Pub Date : 2024-07-01DOI: 10.55524/ijircst.2024.12.4.12
Indri Dayana, Habib Satria
This research aims to this research discusses alternative potato sheets that can replace lithium for future batteries, with a sample of 20 potato experiments. Using laboratory methods with repeated experiments, it was found that the increase in the electrical energy variable was not very significant, around 0.01 Joule.
{"title":"Lithium Replacement Potato Sheets For Future Batteries","authors":"Indri Dayana, Habib Satria","doi":"10.55524/ijircst.2024.12.4.12","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.4.12","url":null,"abstract":"This research aims to this research discusses alternative potato sheets that can replace lithium for future batteries, with a sample of 20 potato experiments. Using laboratory methods with repeated experiments, it was found that the increase in the electrical energy variable was not very significant, around 0.01 Joule.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"127 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141843538","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}
Pub Date : 2024-07-01DOI: 10.55524/ijircst.2024.12.4.1
Ayesha Saeed, Ali Husnain, Anam Zahoor, Mehmood Gondal
The Graph Coloring Problem (GCP) is a significant optimization challenge widely suitable to solve scheduling problems. Its goal is to specify the minimum colors (k) required to color a graph properly. Due to its NP-completeness, exact algorithms become impractical for graphs exceeding 100 vertices. As a result, approximation algorithms have gained prominence for tackling large-scale instances. In this context, the Cat Swarm algorithm, a novel population-based metaheuristic in the domain of swarm intelligence, has demonstrated promising convergence properties compared to other population-based algorithms. This research focuses on designing and implementing the Cat Swarm algorithm to address the GCP. By conducting a comparative study with established algorithms, our investigation revolves around quantifying the minimum value of k required by the Cat Swarm algorithm for each graph instance. The evaluation metrics include the algorithm's running time in seconds, success rate, and the mean count of iterations or assessments required to reach a goal.
{"title":"A Comparative Study of Cat Swarm Algorithm for Graph Coloring Problem: Convergence Analysis and Performance Evaluation","authors":"Ayesha Saeed, Ali Husnain, Anam Zahoor, Mehmood Gondal","doi":"10.55524/ijircst.2024.12.4.1","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.4.1","url":null,"abstract":"The Graph Coloring Problem (GCP) is a significant optimization challenge widely suitable to solve scheduling problems. Its goal is to specify the minimum colors (k) required to color a graph properly. Due to its NP-completeness, exact algorithms become impractical for graphs exceeding 100 vertices. As a result, approximation algorithms have gained prominence for tackling large-scale instances. In this context, the Cat Swarm algorithm, a novel population-based metaheuristic in the domain of swarm intelligence, has demonstrated promising convergence properties compared to other population-based algorithms. This research focuses on designing and implementing the Cat Swarm algorithm to address the GCP. By conducting a comparative study with established algorithms, our investigation revolves around quantifying the minimum value of k required by the Cat Swarm algorithm for each graph instance. The evaluation metrics include the algorithm's running time in seconds, success rate, and the mean count of iterations or assessments required to reach a goal.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"10 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141704026","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}
Generative AI is making buzz all over the globe and has mostly drawn attention due to it's ability to generate variety of content that mimics human behaviour and intelligence along with the ease of access. It comprises of the ability to generate text, images, video, and even audio that are almost unrecognizable from human-created content. Thus there is a huge scope of research in this field due to its vast applicability and motivates this research work. This research work presents comparatively analysis of the three Generative Artificial Intelligence (AI) tool, namely ChatGPT, Gemini, Perplexity AI, based on the content generation, ownership and developing technology, context understanding, transparency, and information retrieval.
