International Journal of Advanced Research in Science, Communication and Technology最新文献

{"title":"Optical Glaucoma Detection using Deep Learning And streamlit","authors":"Tom Antony Agassi J, Dr. C. Meenakshi","doi":"10.48175/ijetir-1233","DOIUrl":"https://doi.org/10.48175/ijetir-1233","url":null,"abstract":"The Project “Glaucoma is a disease that relates to the vision of the human eye”. This disease is considered as the irreversible disease that results in the vision deterioration. Much deep learning (DL) models have been developed for the proper detection of glaucoma so far. So this paper presents architecture for the proper glaucoma detection based on the deep learning by making use of the convolutional neural network (CNN). The differentiation between the patterns formed for glaucoma and non-glaucoma can find out with the use of the CNN. The CNN provides a hierarchical structure of the images for differentiation. Proposed work can be evaluated with a total of six layers. Here the dropout mechanism is also used for achieving the adequate performance in the glaucoma detection. The datasets used for the experiments are the SCES and ORIGA.\u0000Glaucoma is a group of related eye disorders that cause damage to the optic nerve that carries information from the eye to the brain which can get worse over time and lead to blindness. It is very important that glaucoma is detected as early as possible for proper treatment. In this paper, we have proposed a Convolutional Neural Network (CNN) system for early detection of Glaucoma. Initially, eye images are augmented to generate data for Deep learning. The eye images are then pre- processed to remove noise using Gaussian Blur technique and make the image suitable for further processing. The system is trained using the pre-processed images and when new input images are given to the system it classifies them as normal eye or glaucoma eye based on the features extracted during training..","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"24 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141659792","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":"Effective Data Management using Iterative Approach in Data Systems","authors":"Jefferson Singh David V, V. Sumalatha","doi":"10.48175/ijetir-1248","DOIUrl":"https://doi.org/10.48175/ijetir-1248","url":null,"abstract":"In our innovative approach to customer service for household appliance repair, we harness cutting-edge technology to provide efficient and effective solutions to our valued customers. This project not only addresses the repair of household appliances but also revolutionizes the customer service experience. Feature engineering comes into play, creating relevant features such as time since the last maintenance, cumulative operating hours, error logs, temperature readings, and vibration levels. This ensures a comprehensive dataset with features reflective of both the current state of the equipment and indicators of potential issues. The subsequent application of feature selection techniques involves a multi-faceted approach. Filter methods, including correlation coefficients and statistical tests such as chi-square, pinpoint features highly correlated with the target variable, optimizing the system's predictive capabilities.","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"26 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141662403","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 Hybrid Approach for Secured Banking Transaction using Ras and Two Fish Blockcypher Algorithms","authors":"D. Naveen, Dr. C. Meenakshi","doi":"10.48175/ijetir-1228","DOIUrl":"https://doi.org/10.48175/ijetir-1228","url":null,"abstract":"We designed a secure and efficient E-Payment protocol. The new protocol offers an extra layer of protection for cardholders and merchants. Customers are asked to enter an additional password after checkout completion to verify they are truly the cardholder, the authentication is done directly between the cardholder and card issuer using the issuer security certificate and without involving the third- party electronic commerce or e-commerce provides participants, including consumers and merchants, with a number of benefits, such as convenience and time savings. E-commerce transactions can be categorized into business to business (B2B), business to consumer (B2C), consumer to consumer (C2C), and public/private sectors to government we focus on B2C transactions in this paper.","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"33 35","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141659417","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":"Weakly Supervised Deep Embedding for Product Review Sentiment Analysis","authors":"Sandeep P, Dr. D. R. Krithika","doi":"10.48175/ijetir-1221","DOIUrl":"https://doi.org/10.48175/ijetir-1221","url":null,"abstract":"Online reviews have become an important source of information for users before making an informed purchase decision. Early reviews of a product tend to have a high impact on the subsequent product sales. In this project, we take the initiative to study the behavior characteristics of early reviewers through their posted reviews on two real-world large e-commerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime into three consecutive stages, namely early, majority and laggards. A user who has posted a review in the early stage is considered as an early reviewer. We quantitatively characterize early reviewers based on their rating behaviors, the helpfulness scores received from others and the correlation of their reviews with product popularity. We have found that (1) an early reviewer tends to assign a higher average rating score; and (2) an early reviewer tends to post more helpful reviews. Our analysis of product reviews also indicates that early reviewers’ ratings and their received helpfulness scores are likely to influence product popularity. By viewing review posting process as a multiplayer competition game, we propose a novel margin-based embedding model for early reviewer prediction. Extensive experiments on two different e-commerce datasets have shown that our proposed approach outperforms