Category Archives: Uncategorized

Authors: Ms. Y Suma Chamundeswari, Nalli Neeharika, Sneha Dindi, Abbireddy Durga Devi, Nyasavarajula R S Gowtham Datta, Ayanamahanthi Thandava Krishna Murthy

 

 

Abstract: Hearing impairment is one of the most common sensory disorders affecting newborns, infants, and young children worldwide. Early detection of hearing loss is crucial because delayed diagnosis can negatively affect speech development, cognitive growth, social interaction, and educational outcomes in children. However, many developing and underdeveloped regions face a shortage of audiologists and otolaryngologists, which often results in delayed diagnosis and limited access to hearing care services. This situation highlights the need for automated and intelligent diagnostic tools that can assist healthcare professionals in identifying hearing impairments more efficiently. This study proposes an automated hearing loss detection framework based on machine learning techniques to support medical professionals in diagnosing hearing impairments in newborns, infants, and toddlers. The proposed system integrates a hearing test data generation module with a machine learning classification model capable of analyzing audiometry test data and predicting the presence and characteristics of hearing loss. The data generation module creates a comprehensive dataset representing different hearing conditions, which is then used to train and evaluate the machine learning model. By employing multiclass and multi-label classification techniques, the model can identify the type, degree, and configuration of hearing loss with high accuracy. Experimental results demonstrate strong diagnostic performance, achieving a prediction time of approximately 634 milliseconds, a log-loss reduction rate of 98.48%, and macro and micro precision values close to 100%. These results indicate that the proposed framework can provide rapid and reliable diagnostic support for healthcare professionals, enabling earlier intervention and improving access to hearing care in regions with limited medical resources.

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Published by: vikaspatanker

Authors: Mr .B.Janu Naik, Velugubanti Lakshmana Siva Ganesh, Vignesh Mullangi, Vanapalli Veera Satya Sai Praneesh, Maddula Veera Venkata Sai Pradeep

 

 

Abstract: With the rapid expansion of Android applications, malware attacks targeting mobile devices have increased significantly, creating serious security and privacy concerns for users. Traditional malware detection approaches, such as signature-based and rule-based methods, often fail to detect newly emerging or obfuscated malware variants. To overcome these challenges, this study proposes an explainable artificial intelligence-based framework, named XAI-Droid, for effective Android malware detection and classification. The proposed system integrates deep learning techniques with explainable AI (XAI) methods to enhance detection accuracy while ensuring transparency and interpretability in decision-making. Feature extraction is carried out using static analysis techniques, and the extracted features are used to train advanced machine learning and deep learning models. To improve trust and reliability, explanation methods such as feature importance analysis are incorporated to identify the key attributes influencing classification outcomes. Experimental results demonstrate that the proposed framework achieves high detection accuracy while maintaining interpretability, making it suitable for practical cybersecurity applications. By combining strong classification performance with explainability, XAI-Droid contributes to the development of reliable and trustworthy AI-based mobile security systems.

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Published by: vikaspatanker

Authors: Mrs. V. Suvarna, Mavuri Bhuvana, Boddu Rajeev, Choppella Vamsi Kumar, Dhulipudi Sree Vivek

 

 

Abstract: To improve prediction accuracy and enable real-time monitoring of mechanical components, a deep learning-based approach is proposed. The system utilizes a Convolutional Neural Network (CNN) to extract important features from mechanical parts using sensor data. These features are further processed through fully connected layers for information fusion and classification, allowing accurate prediction of remaining useful life and health status. The trained deep learning model is integrated into a monitoring system to create a complete framework for continuous condition monitoring and life prediction. The system is further optimized to enhance prediction accuracy, real-time performance, and adaptability under different working and environmental conditions. Experimental results show that the proposed model achieves high performance with a Mean Absolute Error (MAE) of 2.1, Root Mean Squared Error (RMSE) of 2.5, and Mean Absolute Percentage Error (MAPE) of 10%. These results demonstrate the effectiveness of the approach and its potential for practical applications in industrial maintenance and reliability improvement.

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Published by: vikaspatanker

Authors: Mrs. D. Chakra Satya Tulasi, Bejjipuram Jahnavi, Yarlapati Venkata Naga Durga Varun, Guraja Jayachandra, Peruri Vinay

 

 

Abstract: An advanced wild animal detection and alert system is developed using the YOLOv5 (You Only Look Once version 5) model. The system uses an object detection algorithm to identify wild animals and provide real-time alerts to users. A camera captures live video footage, which is processed by a computer running the YOLOv5 model to accurately detect and classify animals. When a wild animal is detected, the system immediately generates alerts such as warning sounds or notifications to prevent potential danger. These alerts can also act as deterrents to scare animals away and improve safety. The system is useful in areas where wild animal movement is common, such as forest borders, agricultural fields, and highways. Overall, the system provides an efficient and real-time solution for monitoring wildlife and reducing human-animal conflicts. Future improvements can focus on increasing accuracy and enhancing real-world performance under different environmental conditions.

