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A Comprehensive Review on Deep Learning Approach for Prostate Cancer Gleason Grading 前列腺癌格里森分级深度学习方法综述
International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2023-11-10 DOI: 10.32628/cseit2361046
Mona Chavda, S. Degadwala
{"title":"A Comprehensive Review on Deep Learning Approach for Prostate Cancer Gleason Grading","authors":"Mona Chavda, S. Degadwala","doi":"10.32628/cseit2361046","DOIUrl":"https://doi.org/10.32628/cseit2361046","url":null,"abstract":"This comprehensive review explores the transformative role of deep learning in revolutionizing the diagnosis of prostate cancer through a refined Gleason grading approach. Prostate cancer diagnosis has significantly benefited from advancements in deep learning techniques, enabling more accurate and precise Gleason grading—a critical component in assessing the severity of prostate tumors. The abstract delves into the latest developments in deep learning algorithms and their application to Gleason grading, highlighting the potential to enhance diagnostic accuracy, improve prognostic predictions, and ultimately contribute to more effective treatment strategies for prostate cancer patients. The synthesis of current research findings in this review underscores the pivotal role that deep learning plays in reshaping the landscape of prostate cancer diagnosis and emphasizes the promising future prospects for integrating these innovative technologies into clinical practice.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"45 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139280419","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 Comprehensive Review on Machine Learning Methods for Categorizing Liver Tumors 肝脏肿瘤分类的机器学习方法综述
International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2023-11-10 DOI: 10.32628/cseit2361056
Jalpaben Kandoriya, S. Degadwala
{"title":"A Comprehensive Review on Machine Learning Methods for Categorizing Liver Tumors","authors":"Jalpaben Kandoriya, S. Degadwala","doi":"10.32628/cseit2361056","DOIUrl":"https://doi.org/10.32628/cseit2361056","url":null,"abstract":"This comprehensive review delves into the application of machine learning methods for the categorization of liver tumors, offering a thorough examination of the current landscape in medical imaging and diagnostics. The escalating prevalence of liver tumors necessitates precise and efficient classification methods, and this paper systematically explores the diverse array of machine learning techniques employed in this context. From traditional approaches such as support vector machines and decision trees to more advanced deep learning algorithms, the review synthesizes existing literature to provide a holistic understanding of their strengths, limitations, and comparative performances. Furthermore, the article discusses key challenges in the domain, such as data scarcity and interpretability, proposing potential avenues for future research and innovation. With a focus on bridging the gap between clinical needs and technological advancements, this review contributes valuable insights to the evolving field of medical imaging, offering a roadmap for the development of robust and clinically relevant liver tumor classification systems.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139281264","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 Comprehensive Review on Utilization of Deep Learning for Precipitation Nowcasting via Satellite Data 深度学习在卫星数据降水预报中的应用综述
International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2023-11-10 DOI: 10.32628/cseit2361055
Vedanti Patel, S. Degadwala
{"title":"A Comprehensive Review on Utilization of Deep Learning for Precipitation Nowcasting via Satellite Data","authors":"Vedanti Patel, S. Degadwala","doi":"10.32628/cseit2361055","DOIUrl":"https://doi.org/10.32628/cseit2361055","url":null,"abstract":"This comprehensive review delves into the cutting-edge applications of deep learning techniques for precipitation nowcasting using satellite data. As climate variability and extreme weather events become increasingly prominent, accurate and timely precipitation predictions are essential for effective disaster management and resource allocation. The paper surveys the recent advancements in deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), showcasing their efficacy in processing and analyzing satellite-derived information. The discussion encompasses the challenges associated with satellite data, such as spatiotemporal complexities and data quality issues, and elucidates how deep learning architectures address these hurdles. The review also highlights noteworthy studies, methodologies, and benchmarks in the field, providing a comprehensive overview of the state-of-the-art approaches for precipitation nowcasting through the lens of deep learning applied to satellite data.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"182 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139281551","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 Comprehensive Review on Gujarati-Text Summarization Through Different Features 通过不同特征对古吉拉特语文本进行总结的综合评述
International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2023-11-10 DOI: 10.32628/cseit2361051
Riddhi Kevat, S. Degadwala