a number of competitive baselines. In our project we have used algorithms like Decision Tree (DT) and Multi Layer Perceptron (MLP). All are measured in terms of accuracy","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"47 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141660154","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":"Evaluation of Mechanical Properties of Mortar Generated by using Lunar Soil Simulant","authors":"Vaibhav Kulkarni, Rohan Dhoka, Prathamesh Godambe, Shraddha Bagul, Pritee Thodsare","doi":"10.48175/ijarsct-19143","DOIUrl":"https://doi.org/10.48175/ijarsct-19143","url":null,"abstract":"This study investigates the mechanical properties of mortar produced using lunar soil simulant, with the aim of assessing its suitability for construction in extraterrestrial habitats. Various tests including fineness test, specific gravity, standard consistency and compressive test were conduct to evaluate the performance of the mortar. Factor such as particle size distribution, binder type, curing conditions and environmental influences were scrutinized to gain comprehensive insights into the material’s suitability for construction purposes. The findings provide insights into the feasibility of utilizing lunar soil simulant based mortar for future space missions and lunar colonization efforts","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"27 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141660768","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":"Football Player Analysis for Identifying Best Team using Machine Learning","authors":"Aditya Ramnath, R. Priya","doi":"10.48175/ijetir-1245","DOIUrl":"https://doi.org/10.48175/ijetir-1245","url":null,"abstract":"In the game of football (soccer), the evaluation of players for transfer, scouting, squad formation and strategic planning is important. However, due to the vast pool of grassroots level player, short career span, differing performance throughout the individual’s career, differing play conditions, positions and varying club budgets, it becomes difficult to identify the individual player's performance value altogether. The Player Performance Prediction system aims at solving this complex problem analytically and involves learning from various attributes and skills of a football player. It considers the skill set values of the football player and predicts the performance value, which depicts the scope of improvement and the capability of the player. The objective of this project is to help the coaches and team management at the grassroots as well as higher levels to identify the future prospects in the game of football without being biased to subjective conditions like club budget, competitiveness in the league, and importance of the player in the team or region. The system is based on a data-driven approach and we train our models to generate an appropriate holistic relationship between the players’ attributes values, market value and performance value to be predicted. These values are dependent on the position that the football player plays in and the skills they possess.\u0000In This project best player is predicted by algorithms namely Naïve Bayes (NB) as proposed and K Nearest Neighbor (KNN) as existing system and compared in terms of Accuracy. From the results obtained its proved that proposed NB works better than existing KNN..","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"54 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141659939","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":"Sign Tone: A Deep Learning-Based Deaf Companion System for Two Way Communication Between Deaf and Non-Deaf Individuals","authors":"Harish Dr, Dr. C. Meenakshi","doi":"10.48175/ijetir-1230","DOIUrl":"https://doi.org/10.48175/ijetir-1230","url":null,"abstract":"Communication is essential to express and receive information, knowledge, ideas, and views among people, but it has been quite a while to be an obstruction for people with hearing and mute disabilities. Sign language is one method of communicating with deaf people. Though there is sign language to communicate with non-sign people it is difficult for everyone to interpret and understand. The performance of existing sign language recognition approaches is typically limited. Developing an assistive device that will translate the sign language to a readable format will help the deaf-mutes to communicate with ease to the common people. Recent advancements in the development of deep learning, deep neural networks, especially Temporal convolutional networks (TCNs) have provided solutions to the communication of deaf and mute individuals. In this project, the main objective is to design Deaf Companion System for that to develop SignNet Model to provide two-way communication of deaf individuals and to implement an automatic speaking system for deaf and mute people. It provides two-way communication for all classes of people (deaf-and-mute, hard of hearing, visually impaired, and non-signers) and can be scaled commercially. The proposed system, consists of three modules; the sign recognition module (SRM) that recognizes the signs of a deaf individual using TCN, the speech recognition using Hidden Marko Model and synthesis module (SRSM) that processes the speech of a non-deaf individual and converts it to text, and an Avatar module (AM) to generate and perform the corresponding sign of the non-deaf speech, which were integrated into the sign translation companion system called deaf companion system to facilitate the communication from the deaf to the hearing and vice versa. The proposed model is trained on Indian Sign Language. Then developed a web-based user interface to deploy SignNet Model for ease of use. Experimental results on MNIST sign language recognition datasets validate the superiority of the proposed framework. The TCN model gives an accuracy of 98.5%..","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"33 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141660617","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":"Efficient Malware Detection in Android Devices to Improve Cyber Security","authors":"Faizur Rahaman.R, Dr. S. Prasanna","doi":"10.48175/ijetir-1241","DOIUrl":"https://doi.org/10.48175/ijetir-1241","url":null,"abstract":"The project introduces a novel framework for detecting Android malware based on permissions, utilizing multiple linear regression methods. Permissions play a crucial role in the security of the Android operating system, serving as fundamental indicators of an application's behavior. Through static analysis, the framework extracts application permissions and employs machine learning techniques to conduct security analyses.