Mutual funds have emerged as popular investment vehicles globally, offering individuals the opportunity to invest in a diversified portfolio managed by professionals. This study aims to investigate the awareness levels regarding mutual funds among individuals. The research employs a mixed-methods approach, utilizing both quantitative surveys and qualitative interviews to gather comprehensive insights. The quantitative aspect involves administering structured questionnaires to a diverse sample of participants, assessing their familiarity with mutual funds, understanding of their functioning, and reasons for investing or not investing in them. Additionally, demographic factors such as age, income, and education will be analyzed to discern any correlations with awareness levels. Complementing the quantitative data, qualitative interviews will be conducted with select participants to delve deeper into their perceptions, attitudes, and experiences related to mutual funds. This qualitative component aims to provide nuanced insights into the factors influencing awareness and investment decisions.
{"title":"Review Paper: “Study the Awareness About Mutual Fund","authors":"Aatif Latif Shaikh","doi":"10.55041/ijsrem34547","DOIUrl":"https://doi.org/10.55041/ijsrem34547","url":null,"abstract":"Mutual funds have emerged as popular investment vehicles globally, offering individuals the opportunity to invest in a diversified portfolio managed by professionals. This study aims to investigate the awareness levels regarding mutual funds among individuals. The research employs a mixed-methods approach, utilizing both quantitative surveys and qualitative interviews to gather comprehensive insights. The quantitative aspect involves administering structured questionnaires to a diverse sample of participants, assessing their familiarity with mutual funds, understanding of their functioning, and reasons for investing or not investing in them. Additionally, demographic factors such as age, income, and education will be analyzed to discern any correlations with awareness levels. Complementing the quantitative data, qualitative interviews will be conducted with select participants to delve deeper into their perceptions, attitudes, and experiences related to mutual funds. This qualitative component aims to provide nuanced insights into the factors influencing awareness and investment decisions.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"5 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099956","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}
LearnIt is an innovative educational system crafted to identify and support slow learners through advanced technological solutions. It employs the MERN stack (MongoDB, Express.js, React.js, Node.js) to offer a personalized learning environment tailored to meet individual student needs. This system not only enhances the educational experience for students but also provides tools to facilitate effective communication between teachers and parents, ensuring a cohesive support network for the learner. Additionally, LearnIt empowers educators with data-driven insights, allowing for informed decision-making and targeted interventions. The traditional educational model often overlooks the unique needs of slow learners, resulting in disengagement and poor academic performance. LearnIt addresses this challenge by integrating comprehensive assessment tools and personalized learning strategies within its platform. By identifying slow learners early, the system provides timely interventions that can significantly improve their learning trajectory. The use of the MERN stack ensures that LearnIt is both scalable and responsive. The assessment and monitoring module is at the core of LearnIt’s functionality. The development of LearnIt involved a detailed analysis of the educational needs of slow learners, incorporating feedback from educators, parents, and the students themselves. This user-centered approach ensured that the system's design and functionality were closely aligned with real-world requirements. Key modules of LearnIt include user management, assessment and monitoring, and communication tool. The user management module handles registration, authentication, and role-based access control, ensuring secure and personalized experiences for all users. The assessment and monitoring module is at the core of LearnIt’s functionality. It includes tools for creating and administering assessments, tracking student performance, and generating detailed reports. This module helps in identifying slow learners by analyzing their performance data and providing actionable insights to educators. With these insights, teachers can develop customized learning plans that address the specific needs of each student, fostering an inclusive and effective educational environment. Communication is another critical component of LearnIt. The platform includes features that facilitate seamless communication between teachers, parents, and students. This ensures that parents are kept informed about their child's progress and can collaborate with teachers to provide consistent support. Effective communication helps in building a supportive learning ecosystem, which is essential for the success of slow learners. The implementation of LearnIt has shown promising results. Preliminary studies indicate that the system significantly improves the identification and support of slow learners. LearnIt represents a significant advancement in the field of educational technology. By focusing on the