{"title":"A Comprehensive Review on Gujarati-Text Summarization Through Different Features","authors":"Riddhi Kevat, S. Degadwala","doi":"10.32628/cseit2361051","DOIUrl":"https://doi.org/10.32628/cseit2361051","url":null,"abstract":"This comprehensive review delves into the intricacies of Gujarati-text summarization, exploring diverse features employed in the process. With a focus on the nuances of the Gujarati language, the paper investigates various techniques and methodologies applied to extract essential information from textual content. The review systematically examines the effectiveness of distinct features such as linguistic, semantic, and syntactic elements in the context of Gujarati summarization. Additionally, the study provides insights into the challenges specific to Gujarati-language summarization and discusses advancements in natural language processing and machine learning that contribute to the refinement of summarization models. This thorough examination serves as a valuable resource for researchers, practitioners, and enthusiasts seeking a deeper understanding of the complexities and advancements in Gujarati-text summarization.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"41 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139280374","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 Comprehensive Review on Monkeypox Skin Lesion Recognition through Deep Learning 通过深度学习识别猴痘皮损的综述
International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2023-11-10 DOI: 10.32628/cseit2361045
Dhwani Jagani, S. Degadwala
{"title":"A Comprehensive Review on Monkeypox Skin Lesion Recognition through Deep Learning","authors":"Dhwani Jagani, S. Degadwala","doi":"10.32628/cseit2361045","DOIUrl":"https://doi.org/10.32628/cseit2361045","url":null,"abstract":"This comprehensive review delves into the emerging field of Monkeypox skin lesion recognition using deep learning techniques. Monkeypox, a rare viral disease with symptoms resembling smallpox, presents a diagnostic challenge, particularly in resource-limited regions. The paper explores the recent advancements in deep learning methodologies applied to the automated identification and classification of Monkeypox skin lesions, offering a detailed analysis of various neural network architectures, image preprocessing techniques, and dataset considerations. The review highlights the potential of deep learning models in enhancing the accuracy and efficiency of Monkeypox diagnosis, paving the way for improved early detection and timely intervention in affected populations. Additionally, it discusses challenges and future directions in this domain, emphasizing the need for robust and interpretable models to facilitate widespread adoption in clinical settings.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"124 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139280438","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 Comprehensive Review on Multi-Class Recognition of Soybean Leaf Diseases 大豆叶片病害多类识别综述
International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2023-11-10 DOI: 10.32628/cseit2361052
Shivani Shelke, S. Degadwala
{"title":"A Comprehensive Review on Multi-Class Recognition of Soybean Leaf Diseases","authors":"Shivani Shelke, S. Degadwala","doi":"10.32628/cseit2361052","DOIUrl":"https://doi.org/10.32628/cseit2361052","url":null,"abstract":"This paper presents a comprehensive review of the current state-of-the-art methodologies in the multi-class recognition of soybean leaf diseases, addressing the challenges faced by soybean cultivation globally. Focusing on diseases like rust, bacterial blight, anthracnose, and powdery mildew, the review encompasses traditional image processing techniques as well as modern advancements in deep learning, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Topics covered include dataset compilation, preprocessing, feature extraction, and the application of various machine learning algorithms. Special emphasis is placed on exploring the potential of transfer learning, domain adaptation, and the integration of spectral imaging and remote sensing technologies for enhanced disease detection. By providing a thorough comparative analysis, this review aims to guide future research efforts, aiding researchers, agronomists, and practitioners in developing robust and scalable solutions to combat soybean leaf diseases and improve global agricultural productivity.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139280857","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
Java in Action : AI for Fraud Detection and Prevention Java 在行动：用于欺诈检测和预防的人工智能
International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2023-11-01 DOI: 10.32628/cseit239063
Bhuman Vyas
{"title":"Java in Action : AI for Fraud Detection and Prevention","authors":"Bhuman Vyas","doi":"10.32628/cseit239063","DOIUrl":"https://doi.org/10.32628/cseit239063","url":null,"abstract":"In today's increasingly digital world, the financial and e-commerce sectors face a growing threat from fraudulent activities. Fraudsters are becoming more sophisticated, making it essential to employ advanced tools and technologies to combat this menace effectively. This paper presents a comprehensive exploration of using Java-based Artificial Intelligence (AI) systems for fraud detection and prevention. Java has long been a trusted choice for building scalable and robust applications, and AI is revolutionizing how businesses safeguard their financial transactions. By combining these two powerful technologies, organizations can develop intelligent systems that analyze vast amounts of data in real time, detect suspicious patterns, and take swift action to prevent fraudulent activities. This paper delves into the principles and techniques of AI, machine learning, and deep learning, demonstrating how these methodologies can be harnessed within the Java ecosystem. We explore the development and deployment of predictive models, anomaly detection algorithms, and behavioral analysis using Java libraries and tools. Moreover, we will discuss the challenges and considerations when implementing AI-driven fraud detection systems, including data privacy, model accuracy, and scalability. By the end of this presentation, the audience will gain valuable insights into how Java-based AI can be a game-changer in the battle against fraud, enhancing the security and trustworthiness of financial and e-commerce platforms. This abstract provides an overview of the paper's content, emphasizing the significance of Java and AI in the context of fraud detection and prevention, and inviting the audience to learn more about the topic.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139295774","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
