Category Archives: Uncategorized

Authors: Dharunika G, Jack Robin J

Abstract: The increasing scale and dynamic complexity of modern cloud computing environments pose significant challenges for ensuring system reliability and availability, making traditional manual fault diagnosis and recovery insufficient. This paper explores advanced methodologies, contrasting established statistical monitoring techniques with emerging Artificial Intelligence (AI)-driven autonomous self-healing frameworks designed to manage faults, minimize downtime, and optimize resource utilization. Early methods employed correlation analysis using Canonical Correlation Analysis (CCA) and Exponentially Weighted Moving Average (EWMA) control charts for anomaly detection, followed by feature selection using ReliefF and SVM-RFE for problem location. The evolution toward AI-driven solutions leverages machine learning (ML), deep learning (DL), and reinforcement learning (RL) to achieve automated failure prediction and recovery. Recent advancements feature hybrid AI models and self- reflective multi-agent systems (SR-MACHA) that demonstrate enhanced accuracy and self- optimization capabilities, achieving significant reductions in Mean Time to Repair (MTTR) and improving system resilience. However, challenges remain regarding the scalability, computational cost of training complex models, and ensuring real-time performance in dynamic, heterogeneous cloud infrastructures.

Published by: vikaspatanker

Authors: Semran Ojha, Professor Rahul Patidar, Professor Jayshree Boaddh

Abstract: The rapid expansion of the Internet of Things (IoT) has created significant challenges in managing computation and data processing efficiently. As billions of interconnected devices generate massive workloads, traditional cloud infrastructures experience latency and performance bottlenecks. To address these limitations, this research introduces an intelligent edge-level load distribution model using Random Forest techniques. The proposed system leverages edge computing to process tasks closer to data sources, thereby reducing dependency on centralized cloud servers. The Random Forest algorithm is utilized to learn from historical task patterns and predict optimal job scheduling sequences. This is integrated with a wolf optimization strategy that dynamically adjusts load distribution across heterogeneous edge nodes without prior training requirements. Experimental evaluation conducted using MATLAB demonstrates that the proposed model effectively minimizes makespan by 0.79% and enhances edge utilization by 16.25% compared to the existing Preference-Based Stable Matching (PBSM) model. These improvements confirm that machine learning- driven edge load balancing can significantly improve resource allocation, task completion time, and overall network efficiency in large-scale IoT environments.

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Authors: Gayatri dhasade, Soham vishe, Soham Bhintade, Darshan Bhamare, Prof.Poonam Chavan

Abstract: An academic event management system is A digital platform designed to plan, organize, manage and monitor academic and non-academic events held at educational institutions. College Event Management System is a digital platform designed to plan, organize, manage, and monitor academic and non-academic events conducted in educational institutions. The Colleges regularly organize seminars, workshops, cultural programs, sports events, technical competitions and guest lectures. competitions, and guest lectures. Handling these events manually using paperwork or scattered communications often results in inefficiency, communication issues, and data loss. Managing these events manually using paperwork or scattered communication often leads to inefficiency, miscommunication, and data loss. The proposed system provides a centralized solution to manage event creation, registration, scheduling, notifications and reporting. notifications, and reporting. It allows students, faculty and event coordinators to interact through a single platform, improving transparency and coordination. enables students, faculty, and event coordinators to interact through a single platform, improving transparency and coordination. By automating event workflows, the system reduces administrative workload, ensures timely communication and improves overall event execution. communication, and enhances overall event execution.

 

Published by: vikaspatanker

Authors: Aadhithyan K, Pranauv Raaj N

Abstract: Card-based transactions and modern digital payment systems face sophisticated and rapidly evolving security threats, necessitating advanced fraud detection methods. Traditional approaches, often reliant on fixed rules and descriptive analytics, are slow to adapt to new fraud schemes and struggle with the volume of real-time transactions. This presentation analyzes the effectiveness of AI-driven fraud detection, specifically focusing on the integration of Real-Time Analytics, Deep Learning (DL), and Agentic AI systems to enhance security and prevent financial losses. The study highlights that DL models, such as hybrid Recurrent Neural Networks (RNNs) combined with attention mechanisms, offer superior performance by modeling sequential data and addressing challenges like data imbalance. Furthermore, adopting the Deep Learning–Sector–Governance (DLSG) framework is crucial, as it ensures that technical innovations are aligned with sector-specific constraints and regulatory requirements, such as the need for explainability and data privacy. The synthesis of these technologies provides a proactive, adaptive solution to safeguard complex financial ecosystems.

