Category Archives: Uncategorized

Authors: Rohan Deshpande

Abstract: Microservices architecture has emerged as a prominent approach in modern software development, enabling the design and deployment of applications as a collection of loosely coupled, independently deployable services. This study explores the principles, benefits, and challenges of microservices architecture, emphasizing its role in improving scalability, maintainability, and agility in software systems. The paper examines key implementation strategies, including service decomposition, API management, containerization, and orchestration using platforms like Docker and Kubernetes. Additionally, it discusses important aspects such as inter-service communication, data management, fault tolerance, and continuous integration/continuous deployment (CI/CD) pipelines. Challenges including service coordination, monitoring, security, and performance optimization are analyzed, along with practical solutions and best practices. The study also highlights real-world applications across industries such as e-commerce, finance, healthcare, and IoT. Concluding, microservices architecture is positioned as a critical enabler of modern cloud-native applications, facilitating rapid development, scalability, and resilience in complex software ecosystems.

DOI: https://doi.org/10.5281/zenodo.19647478

Published by: vikaspatanker

Authors: Sneha Kulkarni

Abstract: Cloud computing has revolutionized the delivery of computing resources, offering scalability, flexibility, and cost efficiency. However, the dynamic and distributed nature of cloud environments poses significant challenges for network performance, including latency, bandwidth limitations, congestion, and reliability issues. Network optimization techniques are essential to ensure efficient data transfer, reduced communication delays, and improved overall system performance. This study provides a comprehensive analysis of various network optimization strategies in cloud environments, including traffic engineering, load balancing, software-defined networking (SDN), network function virtualization (NFV), and caching mechanisms. The study evaluates the effectiveness of these techniques in enhancing network throughput, minimizing latency, and ensuring high availability in cloud-based applications. Additionally, it addresses challenges such as resource contention, dynamic workload allocation, security considerations, and the integration of heterogeneous network infrastructures. By examining current research trends, practical implementations, and performance metrics, this study demonstrates that effective network optimization is critical for achieving reliable, high-performance, and scalable cloud computing solutions.

DOI: https://doi.org/10.5281/zenodo.19647476

Published by: vikaspatanker

Authors: Daniel Okeke

Abstract: Network monitoring and observability have become essential components in managing modern cloud systems, where distributed architectures, dynamic workloads, and microservices-based applications introduce significant complexity. This review provides a comprehensive analysis of traditional network monitoring techniques and the evolution toward full-stack observability in cloud environments. It examines key concepts such as metrics, logs, and traces, which collectively enable deep visibility into system behavior, performance, and reliability. The study explores cloud-native monitoring tools and frameworks, including Prometheus, Grafana, OpenTelemetry, and distributed tracing systems, highlighting their roles in detecting anomalies, diagnosing issues, and ensuring service availability. Additionally, the integration of artificial intelligence and machine learning in observability platforms is discussed, emphasizing their ability to provide predictive insights and automate incident response through AIOps. Challenges such as data volume management, alert fatigue, latency, and interoperability are critically analyzed, along with best practices for designing scalable and efficient monitoring strategies. The review concludes that effective observability is crucial for maintaining performance, reliability, and user experience in cloud systems, enabling organizations to proactively manage complex distributed infrastructures.

DOI: https://doi.org/10.5281/zenodo.19647452

Published by: vikaspatanker

Authors: Amina Yusuf

Abstract: The rapid adoption of distributed cloud environments has transformed how organizations deploy, manage, and scale applications across multiple geographic locations and cloud providers. While this paradigm offers enhanced flexibility, scalability, and resilience, it also introduces complex security and risk management challenges. This study provides a comprehensive analysis of security frameworks and risk management strategies in distributed cloud ecosystems, focusing on the protection of data, applications, and infrastructure. It examines key security concerns such as data breaches, unauthorized access, misconfigurations, insider threats, and vulnerabilities arising from multi-cloud and hybrid cloud deployments. The paper also explores advanced security mechanisms, including identity and access management (IAM), encryption techniques, zero-trust architecture, and continuous monitoring. Additionally, it highlights the role of compliance standards, governance policies, and automated security tools in mitigating risks. Emerging approaches such as AI-driven threat detection, secure access service edge (SASE), and cloud security posture management (CSPM) are discussed as critical enablers of proactive defense strategies. The findings emphasize that a holistic and layered security approach, combined with effective risk assessment and mitigation practices, is essential for ensuring the integrity, confidentiality, and availability of resources in distributed cloud environments.

