Category Archives: Uncategorized

Authors: Abhendra Pratap Singh, Nandini Sharma, Prince Kumar Sharma, Arpit Dwivedi, Aakriti Sharma

Abstract: The growing use of Artificial Intelligence (AI) in surveillance technologies changes how societies observe, predict, and manage security. From predictive policing to facial recognition, AI surveillance technologies offer real-time analysis, risk detection, and improved efficiency. Still, the rapid proliferation of such technologies brings issues of privacy, ethics, and accountability to the forefront. This review assesses the balance between human rights, AI ethics, and the surveillance technologies themselves. It demonstrates how China, the UK, and the USA have vastly different approaches toward data regulation, transparency, and consent. It also illustrates the major technical issues of algorithmic bias, data abuse, interoperability of privacy frameworks, and the ethics of large-scale surveillance and digital autonomy. By defining the gaps and analyzing the global pattern of such technologies, the paper aims to provide the most responsible and human-centric AI surveillance possible to guarantee privacy while also providing the oversight that people need

DOI: http://doi.org/10.5281/zenodo.17481315

Published by: vikaspatanker

Authors: Abhendra Pratap Singh, Nandini Sharma, Vanshika Dua, Arpit Dwivedi, Aakriti Sharma

Abstract: Composite materials are generated by intermingling two or more diverse components that are individually not able to do various tasks but when put together have become critically important in modern engineering due to their superior mechanical and structural traits. Fiber reinforced polymer (FRP) composites are utilized frequently in the aerospace automotive and construction industries more prominently. Despite their growing adoption, a continuing dilemma involves assessing natural fiber reinforced polymers (NFRP) over synthetic fiber reinforced polymers (SFRP) which differ greatly at the levels of performance cost and environmental impact. Both natural and synthetic composites have their own benefits and drawbacks such that synthetic composites offer excellent strength and durability and natural composites are gaining popularity due to their lightweight renewability and sustainability. This lack of unambiguous data driven comparison often leads to unclear judgment and leads to confusion in choosing the most viable composite for certain technical objectives. To eradicate this gap, the study examines three natural composites flax FRP, hemp FRP and jute FRP and three synthetic composites glass FRP, carbon FRP and aramid FRP. The paper uses computationally intensive analysis and machine learning methods such as linear regression and support vector machine (SVM) to figure out four crucial properties which mostly defines about the composite materials namely density, tensile strength, elastic modulus and moisture absorption. The visualized results of matplotlib based graphs provide a clear insight of how natural and synthetic composites perform individually and collectively through comparative analysis. This research incorporates AI assisted analytical modeling with scientific visualization to give a systematic and sustainable structure for selecting innovative composite materials.

DOI: http://doi.org/10.5281/zenodo.17481163

Published by: vikaspatanker

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.

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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

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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.

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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.

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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.

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Authors: Mr. Akhilesh M. Bhagat, Prof. S. V. Raut

Abstract: The advancement of artificial intelligence and natural language processing has led to the development of intelligent voice assistants capable of performing a wide range of tasks. However, most existing systems such as Siri, Alexa, and Google Assistant lack emotional understanding and real-time adaptability. This paper presents an enhanced version of AURA AI, an intelligent voice assistant built using Python and GPT technology, integrated with emotion recognition and real-time web interaction. The proposed system detects the user's emotional state through speech tone and facial expressions, allowing it to respond more empathetically and contextually. Additionally, real-time web integration enables the assistant to access live information such as weather updates, news, and general knowledge through APIs, providing users with up-to- date and personalized responses. Experimental evaluation demonstrates that the enhanced AURA AI offers improved user engagement, adaptability, and interaction quality compared to traditional voice assistants. This approach contributes toward creating emotionally intelligent and human-like conversational systems for next-generation AI applications.

Published by: vikaspatanker

Authors: Vinod B. Ingale, Ashish Vankudre, Sagar mali , Dhanaji Jadhav, Pramod Shitole

Abstract: The proliferation of digital platforms has generated vast amounts of event-based temporal data, where user interactions are logged as discrete events in continuous time. Traditional recommendation systems often fail to capture the intricate dynamics of such data, including the exact timing, inter-event gaps, and evolving nature of user preferences. This paper proposes a novel hybrid neural architecture that synergistically integrates Temporal Point Processes (TPPs) with a Self-Attention mechanism to model user temporal behavior for next-item recommendation. Our model, the Temporal Self-Attentive Hawkes Process (TSAHP), leverages the self-attention mechanism to capture complex, long-range dependencies within user interaction sequences, while a neural Hawkes process models the continuous-time intensity of these interactions, inherently accounting for the excitement and decay effects of past events. We evaluate the proposed TSAHP model on two real-world datasets: Amazon Electronics and LastFM. Comparative analysis against state-of-the-art methods, including Time-Aware Matrix Factorization, GRU-based models, and standard Hawkes Process models, demonstrates the superiority of our approach. The TSAHP model achieves significant improvements, with an average increase of 12.5% in Hit Rate @10 and 15.3% in NDCG @10 on the Amazon dataset, and 9.8% in HR@10 and 11.7% in NDCG@10 on the LastFM dataset. The results indicate that explicitly modeling both the semantic context through self-attention and the temporal dynamics via point processes is crucial for accurate and timely recommendations in event-based systems.

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Authors: Paul Oduor Oyile, Eric Sifuna Siunudh, Daniel Khaoya Muyobo, Anselemo Peters Ikoha

Abstract: This study examined the impact of prolonged digital device use on cognitive development and attention span among children aged 6-8 years in four counties of Western Kenya: Bungoma, Kakamega, Vihiga, and Busia. Employing a mixed-methods approach, the research combined surveys, interviews, and observational assessments to evaluate how exposure to tablets and computers affects cognitive skills, problem-solving abilities, and attention retention. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative insights revealed behavioral patterns and parental mediation practices. Findings demonstrated a significant negative correlation between increased daily screen time and both cognitive and attention performance. Children exposed to less than one hour of screen time daily scored considerably higher on cognitive and attention measures compared to those with over four hours of exposure. Parental mediation emerged as a crucial moderating factor, with high parental engagement substantially buffering negative outcomes. Gender differences were subtle, though boys engaged more in recreational activities while girls favored educational content. The study supports the displacement hypothesis, suggesting that excessive screen use replaces developmentally essential activities. Results underscore the necessity for balanced technology integration in early education, evidence-based screen time guidelines, and collaborative efforts among policymakers, educators, and parents to maximize educational benefits while safeguarding children's cognitive development and attention capabilities.

DOI: https://doi.org/10.5281/zenodo.17463828

 

Published by: vikaspatanker