AIoT (Artificial Intelligence of Things), represents a significant evolution in technology by merging the capabilities of Artificial Intelligence (AI) and the Internet of Things (IoT). This fusion creates smarter, autonomous systems capable of advanced decision-making, predictive analysis, and optimized control of connected devices in real-time. Traditional IoT systems, which collect and exchange data, are enhanced by AI, allowing these systems to interpret data, learn from it, and act intelligently.
Several enabling technologies are fundamental to AIoT. Edge computing, for instance, allows data processing closer to the devices, ensuring real-time decision-making without the delays of sending data to central servers. Coupled with 5G networks, which provide high-speed, low-latency connectivity, AIoT systems can operate faster and more efficiently. The use of advanced sensors and actuators enables continuous data collection from the environment, while AI algorithms make sense of this data, leading to applications such as predictive maintenance in industries and real-time health monitoring.
Cloud and hybrid AI architectures provide the necessary infrastructure for large-scale data management and complex analytics, combining the benefits of edge computing for real-time insights with the cloud's capacity for deep learning and storage. AIoT systems also leverage machine learning and deep learning models to process vast streams of data, enabling predictions, anomaly detection, and autonomous actions across sectors like healthcare, manufacturing, and transportation. Blockchain technology enhances security by ensuring secure, traceable, and tamper-proof communication between IoT devices, crucial in sectors dealing with sensitive data.
AIoT will evolve toward self-healing and autonomous systems capable of detecting and resolving issues without human intervention. AIoT's role in sustainability will become more prominent, with systems optimizing energy consumption, reducing emissions, and minimizing waste through smart grids, farming, and efficient manufacturing. In healthcare, AIoT will transform patient monitoring, enabling early detection of diseases and personalized treatment plans through AI-powered wearables. This session will be majorly focused on Enabling technologies for AIoT and future advances.
Topics of Interest:
Following are the expected topics for this session:
- AIoT Applications and Case Studies
- Leveraging Edge Computing and AI for Real-Time Decision-Making in AIoT Systems
- Machine Learning and Deep Learning for IoT
- Integrating ML with IoT Devices for Predictive Maintenance in Industrial AIoT Applications
- AI-driven Analytics and Decision-making
- Federated learning techniques in AIoT
- AI-Driven Optimization of IoT Networks: Enhancing Scalability, Efficiency, and Security
- AIoT-Enabled Smart Cities: Advancing Urban Infrastructure
- Security and Privacy in AIoT
- AIoT in Healthcare: Revolutionizing Remote Monitoring and Predictive Health
- Explainable AI and Generative AI for IoT Systems.
Details of Session Chair and Co-Chair:
Dr. Gaurav Singal, Assistant Professor, Netaji Subhas University of Technology Delhi, India, Email: gauravsingal789@gmail.com
Dr. Chhagan Lal, Researcher, Critical Infrastructure Security and Resilience (CISaR) group, Dept. of Information Security and Communication Technology, NTNU, Norway, email: chhagan.lal@ntnu.no
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are transforming numerous aspects of society, offering tools and techniques that drive innovation and uplift communities. These technologies are increasingly applied in healthcare, education, agriculture, Intelligent Transport Systems, Speech and Communication, and disaster management, addressing critical challenges and improving quality of life. In healthcare, AI and ML models are used for diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. In education, adaptive learning platforms use AI and ML to tailor content to individual student needs, helping to bridge learning gaps. Agriculture also benefits from AI and DL models, such as reinforcement learning and computer vision. These tools optimize crop yield predictions, detect plant diseases, and automate irrigation systems, leading to more efficient farming practices.
Precision agriculture techniques, powered by AI, help farmers conserve resources, reduce costs, and increase productivity. Disaster management is another critical area where AI and ML techniques play a vital role. AI models analyze satellite imagery and weather data to predict natural disasters like floods and wildfires, enabling early warning systems and faster response efforts. Overall, state-of-the-art AI, ML, and DL models are powerful tools that enhance societal well-being, fostering a future where technology serves as a force for good.
Topics of Interest:
We invite the original and novel (un-published) research contributions based on the above mentioned theme including but not limited to the following topics:
- AI-Driven Solutions for Early Disease Detection and Diagnosis
- Personalized Learning Systems Using Adaptive AI Algorithms
- AI and ML for Precision Agriculture in Resource-Constrained Settings
- Deep Learning Models for Disaster Prediction and Early Warning Systems
- AI for Mental Health Support: Chatbots and Virtual Therapists
- Ethical AI: Ensuring Fairness and Bias Mitigation in Societal AI Applications
- AI-Powered Public Health Surveillance and Disease Spread Prediction
- AI and ML in Smart City Infrastructure for Sustainable Urban Development
- AI in Assistive Technologies for the Differently-Abled
- Explainable AI in Critical Applications: Enhancing Transparency and Trust
- AI, ML, and DL to Design Intelligent Transport System for Smart Cities.
