Category Archives: Uncategorized

Authors: Brian Levi Okimaru, Betty Mayeku, Humphrey Juma kilwake

Abstract: The transition from junior to senior school under Kenya's Competency-Based Education (CBE) requires learners to select academic pathways that align with their competencies and interests. This transition presents a challenge because pathway selection requires personalized guidance while ensuring the privacy of sensitive student information. Existing educational recommender systems predominantly rely on centralized data processing, exposing learner data to privacy risks and limiting the secure exchange of information across institutions. This study proposes a privacy-preserving personalized pathway recommender system that integrates federated learning, cosine similarity, and Random Forest to support academic pathway recommendation without sharing raw student data. Cosine similarity was employed to model learner competency profiles and measure their alignment with predefined pathway requirements. The resulting similarity scores were incorporated into a Random Forest classifier through feature engineering to improve pathway prediction accuracy. A horizontal federated learning framework enabled multiple schools to collaboratively train the recommendation model by exchanging only model updates while retaining student records locally. The proposed model was evaluated using accuracy, precision, recall, and F1-score. Experimental results showed that integrating cosine similarity with Random Forest improved pathway classification performance, while the federated recommender system achieved an accuracy of 86.54%, outperforming the centralized recommender approach while preserving student privacy. The proposed framework provides an effective and privacy-preserving decision-support tool for personalized academic pathway recommendation within Kenya's Competency-Based Education. The study demonstrates that integrating federated learning with content-based filtering and machine learning can simultaneously enhance recommendation accuracy, personalization, and data privacy in educational environments.

DOI: http://doi.org/10.5281/zenodo.21126397

Published by: vikaspatanker

Authors: Kamatam Munna Kiran, Associate Professor Dr. S. Solomon Raj

Abstract: The increasing demand for lightweight, safe, and fuel-efficient vehicles has driven structural optimisation of automotive body frames. A passenger vehicle body shell was designed in SolidWorks and analysed in ANSYS Workbench 2024 R1 under frontal and rear impact at 60, 80, and 100 km/h using five material configurations: ABS, structural steel, Carbon Fiber Reinforced Polymer (CFRP), Glass Fiber Reinforced Polymer (GFRP), and a hybrid CFRP+GFRP laminate. Performance metrics — total deformation, equivalent stress, equivalent strain, and factor of safety (FOS) were extracted for each scenario. Modal analysis extracted the first six natural frequencies and mode shapes. Results show CFRP achieves superior crashworthiness (FOS > 2.40 at all speeds) and highest natural frequencies (86.38–153.62 Hz), while the hybrid composite nearly replicates CFRP performance at reduced cost. ABS is structurally unsuitable and steel approaches failure at 100 km/h. The hybrid CFRP+GFRP laminate is the optimal lightweight alternative to conventional steel for passenger car body shell applications.

DOI: https://doi.org/10.5281/zenodo.21193036

Published by: vikaspatanker

Authors: Associate Professor Dr. Surender Singh

Abstract: Artificial Intelligence (AI) systems have become essential across industries, supporting decision-making, automation, healthcare, finance, and cybersecurity. However, the increasing complexity of AI pipelines introduces significant risks related to data integrity, model drift, security vulnerabilities, regulatory compliance, and operational failures. Embedding governance within AI pipelines provides a structured framework to ensure accountability, transparency, recoverability, and resilience. This paper examines governance mechanisms integrated throughout the AI lifecycle and demonstrates how embedding governance enhances system recovery while minimizing operational, ethical, and security risks. The study proposes a governance-driven AI pipeline architecture incorporating continuous monitoring, version control, audit trails, explainability, and automated rollback mechanisms. The findings indicate that governance significantly improves reliability, trustworthiness, and regulatory compliance while reducing downtime and model-related failures.

DOI: https://doi.org/10.5281/zenodo.21096755

Published by: vikaspatanker

Authors: Professor V.Abirami

Abstract: Calcium deficiency is a major but underrecognized nutritional concern among Indian women, particularly during the premenopausal transition. Emerging evidence indicates that bone mineral loss and biochemical deficiencies begin before menopause. This review synthesizes recent literature (2020–2025) on calcium intake, associated symptoms, and risk factors among Indian women aged 35–50 years. Available data suggest that a large proportion of women consume inadequate calcium, often compounded by widespread vitamin D deficiency. Clinical manifestations during premenopause are frequently subtle, including musculoskeletal discomfort, fatigue, and mood disturbances, contributing to underdiagnosis. Early screening and preventive interventions are critical to reduce long-term skeletal morbidity.

