In the dynamic landscape of marketing, understanding the intricate relationship between advertising and consumer behavior remains pivotal for businesses aiming to thrive. This study delves into the multifaceted impact of advertising on consumer buying behavior, exploring various dimensions such as cognitive, affective, and behavioral responses. Through a comprehensive review of existing literature and empirical evidence, this paper elucidates the mechanisms through which advertising influences consumer perceptions, attitudes, and ultimately purchasing decisions. Factors such as message content, media channels, and consumercharacteristics are examined to unveil the nuances shaping the effectiveness of advertisingcampaigns. Additionally, emerging trends in digital advertising and the advent of personalized marketing strategies are analyzed to discern their implications on consumer behavior. By synthesizing theoretical insights with practical implications, this research contributes to a deeper understanding of the role of advertising in shaping consumer preferences and offers
{"title":"IMPACT OFADVERTISING ON CONSUMER BUYING BEHAVIOUR","authors":"Priyanshu Priya","doi":"10.55041/ijsrem36828","DOIUrl":"https://doi.org/10.55041/ijsrem36828","url":null,"abstract":"In the dynamic landscape of marketing, understanding the intricate relationship between advertising and consumer behavior remains pivotal for businesses aiming to thrive. This study delves into the multifaceted impact of advertising on consumer buying behavior, exploring various dimensions such as cognitive, affective, and behavioral responses. Through a comprehensive review of existing literature and empirical evidence, this paper elucidates the mechanisms through which advertising influences consumer perceptions, attitudes, and ultimately purchasing decisions. Factors such as message content, media channels, and consumercharacteristics are examined to unveil the nuances shaping the effectiveness of advertisingcampaigns. Additionally, emerging trends in digital advertising and the advent of personalized marketing strategies are analyzed to discern their implications on consumer behavior. By synthesizing theoretical insights with practical implications, this research contributes to a deeper understanding of the role of advertising in shaping consumer preferences and offers","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"68 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808314","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}
Gangadharayya Vh, Abhishek Dc, Naveen Nb, Dr.Md.Irshad Hussain B
Predicting house prices is an important research and application area in the fields of real estate economics and social sciences. This study uses statistics that include various characteristics of houses, such as location, size, age and quality, to create a method for estimating house prices. Accurate predictions are achieved through powerful data processing, feature selection and modeling techniques, including background analysis and machine learning algorithms. The results show that factors such as location, size, and neighborhood characteristics have a significant impact on home prices. Additionally, research shows that advanced techniques such as geographic analysis and economic analysis are used to improve forecast accuracy. The findings underscore the importance of using accurate statistics and analytical methods to predict house prices, providing valuable information to stakeholders in real estate investment, urban planning and policy making. This retrospective focuses on summarizing the methodology, key findings and conclusions of research in the field of house price forecasting. Adjustments may be made based on the specific results and methods used in a particular study Keyword: House Price Prediction, Machine Learning.
