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

{"title":"AI Based Smart Voting using Face Recognition","authors":"Dr. Anitha T N, Sai Kian T N, B P Satwick, Ameetreddy, Shravan M","doi":"10.48175/ijarsct-18454","DOIUrl":"https://doi.org/10.48175/ijarsct-18454","url":null,"abstract":"As society changes and technology plays a bigger role in shaping daily life, the security, effectiveness, and accessibility of traditional voting systems are being questioned. This research presents a novel approach to create an online smart voting system using facial recognition technology. The technology promises to improve the convenience and integrity of electoral processes by integrating cutting-edge facial recognition algorithms with secure online voting platforms. It also addresses common problems such as voter fraud, long lines, and geographical restrictions. The system verifies each vote's legitimacy by registering voters' facial biometric information, securely storing it in a centralized database, and providing live facial authentication while voting. Strong security protocols, such as multi-factor authentication and encryption methods, are notably used to protect voter privacy and prevent election results manipulation. Additionally, the system's removal of the requirement for voters to physically be present at polling places improves voter accessibility, especially for underprivileged or geographically separated groups, which encourages higher levels of political engagement. Through extensive testing and validation that includes both simulated and real-world voting scenarios, the efficacy of this novel approach will be closely examined. Consent, transparency, and data privacy are ethical issues that need to be properly addressed in order to ensure compliance with legal and regulatory requirements. In conclusion, the face recognition online smart voting system is a ground-breaking advancement in electoral technology that might fundamentally alter the way elections are held by enhancing their integrity, efficiency, and inclusivity","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"1 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099412","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 Workplace Safety Based on Internet of Things to Provide Intelligent Decision for Preventing Working at Height Fall Incidents for Occupational Workers","authors":"Pravin Tathod, Mrityunjay Singh","doi":"10.48175/ijarsct-18476","DOIUrl":"https://doi.org/10.48175/ijarsct-18476","url":null,"abstract":"In Under develop country like India fall incidents are increasing rapidly at workplace not only in the industrial sector even though construction and private sector too. Fall injury is the highest cause of death/ fatal/severe injuries. The severity of working at height incident depending upon the nature of job, height of application, uneven/cluttered surface etc. In this research paper a unique solution has been studied to bring revolutionary change in personal protective equipment which is used while working at height. There are many technologies are available,in chemical industry if any parameter deviate automatically machine will be shut-down and safe operating protocols will be activated but in conventional personal protective equipment it is hardly say there is still area of research where the technological improvement is needed to bring the robust safety system. Air bags are a very good example to correlate the smart PPE, if any passenger vehicle have air bag system collision with other vehicle automatically within milli second the airbag will be activated which saves the life of driver and passenger as well. Similarly Smart PPE has unique provision if PPE compliance is deviated at workplace immediately the violation shall be captured, and warning alert shall be given to user to remind them to compliance. This will help us to improve workplace safety. It is an error proof system that neither required manual surveillance nor manual recording of violations. In this research paper IOT internet of thing based smart safety harness has been studied and the workplace deviation were compared earlier with manual monitoring data to analyze the human error with respect to compliance of personal protective equipment","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"21 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099593","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":"SJCIT Transport Management System","authors":"Shreekar Bharadwaj M N, Dhanush Reddy H M, Deeraj C, Akshath Sai, Narendra Babu","doi":"10.48175/ijarsct-18464","DOIUrl":"https://doi.org/10.48175/ijarsct-18464","url":null,"abstract":"Innovative software developed with great care to maximize transportation inside of educational institution called the SJCIT Transport Management System (TMS). Acting as a single point of contact for all student and faculty transportation-related information, including records, routes, diversions, and more, this system includes sophisticated features like effective database management, user-friendly interfaces, and aesthetically pleasing design elements. Its rapid adaptation minimizes transition times by guaranteeing quick user onboarding. A key instrument in coordinating the smooth movement of people across multiple locations and schedules is the SJCIT TMS, which is centred around the promotion of safe, efficient, dependable, and sustainable transportation practices. Its adaptability makes it possible for it to function seamlessly in both standalone and integrated configurations, which makes it an essential tool for improving transportation management in learning environments","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"10 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099119","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":"Augmented Reality in Identification of Pests on Crops","authors":"Ms. Chandana KR, Ms. Chaithra Shree M, Ms. Deepthi B, Ms. S P Preethi, Ms. Sankalana CM","doi":"10.48175/ijarsct-18469","DOIUrl":"https://doi.org/10.48175/ijarsct-18469","url":null,"abstract":"The agriculture division can benefit from improved methods for identifying and managing pests to ensure a steady supply of safe and nutritious food. Traditional pest identification methods, which rely on the expertise of taxonomists to identify pests based on morphological features, can be time-consuming and require significant resources. To address this challenge, a new pest classification system has been developed that uses close-up image extraction and