生成式人工智能（Generative AI）正在全球范围内引起热议，它之所以备受关注，主要是因为它能够生成各种模仿人类行为和智能的内容，而且易于访问。它能够生成文本、图像、视频甚至音频，而这些内容几乎无法与人类创建的内容相提并论。因此，由于其广泛的适用性，该领域的研究空间巨大，这也是本研究工作的动力所在。本研究工作从内容生成、所有权和开发技术、上下文理解、透明度和信息检索等方面对 ChatGPT、Gemini 和 Perplexity AI 这三种生成式人工智能（AI）工具进行了比较分析。
{"title":"A Comparative Study of ChatGPT, Gemini, and Perplexity","authors":"Manali Shukla, Ishika Goyal, Bhavya Gupta, Jhanvi Sharma","doi":"10.55524/ijircst.2024.12.4.2","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.4.2","url":null,"abstract":"Generative AI is making buzz all over the globe and has mostly drawn attention due to it's ability to generate variety of content that mimics human behaviour and intelligence along with the ease of access. It comprises of the ability to generate text, images, video, and even audio that are almost unrecognizable from human-created content. Thus there is a huge scope of research in this field due to its vast applicability and motivates this research work. This research work presents comparatively analysis of the three Generative Artificial Intelligence (AI) tool, namely ChatGPT, Gemini, Perplexity AI, based on the content generation, ownership and developing technology, context understanding, transparency, and information retrieval.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141698221","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}
Pub Date : 2024-07-01DOI: 10.55524/ijircst.2024.12.4.13
Shikai Wang, Kangming Xu, Zhipeng Ling
This paper explores the integration of deep learning techniques in Electronic Design Automation (EDA) tools, focusing on chip power prediction and optimization. We investigate the application of advanced AI technologies, including attention mechanisms, machine learning, and generative adversarial networks (GANs), to address complex challenges in modern chip design. The study examines the transition from traditional heuristic-based methods to data-driven approaches, highlighting the potential for significant improvements in design efficiency and performance. We present case studies demonstrating the effectiveness of AI-driven EDA tools in functional verification, Quality of Results (QoR) prediction, and Optical Proximity Correction (OPC) layout generation. The research also addresses critical challenges, such as model interpretability and the need for extensive empirical validation. Our findings suggest that AI/ML technologies have the potential to revolutionize EDA workflows, enabling more efficient chip designs and accelerating innovation in the semiconductor industry. The paper concludes by discussing future directions, including the integration of quantum computing and neuromorphic architectures in EDA tools. We emphasize the importance of collaborative research between AI experts and chip designers to fully realize the potential of these technologies and address emerging challenges in advanced node designs.
{"title":"Deep Learning-Based Chip Power Prediction and Optimization: An Intelligent EDA Approach","authors":"Shikai Wang, Kangming Xu, Zhipeng Ling","doi":"10.55524/ijircst.2024.12.4.13","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.4.13","url":null,"abstract":"This paper explores the integration of deep learning techniques in Electronic Design Automation (EDA) tools, focusing on chip power prediction and optimization. We investigate the application of advanced AI technologies, including attention mechanisms, machine learning, and generative adversarial networks (GANs), to address complex challenges in modern chip design. The study examines the transition from traditional heuristic-based methods to data-driven approaches, highlighting the potential for significant improvements in design efficiency and performance. We present case studies demonstrating the effectiveness of AI-driven EDA tools in functional verification, Quality of Results (QoR) prediction, and Optical Proximity Correction (OPC) layout generation. The research also addresses critical challenges, such as model interpretability and the need for extensive empirical validation. Our findings suggest that AI/ML technologies have the potential to revolutionize EDA workflows, enabling more efficient chip designs and accelerating innovation in the semiconductor industry. The paper concludes by discussing future directions, including the integration of quantum computing and neuromorphic architectures in EDA tools. We emphasize the importance of collaborative research between AI experts and chip designers to fully realize the potential of these technologies and address emerging challenges in advanced node designs.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"18 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141844714","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}
Pub Date : 2024-07-01DOI: 10.55524/ijircst.2024.12.4.8
Dr. Zalak Thakrar, Krupal J. Buddhadev, Harsh D. Bhatt, Nakul H. Bhadrecha, Mathan D. Bhogayata
The perilous encounters between swimmers and marine animals pose a significant risk to both human safety and the well-being of aquatic creatures. Every year, a distressing number of swimmers succumb to attacks by marine animals, often with neither party at fault. In response to this ongoing threat, the Swimmer Alert System emerges as a groundbreaking technology aimed at safeguarding both humans and marine life, ensuring their mutual protection without harm to either party. By utilizing advanced sensors and real-time monitoring, this system detects the presence of potentially dangerous marine animals in swimmer-populated areas, alerting both swimmers and authorities to take necessary precautions. Through proactive intervention and awareness, the Swimmer Alert System endeavors to mitigate the frequency of unfortunate incidents, fostering harmonious coexistence between humans and the marine ecosystem. As a result, lives are spared, and ecosystems remain undisturbed, offering a promising solution to a longstanding challenge.
