2nd International Conference on Communication, Security and Artificial Intelligence (ICCSAI 2023) invites original contributions based on the results of research and developments. Prospective authors are requested to submit their papers in not more than 6 pages, as PDF prepared in the two column IEEE format. Accepted papers will be submitted for inclusion into IEEE Xplore subject to meeting IEEE Xplore’s scope and quality requirements Which indexed with world’s leading Abstracting & Indexing (A&I) databases, including ISI / SCOPUS/ Google Scholar.Authors interested in presenting manuscripts that demonstrate original unpublished research in the areas of Informatics are invited to submit their Full Papers.
Chair : Dr Akash Saxena, Compucom Institute of Information Technology and Management, Jaipur, Rajasthan, India
Co Chair 1 : Dr. Navneet Sharma, IIS Deemed to be University, Jaipur, Rajasthan, India
Co Chair 2 : Dr.Mahaveer Kumar Sain, Department of Computer Science,Maharishi Arvind Institute of Science and Management, Jaipur, Rajasthan, India
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, Application of Block Chain, Cryptography, Water Marking, Privacy and Preservation, Information Security, Digital Services and Protection Mechanism, Cyber Security.
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.
Chair : Dr. Yogesh Kumar, Pandit Deendayal Energy University, Ghandinagar, Gujarat
Co Chair 1 : Dr. Ankur Changela, Pandit Deendayal Energy University, Ghandinagar, Gujarat
Co Chair 2 : Dr Harjeet Singh, Chitkara University Rajpura, Punjab
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.
Component based and service-oriented software systems. Software engineering, reliability and testing, information retrieval & security, knowledge engineering & management, information, system modelling and simulation techniques.
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.
Chair : Prof. Ashish Sharma, Chandigarh University, Mohali, Punjab, India
Co Chair : Prof. Gagandeep Kaur, M.R.S. Punjab Technical University, Bathinda, Punjab, India
There is a growing need for clean energy sources such as nuclear, wind, solar, geothermal, hydropower and biomass. Yet, truly sustainable growth would include the intelligent integration of renewable energy technology into existing infrastructure, as well as dramatically enhanced efficiency in non-renewable energy consumption. The analytical and practical skills for planning, constructing, running, and improving sustainable energy systems that combine energy generation, distribution, and consumption in an ecologically responsible and economically effective manner. Smart Technology for Power Production and Distribution and Efficient Energy Generation and Conversion is need.
We invite original (un-published) research contributions based on the above-mentioned theme including following topics but not limited to: Active Filters, Biofuel or Solar Economics and Commercialization, Biomass Conversion Technologies, Biomass for Bioenergy, Distributed Generation, Distribution Power System, Eco-Design, Efficient Energy Generation and Conversion, Electric Drives, Electric Vehicles, Electrical Machinery, Electricity Storage, Electromagnetic Compatibility, Energy Efficiency, Energy Harvesting, Green Facilities and Industries, Green Technology, High voltage engineering, Hydroelectric Power, Industrial Power Systems
Distributed & Parallel Computing, High Performance Computing, Cloud Quality Management & Service level agreement, Cluster, Cloud, & Grid Computing, Mobile Computing, Edge Computing, Fog Computing, Cognitive Computing.
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.
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.
Chair : Prof. S. Jayachitra, PSNA College of Engineering and Technology, Dindigul, Tamilnadu, India
Co Chair : Dr. A. Prasanth, Sri Venkateswara College of Engineering, Sriperumbudur, Near Chennai, India
In the era of artificial intelligence, the enormous high-quality data supports the development of various emerging areas. The artificial intelligence significantly enhances the ability of big data management and the utilization level of data mining. Although artificial intelligence can enhance the value of big data and it also have serious threats to personal privacy data and enterprise business secrets. The conventional privacy protection technologies face great challenges in efficiency, function, and applicability when solving the privacy protection problem for big data. Therefore, how to use the relevant advanced cryptography, privacy protection, and AI technologies will help effectively promote secure data sharing and enable the high-quality development of artificial intelligence technology.
With the rapid development and prevalence of the latest emerging technologies such as advanced sensing, mobile computing, the Internet of Things, pervasive computing, data mining and deep leaning, a new wave of user-centred applications, such as personalized care and medicine, just-in-time independent living, self-care and self-management, early risk detection and intervention, have attracted increasing attention. Such applications include built upon the conception of smart homes, smart cities, smart healthcare, intelligent transport, service robots, and thus having the huge potential of impacting the society and economy. User-centred applications and systems place special emphases on data intelligence, including computational intelligence, interactive intelligence and cognitive intelligence, and co-design, co-development, user experience, accessibility particularly in the envisioned future smart world environments.
