EPRA International Journal of Research & Development (IJRD)最新文献

{"title":"DEEP LEARNING FOR VISUAL RECOGNITION","authors":"G.Vijaya Lakshmi, B.Amrutha Varshini, Ch.Goutham Naidu, P.Viswanth Reddy, A.Raheem Khan","doi":"10.36713/epra16464","DOIUrl":"https://doi.org/10.36713/epra16464","url":null,"abstract":"The design and development of an advanced object detection system are presented in this work, which was guided by a thorough literature review and feasibility assessment. The literature review emphasises how object detection techniques have evolved, highlighting the shift from conventional techniques to deep learning approaches. Important developments are highlighted, such as feature pyramid networks, anchor-based localization, and region-based and single-stage detectors. Furthermore, offered are insights about assessment metrics, transfer learning, and data augmentation. A feasibility study assesses the suggested systems operational, technological, and financial viability and finds that it is highly feasible in each of these areas. The architecture of the system is modular and scalable, including backend services, data management, an object detection engine, and a user interface among its constituent parts. Specifications for both functional and non-functional needs are provided, which direct the development of the system. The development phases, resource allocation, development process, and quality assurance procedures are all outlined in the implementation plan. Through the integration of deep learning techniques, the suggested system seeks to achieve high-performance object identification capabilities that are appropriate for a variety of applications while being scalable, reliable, and user-friendly.","PeriodicalId":114964,"journal":{"name":"EPRA International Journal of Research & Development (IJRD)","volume":"113 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140695251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FRAUD DETECTION IN UPI TRANSACTIONS USING ML","authors":"J. Kavitha, G. Indira, A. Anil kumar, A. Shrinita, D. Bappan","doi":"10.36713/epra16459","DOIUrl":"https://doi.org/10.36713/epra16459","url":null,"abstract":"Significant obstacles to financial security have arisen as a result of the quick uptake of Unified Payments Interface (UPI) for online transactions and a commensurate rise in fraudulent activity. This paper suggests a novel fraud detection method that makes use of cutting-edge machine learning (ML) algorithms to address this urgent issue. It focuses on integrating a Hidden Markov Model (HMM) into the UPI transaction process. In order to enable the system to identify departures from these learnt behaviors as possibly fraudulent, the HMM is trained to predict the typical transaction patterns for particular cardholders. The suggested system uses a variety of contemporary approaches, such as Kmeans Clustering, Auto Encoder, Local Outlier Factor, and artificial neural networks, to improve algorithmic diversity and flexibility to changing fraud patterns. In addition to addressing issues like test data creation for training and validation, the system emphasizes a heuristic approach to solving high-complexity computational problems, guaranteeing efficacy in a variety of settings. This study, which is positioned as a proactive and adaptable solution, emphasizes how crucial it is to stop UPI fraud and provides a thorough foundation for reliable fraud detection in the ever-changing world of online transactions.","PeriodicalId":114964,"journal":{"name":"EPRA International Journal of Research & Development (IJRD)","volume":"313 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140698260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SMART BUILDING CONTROL THROUGH INTERNET OF THINGS/MACHINE-TO-MACHINE DEVICE MANAGEMENT VIA HETEROGENEOUS WIRELESS NETWORKS","authors":"G. Suneetha, S. Harinadh, N.Suvarna Rekha, K.Preethi, CH.Akash","doi":"10.36713/epra16417","DOIUrl":"https://doi.org/10.36713/epra16417","url":null,"abstract":"The increasing use of wireless communication technologies has made IoT and machine-to-machine (M2M) connections crucial to a wide range of industries. Smart buildings are an excellent example of an IoT/M2M application. Issues such as a lack of a convergent solution, high costs, restricted wireless transmission range, user-unfriendly interfaces, and the inability to integrate IoT and M2M technologies. Consequently, this article suggests a more effective method for for Machine-to-Machine (M2M) and The Internet of things smart building systems: heterogeneous wireless networks that include both WSNs and MCNs. The suggested system is a cost-effective embedded system that includes Several functional actuators, sensors, and modules for Arduino and NodeMCU, ESP32CAM boards allow for data collection and control across a variety of heterogeneous communication methods like Bluetooth, Wi-Fi, and global system