International Journal of Innovative Research in Computer Science and Technology最新文献

Lithium Replacement Potato Sheets For Future Batteries 未来电池的锂替代土豆片

International Journal of Innovative Research in Computer Science and Technology Pub Date : 2024-07-01 DOI: 10.55524/ijircst.2024.12.4.12

Indri Dayana, Habib Satria

引用次数: 0

A Comparative Study of Cat Swarm Algorithm for Graph Coloring Problem: Convergence Analysis and Performance Evaluation 图形着色问题的猫群算法比较研究：收敛性分析与性能评估

International Journal of Innovative Research in Computer Science and Technology Pub Date : 2024-07-01 DOI: 10.55524/ijircst.2024.12.4.1

Ayesha Saeed, Ali Husnain, Anam Zahoor, Mehmood Gondal

{"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}

引用次数: 0

A Comparative Study of ChatGPT, Gemini, and Perplexity ChatGPT、Gemini 和 Perplexity 的比较研究

International Journal of Innovative Research in Computer Science and Technology Pub Date : 2024-07-01 DOI: 10.55524/ijircst.2024.12.4.2

Manali Shukla, Ishika Goyal, Bhavya Gupta, Jhanvi Sharma

引用次数: 0

Deep Learning-Based Chip Power Prediction and Optimization: An Intelligent EDA Approach 基于深度学习的芯片功耗预测与优化：智能 EDA 方法

International Journal of Innovative Research in Computer Science and Technology Pub Date : 2024-07-01 DOI: 10.55524/ijircst.2024.12.4.13

Shikai Wang, Kangming Xu, Zhipeng Ling

{"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}

引用次数: 0

Swimmer Safety Alert System for Encounters with Unidentified Marine Aquatic Animals 遇到不明海洋水生动物的游泳者安全警报系统

International Journal of Innovative Research in Computer Science and Technology Pub Date : 2024-07-01 DOI: 10.55524/ijircst.2024.12.4.8

Dr. Zalak Thakrar, Krupal J. Buddhadev, Harsh D. Bhatt, Nakul H. Bhadrecha, Mathan D. Bhogayata

引用次数: 0

Comprehensive Review on Machine Learning Applications in Cloud Computing 云计算中的机器学习应用综述

International Journal of Innovative Research in Computer Science and Technology Pub Date : 2024-07-01 DOI: 10.55524/ijircst.2024.12.4.3

Sampada Zende, Tanisha Singh, Dr. Mahendra Suryavanshi

引用次数: 0

A Comprehensive Review- Building A Secure Social Media Environment for Kids- Automated Content Filtering with Biometric Feedback 全面回顾--为儿童构建安全的社交媒体环境--利用生物识别反馈自动过滤内容

International Journal of Innovative Research in Computer Science and Technology Pub Date : 2024-07-01 DOI: 10.55524/ijircst.2024.12.4.4

Pandya Vishal Kishorchandra, Vadher B, Meghnathi R, Raychura M, Keshwala K.

引用次数: 0

Enhancing Momentum Trading with Macroeconomic Indicators- A Strategic Approach 利用宏观经济指标加强动量交易--一种战略方法

International Journal of Innovative Research in Computer Science and Technology Pub Date : 2024-07-01 DOI: 10.55524/ijircst.2024.12.4.11

Mohit Apte

引用次数: 0

Diffusion Dynamics Applied with Novel Methodologies 用新方法应用扩散动力学

International Journal of Innovative Research in Computer Science and Technology Pub Date : 2024-07-01 DOI: 10.55524/ijircst.2024.12.4.9

Anmol Chauhan, Sana Rabbani, Prof. (Dr.) Devendra Agarwal, Dr. Nikhat Akhtar, D. Perwej

{"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}

引用次数: 0

Exploring the Synergy of Web Usage Data and Content Mining for Personalized Effectiveness 探索网络使用数据与内容挖掘的协同作用，实现个性化效果

International Journal of Innovative Research in Computer Science and Technology Pub Date : 2024-07-01 DOI: 10.55524/ijircst.2024.12.4.5

Ambareen Jameel, Mohd Usman Khan

{"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}

引用次数: 0