DOI: https://doi.org/10.5281/zenodo.19647429

Published by: vikaspatanker

Authors: Soumya M Achari, Pakhi Singha, Nithin Ramakrishnan

Abstract: On demand delivery began as a competitive edge in the consumer market. Quick commerce sites provided customers access to products within the shortest possible time to stand out from rivaling brands. However, this fast growth of 10 minute and 1 day delivery services leave traditional delivery services irrelevant. Due to the customer’s opting for convience and speed, retailers selling stock struggle to meet these expectations and lose profitability. Access to real time data updates and optimisation has hence become significant in ensuring delivery to correct locations, punctually and efficiently. Current local systems struggle to respond to dynamic data, leading to missed delivery time slots, manual intervention requirement, excessive fuel and time wastes, poor customer feedback and so on. In order to remain competitive in such consumer markets, business require real time updates on demand and supply chains, delivery agent availability, client shopping patterns and traffic volume information. To counter these challenges artificial intelligence can be used to understand real time data and set parcel delivery time slots automatically while routing delivery agents through optimal pathways and monitoring the system of the agents and customer to align with their available schedules. The AI will utilise previous ETA, traffic congestion, pattern recognition in relation to prior on time articles that were received and user presence to define schedules for delivery and update the consumers, drivers and supervisors accordingly. This proposed intelligent system would solve the common E-Commerce problems faced by traditional delivery systems by ensuring routes are mapped to avoid redundancy, increase time efficiency, deliver as per consumer availability, especially for cash on delivery where the client is required at the home for payment, provide real time transportation status of the products to supervisors and customers, therefore increasing the trust of the user in the brand and providing an avenue for the manager to handle mismanaged deliveries. Such a system would bolster customer satisfaction and also reduce fuel and time consumption for the drivers, enhancing their work life balance. Deliveries that are more likely to be missed or routes that could result in accidents would be information sent to the supervisor, customer and delivery agents respectively, hence, preventing missed deliveries, injuries and delays. Such systems have been applied experimentally at a smaller scale and proven successful in reducing time, fuel, costs and injury risk, while improving customer satisfaction, making them a worthwhile subject of research.

DOI:

Published by: vikaspatanker

Authors: Manasi Rathod, Jayesh Mahajan, Rutwij Landge

Abstract: Storytelling represents one of the most effective techniques for communication, education, and knowledge transfer across diverse domains. However, traditional text-based storytelling methods often fail to maintain engagement among modern learners who increasingly prefer visually rich multimedia experiences. Creating animated storytelling videos manually requires expertise in scripting, illustration, animation design, narration recording, and editing tools. This paper presents STORYTALES – AI Tells the Story, an automated Generative Artificial Intelligence framework that converts textual narratives into animated storytelling videos with synchronized narration and scene-wise visualization. The system integrates Large Language Models for semantic scene segmentation, Stable Diffusion XL for visual synthesis, Stable Video Diffusion for animation generation, Coqui XTTS for narration synthesis, and FFmpeg for automated multimedia composition. Experimental evaluation confirms that the proposed architecture significantly reduces multimedia production complexity while improving accessibility for educators and content creators.

DOI: https://doi.org/10.5281/zenodo.19644537

Published by: vikaspatanker

Authors: Prof. Biju Balakrishnan, Deep Patel, Pankitkumar Patel, Virajkumar Suthar, Dharmik Kanojia

Abstract: Healthcare delivery in its conventional form continues to face persistent operational hurdles — prolonged patient waiting periods, excessive administrative burden, and geographic constraints. This paper introduces MEDICO, a security-focused and patient-centred web portal engineered to establish a fluid digital healthcare environment. The platform was built using the Python Django framework, adopting a Waterfall development methodology and implementing a Model-View-Template (MVT) architecture to support dual-role access control and an intelligent appointment scheduling engine. System capabilities — including practitioner discovery, profile browsing, and automated notification dispatch — were evaluated through Unit, Integration, and System-level testing. Quantitative stress testing demonstrated complete transactional integrity while concurrently processing 50 simultaneous appointment requests, recording zero system failures or scheduling conflicts. This lays a dependable technical foundation directly combating the inefficiencies of conventional booking methods. Subsequent development phases will focus on Artificial Intelligence (AI) for personalised doctor recommendations and a fully integrated Electronic Health Record (EHR) management module.

DOI: https://doi.org/10.5281/zenodo.19642551

Published by: vikaspatanker

Authors: Rathod Alfaz, Ravi Ranjan Kumar Pandey

Abstract: Artificial Intelligence (AI) has changed many industries, and its influence on cybersecurity is very significant. This research paper studies the progress of AI and its role in handling the changing challenges of cybersecurity. It examines the possible benefits of AI in threat detection, vulnerability assessment, incident response, and predictive analytics. In addition, the paper discusses the ethical concerns and possible risks connected with AI in cybersecurity. Through the study of current research, case studies, and industry practices, this paper aims to provide clear insights into the opportunities and challenges created by the use of AI in the field of cybersecurity.

Published by: vikaspatanker

Authors: Ansari Zain, Khan Fahad, Rajput Burhan, Khan Shifa, Chandramohan Konduri

Abstract: A Learning Management System (LMS) is a comprehensive web-based application developed to streamline the process of teaching, learning, and academic administration. The main objective of the LMS is to provide a unified digital platform where educators can create, organize, and manage learning content, while learners can easily access courses, participate in discussions, submit assignments, and track their academic progress. The system eliminates geographical and time limitations, enabling flexible and self-paced learning for students across different devices. The proposed LMS includes essential modules such as user authentication, course management, content uploading, online assessments, grading, progress tracking, and communication tools like notifications and discussion forums. It leverages database management systems to securely store and retrieve user data, ensuring reliability and scalability.

DOI: https://doi.org/10.5281/zenodo.19642420

 

Published by: vikaspatanker

Authors: Smit Sudani

Abstract: With all the amount of data that is now available about the students in a school environment, there is no way one could analyze such data manually. The Student Performance Predictor is a web application I designed to help determine the final score that a particular student will get from mathematics class, basing on his demographics and background. The whole machine learning pipeline was implemented by me using the Python language. After experimenting with various models in Jupyter Notebooks and having my kernel crash quite a few times, I managed to find the most accurate one – Random Forest Regressor with an 80% accuracy rate. Next, I embedded this algorithm in my application, which uses the Flask server. User only needs to input some values in three fields to get the prediction instantly.

Published by: vikaspatanker