Details of Session Chair and Co-Chair:
Dr. Hiren Kumar Thakkar (Session Chair), Dept. of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India. Email: hiren.pdeu@gmail.com.
Dr. Shakti Sharma (Session Co-chair), Department of Computer Science and Engineering, Bennett University, Greater Noida, UP, India, Email: Shakti.sharma1@bennett.edu.in.
Dr. Mayank Swarnkar (Session Co-chair), Department of Computer Science and Engineering, Indian Institute of Technology, (IIT-BHU), Banaras, Uttar Pradesh, India, Email: mayank.cse@itbhu.ac.in
Dr. Djeane Debora Onthoni, (Session Co-chair), Institute of Population Health Sciences, National Health Research Institutes (NHRI), Miaoli County, Taiwan, Email: Djeane@nhri.edu.tw
This exploration delves into the transformative impact of combining image processing, computer vision, artificial intelligence (AI), machine learning (ML), and generative AI across diverse domains. As these technologies converge, they enhance our ability to analyze and interpret visual data, leading to groundbreaking applications in healthcare, automotive, entertainment, and more. By leveraging advanced algorithms and neural networks, organizations can create smarter systems that improve efficiency, accuracy, and creativity. This synergy not only streamlines processes but also fosters innovation, enabling businesses to deliver unprecedented solutions and experiences. As we move forward, understanding and harnessing these technologies will be crucial in shaping the future of innovation across all domains.
Topics of Interest:
Topics to be discussed in this special session include (but are not limited to) the following:
- AI-Driven Image Enhancement
- Ethical Considerations and Bias in AI
- AI/ML in Natural Language Processing
- AI/ML in disaster management
- AI/ML in healthcare
- Application of Deep Learning in Large Language Model (LLM)
- Machine Learning and AI techniques for Big Data
- Predictive Modelling
- Bio-Inspired Computational Intelligence
Details of Session Chair and Co-Chair:
Dr. Sandhya Tarwani, Assistant Professor, Vivekananda Institute of Professional Studies-Technical campus, Delhi, India, Email: sandhya.tarwani@gmail.com
Dr. Lakshita Aggarwal, Assistant Professor, Vivekananda Institute of Professional Studies-Technical campus, Delhi, India, Email: lakshitaaggarwal31@gmail.com
The conference covers a diverse set of research in cross-disciplinary themes. Its applications range from computer vision, image processing, artificial intelligence, and machine learning to Data Science and Big Data analytics. The text provides an in-depth, multidisciplinary discussion of recent advancements and state-of-the-art methods in disciplines. The publication is ideally designed for academicians, technology professionals, students, and researchers interested in uncovering the latest innovations in these fields. It also features explanatory, illustrations of Algorithms, architecture, applications, software systems, and data analytics in the scope of a specified domain.
Topics of Interest:
Following is the list of sub-topics suitable for this special conference:
- Deep Learning towards smart computing systems
- Artificial intelligence technologies like machine learning in Health care.
- Data Science and its application business.
- Federated learning for AI-powered IoT systems
- Artificial intelligence technologies like Deep Learning in Agriculture.
- AI-enabled monitoring systems
- AI-enabled medical Imaging
Details of Session Chair and Co-Chair:
Dr. Mahesh Kumar Singh, Associate Professor, Department of Computer Science Engineering, Dronacharya Group of Institutions, Greater Noida, UP, Email: maheshkrsg@gmail.com
Dr. Pushpendra Singh, Associate Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi NCR campus Modinagar, Ghaziabad, U.P. India, Email: pushpendra.singh1@gmail.com
Dr. Pushpa Choudhary, Professor, Department of Computer Sciences and Engineering, Galgotias College of Engineering and Technology, Greater Noida, Email: pushpak2728@gmail.com
Dr. Arun Kumar Singh, Professor, Department of Computer and Engineering, Greater Noida Institute of Technology, Gr Noida, Email: arun.k.singh.iiit@gmail.com
The Sustainable Development Goals (SDGs) represent a global blueprint for a sustainable future, and machine learning (ML) offers transformative potential in driving progress across these goals. This special session invites researchers and practitioners to submit original research papers involving innovative ML methods and their applications in various sectors aimed at achieving the SDGs. We encourage submissions that demonstrate how machine learning can address pressing challenges in sustainability, equity, and resilience. By fostering interdisciplinary collaboration, this session aims to inspire actionable insights and strategies for a more sustainable world.