{"title":"House Price Prediction Using Machine Learning","authors":"Gangadharayya Vh, Abhishek Dc, Naveen Nb, Dr.Md.Irshad Hussain B","doi":"10.55041/ijsrem36791","DOIUrl":"https://doi.org/10.55041/ijsrem36791","url":null,"abstract":"Predicting house prices is an important research and application area in the fields of real estate economics and social sciences. This study uses statistics that include various characteristics of houses, such as location, size, age and quality, to create a method for estimating house prices. Accurate predictions are achieved through powerful data processing, feature selection and modeling techniques, including background analysis and machine learning algorithms. The results show that factors such as location, size, and neighborhood characteristics have a significant impact on home prices. Additionally, research shows that advanced techniques such as geographic analysis and economic analysis are used to improve forecast accuracy. The findings underscore the importance of using accurate statistics and analytical methods to predict house prices, providing valuable information to stakeholders in real estate investment, urban planning and policy making. This retrospective focuses on summarizing the methodology, key findings and conclusions of research in the field of house price forecasting. Adjustments may be made based on the specific results and methods used in a particular study Keyword: House Price Prediction, Machine Learning.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"53 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807528","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}
As the internet continues to evolve, robust security measures are becoming increasingly vital at every stage. One crucial component, the Domain Name System (DNS), plays a pivotal role in helping users access websites. However, its lack of inherent security mechanisms makes it vulnerable to exploitation. Unsecured DNS can be manipulated, leading to threats like DNS tunneling, hijacking, and cache poisoning. To address these vulnerabilities, DNSSEC (Domain Name System Security Extension) offers a critical layer of security for a safer DNS system. Keywords: DNS, DNSSEC, Tunneling, Cache poisoning, Hijacking
随着互联网的不断发展，强大的安全措施在每个阶段都变得越来越重要。域名系统（DNS）是其中一个关键组件，在帮助用户访问网站方面发挥着举足轻重的作用。然而，由于缺乏固有的安全机制，它很容易被利用。不安全的 DNS 可能会被操纵，从而导致 DNS 隧道、劫持和缓存中毒等威胁。为了解决这些漏洞，DNSSEC（域名系统安全扩展）为更安全的 DNS 系统提供了一个关键的安全层。关键词：DNSDNS、DNSSEC、隧道、缓存中毒、劫持
{"title":"Enhancing Cyber Defense: A Comprehensive Study of DNS Security","authors":"Vaishnavi Gulhane, Dr.R.S Bansode","doi":"10.55041/ijsrem36765","DOIUrl":"https://doi.org/10.55041/ijsrem36765","url":null,"abstract":"As the internet continues to evolve, robust security measures are becoming increasingly vital at every stage. One crucial component, the Domain Name System (DNS), plays a pivotal role in helping users access websites. However, its lack of inherent security mechanisms makes it vulnerable to exploitation. Unsecured DNS can be manipulated, leading to threats like DNS tunneling, hijacking, and cache poisoning. To address these vulnerabilities, DNSSEC (Domain Name System Security Extension) offers a critical layer of security for a safer DNS system. Keywords: DNS, DNSSEC, Tunneling, Cache poisoning, Hijacking","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"16 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808941","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 purpose of this research is to evaluate the bond strength between various combinations of bituminous layers in a laboratory setting. The bond between these layers is critical for the overall performance of pavement under traffic stresses. Bituminous tack coatings are widely used to improve interlayer adherence. This study examines two layer combinations: bituminous concrete (BC) on dense bituminous macadam (DBM) and semi-dense bituminous concrete (SDBC) on BM. A variety of tack coat materials are employed, including bitumen, Cationic Rapid Setting with low viscosity (CRS-1), and Cationic Medium Setting with high viscosity (CMS-2) emulsions. Bond strength is tested on cylindrical specimens at typical service temperatures (25°C, 30°C, 35°C, and 40°C) and various tack coat application rates. The testing procedure follows the standard Marshall Procedure, where the tack coat is applied, and the top layer is suitably covered in the same mould. The bond strength between layers is then evaluated using a specially designed attachment known as the "bond strength device," which is connected to the loading frame of the Modified Marshall Testing Apparatus. The results indicate that interlayer bond strength is influenced by test temperature, with a reduction observed as temperature increases. The type of tack coat and the specific layer combination also affect binding strength. The required amount of tack coat varies depending on the tack coat type and layer combination. Overall, this study provides insights into improving the bond between bituminous layers in pavements, thereby enhancing their performance and durability under traffic-induced stresses. Keywords: Interlayer bond strength, Tack coat, Bituminous layer combination, Bond strength device.