for Mobile (GSM). All collected data is issued to the ThingSpeak platform, allowing the building system to be monitored via the Thing Speak webpage or the UR Smarthome app. One of the researchs most important results is that it can give the server precise information with extremely little delay, allowing users to quickly control and monitor remotely the proposed system that consists of several innovative services names indoor applications(fire detection and gas leakage detection, intrusion alarm by ultrasonic, medicine reminder by RTC module, light and fan control, smart door) and outdoor applications(garden irrigation by soil moisture and DHT11, air quality, smart parking ). Our free mobile application UR Smarthome, which is a local server that controls and monitors the building from a distance, is used to plan and execute all of these services that provides remote control and monitor of the building via Bluetooth/ Cellular networks and Wi-Fi access. With its customizable features, this IoT/M2M smart building system can be tailored to the demands of its users, enhancing their quality of life and safety while using less energy. In addition, identifying and lowering risks, it assists in preventing the loss of resources and human lives. \u0000KEYWORDS— Converged networks, heterogeneous networks (HetNets), Internet of Things (IoT)/machine-to-machine (M2M), mobile application, mobile cellular networks (MCNs), smart building, wireless sensor networks (WSNs)","PeriodicalId":114964,"journal":{"name":"EPRA International Journal of Research & Development (IJRD)","volume":"43 48","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140701571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ENHANCING UNDERWATER IMAGE QUALITY THROUGH ADAPTIVE COLOR CORRECTION AND MULTI-SCALE HISTOGRAM EQUALIZATION","authors":"Mr.T.Sreedhar, B.Ajay Jeevith Kumar, P.Venkateswarlu, G.Bhargav Vamsi, U.Venkata Manikanta","doi":"10.36713/epra16447","DOIUrl":"https://doi.org/10.36713/epra16447","url":null,"abstract":"Due to the selective attenuation of light in water, underwater images are poorly visible and pose significant challenges in visual activities. The structural and statistical properties of different areas of degraded underwater images are damaged to different levels, resulting in an overall uneven drift of object representation and further degrading the image quality. In order to solve these problems, we introduce a method for enhancing underwater images through multi-bin histogram perspective equalization under to solve the problems caused by underwater images. We estimate the degree of feature variation in each image region by extracting the statistical features of the image and using this information to control feature enhancement to achieve adaptive feature enhancement, thereby improving the visual effect of degraded images. We first design a vibration model that exploits the difference between data elements and regular elements to improve the color correction performance of the linear transformation-based sub-interval method. In addition, a multiple threshold selection method was developed that adaptively selects a set of thresholds for interval division. Finally, a multi-bin sub-histogram equalization method is presented, which performs histogram equalization in each sub-histogram to improve image contrast. Underwater imaging experiments in various scenarios show that our method significantly outperforms many state-of-the-art methods in terms of quality and quantity.\u0000INDEX TERMS: Multiple intervals, multi-scale fusion (MF), sub histogram equalization (SHE), underwater image.","PeriodicalId":114964,"journal":{"name":"EPRA International Journal of Research & Development (IJRD)","volume":"59 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140699178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ADVANCING LYME DISEASE PREVENTION THROUGH COMPUTER VISION: A ROBUST APPROACH FOR TICK IDENTIFICATION","authors":"Mr. P. Suresh, Ch. Lahari Priyanka, K. Murali Krishna, P.Kamal Srinivas, Y. Pavan Kumar","doi":"10.36713/epra16351","DOIUrl":"https://doi.org/10.36713/epra16351","url":null,"abstract":"Lyme is a disease that is caused by Borrelia burgdorferi, a bacterium that is spread by ticks. The prevalence of Lyme disease has made it a major public health problem. Immediate identification of the bacteria-carrying parasites is important in preventing the epidemic. This research suggests an alternative approach which uses computer vision to identify Lyme diseases related to ticks. A dataset containing images of ticks was used to create and train a Convolutional Neural Network (CNN) model. Preprocessing and augmentation were done on the dataset with split data into training and testing sets prior to boosting model generalization. The architecture of the CNN consists of convolutional, batch normalization and pooling layers followed by fully connected layers for