Artificial Intelligence and Machine Learning

Clustering & Classification, Online learning, Reinforcement learning, Semi-supervised and Unsupervised learning, Time series analysis, Deep learning architectures, Generative models, Deep reinforcement learning, Learning Theory, Optimization Techniques, Nature inspired optimization techniques, Probabilistic Inference, Trustworthy Machine Learning, Applications of Machine Learning, Natural Language Processing, Computer Vision.

Block Chain, and Cyber Physical System

Block Chain, Application of Block Chain, Cryptography, Water Marking, Privacy and Preservation, Information Security, Digital Services and Protection Mechanism, Cyber Security.

Computer Communication and Networking

Mobile ad-hoc networks, Mobile Identity Management, Security access policies, Network security management, Privacy-respecting authentication issues, Sensor N/W, Data Communication, Wireless Sensor Network, Wireless Communication, Architectural Structure, Design Decisions and Philosophies, Autonomic Management of Ubiquitous Systems, Ubicomp Human-Computer Interaction for Devices, Ubiquitous Systems and Trust, Vehicular ad-hoc network, Flying ad-hoc network.

Data Science, Big Data and Data Mining

Big Data Analytics, Semantic web, Data warehousing and Mining, Object and multi agent system, Data mining, Database systems, Web search & Web Security, Search engines, Dataset Dynamics and Synchronization, Web-Based Health- and Bio- Information Systems, Nature-Inspired Models and Approaches for Web Intelligence, Content Management and Semantic Web, Web Mining, Business Analytics.

Software Reliability and Engineering    

Component based and service-oriented software systems. Software engineering, reliability and testing, information retrieval & security, knowledge engineering & management, information, system modelling and simulation techniques.

Internet of Things (IOT) and Robotics        

Robotics and Automation Autonomous Agents, Engineering Applications on Robotics and Automation, Systems Mobile Robots and Intelligent Autonomous Systems Modelling, Simulation and Architecture Network Robotics Perception and Awareness Robot Design, Development and Control Space and Underwater Robots Surveillance, Fault Detection and Diagnosis Telerobotic and Teleoperation Vehicle Control Applications Virtual Environment, Virtual and Augmented Reality Vision, Human Computer Interface, Bio-Inspired Robotics, Cyborg, Digital Personality, Molecular Computing.

Sustainable and Renewable Energy       

Renewable Energy, Green Design, Products and Manufacturing Processes, Emerging and Advanced Green Energy Technologies, Energy Efficient Designs and Standards, Supply and Technologies, Energy Management Systems, Green Technologies in Information, Green Energy in Transport, Sustainable Cities, Architecture and Green Buildings, Green Nanotechnology, Rural Development through Green Energy, Global Warming, Automation Applications in Environmental Technology, Waste Management and Waste Water Treatment, Low Carbon Economy and Society, Environmental Chemistry and Pollution Control, Solid Waste Valorisation, Resource and Environment Greenhouse Gas Mitigation and Climate Change, Computational Models for Sustainable Development, Biofuels / Biofuels Cells, Environmental Remote Sensing Applications, Environmental Process Simulation, Solar Energy and Photovoltaic Technology.

Distributed Computing                                  

Distributed & Parallel Computing, High Performance Computing, Cloud Quality Management & Service level agreement, Cluster, Cloud, & Grid Computing, Mobile Computing, Edge Computing, Fog Computing, Cognitive Computing.

Signals and Systems, VLSI                                  

Recognition and Reconstruction Nonlinear Signals and Systems Optimization Problems in Signal Processing Real-Time Systems Control Sensors Fusion Signal Reconstruction System Identification System Modelling Time-Frequency Analysis, Logic based planning, VLSI Designing & Modelling, Simulation & Automation, Process Monitoring, Reliability & Fault tolerance, Embedded Software and Hardware Architecture Techniques.

