Published by: vikaspatanker

Authors: Mr.Y.H.S.S. Phaneedra, Polisetty Nikhitha Sowmya, Kolla Triveni, Garaga Naveen Kumar, Kadali Nikitha Sri Satya Gayatri

Abstract: Network Intrusion Detection Systems (NIDS) play a critical role in modern cybersecurity infrastructures by monitoring network traffic and identifying suspicious or malicious activities. In recent years, machine learning techniques have significantly improved the performance of intrusion detection systems by enabling automated traffic analysis and anomaly detection. However, the integration of machine learning into security systems also introduces new vulnerabilities that can be exploited by attackers. One such threat is adversarial machine learning, where malicious actors manipulate training or testing data to deceive machine learning models and degrade their performance. This study presents a comprehensive analysis of adversarial machine learning attacks targeting network intrusion detection systems. The work explores how adversarial samples are generated by introducing small perturbations into original datasets, which results in incorrect predictions by the intrusion detection model. Furthermore, the paper classifies adversarial attacks based on several criteria, including attacker knowledge level, misclassification objectives, affected learning phase, and the intended security violation. Understanding these attack strategies is essential for designing more robust and secure intrusion detection systems capable of defending against adversarial manipulation.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.145

Published by: vikaspatanker

Authors: Mr.Y.Manas Kumar, Sathi Chaitanya Sai Durga, Kollu Ruby Sophia, Gaduthuri Alekhya, Nalluri Lishitha Devi, Pallala Sasi Kiran Reddy

Abstract: The rapid growth of renewable energy sources has increased the importance of accurate wind power forecasting for reliable power system operation. Wind power generation is inherently variable due to changing weather conditions, making prediction a challenging task. This paper presents an intelligent wind power forecasting framework based on Bayesian Feature Selection combined with machine learning models. The proposed approach processes numerical weather prediction data and removes irrelevant spatial features to improve prediction accuracy. A dimensionality reduction technique is applied to select the most informative sub-areas of weather data, thereby reducing computational complexity while maintaining important predictive information. Various machine learning algorithms such as Support Vector Machines, Artificial Neural Networks, and Convolutional Neural Networks are employed for forecasting regional wind power output. The proposed model enhances prediction performance by optimizing feature selection and improving model efficiency. Experimental evaluation demonstrates that the system significantly improves forecasting accuracy while reducing the dimensionality of input data. The framework can assist energy providers and power grid operators in planning and managing renewable energy resources more effectively.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.144

Published by: vikaspatanker

Authors: Dr Manjula Devarakonda Venkata, Vasa Neeharikasri, Vudatha Rama Subrahmanyam, Suravarapu Venkatesh, Malagala Pavan, Mattaparthi Jaya Praneeth

Abstract: Unmanned Aerial Vehicles (UAVs), commonly known as drones, are increasingly used in various applications such as surveillance, logistics, environmental monitoring, and disaster management. Despite their numerous benefits, the rapid adoption of UAV systems has introduced significant cybersecurity challenges. UAV communication networks are vulnerable to different types of cyber threats including GPS spoofing, data injection attacks, and network intrusions, which can compromise system functionality, mission objectives, and data security. To address these challenges, this study proposes a machine learning-based framework for detecting cyber attacks in UAV systems. The proposed approach combines supervised and unsupervised learning techniques to analyse UAV telemetry data, communication signals, and operational parameters in real time. By performing behavioural analysis and anomaly detection, the system can identify abnormal patterns and isolate potential cyber threats with high accuracy and minimal false positives. Experimental evaluation demonstrates that the proposed framework can effectively detect various attack scenarios while maintaining efficient response time and reliable performance. The integration of machine learning techniques into UAV cybersecurity systems provides a robust solution for enhancing the safety and reliability of drone communication networks.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.143

Published by: vikaspatanker

Authors: Ms.MD.Apsar Jaha, Gollapalli Mounika Subhash Chandra, Govindaraju Sri Lakshmi Swathi, Soorneedi Navaneeth Preetham, Nagulla Seshu Pavan, Kanumenu Siva Senkara Varaprasad

Abstract: Speech disorders significantly affect an individual’s ability to communicate effectively and reduce overall quality of life. Dysarthria is a neurological speech disorder that results from damage to the nervous system and affects the muscles involved in speech production. Traditional assessment of dysarthria severity is usually performed by speech-language pathologists through perceptual evaluation, which can be subjective and time-consuming. Recent advancements in artificial intelligence and machine learning have enabled the development of automated systems capable of analysing speech characteristics and identifying different levels of dysarthria severity. This study presents an overview of intelligent techniques used for the automatic detection and classification of dysarthria severity levels. The proposed approach focuses on analysing speech features such as acoustic patterns, prosodic characteristics, and spectral features extracted from speech signals. Machine learning and deep learning models are then used to classify the severity of dysarthria based on these extracted features. By utilizing AI-based models, the system can provide objective and efficient evaluation of speech impairments. The proposed framework can assist clinicians in improving diagnostic accuracy and developing personalized rehabilitation strategies for individuals affected by dysarthria.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.142

Published by: vikaspatanker

Authors: Mrs.M. Saranya, Parimi Sai Neeraja, Akumarti Venkat, Sistu Sai Purna Sriram, Makada Ravikiran, Padala Chaitanya

