In terms of the variety of rural jobs available, India offers enormous opportunity for the growth of entrepreneurship. Since India produces more milk than any other country in the world (108 million tons), dairy farmers play a crucial role in the growth of the dairy industry and the socioeconomic fabric of the nation. One of the major occupations of the rural people in our nation is dairy farming. Any nation's entrepreneurs play a crucial role in fostering technical advancement and economic expansion. Milk is an incredibly valuable and nourishing food that must be handled and kept with caution because of its limited shelf life. Due to its natural qualities, milk is a great medium for many microorganisms to thrive. These germs can enter milk through the process of milking, handling, storing, or transporting it to markets. Maintaining animal health, adhering to optimal practices for milking, and upholding cleanliness standards in the milking parlor are essential for reducing the microbial burden in raw milk. The current study offers recommendations to prevent milk contamination and aids in understanding the many forms of hygiene that should be maintained in the dairy. Index Terms— Entrepreneurship skills, Dairy Industry, Milk production, Hygienic conditions.
{"title":"Entrepreneurship skills in Dairy Industry: A Critical Study on Importance of Hygienic Conditions in Dairy Industry","authors":"Dr. Swapnali Amol Kulkarni, Mr. Sachin Hadole","doi":"10.55041/ijsrem36981","DOIUrl":"https://doi.org/10.55041/ijsrem36981","url":null,"abstract":"In terms of the variety of rural jobs available, India offers enormous opportunity for the growth of entrepreneurship. Since India produces more milk than any other country in the world (108 million tons), dairy farmers play a crucial role in the growth of the dairy industry and the socioeconomic fabric of the nation. One of the major occupations of the rural people in our nation is dairy farming. Any nation's entrepreneurs play a crucial role in fostering technical advancement and economic expansion. Milk is an incredibly valuable and nourishing food that must be handled and kept with caution because of its limited shelf life. Due to its natural qualities, milk is a great medium for many microorganisms to thrive. These germs can enter milk through the process of milking, handling, storing, or transporting it to markets. Maintaining animal health, adhering to optimal practices for milking, and upholding cleanliness standards in the milking parlor are essential for reducing the microbial burden in raw milk. The current study offers recommendations to prevent milk contamination and aids in understanding the many forms of hygiene that should be maintained in the dairy. Index Terms— Entrepreneurship skills, Dairy Industry, Milk production, Hygienic conditions.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"30 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924869","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}
Deepfake technology has emerged as a significant challenge in digital media, posing risks related to misinformation and identity theft. This paper provides a comprehensive review of deepfake detection techniques, highlighting advancements in traditional machine learning, deep learning models, hybrid approaches, and attention mechanisms. We evaluate the effectiveness of various methods based on accuracy, computational efficiency, and practical applicability, using key datasets and benchmarking systems. Our review underscores the progress made in detecting deepfakes and identifies areas for future research, including real-time detection, multimodal approaches, and improvements in computational efficiency. Key Words: Deepfake detection, machine learning, deep learning, convolutional neural networks, transformers, attention mechanisms, multimodal data, benchmarking systems, datasets.
{"title":"A Survey of Deepfake Detection Methods: Innovations, Accuracy, and Future Directions","authors":"Parminder Singh","doi":"10.55041/ijsrem37000","DOIUrl":"https://doi.org/10.55041/ijsrem37000","url":null,"abstract":"Deepfake technology has emerged as a significant challenge in digital media, posing risks related to misinformation and identity theft. This paper provides a comprehensive review of deepfake detection techniques, highlighting advancements in traditional machine learning, deep learning models, hybrid approaches, and attention mechanisms. We evaluate the effectiveness of various methods based on accuracy, computational efficiency, and practical applicability, using key datasets and benchmarking systems. Our review underscores the progress made in detecting deepfakes and identifies areas for future research, including real-time detection, multimodal approaches, and improvements in computational efficiency. Key Words: Deepfake detection, machine learning, deep learning, convolutional neural networks, transformers, attention mechanisms, multimodal data, benchmarking systems, datasets.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"52 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141923914","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}
Agriculture automation is becoming more and more sophisticated, utilizing Deep Neural Networks (DNN) and the Internet of Things (IoT) to create and implement a wide range of fine-grained controlling, monitoring, and tracking applications. Managing the interaction with the factors outside the agricultural ecosystem, such wildlife, is a pertinent open topic in this quickly changing situation. One of the main concerns of today's farmers is protecting crops from wild animals’ attacks. There are different traditional approaches to address this problem which can be lethal (e.g., shooting, trapping) and non-lethal (e.g., scarecrow, chemical repellents, organic substances, mesh, or electric fences). Nevertheless, some of the traditional methods have environmental pollution effects on both humans and ungulates, while others are very expensive with high maintenance costs, with limited reliability and limited effectiveness. In this project, we develop a system, that combines AI Computer Vision using DCNN for detecting and recognizing animal species, and specific ultrasound emission (i.e., different for each species) for repelling them. Keywords: Animal Recognition, Repellent, Artificial Intelligence, Edge Computing, Animal Detection, Deep Learning, DCNN.