{"title":"Swimmer Safety Alert System for Encounters with Unidentified Marine Aquatic Animals","authors":"Dr. Zalak Thakrar, Krupal J. Buddhadev, Harsh D. Bhatt, Nakul H. Bhadrecha, Mathan D. Bhogayata","doi":"10.55524/ijircst.2024.12.4.8","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.4.8","url":null,"abstract":"The perilous encounters between swimmers and marine animals pose a significant risk to both human safety and the well-being of aquatic creatures. Every year, a distressing number of swimmers succumb to attacks by marine animals, often with neither party at fault. In response to this ongoing threat, the Swimmer Alert System emerges as a groundbreaking technology aimed at safeguarding both humans and marine life, ensuring their mutual protection without harm to either party. By utilizing advanced sensors and real-time monitoring, this system detects the presence of potentially dangerous marine animals in swimmer-populated areas, alerting both swimmers and authorities to take necessary precautions. Through proactive intervention and awareness, the Swimmer Alert System endeavors to mitigate the frequency of unfortunate incidents, fostering harmonious coexistence between humans and the marine ecosystem. As a result, lives are spared, and ecosystems remain undisturbed, offering a promising solution to a longstanding challenge.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"390 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141852556","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}
Pub Date : 2024-07-01DOI: 10.55524/ijircst.2024.12.4.3
Sampada Zende, Tanisha Singh, Dr. Mahendra Suryavanshi
Cloud computing provides on-demand access to a variety of processing, storage, and network resources. Over the past few years, cloud computing has become a widely accepted computing paradigm and one of the fastest-growing model in the IT industry. It turns out to be a new computing evolution after the evolution of mainframe computing, client-server computing and mobile computing. Cloud computing model faces various challenges such as security, resource allocation, load balancing, incast, interoperability. Machine learning is the study of computer algorithms that get better on their own via experience. Algorithms for machine learning are strong analytical techniques that let computers see patterns and help people learn. In this review paper, we present an analysis of various cloud computing issues and machine learning algorithms. Furthermore, we have comprehensively analyzed applications of numerous machine learning algorithms that are used to mitigate a variety of cloud computing issues.
云计算提供对各种处理、存储和网络资源的按需访问。在过去几年里，云计算已成为一种广为接受的计算模式，也是 IT 行业发展最快的模式之一。它是继大型机计算、客户服务器计算和移动计算之后的又一次新的计算进化。云计算模式面临着各种挑战，如安全性、资源分配、负载平衡、不同步、互操作性等。机器学习是一门研究计算机算法的学科，计算机算法会通过经验自我完善。机器学习算法是一种强大的分析技术，能让计算机看到模式并帮助人们学习。在这篇综述论文中，我们分析了各种云计算问题和机器学习算法。此外，我们还全面分析了众多机器学习算法的应用，这些算法用于缓解各种云计算问题。
{"title":"Comprehensive Review on Machine Learning Applications in Cloud Computing","authors":"Sampada Zende, Tanisha Singh, Dr. Mahendra Suryavanshi","doi":"10.55524/ijircst.2024.12.4.3","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.4.3","url":null,"abstract":"Cloud computing provides on-demand access to a variety of processing, storage, and network resources. Over the past few years, cloud computing has become a widely accepted computing paradigm and one of the fastest-growing model in the IT industry. It turns out to be a new computing evolution after the evolution of mainframe computing, client-server computing and mobile computing. Cloud computing model faces various challenges such as security, resource allocation, load balancing, incast, interoperability. Machine learning is the study of computer algorithms that get better on their own via experience. Algorithms for machine learning are strong analytical techniques that let computers see patterns and help people learn. In this review paper, we present an analysis of various cloud computing issues and machine learning algorithms. Furthermore, we have comprehensively analyzed applications of numerous machine learning algorithms that are used to mitigate a variety of cloud computing issues.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"49 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141689817","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}
Pub Date : 2024-07-01DOI: 10.55524/ijircst.2024.12.4.4
Pandya Vishal Kishorchandra, Vadher B, Meghnathi R, Raychura M, Keshwala K.
This review paper is all about how important it is to use smart technology to keep kids safe on social media while helping them learn better. By adding things like better controls for parents, filters that stop bad stuff, and tools that check how kids are feeling, we can make sure they don't run into anything harmful online. In today's world where kids spend a lot of time online, it's super important to make sure they're safe. If social media platforms start using cool new tech like biometric sensors and wearable gadgets, they can create safer spaces for kids to have fun and learn. This paper also talks about why we need to do things ahead of time to deal with problems like spending too much time on screens or seeing things that might not be right for us. By giving practical ideas for researchers, people who make rules, and companies, this paper wants to make sure kids can enjoy the good parts of social media without any worries.
