Category Archives: Uncategorized

Authors: Mr. Suryaashokkumar Siriki, Miss. Savarapu Suhasini

Abstract: Rapid and accurate fire detection is essential for minimizing human casualties, reducing property damage, and enabling timely emergency response. Conventional fire detection systems primarily depend on smoke, heat, and gas sensors, which often experience delayed response, high false alarm rates, and limited effectiveness in complex or large-scale environments. Recent advances in deep learning and computer vision have enabled intelligent visual monitoring systems capable of identifying fire incidents directly from surveillance imagery. This paper presents a deep learning-based intelligent fire detection and early warning framework that employs Convolutional Neural Networks (CNNs) to automatically classify surveillance images into fire and non-fire categories. The proposed framework utilizes a comprehensive image preprocessing pipeline, including resizing, normalization, and data augmentation techniques such as rotation, scaling, zooming, and horizontal flipping to improve model robustness and generalization. Training optimization strategies, including Early Stopping and ReduceLROnPlateau, are incorporated to enhance learning stability and prevent overfitting. The performance of the proposed CNN model is compared with conventional machine learning algorithms, including Logistic Regression, K-Nearest Neighbors (KNN), and AdaBoost, using evaluation metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC analysis. Experimental results demonstrate that the CNN-based framework achieves superior classification performance by effectively learning complex visual characteristics of flames and smoke while maintaining high detection accuracy and a low false alarm rate. The system further integrates an automated alert mechanism that instantly generates notifications upon fire detection, supporting rapid emergency intervention. The proposed framework provides an intelligent, scalable, and cost-effective solution for real-time fire monitoring and can be effectively deployed in smart buildings, industrial facilities, public infrastructures, and smart city surveillance systems to strengthen fire safety management and disaster prevention.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.475

Published by: vikaspatanker

Authors: Miss. Kathula Lakshmi, Miss. Savarapu Suhasini

Abstract: The rapid adoption of Unmanned Aerial Vehicles (UAVs) in applications such as surveillance, precision agriculture, disaster response, logistics, and intelligent transportation has significantly increased the demand for secure and reliable communication networks. However, the wireless nature of UAV communication exposes these systems to a wide range of cyber threats, including GPS spoofing, data injection, denial-of-service (DoS), and network intrusion attacks, which can compromise mission integrity and operational safety. To address these security challenges, this paper presents a machine learning-based cyber attack detection framework for UAV communication networks. The proposed framework employs comprehensive data preprocessing, feature engineering, and intelligent classification techniques to analyze UAV telemetry data, communication signals, and operational parameters for identifying malicious activities. Multiple machine learning models are utilized to distinguish normal UAV behavior from cyber attack scenarios through behavioral pattern analysis and anomaly detection. The framework is evaluated using standard performance metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, to assess its detection capability and reliability. Experimental results demonstrate that the proposed framework effectively detects various cyber attacks with high detection accuracy, low false positive rates, and efficient response time. By integrating intelligent machine learning algorithms into UAV cybersecurity, the proposed approach enhances communication security, improves system resilience, and supports the development of reliable and autonomous drone operations in dynamic network environments.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.474

Published by: vikaspatanker

Authors: Rakesh Dondapati

Abstract: The rapid diffusion of generative AI tools and autonomous agents has generated a pervasive and largely ungoverned organizational phenomenon: shadow AI, whereby employees and teams deploy AI capabilities outside formal information technology governance and procurement processes. While shadow AI may generate local productivity improvements and serve as an incubator for grassroots innovation, it simultaneously exposes organizations to compounding risks across data security, regulatory compliance, intellectual property control, and operational integrity domains. This study investigates the dual character of shadow AI — as both an organizational threat and an innovation catalyst — and examines the conditions under which adaptive governance structures enable firms to convert unauthorized AI experimentation into sanctioned strategic capability. Drawing on a multi-source dataset comprising IT leader survey responses, employee-level AI usage telemetry, security incident reports, patent disclosures, and longitudinal firm performance data from 487 firms across seven industry sectors (2022–2026), the study develops and validates the Shadow AI Prevalence Index (SAPI) and the Governance Adaptiveness Score (GAS). Structural equation models demonstrate that SAPI is positively associated with risk exposure (β = 0.48, p < .001) but that governance adaptiveness significantly moderates this relationship (interaction β = –0.27, p < .001), and independently predicts innovation output (β = 0.41, p < .001) and organizational resilience (β = 0.48, p < .001). Six inductively derived qualitative themes from 48 executive interviews illuminate the mechanisms linking governance adaptiveness to shadow AI outcomes. The study advances a theory of adaptive AI governance, provides the first large-scale empirical examination of the shadow AI prevalence-performance relationship, and delivers a practical Shadow-to-Sanctioned AI conversion framework for enterprise practitioners. Findings indicate that the critical governance imperative is not the elimination of shadow AI — which is both practically infeasible and strategically self-defeating — but its structured transformation from hidden organizational risk into visible competitive capability.