{"title":"AI-Powered Animal Repellent System for Smart Farming","authors":"K. Revathi, Dr.K.M Alaaudeen","doi":"10.55041/ijsrem37016","DOIUrl":"https://doi.org/10.55041/ijsrem37016","url":null,"abstract":"Agriculture automation is becoming more and more sophisticated, utilizing Deep Neural Networks (DNN) and the Internet of Things (IoT) to create and implement a wide range of fine-grained controlling, monitoring, and tracking applications. Managing the interaction with the factors outside the agricultural ecosystem, such wildlife, is a pertinent open topic in this quickly changing situation. One of the main concerns of today's farmers is protecting crops from wild animals’ attacks. There are different traditional approaches to address this problem which can be lethal (e.g., shooting, trapping) and non-lethal (e.g., scarecrow, chemical repellents, organic substances, mesh, or electric fences). Nevertheless, some of the traditional methods have environmental pollution effects on both humans and ungulates, while others are very expensive with high maintenance costs, with limited reliability and limited effectiveness. In this project, we develop a system, that combines AI Computer Vision using DCNN for detecting and recognizing animal species, and specific ultrasound emission (i.e., different for each species) for repelling them. Keywords: Animal Recognition, Repellent, Artificial Intelligence, Edge Computing, Animal Detection, Deep Learning, DCNN.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"8 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925129","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}
A surge in digital monetary transactions has resulted in a rise in cyber threats on such platforms. Conventional security measures are slowly eroding and are, therefore, failing to a great extent in curbing these emerging risks. Artificial Intelligence (AI) holds out much promise toward robust cybersecurity through mechanisms with machine learning and anomaly detection techniques, especially natural language processing. This paper tries to explore technical insight into the AI-based framework, approaches, applications, benefits, issues, ethical concerns, and the way forward for the security of financial transactions. Key Words: AI-driven approaches, Cybersecurity, Financial transactions, Machine learning, Natural language processing (NLP), Anomaly detection, Deep learning architectures, Supervised learning, Unsupervised learning, Reinforcement learning, Adversarial machine learning, Data preprocessing, Real-time monitoring, Blockchain integration, Predictive analytics, Explainable AI, Ethical and privacy issues, Regulatory compliance, Quantum computing, Edge AI
{"title":"AI-Driven Approaches to Enhance Cybersecurity in Financial Transactions","authors":"Maheshwaran C V, Amirdavarshni V","doi":"10.55041/ijsrem37015","DOIUrl":"https://doi.org/10.55041/ijsrem37015","url":null,"abstract":"A surge in digital monetary transactions has resulted in a rise in cyber threats on such platforms. Conventional security measures are slowly eroding and are, therefore, failing to a great extent in curbing these emerging risks. Artificial Intelligence (AI) holds out much promise toward robust cybersecurity through mechanisms with machine learning and anomaly detection techniques, especially natural language processing. This paper tries to explore technical insight into the AI-based framework, approaches, applications, benefits, issues, ethical concerns, and the way forward for the security of financial transactions. Key Words: AI-driven approaches, Cybersecurity, Financial transactions, Machine learning, Natural language processing (NLP), Anomaly detection, Deep learning architectures, Supervised learning, Unsupervised learning, Reinforcement learning, Adversarial machine learning, Data preprocessing, Real-time monitoring, Blockchain integration, Predictive analytics, Explainable AI, Ethical and privacy issues, Regulatory compliance, Quantum computing, Edge AI","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"86 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922348","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}
The Comprehensive Student Monitoring Solution is a state-of-the-art integrated system designed to streamline attendance tracking, drowsiness detection, and advanced proctoring functionalities within educational settings. This innovative solution combines cutting-edge technologies to provide real-time monitoring and analysis of student activities, ensuring a secure and engaging learning environment. The system offers seamless attendance tracking capabilities, allowing educators to easily monitor and manage student attendance records. Furthermore, the inclusion of drowsiness detection technology enhances student safety by alerting instructors to signs of fatigue or lack of engagement. Additionally, the advanced proctoring functionality of the system enables educators to remotely supervise exams and assessments, ensuring academic integrity and preventing cheating. With its user-friendly interface and robust features, the Comprehensive Student Monitoring Solution is a valuable tool for educators seeking to enhance student engagement and academic performance. Attendance Tracking, Drowsiness Detection, Proctoring Functionality.