{"title":"A Comprehensive Review- Building A Secure Social Media Environment for Kids- Automated Content Filtering with Biometric Feedback","authors":"Pandya Vishal Kishorchandra, Vadher B, Meghnathi R, Raychura M, Keshwala K.","doi":"10.55524/ijircst.2024.12.4.4","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.4.4","url":null,"abstract":"This review paper is all about how important it is to use smart technology to keep kids safe on social media while helping them learn better. By adding things like better controls for parents, filters that stop bad stuff, and tools that check how kids are feeling, we can make sure they don't run into anything harmful online. In today's world where kids spend a lot of time online, it's super important to make sure they're safe. If social media platforms start using cool new tech like biometric sensors and wearable gadgets, they can create safer spaces for kids to have fun and learn. This paper also talks about why we need to do things ahead of time to deal with problems like spending too much time on screens or seeing things that might not be right for us. By giving practical ideas for researchers, people who make rules, and companies, this paper wants to make sure kids can enjoy the good parts of social media without any worries.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141716000","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}
Pub Date : 2024-07-01DOI: 10.55524/ijircst.2024.12.4.11
Mohit Apte
Traditional momentum trading strategies capitalize on existing market trends but often overlook broader macroeconomic contexts, potentially limiting their effectiveness during periods of economic fluctuation. This paper introduces an enhanced momentum trading strategy that incorporates key economic indicators—GDP, inflation, unemployment rates, and interest rates—to provide a more robust framework capable of adapting to changing economic conditions. By integrating these macroeconomic factors, the strategy aims to improve predictive accuracy and performance stability. Using data from the S&P 600 SmallCap Index, we modified the conventional momentum calculation to include weighted contributions from these indicators, creating a comprehensive 'new momentum' score. Preliminary back testing, comparing this enhanced strategy against traditional methods, shows promising improvements in risk-adjusted returns. This paper not only details the methodology and results of integrating economic indicators into momentum trading but also discusses the implications for risk management and potential areas for future research.
{"title":"Enhancing Momentum Trading with Macroeconomic Indicators- A Strategic Approach","authors":"Mohit Apte","doi":"10.55524/ijircst.2024.12.4.11","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.4.11","url":null,"abstract":"Traditional momentum trading strategies capitalize on existing market trends but often overlook broader macroeconomic contexts, potentially limiting their effectiveness during periods of economic fluctuation. This paper introduces an enhanced momentum trading strategy that incorporates key economic indicators—GDP, inflation, unemployment rates, and interest rates—to provide a more robust framework capable of adapting to changing economic conditions. By integrating these macroeconomic factors, the strategy aims to improve predictive accuracy and performance stability. Using data from the S&P 600 SmallCap Index, we modified the conventional momentum calculation to include weighted contributions from these indicators, creating a comprehensive 'new momentum' score. Preliminary back testing, comparing this enhanced strategy against traditional methods, shows promising improvements in risk-adjusted returns. This paper not only details the methodology and results of integrating economic indicators into momentum trading but also discusses the implications for risk management and potential areas for future research.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"5 9‐10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141849307","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}
Pub Date : 2024-07-01DOI: 10.55524/ijircst.2024.12.4.9
Anmol Chauhan, Sana Rabbani, Prof. (Dr.) Devendra Agarwal, Dr. Nikhat Akhtar, D. Perwej
An in-depth analysis of using stable diffusion models to generate images from text is presented in this research article. Improving generative models' capacity to generate high-quality, contextually appropriate images from textual descriptions is the main focus of this study. By utilizing recent advancements in deep learning, namely in the field of diffusion models, we have created a new system that combines visual and linguistic data to generate aesthetically pleasing and coherent images from given text. To achieve a clear representation that matches the provided textual input, our method employs a stable diffusion process that iteratively reduces a noisy image. This approach differs from conventional generative adversarial networks (GANs) in that it produces more accurate images and has a more consistent training procedure. We use a dual encoder mechanism to successfully record both the structural information needed for picture synthesis and the semantic richness of text. outcomes from extensive trials on benchmark datasets show that our model achieves much better outcomes than current state-of-the-art methods in diversity, text-image alignment, and picture quality. In order to verify the model's efficacy, the article delves into the architectural innovations, training schedule, and assessment criteria used. In addition, we explore other uses for our text-to-image production system, such as for making digital art, content development, and assistive devices for the visually impaired. The research lays the groundwork for future work in this dynamic area by highlighting the technical obstacles faced and the solutions developed. Finally, our text-to-image generation model, which is based on stable diffusion, is a huge step forward for generative models in the field that combines computer vision with natural language processing.