DOI: http://doi.org/10.5281/zenodo.20958484

Published by: vikaspatanker

Authors: Mr. Appalla Yazna Surya Sai Kiran, Miss. Savarapu Suhasini

Abstract: The rapid growth of social media platforms and online communication has significantly increased the volume of user-generated content, creating new challenges in identifying toxic language, hate speech, cyberbullying, and abusive comments. These harmful interactions negatively affect online communities, user well-being, and digital safety, highlighting the need for intelligent and automated content moderation systems. This paper presents ToxiShield, a next-generation intelligent framework for toxic comment detection that integrates Machine Learning (ML) and Natural Language Processing (NLP) techniques to accurately classify online comments as toxic or non-toxic. The proposed framework employs comprehensive text preprocessing, including tokenization, stop-word removal, text normalization, lemmatization, and feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF) and word embedding techniques to generate meaningful textual representations. To evaluate the effectiveness of the proposed framework, multiple classification algorithms, including Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Random Forest, and Convolutional Neural Networks (CNN), are implemented and comparatively analysed using performance metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that deep learning-based models, particularly CNN, achieve superior performance in identifying complex contextual toxicity patterns compared with traditional machine learning methods. The proposed ToxiShield framework provides an efficient, scalable, and intelligent solution for automated online content moderation, contributing to safer digital communication environments and promoting respectful interactions across social media platforms and online communities.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.472

Published by: vikaspatanker

Authors: Professor Mayuri Dongre, Saurabh Bhoyar, Sanskar Karnewar

Abstract: Fraudulent voice calls have become a prominent cyber threat in the contemporary telecommunication environment as the usage of online banking, UPI transactions, mobile wallets, and instant messaging services becomes widespread. The perpetrators of cybercrime resort to fraudulent activities such as voice calls, phishing attacks, OTP manipulation, lottery scams, insurance scams, loan scams, and identity deception. The consequences include substantial monetary damage and grave security vulnerabilities. Existing techniques for spam detection in phone calls depend upon manual reporting, blacklisting, and basic rules-based filtering algorithms. However, these methods prove ineffective against newly emerging and evolving forms of fraud, particularly when the perpetrator changes their phone number and employs advanced social engineering techniques. Therefore, there is a need to develop an efficient and automated fraud detection system. In this paper, we propose a machine learning-based method to detect and analyze fraudulent phone calls. Using the following indicators for call behavior analysis, duration of a call, frequency of calls, suspicious phrases, time of calls, and voice pattern recognition, our approach is intended to identify and classify every call as either fraudulent or legitimate. For better prediction and detection, such machine learning models as Naive Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM) will be applied.

DOI: https://doi.org/10.5281/zenodo.20956340

Published by: vikaspatanker

Authors: Mayuri Dongre, Hrutuja Meshram, Vidhi Ugale

Abstract: Digital transformation is really changing the way we learn in education. This is why Learning Management Systems are so important now. Even though a lot of schools are using Learning Management Systems they often do not keep students because the content is not interactive and it is not personalized for each student. This paper is about a kind of Learning Management Platform that we call Interactive Learning Management Platform. The Interactive Learning Management Platform uses four ideas to make learning more engaging for students: combining different ways of teaching, making the content fit each student’s needs, using games to make learning fun for students, analysing how students learn. We based our ideas for the Interactive Learning Management Platform on what other researchers have found and, on theories that are well established. We think that our Interactive Learning Management Platform can really help students stay engaged when they are learning online. We talk about how each part of our Interactive Learning Management Platform's based on research and how all these parts work together to help students. Our goal is to help students behave think and feel in a way that makes them want to learn. At the end we discuss how to make our Interactive Learning Management Platform a reality and what we need to do to test it.