{"title":"An Advance Learning Platform for Slow Learners","authors":"Dr. Prof. K.S. Mahajan","doi":"10.55041/ijsrem34581","DOIUrl":"https://doi.org/10.55041/ijsrem34581","url":null,"abstract":"LearnIt is an innovative educational system crafted to identify and support slow learners through advanced technological solutions. It employs the MERN stack (MongoDB, Express.js, React.js, Node.js) to offer a personalized learning environment tailored to meet individual student needs. This system not only enhances the educational experience for students but also provides tools to facilitate effective communication between teachers and parents, ensuring a cohesive support network for the learner. Additionally, LearnIt empowers educators with data-driven insights, allowing for informed decision-making and targeted interventions. The traditional educational model often overlooks the unique needs of slow learners, resulting in disengagement and poor academic performance. LearnIt addresses this challenge by integrating comprehensive assessment tools and personalized learning strategies within its platform. By identifying slow learners early, the system provides timely interventions that can significantly improve their learning trajectory. The use of the MERN stack ensures that LearnIt is both scalable and responsive. The assessment and monitoring module is at the core of LearnIt’s functionality. The development of LearnIt involved a detailed analysis of the educational needs of slow learners, incorporating feedback from educators, parents, and the students themselves. This user-centered approach ensured that the system's design and functionality were closely aligned with real-world requirements. Key modules of LearnIt include user management, assessment and monitoring, and communication tool. The user management module handles registration, authentication, and role-based access control, ensuring secure and personalized experiences for all users. The assessment and monitoring module is at the core of LearnIt’s functionality. It includes tools for creating and administering assessments, tracking student performance, and generating detailed reports. This module helps in identifying slow learners by analyzing their performance data and providing actionable insights to educators. With these insights, teachers can develop customized learning plans that address the specific needs of each student, fostering an inclusive and effective educational environment. Communication is another critical component of LearnIt. The platform includes features that facilitate seamless communication between teachers, parents, and students. This ensures that parents are kept informed about their child's progress and can collaborate with teachers to provide consistent support. Effective communication helps in building a supportive learning ecosystem, which is essential for the success of slow learners. The implementation of LearnIt has shown promising results. Preliminary studies indicate that the system significantly improves the identification and support of slow learners. LearnIt represents a significant advancement in the field of educational technology. By focusing on the ","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141102568","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}
Now days concrete is the most widely used construction material due to its high strength and durability.Concrete needs a pleasant atmosphere for the development of strength, which will be provided by curing as prescribed by IS Code. Any neglectful in curing will effect the strength of the concrete. The water demand is increasing day by day and the sources are depleting. To counter this water demand we have made a study on ‘Self-Curing Concrete by the use of polyethylene glycol in M30 grade of concrete' which can drastically save the water used on the construction site.This study involves the use self curing agent-PolyEthylene Glycol (PEG400) which helps in self-curing of the concrete. Key Words: Concrete,Glass Fibers, Aggregate,Polyethylene Glycol,Superplastisizer
如今，混凝土因其高强度和耐久性而成为最广泛使用的建筑材料。混凝土需要一个舒适的环境来发展强度，而这将通过 IS 规范规定的养护来实现。任何疏忽都会影响混凝土的强度。水资源需求与日俱增，而水源却日渐枯竭。为了应对这种用水需求，我们进行了一项关于 "在 M30 级混凝土中使用聚乙二醇自养护混凝土 "的研究，这项研究可以大大节约施工现场的用水。关键字混凝土、玻璃纤维、骨料、聚乙二醇、超塑化剂
{"title":"USE OF SELF CURING AGENT POLYETHELENE GLYCOL (PEG400) IN M30 GRADE CONCRETE","authors":"Prof. Suryawanshia S.R","doi":"10.55041/ijsrem34552","DOIUrl":"https://doi.org/10.55041/ijsrem34552","url":null,"abstract":"Now days concrete is the most widely used construction material due to its high strength and durability.Concrete needs a pleasant atmosphere for the development of strength, which will be provided by curing as prescribed by IS Code. Any neglectful in curing will effect the strength of the concrete. The water demand is increasing day by day and the sources are depleting. To counter this water demand we have made a study on ‘Self-Curing Concrete by the use of polyethylene glycol in M30 grade of concrete' which can drastically save the water used on the construction site.This study involves the use self curing agent-PolyEthylene Glycol (PEG400) which helps in self-curing of the concrete. Key Words: Concrete,Glass Fibers, Aggregate,Polyethylene Glycol,Superplastisizer","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"21 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100328","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}
An efficient vertical transit system is a crucial feature of contemporary office buildings. Modern machine learning (ML) algorithms make it simple to optimise lift control strategies. This study proposes a revolutionary way to use Raspberry Pi camera module data for the best possible dispatching of traditional passenger lifts. It is assumed that an image processing system processes a real-time video to ascertain the quantity of people and objects utilising the lifts and waiting for a lift vehicle in the halls. These numbers are assumed to be connected to a specific uncertain probability. The efficiency of our unique lift control algorithm is derived from the need to serve a crowded floor completely, sending as many lifts as possible there and filling them to the maximum weight permitted, in addition to the probabilistic utilisation of the number of people and/or items waiting. The suggested technique introduces the idea of the effective number of persons and items to account for the uncertainty that may arise from the image processing system's imperfection. Reducing wait time, energy conservation and optimization are main the goals of this research. The proposed approach was implemented, and the simulation results showed that the passenger journey time was reduced. A three-story office building was the intended application for the prototype model. Key Words: Elevator, Raspberry Pi, Machine Learning, Optimization, Image Processing