{"title":"Monitoring of Participation Monitoring, Optical Somnolence Recognition and Proctorial Supervision","authors":"R. Lavanya, M. Meenatchi, R. Saranya","doi":"10.55041/ijsrem37014","DOIUrl":"https://doi.org/10.55041/ijsrem37014","url":null,"abstract":"The Comprehensive Student Monitoring Solution is a state-of-the-art integrated system designed to streamline attendance tracking, drowsiness detection, and advanced proctoring functionalities within educational settings. This innovative solution combines cutting-edge technologies to provide real-time monitoring and analysis of student activities, ensuring a secure and engaging learning environment. The system offers seamless attendance tracking capabilities, allowing educators to easily monitor and manage student attendance records. Furthermore, the inclusion of drowsiness detection technology enhances student safety by alerting instructors to signs of fatigue or lack of engagement. Additionally, the advanced proctoring functionality of the system enables educators to remotely supervise exams and assessments, ensuring academic integrity and preventing cheating. With its user-friendly interface and robust features, the Comprehensive Student Monitoring Solution is a valuable tool for educators seeking to enhance student engagement and academic performance. Attendance Tracking, Drowsiness Detection, Proctoring Functionality.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"73 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922484","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}
THE RESEARCH IS ABOUT BRAND LOYALTY AMONG THE CUSTOMERS TOWARDS BRANDED SHIRTS. THE OBJECTIVE OF THE STUDY IS TO FIND OUT THE MOST PREFERRED BRAND IN SHIRTS BY CUSTOMERS, AND THE FACTORS THAT INFLUENCE THE CUSTOMERS TO PURCHASE BRANDED SHIRTS. THE DATA HAS BEEN COLLECTED FROM 385 CUSTOMERS BY USING A STRUCTURED QUESTIONNAIRE. AND THE DATA WAS COLLECTED FROM COIMBATORE AND MADURAI. WE USED THE STATISTICAL PACKAGE FOR SOCIAL SCIENCES (SPSS) ASSISTED FOR DATA ANALYSIS. THE DATA COLLECTED WERE ANALYSED BY USING STATISTICAL TOOL SUCH AS AVERAGE, CHI SQUARE, AND REGRESSION. THE FINDINGS SHOW THAT OTTO IS THE MOST PREFERRED BRAND IN SHIRTS. AND THE FACTORS LIKE BRAND LOYALTY, BRAND AWARENESS, AND BRAND ASSOCIATION ARE INDEPENDENT VARIABLES THAT ARE STATISTICALLY SIGNIFICANT. Keywords: Customers, Brand Loyalty, Branded Shirts, Shirts.