DOI: https://doi.org/10.5281/zenodo.20956129

Published by: vikaspatanker

Authors: Mayuri Dongre, Kalyani Parihar

Abstract: This paper describes the design and implementation of a CMS system using the Salesforce CRM platform. Our goal is to automate current processes for educational institutions with a cloud-based, user-friendly system replacing manual, and paper-based procedures. It has four most important modules: Student Module, Fee Management Module, Teacher/Faculty Module and Admin Module. These modules modernize academic processes, reducing operational costs, minimize data redundancy, and enhancing efficiency. Unlike the traditional data warehouse, where problems arise with storage systems and access to remote data usually takes time as well, this system utilizes Salesforce cloud infrastructure. The appropriate communication and streamlined processes are the key steps to attracting and retaining more students, as well as staying competitive; therefore, an adequate use of a CRM system can support taking advantage of these success factors. The paper proposes a comparative analysis of existing leading CRM systems in the field of higher education, a summarization of the benefits and the need for their deployment.

DOI: https://doi.org/10.5281/zenodo.20955858

Published by: vikaspatanker

Authors: Professor Mayuri Dongre, Aniket Manoj Singh, Deepak Albankar

Abstract: With the exponential growth of user-generated con-tent on social media platforms, recommendation systems have become the primary mechanism for content curation, user engagement, and personalized information delivery. Traditional recommendation approaches, such as collaborative filtering and content-based filtering, increasingly struggle with inherent limi-tations, including data sparsity, cold-start issues, and the highly dynamic, multimodal nature of modern social media networks. This paper provides a comprehensive analysis of contemporary optimization techniques designed to enhance the precision, scala-bility, and diversity of social media recommendation engines. We systematically review the integration of deep learning architec-tures, Graph Neural Networks (GNNs) for structural relationship mapping, and advanced embedding strategies. Furthermore, we investigate critical operational challenges, including algorithmic bias, real-time computational latency, and data privacy regula-tions. Finally, this study outlines pivotal future research direc-tions, highlighting the paradigm shift toward Large Language Model (LLM) integration and autonomous agentic workflows to build next-generation, context-aware, and explainable recommen-dation frameworks.

DOI: https://doi.org/10.5281/zenodo.20955477

Published by: vikaspatanker

Authors: Mayuri Dongre, Tanmay Lanjewar, Vedant Chaple

Abstract: With the rapid expansion of internet-based applications, cloud services, and digital communication platforms, cybersecurity threats have become increasingly complex and harmful. Among these threats, Distributed Denial of Service (DDoS) attacks are considered one of the most disruptive network-based attacks because they overwhelm targeted servers or networks with excessive traffic, causing downtime, service interruption, and financial loss. Traditional security mechanisms such as firewalls and rule-based intrusion detection systems often fail to detect evolving DDoS attack patterns in their early stages. This research focuses on applying supervised machine learning techniques for early DDoS attack detection by analyzing network traffic behavior and classifying malicious activities. The proposed system performs data preprocessing, feature extraction, traffic analysis, model training, and attack classification using supervised learning algorithms such as Decision Tree, Random Forest, Support Vector Machine (SVM), Logistic Regression, and K-Nearest Neighbors (KNN). The study aims to improve detection accuracy, reduce false alarms, and strengthen real-time cybersecurity monitoring. Results indicate that supervised learning models provide reliable performance in identifying suspicious traffic patterns and can significantly enhance proactive defense mechanisms in network infrastructures.

DOI: https://doi.org/10.5281/zenodo.20955167

Published by: vikaspatanker

Authors: Deepa Barethiya, Kajal Lanjewar, Damini Mondhe

Abstract: Malware — malicious software — represents one of the most pervasive and rapidly evolving threats in modern cybersecurity. Traditional signature-based detection systems, while effective against known threats, are fundamentally inadequate against polymorphic, metamorphic, and zero-day malware variants. This paper presents a comprehensive study and implementation of a machine-learning-based malware detection framework that overcomes the limitations of conventional approaches. The proposed system employs static analysis (PE header features, API call sequences, n-gram byte patterns), dynamic analysis (system call traces, network behaviors), and hybrid analysis to extract discriminative feature sets. Several supervised classification algorithms — including Random Forest, Support Vector Machine (SVM), Gradient Boosting (XGBoost), and a custom Convolutional Neural Network (CNN) — are evaluated on the EMBER 2018 and VirusShare benchmark datasets. Experimental results demonstrate that the ensemble model achieves a detection accuracy of 98.7%, a false-positive rate below 0.4%, and an average inference time of 12 ms, outperforming state-of-the-art baselines by a significant margin. The paper further discusses real-time deployment considerations, adversarial robustness, and future research directions.

DOI: https://doi.org/10.5281/zenodo.20954815

Published by: vikaspatanker