{"title":"Optimization of Elevator Usage by Image Processing for Optimal Energy Conservation","authors":"Abhishek M","doi":"10.55041/ijsrem34599","DOIUrl":"https://doi.org/10.55041/ijsrem34599","url":null,"abstract":"An efficient vertical transit system is a crucial feature of contemporary office buildings. Modern machine learning (ML) algorithms make it simple to optimise lift control strategies. This study proposes a revolutionary way to use Raspberry Pi camera module data for the best possible dispatching of traditional passenger lifts. It is assumed that an image processing system processes a real-time video to ascertain the quantity of people and objects utilising the lifts and waiting for a lift vehicle in the halls. These numbers are assumed to be connected to a specific uncertain probability. The efficiency of our unique lift control algorithm is derived from the need to serve a crowded floor completely, sending as many lifts as possible there and filling them to the maximum weight permitted, in addition to the probabilistic utilisation of the number of people and/or items waiting. The suggested technique introduces the idea of the effective number of persons and items to account for the uncertainty that may arise from the image processing system's imperfection. Reducing wait time, energy conservation and optimization are main the goals of this research. The proposed approach was implemented, and the simulation results showed that the passenger journey time was reduced. A three-story office building was the intended application for the prototype model. Key Words: Elevator, Raspberry Pi, Machine Learning, Optimization, Image Processing","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"1 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099420","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}
It's challenging to automatically produce brief descriptions of an image's meaning because it can have diverse connotations in different languages. However, due to the vast amount of information packed into a single image, it is challenging to parse out the necessary context to use it to build sentences. It's a great way for the visually impaired to get around independently. This type of system can be built using the emerging programming technique of deep learning. This paper presents the development of an Image Caption Bot designed to aid individuals with visual impairments. We achieve enhanced accuracy in caption generation by modeling on the MSCOCO dataset using a Transformer encoder and Inception v3 for image processing. Image captioning, which entails generating textual descriptions for images, is the primary focus of our research. We achieve enhanced accuracy in caption generation by utilizing a Transformer encoder during training. The MSCOCO dataset serves as a valuable The results of the model are translated into speech for the benefit of the visually handicapped. Keywords—CNN, Google Text To Speech, MS-COCO, Inspection v3.
{"title":"Image Caption Bot for Assistive Vision","authors":"Prof.Anandkumar Birajdar","doi":"10.55041/ijsrem34573","DOIUrl":"https://doi.org/10.55041/ijsrem34573","url":null,"abstract":"It's challenging to automatically produce brief descriptions of an image's meaning because it can have diverse connotations in different languages. However, due to the vast amount of information packed into a single image, it is challenging to parse out the necessary context to use it to build sentences. It's a great way for the visually impaired to get around independently. This type of system can be built using the emerging programming technique of deep learning. This paper presents the development of an Image Caption Bot designed to aid individuals with visual impairments. We achieve enhanced accuracy in caption generation by modeling on the MSCOCO dataset using a Transformer encoder and Inception v3 for image processing. Image captioning, which entails generating textual descriptions for images, is the primary focus of our research. We achieve enhanced accuracy in caption generation by utilizing a Transformer encoder during training. The MSCOCO dataset serves as a valuable The results of the model are translated into speech for the benefit of the visually handicapped. Keywords—CNN, Google Text To Speech, MS-COCO, Inspection v3.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"87 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101523","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}
Web servers play a crucial role in delivering web content efficiently to users. When a web server receives a request, it processes the request for the requested resources. One method to optimize this process is through the use of cache servers. Cache servers store frequently accessed data in memory, reducing the need to retrieve data from the original source each time a request is made. By leveraging cache servers, web servers can significantly enhance their performance by reducing response times. When a cache server successfully serves a request with a cache hit, it eliminates the need for the web server to process the request and retrieve the data, thereby speeding up the response time. This efficiency is crucial for improving user experience, as faster response times lead to quicker loading of web pages and reduced latency. Through this technical exploration of Apache, Nginx, and Varnish cache servers, we aim to analyze and compare their performance metrics to determine the most effective solution for reducing response times and optimizing web server efficiency. Understanding how cache servers impact performance in terms of cache hit ratio, cache miss ratio, client connections, CPU usage, memory usage, error rates, requests per second, and bandwidth will provide valuable insights into selecting the best cache server solution for improved web server performance. Keywords— Cache Server, Cache, Response time, Apache, Nginx, Varnish.