{"title":"Experimental Analysis of Bond Strength between Bituminous Paving Layers in Laboratory Settings","authors":"Rajendra Tigga, Durgesh Kumar Sahu","doi":"10.55041/ijsrem36771","DOIUrl":"https://doi.org/10.55041/ijsrem36771","url":null,"abstract":"The purpose of this research is to evaluate the bond strength between various combinations of bituminous layers in a laboratory setting. The bond between these layers is critical for the overall performance of pavement under traffic stresses. Bituminous tack coatings are widely used to improve interlayer adherence. This study examines two layer combinations: bituminous concrete (BC) on dense bituminous macadam (DBM) and semi-dense bituminous concrete (SDBC) on BM. A variety of tack coat materials are employed, including bitumen, Cationic Rapid Setting with low viscosity (CRS-1), and Cationic Medium Setting with high viscosity (CMS-2) emulsions. Bond strength is tested on cylindrical specimens at typical service temperatures (25°C, 30°C, 35°C, and 40°C) and various tack coat application rates. The testing procedure follows the standard Marshall Procedure, where the tack coat is applied, and the top layer is suitably covered in the same mould. The bond strength between layers is then evaluated using a specially designed attachment known as the \"bond strength device,\" which is connected to the loading frame of the Modified Marshall Testing Apparatus. The results indicate that interlayer bond strength is influenced by test temperature, with a reduction observed as temperature increases. The type of tack coat and the specific layer combination also affect binding strength. The required amount of tack coat varies depending on the tack coat type and layer combination. Overall, this study provides insights into improving the bond between bituminous layers in pavements, thereby enhancing their performance and durability under traffic-induced stresses. Keywords: Interlayer bond strength, Tack coat, Bituminous layer combination, Bond strength device.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"76 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808101","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}
With the Internet of Things (IoT) gradually evolving as the subsequent phase of the evolution of the Internet, It becomes crucial to recognize the various potential domains for. Application of IoT, and the research challenges that are associated with these applications. Ranging from smart cities, to health care, smart agriculture, logistics and retail, to even smart living and smart environments IoT is expected to infiltrate into virtually all aspects of daily life. Even though the current IoT enabling technologies have greatly improved in the recent years, there are still numerous problems that require attention. Since the IoT concept ensues from heterogeneous technologies, many research challenges are bound to arise. The fact that IoT is so expansive and affects practically all areas of our lives, makes it a significant research topic for studies in various related fields such as Information technology and computer science. Thus, IoT is paving the way for new dimensions of research to be carried out. This paper presents the recent development of IoT technologies and discusses future applications and research challenges. Keywords—Internet of Things; IoT applications; IoT Challenges; future technologies; smart cities; smart environment; Smart agriculture; smart living.
{"title":"Internet of Things (IOT): Research Challenges and Future Applications","authors":"Santosh Kumar Pradhan, Kamalkanta Shaw","doi":"10.55041/ijsrem36555","DOIUrl":"https://doi.org/10.55041/ijsrem36555","url":null,"abstract":"With the Internet of Things (IoT) gradually evolving as the subsequent phase of the evolution of the Internet, It becomes crucial to recognize the various potential domains for. Application of IoT, and the research challenges that are associated with these applications. Ranging from smart cities, to health care, smart agriculture, logistics and retail, to even smart living and smart environments IoT is expected to infiltrate into virtually all aspects of daily life. Even though the current IoT enabling technologies have greatly improved in the recent years, there are still numerous problems that require attention. Since the IoT concept ensues from heterogeneous technologies, many research challenges are bound to arise. The fact that IoT is so expansive and affects practically all areas of our lives, makes it a significant research topic for studies in various related fields such as Information technology and computer science. Thus, IoT is paving the way for new dimensions of research to be carried out. This paper presents the recent development of IoT technologies and discusses future applications and research challenges. Keywords—Internet of Things; IoT applications; IoT Challenges; future technologies; smart cities; smart environment; Smart agriculture; smart living.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"30 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809200","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}
Abstract—Developing systems that can automatically recognize and interpret human emotions from facial expressions is the aim of the quickly expanding field of facial emotion identification and detection research. This technology finds applications in a wide range of areas, including as healthcare, marketing, security, and human-computer interface. Using computer vision and machine learning algorithms, facial emotion recognition systems analyze a face’s features and classify it into numerous emotional categories, including joyful, sorrowful, angry, fearful, and surprised.The three steps in the multi-step process that goes into identifying facial emotions are face detection, facial feature extraction, and emotion categorization. Thanks to recent advances in deep learn- ing, facial emotion detection systems can now identify emotions with high resilience and precision. Further, the development of real-time face expression recognition systems has opened up new avenues for applications such as sentiment analysis, emotional intelligence, and affective computing. This technology could fundamentally alter human-machine interactions and open the way to more compassionate and personalized relationships.A multitude of applications, including virtual assistants, mental health aids, and human-centered technology, will be greatly impacted by the development of systems for identifying and de- tecting facial expressions. Artificial intelligence (AI) technologies that recognize emotions allow people to interact with the digital world in more intelligent and flexible ways. But the complexity of emotion identification lies in the fact that it requires context and geometric elements in addition to facial expressions.