\u0000Specifically, the framework employs multiple linear regression techniques to develop two classifiers for permission-based Android malware detection. These classifiers leverage the relationships between various permission attributes to accurately identify potentially malicious applications. Notably, the framework achieves notable performance levels using classification algorithms without the need for overly complex models.\u0000In the project, the existing system utilizes the Random Forest (RF) algorithm, while the proposed system adopts the Support Vector Machine (SVM) algorithm. Both algorithms are evaluated in terms of accuracy, with the results demonstrating that the proposed SVM approach outperforms the existing RF method. This highlights the effectiveness of SVM in accurately detecting Android malware based on permission analysis.","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"2 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141658758","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 Comprehensive Framework for Pathology Classification Bridging Precision and Interpretability","authors":"Koushik K .V, V. Sumalatha","doi":"10.48175/ijetir-1247","DOIUrl":"https://doi.org/10.48175/ijetir-1247","url":null,"abstract":"Pathology classification is an indispensable component of medical diagnostics, facilitating accurate disease identification, prognosis determination, and treatment planning. However, the increasing complexity and heterogeneity of pathological manifestations pose significant challenges to traditional classification methodologies. This abstract presents a novel framework that integrates advanced machine learning techniques with domain-specific expertise to enhance the precision and interpretability of pathology classification. Our framework adopts a multi-modal approach, leveraging diverse data sources including histopathological images, clinical records, genomic profiles, and molecular biomarkers. Through feature fusion and dimensionality reduction techniques, we effectively capture intricate patterns and latent relationships embedded within the data, enabling robust classification across diverse pathological conditions. Furthermore, interpretability is prioritized through the incorporation of explainable AI methodologies, facilitating the identification of salient features and decision rationales underlying classification outcomes. This ensures transparency and trustworthiness in the diagnostic process, empowering clinicians to make informed decisions and refine treatment strategies. Validation of our framework across various pathological contexts demonstrates superior performance compared to conventional approaches, exhibiting high accuracy, sensitivity, and specificity. Moreover, its modular architecture facilitates customization and scalability, accommodating evolving diagnostic needs and emerging technological advancements. In conclusion, our proposed framework represents a significant advancement in pathology classification, offering a synergistic blend of computational sophistication and clinical relevance. By seamlessly integrating cutting-edge technologies with domain knowledge, it holds promise for revolutionizing diagnostic practices and improving patient outcomes in the realm of precision medicine.","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"18 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141662468","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":"An Empirical Study on Hazards Faced by Marine Organisms","authors":"K.S Rayvanth Kumar, Mrs. Anju Mohan","doi":"10.48175/ijarsct-19136","DOIUrl":"https://doi.org/10.48175/ijarsct-19136","url":null,"abstract":"Marine ecosystems and aquatic habitats face a number of threats from humans. Serious conservation attention and efforts should be drawn and directed towards restoration of fragmented marine habitats and estuarine ecosystems. Threats are manifold: i.e., primarily from overexploitation of marine resources, overfishing, climate warming, sewage disposal, industrial chemical discharge, oil spills, invasive species, and dredging. MPAs are areas of the ocean that are set aside for conservation and have strict regulations on human activities. MPAs can help to protect marine life from overfishing, pollution, and other threats. India has established a number of MPAs, including the Andaman and Nicobar Islands Marine National Park, which is home to a diverse range of marine life. India has also enacted a number of fishing regulations, such as the Marine Fisheries Act, which is designed to protect fish stocks. The research method followed here is empirical Research. A total of 200 samples have been taken out of which is taken through Random sampling. The sample frame taken by the research through the general public based on a questionnaire .The primary sources are taken from the general public in the form of survey method. The information was collected from secondary sources from journal articles, books and reports of presidency non governmental organisations. The independent variable taken here is age, gender, education, occupation. The dependent variables are causes of marine pollution, marine organisms cruelty, marine habitat, transport etc. The statistical tool used here in this research is graph(mean) and scaling . The main aim of this research is to study and analyze the hazards faced by marine life and resources and the steps to protect them","PeriodicalId":341984,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"122 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141666384","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}