Published by: vikaspatanker

Authors: Dr R.K.Dubey, Mohd Wahid Mansury

Abstract: This Paper analyzed the effects of the cosmological constantΛ in the context of the Bianchi Type I cosmological model. The Bianchi Type I model represents an anisotropic but spatially flat universe, where expansion rates can differ along three spatial directions.This study analyzes the effects of the spatial directions with respect to axes . The cosmological, plays a significant role in the universe expansion. This work aims to understand how influences the expansion, energy density, and anisotropy of the universe. Einstein's field equations with variable cosmological constant if considered in the presence of a perfect fluid for a Bianchi type I universe by assuming that the cosmological term is proportional to the square of the Hubble parameter. The variation law for vacuum density was recently proposed by many researches on the basis of the quantum field estimation in a curved expanding background. The cosmological term tends asymptotically to a genuine cosmological constant and the model tends to a de-Sitter universe. More obtained some new results by using a slightly different method from that of other researchers obtained the result that the present universe is accelerating with a large fraction of cosmological density in the form of a cosmological term.

Published by: vikaspatanker

Authors: Lavish Kumar, Mohd Aamish, Murad Aalam, Himanshu Kumar Thakur, Ashwani Dubey, Dr. Raj Kumar

Abstract: The use of artificial intelligence (AI) technology for medical image analysis has gained significant importance in contemporary health care systems. AI models assist physicians in diagnosing diseases based on medical images like x-rays, MRIs, CT scans, and ultrasound scans with great precision and fast diagnosis. Nonetheless, medical datasets involve critical and sensitive data about patients, thus raising serious concerns regarding privacy protection in the context of AI applications. The exposure or unauthorized access of medical data can result in severe ethical and legal problems [2], [9], [16]. In this paper, we present a privacy-aware medical image analysis system based on the implementation of convolutional neural networks (CNN), PyTorch framework, Streamlit toolkit, and Differential Privacy technology. CNN is used to extract the features of the medical images automatically and classify the underlying diseases. PyTorch is used for developing the proposed model efficiently, and Streamlit provides a user-friendly interface for physicians. Moreover, the training process is implemented based on differential privacy in order to maintain the privacy of the data. [4], [5]. The proposed framework can support hospitals, diagnostic centers, and telemedicine systems in secure healthcare applications [6], [20].

DOI: http://doi.org/10.5281/zenodo.21063384

Published by: vikaspatanker

Authors: Anjali Kapoor, Dr Mridula

Abstract: In order to facilitate dynamic customization in contemporary learning environments, this study introduces a Smart Autonomous System for Adaptive Student Profiling. To create constantly changing student profiles, the suggested architecture incorporates a variety of educational data sources, such as academic achievement, behavioral patterns, cognitive traits, environmental data, social interactions, and emotional indications. To handle missing data, the system uses KNN-based imputation Convolutional Neural Networks (CNNs) for emotion identification, Principal Component Analysis (PCA) for feature reduction, and XG Boost for academic risk prediction and student profile. A Deep Q-Network (DQN)-based reinforcement learning method that modifies suggestions and interventions based on learner needs enables autonomous decision-making. A hybrid recommendation engine also facilitates optimum learning paths and individualized material distribution. Real-time profiling, ongoing monitoring, proactive intervention, and adaptive feedback mechanisms are made possible by the framework's implementation within a multi-agent architecture. Learner engagement, academic achievement, and early detection of at-risk kids have all improved, according to experimental evaluation utilizing benchmark educational datasets. The findings show that the suggested approach is reliable, scalable, and successful in facilitating intelligent, customized learning environments.

DOI: https://doi.org/10.5281/zenodo.21063202

Published by: vikaspatanker

Authors: Mr. Kuldeep, Associate Professor Dr. Pramod Kumar

Abstract: The Industrial Internet of Things (IIoT) has transformed industrial ecosystems by enabling real-time monitoring, automation, and data-driven decision-making. Deep learning techniques have emerged as powerful tools for predictive analytics, supporting applications such as anomaly detection, fault diagnosis, and predictive maintenance. However, centralized deep learning approaches introduce significant security and privacy risks, including data leakage, adversarial attacks, and model poisoning. This research proposes a secure hybrid deep learning framework integrating CNN-LSTM with ANFIS, along with federated learning, blockchain, and differential privacy to ensure secure, privacy-preserving, and explainable predictive analytics in IIoT environments. The framework enhances prediction accuracy while maintaining data confidentiality, robustness, and real-time performance.