{"title":"Brand Loyalty Among the Customers towards Branded Shirts","authors":"Dr.K Rajamani, Mrs. S Suganya, Ms.M Karthika","doi":"10.55041/ijsrem36997","DOIUrl":"https://doi.org/10.55041/ijsrem36997","url":null,"abstract":"THE RESEARCH IS ABOUT BRAND LOYALTY AMONG THE CUSTOMERS TOWARDS BRANDED SHIRTS. THE OBJECTIVE OF THE STUDY IS TO FIND OUT THE MOST PREFERRED BRAND IN SHIRTS BY CUSTOMERS, AND THE FACTORS THAT INFLUENCE THE CUSTOMERS TO PURCHASE BRANDED SHIRTS. THE DATA HAS BEEN COLLECTED FROM 385 CUSTOMERS BY USING A STRUCTURED QUESTIONNAIRE. AND THE DATA WAS COLLECTED FROM COIMBATORE AND MADURAI. WE USED THE STATISTICAL PACKAGE FOR SOCIAL SCIENCES (SPSS) ASSISTED FOR DATA ANALYSIS. THE DATA COLLECTED WERE ANALYSED BY USING STATISTICAL TOOL SUCH AS AVERAGE, CHI SQUARE, AND REGRESSION. THE FINDINGS SHOW THAT OTTO IS THE MOST PREFERRED BRAND IN SHIRTS. AND THE FACTORS LIKE BRAND LOYALTY, BRAND AWARENESS, AND BRAND ASSOCIATION ARE INDEPENDENT VARIABLES THAT ARE STATISTICALLY SIGNIFICANT. Keywords: Customers, Brand Loyalty, Branded Shirts, Shirts.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"12 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921504","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}
Improving patient outcomes depends critically on early identification of breast cancer. In order to detect breast cancer up to five years before a clinical diagnosis, artificial intelligence (AI) has the potential to completely transform breast cancer screening. This paper examines this possibility. We explore the most recent developments in AI algorithms and how they relate to imaging in medicine, namely mammography. The paper looks at how AI can identify precancerous alterations that are invisible to the human eye by analysing minute patterns in breast tissue. We go over the difficulties and possibilities in creating and evaluating AI models for early detection, including model interpretability, data quality, and ethical issues. The ultimate goal of this analysis is to demonstrate how artificial intelligence (AI) has the potential to drastically lower breast cancer mortality by enabling much earlier detection. Keywords-Artificial Intelligence, Breast Cancer, Personalized medicine,Digital Mammography
改善患者的治疗效果关键取决于乳腺癌的早期识别。为了在临床诊断前五年发现乳腺癌，人工智能（AI）有可能彻底改变乳腺癌筛查。本文探讨了这种可能性。我们探讨了人工智能算法的最新发展，以及它们与医学成像（即乳腺 X 射线照相术）的关系。本文探讨了人工智能如何通过分析乳腺组织中的微小模式来识别肉眼无法看到的癌前病变。我们探讨了创建和评估用于早期检测的人工智能模型的困难和可能性，包括模型的可解释性、数据质量和伦理问题。这项分析的最终目的是展示人工智能（AI）如何通过实现更早的检测来大幅降低乳腺癌死亡率。关键词--人工智能、乳腺癌、个性化医疗、数字乳腺 X 射线照相术
{"title":"Artificial Intelligence in Early Detection: Identifying Breast Cancer Before Clinical Diagnosis","authors":"Prasurjya Saikia","doi":"10.55041/ijsrem37010","DOIUrl":"https://doi.org/10.55041/ijsrem37010","url":null,"abstract":"Improving patient outcomes depends critically on early identification of breast cancer. In order to detect breast cancer up to five years before a clinical diagnosis, artificial intelligence (AI) has the potential to completely transform breast cancer screening. This paper examines this possibility. We explore the most recent developments in AI algorithms and how they relate to imaging in medicine, namely mammography. The paper looks at how AI can identify precancerous alterations that are invisible to the human eye by analysing minute patterns in breast tissue. We go over the difficulties and possibilities in creating and evaluating AI models for early detection, including model interpretability, data quality, and ethical issues. The ultimate goal of this analysis is to demonstrate how artificial intelligence (AI) has the potential to drastically lower breast cancer mortality by enabling much earlier detection. Keywords-Artificial Intelligence, Breast Cancer, Personalized medicine,Digital Mammography","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"78 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922117","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}
The Online E-commerce System (OSMS) serves as a vital tool in the ecommerce sector, ensuring the efficient management and distribution of various products. This abstract presents an overview of the OSMS, highlighting its functionalities, benefits, and potential impact on ecommerce delivery. The purpose of Online E-commerce System is to automate the existing manual system by the help of computerized equipment’s and full-fledged computer software, fulfilling their requirements, so that their valuable data/information can be stored for a longer period with easy accessing and manipulation of the same. The required software and hardware are easily available and easy to work with. Key Features of the system are: 1) Integration of all records of the order.Top of Form 2) Managing the information of the products. 3) Manage the Delivery address, Customer details, Order details. 4) Shows the information and description of the various products.