classification. The Adam optimizer trains the model with a piecewise learning rate schedule. Test set evaluation shows promising results with high accuracy in categorizing tick pictures. Furthermore, this study calculates precision, recall and F1 score metrics which indicates strong performance from this model. A confusion matrix as well as visualization is also used to prove that model can distinguish between different tick classes. This computer vision approach provides a powerful tool for automatic tick recognition thus aiding in early detection as well as prevention of Lyme disease\u0000KEYWORDS; Image analysis, deep learning, tick identification, epidemiological surveillance, disease management, public health interventions, artificial intelligence, zoonotic diseases, tick-borne pathogens, predictive modeling.","PeriodicalId":114964,"journal":{"name":"EPRA International Journal of Research & Development (IJRD)","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140720563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GENDER DIFFERENCES IN ATTITUDE TOWARDS MATHEMATICS AND ACADEMIC ACHIEVEMENT AMONG SECONDARY LEVEL STUDENTS","authors":"Dr. S. Ramaprabha, Dr. R. Selvaganapathy","doi":"10.36713/epra16372","DOIUrl":"https://doi.org/10.36713/epra16372","url":null,"abstract":"Attitudes towards mathematics significantly influence academic success. This study explores the relationship between attitude towards mathematics and academic achievement among secondary level students, particularly examining gender differences. A sample of 500 students from four schools (two boys' and two girls schools) participated, with 200 boys and 300 girls, aged mostly 15 and 16. Data was collected through a 25-item questionnaire adapted from existing literature and modified by Steinback and Gwizdala. Academic achievement was assessed using students recent mathematics examination scores. Results indicate that girls outperformed boys in mathematics achievement, yet attitude towards mathematics did not correlate with academic success. This study underscores the importance of understanding attitudes towards mathematics, particularly in fostering academic performance, especially among girls.\u0000KEYWORDS: Attitude towards mathematics, academic achievement, secondary level students, gender differences, mathematics education, gender equity, attitudes, gender stereotypes.","PeriodicalId":114964,"journal":{"name":"EPRA International Journal of Research & Development (IJRD)","volume":"19 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140729327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A RANDOM FOREST-BASED MODEL OF SCORE FLUCTUATIONS IN PROFESSIONAL TENNIS MATCHES","authors":"Yanqi Zhang, Jie Zhang, Mingxu Zhou, Liu Tao, Dr. Hatem Hassanin","doi":"10.36713/epra16361","DOIUrl":"https://doi.org/10.36713/epra16361","url":null,"abstract":"In tennis matches, the victory and turning points of the game are often influenced by various factors. To explore the factors that affect match fluctuations (changes in the flow of scoring) and to provide suggestions for athletes match strategies, this paper first identifies general indicators through a literature review and uses logistic regression to determine the effectiveness of the chosen model. Secondly, it employs the Fourier function fitting to identify turning points in the match. Considering the scarcity of turning points in the game, this paper uses the SMOTE method to expand the dataset. Subsequently, it tests with a random forest classification model, achieving an accuracy of 93.433%. To improve the models accuracy, several indicators were added to the original model, resulting in a correct rate of 98.51%. Finally, to verify the models results and applicability, the model was applied to other matches with good results. A sensitivity analysis was conducted, revealing good model stability. The model results indicate that the main factors affecting the appearance of turning points include the players movement distance during the match, whether there are changes in the depth and width of the return, score differences, and the maximum number of consecutive wins. When tested in other types of matches, we found that the importance of these factors may change to some extent, but the results remain satisfactory.