This special session offers an excellent international forum for sharing information, results in theory, approach and emerging predictive models for diverse applications. Machine Learning especially deep learning is efficient for handing complex prediction models due to their outstanding performance in handling large scale data sets with uniform characteristics. The objective of this session is to bring together researchers, practitioners, academicians, and industrialists from different disciplines related to machine learning to share ideas, algorithms on current as well as future use of machine learning, image processing and computer vision algorithms in real-life. The main idea of the special session is what more can be achieved with the help of current technology. Here, all the participants will get a chance to interact and establish professional relations for future collaboration.
Topics will be covered: AI-based techniques and applications, Applications of Fuzzy systems and Neural networks, Convolutional neural networks, Evolutionary algorithms and its applications, Machine learning methods for security and privacy-preservation, Deep learning and statistical methods for data mining and its application, Decision support, recommendation techniques, Computational intelligence, interactive intelligence and cognitive intelligence, Image processing, classification, segmentation, enhancement, restoration, retrieval, Object detection, recognition, tracking, Medical Image analysis, Big data in computer vision and image analysis, Deep learning, transfer learning for smart healthcare, Computational intelligence in smart healthcare
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
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.
Chair : Dr. D. Sumathi, VIT-AP University, Andhra Pradesh, India
Co Chair : Dr. S. Karthikeyan, Department of AI and ML, KPR Institute of Engineering and Technology, Coimbatore, India
Machine learning and deep learning techniques have revolutionized the field of computational biology by enabling the processing and analysis of vast amounts of biological data. In recent years, these methods have been used to improve our understanding of complex biological systems, predict protein structures and functions, identify disease biomarkers, and design new drugs. Machine learning algorithms, such as decision trees, support vector machines, and random forests, are commonly used for classification and prediction tasks, while deep learning techniques, such as convolutional neural networks and recurrent neural networks, are well-suited for processing and analyzing large-scale biological data, such as genomic and imaging data. The integration of machine learning and deep learning approaches with computational biology has the potential to transform biomedical research and improve human health by enabling the development of personalized medicine and accelerating drug discovery.
The current theme of this track is to take the submissions in the following areas but not limited to: Protein Structures and Analysis, Drug Discovery, Transcriptomics, Web services in bioinformatics, Cancer drug remodeling, Genomic sequence analysis, Immunotherapy, Emerging techniques in bioinformatics, Eco informatics- applications and analysis.
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
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.
Chair : Prof. Anupam Baliyan, Chandigarh University, Mohali, Punjab India
Co Chair-1 : 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.
Chair : Dr. M. Nalini, Electronics and Instrumentation Engineering
People may now engage with machines in completely new ways thanks to consumer electronics advancements like the iPhone 5s, Google Glasses, Xbox Kinect, and others. These developments are the result of recent breakthroughs in advanced technologies like speech recognition, object identification, and motion recognition. These examples highlight the value and significance of signal and image processing research, even if many of these technologies are still far from ideal. The field of signal and image processing has endured much worse circumstances in the past and is still active today. The future of signal processing is increasingly looking very bright and long-lasting in terms of both time and other dimensions.The aim of this special issue is to consider the recent advancements in signal and image processing.
The focus of session will be on Image / Video / Multimedia Signal Processing, Audio / Speech / Spoken Language Processing, Digital & Multirate Signal Processing, Signal Processing Algorithms and Architectures, Pattern Recognition and Object Tracking, Compressive Sensing and High-Dimensional Statistics, Sensing, Representation, Modeling, and Registration, Motion Estimation, Registration, and Fusion, Synthesis, Rendering, and Visualization, Deep Learning for Images and Videos, Computational Imaging, Learning with Limited Labels, Restoration and Enhancement, Image & Video Interpretation and Understanding, Compression, Coding, and Transmission, Detection, Recognition, Retrieval, and Classification, Color, Multi-spectral, and Hyper-spectral Imaging Biometrics, Forensics, and Security, Stereoscopic, Multi-view, and 3D Processing, Biomedical and Biological Image Processing, Image & Video Quality Models, Emerging Applications and Systems, Applied Signal Processing Systems, Audio & Acoustic Signal Processing, Signal Processing for Big Data, Signal Processing for Communication, Signal Processing for Cyber Security, Signal Processing for Education, Signal Processing for Robotics. Signal Processing Over Graphs, Signal Processing Theory & Methods, Speech and Language Processing.