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Authors: Shubhangi Sondhiya, Deepesh Malviya

Abstract: These seismic risks are considered the prime cause for concern in seismic zones and earthquake-prone areas around the world. Over time, a sequence of earthquake motions with different seismic intensity has been used to conduct the investigation and analyze the structural dynamics. Analyses considering the effect of isolated structures showed that the isolators restrict the lateral loads transmitted to the structure, which, in turn, has the tendency to reduce the sizes of building components. In this study, design, operation, testing, and applicability of base isolation are analyzed in detail as per Indian Standards. Base isolation has been found to be one of the popular design approaches in recent times. A building structure is taken as a case study model for this study, and contemporary design tools are also applied for the analysis. Conclusions are drawn from the results obtained. We'll discuss the probable advantages of base isolation over the conventional dynamic analysis. This chapter deals with the design details of the models and step followed to design the building. This study is conducted on a G+11-story building located on soft soil. The structure is designed as a college building (with plan irregularity) situated in Seismic Zone V and analyzed using ETABS 2022. For the analysis, two models are considered: Model M-1, which has a fixed base, and Model M-2, which incorporates a base isolator. The comparative analysis between the fixed-base model (M–1) and the base-isolated model (M–2) clearly demonstrates the effectiveness of base isolation in improving seismic performance. The base-isolated structure shows reduced base shear, displacement, and overturning moments by approximately 25–30%, indicating enhanced stability and safety. Although story drift slightly increases due to controlled base movement, this behavior helps in dissipating seismic energy and reducing damage to the superstructure. Overall, base isolation significantly enhances structural resilience, minimizes earthquake-induced forces, and provides an efficient and reliable solution for earthquake-resistant design in multi-storey buildings. Overall, the comparative study clearly demonstrates that the implementation of base isolation considerably enhances the seismic performance of structures. It reduces base shear, displacement, and overturning moments while allowing controlled drift, thereby ensuring improved safety, flexibility, and durability. These outcomes confirm that base isolation is a highly effective and reliable strategy for seismic risk mitigation in multi-storey buildings.

DOI: http://doi.org/10.5281/zenodo.18066513

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Authors: A. Mohamed Sikkander, Joel J. P. C. Rodrigues, Manoharan Meena

Abstract: Artificial Intelligence (AI), also referred to as machine learning (ML) or deep learning (DL), is rapidly revolutionizing cancer treatment by using genomic information for improving diagnosis, prognosis, treatment decision, and drug discovery. Being a result of genetic and molecular changes, it is important to understand cancer’s genomic patterns and profiles. In conventional genomic analyses, common methodologies fail to handle high-dimensional genomic data produced from next-generation sequencing (NGS) and multi-omics platforms; on the other hand, AI approaches excel in detecting intricate patterns from large genomic datasets. This AI system trained from a large public genomic database such as ‘The Cancer Genome Atlas (TCGA),’ ‘Genomic Data Commons (GDC),’ or generally from the ‘Catalogue of Somatic Mutations in Cancer (COSMIC)’ has already facilitated accurate classifications of cancer subtypes and their treatment predictions or discovery of effective biomarkers for treatment of cancer subtypes that are accurate to a great extent. Deep learning from somatic mutation sequences showed an accuracy of approximately 0.98 for clinical biomarkers such as microsatellite instability (MSI), which is a considerably high improvement over other existing methodology. Integration of AI with multi-omics genomic, transcriptomic, proteomic data types further helps to increase efficiency of predictions regarding patient outcomes. Though AI is a revolution in genomic study thereby bringing a revolution in cancer treatment approaches following a detailed precise treatment decision of cancer treatment from an individual’s genomic study followed by inducing a global revolution in cancer treatment true to precision medicine practices around the world.