{"title":"Advanced Techniques for Real-time Facial Expression Recognition Using Deep Learning","authors":"Bhoomika J, Nagesh B S","doi":"10.55041/ijsrem36731","DOIUrl":"https://doi.org/10.55041/ijsrem36731","url":null,"abstract":"Abstract—Developing systems that can automatically recognize and interpret human emotions from facial expressions is the aim of the quickly expanding field of facial emotion identification and detection research. This technology finds applications in a wide range of areas, including as healthcare, marketing, security, and human-computer interface. Using computer vision and machine learning algorithms, facial emotion recognition systems analyze a face’s features and classify it into numerous emotional categories, including joyful, sorrowful, angry, fearful, and surprised.The three steps in the multi-step process that goes into identifying facial emotions are face detection, facial feature extraction, and emotion categorization. Thanks to recent advances in deep learn- ing, facial emotion detection systems can now identify emotions with high resilience and precision. Further, the development of real-time face expression recognition systems has opened up new avenues for applications such as sentiment analysis, emotional intelligence, and affective computing. This technology could fundamentally alter human-machine interactions and open the way to more compassionate and personalized relationships.A multitude of applications, including virtual assistants, mental health aids, and human-centered technology, will be greatly impacted by the development of systems for identifying and de- tecting facial expressions. Artificial intelligence (AI) technologies that recognize emotions allow people to interact with the digital world in more intelligent and flexible ways. But the complexity of emotion identification lies in the fact that it requires context and geometric elements in addition to facial expressions.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809275","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}
Tires are crucial components of vehicles, continuously in contact with the road. Monitoring tire conditions is vital for safety and performance, as degradation in tire treads and sidewalls can affect traction, fuel efficiency, longevity, and road noise. This research leverages both VGG19 and Efficient Net B7 algorithms to enhance tire image rendering, addressing limitations of traditional techniques. Using a binary classification algorithm, we classify tire images as healthy or cracked. By fine-tuning VGG19 and EfficientNet B7 on a specialized tire dataset, we achieve high-quality, photorealistic renderings. Our results demonstrate remarkable improvements in texture quality and visual realism compared to traditional methods. The rendered images exhibit finer details and more accurate representations of the tire’s tread patterns and material properties. This research contributes to the field of computer graphics by presenting a novel application of deep learning techniques to a specific industrial need, paving the way for future advancements in high-quality rendering of complex tire textures. Key Words: VGG19,Photorealistic rendering, deep learning.
{"title":"Tire Texture Monitoring (VGG 19 VS Efficient Net b7)","authors":"Saloni Jain1, Sunita GP2, Sampath Kumar S3","doi":"10.55041/ijsrem36751","DOIUrl":"https://doi.org/10.55041/ijsrem36751","url":null,"abstract":"Tires are crucial components of vehicles, continuously in contact with the road. Monitoring tire conditions is vital for safety and performance, as degradation in tire treads and sidewalls can affect traction, fuel efficiency, longevity, and road noise. This research leverages both VGG19 and Efficient Net B7 algorithms to enhance tire image rendering, addressing limitations of traditional techniques. Using a binary classification algorithm, we classify tire images as healthy or cracked. By fine-tuning VGG19 and EfficientNet B7 on a specialized tire dataset, we achieve high-quality, photorealistic renderings. Our results demonstrate remarkable improvements in texture quality and visual realism compared to traditional methods. The rendered images exhibit finer details and more accurate representations of the tire’s tread patterns and material properties. This research contributes to the field of computer graphics by presenting a novel application of deep learning techniques to a specific industrial need, paving the way for future advancements in high-quality rendering of complex tire textures. Key Words: VGG19,Photorealistic rendering, deep learning.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"41 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809749","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 study explores coping mechanisms and stress management techniques utilized by working women, focusing on their effectiveness in mitigating work-related stress. The research aims to identify common stressors and evaluate coping strategies, such as time management, physical exercise, social support, mindfulness, and professional counseling. By employing a mixed-methods approach, the study combines quantitative surveys and qualitative interviews to gather comprehensive data. The findings indicate that effective stress management techniques significantly improve mental health and job performance. Additionally, the study highlights the importance of organizational support in fostering a healthy work environment. The results contribute to the understanding of gender- specific stress management and offer practical insights for organizations to support their female employees better. Keywords: Stress Management, Coping Mechanisms, Working Women, Mental Health, Organizational Support, Mixed-Methods.