\u0000KEYWORDS: Volatility prediction, Random Forest, Logistic regression, Sensitivity analysis.","PeriodicalId":114964,"journal":{"name":"EPRA International Journal of Research & Development (IJRD)","volume":"63 S15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AUTOMATED HELMET MONITORING SYSTEM USING DEEP LEARNING","authors":"Kavuri.K.S.V.A.Satheesh, Nandam Sai Akhila, Dondapati Amarnadh, Paruchuri Sagar Swetha, Avula Venkata Sohan, Vasireddy Pardhiv","doi":"10.36713/epra16328","DOIUrl":"https://doi.org/10.36713/epra16328","url":null,"abstract":"Safety and compliance are the uppermost and fundamental concerns in the modern transport subsystems. As a result, the project is essentially designed to come up with an advanced solution by combining YOLOv8 for precise identification of objects and, on the other hand, Easy OCR for reading characters. The key goals are to detect helmets, those without helmets, and identify number plates of the respective motor vehicle. With YOLOv8, we start training the model to identify not only helmets but the lack of helmets, which is necessary for compliance monitoring based on the law. Further, YOLOv8 is also designed to determine the Regions of Interest . Regarding vehicles, the model focuses mainly on license plates which are key objects. After finding the appropriate areas, Easy OCR is designed for applying optical character recognition, helping to obtain vehicle numbers of any type in the most organized, quick way. Therefore, combining YOLOv8 at the stage of object detection and Easy OCR for the recognition of characters creates a novel but, at the same time susceptible system for a vehicle control company.\u0000This integrated system is a sophisticated device for monitoring helmeted and un helmeted riders, promoting a safe and stable journey gadget. By leveraging real-time records, our answers provide precious insights into protection compliance. In summary, the aggregate of YOLOv8 and Easy OCR presents a effective answer for item popularity and conduct reputation, so that our system contributes to the development of secure and green urban mobility by means of preserving rider protection and safety.\u0000s.\u0000Index Terms - Helmet, Deep Learning, Object Detection, Character Recognition, ROI","PeriodicalId":114964,"journal":{"name":"EPRA International Journal of Research & Development (IJRD)","volume":"22 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140732883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CHRONIC KIDNEY DISEASE DETECTION USING ENSEMBLE LEARNING TECHNIQUES AND COMPARATIVE STUDY","authors":"A. Gowtham, Ch. Kesava Manikanta, Ch. Prasanth Kumar, Ch. Sai, Sundara Raghuram, B. S. Jyothi","doi":"10.36713/epra16316","DOIUrl":"https://doi.org/10.36713/epra16316","url":null,"abstract":"A common health problem around the globe, chronic kidney disease (CKD) must be identified early in order to be effectively managed. The accuracy of CKD diagnosis may be increased with the use of machine learning approaches, especially ensemble learning. In order to determine which model performs best for CKD detection, this research will compare and contrast several ensemble learning strategies. Ten distinct models are evaluated in the study: Bagging, Random Forest, Gradient Boosting, Ada Boosting, XGBoost, K-Nearest Neighbours (KNN), Decision Tree, Decision Tree after Pruning, Logistic Regression, and Linear Discriminant Analysis. A CKD dataset is used to evaluate these models based on criteria including accuracy, precision score, and recall score. The comparative study results demonstrate how ensemble learning techniques might raise CKD detection accuracy. The findings provide crucial details about the optimal model for CKD detection, which can help with early diagnosis and better patient outcomes.\u0000 KEYWORDS: Chronic Kidney Disease (CKD), Ensemble Learning, Machine Learning, Accuracy, Early Diagnosis","PeriodicalId":114964,"journal":{"name":"EPRA International Journal of Research & Development (IJRD)","volume":"127 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140740691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BLOCKCHAIN BASED E-COMMERCE ONLINE APPLICATION","authors":"J. Divya, B. Harshitha, A. Harika, A. Santhisree, A. Kalavathi","doi":"10.36713/epra16271","DOIUrl":"https://doi.org/10.36713/epra16271","url":null,"abstract":"In existing E-commerce application all customers and product details will be stored and managed in single centralized server and if this server crashed due to too many requests and or if server is hacked then services will not be available to other customers and to overcome from this problem, we are migrating E-commerce application to Blockchain which will maintain data at multiple nodes/servers and if one node down then customers can get data from other working nodes. Another advantage of Blockchain is inbuilt support for data encryption and immutability (data cannot be altered by unauthorized users) and it will consider each data as block/transaction and associate each block storage with unique hash code and before storing new records. Blockchain will verify hash code of previous blocks and if all nodes’ blocks verification successful then data is consider as secured.\u0000KEYWORDS – Blockchain, Decentralize, E-Commerce, Smart Contracts, Solidity, Hash code.","PeriodicalId":114964,"journal":{"name":"EPRA International Journal of Research & Development (IJRD)","volume":"74 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140751647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}