Topics of Interest:
Following is the list of sub-topics suitable for this special conference:
- Machine Learning for Climate Change Mitigation and Adaptation
- AI-Driven Solutions for Sustainable Agriculture and Food Security
- Smart Water Management: Applications of Machine Learning
- Disease Diagnosis and Monitoring using Machine Learning
- Enhancing Health Outcomes with Machine Learning in Public Health
- ML based Intelligent Systems for Sustainable Urban Development
- Machine Learning in Biodiversity Conservation and Ecosystem Management
- Data Analytics for Renewable Energy Optimization
- Ethical Considerations in AI for Sustainable Development
- Predictive Modelling for Disaster Risk Reduction
- Case Studies of Successful ML Applications in SDGs
- Interdisciplinary Approaches to Leveraging ML for Sustainable Development
- Secure Cyber Physical Systems using ML
Details of Session Chair and Co-Chair:
Dr. Sanoj Kumar, Data Science Cluster, School of Computer Science, UPES, Dehradun, Uttarakhand, Email: sanoj.kumar@ddn.upes.ac.in
Dr. C M Sharma, AIML Cluster, School of Computer Science, UPES, Dehradun, Uttarakhand, Email: cmsharma@ddn.upes.ac.in
AI-powered software allows machines to enhance functions such as learning, reasoning, and decision-making, which are essential for automating several tasks. Software Automation has become an important aspect for Business Process. Below are the key roles of software in AI-driven automation.
In this special session we invite the papers from the researchers/academician/industry people to share their ideas, contributions, work in various fields where different softwares automation can be implemented using AI.
Topics of Interest:
The session aims to provide a platform for researchers and engineers across the world to exchange and explore state-of-art advances and innovations, in following areas (but not limited to)
- Software Automation for Artificial Intelligence of Things (AIoT)
- Scalable IoT Models using AI
- Secured IoT software design models
- Software for Edge Computing using AI
- Edge Computing Applications for real-time processing
- Edge Computing in industrial IoT
- Impact of AI in Blockchain Applications
- Developing decentralized applications (dApps) for various sectors
- Smart contracts: design, implementation, and security issues
- Role of AI in Augmented Reality (AR) and Virtual Reality (VR) Development
- Software tools for creating immersive AR/VR experiences
- Use cases for AR/VR in education, healthcare, and training
- AI Based Quantum Computing Software
- Programming languages and frameworks for quantum computing (e.g., Qiskit, Cirq)
- Applications of quantum algorithms in real-world problems
- AI Based Business Process Automation Using DevOps
- Automate business process using DevOps Tools
- Automation of CICD Technique using DevOps Tools
- Software for Data Analysis using AI
- Softwares for Data Visualization
- Cloud Based Data Analysis Platforms
- AI-Powered Low-Code/No-Code Platforms
- Low-code platforms for rapid application development
- Building applications with minimal coding
- Bubble: A no-code platform for web application development
- Effect of AI in Software-Defined Networking (SDN)
- Network Automation
- Troubleshooting tools for Network analysis
- Network security Management tools
- Software Development for Full stack based on AI-Models
- Web/mobile based application using Full Stack
- Comparative analysis of different tools used in Full Stack based applications
Details of Session Organizers:
Dr. Madhushi Verma, Associate Professor, SCSET, Bennett University, Greater Noida, India, Email: madhushi.verma@bennett.edu.in
Dr. Ashutosh Srivastava, Assistant Professor, Bennett University, Greater Noida, India, Email: ashutosh.srivastava@bennett.edu.in
Details of Session Chair:
Dr. Samya Muhuri, Assistant Professor, Thapar Institute of Engineering and Technology, Patiala, India, Email: smuhuri13@gmail.com
Dr. Divya Srivastava, Assistant Professor, Bennett University, Greater Noida, India, Email: divya.srivastava@bennett.edu.in
Details of Keynote Speaker:
Dr. Anurag Goswami, Associate Professor, SCSET, Bennett University, Greater Noida, India, Email: anurag.goswami@bennett.edu.in
Dr. Divya Srivastava, Assistant Professor, Bennett University, Greater Noida, India, Email: divya.srivastava@bennett.edu.in
Technical Program Committee:
Dr. Vipul Mishra, Assistant Professor, Gati Shakti Vishwavidyalaya, Vadodara, Gujarat, India
Dr. Arpit Bhardwaj, Associate Professor, Gautam Budh University, Greater Noida, India
Dr. Vishal Srivastava, Assistant Professor, NIT Allahabad, Prayagraj, India
Dr. Shashank Sheshar, Assistant Professor, Thapar Institute of Engineering and Technology, Patiala, India
Dr. Gaurav Singal, Assistant Professor, Computer Science Engineering Department NSUT Delhi, India
Dr. Deepak Singh, Assistant Professor, NIT Raipur, India/p>
Dr. Mohit Sajwan, Assistant Professor, NSUT, New Delhi, India
Dr. Jagritee Talukdar, Assistant Professor, NIT Meghalaya, India
Dr. Paramita Sarkar, Assistant Professor, BMS College of Engineering, Bangalore