DOI: https://doi.org/10.5281/zenodo.21060033

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Authors: Sk Samsul Hoda, Professor Dr Vipin kumar

Abstract: Bending and focusing magnets, both normal- or super-conducting, are crucial elements for the performance of any particle accelerator. Their design require-ments are always more tighten regarding components’ misalignment and mag-netic properties. This dissertation proposes new solutions for characterizing mag-netic materials and monitoring solenoids’ magnetic axis misalignments. A superconducting permeameter is designed to characterize the new-generation superconducting magnet yokes at their operational temperature and saturation level. As proof of principle, the magnetic characterization of ARMCO®Pure Iron was performed at the cryogenic temperature of 4.2 K and a saturation level of nearly 3 T. A case study based on the new HL-LHC superconducting magnets quantifies the impact of the magnetic properties of the yoke on the performances of the superconducting magnets. A flux-metric based method is proposed to identify the relative magnetic perme-ability of weakly magnetic materials. As proof of principle, the magnetic prop-erties of the ITER TF coils quench detection stainless steel are analyzed. This method is not suitable to test materials with a relative permeability lower than 1.1. Hence, a measurement system based on a new magneto-metric method is conceived and validated employing a standard reference sample. The methods proposed in this thesis are currently employed at CERN’s magnetic laboratory to face an increasing number of requests concerning not only the magnetic charac-terization of materials for magnets but also for shielding systems and compatibil-ity of various components with high magnetic fields. In this thesis, the results of the evaluation of ARMCO®Pure Iron as the yoke of the new LHC superconducting magnets and CRYOPHY as the magnetic shield for the cryomodule prototypes of HL-LHC Crab Cavities are reported. Finally, a new Hall-sensor method is conceived and implemented for monitor-ing the coils alignment in multi-coil magnets, directly during their operation in particle accelerators. The proposed method is suitable even for those cases when almost the whole magnet aperture is not accessible. Requiring only a few mea-surements of the magnetic field at fixed positions inside the magnet aperture, the method overcomes the main drawback of the other Hall sensor-based methods which is having to deal with sturdy mechanics of the moving stages. The method is validated numerically on a challenging case study related to the Solenoid B of the project ELI-NP.

DOI: https://doi.org/10.5281/zenodo.21058750

Published by: vikaspatanker

Authors: Ruchi Gandhi

Abstract: Religious tourism is one of the most important and significant sectors of the Indian tourism industry. It plays a major role in contributing to economic growth, employment generation, infrastructure development, cultural preservation, and regional development. Gujarat is the one of the states from India which has rich religious heritage and some of them are known worldwide such as Somnath, Dwarka, Ambaji, Dakor and Palitana. However, some other religious destinations are underdevelopment though they have tourism potential. One such destination is Kameshwar Mahadev Temple, situated on the bank of the Ambika River in Gadat village, Navsari District, Gujarat. The main purpose of this research is to investigate the potential for sustainable development of Kameshwar Mahadev Temple as a regional religious tourism destination. The study evaluates the temple’s historical significance, geographical setting, tourism resources, visitor characteristics, existing infrastructure, environmental attributes, and socio-economic context. Furthermore, it examines opportunities and constraints associated with tourism development through SWOT analysis and sustainable tourism assessment frameworks. The research uses the mixed-method approach which is based on secondary data, demographic analysis, tourism statistics, infrastructure assessment, policy review, and qualitative evaluation. Findings indicate that Kameshwar Mahadev Temple possesses significant strengths including religious importance, strategic accessibility, natural landscapes, cultural heritage, and an established visitor base. Nevertheless, deficiencies in tourism infrastructure, accommodation facilities, sanitation, destination marketing, and community participation continue to constrain its development potential. This study introduces a master plan for tourism that combines better roads and facilities, environmental protection, community involvement, smart marketing, and teamwork among local authorities. The findings show that focusing on sustainable tourism can turn the Kameshwar Mahadev Temple into a major regional pilgrimage site. This development will boost the local economy and create jobs for residents while fully protecting the surrounding natural resources.

DOI: http://doi.org/10.5281/zenodo.21033316

Published by: vikaspatanker