object recognition to identify pests in the IP102 dataset. This system achieved high classification rates of 91.5% and 90% for nine and 24 class pests, respectively, using a convolutional neural network (CNN) model. In addition to this classification system, an innovative application of Augmented Reality (AR) is being developed to assist farmers in pest identification and management. This system aims to help farmers distinguish between harmful and beneficial insects and provide recommendations for appropriate pesticides and treatments. By providing farmers with this information in real-time, the AR system can help improve crop yields and reduce the negative impacts of pests on the environment.","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"17 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141102441","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":"Enhanced Player Discovery via Machine Learning","authors":"Nithin M, Dr. S. Nagasundaram","doi":"10.48175/ijarsct-18412","DOIUrl":"https://doi.org/10.48175/ijarsct-18412","url":null,"abstract":"Forecasting the number of Olympic medals for each nation is highly relevant for different stakeholders: Ex ante, sports betting companies can determine the odds while sponsors and media companies can allocate their resources to promising teams. Ex post, sports politicians and managers can benchmark the performance of their teams and evaluate the drivers of success. To significantly increase the Olympic medal forecasting accuracy, we apply machine learning, more specifically a two-staged, thus outperforming more traditional naïve forecast for three previous Olympics held in the past years.\u0000In our project best player is predicted by algorithms namely Naïve Bayes (NB) as existing and K Nearest Neighbor (KNN) as proposed system and compared in terms of Accuracy. From the results obtained its proved that proposed KNN works better than existing NB. This project aims to develop a machine learning solution in Python for searching and ranking the best players based on their performance metrics.\u0000 The project involves collecting and preprocessing relevant player data, including statistics and attributes. Various machine learning algorithms, such as regression or ranking models, are explored to predict player performance. The trained model is then deployed to make real-time predictions, assisting sports teams or gaming platforms in selecting the most suitable players. The project highlights the potential of machine learning in optimizing player selection processes, offering a scalable and data-driven approach to identifying top performers.","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"49 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101956","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":"Tomato Leaf Disease Detection using Flask Frame Work","authors":"Bharad Raj, R. Priya","doi":"10.48175/ijarsct-18417","DOIUrl":"https://doi.org/10.48175/ijarsct-18417","url":null,"abstract":"In recent years, plant leaf diseases has become a widespread problem for which an accurate research and rapid application of deep learning in plant disease classification is required, tomato is also one of the most important plants and seeds which are used worldwide for cooking in either dried or fresh form, tomato are a great source of protein that offer many health benefits, but there are a lot of diseases associated with tomato leaf which hinder its production. Thus, an accurate classification of tomato leaf diseases is needed to solve the problem in the early stage. A deep learning approach is proposed to identify and classify leaf disease by using public dataset of leaf image and CNN model with the open source library TensorFlow. In this project, we proposed a method to classify tomato leaf disease and to find and describe the efficient network architecture (hyper parameters and optimization methods). Moreover, after applying each architecture separately, we compared their obtained results to find out the best architecture configuration for classifying tomato leaf diseases and their results. Furthermore, to satisfy the classification requirements, the model was trained using CNN architecture check if we could get faster training times, higher accuracy and easier retraining. Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this project, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this project will be a valuable resource for researchers who study the detection of plant diseases. At the same time, we also discussed some of the current challenges and problems that need to be resolved.","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"9 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099512","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":"Facial Feature-Based Attention Tracking System for Enhanced Online Learning Engagement","authors":"Krishnaraj. V, Sumalatha. V","doi":"10.48175/ijarsct-18419","DOIUrl":"https://doi.org/10.48175/ijarsct-18419","url":null,"abstract":"Recognizing and enhancing student engagement is crucial for improving learning outcomes, particularly in the context of online classes where monitoring can be challenging. Traditional methods of attendance tracking, such as calling out names, are impractical and susceptible to manipulation in the virtual environment. Students might appear 'online' without actively participating, and the absence of video feeds makes it difficult for teachers to verify attendance and attention. In order to realize a highly efficient and robust attendance management and engagement level prediction system for online learning, In the proposed\u0000System, the learner’s face is monitored by a video camera while attending a video lecture. Facial features were analyzed to predict reaction time (RT) to a task-irrelevant stimulus, which was assumed to be an index of the level of attention. Then apply a machine learning method, light Gradient Boosting Machine (LightGBM), to estimate RTs from facial features extracted as action units (AUs) corresponding to facial muscle movements by an open-source software (OpenFace). This project is to develops a user-friendly system integrated with private online learning and attendance recording system for teachers that can automatically record students ‘engagement state and attendance then generate attendance reports for online classrooms. It encompasses a novel design using the AI based FFCNN (Face Fiducial Convolution Neural Network) model to capture face biometric randomly from students’ video