{"title":"ONLINE E-COMMERCE STSTEM","authors":"Manish Singh,","doi":"10.55041/ijsrem35244","DOIUrl":"https://doi.org/10.55041/ijsrem35244","url":null,"abstract":"The Online E-commerce System (OSMS) serves as a vital tool in the ecommerce sector, ensuring the efficient management and distribution of various products. This abstract presents an overview of the OSMS, highlighting its functionalities, benefits, and potential impact on ecommerce delivery. The purpose of Online E-commerce System is to automate the existing manual system by the help of computerized equipment’s and full-fledged computer software, fulfilling their requirements, so that their valuable data/information can be stored for a longer period with easy accessing and manipulation of the same. The required software and hardware are easily available and easy to work with. Key Features of the system are: 1) Integration of all records of the order.Top of Form 2) Managing the information of the products. 3) Manage the Delivery address, Customer details, Order details. 4) Shows the information and description of the various products.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"43 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231599","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}
This paper presents a biometric fingerprint attendance system designed to enhance accuracy, security, and efficiency in attendance tracking. The system utilizes fingerprint recognition technology for data acquisition, preprocessing, feature extraction, and matching. Key challenges, including data privacy and security, are addressed with robust solutions. Comprehensive testing demonstrates the system's effectiveness in reducing time theft and improving employee accountability. The findings highlight the potential of biometric systems to revolutionize attendance management, suggesting avenues for future technological advancements. biometric fingerprint attendance system aimed at improving accuracy and security in attendance tracking. Utilizing fingerprint recognition technology, the system effectively handles data acquisition, processing, and matching. Key issues such as data privacy and security are addressed with robust solutions. Testing shows significant improvements in reducing time theft and enhancing employee accountability, highlighting the system's potential to revolutionize attendance management.
{"title":"BIOMETRIC ATTENDANCE SYSTEM","authors":"Seependra Singh,","doi":"10.55041/ijsrem35127","DOIUrl":"https://doi.org/10.55041/ijsrem35127","url":null,"abstract":"This paper presents a biometric fingerprint attendance system designed to enhance accuracy, security, and efficiency in attendance tracking. The system utilizes fingerprint recognition technology for data acquisition, preprocessing, feature extraction, and matching. Key challenges, including data privacy and security, are addressed with robust solutions. Comprehensive testing demonstrates the system's effectiveness in reducing time theft and improving employee accountability. The findings highlight the potential of biometric systems to revolutionize attendance management, suggesting avenues for future technological advancements. biometric fingerprint attendance system aimed at improving accuracy and security in attendance tracking. Utilizing fingerprint recognition technology, the system effectively handles data acquisition, processing, and matching. Key issues such as data privacy and security are addressed with robust solutions. Testing shows significant improvements in reducing time theft and enhancing employee accountability, highlighting the system's potential to revolutionize attendance management.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"63 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231377","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}
In the contemporary digital landscape, enhancing human-computer interaction efficiency and intuitiveness is essential. Traditional input devices like mice and keyboards are being augmented by innovative approaches such as hand gesture recognition, which provides a more natural method of interaction. This paper aims to generate a virtual mouse controlled by hand gestures using computer vision and deep learning techniques. The system employs a webcam to capture live video of the user's hand movements. These movements are analyzed using convolutional neural networks (CNNs) to identify specific gestures, which are then translated into mouse operations like cursor movement, clicking, and scrolling. This solution is hardware-independent, utilizing only the device's camera, making it accessible and straightforward to use. The goal is to create a seamless and efficient interaction method, allowing users to control their computers with simple hand gestures from a distance. Keywords: Convolutional Neural Network, Deep Learning, Hand Gesture Recognition, Virtual Mouse, Computer Vision, OpenCV
{"title":"AI Virtual Mouse System Using Computer Vision to avoid COVID-19 spread","authors":"Dr. Santhosh Kumar S","doi":"10.55041/ijsrem35254","DOIUrl":"https://doi.org/10.55041/ijsrem35254","url":null,"abstract":"In the contemporary digital landscape, enhancing human-computer interaction efficiency and intuitiveness is essential. Traditional input devices like mice and keyboards are being augmented by innovative approaches such as hand gesture recognition, which provides a more natural method of interaction. This paper aims to generate a virtual mouse controlled by hand gestures using computer vision and deep learning techniques. The system employs a webcam to capture live video of the user's hand movements. These movements are analyzed using convolutional neural networks (CNNs) to identify specific gestures, which are then translated into mouse operations like cursor movement, clicking, and scrolling. This solution is hardware-independent, utilizing only the device's camera, making it accessible and straightforward to use. The goal is to create a seamless and efficient interaction method, allowing users to control their computers with simple hand gestures from a distance. Keywords: Convolutional Neural Network, Deep Learning, Hand Gesture Recognition, Virtual Mouse, Computer Vision, OpenCV","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"20 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231761","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}
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