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Published by: vikaspatanker

Authors: Mrs. A. Daiva Krupa Nirmala, Polina Sai Satvika, Vennapu Lingeswara Rao, Dasari Manvanth, Syed Irfan

Abstract: This paper proposes a smart car parking system that uses image processing and real-time CCTV monitoring to efficiently detect parking space availability and recognize vehicle license plates. The system is designed for both open parking areas and multi-storey parking environments. It uses Python along with the OpenCV library to analyze video input and determine whether parking slots are occupied or vacant based on pixel-level analysis and image processing techniques. In addition, the system integrates Optical Character Recognition (OCR) using the Tesseract engine to automatically extract license plate information from captured images. To improve accuracy, multiple preprocessing techniques are applied to handle variations in image quality, lighting, and noise. The proposed system enables automated parking management, reduces manual effort, and enhances monitoring efficiency, making it suitable for real-time smart parking applications.

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Published by: vikaspatanker

Authors: Mrs. K. Harika, Rowthu Kavyanjali Priya, Madeti Vineetha, Samanthakurthy Rajavardhan, Sachin Pandit, Penky Adi Seshu

 

 

Abstract: Agriculture plays a vital role in maintaining food security and contributing to the global economy. However, selecting the most appropriate crop for a specific region remains a significant challenge for farmers due to variations in soil nutrients, climatic conditions, and environmental factors. Incorrect crop selection can result in low productivity, inefficient resource utilization, and financial losses. With the growing availability of agricultural data and advancements in artificial intelligence, machine learning techniques have become effective tools for improving decision-making in agriculture. This study proposes an intelligent crop recommendation system that combines machine learning and deep learning models to assist farmers in selecting the most suitable crop based on soil and environmental conditions. The system analyses key agricultural parameters such as nitrogen (N), phosphorus (P), potassium (K), rainfall, soil pH, temperature, and humidity. These features are used to train predictive models capable of recommending the most appropriate crop for cultivation. Various machine learning and deep learning algorithms, including Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Temporal Convolutional Networks (TCN), are implemented and evaluated. The models are trained using a publicly available agricultural dataset containing multiple crop types along with environmental attributes. Performance is assessed using evaluation metrics such as accuracy, precision, recall, and F1-score to identify the most effective model. Experimental results show that ensemble and deep learning models achieve high prediction accuracy in recommending suitable crops. The system also provides a user-friendly interface that enables farmers to input soil and environmental parameters and receive crop recommendations in real time. The proposed approach supports precision agriculture by enabling data-driven farming practices, improving crop yield, and assisting farmers in making informed decisions.

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Published by: vikaspatanker

Authors: Mrs. K. Tulya Sree Simla, Penmatsa Dhathri Vidya Prabha, Bikkina Anusha, Gubbala Chandra Mouli, Nimmagadda Sanjith, Vakadi Ayyappa Surya Sri Harsha

 

 

Abstract: Traffic violations are a major contributor to road accidents and fatalities, especially in densely populated urban regions. Common violations such as jumping red lights, triple riding on two-wheelers, reckless driving, and riding without helmets significantly increase the likelihood of accidents. Conventional traffic monitoring systems largely depend on manual supervision by traffic police or basic sensor-based methods, which are often inefficient, time-consuming, and susceptible to human error. To overcome these limitations, intelligent traffic monitoring systems based on computer vision and deep learning have gained increasing importance. This paper presents a deep learning-based automated traffic violation detection system using the YOLOv7 object detection model. The proposed system processes video streams captured from roadside surveillance cameras and analyses them frame by frame to detect various traffic violations. The YOLOv7 model is used to identify vehicles and generate bounding boxes around detected objects. A predefined threshold line is applied to determine whether a vehicle crosses the signal during a red light, thereby detecting signal violations. Additionally, the system identifies overloading or triple riding on two-wheelers by analysing the number of riders within a single vehicle bounding box. Helmet violations are also detected by determining whether riders on motorcycles are wearing helmets. If a rider is identified without a helmet, the system classifies it as a violation. The system utilizes publicly available datasets such as the MS COCO dataset for vehicle detection and a custom annotated dataset for detecting overloading and helmet violations. The model is trained and evaluated using performance metrics including precision, recall, F-measure, and mean Average Precision (mAP). Experimental results indicate that the proposed system can accurately detect multiple traffic violations while maintaining efficient real-time performance. The proposed approach offers a cost-effective, automated, and scalable solution for traffic monitoring. It can assist traffic authorities in improving road safety and reducing the burden of manual monitoring. Furthermore, the system can be integrated with existing smart city surveillance infrastructure to support intelligent transportation management and law enforcement.

