LATE G. N. SAPKAL COLLEGE OF ENGINEERING

Barriers and Limitations in Artificial Intelligence for Decoding Non-Human Signals

Advances in artificial intelligence, data analysis, and bioacoustics help bridge the gap between human and non-human species. By decoding animal vocalizations, gestures, and behavioral patterns, AI technologies offer new ways to understand animal emotions, intentions, and social interactions. This emerging field helps improve animal welfare, conservation efforts, and creates a broader understanding of cognition across species. Harnessing AI for communication challenges organizations to rethink the boundaries of language, intelligence, and empathy between humans and the natural world.

Exploring Deep Neural Architectures for Skin Lesion Detection and Classification

Skin cancer, such as melanoma, is an important global public health issue, in which the early detection and appropriate classification contributed to establish an early diagnosis that resulted in a more effective and efficient treatment. Recent advances in deep learning have sparked a new revolution in medical image analysis that has led to powerful automatic methods for skin lesion detection and classification. In this paper, we discuss the design and application of deep neural architectures, i.e., Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Recurrent Neural Networks (RNNs), and hybrid models, for the purpose of dermatology diagnostics.

"Implementation on Machine learning , blockchain & decision process for Securing Smart Grid"

Implementation of Machine Learning, Blockchain, and Decision Process for Securing Smart Grid presents a secure framework that integrates Machine Learning, Blockchain technology, and intelligent decision-making processes to enhance the security and reliability of smart grid systems. The proposed approach enables real-time threat detection, secure data sharing, and decentralized energy management while protecting the grid from cyberattacks and unauthorized access. The paper discusses the system architecture, implementation, security analysis, and performance evaluation, demonstrating how the combined technologies improve the resilience, efficiency, and trustworthiness of modern smart grid infrastructure.