Category Archives: Uncategorized

Authors: Vinod Ingale, Sayli Jadhav, Priyanka Telshinge, Rahin Tamboli, Ashwini Mahind

Abstract: The proliferation of digital platforms has led to an explosion of complex user interaction data, characterized by its sequential nature and rich contextual information. Traditional collaborative filtering (CF) methods often fall short by treating user preferences as static and ignoring the nuanced impact of temporal context and real-world events. This paper proposes a novel recommendation framework, the Temporal-Event-aware Support Vector Machine (TE-SVM), designed to effectively model the dynamic evolution of user preferences by integrating temporal dynamics and event-based contextual signals. The TE-SVM model formulates the recommendation task as a classification problem, where the objective is to find an optimal hyperplane that separates user preferences for items at a given time under specific event conditions. We engineer a comprehensive feature set that captures temporal patterns (e.g., time decay, periodicity) and event embeddings derived from external knowledge sources. A thorough comparative analysis is conducted against established models, including Matrix Factorization (MF), TimeSVD++, and Recurrent Neural Networks (RNN). Experimental results on a large-scale e-commerce dataset demonstrate that the proposed TE-SVM model achieves a significant improvement, with a 12.7% increase in Precision@10 and a 9.8% increase in NDCG@20 compared to the best-performing baseline. The findings underscore the efficacy of SVM in handling high-dimensional, heterogeneous feature spaces for temporal and event-aware recommendation tasks, providing a robust and interpretable alternative to deep learning-centric approaches.

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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

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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.

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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.

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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

 

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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

 

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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

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Authors: Charitidis J. Panagiotis

Abstract: The oil and gas (O&G) industry increasingly requires advanced materials capable of withstanding harsh operating environments such as deepwater, ultra-deepwater, and high-temperature/high-pressure (HTHP) reservoirs. Fibre- reinforced polymer (FRP) composites have already provided benefits in corrosion resistance, weight reduction, and fatigue performance, yet their broader adoption remains limited by challenges such as poor impact resistance and inadequate fire performance. Nanocomposites—polymers reinforced with nanoparticles, including clays, metal oxides, carbon nanotubes, and graphene—offer a pathway to overcoming these limitations. Even at low filler concentrations, they can deliver significant improvements in mechanical strength, thermal stability, fire resistance, and barrier properties, while also enabling new functionalities in drilling fluids, cementing, and enhanced oil recovery (EOR). This review examines the state of nanocomposite research in the O&G sector, evaluates their potential to enhance both structural and fluid applications, and discusses the technical, economic, and regulatory challenges that must be addressed to achieve commercial deployment.

DOI: http://doi.org/10.5281/zenodo.17439618

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Authors: Sushanth.Chandrashekar, Shantanu Nagaraj, Ashish Naidu

Abstract: The tender bidding process remains a critical yet inefficient cornerstone of global procurement, plagued by manual document analysis, compliance errors, and resource-intensive workflows. This paper introduces Agentic Graph RAG, an innovative AI system that redefines bid preparation by integrating Retrieval-Augmented Generation (RAG), dynamic knowledge graph and multi-agent collaboration to automate and optimize the end-to-end bidding pipeline. Our architecture combines three transformative pillars: a cognitive document processor, a living knowledge graph, and a specialized agent framework. Validated on real-world tenders, the system demonstrates 98% clause extraction accuracy, 80% faster bid preparation, and a 6.4x ROI through compliance assurance and strategic positioning. This work bridges cutting-edge AI research with practical procurement challenges, offering a scalable blueprint for intelligent automation in competitive bidding.

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Authors: Aswin S k, Dr. M Sivaprakash, Linsha Pushparaj, Vinoth M, Dan Abishek A S, Infant Mazhak

Abstract: The global transition toward sustainable mobility has increased interest in electric vehicles (EVs) as alternatives to internal combustion engine–based transportation. This work presents the design, fabrication, and performance evaluation of an electric three-wheeler prototype intended for urban commuting and short-distance goods or passenger transport. The prototype integrates a lightweight chassis, a Brushless DC (BLDC) motor drive, a lithium-ion battery pack, and a basic control system to achieve affordability, reliability, and energy efficiency. The methodology included load analysis, torque and speed requirement estimation, chassis fabrication, motor and controller integration, and testing under real operating conditions. Results demonstrated a top speed of 40 km/h, a load capacity of 300 kg, and an average operational range of 65 km per charge. Compared with conventional three-wheelers, the prototype eliminates fuel costs, reduces maintenance requirements, and achieves zero tailpipe emissions. The findings suggest that electric three-wheelers can provide a sustainable and cost-effective solution for last-mile connectivity and urban transport, especially in developing economies.

DOI: https://doi.org/10.5281/zenodo.17433866

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