (2026). AI, blockchain and social network on urban crisis management: Leveraging emerging technologies for disaster response and resilience in smart cities (Vol. 142). Academic Press. https://shop.elsevier.com/books/ai-blockchain-and-social-network-on-urban-crisis-management-leveraging-emerging-technologies-for-disaster-response-and-resilience-in-smart-cities/dutta/978-0-443-31368-4

Thematic overview: Explores AI analytics, blockchain, and social network intelligence for crisis detection, coordination, and resilience in urban environments.

50. Trivedi, S., Dutta, P. K., Ananth, C., & Kumar, A. (Eds.). (2026). Green policies and augmented reality in InsurTech: Catalysts for sustainability and innovation. Emerald Publishing.

Thematic overview: Investigates how green policy frameworks and AR technologies transform InsurTech products, risk assessment, and customer engagement for sustainability.

51. Pattanaik, P., Dutta, P., & Mohanty, S. (Eds.). (2026). Multimedia and AI applications for sustainable smart mining and energy. IGI Global.

Thematic overview: Discusses multimedia sensing and AI analytics to improve safety, efficiency, and environmental performance in mining and energy sectors.

52. Dutta, P. K., Nguyen, K., Gayathri, N., Soundarabai, P. B., & Raj, P. (Eds.). (2026). Artificial intelligence (AI) for climate change mitigation: Key motivations and combating methods. Cambridge Scholars Publishing. https://cambridgescholars.com/product/978-1-0364-6095-2/

Thematic overview: Focuses on AI methods for mitigation—emissions monitoring, climate modeling, optimization of energy systems, and climate-smart policies.

53. Kautish, S., Dutta, P. K., Nagpal, N., Karantharaj, P., Bhattacharya, P., & Gupta, V. P. (Eds.). (2025). Digital transformation and innovation in higher education: Faculty development and student-centric paradigms (Advances in Data Science Driven Technologies; Vol. 6). Bentham Science Publishers. https://doi.org/10.2174/97988988112971250601

Thematic overview: Explores data-driven transformation of higher education with a focus on faculty upskilling, student-centered pedagogy, and analytics-informed governance.

54. Kautish, S., Dutta, P. K., Nagpal, N., Karantharaj, P., Bhattacharya, P., & Gupta, V. P. (Eds.). (2025). Data-informed leadership in higher education: An executive playbook for institutional excellence (Advances in Data Science Driven Technologies; Vol. 5). Bentham Science Publishers. https://doi.org/10.2174/97988988112661250501

Thematic overview: Provides an executive playbook for data-informed leadership in universities, covering strategy, quality assurance, and performance management.

55. Dutta, P. K., Singh, B., Paliktzoglou, V., Ibeh, L., & Prianto, A. L. (Eds.). (2026). Digital education for a sustainable future: Technological integration, SDGs, and empowering future generations (Sustainable Development Goals Series). Palgrave Macmillan. https://link.springer.com/book/9783032154323

Thematic overview: Links digital education strategies with SDGs, focusing on inclusive pedagogy, technology integration, and empowerment of future learners.

n large-scale, high-safety critical systems

In the fields of genomic selection and mating optimization, current research often focuses on exploring genotype-to-phenotype relationships and improving mating strategies. I am wondering whether there has been any research on predicting offspring genotypes based on parental genotypes, and whether it would be feasible to use artificial intelligence to build a model for this purpose.

From many data, situations, examples, etc., it is clear that generative artificial intelligence (AI) technology significantly supports scientific research at every stage of its implementation, from concept design to the development of results. In the preparation phase of research projects, generative AI can help analyze scientific literature quickly and comprehensively. Natural language processing algorithms make it possible to search for relevant publications, identify key trends in the literature and pinpoint research gaps. This allows researchers to create more precise research questions and legitimate hypotheses, which is crucial to the quality and value of research. But does generative artificial intelligence technology help research mainly in terms of processing large data sets in a relatively short period of time, or is it also helpful in many other issues of ongoing scientific research? And how about the ethics and objectivity of conducting scientific research? Does generative artificial intelligence technology help with these issues as well?

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

Please write what you think in this issue? Do you see rather threats or opportunities associated with the development of artificial intelligence technology?

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,

Warm regards,

I would like to invite you to join me in scientific cooperation,

Dariusz Prokopowicz

So, https://paperswithcode.com no longer exist. It was a very convenient way to find repositories implementing research papers in ML.

