Category Archives: Uncategorized

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

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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

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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.

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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.

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Authors: Dheeraj Patil, Sanika Dixit, Aditya Dixit, Meenakshi Deotare

Abstract: Air pollution is one of the most serious environmental threats in urban areas, affecting both human health and climate. Traditional air quality monitoring systems provide only point-based information; hence, this limits their ability to show distributions across a city. Herein, this work describes HEAL, a web-based system for pollution hotspot predictions and visualizations through the utilization of machine learning and data visualization techniques. This system collects air quality data from APIs or sensors, processes it, and generates dynamic heat maps that showcase the levels of pollution in real time. Interpreting the interaction among environmental, traffic, and meteorological data, HEAL offers citizens, policymakers, and researchers new localized insights into air quality variations, which will result in better decision-making.

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Authors: Dr. A.Avinash, Kanchi Dhanusha, Saladi Rudra Naga Prasanna Lakshmi, Thiguti Sri Ajitesh, Kesanakurthi Satya Karthikeeyan, Vangapandu Lokesh

Abstract: Thyroid disease is one of the most common endocrine disorders affecting millions of people worldwide. The thyroid gland plays a crucial role in regulating metabolism, growth, and overall body functions. Any imbalance in thyroid hormone production can lead to conditions such as hypothyroidism or hyperthyroidism. Early detection of thyroid disorders is important to prevent serious health complications and to ensure timely treatment. Traditional methods of diagnosing thyroid disease rely on laboratory tests and manual evaluation, which may be time-consuming and sometimes prone to errors. With the advancement of artificial intelligence, deep learning techniques can assist medical professionals in improving diagnostic accuracy and reducing workload. In this project, a deep learning-based Multi-Layer Recursive Neural Network (ML-RNN) is proposed for thyroid disease detection and classification. The system includes data preprocessing, feature selection using the Fisher Score method, and classification using the ML-RNN model. The dataset used for analysis is obtained from a standard repository and includes various thyroid-related attributes. The performance of the proposed model is evaluated using metrics such as accuracy, recall, precision, and error rate. Experimental results show that the ML-RNN model achieves better performance compared to traditional machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Random Forest (RF). The proposed approach provides an effective and reliable method for thyroid disease detection.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.155

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Authors: Dr. Y. Jayababu, Gokeda Veera Satya Sri Pravallika, Ayyarapu Teja, Appasani Hari Kailash Chowdary, Addanki Yuva Sai Surya Prakash, Garapati Poorna Venkata Ranjit Kumar

Abstract: The study suggests an accessible and secure machine learning model for forecasting brake failures in large commercial vehicles. We support this proposal with evidence. Heavy transport vehicles' Air Pressure System (APS) is constantly monitored by IoT-based sensors in modern day heavy transport systems, generating vast amounts of operational data. Detecting brake failures manually with large and highly unbalanced datasets is time-consuming and inefficient. Our approach to these problems involves the use of K-Nearest Neighbour (KNN) imputation for missing values and SMOTE for dealing with class imbalance. Both methods are effective in both situations. Logistic Regression, Decision Tree, Support Vector Machine, Gradient Boosting, and Random Forest are among the machine learning algorithms that undergo stratified cross-validation during implementation and evaluation. The Random Forest classifier's accuracy, precision, recall, F1-score and ROC-AUC are shown to be more than satisfactory using experimental data. Enhanced transparency and trust in the prediction process are achieved through the use of Explainable Artificial Intelligence (XAI) techniques like SHAP and LIME, which can interpret model decisions. They also use methods of selecting features that reduce computational complexity while preserving high levels of accuracy in making predictions. This proposed framework improves fault detection reliability, reduces maintenance costs and allows for predictive maintenance in heavy transport systems.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.154

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Authors: Mr.M.Raja Kumar, Pepakayala Bhavani Sri Alekhya, Dasari Asritha, Sada Uma Maheswara Rao, Thimmasatthi Venkateswarlu, Kollu Rajesh

Abstract: The rapid growth of Internet of Things (IoT) devices has significantly improved automation, connectivity, and data-driven decision-making across various domains such as healthcare, smart cities, agriculture, and industrial systems. However, the increasing number of interconnected devices has also introduced serious security challenges. IoT-enabled cyber-physical systems are highly vulnerable to cyber-attacks such as Distributed Denial of Service (DDoS), data injection, botnet attacks, and unauthorized access. Traditional machine learning techniques often struggle to provide high detection accuracy due to imbalanced datasets, high-dimensional features, and inefficient parameter tuning. In this project, a hybrid deep learning-based intrusion detection framework is proposed for identifying security attacks in IoT-enabled cyber-physical systems. The proposed model combines Convolutional Neural Network (CNN) and Deep Belief Network (DBN) to improve feature learning and classification performance. To enhance the model’s efficiency and convergence speed, a novel hybrid optimization technique called Seagull Adopted Elephant Herding Optimization (SAEHO) is employed for tuning the classifier weights. The proposed framework is evaluated using standard IoT intrusion detection datasets such as UNSW-NB15 and BoT-IoT. Performance is measured using metrics including accuracy, precision, sensitivity, specificity, False Positive Rate (FPR), False Negative Rate (FNR), and Matthews Correlation Coefficient (MCC). Experimental results demonstrate that the hybrid classifier optimized using SAEHO outperforms conventional machine learning and optimization-based models in terms of detection accuracy and reduced error rates. The developed system provides an effective and scalable solution for enhancing security in IoT-enabled cyber-physical environments.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.153

Published by: vikaspatanker