Category Archives: Uncategorized

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

 

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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

Authors: Mohammad Sameer Hussain, Jaspreet Kaur, Er. Gundeep Kaur

Abstract: Decision making systems are using a combination of style rules and new style artificial intelligence to help people make good choices. The old style rules are good because they are clear and easy to understand and they make sure people follow the rules. The old style rules have some problems though. They are hard to scale up. They cost a lot to maintain. Decision making systems that use style rules do not adapt well to new situations. On the hand the new style artificial intelligence like the kind that understands human language can find patterns and help with tough decisions. The style artificial intelligence is really good, at helping people make good choices because it can understand what people are saying and find patterns that the old style rules cannot. The style artificial intelligence is a big help to decision making systems because it can do things that the old style rules cannot. Decision making systems that use the style artificial intelligence can make better choices because they have more information and can understand what people are saying. This kind of intelligence has some problems. Artificial intelligence can make things up. It can be hard to understand intelligence. Also when something goes wrong with intelligence systems like these artificial intelligence systems it is not clear who is responsible, for the artificial intelligence. This paper reviews expert perspectives on both approaches and compares them in terms of interpretability, robustness, data dependence, deployment constraints, and evaluation. Evidence across multiple domains suggests that hybrid architectures integrating explicit rules, structured knowledge, and generative components provide a practical path toward trustworthy and adaptive decision- making.

DOI: http://doi.org/10.5281/zenodo.18359722

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Authors: Leo. Tanyam. Encho, Abraham Okolo

Abstract: The Gompertz distribution is widely applied in describing human mortality, establishing actuarial tables, and various other fields. Historically, it was originally introduced by Benjamin Gompertz (1825) in connection with human mortality. This study aims to derive and analyze the mathematical and statistical properties of the Gompertz distribution, providing explicit expressions for parameter estimation from both frequentist and Bayesian perspectives. We then apply these estimation methodologies to analyze COVID-19 data in Cameroon. We investigate and compare numerous frequentist approaches for parameter estimation, including maximum likelihood, method of moments, pseudo-moments, modified moments, L-moments, percentile-based, least squares (including weighted), maximum product of spacings, minimum spacing absolute distance, minimum spacing absolute-log distance, Cramér-von-Mises, and Anderson-Darling (including right-tail) estimators. Their performance is evaluated using extensive numerical simulations, and their coverage probabilities are also assessed. Our results indicate that among the frequentist estimators, modified moments and moments estimators generally perform better than their counterparts. For Bayesian estimators, those based on the Mean Squared Error Loss Function (MSELF) and Kullback-Leibler Loss Function (KLF) demonstrate superior performance. The maximum product of spacings estimators also exhibit competitive performance.

DOI: https://doi.org/10.5281/zenodo.18359495

 

Published by: vikaspatanker