Chair : Dr. Bhanu Sharma, Chitkara University, Punjab, India
Co Chair : Dr. Satyam Kumar Aggrwal, Chitkara University, Punjab, India
Trailblazing and imminent contrivances in technology are among the most important aspects of modern technological development. Trailblazing is the process of discovering and developing new technology, while imminent contrivances are the applications of existing technology to new tasks or problems. Both elements are essential for the creation and advancement of technology. In regard to trailblazing, it is often the case that a particular technology is seen as being ‘cutting-edge’ or ‘groundbreaking’. These technologies often come with the potential to revolutionize the way that we do things and are often seen as being the first of their kind. Examples of trailblazing technologies include the invention of the internet, the invention of the mobile phone, and the invention of the personal computer. As for imminent contrivances, this refers to the application of existing technology to new tasks or problems. Basically, the theme of the Trailblazing and Imminent Contrivances in Technology session is the exploration of the newest, most innovative, and most creative technologies that are pioneering a new era of technological advancement. This session will focus on the development of new technologies, the emergence of AI, machine learning, deep learning, AR/VR/Metaverse/IoT, Blockchain, and robotics, and how these technologies are transforming the way businesses and individuals interact with technology.
The focus of session will be on Special Child Education, Augmented Reality/Virtual Reality/Metaverse, Molecular Biology, Internet of Things, Sensors, Artificial Intelligence and Early Cancer Detection, Artificial Intelligence and Healthcare, Brain-Computer Interface, Automation and Robotics, Immersive Environments for Biology, Edge computing, Digital Image Processing, Quantum Computing, Neural Network, Cognitive Science/Neuroscience
Chair : Prof. Raj Gaurang Tiwari, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
Co Chair :Prof. Ambuj Kumar Agarwal, School of Engineering and Technology, Sharda University, Greater Noida, India
The special session focuses on Computational Intelligence (CI), which is represented by the three subjects Evolutionary Computation (EC), Fuzzy Logic (FL), and Neural Networks (NNs). Starting with fuzzy neural or genetic systems and progressing to more recent computational frameworks such as deep learning and natural language processing, the subject of CI has widened and grown to encompass many disciplines and application domains. Intelligent computing systems give effective answers to complicated problems in rising scientific and technology sectors. Computational intelligence models and systems use computational approaches to solve real-world problems and provide a comprehensive answer based on the computer system's mathematical basis. The conference seeks papers from researchers, academics, and other professionals working in Artificial Intelligence, Big Data, Sensors, Intelligent Computing, Machine Learning, and related fields. At the same time, this expansion has shown a gap in theoretical understanding based on CI systems that allows them to reach their full potential when dealing with real-world problems. The conference will provide a major international forum for the creation and exchange of ideas related to Recent Advances in Computational Intelligence Techniques and Applications in the most diverse fields, including finance, medicine, precision agriculture, and aviation, as well as in a variety of information systems.
The focus of session will be on Big data analytics, Application of deep learning, E-governance, Hybrid systems based on intelligent computational techniques, Machine Learning Perspective on Social Network Analysis, Recommendation Systems, Health-monitoring, E-learning, Precision Agriculture, Crop Recommendation System, Smart cities and Sustainable Goals Developments, Expert systems, Applications of intelligent computing in various domains: Big data, IoT, industry 4.0, etc., Intelligent computing algorithms, Evolutionary Computing, Artificial Immune Systems, Fuzzy Logic & Soft Computing Techniques, Design System & Algorithm
Chair : Prof. Akash Saxena, Compucom Institute of Information Technology and Management, Jaipur, Rajasthan, India
Co Chair 1 : Dr Mahaveer Kumar Sain, Maharshi Arvind Institute of Science and Management, Jaipur, Rajasthan, India
Co Chair 2 : Dr Navneet Sharm, IIS (Deemed to be University), Jaipur, Rajasthan, India
With the use of IoT Organizations are starting their investments in Internet of Things (IoT) initiatives. RPA and its use cases, and how it perfectly complements IoT technologies’ characteristics to help companies boost efficiency and drive higher profitability using intelligent automation in the form of Robotic Process Automation (RPA) coupled with artificial intelligence (AI) can drive even faster and higher returns for organizations that recognize the synergies between the two technologies.One of the most promising and emerging solution to these process is artificial intelligence and RPA. With the help of Both the technologies (Machine learning and artificial intelligence (AI) ) with IoT RPA is very much adequate and effective for gaining more profit in the business. This session aims to provide and anticipates various RPA with IoT enabled tools based on AI & Machine Learning techniques, current research findings, gaps, problems with a varied range of other aspects within IoT Enabled AI & ML technologies.
