In recent years an exponential growth in published literature on social science domain has been observed in the databases like web of science and Scopus. As the volume of publications increases, it becomes difficult for researchers and practitioners to accumulate sense making information from the scattered literature. To overcome this challenge, advance quantitative methodologies, and software tools are being employed to conduct literature review more efficiently and effectively. Systematic literature review (SLR) and bibliometric analysis (BA) are prominent methodologies for extraction and synthesis of available literature and to develop its knowledge structure.

2023 IEEE CIS Summer School on Computational Advances in Deep Learning and Applications (CADLA) is a one week short-term course organized by Computer Vision and Biometrics Lab (CVBL) at Indian Institute of Information Technology, Allahabad during 17-21 July 2023. Breakthroughs in the field of data science and computing power over the past two decades has made computational intelligence and deep learning the most powerful tools in the field of artificial intelligence. Deep learning algorithms have shown promising success in numerous applications of Computer Vision, Natural Language Processing, Robotics and Automation. Present day state-of-the-art algorithms have amazed everyone with their human level performance in classification, generating life-like images and videos, automated conversations and even beating world-champions at their own games.

Satellite Image Processing is a prominent field of research and development which has become a keen interest of researchers in past few decades. There are various applications where satellite imaging is the only tool to produce the effective results, e.g., mapping of land cover, deforestation maps, growth of urban area, flood mapping etc. since a large geographical area can be monitored with the help of satellite imaging. India has a vast history of satellite missions which were successfully operated in past and continued with more number of satellites every year.
Registration link: https://apply.iiita.ac.in/event/register/

The One Month Summer Training (26 June - 22 July 2023) on Artificial Intelligence Tools And Applications will provide a comprehensive understanding of the four key areas of AI – Deep Learning, Machine Learning, Reinforcement Learning, and Robotics. Participants will learn how to design, develop, and deploy AI-based solutions for real-world problems. The course will be conducted by experienced AI professionals who will provide insights into the latest developments in the field. The training will include lectures, interactive sessions, and practical to ensure a comprehensive understanding of the subject

The Scheme “Synergistic Training Program Utilizing the Scientific and Technological Infrastructure” (STUTI) is intended to build human resources and its knowledge capacity through open access S & T Infrastructure across the country. As a complement to the various schemes of DST funding for expansion of R & D Infrastructure at academic institution. STUTI schemes envisions a hands-on training program and sensitization of the state-of-the-art equipment as well as towards sharing while ensuring transparent access of S & T facilities.

Behavioural finance is an emerging area of study which considers the theories based on psychology to elucidate the anomalies in the financial market. Of late there is a lot of researches happening in the field of behavioural finance. The development of technology has made accessible information to the general public. Any information is available to investors at a relatively faster rate than ever before.

Process mining, positioned at the interface between ‘Process science’ and ‘Data science’, combines event data with process models and intends to gain insights, identify bottlenecks, predict problems, and optimize organizational processes. Process mining, already being used for high-volume processes in large organizations, will soon become the ‘new normal’ for smaller organizations and processes with fewer cases as well. Despite a huge surge in research endeavors in Process Discovery, Conformance Checking, and Model Enhancement, positioning them as three verticals of Process Mining, there are a number of research challenges that need to be overcome to realize the vision of data driven optimization of business processes. The optimization paradigm in the process mining context is being explored at the following levels: 1. When it comes to creating process models, event logs generated by process-oriented information systems are treated as a critical resource. Conformance checking can be formulated as an optimization problem with the model and log repair. Thus, conformance checking corresponds to solving optimization problems that grow exponentially in the size of the model and the length of traces in the event log 2. Optimization metaheuristics have also been widely applied in the context of automated process discovery, with the goal of gradual discovery and advancement of process models to achieve a tradeoff between accuracy and simplicity. The most notorious of these approaches are those based on evolutionary (genetic) algorithms. However, several other metaheuristics have been researched, such as Imperialist competition algorithms, swarm particle optimization, and simulated glow in this context 3. Data ingestion from diverse source systems is supported by AI, which allows to identify and customize structured and unstructured data from various sources. Thus, various optimization techniques can be used to improve the performance of the data transformation discovery techniques in the context of the synthesis of routine specifications. With rapidly growing applications, this special session invites original, unpublished research contributions that demonstrate current findings in the area of application of data science and optimization techniques for process mining, with special reference to algorithms for process discovery, conformance checking, and process model enhancement. Organizer : Om Prakash Vyas (Indian Institute of Information Technology, Allahabad , India) and  Jerome Geyer-Klingeberg (Celonis, Munich, Germany)

Motivate researchers to work in the area of Machine Learning, Data Analytics and Data Security. Bring academicians, researchers, industry practitioners and students to discuss the importance of machine learning, data mining and data security. This is the sixth workshop in the series and will be conducted online. Workshop will address the basics and recent research work on machine learning, security and data analytics. In addition, the integration of three dimensions will be discussed with its importance and a project.