Emerging Perspectives of WSN & Artificial Intelligence for IoT Applications

Chair 1 : Dr. Shalli Rani, Associate Professor, Chitkara University Institute of Engineering and Technology, Punjab

Chair 2 : Dr. Himanshi Babbar, Assistant Professor, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab

The ability to organize hardware in standard methods gives boost to modernize smart & intelligent device control. A great deal of enterprise is starting to receive this innovation on to their activities to build their profitability and improve effectiveness. IoT offers some intriguing applications to our lives simpler like in Healthcare, Transportation, and Agriculture. Different subareas like machining learning, security, privacy, data analytics, clouding computing & protection are considered the crucial part of IoT. To convert smart cities roadmap into reality, all the above areas need to be evaluated and considered for new research plethora. The aim of this special session on Security Issues in WSN and Use of Machine Learning in IoT Applications is to bring together innovative Academics, Researchers and Industrial experts to discuss the theoretical and practical challenges encountered and the solutions adopted
Topics of Interest: Security Issues in WSN, Cloud Computing and AI, Secure Software Defined Networking (SDN) , Named Data Networking (NDN) Security, Secure Data Mining in IoT Applications, Security & Privacy in Machine Learning, Industrial Automation over Secure IoT, Smart and Secure Society in WSN, Information Security using Data Mining, Network Security using Machine Learning/Data Mining, Blockchain, Fog/ Edge Computing.

Navigation of Artificial Intelligence and Software-Defined Networking (SDN) Technologies for New Age Networks

Chair : Dr. Sumit Badotra, Bennett University, Greater Noida, Uttar Pradesh

Co Chair 1: Dr Sarvesh Tanwar, Amity University Institute of Technology, Noida, Uttar Pradesh

Co Chair 2: Dr Gurpreet Singh,Lovely Professional University, Phagwara, Punjab

With the use of SDN the dynamic and very much efficient management of all networks related resources in order to deliver varied services is very easy. This leads to easy configurability and thus leveraging the SDN controller the ultimate controller intelligence and global network programmability. The radical new idea of SDN helps the network systems to deliver an abstracted view and at the same time maximizes the resource utilization. One of the most promising and emerging solution to these networks is artificial intelligence. It becomes a track way to speed the core intelligence of the brain of SDN architecture i.e. SDN controllers. Both the technologies (Machine learning and artificial intelligence (AI) ) are very much adequate and effective for the SDN network communication processability. With the use of ML and AI SDN controllers can be trained in a more sophisticated manner and various network policies and provisioning can be achieved easily.
This session aims to provide and anticipates various cutting-edge SDN technologies based on ML and AI techniques, current research findings, gaps, problems with a varied range of other aspects within intelligent SDN technology (Data Plane, Application Plane and Control Plane) for modern generation networks and architectures.

Topics of Interest: Load balancing in energy constrained environments using AI/SDN, Software Defined Networking, AI Enabled Security Leveraging AI/SDN/NFV for network slicing in 5G and Beyond Networks, Use of Machine learning in smart networks, IoT enabled Network security Security and functionality of industrial IoT network, High security and privacy with AI/SDN, Application of software-defined networks/AI in Fog computing, UAV, resource management, and edge computing, AI/SDN for mission-critical applications, Network anomaly detection, botnet detection and traffic re-routing, SDN network complexity by using machine learning traffic flow classification techniques, High level SDN policies for various derived flow classes in machine learning.

