Daily Archives: October 25, 2025

Authors: Samarth D

Abstract: This project provides a comprehensive analysis and prediction of California housing prices using machine learning techniques. The project is implemented in Python and uses a Linear Regression model to predict housing prices based on various factors such as median income, housing median age, total rooms, population, and geographical location. The report is structured to provide an in-depth understanding of the problem, methodology, implementation, results, and potential future work. The accompanying Python code trains the model, evaluates its performance, and produces visualizations to aid in understanding the relationships between features and housing prices

Published by: vikaspatanker

Authors: Mr. Akash Godere, Mr. Javeed Khan

Abstract: Micro services architectures on cloud platforms offer scalability and flexibility, but performance optimization remains a key challenge. This paper presents a comparative study of different optimization techniques for cloud-based microservices, focusing on resource utilization, load balancing, and response time reduction. Experimental evaluation on AWS and Kubernetes demonstrates significant improvements in throughput and latency when employing container- level optimization, dynamic scaling, and efficient service orchestration. The study provides actionable insights for cloud architects and developers to achieve optimal performance in microservices deployments.

Published by: vikaspatanker

Authors: Mr. Akash Godre, Mr. Javeed Khan

Abstract: Cloud-native applications increasingly rely on microservices architectures to achieve scalability, fault tolerance, and maintainability. This paper presents a scalable microservices architecture design suitable for cloud platforms. The proposed architecture leverages containerization, orchestration, and dynamic scaling mechanisms to ensure high availability and optimal resource utilization. Performance evaluation demonstrates improved scalability, fault tolerance, and reduced response time compared to monolithic and traditional microservices designs. This work provides practical guidelines for deploying scalable microservices on cloud platforms like AWS, Azure, and Google Cloud.

Published by: vikaspatanker

Authors: Ayush Kumar, Abhendra Pratap Singh, Dr.Uma Gautam, Nandini Sharma

Abstract: Upper limb amputees face significant challenges due to the high cost and limited availability of advanced prosthetic hands. Recent advances in 3D printing, combined with electromyography (EMG) and electrocardiography (ECG) sensing, have enabled the development of affordable, customizable and functionally capable prosthetic devices. This review paper focusses on the current literature on 3d printed bionic hands controlled by EMG and ECG signals, highlighting design strategies, materials, actuations mechanism, and control system. The integration of hybrid bio signals, adaptive algorithms, and additive manufacturing has improved prosthetic performance, responsiveness and user comfort. The review also discusses the role of artificial intelligence and machine learning in enhancing signal processing, gesture recognition, and motion prediction as well as the potential of IoT-enabled monitoring and patient support. Moreover, limitations of current approaches and future directions for more intelligent, reliable and accessible prosthetic solutions are outlined for identification of scope for further advancement in this domain.

DOI: https://doi.org/10.5281/zenodo.17442105

 

Published by: vikaspatanker

Authors: Sidhant Chadha

Abstract: Vision-based object recognition has emerged as a transformative technology in modern retail, revolutionizing how products are identified, tracked, and managed across the supply chain. Leveraging computer vision and deep learning techniques, these systems enable automated product detection, shelf monitoring, customer behavior analysis, and inventory management with high precision and speed. This study explores the design and implementation of vision-based object recognition systems within retail environments, emphasizing the role of convolutional neural networks (CNNs), transfer learning, and real-time image processing frameworks. By integrating cameras, sensors, and AI-driven analytics, retailers can enhance operational efficiency, minimize human error, and provide personalized shopping experiences. The paper also examines challenges such as occlusion, lighting variation, and scalability, along with potential solutions through model optimization and data augmentation. The findings suggest that vision-based recognition systems are key enablers of intelligent retail automation, contributing significantly to the advancement of smart retail ecosystems and Industry 4.0 integration.

DOI: https://doi.org/10.5281/zenodo.17439901

 

Published by: vikaspatanker

Authors: Sarang Vehale, Ruchita Vehale

Abstract: Traditional federated learning (FL) frameworks face critical challenges in privacy, scalability, and auditability when deployed across multiple enterprises with strin- gent regulatory requirements. Quantum-secure protocols such as Quantum Key Distribution (QKD) and post-quantum cryptography can harden communica- tion channels against both classical and emerging quantum attacks. Meanwhile, variational quantum algorithms (VQAs) promise computational speedups for high-dimensional aggregation tasks that become bottlenecks in large-scale FL systems. We propose a hierarchical, multi-tier Quantum-Federated Learning (QFL) architecture in which local enterprises perform classical model training, regional “quantum hubs” execute VQA-accelerated aggregation and anomaly detection, and a global coordinator enforces UN/ISO AI governance via verifiable zero-knowledge proofs (ZKPs). By bounding quantum resource usage to interme- diate nodes and combining QKD on backbone links with lattice-based encryption at the edge, our design achieves near-term implementability, cost-effectiveness, and end-to-end privacy guarantees. Preliminary simulations demonstrate that the proposed scheme reduces communication overhead by over 60% and resists gradient-poisoning attacks with negligible impact on model accuracy. This work lays the foundation for a globally scalable, audit-ready AI governance ecosystem suitable for international deployments

DOI: http://doi.org/10.5281/zenodo.17441935

Published by: vikaspatanker