Authors: Neh Sharma

Abstract: GenAI has altered how businesses think about data and how they use it to make decisions. After 2020, better large language models (LLMs), retrieval-augmented generation (RAG), and agentic pipelines have made it possible for analytics systems to go from only reporting on data to coming up with fresh insights. But this change makes people very worried about fairness, openness, and data privacy, especially since models affect how businesses make decisions and how people from different backgrounds work together. This paper looks at new developments in architecture and talks about the ongoing ethical and evaluative problems that come up in generative analytics. A single Generative Analytics System Framework (GASF) is proposed, integrating architectural, evaluative, and ethical dimensions to achieve a balance between analytical efficacy and accountability. A simulation demonstrates that various departments and demographic user groups utilise LLM-based analytics in distinct manners. The findings indicate that user skill and contextual diversity influence factual accuracy, delay, and trust in distinct ways. This means we need to make systems that are fair and keep people's information safe. The report concludes with a proposal for research aimed at developing generative analytics ecosystems that are ethical, comprehensible, and adaptable to diverse populations.

Published by: vikaspatanker

Authors: Narendra Reddy Burramukku

Abstract: The increasing adoption of multi-vendor network architectures in enterprise, data center, cloud, and hybrid environments has significantly enhanced flexibility, cost efficiency, and technological innovation. However, the heterogeneity of hardware, software, firmware, and management interfaces across vendors introduces substantial challenges in maintaining a consistent and resilient security posture. Security vulnerability management in such environments is particularly complex due to interoperability limitations, asynchronous patch cycles, fragmented monitoring systems, and inconsistent policy enforcement. This paper presents a comprehensive review of security vulnerability management strategies in multi-vendor network environments, focusing on vulnerability identification, classification, prioritization, and remediation. It examines traditional and modern vulnerability assessment techniques, including automated scanning, penetration testing, threat modeling, and standardized vulnerability databases. The study further analyzes vulnerability management frameworks encompassing patch management, policy-based security, integration with SIEM and threat intelligence platforms, and automation through orchestration. Key challenges related to scalability, real-time monitoring, compliance, and governance are critically discussed. Performance and effectiveness metrics such as remediation time, detection accuracy, operational efficiency, and risk reduction are evaluated to assess practical deployment feasibility. Emerging approaches, including AI- and ML-driven vulnerability management, zero-trust architectures, micro-segmentation, blockchain-based security mechanisms, and cloud-native platforms, are explored as potential solutions to existing limitations. By synthesizing current research, identifying literature gaps, and outlining future research directions, this review provides a structured reference for researchers, network architects, and security practitioners seeking to enhance vulnerability management in complex, heterogeneous network infrastructures.

DOI: https://doi.org/10.5281/zenodo.18383985

 

Published by: vikaspatanker

Authors: Narendra Reddy Burramukku

Abstract: The rapid expansion of large-scale computer networks has introduced significant complexity in managing diverse network configurations. Manual classification and analysis of configurations are time-consuming, error-prone, and increasingly infeasible in dynamic environments. This paper presents a novel framework for automated classification of large-scale network configurations using machine learning combined with semantic vectorization. Network configuration files are first pre-processed and transformed into high-dimensional vector representations that capture both semantic and hierarchical relationships among configuration commands, protocols, and policies. These embeddings serve as input to supervised machine learning models, including Random Forest, Support Vector Machines, and Neural Networks, enabling accurate classification of network devices, roles, and compliance profiles. Experiments are conducted on real-world enterprise, cloud, and synthetic network datasets, comprising thousands of configuration files with diverse structures and device types. Results demonstrate that the proposed framework significantly outperforms traditional rule-based and feature-based approaches, achieving up to 94.5% F1-score with graph-based embeddings. Scalability analysis indicates the method can efficiently handle large volumes of configurations while maintaining high accuracy. The study highlights the effectiveness of semantic vectorization in capturing complex configuration semantics and facilitating robust automated classification. This framework provides a foundation for intelligent, scalable network management, supporting proactive policy enforcement, misconfiguration detection, and operational efficiency. Future work explores real-time classification, integration with network orchestration systems, and transformer-based embeddings for richer semantic representation.