Abstract: The rapid growth of smartphone usage has made mobile devices an essential part of everyday life, supporting activities such as communication, online banking, education, and social networking. However, the increasing popularity of Android-based devices has also made them a major target for cyber attackers who develop malicious applications to exploit system vulnerabilities and steal sensitive information. To address this challenge, an intelligent malware detection and prevention framework for Android devices is proposed. The proposed system integrates real-time threat intelligence gathered from social media platforms with deep learning-based malware classification techniques. Malware signatures shared through social media sources are periodically collected and stored in a centralized malware hash database to ensure the system remains updated with newly discovered threats. In addition, the system employs a deep learning model based on a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) architecture to analyze Android application permissions and classify applications as benign or malicious. By combining real-time malware signature updates with deep learning-based behavioural analysis, the proposed framework enhances the accuracy and efficiency of Android malware detection. Experimental evaluation demonstrates that the system achieves high detection accuracy and provides a robust solution for protecting Android devices against emerging malware threats.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.141

Published by: vikaspatanker

Authors: Mrs.T.N.V. Durga, Kona Lasya, Golla Vidya Prasanthi, Allam Hema Siva Sankar, Kola Amrutha Lakshmi

Abstract: Flooding is one of the most destructive natural hazards, particularly in urban environments where population density and infrastructure development increase vulnerability to extreme weather events. Accurate identification of flood-prone areas is essential for effective disaster management and urban planning. This study presents an ensemble machine learning framework for urban flood hazard assessment by integrating multiple predictive models. The proposed approach combines the strengths of individual machine learning algorithms such as Classification and Regression Trees (CART), Random Forest (RF), and Boosted Regression Trees (BRT) to generate a more reliable flood susceptibility map. Several environmental and geographical factors, including slope, elevation, rainfall, land use, and distance to rivers, are analysed to evaluate their influence on flood occurrence. The ensemble model aggregates the predictions of individual models using weighted averaging techniques to improve prediction accuracy and reduce model bias. Experimental results demonstrate that the ensemble approach outperforms individual models in terms of predictive performance and reliability. The generated flood hazard maps provide valuable insights for identifying high-risk zones and supporting decision-makers in developing effective flood mitigation strategies.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.140

Published by: vikaspatanker

Authors: Mr.S.K. Sankar, Janga Sanjay, Didde Vaishali, Dwarampudi Tejo Madhuri, Chitturi Nikhitha

Abstract: Early detection of cancer plays a crucial role in improving patient survival rates and enabling effective treatment strategies. However, traditional diagnostic methods often face challenges such as high-dimensional biomedical data, feature redundancy, and computational inefficiency. Recent advancements in machine learning have improved diagnostic capabilities, but conventional algorithms still struggle to efficiently process complex genomic and medical imaging datasets. To address these limitations, this study proposes a novel framework that integrates quantum computing with machine learning techniques for enhanced cancer detection. The proposed approach employs a sequence of intelligent modules including Quantum-Normalized Adaptive Refinement (Q-NAR) for data preprocessing, Wrapper Component Attribute Analysis (WCAA) for feature ranking, and Swing L-Bee Mustard Optimization (SLBMO) for selecting the most relevant features. Finally, a hybrid predictive model known as the Quantum Boosted Vector Fusion Network (QBVFN) is utilized to perform cancer prediction and treatment outcome analysis. The framework is evaluated using the Cancer Genome Atlas (TCGA) dataset in a Python environment. Experimental results demonstrate significant improvements in feature optimization, prediction accuracy, and computational efficiency for early-stage cancer detection. This research highlights the potential of quantum-assisted machine learning techniques to support next-generation intelligent cancer diagnostic systems.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.139

Published by: vikaspatanker

Authors: Dadarkar Ehaan Mubasshir, Ansari Ayan Atif Masood Iqbal, Khan Zain MD Irfan

Abstract: MentorAI is a comprehensive, web-based intelligent learning assistant designed to transform how students study, retain knowledge, and engage with educational content. The platform integrates a personalized AI Tutor powered by large language models, a Voice Recall system for active retrieval practice using the Web Speech API, an SM-2 algorithm-driven Spaced Repetition Flashcard engine, an AI-generated adaptive Quiz Engine, an AI- assisted rich-text Workspace for notes and PDF imports, a 3D Knowledge Graph for visual concept mapping using D3.js force-directed visualization, and a command-palette-style Nexus navigation system. A central Dashboard aggregates learning metrics, study streaks, mastery scores, and AI-detected weak topics in real time. This paper presents the complete system architecture, feature design rationale, technology stack, database design, security model, testing methodology, and results achieved during the development of MentorAI as a final year engineering capstone project.

Published by: vikaspatanker

Authors: Sparsh S. Misal, Yash R. Lodha, Parth P. Gargote, Shivam S. Daundkar

Abstract: Road infrastructure plays a critical role in transportation, but issues like potholes significantly affect safety, efficiency, and maintenance costs. Traditional pothole detection methods rely heavily on manual inspection and public reporting, which are often delayed and inefficient. This project proposes a smart pothole detection and reporting system using computer vision and machine learning. The system uses a live webcam feed to detect potholes in real-time using a model trained with Teachable Machine and deployed using TensorFlow.js. When a pothole is detected, the system captures an image, records the location, date, and time, and automatically generates a complaint ticket. The backend, built using Flask, stores the report data and provides a history of detected potholes. This system offers a low-cost, scalable, and automated solution that can be extended for smart city applications and real-time road monitoring systems.

Published by: vikaspatanker