{"title":"Diffusion Dynamics Applied with Novel Methodologies","authors":"Anmol Chauhan, Sana Rabbani, Prof. (Dr.) Devendra Agarwal, Dr. Nikhat Akhtar, D. Perwej","doi":"10.55524/ijircst.2024.12.4.9","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.4.9","url":null,"abstract":"An in-depth analysis of using stable diffusion models to generate images from text is presented in this research article. Improving generative models' capacity to generate high-quality, contextually appropriate images from textual descriptions is the main focus of this study. By utilizing recent advancements in deep learning, namely in the field of diffusion models, we have created a new system that combines visual and linguistic data to generate aesthetically pleasing and coherent images from given text. To achieve a clear representation that matches the provided textual input, our method employs a stable diffusion process that iteratively reduces a noisy image. This approach differs from conventional generative adversarial networks (GANs) in that it produces more accurate images and has a more consistent training procedure. We use a dual encoder mechanism to successfully record both the structural information needed for picture synthesis and the semantic richness of text. outcomes from extensive trials on benchmark datasets show that our model achieves much better outcomes than current state-of-the-art methods in diversity, text-image alignment, and picture quality. In order to verify the model's efficacy, the article delves into the architectural innovations, training schedule, and assessment criteria used. In addition, we explore other uses for our text-to-image production system, such as for making digital art, content development, and assistive devices for the visually impaired. The research lays the groundwork for future work in this dynamic area by highlighting the technical obstacles faced and the solutions developed. Finally, our text-to-image generation model, which is based on stable diffusion, is a huge step forward for generative models in the field that combines computer vision with natural language processing.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"11 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141853696","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}
Pub Date : 2024-07-01DOI: 10.55524/ijircst.2024.12.4.5
Ambareen Jameel, Mohd Usman Khan
In light of the exponential growth of web data and user volume, individuals are increasingly overwhelmed by information overload on the internet. Addressing this challenge, our study focuses on enhancing web information retrieval and presentation by leveraging web data mining techniques to uncover intrinsic relationships within textual, linkage, and usability data. Specifically, we aim to improve the performance of web information retrieval and presentation by analysing web data features. Our approach centres on web usage mining to identify usage patterns and integrate this knowledge with user profiles for personalized content delivery. Personalization, tailored to user’s characteristics and behaviours, serves to enhance engagement, conversion, and long-term commitment to websites. The objective of our research is to develop a web personalization system that enables users to access relevant website content without the need for explicit queries. This paper presents an extensive survey of various approaches proposed by researchers in the field of web personalization. It highlights the diverse methodologies and techniques employed to enhance user experience and engagement on the web. The paper identifies key challenges that require urgent attention to advance the field of web personalization.
{"title":"Exploring the Synergy of Web Usage Data and Content Mining for Personalized Effectiveness","authors":"Ambareen Jameel, Mohd Usman Khan","doi":"10.55524/ijircst.2024.12.4.5","DOIUrl":"https://doi.org/10.55524/ijircst.2024.12.4.5","url":null,"abstract":"In light of the exponential growth of web data and user volume, individuals are increasingly overwhelmed by information overload on the internet. Addressing this challenge, our study focuses on enhancing web information retrieval and presentation by leveraging web data mining techniques to uncover intrinsic relationships within textual, linkage, and usability data. Specifically, we aim to improve the performance of web information retrieval and presentation by analysing web data features. Our approach centres on web usage mining to identify usage patterns and integrate this knowledge with user profiles for personalized content delivery. Personalization, tailored to user’s characteristics and behaviours, serves to enhance engagement, conversion, and long-term commitment to websites. The objective of our research is to develop a web personalization system that enables users to access relevant website content without the need for explicit queries. This paper presents an extensive survey of various approaches proposed by researchers in the field of web personalization. It highlights the diverse methodologies and techniques employed to enhance user experience and engagement on the web. The paper identifies key challenges that require urgent attention to advance the field of web personalization.","PeriodicalId":218345,"journal":{"name":"International Journal of Innovative Research in Computer Science and Technology","volume":"15 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141700808","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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