DOI: https://doi.org/10.5281/zenodo.18383730

 

Published by: vikaspatanker

Authors: M. Gowsalya,, N. Devapriya, K. Abinaya

Abstract: In the modern digital era, the increasing demand for intelligent monitoring systems has become a critical concern across domains such as healthcare, surveillance, and smart environments. Conventional monitoring approaches primarily rely on single- modality data sources, which often limit their accuracy, reliability, and adaptability in real-world conditions. To address these limitations, this paper proposes a Smart Multimodal Analysis System (SMAS) that integrates multiple data modalities, including visual, audio, sensor, and textual information, into a unified intelligent framework. The proposed system leverages advanced machine learning and deep learning techniques to perform real-time data acquisition, preprocessing, feature extraction, and multimodal fusion. By combining information at both feature and decision levels, SMAS enhances detection accuracy and robustness, even in the presence of noisy or incomplete data. The system supports intelligent classification, anomaly detection, and predictive analysis, enabling timely alerts and informed decision-making. Experimental evaluation demonstrates that the multimodal approach outperforms traditional single-modality systems in terms of accuracy and reliability. The results highlight the potential of SMAS as an effective and scalable solution for next-generation smart monitoring applications.

DOI: https://doi.org/10.5281/zenodo.18410791

 

Published by: vikaspatanker

Authors: David Mark Abayomi, Obafaiye Pauline Olayemi

Abstract: The integration of Artificial Intelligence (AI) into healthcare is revolutionizing the paradigms of diagnosis and treatment (Topol, 2019). This paper provides a comprehensive review of contemporary AI applications, focusing on machine learning (ML) and deep learning (DL) models in image analysis, predictive analytics, and precision medicine. We conducted a systematic literature review of peer-reviewed articles and major clinical trials published between 2018 and 2023. Our analysis demonstrates that AI algorithms, particularly con- volutional neural networks (CNNs), now match or exceed human expert performance in diagnosing specific conditions from radiological (e.g., mammography, chest X-rays) and pathological images (Liu et al., 2021). In treatment, AI-driven tools are enhancing radiotherapy planning, predicting patient-specific drug responses, and powering clinical decision support systems (He et al., 2019). The discussion highlights transformative case studies, including AI for early sepsis detection and diabetic retinopathy screening, while critically addressing significant challenges: algorithmic bias (Obermeyer et al., 2019), the ”black box” problem, data privacy concerns, and the necessity for robust clinical vali- dation and regulatory frameworks (FDA, 2021). We conclude that AI holds immense potential to augment clinical decision-making, improve diagnostic accuracy, personalize treatment, and alleviate administrative burdens. However, its successful translation into routine care necessitates a collaborative focus on ethical AI development, interdisciplinary education, and human-centered design to ensure these tools are equitable, transparent, and effectively integrated into the clinical workflow.

DOI: https://doi.org/10.5281/zenodo.18372826

 

Published by: vikaspatanker

Authors: Dr. Kalyana Rajasekhar Babu

Abstract: Deaf and dumb individuals often face significant barriers in learning and engaging with team sports such as volleyball, primarily due to challenges in communication and instruction. Recent advancements in computer vision and machine learning have enabled the development of hand gesture recognition systems that can bridge this gap. This paper proposes a self-assistive tool that leverages hand gesture recognition for facilitating the learning of volleyball among deaf and dumb beginners. By integrating gesture interpretation, real-time feedback, and interactive instruction, this approach aims to foster inclusivity within sports education. Drawing upon recent studies in gesture recognition, human-computer interaction, and assistive technologies, this research outlines the system’s architecture, underlying algorithms, and potential impact on accessibility in sports training. The findings indicate that such tools, grounded in deep learning and computer vision frameworks, can empower deaf and dumb learners, enhance communication, and foster greater participation in athletic activities.