Shiksha Mitra: An Assamese Language AI Chatbot Using Deep Learning Shiksha Mitra：使用深度学习的阿萨姆语人工智能聊天机器人
International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2023-10-14 DOI: 10.32628/cseit2390572
Surajit Sarma, Nabankur Pathak
{"title":"Shiksha Mitra: An Assamese Language AI Chatbot Using Deep Learning","authors":"Surajit Sarma, Nabankur Pathak","doi":"10.32628/cseit2390572","DOIUrl":"https://doi.org/10.32628/cseit2390572","url":null,"abstract":"This research paper presents “Shiksha Mitra”, an artificial intelligence chatbot that answers user queries in Assamese for educational purposes. The chatbot uses Assamese Natural Language Processing (ANLP) and deep learning techniques to identify relevant sentences and provide responses. Unlike many organizations that use English chatbots, this research aims to develop a data-driven, retrieval-based, closed domain chatbot that can interact with users in Assamese. The chatbot is trained with corpus data encoded in UTF-8 format using a train function adapter. A feedforward neural network is used to find the best match from the corpus and generate a suitable answer.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139319450","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
Enabling An Efficient Smart Grid Infrastructure Through IOT Integration 通过物联网集成实现高效的智能电网基础设施
International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2023-10-12 DOI: 10.32628/cseit2390561
None Sahil Bashir
{"title":"Enabling An Efficient Smart Grid Infrastructure Through IOT Integration","authors":"None Sahil Bashir","doi":"10.32628/cseit2390561","DOIUrl":"https://doi.org/10.32628/cseit2390561","url":null,"abstract":"In the ever-evolving landscape of energy management, the marriage of Smart Grid technology with the Internet of Things (IOT) emerges as a ground breaking paradigm. This visionary approach revolutionizes how we harness and distribute electricity. Imagine a grid that not only delivers power but also communicates dynamically with its components. Smart sensors and advanced computing work in tandem to provide real-time insights, ensuring optimal performance even in times of high demand. Beyond efficiency, this Smart Grid paves the way for seamless integration of renewable energy sources, transforming intermittent energy flows into a stable, sustainable resource. It empowers consumers, offering them personalized insights into their energy consumption patterns, and encouraging informed choices for a greener tomorrow. Yet, this innovation demands diligence in security. Robust protocols fortify against cyber threats, guaranteeing the integrity of critical infrastructure. Moreover, data privacy is held sacrosanct, with transparent handling practices in place. In essence, the Smart Grid with IOT is a catalyst for a more sustainable and efficient energy future. It not only economizes energy usage but also opens doors to a new era of renewable energy integration. With the right safeguards, this transformative technology promises a brighter, greener, and more connected world.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136058554","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
Integrating Solar Heaters with Building Energy Systems : A Simulation Study 集成太阳能加热器与建筑能源系统:模拟研究
International Journal of Scientific Research in Computer Science, Engineering and Information Technology Pub Date : 2023-10-10 DOI: 10.32628/cseit2390564
None Dr. Vipul M Goti
{"title":"Integrating Solar Heaters with Building Energy Systems : A Simulation Study","authors":"None Dr. Vipul M Goti","doi":"10.32628/cseit2390564","DOIUrl":"https://doi.org/10.32628/cseit2390564","url":null,"abstract":"This paper looks at the topic of high-tech solar water heating systems being incorporated into existing building energy infrastructure. Hybrid systems that use solar water heating in conjunction with other renewable energy sources are also discussed, as are technical developments in collector designs, the use of cutting-edge control and monitoring systems, and the like. Reduced carbon emissions and optimized resource utilization are only two of the environmental advantages highlighted in the report. It also emphasizes the need of precise system sizing and regional life cycle assessments (LCAs) in achieving maximum energy efficiency. The paper highlights knowledge gaps in the areas of performance analysis, localized environmental impact studies, integration difficulties, and economic assessments. By filling up these spaces, it hopes to promote more eco-friendly and economical construction methods. Sustainable construction, energy savings, less of an influence on the environment, and new innovations in solar water heating are some of the terms that come to mind.","PeriodicalId":313456,"journal":{"name":"International Journal of Scientific Research in Computer Science, Engineering and Information Technology","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136360345","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
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