DOI: http://doi.org/10.5281/zenodo.18066233

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Authors: Ujjwal Kumar, Ritik Kumar, Kavita kumari, Nitika Vats

Abstract: Apigenin is a low-toxicity flavonoid with several beneficial bioactivities. It is a secondary metabolite bioflavonoid that shows pharmacological activities such as antibacterial, anticancer, antidiarrheal, antiemetic, and hemostatic effects. Apigenin is extracted from different parts of parsley plants by using the Soxhlet Extraction method. Parsley (Petroselinum crispum), a flowering species of the Apiaceae family, is native to Greece, Morocco, and the former Yugoslavia. In traditional medicine, parsley has been used as a carminative, gastrotonic, diuretic, urinary tract antiseptic, anti-urolithiasis, antidote, and anti-inflammatory agent. It is also used for treating dermal diseases, amenorrhea, dysmenorrhea, gastrointestinal disorders, hypertension, cardiac and urinary diseases, otitis, cold, and diabetes. Apigenin is a phytochemical that occurs along with tannins, alkaloids, triterpenoids, steroids, flavonoids, and saponins. Rich in parsley, celery, celeriac, and chamomile tea, apigenin has health-promoting properties, including use as a natural sleep aid, antidiabetic, and anticancer compound. This flavonoid induces relaxation as it binds to brain receptors that promote sleep. The main aim of this paper is to extract apigenin flavonoid from parsley plant parts. Parsley leaves and flowers are abundant in phenolic compounds, present as aglycones (flavones and flavonols) and glycosides. In this study, apigenin was extracted from parsley leaves using Soxhlet extraction, followed by hydrolysis and recrystallization. A combination of apigenin and lecithin was also synthesized using a solvent method. Several extraction parameters were tested to evaluate yield, with Soxhlet extraction 5.5 h, 65 °C, solid-to-solvent ratio 8:400 as the reference and the purification done by colume chromotography. UV-Visible analysis confirmed that the structure of apigenin remained stable after extraction and purification.

DOI: https://doi.org/10.5281/zenodo.18058776

 

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Authors: Dr. Rachna Rana, Er. Gundeep Kaur, Er. Manpreet Kaur, Mr. Sachin Sharma

Abstract: Artificial Intelligence (AI) is reshaping educational systems worldwide through personalized learning, predictive analytics, intelligent tutoring systems, automation, and institutional decision-support technologies. AI applications in education have transitioned from experimental prototypes to widely adopted tools used for assessment, student support, curriculum design, and governance. This paper presents a comprehensive analysis of the current landscape of AI in education, with emphasis on machine learning (ML) frameworks, learning analytics (LA), natural language processing (NLP), and predictive analytics used for monitoring academic quality assurance (QA). The paper synthesizes findings from recent empirical and conceptual studies, discusses the system-level implications of AI-enabled educational data mining, and identifies ethical, pedagogical, and institutional challenges that influence adoption. A section is dedicated to the integration of AI-driven predictive models into QA processes, including early warning systems, risk-prediction algorithms, and data-driven continuous-improvement frameworks. The paper concludes with recommendations for responsible AI deployment, future research trajectories, and policy considerations.

DOI: http://doi.org/10.5281/zenodo.18081433

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Authors: Vaibhav Sinha, Abhishek Kumar Singh, Dr. Partap Singh

Abstract: The integration of 5G technology and Artificial Intelligence (AI) marks a transformative phase in digital communications and intelligent connectivity. As 5G networks offer unprecedented speed, ultra-low latency, and massive device connectivity, AI brings the intelligence required to optimize and automate 5G systems. This research paper critically examines how AI empowers 5G networks, explores key applications, discusses challenges, and highlights future prospects across industries. With supporting pictorial references, the paper presents a comprehensive, humanized view suitable for academic and professional audiences.

DOI: http://doi.org/10.5281/zenodo.18053277

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Authors: Satyaki Adak

Abstract: Distributed Machine Learning (DML) enables efficient training over massive datasets by distributing computation across multiple nodes; however, it also increases vulnerability to data poisoning attacks, where adversaries inject malicious or mislabeled data to corrupt the learning process. Ensuring model integrity in such environments is a critical security challenge. This project classifies DML systems into basic-DML and semi-DML based on whether the central server participates in dataset training. For the basic-DML scenario, a novel cross-learning–based data poisoning detection scheme is proposed, where training results from distributed workers are compared through multiple training loops to identify anomalous behaviour. A mathematical model is developed to determine the optimal number of training loops that maximizes detection accuracy while minimizing overhead. For the semi-DML scenario, an enhanced poison detection mechanism is introduced by leveraging the central server’s computing resources, along with an optimal resource allocation strategy to reduce unnecessary computation. Experimental results demonstrate that the proposed schemes significantly improve model accuracy—up to 20% for Support Vector Machines and 60% for Logistic Regression in basic-DML—while reducing wasted resources by 20–100% in semi-DML. The proposed framework offers a general, efficient, and scalable defence against data poisoning attacks in distributed learning environments.

DOI: http://doi.org/10.5281/zenodo.18051593

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