Daily Archives: October 29, 2025

Authors: Om Dwivedi, Neelam Singh Parihar

Abstract: Skin cancer remains one of the most prevalent and life-threatening diseases globally, necessitating early and precise diagnosis. This research proposes an optimized deep learning framework using ResNet152 for automated skin lesion classification. The model integrates preprocessing, segmentation, and feature extraction to enhance lesion detection and classification accuracy. Experimental results demonstrate superior performance, achieving 97% accuracy, 98% precision, and 97% recall, outperforming existing ResNet variants. The framework’s robustness and adaptability make it suitable for clinical and remote diagnostic applications, promoting early intervention and reducing diagnostic errors.

Published by: vikaspatanker

Authors: Sakshi Keshri, Nitin Namdev

Abstract: This paper presents a comprehensive review of deep learning techniques designed to enhance violence detection in surveillance systems. With the rapid advancement of surveillance technologies, the accurate identification of violent activities has become crucial for ensuring public safety. Conventional approaches often fail to cope with the complexity of video data, which inherently involves both spatial and temporal dynamics. To overcome these limitations, modern deep learning models such as Convolutional Neural Networks (CNNs), InceptionV3, Long Short-Term Memory (LSTM) networks, and hybrid architectures have been widely adopted. These methods excel at capturing spatial representations while simultaneously modeling temporal dependencies, making them well-suited for real-time violence detection tasks. The review further discusses essential preprocessing strategies—including noise reduction, feature extraction, and data augmentation—that significantly improve model robustness. In addition, it outlines persistent challenges such as class imbalance, scalability issues, and high computational costs, which remain key barriers to practical deployment

Published by: vikaspatanker

Authors: Priyesh Mahajan, Nitin Namdev

Abstract: The rise of digital payments, credit card fraud has also grown, becoming a major challenge for the financial sector. To address this, more advanced detection systems are needed. Machine Learning (ML) and Deep Learning (DL) have proven to be powerful tools in this fight. These technologies learn from large volumes of transaction data, spotting patterns and unusual behavior that may signal fraud. Unlike traditional systems, ML and DL models can adapt and improve over time, making them effective against constantly changing fraud tactics. Integrating these models into fraud detection systems has already shown strong results, reducing the success rate of fraud attempts and helping to protect the security of credit card transactions. This review highlights the importance of ML and DL in strengthening fraud detection and improving trust in financial systems.

Published by: vikaspatanker

Authors: Prof. K.S.Tamboli, Gaiwad Nikhil Ganesh, Meher Karan Dnyandev, Kate Dhruv Balsabheb

Abstract: The global shift towards sustainable and eco-friendly transportation has intensified interest in electric vehicles (EVs) as a viable alternative to conventional internal combustion engine (ICE) vehicles. However, replacing every gasoline or diesel-powered vehicle with a brand-new EV is not only economically challenging but also environmentally taxing due to the resources and energy required for manufacturing new vehicles. As a practical and cost-effective solution, retrofitting existing vehicles into electric vehicles has emerged as an innovative approach to accelerate the transition to clean mobility. Retrofitting involves replacing the conventional drivetrain of a vehicle including the engine, fuel system, and exhaust with an electric motor, battery pack, and related control systems, thereby converting the vehicle into a fully electric one. This process extends the lifespan of vehicles, reduces emissions, and allows vehicle owners to enjoy the benefits of electric mobility without the need to purchase a new EV. This approach is especially relevant in developing countries, where the existing fleet of vehicles is large and often aging. Retrofitting not only helps in meeting stringent emission norms but also supports local industries and job creation by fostering a circular economy in the automotive sector. In this context, retrofitting serves as a bridge between current transportation realities and a more sustainable future, offering a promising pathway for reducing the carbon footprint of road transport while maximizing the utility of existing automotive assets.

Published by: vikaspatanker

Authors: Ms. Rasika R. Patil, Renuka S. Durge

Abstract: Cognitive computing represents an advanced approach in artificial intelligence that aim to simulate human reasoning, learning and decision-making process. Unlike traditional AI systems that follow fixed algorithm, cognitive systems learn from continuously learn from experiences, adapt to new data and response intelligently to changing a new context. These systems integrate disciplines such as machine learning, deep natural networks and natural language processing to analyze large volume of structured and unstructured information. Cognitive computing enhances human machine interaction by enabling contextual understanding, pattern recognition and predictive reasoning. This pepar explores this architecture, working principles, and real-world application of cognitive computing in healthcare, business analytics, and autonomous systems. It also discusses current challenges, including data privacy, interpretability, and ethical implementation. The study concludes that cognitive computing holds to potential to create adaptive, transparent, and human like intelligent systems that redefine the future of decision making and automations.

Published by: vikaspatanker