Published by: vikaspatanker

Authors: Ibrahim Abdurrashid, Ms. Ritu Sharma, Dr. Harish Saraswat, Dr. Giriraj, Jeevan Singh, Abubakar Musa Shuaibu

Abstract: This study investigated the impact of storage conditions room temperature, heat and cold on the levels of ascorbic acid (vitamin C) of chosen fruit juices like lemon, orange, apple, tomato and mango. Vitamin C was quantified by iodometric titration and the concentration of each fruit was recorded for the three conditions. the results revealed significant discrepancies both among the different fruits and the storage methods. Lemon juice always maintained the maximum ascorbic acid content of 2.1 at room temperature, 2.0 heated and 2.05 refrigerated, followed by orange at 1.8, 1.72 and 1.76 respectively. Mango has 1.1, 1.0 and 1.07, and apple at 0.92, 0.83 and 0.88 were moderately present, while tomato contained the lowest levels 0.72, 0.64 and 0.71. a common trend suggested that warming reduced ascorbic acid content in all fruit juices, validating vitamin C is heat labile nature. alternatively refrigeration preserved ascorbic acid content significantly better than room temperature and warming with values closer to initial concentrations.

DOI: https://doi.org/10.5281/zenodo.18372692

 

Published by: vikaspatanker

Authors: Jigarkumar Ambalal Patel, Mayur Girish Taunk

Abstract: The food industry is one of the largest global employers, yet it faces ongoing challenges in demand–supply chain management and food safety due to heavy reliance on manual processes and human error. Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being adopted to transform the industry across the entire "farm to fork" pipeline by improving efficiency, accuracy, and safety. This paper reviews key AI- and ML-driven applications, including smart farming for crop monitoring and yield optimization, automated product sorting and grading, electronic noses for spoilage detection, and vision-based dietary assessment. Despite these advances, significant challenges remain, such as inaccurate image segmentation, high intra-class variation in food appearance, and the lack of large, standardized datasets. Overcoming these limitations is crucial for enabling reliable and scalable real-world deployment of AI.

DOI: http://doi.org/10.5281/zenodo.18359803

Published by: vikaspatanker

Authors: Jigarkumar Ambalal Patel, Mayur Girish Taunk

Abstract: The food industry is one of the largest global employers, yet it faces ongoing challenges in demand–supply chain management and food safety due to heavy reliance on manual processes and human error. Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being adopted to transform the industry across the entire "farm to fork" pipeline by improving efficiency, accuracy, and safety. This paper reviews key AI- and ML-driven applications, including smart farming for crop monitoring and yield optimization, automated product sorting and grading, electronic noses for spoilage detection, and vision-based dietary assessment. Despite these advances, significant challenges remain, such as inaccurate image segmentation, high intra-class variation in food appearance, and the lack of large, standardized datasets. Overcoming these limitations is crucial for enabling reliable and scalable real-world deployment of AI.

DOI: http://doi.org/10.5281/zenodo.18359803

Published by: vikaspatanker

Authors: Jigarkumar Ambalal Patel, Mayur Girish Taunk

Abstract: The food industry is one of the largest global employers, yet it faces ongoing challenges in demand–supply chain management and food safety due to heavy reliance on manual processes and human error. Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being adopted to transform the industry across the entire "farm to fork" pipeline by improving efficiency, accuracy, and safety. This paper reviews key AI- and ML-driven applications, including smart farming for crop monitoring and yield optimization, automated product sorting and grading, electronic noses for spoilage detection, and vision-based dietary assessment. Despite these advances, significant challenges remain, such as inaccurate image segmentation, high intra-class variation in food appearance, and the lack of large, standardized datasets. Overcoming these limitations is crucial for enabling reliable and scalable real-world deployment of AI.

DOI: http://doi.org/10.5281/zenodo.18359803

Published by: vikaspatanker