The theme of this technical session aims to create a space for researchers, academics, and industry professionals to share their expertise and insights into the fields of data mining, big data analytics, and cloud computing. By fostering collaboration and knowledge exchange, this session seeks to advance the development and application of computational intelligence solutions that contribute to sustainable computing practices. The session will provide a platform for researchers, engineers, and practitioners from India and around the world to discuss the latest trends, innovations, and findings in the areas of intelligent systems, security, telecommunication, computing, and big data analytics. By sharing their ideas and experiences, participants can contribute to the advancement of research and development activities in these fields. The technical session is to promote the development and application of computational intelligence techniques that can help address the challenges of sustain
Topics of Interest:
- Software Automation for Artificial Intelligence of Things (AIoT)
Details of Session Chair:
Dr. Praveen Kumar, Email: praveen.kumar@astanait.edu.kz
This special session is designed to bring focus on the potential on next-generation Artificial In-telligence (AI) solutions in cancer research. Cancer remains a global challenge, with the need for improved diagnostic accuracy, personalized treatments, and effective disease monitoring. By focusing on cutting-edge AI technologies, this session will demonstrate how next-gen AI can bridge current gaps in cancer care, enhance precision, and significantly reduce diagnostic delays, offering novel approaches that improve patient outcomes and save lives.
Next-generation AI technologies have the capacity to reshape cancer research across multiple dimensions. With advancements in deep learning, machine learning, and neural networks, AI can offer earlier and more accurate diagnoses by analyzing complex medical imaging, genetic profiles, and multi-omics data. AI can also be harnessed to predict cancer progression and re-sponse to therapies, enabling personalized treatment strategies. Furthermore, AI-driven drug discovery can accelerate the identification of novel and comfortable line of treatment, while re-al-time monitoring and predictive modeling can improve post-treatment care.
Topics of Interest:
Some potential topics or names of next-generation AI algorithms and models that could play a significant role in cancer research, diagnosis, and treatment:
- Graph Neural Networks (GNNs) for Tumor Cell Interaction Analysis
- Attention Mechanisms in Cancer Genomics for Feature Selection and Interpretation
- Transformer-Based Models for Multi-Omics Integration in Cancer Diagnosis
- Reinforcement Learning (RL) for Adaptive Cancer Therapy and Treatment Optimiza-tion
- Self-Supervised Learning (SSL) for Label-Efficient Medical Image Analysis
- Few-Shot Learning (FSL) for Rare Cancer Types Detection
- Federated Learning for Collaborative and Privacy-Preserving Cancer Research
- Generative Adversarial Networks (GANs) for Synthetic Data Augmentation in Oncology Imaging
- Explainable AI (XAI) Models for Interpretable Cancer Diagnosis and Prognosis
- AutoML Frameworks for Automated Cancer Biomarker Discovery
- Bayesian Neural Networks (BNNs) for Uncertainty Estimation in Cancer Treatment Planning
- Hypergraph Learning for Complex Multi-Omics Data Fusion in Cancer Research
- Variational Autoencoders (VAEs) for Latent Feature Extraction from Genomic Data
- Neural Architecture Search (NAS) for Custom AI Model Development in Cancer Analysis
- Multimodal Deep Learning Models for Integrating Imaging, Genomics, and Clinical Data
- Meta-Learning Algorithms for Personalized Cancer Therapy Prediction
- Quantum Machine Learning (QML) for Accelerated Drug Discovery in Cancer Research
- Causal Inference Models for Understanding Treatment Effects and Disease Progression
- Graph Convolutional Networks (GCNs) for Predicting Drug-Drug and Drug-Tumor Interactions
- Sparse Neural Networks for Efficient Cancer Screening in Low-Resource Settings
Details of Session Chair:
Dr. Dinesh Kumar, Associate Professor, School of Artificial Intelligence, Bennett University, Email: dinesh.kumar2@bennett.edu.in
Dr. Yajnaseni Dash, Assistant Professor, School of Computer Science Engineering and Technology, Bennett University, Email: yajnaseni.dash@bennett.edu.in
Dr. Anjali Diwan, Assistant Professor, Computer Engineering – AI & Big Data Analytics, Marwadi University, Email: anjali.diwan@marwadieducation.edu.in
Dr. Naween Kumar, Assistant Professor, School of Computer Science Engineering and Tech-nology, Bennett University, Email: naween.kumar1@bennett.edu.in
In the last few years, Prompt engineering has taken the generative AI world by storm which has continued to evolve having a large impact in every area of our life. Prompt engineering is a field that is expected to continue to grow as AI models become more sophisticated and integrated into more applications. Crafting Prompts carefully can have a great impact in future due to many reasons such as fine tuning, understanding, improving accessibility of AI models, and enabling real-time translation etc. This session aims to cultivate a platform for sharing innovative ideas, novel concepts, original research findings, and practical experiments that can combine theory with application on Prompt Engineering and its future directions which are tailored to meet the evolving needs of society. Our goal is to drive scientific and educational endeavors forward, advancing the tracks of technology, by uniting industry experts, researchers, academicians, scholars, and students.