{"title":"Exploring Coping Mechanisms: A Study on Stress Management Techniques for Working Women","authors":"Origanti Shalini","doi":"10.55041/ijsrem36756","DOIUrl":"https://doi.org/10.55041/ijsrem36756","url":null,"abstract":"This study explores coping mechanisms and stress management techniques utilized by working women, focusing on their effectiveness in mitigating work-related stress. The research aims to identify common stressors and evaluate coping strategies, such as time management, physical exercise, social support, mindfulness, and professional counseling. By employing a mixed-methods approach, the study combines quantitative surveys and qualitative interviews to gather comprehensive data. The findings indicate that effective stress management techniques significantly improve mental health and job performance. Additionally, the study highlights the importance of organizational support in fostering a healthy work environment. The results contribute to the understanding of gender- specific stress management and offer practical insights for organizations to support their female employees better. Keywords: Stress Management, Coping Mechanisms, Working Women, Mental Health, Organizational Support, Mixed-Methods.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"30 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807762","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 "AI Based Multilingual Chatbot: Advancing Higher Education in Rural Communities" addresses the pressing need for educational support in rural areas. With advancements in AI and natural language processing (NLP), chatbots have emerged as promising tools to bridge educational gaps. However, existing solutions often lack multilingual support and fail to cater to the unique needs of rural communities. This project aims to develop a multilingual chatbot tailored specifically for rural contexts, enabling access to educational resources and support in local languages. By leveraging AI and NLP technologies, the chatbot will provide personalized assistance to rural students, empowering them to pursue higher education aspirations. Through this project, we aim to address the challenges faced by rural communities in accessing quality education and contribute to their educational advancement. The methodology of the project involves leveraging machine learning algorithms and natural language processing techniques to develop the chatbot. Objectives include designing a user-friendly interface, implementing multilingual support, and ensuring robust data storage and retrieval. Design and experimental tools such as Python, Flask, NLTK, and scikit-learn will be utilized. Key specifications include handling diverse user queries, ensuring data security, and optimizing response generation algorithms. The sequence involves initial data gathering, followed by algorithm development, system testing, and iterative refinement based on user feedback. The key findings of the project showcase significant improvements in user interaction and system performance. Experimental data demonstrates high accuracy in query processing and response generation. The developed chatbot exhibits a working efficiency of over 90%, as indicated by user satisfaction surveys and performance metrics. These outcomes validate the effectiveness of the implemented methodologies and design choices. Key Words: User Experience, Product Recommendation, Neural Network (CNN’s)
{"title":"AI Based Multilingual Chatbot: Advancing Higher Education in Rural Communities","authors":"Chethan K, Preethi K P","doi":"10.55041/ijsrem36726","DOIUrl":"https://doi.org/10.55041/ijsrem36726","url":null,"abstract":"The project \"AI Based Multilingual Chatbot: Advancing Higher Education in Rural Communities\" addresses the pressing need for educational support in rural areas. With advancements in AI and natural language processing (NLP), chatbots have emerged as promising tools to bridge educational gaps. However, existing solutions often lack multilingual support and fail to cater to the unique needs of rural communities. This project aims to develop a multilingual chatbot tailored specifically for rural contexts, enabling access to educational resources and support in local languages. By leveraging AI and NLP technologies, the chatbot will provide personalized assistance to rural students, empowering them to pursue higher education aspirations. Through this project, we aim to address the challenges faced by rural communities in accessing quality education and contribute to their educational advancement. The methodology of the project involves leveraging machine learning algorithms and natural language processing techniques to develop the chatbot. Objectives include designing a user-friendly interface, implementing multilingual support, and ensuring robust data storage and retrieval. Design and experimental tools such as Python, Flask, NLTK, and scikit-learn will be utilized. Key specifications include handling diverse user queries, ensuring data security, and optimizing response generation algorithms. The sequence