{"title":"Decoding Efficiency: A Technical Exploration of Apache, Nginx and Varnish Cache Server through Comprehensive Performance Metrics","authors":"Sakshi. S. Sawant","doi":"10.55041/ijsrem34568","DOIUrl":"https://doi.org/10.55041/ijsrem34568","url":null,"abstract":"Web servers play a crucial role in delivering web content efficiently to users. When a web server receives a request, it processes the request for the requested resources. One method to optimize this process is through the use of cache servers. Cache servers store frequently accessed data in memory, reducing the need to retrieve data from the original source each time a request is made. By leveraging cache servers, web servers can significantly enhance their performance by reducing response times. When a cache server successfully serves a request with a cache hit, it eliminates the need for the web server to process the request and retrieve the data, thereby speeding up the response time. This efficiency is crucial for improving user experience, as faster response times lead to quicker loading of web pages and reduced latency. Through this technical exploration of Apache, Nginx, and Varnish cache servers, we aim to analyze and compare their performance metrics to determine the most effective solution for reducing response times and optimizing web server efficiency. Understanding how cache servers impact performance in terms of cache hit ratio, cache miss ratio, client connections, CPU usage, memory usage, error rates, requests per second, and bandwidth will provide valuable insights into selecting the best cache server solution for improved web server performance. Keywords— Cache Server, Cache, Response time, Apache, Nginx, Varnish.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"4 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100347","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 today's increasingly competitive and complex business environment, organizations are placing greater emphasis on meeting their talent management needs. Attracting and retaining the right talent has become paramount, leading organizations to develop and project a brand image that reflects their values and philosophy. This process, known as employer branding, draws on principles from marketing and branding literature to position the organization as an employer of choice. Employer branding not only helps attract experienced employees but also serves as an enabler for internal brand building efforts within the organization. By creating a positive and compelling brand image, organizations can cultivate brand ambassadors who further enhance the organization's reputation as an employer of choice. Successfully managing employer branding requires a comprehensive approach and the commitment of all stakeholders within the organization. When executed effectively, employer branding can have a significant impact on talent management outcomes. This paper reviews existing literature to understand the influence of employer branding on talent management, explores strategies for branding organizations, and examines how global organizations leverage effective branding to attract and retain top talent.
{"title":"Employer Brand Building for Effective Talent Management","authors":"Raunak Bothra","doi":"10.55041/ijsrem34549","DOIUrl":"https://doi.org/10.55041/ijsrem34549","url":null,"abstract":"In today's increasingly competitive and complex business environment, organizations are placing greater emphasis on meeting their talent management needs. Attracting and retaining the right talent has become paramount, leading organizations to develop and project a brand image that reflects their values and philosophy. This process, known as employer branding, draws on principles from marketing and branding literature to position the organization as an employer of choice. Employer branding not only helps attract experienced employees but also serves as an enabler for internal brand building efforts within the organization. By creating a positive and compelling brand image, organizations can cultivate brand ambassadors who further enhance the organization's reputation as an employer of choice. Successfully managing employer branding requires a comprehensive approach and the commitment of all stakeholders within the organization. When executed effectively, employer branding can have a significant impact on talent management outcomes. This paper reviews existing literature to understand the influence of employer branding on talent management, explores strategies for branding organizations, and examines how global organizations leverage effective branding to attract and retain top talent.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"37 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141102339","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}
People with normal vision can easily see the world around them and can read and write without difficulty. For those who are visually impaired, the Braille script enables them to read and write just like sighted individuals. According to WHO data from 2023, about 15 million people worldwide have significant vision loss. The Braille system uses cells with six raised dots, each dot numbered from one to six, arranged in two columns. This system is crucial for visually impaired individuals to keep up with the world around them. Providing Braille-assisted technology and incorporating it into daily life is essential to make life more comfortable and efficient for visually impaired people, enabling better communication with others. Key Words: Braille script, OBR.