Data Communication, Computer Networks and Artificial Intelligence

Chair : Dr. Ihtiram Raza Khan, Jamia Hamdard, Delhi, India

Co Chair : Dr. Neetu Faujdar, GLA University, Mathura, Uttar Pradesh, India

The ubiquity of information technology and computer power can be applied in many areas ranging from industrial production to everyday. Therefore, it is essential to develop better understandings and design methodologies for large scale pervasive systems for various domains, and societal challenges. Pervasive computing has been greatly supported by the internet of things. The use of internet of things has created the environment that helps in the management of the different modules that are the part of the complete system, which can work effectively without the interference with the other components of the system. The cloud environment with the internet of things can help in getting the greater extend of data sharing. Little attention has been provided to the security of the various stakeholders that are the part of the system. IoT cloud integration involved privacy, security, and personal safety risk of the stakeholders. Not only are these types of security attacks possible, but there is also the possibility of attack on the IoT components like hardware manipulation to disrupt the services. As we are on the network, all the communication attacks of network are also possible. The ever-growing usage of computer systems in a wide variety of fields has generated a large demand of computer professionals. Artificial Intelligence - Human intelligence exhibited by the machines has occupied the glossary of succeeding era of science fiction admirer, computer scientists and medical researchers. Artificial Intelligence is poised to transform medicine at a basic science, clinical, healthcare management, and financial level. Machine Learning – Essence of computer algorithms that can learn patterns and complex relationships from pragmatic data and produces precise decisions. Machine Learning is the growing field in computer science and Health Informatics is amongst the greatest application challenges, giving accuracy and benefits in medical diagnoses, pharmaceutical development and disease analysis. Healthcare Industry is facing complex challenges and hence it needs an intelligent tool to tackle it. All the programs feature a combination of theoretical and practical elements in order to provide the students a platform for correlating the learning.

Topics will be covered: Computer Networks, Ad-hoc Networks, Wireless and Sensor Network, P2P Networks, Software Defined Network, Network beyond 5G, Network Security, Network Technology, Network Management and Traffic Engineering, Cross-Layer Design and Optimization, Artificial Intelligence and Healthcare, Bio-Informatics and Machine Learning, Biomedical Engineering

Recent Advancements in Machine Intelligent Paradigms for Healthcare Big Data Analytics

Chair : Dr. Shakeel Ahmed, King Faisal University, Al-Ahsa 31982, Saudi Arabia

Co Chair : Dr. Parvathaneni Naga Srinivasu, Prasad V Potluri Siddhartha Institute of Technology, Vijayawada, India

In recent years, the domain of Big Data for healthcare has emerged as the most sought-after field for research, encompassing various interdisciplinary technologies such as mathematical and statistical assessment of domain and business knowledge via algorithms such as evolutionary algorithms, Differential Algorithms, Genetic Algorithms, Swarm Intelligence, Optimization algorithms, and nature-inspired algorithms. Big Data is connected with data mining, which is used to make future forecasts and risk assessments using structured and unstructured data collected from diverse sources of information.

Conventional data management technologies are incapable of handling such massive amounts of data. In this case, combining several algorithms with soft computing approaches and optimization algorithms might provide successful results. All of these hybrid methodologies may be used in a variety of industries, including health care monitoring, market forecasting, e-commerce, consumer behaviour research, agriculture sector business analysis, and the industrial sector. Big Data encompasses a number of stages in the process of contextual and predictive analysis, such as data discovery, data preparation, data analysis, feature engineering, communication, and report generation, all of which are enabled by the hybridization of Meta-Heuristic approaches with soft computing approaches and real-time processing algorithms.

The session focuses on emerging Big Data tools, methodologies, and technologies that address the collecting and interpretation of complex data in healthcare sector to handle data of divergent size, variety, and velocity. It provides information on scalable computing models for data-driven decision models and context-based research projects. This session includes novel ideas and suggestions for data science applications that combine state-of-the-art theory, techniques, and implementations.

The current theme of this track is to take the submissions in the following areas but not limited to: Recent technologies in risk assessment in healthcare using machine learning, Processing information gathered by diagnostic imaging machines, Studies on IoMT's potential in the realms of diagnosis and risk assessment, Developments in imaging and treatment in patient-centric infrastructure, IoT-enabled healthcare services, Human behavior modeling and analysis in healthcare, Using Deep Learning to Analyze Past Medical Records, Privacy and security in healthcare, Ambient Assisted Living Environment for well-being of senior citizens.,Explainable AI models for Decision support systems for healthcare and wellbeing.