involves initial data gathering, followed by algorithm development, system testing, and iterative refinement based on user feedback. The key findings of the project showcase significant improvements in user interaction and system performance. Experimental data demonstrates high accuracy in query processing and response generation. The developed chatbot exhibits a working efficiency of over 90%, as indicated by user satisfaction surveys and performance metrics. These outcomes validate the effectiveness of the implemented methodologies and design choices. Key Words: User Experience, Product Recommendation, Neural Network (CNN’s)","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"56 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808961","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, titled "Navigating the Frontier: Benefits and Limitations of AI in Animation," explains the transformative role of artificial intelligence (AI) within the animation industry and also shows how it began. Beginning with a synoptic overview of the evolution of animation technology, the paper unfolds the journey from early hand-drawn animations to contemporary techniques such as computer-generated imagery (CGI) and motion capture and many more. The discussion then shifts to the advantages of AI in animation. Key benefits include enhanced efficiency through automation, increased realism via AI-driven simulations, and cost reductions from optimized production workflows. AI also aids in fostering creativity, ensuring consistency, and maintaining high quality in animation projects. On the flip side, the paper addresses several limitations associated with AI in animation. These include the high initial investment required for AI implementation, the dependence on high-quality data, and potential constraints on artistic control. Ethical concerns such as copyright issues and job displacement are also explored, alongside the challenges of integrating AI tools into established workflows and the current limitations of AI algorithms in capturing subtle human expressions. Using case studies and real-world examples, the paper illustrates both successful applications of AI in animation and the associated challenges. It concludes with a discussion on future prospects for AI in animation, offering recommendations for studios on how to balance technological advancements with maintaining artistic integrity. KEYWORDS Artificial Intelligence (AI), Animation Technology, AI in Animation, Computer-Generated Imagery (CGI), Motion Capture, Automation in Animation, Realism in Animation, AI-Driven Simulations, Animation Efficiency, Creative Assistance, Cost Reduction in Animation, Ethical Considerations in AI, AI Integration.
{"title":"The AI Evolution in Animation: Balancing Technology and Artistic Integrity","authors":"Prerana V","doi":"10.55041/ijsrem36792","DOIUrl":"https://doi.org/10.55041/ijsrem36792","url":null,"abstract":"This paper, titled \"Navigating the Frontier: Benefits and Limitations of AI in Animation,\" explains the transformative role of artificial intelligence (AI) within the animation industry and also shows how it began. Beginning with a synoptic overview of the evolution of animation technology, the paper unfolds the journey from early hand-drawn animations to contemporary techniques such as computer-generated imagery (CGI) and motion capture and many more. The discussion then shifts to the advantages of AI in animation. Key benefits include enhanced efficiency through automation, increased realism via AI-driven simulations, and cost reductions from optimized production workflows. AI also aids in fostering creativity, ensuring consistency, and maintaining high quality in animation projects. On the flip side, the paper addresses several limitations associated with AI in animation. These include the high initial investment required for AI implementation, the dependence on high-quality data, and potential constraints on artistic control. Ethical concerns such as copyright issues and job displacement are also explored, alongside the challenges of integrating AI tools into established workflows and the current limitations of AI algorithms in capturing subtle human expressions. Using case studies and real-world examples, the paper illustrates both successful applications of AI in animation and the associated challenges. It concludes with a discussion on future prospects for AI in animation, offering recommendations for studios on how to balance technological advancements with maintaining artistic integrity. KEYWORDS Artificial Intelligence (AI), Animation Technology, AI in Animation, Computer-Generated Imagery (CGI), Motion Capture, Automation in Animation, Realism in Animation, AI-Driven Simulations, Animation Efficiency, Creative Assistance, Cost Reduction in Animation, Ethical Considerations in AI, AI Integration.","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"46 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809705","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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