{"title":"Smart Book Reader for Visual Impairment Person Using IoT Device and Deep Learning","authors":"Narendra Kumar S","doi":"10.55041/ijsrem34565","DOIUrl":"https://doi.org/10.55041/ijsrem34565","url":null,"abstract":"People with normal vision can easily see the world around them and can read and write without difficulty. For those who are visually impaired, the Braille script enables them to read and write just like sighted individuals. According to WHO data from 2023, about 15 million people worldwide have significant vision loss. The Braille system uses cells with six raised dots, each dot numbered from one to six, arranged in two columns. This system is crucial for visually impaired individuals to keep up with the world around them. Providing Braille-assisted technology and incorporating it into daily life is essential to make life more comfortable and efficient for visually impaired people, enabling better communication with others. Key Words: Braille script, OBR.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"42 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141102247","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}
Facial analysis, encompassing emotion, age, and gender detection, shows potential in various applications such as human-computer interaction, business, security, and health. This study delves into the development and evaluation of a deep neural network (DNN) model for facial emotion, age, and gender detection. Utilizing a convolutional neural network (CNN) architecture trained on diverse datasets for each task, our model proves effective in predicting facial features. The accuracy of needs assessment is X%, the marginal error (MAE) of age estimation is Y years, and the accuracy of gender classification is Z%.
面部分析包括情感、年龄和性别检测，在人机交互、商业、安全和健康等各种应用领域都显示出潜力。本研究深入探讨了用于面部情绪、年龄和性别检测的深度神经网络（DNN）模型的开发和评估。我们的模型利用卷积神经网络（CNN）架构，在不同任务的数据集上进行训练，证明能有效预测面部特征。需求评估的准确率为 X%，年龄估计的边际误差（MAE）为 Y 年，性别分类的准确率为 Z%。
{"title":"FACIAL IMAGE CAPTIONING USING DNN","authors":"Vijayalakshmi B","doi":"10.55041/ijsrem34576","DOIUrl":"https://doi.org/10.55041/ijsrem34576","url":null,"abstract":"Facial analysis, encompassing emotion, age, and gender detection, shows potential in various applications such as human-computer interaction, business, security, and health. This study delves into the development and evaluation of a deep neural network (DNN) model for facial emotion, age, and gender detection. Utilizing a convolutional neural network (CNN) architecture trained on diverse datasets for each task, our model proves effective in predicting facial features. The accuracy of needs assessment is X%, the marginal error (MAE) of age estimation is Y years, and the accuracy of gender classification is Z%.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"22 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141102858","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 project, titled “Smart Traffic Signaling using Machine Learning and IoT," introduces an innovative solution for optimizing traffic signal control. By harnessing the power of image processing, IoT, and machine learning, this project will be a real-time system that accurately assesses vehicle density at intersections. The project focuses on training a machine learning model to recognize various vehicle types, including bikes, cars, trucks, and heavy vehicles. This adaptive control mechanism aims to enhance traffic flow efficiency, reduce congestion, and contribute to the advancement of intelligent transportation systems. systems. Key Words: Machine learning, IoT, Image processing, Smart Traffic.
{"title":"Smart Traffic Signaling Using Machine Learning and IoT","authors":"Sandeep B","doi":"10.55041/ijsrem34607","DOIUrl":"https://doi.org/10.55041/ijsrem34607","url":null,"abstract":"The project, titled “Smart Traffic Signaling using Machine Learning and IoT,\" introduces an innovative solution for optimizing traffic signal control. By harnessing the power of image processing, IoT, and machine learning, this project will be a real-time system that accurately assesses vehicle density at intersections. The project focuses on training a machine learning model to recognize various vehicle types, including bikes, cars, trucks, and heavy vehicles. This adaptive control mechanism aims to enhance traffic flow efficiency, reduce congestion, and contribute to the advancement of intelligent transportation systems. systems. Key Words: Machine learning, IoT, Image processing, Smart Traffic.","PeriodicalId":13661,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"13 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141100490","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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