stream and record their attendance automatically. This integrated solution not only streamlines attendance management but also provides valuable insights into students' engagement levels through facial feature analysis.","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100876","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":"Machine Learning and Deep Learning Approach for Medical Image Analysis Summary Generator","authors":"Sam Sudhakar J, Dr. Krithika. D. R.","doi":"10.48175/ijarsct-18408","DOIUrl":"https://doi.org/10.48175/ijarsct-18408","url":null,"abstract":"Colorectal cancer, which is frequent, recognized tumours in both genders around the globe. As per the report generated by WHO in 2018, colon cancer placed in the third position, whereas 1.80 million individuals are affected. Precisely, it is the succeeding leading cancer, which is the second most common cause of cancer in females, and the third for males. The loss of control over the integrity of epidermal cells in bowel or malignancy can be the cause of colorectal cancer. An effective way to recognize colon cancer at an early stage and substantial treatment can reduce the ensuing death rates to a great extent. To perform Screening of Morphology of Malignant Tumor Cells in the colon, a Gastroenterologist may refer to cancer diagnosis tests for pathological images. In any Histology method, the process takes a significant duration of time due to infinite numbers of glands in the gastrointestinal system, which may lead to irreconcilable outcomes. By diagnosing through computer algorithms, can give practical and contributory results. Hence, accurate gland segmentation is one crucial prerequisite stage to get reliable and informative morphological image data. In recent times, the scholars applied machine learning algorithms to pathological image analysis for the diagnosis of cancer disease. We propose that features extracted from the diagnostic tests, given as input to a machine learning architecture used along with semantic segmentation algorithm, provide results that are accurate than the existing image segmentation algorithms. This work is the extensive review of machine learning architectures used for semantic segmentation on the histological images of the colon.In our project we will be using the following algorithms such as Adaboost as existing and Convolution Neural Network (CNN) as proposed and its accuracy is been calculated and well compared to other algorithms. It is found that CNN performs less than other algorithms","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"80 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101440","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":"Career Bot for Career Prediction of Higher Secondary Students using Decision Tree","authors":"S. Selvakumar, A. Poongodi","doi":"10.48175/ijarsct-18414","DOIUrl":"https://doi.org/10.48175/ijarsct-18414","url":null,"abstract":"Career guidance refers to a process that assists individuals, typically students in making informed decisions about their career paths. Career guidance can be delivered through various channels, including career counsellors, educational institutions, online platforms, and self-help resources. It plays a vital role in helping individuals make informed choices that align with their aspirations, values, and capabilities. Traditional career prediction models often lack transparency and fail to consider the diverse and dynamic factors that influence students' career choices. The existing systems may exhibit biases and limitations that hinder accurate and personalized career guidance. The project aims to tackle these problems by developing an Explainable ML (XML) model that provides transparent, personalized, and adaptable recommendations to higher secondary students. The proposed system incorporates Decision Tree algorithms within an Explainable ML framework to provide clear and comprehensible insights into the factors influencing career predictions. It takes into account a diverse set of input features, including academic performance, skills, interests, and extracurricular activities, to offer personalized career guidance to individual students. The project also addresses potential biases in the model to ensure fair and equitable career recommendations for students from varied backgrounds. By combining the power of Decision Tree algorithms with Explainable ML, the project aims to empower higher secondary students in making well-informed decisions about their future careers. The transparency provided by the Explainable ML model enhances user trust and understanding, fostering a more engaging and personalized career prediction system. The project's outcomes are expected to contribute significantly to the field of career guidance, providing a model that is not only accurate but also accessible and comprehensible for students navigating the critical phase of choosing their career paths.","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"81 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101708","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":"Plant Leaf Disease Detection using Deep Learning Algorithms","authors":"Mr. Srinath G M, Ms. Arfa Thareen K, Ms. Noor Fathima M, Ms. Vandana C K, Ms. Vinutha C R","doi":"10.48175/ijarsct-18475","DOIUrl":"https://doi.org/10.48175/ijarsct-18475","url":null,"abstract":"The Plant Leaf Diseases Detection System addresses the critical challenge of early detection and management of plant diseases, significantly impacting agricultural productivity and food security. Utilizing advanced technologies, this cutting-edge agricultural solution employs a Convolutional Neural Network (CNN) model, specifically based on the VGG19 architecture implemented using Keras. This robust deep learning model is trained on a diverse dataset containing images of both healthy and diseased leaves, allowing it to extract intricate features and accurately classify various plant diseases automatically. The system seamlessly integrates HTML, CSS, and Flask for the front end, while Keras powers the back end, resulting in a user-friendly web application interface. Incorporating this technology not only enhances the efficiency of disease detection but also facilitates user interaction and accessibility","PeriodicalId":472960,"journal":{"name":"International Journal of Advanced Research in Science, Communication and Technology","volume":"13 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100210","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}