Recent Advances and Challenges in Natural Language Processing and Machine Translation

Chair : Ms. Shweta Roy, ABES Engineering College, Ghaziabad, India

Co Chair : Ms. Sandhya Avasthi, ABES Engineering College, Ghaziabad, India

The discipline of computational linguistics, also known as natural language processing (NLP), combines linguistics and computer science. It is a subfield of artificial intelligence concerned with the linguistic study of language computing, data mining, machine learning, knowledge engineering, and knowledge acquisition. This topic is difficult due to the diversity, ambiguity, robustness, information dependence, and context of language. There is a close relationship between machine translation (MT), which refers to the use of computers to autonomously translate one language into another, and the rise of natural language processing. Due to the substantial potential of this discipline as a disruptive technology, it is becoming an increasingly popular area of study. Utilizing a variety of strategies, tools, and techniques, it confronts various language barriers head-on in order to facilitate effective translation and cross-language communication. Due to the fact that NLP and MT systems rely on vast data sets and computing power, there are still numerous issues to be resolved. Machine translation (or MT for short) Automatic language translation (MT) is a branch of computational linguistics that studies how computers can be programmed to interpret written or spoken language. Word-for-word translation (WFT) is the foundation of MT; however, this alone rarely yields a decent translation, as recognition of complete phrases and their closest equivalents in the target language is also needed.

Topics of Interests : Techniques of machine translation, Question answering systems, Machine Learning for NLP, Machine translation System and deployment, Evaluation of machine translation quality, NLP for machine translation, Semantics and syntax parsing, Language model, Interpretability and Analysis of models for NLP, Speech and multimodality

Recent Trends and Future Prospects in Non-Conventional Machining of Hard Materials and Thermal Engineering

Chair : Dr. Himanshu Payal, Sharda University, Greater Noida, India

Co Chair : Dr. Akanksha Mishra, Sharda University, Greater Noida, India

The aim of this special session is to give insights on Non-Conventional Machining of Hard Materials and recent advances of science and technology in thermal engineering, heat exchanger, cryogenics and its application. It will address issues related to advances in the non-conventional machining of a wide range of materials, such as metals, ceramics, composites and alloys. The utilization of non-conventional machining technology is to machine difficult-to-hard materials in terms of accuracy and precision, overcoming the limitations of traditional machining processes such as the deterioration of machined surfaces due to heat exposure. The motivation behind this special issue is to gather important research articles in which further developed methods are presented with significant contributions to the machining process and application of experimental, analytical, or theoretical thermal and energy engineering. This issue focuses at the accumulation of recent trends and developments in the field—including experimental, simulation, and reviews.

The focus of session will be on Recent developments in the non-conventional machining process, New applications of machining in high-added value components, for aeronautics, automotive, windmill, energy, and other key sectors, Machinability of new composites, brittle and emerging materials, Modeling/simulation of non-conventional machining processes, Hybrid non-conventional machining processes, Renewable and clean-energy technologies, Thermal systems design to produce, store and efficiently use energy in industrial application, Components, devices and equipment for thermal engineering processes, Thermodynamics modeling and design analysis, Economic assessment of thermal engineering applications, Applications of heat and fluid flow, Thermoeconomic analysis, evaluation and optimization. Multi-Phase Flow and Heat Transfer, Heat and Mass transfer in the ecosystem, Heat and Mass transfer in Biomedical devices, Biofuels and Internal Combustion Engines, Micro/nano Heat Transfer, Biological Heat Transfer, Double Diffusive Convection, Air Conditioning and Refrigeration

Intelligent Computing Systems for Data Analysis                                 

Chair : Dr Sanjaya Kumar Panda, CSE, NIT Warangal, India

Co Chair 1 : Dr. Sarat Chandra Nayak, Department of ComputerScience, Yonsei University, Seoul, South Korea
Co Chair 2 : Dr. Sanjib Kumar Nayak, VSSUT, Burla