The focus of session will be on Robotic Process Automation Using AI, Software Defined Process Automation, AI Based Application Security on RPA, Iot Enabled Data Security & Network Security, Iot Services And Its Applications, Iot Enabled Software Architecture, Role Of Iot In Mobile Applications, Challenges On Iot Based Applications Implementation, Cloud Based Iot Enabled Rpa System, Intelligent Industry Automation System, Efficient Iot Based Application Developmemnt Services, Iot Based Virtual Communication On Augmented Reality
Chair : Dr. Mithlesh Arya Swami Keshwanand, Institute of Technology, Jaipur
Co Chair-1 : Dr. Varun Malik, Chitkara University, Rajpura, Punjab
Co Chair-2 : Dr. Ruchi Mittal, Chitkara University, Rajpura, Punjab
This Session is on " Machine Learning in Engineering and Health Sciences”. Machine learning area have become prominent research interests spreading across multiple domains such as Cyber security, Healthcare, Image processing, Agriculture, Financial services, Data mining, Artificial Intelligence and Data Science applications. Because of growing importance, researchers from academia and industry are evaluating possibility of developing computationally efficient approaches by applying machine learning, and deep learning principles and techniques. Although, numerous approaches are explored to build highly efficient and effective learning models, but they are confronted with many difficulties when dealing with complex data, such as failing to capture intricate feature interactions, extract good feature representations. Machine Learning and Deep learning techniques have shown very promising performance in tackling different types of complex data in a broad range of tasks/problems, but its development in view of healthcare, time series, temporal, spatio-temporal, data fusion, deep fusion applications is limited. This special issue aims to promote machine Learning and deep Learning theme of interest.
The focus of session will be onAdversarial Machine Learning, Artificial Intelligence , ML and DL Applications in speech and image processing, Anomaly or Outlier detection using benchmark datasets, Classification and clustering of structured, unstructured and semi structured datasets, Data mining techniques in machine learning, Feature representation, Feature extraction of massive datasets, Healthcare applications using machine learning or deep learning techniques, Computer vision and Pattern recognition, Natural language processing, Disease diagnosis using machine learning and deep learning methods, Convolution neural networks.
Chair : Prof. (Dr) Vivek Jaglan, Amity University, Madhya Pradesh, Gwalior India
Co Chair : Dr. Surjeet Dalal, Amity University, Haryana, Gurugram India
This Session is on "Trustworthy Artificial Intelligence in Cyber security: Applications, Solutions & Challenges”. The term "trustworthy AI" is used to describe the creation and implementation of AI systems that are trustworthy, secure, and moral. In order to detect and prevent cyber risks like malware, intrusions, and suspicious activities, AI may be used to analyze massive volumes of data, discover trends, and detect abnormalities in real time. To better detect insider threats and illegal access attempts, AI algorithms may be used to evaluate user activity patterns and discover deviations from usual behaviors. Cybersecurity entails safeguarding these systems against unauthorized access, data breaches, and other dangerous actions, whereas trustworthy AI refers to AI systems that are designed and implemented in a fair, transparent, and ethical manner. Organizations may increase the reliability of their AI applications and shield themselves from danger by including cybersecurity concepts and practices into the creation and rollout of AI systems. Keeping up with new security threats and developing effective defenses requires not just regular updates but also close engagement with the cybersecurity community. Security activities like vulnerability assessment, patch management, and incident response may be streamlined with the use of AI-powered automation, allowing for more rapid and effective cyber defense. Financial transactions and e-commerce platforms may be made safer by employing trustworthy AI algorithms to evaluate transactional data and detect fraudulent behaviors.