Topics of Interest:
Topics to be discussed in this special session include (but are not limited to) the following:
- Prompt Engineering and its impact in future
- Methods and Sustainable Algorithms of Crafting Prompts
- Advanced Prompting in Generative AI
- Image Prompting in Generative AI
- Impact of Prompt Engineering in Tuning AI models
- Natural Language Processing and Prompt Engineering
- Prompt Engineering for Innovative Applications of AI
- Creativity in Prompt Engineering
- Generative AI Algorithms and Prompt Engineering
Details of Session Chair:
Dr. Shiladitya Munshi, Associate Professor, School of Computer Science, Techno India University Tripura, India Email: shiladitya.explorer@gmail.com
Dr. Tanusree Chatterjee, Department of Computer Science & Engineering, Future Institute of Technology (a unit of Techno India Group), Kolkata, West Bengal, India, Email: tnsr.chatterjee@gmail.com
Dr. Rajib Banerjee, Associate Professor, School of Computer Science, UPES, Dehradun, India, Email: rajib123banerjee@gmail.com
Dr. Arindam Mondal, Professor, Department of Electrical Engineering, Dr. B.C. Roy, Engineering, College, Durgapur, Email: arininstru@gmail.com
The primary objective of this session is to encourage a distinctive environment where participants can shape professional connections within their respective fields bridging the gap between academia and industry. Today, the growing trends and impact of Artificial Intelligence (AI) in every area of life is undeniable. This session explores various areas and applications of smart cities including smart healthcare, infrastructure, education, transportation, and other areas where the continuous progress of AI can lead towards long lasting impact in society and life-changing results. Our aim is to drive scientific and educational endeavors forward, advancing the orbits of technology, by uniting industry experts, researchers, academicians, scholars, and students. The session directs to cultivate a platform for sharing innovative ideas, novel concepts original research findings, and practical experiments that can combine theory with application and are tailored to meet the evolving needs of society.
Topics of Interest:
Topics to be discussed in this special session include (but are not limited to) the following:
- Sustainable Trends of AI in Smart Transportation
- Future Trends of AI-based Smart Healthcare
- AI-based Sustainable Machine Learning Algorithms in Smart Infrastructure
- Future Trends of AI in Smart Education
- Advancement of Smart Irrigation in Future with AI-based Algorithms
- Future Trends of AI improving Traffic System in Smart City
- Sustainable Machine Learning Algorithms Improving Waste Management Systems in Smart City
- Impact of Generative AI based Algorithms in Smart City
Details of Session Chair:
Dr. Rajib Banerjee, Associate Professor, School of Computer Science, UPES, Dehradun, India, Email: rajib123banerjee@gmail.com
Dr. Ajay Prasad, Professor, School of Computer Science, UPES, Dehradun, India, Email: aprasad@ddn.upes.ac.in
Dr. Tanusree Chatterjee, Department of Computer Science & Engineering, Future Institute of Technology (a unit of Techno India Group), Kolkata, West Bengal, India, Email: tnsr.chatterjee@gmail.com
Dr. Sandip K Chaurasiya, Associate Professor, School of Computer Science, UPES, Dehradun, India, Email: schaurasiya@ddn.upes.ac.in
Dr.Shiladitya Munshi, Associate Professor, School of Computer Science, Techno, University, Tripura, India, Email: shiladitya.explorer@gmail.com