Intelligent computing systems have revolutionized the way data is analyzedandprocessed in various fields such as healthcare, finance, marketing, and manufacturing. These systems use advanced algorithms and artificial intelligence (AI) techniquestoanalyze vast amounts of data and derive valuable insights that can informdecision- making processes. The field of data analysis has been transformed by the emergenceof intelligent computing systems. These systems can performcomplex analysesoflarge data sets in a fraction of the time it would take a human analyst to doso. Thisspeed and efficiency allow businesses and organizations to make data-drivendecisionsmore quickly, giving them a competitive edge in their respective markets. Intelligent computing systems can also be used for predictive analytics, whichinvolvesusing data to make predictions about future events or trends. Predictive analyticscanbe applied in fields such as healthcare, where machine learning algorithms canbeusedto predict patient outcomes or identify individuals at risk of developingcertainconditions. In addition to their speed and efficiency, intelligent computing systems can alsoprovidemore accurate and reliable results than traditional data analysis methods. For example, in medical diagnosis, AI systems can analyze large amounts of patient data andidentifypatterns that may be missed by human doctors. This can lead to more accuratediagnoses and more effective treatments. Despite the many benefits of intelligent computing systems for data analysis, therearealso potential drawbacks to consider. One concern is the risk of algorithmic bias, wherethe algorithms are based on biased data sets and therefore produce biased results. Biascan also arise from the design of the algorithms themselves, which may reflect thebiases of the developers who created them. In conclusion, intelligent computing systems have transformed the field of dataanalysisby enabling faster, more accurate, and more efficient processing of large datasets. These systems have a wide range of applications in various industries, includinghealthcare, finance, and marketing. However, it is important to be aware of potential drawbacks and to consider how these systems can be used in a way that benefitseveryone involved.

The focus of session will be onMachine learning algorithms for data analysis, Natural language processing for sentiment analysis and text data miningPredictive analytics and forecasting using intelligent computing systems, Deep learning and neural networks for image and pattern recognition, Big data processing and analysis using cloud computing and distributedsystemsData visualization and interactive data exploration using intelligent computing systems, Blockchain technology for secure and decentralized data analysis, Ethics and bias in data analysis using intelligent computing systems, Real-time data analysis and decision-making using intelligent computing systems, Innovative applications of intelligent computing systems in specific fields, suchas healthcare, finance, and marketing.

Real-Time Intelligence and Soft Computing: Powering the Digital Revolution

Chair 1 : Prof. Anupam Baliyan, Chandigarh University, Mohali, Punjab India
Chair 2 : Dr. Abhishek Kumar, Chandigarh University, Mohali, Punjab India

The session focuses on the use of computational intelligence and soft computing techniques for real-time data analytics and machine learning in various domains such as finance, healthcare, and transportation. It can explore the use of soft computing techniques for real-time image and signal processing, which is essential for various applications such as medical diagnosis, surveillance, and communication systems. It highlights the application of computational intelligence and soft computing techniques for real-time control and automation in various domains such as manufacturing, energy, and robotics. The session covers the use of soft computing techniques for real-time decision making in complex systems, such as traffic control, emergency response, and supply chain management and focuses on recent advances and emerging trends in soft computing techniques for real-time systems, such as the use of deep learning, reinforcement learning, and swarm intelligence.

The focus of session will be on Fuzzy Logic-Based Real-Time Scheduling for Cyber-Physical Systems, Real-Time Machine Learning for Anomaly Detection in IoT Networks, Real-Time Sentiment Analysis Using Deep Learning for Social Media Monitoring, Intelligent Real-Time Traffic Control Using Swarm Intelligence, Real-Time Predictive Maintenance of Industrial Machines Using Soft Computing Techniques, Real-Time Brain-Computer Interfaces Using Neural Networks and Fuzzy Logic, Soft Computing-Based Real-Time Image and Video Processing for Autonomous Vehicles, Real-Time Soft Computing for Human-Robot Collaboration in Manufacturing, Real-Time Soft Computing for Smart Grid Management and Control, Security Issues in WSN, Cloud Computing and AI, Secure Software Defined Networking (SDN), Named Data Networking (NDN) Security, Secure Data Mining in IoT Applications, Security & Privacy in Machine Learning, Industrial Automation over Secure IoT, Smart and Secure Society in WSN, Information Security using Data Mining, Network Security using Machine Learning/Data Mining, Blockchain, Fog/ Edge Computing, Real-Time Soft Computing Techniques for Dynamic Resource Allocation in Cloud Computing