The focus of session will be on Adversarial Machine Learning, Privacy-Preserving AI, Explainable AI and Interpretability, Fairness and Bias in AI, Secure AI Model Deployment, Threat Hunting with AI, Secure AI Governance and Regulations, Human-AI Collaboration in Cybersecurity, AI for Vulnerability Assessment and Patch Management, Continuous Monitoring and Adaptive Defense, Secure AI Training and Data Integrity, Robustness and Resilience of AI Systems, Trust and Trustworthiness in AI, Ethical Considerations in AI-based Cybersecurity, Secure AI for Internet of Things (IoT) Devices, Secure Federated Learning and Distributed AI, AI-enabled Intrusion Detection and Prevention Systems, AI-based Malware Detection and Analysis, Secure AI in Critical Infrastructure Protection, AI-enabled Cyber Threat Intelligence and Analytics.
Chair : Prof. Manik Rakhra, Department of Computer Science and Engineering, Lovely Professional University, India
Co Chair : Prof. Gaurav Dhiman, Department of Computer Science, Government Bikram College of Commerce, India
The "Revolutionizing Agriculture through Artificial Intelligence" conference session aims to explore the cutting-edge applications and transformative impact of AI in the agricultural sector. This session will bring together leading experts, researchers, and industry professionals to discuss how AI-driven technologies are reshaping farming practices, improving productivity, and ensuring sustainable agricultural development.
Intelligent Equipment Rental and Sharing System: Advancing Access and Efficiency.
Precision Farming and Crop Optimization: Explore how AI-based data analytics, remote sensing, and IoT devices are enabling precision agriculture techniques for optimal crop production, reduced resource wastage, and increased yield.
AI in Crop Disease Detection and Pest Management: Discuss how AI-powered image recognition and machine learning algorithms are helping farmers detect crop diseases, identify pests, and implement targeted control measures.
Sustainable Agriculture and Resource Management: Examine how AI is facilitating sustainable agricultural practices, including water management, soil health monitoring, and biodiversity preservation.
Robotics and Automation in Agriculture: Delve into the role of AI-driven robotics and autonomous machinery in various agricultural tasks, such as planting, harvesting, and weeding, to enhance efficiency and reduce labor costs.
AI for Climate Resilience: Discuss how AI technologies are being utilized to predict and adapt to climate change effects on agriculture, ensuring resilient and climate-smart farming practices.
Agri-FinTech and AI: Explore the integration of AI with financial technology in agriculture, including AI-based credit scoring, risk assessment, and blockchain solutions for transparent supply chains.
AI and Livestock Farming: Examine the applications of AI in livestock farming, including smart animal health monitoring, precision feeding, and automated milking systems.
Data Privacy and Security in Agri-AI: Address the importance of data privacy and cybersecurity in AI-driven agriculture systems and the measures taken to protect farmers' data.
Chair : Prof. Dalwinder Singh, Department of Computer Science and Engineering, Lovely Professional University, India
Co Chair : Prof. Arun Singh, Department of Computer Science and Engineering, Lovely Professional University, India
The "Advancements in Healthcare through Artificial Intelligence" conference session aims to explore the latest developments, challenges, and opportunities in the intersection of healthcare and artificial intelligence (AI). The session will bring together leading experts, researchers, and industry professionals to discuss the transformative potential of AI in the healthcare domain.
AI-driven Diagnostics and Disease Detection: Explore how AI algorithms are enhancing medical imaging analysis, pathology, and other diagnostic methods, leading to faster and more accurate detection of diseases and conditions.
Precision Medicine and Personalized Treatment: Discuss how AI is enabling personalized treatment plans based on patients' genetic, molecular, and clinical data, leading to better therapeutic outcomes.
AI and Drug Discovery: Delve into the use of AI in drug discovery, including identifying new drug candidates, predicting drug interactions, and optimizing drug development processes.
AI in Healthcare Operations: Explore how AI-powered systems are optimizing hospital workflows, resource allocation, and improving patient care through smarter decision-making.
Ethical and Regulatory Challenges: Address the ethical considerations surrounding AI in healthcare, including patient privacy, bias, explainability, and the importance of transparent and accountable AI systems.
AI and Remote Patient Monitoring: Discuss the potential of AI-driven wearables and remote monitoring devices in tracking patients' health conditions and enabling telemedicine services.
AI and Mental Health: Examine the role of AI in mental health support, including chatbots, sentiment analysis, and personalized mental health treatment recommendations.
Future Prospects and Challenges: Reflect on the future of AI in healthcare and the potential challenges that need to be addressed to fully harness its benefits.