What would be needed to replicate such website? they made their database available (as for now) https://github.com/paperswithcode/paperswithcode-data

Are there current alternatives?

I am currently conducting research on integrating variational quantum algorithms with classical deep learning models to overcome the challenges of training high-dimensional networks. In my work, I am exploring whether quantum subroutines; such as quantum amplitude amplification or quantum natural gradient methods; can help speed up convergence and escape local minima more effectively than classical optimizers.

I would appreciate detailed theoretical analyses, simulation studies, or experimental benchmarks that compare these hybrid methods with traditional deep learning optimizers.

In a situation where in many service companies and many manufacturing companies, as part of the so-called cost optimisation and profitability improvement, a significant part, the majority of the employed workers or the entire workforce will be replaced by artificial intelligence technology, the tax revenue going to the state budget from income taxes of the previously employed workers and the amounts from para-taxes, contributions to the social security system and others will significantly decrease if the tax system is not applied modified and adapted to the fourth technological revolution currently taking place. In addition, a long-standing process of change in the demographic structure of society, known as ageing, is taking place in developed countries. This means a successive decrease in the number of people in many productive years against people who have already reached retirement age. This will further weaken the state's public finance system in the years to come. If, in the future, the state is to ensure convenient provision of public goods and services for the next generations of citizens, the social security system, the participatory pension system, etc. are to function effectively, the necessary changes, including in the area of fiscal policy, should already be introduced. However, the issue of shaping socio-economic policy, including fiscal policy, social policy, provision of public goods by the state to citizens, etc., may be a problem mainly in the short term (a few months) or medium term (up to a few years) instead of the long term (at least a few decades of time).

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

What is your opinion on this?

What is your opinion on this subject?

Please respond,

I invite you all to discuss,

Thank you very much,

The following articles are related to the above issues in some respects:

I invite you to scientific cooperation,

Best regards,

Dariusz Prokopowicz

Mayores detalles

What are the ethical challenges of applying deep learning models?

in large-scale, high-safety critical systems

IEEE-2025 5th International Conference on Computational Modeling, Simulation and Data Analysis (CMSDA 2025) will be held on December 12-14, 2025 in Qingdao, China.

More Details: https://ais.cn/u/z2e2um

Paper Submission: https://ais.cn/u/BVryAj

Registration: https://ais.cn/u/JRBN7v

---𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗗𝗮𝘁𝗲𝘀---

Full Paper Submission Date: November 25, 2025

Registration Deadline: December 5, 2025

Full Paper Submission Date: December 5, 2025

Conference Dates: December 12-14, 2025

---𝗖𝗮𝗹𝗹 𝗙𝗼𝗿 𝗣𝗮𝗽𝗲𝗿𝘀---

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

Model Verification and Validation

Intelligent and Expert Systems

Computer Network Modeling and Analysis

Circuit Simulation

Discrete Event and Data Simulation

Industrial Simulation Modeling

Robot System Simulation

Internet of Things Modeling

Education Modeling

Material Engineering Simulation

Power System Simulation

Fuzzy Modeling

Process Simulation and Modeling

Performance Evaluation and Modeling

Mobile System Modeling

......

Data Mining

Big Data Visualization

Computer Simulation

Model Optimization

Model Visualization

Modeling Analysis

Linear Regression Analysis

Multimodal Interactive

Deep Learning

Data Classification

Edge Computing

Mobile Computing

---𝗦𝗽𝗲𝗮𝗸𝗲𝗿𝘀---

---𝗣𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻---

Submitted paper will be peer reviewed by conference committees, and accepted papers after registration and presentation will be published in the IEEE(ISBN: 979-8-3315-8795-6), which will be submitted for indexing by IEEE Xplore, EI Compendex, Scopus.

--- 𝗣𝗮𝗽𝗲𝗿 𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻---

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

The 7th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP 2025) will be held in Xi‘an, China during November 28-30, 2025.

𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗪𝗲𝗯𝘀𝗶𝘁𝗲: https://ais.cn/u/EZFzUn

---𝗖𝗮𝗹𝗹 𝗳𝗼𝗿 𝗽𝗮𝗽𝗲𝗿𝘀---

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

◕ Intelligent Control and Automation

(1) Control Theory and Application

(2) Intelligent and Optimal Control System

(3) System Science and System Engineering

(4) System Modeling, Analysis and Synthesis

(5) Power system and its automation

(6) Power electronics and power drive

◕ Artificial Intelligence and Robots

(1) Artificial Intelligence and Deep Learning

(2) Statistical learning and pattern recognition

(3) Computer Vision

(4) Robots

(5) Application of Big Data and Artificial Intelligence in Oil and Gas Field Development

(6) Oilfield digitalization, automation and intellectualization

◕ Signal and Information Processing

(1) New methods and techniques of logging signal processing

(2) Signal Acquisition and Processing of Geophysical Exploration Instruments

(3) New methods and technologies of digital signal processing

(4) Real-time signal and random signal acquisition and processing technology

(5) Array Signal Processing Technology

(6) Advanced filtering technology

◕ Photoelectric Detection and Measurement Technology

(1) The theory and technology of optical fiber sensing

(2) Distributed Fiber and Grating Measurement Technology

(3) Visible and Infrared Imaging and Image Processing

(4) Electromagnetic measurement and testing

(5) Laser measurement technology and its application

(6) X-ray detection technology and its application

◕ Measurement and Control Technology and Intelligent Instruments

(1) Rotary steering drilling equipment and measurement while drilling technology

(2) New technologies and methods of logging tools

(3) Geophysical exploration methods and instruments

(4) Advanced measurement and control technology and its application

(5) Intelligent instruments

(6) Advanced sensors

---𝗣𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻---

All papers, both invited and contributed, the accepted papers, will be published for inclusion into Conference proceedings with meeting scope and quality requirements, and also submitted to EI Compendex and Scopus for indexing. All conference proceedings paper can not be less than 4 pages.

---𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗗𝗮𝘁𝗲𝘀---

Full Paper Submission Date: October 31, 2025

Registration Deadline: November 21, 2025

Final Paper Submission Date: November 21, 2025

Conference Date: November 28-30, 2025

--- 𝗣𝗮𝗽𝗲𝗿 𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻---

Please send the full paper(word+pdf) to Submission System: https://ais.cn/u/EZFzUn

I am planning a research project aimed at improving the quality and efficiency of deep learning datasets through reconstruction. The core ideas are as follows:

I look forward to receiving feedback and suggestions from experts. Thank you!

I am seeking comprehensive book recommendations to build a strong theoretical foundation in several advanced machine learning algorithms for my research. My focus is on understanding the underlying principles and statistical background, and the ultimate goal of implementing these methods in Python.

I am particularly interested in books that cover the following topics:

Could you suggest any textbooks covers the theory, statistical understanding, and algorithmic design of these topics using Python?

Thank you for your guidance and insights.

## Quick Discussion: ##

Deep learning models (CNNs, LSTMs, and transformers) are now common in fault diagnosis — but they often act like 'black boxes'. Engineers in industry usually ask, “Why did the model classify this as a fault?”

This raises a key question:

Should we consider the need for 'Explainable AI (XAI)' in fault diagnosis, or is achieving high accuracy sufficient?

Curious to know—in your experience, which matters more in practice: "interpretability or maximum accuracy"?

