Daily Archives: April 15, 2026

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

Authors: Mr. K. Srikanth, Beeraka Sharmila, Puppala Madhuri Lakshmi, Darla Ratan Abhishek, Pitchuka Veerababu, Seeram Jaya Venkata Somesh

 

 

Abstract: Pneumonia is a significant respiratory disease and one of the top causes of illness and death around the world, especially among children and the elderly. Timely and accurate diagnosis is essential for effective treatment and better patient outcomes. Recent developments in deep learning, particularly Convolutional Neural Networks (CNNs), have shown impressive results in medical image analysis by automatically identifying important patterns in complex image data. This project introduces a real-time pneumonia identification system that combines CNN-based classification with Explainable Artificial Intelligence (XAI) techniques to improve diagnosis accuracy and model clarity. The proposed system processes digitized chest X-ray images through an efficient preprocessing pipeline. This includes noise removal, image normalization, and background feature consideration before sending the images to a trained deep learning model. The ensemble model merges two strong CNN architectures, VGG16 and ResNet50, and uses their complementary feature extraction abilities to boost classification performance. The model classifies Bacterial Pneumonia, Viral Pneumonia, and normal cases, providing clearer clinical insights. Experimental results show high accuracy, strong sensitivity, and real-time inference capability. This allows for pneumonia detection within seconds, which is vital in clinical settings that need quick diagnoses. To tackle the black-box issue of deep learning models, Explainable AI techniques like Grad-CAM++ (Gradient-weighted Class Activation Mapping++) and Score-CAM are used to visualize the key lung areas that affect the model’s predictions. The system also offers confidence scores with visual explanations, enhancing understanding and aiding clinical decision-making. Overall, the proposed CNN and XAI framework offers an efficient, clear, and clinically helpful solution for automated pneumonia detection. The system has strong potential to assist radiologists, boost diagnostic confidence, and contribute to early disease detection and improved patient care.

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Published by: vikaspatanker

Authors: Mrs. N. V. S. Sowjanya, Chavvakula Lasyavalli, Sunkara Vijay Kishore, Kammakatla Shreya, Yerra Sai Rajesh, Chelli Tarun Teja

 

Abstract: Maritime surveillance plays a crucial role in ensuring the safety, security, and regulation of activities in open sea environments. One of the major challenges faced by maritime authorities is the detection of vessels that intentionally disable their Automatic Identification System (AIS) transponders to conceal illegal activities such as unauthorized fishing, smuggling, or unauthorized entry into restricted maritime zones. AIS messages transmitted by ships are widely used for monitoring vessel trajectories; however, missing AIS signals may occur due to multiple reasons including satellite reception limitations, weather disturbances, or intentional shutdown of AIS devices. Distinguishing between these scenarios becomes difficult when dealing with massive volumes of satellite AIS data. This study proposes an intelligent deep learning framework for detecting intentional AIS shutdown events using self-supervised learning techniques. The proposed approach processes large-scale AIS datasets collected from satellite-based maritime surveillance systems and extracts trajectory-based features such as vessel position, speed, time intervals between messages, and movement patterns. A transformer-based deep learning architecture is used to analyse sequential AIS message data and predict whether a new AIS message is expected within a specific time window. By comparing the predicted results with the actual observations, the system identifies abnormal missing AIS receptions that may indicate intentional signal shutdown. The self-supervised learning approach allows the model to generate pseudo-labels from unlabelled AIS data, eliminating the need for manually labelled datasets. Experimental analysis demonstrates that the proposed framework can process millions of AIS messages in near real-time while achieving high prediction accuracy in detecting abnormal vessel behaviour. The integration of deep learning techniques improves the reliability and scalability of maritime surveillance systems, enabling authorities to identify suspicious vessel activities more efficiently. This framework contributes to enhancing maritime security, improving monitoring capabilities in open sea environments, and supporting timely detection of illegal maritime operations

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Published by: vikaspatanker

Authors: Mr. M. V. Rajesh, Balla Aarathisree, S S V Sumanvitha Palivela, Nagala Bhavya Pragna, Kamalesh Chitra, Taneti Ritesh

 

 

Abstract: The rapid growth of encrypted internet protocols such as HTTP/3 and QUIC has significantly improved communication speed and security on modern networks. However, these protocols also introduce new challenges for network security, particularly in detecting Distributed Denial of Service (DDoS) traffic flood attacks. Traditional monitoring techniques rely on packet inspection, which becomes difficult when network traffic is encrypted. This study proposes an intelligent machine learning framework for detecting QUIC-based traffic flood attacks in encrypted HTTP/3 network environments. The proposed system analyses network flow behaviour rather than packet content, enabling effective detection even when traffic payloads are encrypted. To build the detection model, network traffic data are captured and processed into flow-based features such as packet rate, packet size distribution, inter-arrival time, and connection statistics. Data preprocessing techniques are applied to prepare the dataset for machine learning training. Multiple classification algorithms including Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Random Forest are implemented and evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and ROC–AUC. Experimental results demonstrate that the Random Forest classifier achieves the highest detection accuracy and provides reliable performance for distinguishing between normal and malicious QUIC traffic patterns. To improve transparency and interpretability of the prediction process, Explainable Artificial Intelligence (XAI) techniques such as SHAP and LIME are incorporated into the framework. These methods highlight the most influential network features contributing to attack detection and help security analysts understand the reasoning behind model predictions. The proposed framework enhances the reliability of encrypted traffic monitoring, improves early detection of QUIC traffic flood attacks, and contributes to strengthening the security of next-generation web communication protocols.

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Published by: vikaspatanker