The Second edition of IEEE International Conference on Contemporary Computing and Communications (InC4) organized by IEEE Computer Society Bangalore Chapter and IEEE Computer Society Student Branch Chapter CHRIST University Bangalore in association with the Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Bengaluru, India and IEEE Student Branch, CHRIST University, Bangalore. Recent advancements in computing and communication attract people from academia and industry. It focuses on topics of contemporary interest to computer and computational scientists and engineers. The InC4 -2024 will bring together researchers and practitioners from academia, industry, and government to deliberate upon contemporary computing and communication's algorithmic, systemic, applied, and educational aspects. The conference will witness multiple eminent keynote speakers from academia and industry worldwide and the presentation of accepted peer-reviewed articles.

A map depicts a phenomenon at a smaller scale and thus it has a constraint of how to present the map elements graphically the scale. Map Generalisation, also known as Cartographic Generalisation is the selection and simplified representation of detail appropriate to the scale and/or the purpose of a map. This is a very essential tool in modern cartography and Geographical Information Systems (GIS). The objective of generalisation is to supply information on a content and detail level corresponding to the necessary information for correct geographical reasoning. The available space on a map for cartographic representations of all objects and elements of a landscape is very small and decreases disproportionately from scale to scale. This needs efficient algorithms to select and simplify map elements so that the essential map elements and objects can be kept appropriate for the reduced an image area from scale to scale. The current workshop looks into a theoretical and practical approach to various map simplification algorithms.   Learning Objectives: Generalisation necessity for cartography and GIS Common rules for Map generalisation Map Simplification Techniques Analysis simplification algorithms like Douglas-Peucker, Visvalingam–Whyatt algorithm etc. Practical Demonstration of Map Simplification using GIS Software

Unleash your creativity with Generative AI! Dive into the fascinating world of AI-driven creativity at our hands-on workshop. Led by experts, explore the fundamentals and applications of generative AI. Whether you're a seasoned enthusiast or new to AI, join us to discover how this technology is transforming art, music, and more. Don't miss out on this opportunity to expand your knowledge, connect with peers, and shape the future of technology. Secure your spot now and be part of the creative revolution!

The Student Meetup Program, where innovation and collaboration converge. This event is tailored specifically for office bearers of IEEE CS Student Branch chapters, offering a unique opportunity to connect with peers, share insights, and advance your professional journey. This meetup promises a vibrant exchange of ideas, networking opportunities, and valuable experiences for all attendees. Additionally, participants will have the opportunity to engage in a hands-on workshop on generative AI, exploring the cutting-edge intersection of creativity and technology. Please remember to bring your own laptop to fully participate in this interactive session. Don't miss out on this enriching experience! Whether you're seeking to expand your knowledge, establish new connections, or simply engage with like-minded individuals, this event is your gateway to unlocking endless possibilities in the world of computer science. Secure your spot today by filling out the attached form and join us in shaping the future of technology together.

HANDS-ON DEEP LEARNING ALGORITHMS FOR LAND COVER MAPPING FROM SATELLITE IMAGES Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth and Fifth Industrial Revolution (Industry 4.0 and Industry 5.0). Due to its learning capabilities from raw data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application research areas like healthcare, remote sensing, semiconductor manufacturing industry, visual recognition, natural language processing, text analytics, cybersecurity, 5G Networks and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level for societal applications. This session presents DL algorithms for Land Cover Mapping using Python Programming and deep Learning TensorFlow Library. This session also summarizes real-world application areas where deep learning techniques can be used. Finally, Practical demonstration of UNet and DPPNet model for Land Cover Mapping using Satellite Images and Python Deep Learning TensorFlow Library. Learning Objectives: The primary objective of the expert talk is to enlighten the audience on the potential and challenges of using Deep Learning Algorithms for Land Cover Mapping from Satellite Images both theoretical and practical. At the end of the session, the participants should be able to understand: Understand fundamental concepts of 2D U-Net and DPPNet CNN Models The challenges of working with Land Cover Mapping from Satellite Image Understand the advance deep learning algorithms How to practically implement DL techniques using TensorFlow Target Audience: Students, researchers, academic and industry professionals who have foundational theoretical knowledge of Probability, Linear Algebra, Computer Vision, Image Processing and Fundamental of Artificial Neural Networks.