Conducting a SWOT analysis of the applications of artificial intelligence technology in business, in the business activities of companies and enterprises, shows that there are both many already and developing many more business applications of the said technology, i.e., many potential development opportunities are recognized in this field of using the achievements of the current fourth and/or fifth technological revolution in various spheres of business activity, as well as there are many risks arising from inappropriate, incompatible with the prevailing social norms, standards of reliable business activity, incompatible with business ethics use of new technologies. Among some of the most recognized negative aspects of improper use of generative artificial intelligence technology is the use of AI-equipped graphic applications available on the Internet that allow for the simple and easy generation of photos, graphics, images, videos and animations that, in the form of very realistically presented images, photos, videos, etc., depict something that never happened in reality, i.e., they graphically present images or videos presenting what could be described as “fictitious facts” in a very professional manner. In this way, Internet users can become disinformation generators in online social media, where they can post the said generated images, photos, videos, etc. with added descriptions, posts, comments, in which the said “fictitious facts” presented in the photos or videos will also be described in an editorially correct manner. Besides, the mentioned descriptions, posts, entries, comments, etc. can also be edited with the help of intelligent chatbots available on the Internet like Chat GPT, Copilot, Gemini, etc. However, misinformation is not the only serious problem as it has significantly intensified after OpenAI released the first versions of ChatGPT chatbot online in November 2021. A new category of technical operational risk associated with the new AI technology applied has emerged in companies and enterprises that implement generative artificial intelligence technology into various spheres of business. In addition, there is a growing scale of risks arising from conflicts of interest between business entities related to not fully regulated copyright issues of works created using applications and information systems equipped with generative artificial intelligence technology. Accordingly, there is a demand for the development of a standard of a kind of digital signature with the help of which works created with the help of AI technology will be electronically signed, so that each such work will be unique, unrepeatable and whose counterfeiting will thus be seriously hampered. However, these are only some of the negative aspects of the developing applications of AI technologies, for which there are no functioning legal norms. In the middle of 2023 and then in the spring of 2024, European Union bodies made public the preliminary versions of the developed legal norms on the proper, business-ethical use of technology in business, which were given the name AI Act. The legal normatives, referred to as the AIAct, contain a number of specific, defined types of AI technology applications deemed inappropriate, unethical, i.e. those that should not be used. The AIAct contains classified according to different levels of negative impact on society various types and specific examples of inappropriate and unethical use of AI technologies in the context of various aspects of business as well as non-business activities. An important issue to consider is the scale of the commitment of technology companies developing AI technologies to respect such regulations so that issues of ethical use of this technology are also defined as much as possible in technological aspects in companies that create, develop and implement these technologies. Besides, in order for AIACT's legal norms, when they come into force, not to be dead, it is necessary to introduce both sanction instruments in the form of specific penalties for business entities that use artificial intelligence technologies unethically, antisocially, contrary to AIAct. On the other hand, it would also be a good solution to introduce a system of rewarding those companies and businesses that make the most proper, pro-social, in accordance with the provisions of the AIAct, fully ethical use of AI technologies. In view of the fact that AIACT is to come into force only in more than 2 years so it is necessary to constantly monitor the development of AI technology, verify the validity of the provisions of AIAct in the face of dynamically developing AI technology, successively amend the provisions of the said legal norms, so that when they come into force they do not turn out to be outdated. In view of the above, it is to be hoped that, despite the rapid technological progress, the provisions on the ethical applications of artificial intelligence technology will be constantly updated and the legal normatives shaping the development of AI technology will be amended accordingly. If AIAct achieves the above-mentioned goals to a significant extent, ethical applications of AI technology should be implemented in the future, and the technology can be referred to as ethical generative artificial intelligence, which is finding new applications.

The key issues of opportunities and threats to the development of artificial intelligence technology are described 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

Call for papers: 2025 4th International Conference on Cloud Computing, Performance Computing and Deep Learning(CCPCDL 2025) will be held on October 31-November 2 2025, in Shanghai, China.

大会官网(投稿网址）: https://ais.cn/u/zEZjYz

大会时间: 2025年10月31-11月2日

会议地点：中国-上海

提交检索：EI Compendex, Scopus

第四届云计算、性能计算与深度学习国际学术会议 (CCPCDL 2025)将于2025年10月31日-11月2日在上海召开。

CCPCDL 已成功召开三届，第四届会议将继续深耕云计算、性能计算、深度学习等前沿研究领域，助力促进全球范围内的科学创新。CCPCDL 2025通过链接国内外最先进的学术资源、聚焦技术前沿、推进生态协同，为学界同仁提供一个分享最新研究成果、理论进展和实践应用的学术交流与产业合作平台。欢迎广大专家、学者踊跃投稿参会！

1、性能计算（边缘/雾计算/众包系统/网络物理系统/数据中心和云计算/嵌入式系统/ 物联网等）

2、云计算（云应用程序架构/云计算和语义web技术/云经济/云管理与运营等）

3、智能计算（智能计算系统/智能计算应用/智能计算算法等）

4、大数据（大数据搜索的算法与系统/大数据可视化分析/大数据搜索的架构/可扩展性和效率/计算建模与数据集成等）

5、深度学习（认知架构/ 机器学习/自主计算/图像处理/信息检索和重用/机器学习等）

6、 其他相关主题

所有的投稿都必须经过2-3位同行专家评审，经过严格的审稿之后，最终所有录用的论文将被提交SPIE - The International Society for Optical Engineering(ISSN:0277-786X)出版，并提交EI Compendex(EI核心) 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，可在会议现场进行口头报告或海报展示等;

2. 主讲嘉宾:申请主题演讲，由组委会审核口头报告申请口头报告，时间为10-15分钟;

3. 海报展示:申请海报展示，A1尺寸，彩色打印;

4. 听众参会:不投稿仅参会，也可申请演讲及展示