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Published by: vikaspatanker

Authors: Mr. V. Hemanth Sai, Devulapalli Srujan, Mattaparthi Teja Nirgun, Mummidi Lohith Naga Ratan, Poluparthi Abhishek, Kasireddi Naga Venkata Sai Navadeep

 

 

Abstract: Social media platforms (SMPs) are widely used for communication and information sharing, but they are also increasingly exploited for criminal activities. These activities include forming illegal groups, spreading false information, stealing personal data, and conducting cyberattacks. The ease of access and anonymity provided by SMPs make them attractive for criminals to perform such actions. Sensitive information such as passwords, financial details, and personal data can be misused, leading to serious threats like identity theft, data breaches, and malware attacks. This paper focuses on detecting criminal activities on social media using machine learning techniques. By analyzing user-generated content, the system can identify suspicious patterns and classify potentially harmful activities. The proposed approach aims to improve early detection and help in preventing cybercrimes effectively. Additionally, it highlights the importance of user awareness and responsible data sharing to reduce risks associated with social media usage.

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Published by: vikaspatanker

Authors: Mrs. N. Nikhitha, Malla Sudarsan Sai Sunny, Guthula Surya Sindhu, Neelapalli Sri Durga Abhilasha, Govada Pavan Sai, Palisetti Siva Ram

 

 

Abstract: The increasing rate of criminal activities and the limitations of existing police complaint systems highlight the need for a more transparent, secure, and efficient method for managing complaints and First Information Reports (FIRs). In many cases, complaints remain unreported or are not officially registered due to procedural delays, corruption, or lack of proper documentation systems. Although online portals such as the Crime and Criminal Tracking Network and Systems (CCTNS) have been introduced, they still operate on centralized architectures that may suffer from issues such as single points of failure, limited transparency, and vulnerability to data tampering. To address these challenges, this study proposes a blockchain-based police complaint management system designed to provide secure, decentralized, and tamper-proof storage of complaint records. In the proposed system, complaint details and FIR records are encrypted and stored using the InterPlanetary File System (IPFS), while the corresponding hash values are recorded on a blockchain network to ensure immutability and data integrity. The decentralized nature of blockchain technology ensures that complaint records cannot be altered or deleted without network consensus, thereby preventing unauthorized modifications and enhancing system transparency. Additionally, timestamped blockchain entries provide verifiable proof of complaint submission, enabling citizens to demonstrate that their complaint was officially recorded even if authorities deny receiving it. By integrating blockchain with distributed file storage technologies, the proposed system enhances trust between citizens and law enforcement agencies while ensuring secure and transparent management of police complaints. The framework also supports the broader goals of e-governance by improving accountability, data security, and accessibility in public service systems.

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Published by: vikaspatanker

Authors: Mrs. Ch. Veera Gayatri, Palivela Geethasri, Kothapalli Venkannababu, Madhavarapu Chandhra Sekhar Sri Sai, A Lakshmi Chinmayi, Anupoju Sainadh

 

 

Abstract: The rapid growth of e-commerce platforms has increased the need for intelligent pricing strategies that can adapt to continuously changing market conditions. Traditional pricing methods used in online retail are often static and rely heavily on historical data analysis, making them inefficient in responding to dynamic market factors such as customer demand, competitor pricing, and seasonal trends. In modern digital marketplaces, businesses generate large volumes of transactional and behavioural data, which creates opportunities for applying machine learning techniques to improve pricing decisions. This study proposes a machine learning-enabled business intelligence framework for dynamic pricing optimization in e-commerce environments. The proposed system integrates data preprocessing, predictive modelling, and business intelligence analytics to support real-time pricing decisions. During the preprocessing stage, historical pricing data, market trends, competitor price information, and customer behavior patterns are collected and processed to improve data quality and consistency. Support Vector Machine (SVM) is employed as the primary machine learning algorithm due to its ability to handle complex and non-linear relationships within large datasets. The business intelligence component of the framework enables efficient data visualization, monitoring, and analysis of market conditions through interactive dashboards and analytical tools. This integration allows businesses to combine predictive insights from machine learning with data-driven business intelligence reports to determine optimal pricing strategies. The proposed system dynamically adjusts product prices by analyzing multiple influencing factors such as demand fluctuations, competitor behavior, and customer purchasing patterns. Experimental evaluation demonstrates that the integration of machine learning and business intelligence significantly improves pricing accuracy, market responsiveness, and decision-making efficiency. By enabling automated and adaptive pricing strategies, the proposed framework helps businesses maximize revenue, enhance competitiveness, and respond effectively to rapidly changing e-commerce environments.

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Published by: vikaspatanker