检索结果-内蒙古大学图书馆

Expert Clouds and Applications (ICOECA), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Thenmozhi K K.V. Shiny T Sujatha Wilfred Blessing N.R Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India Department of Compute Science Kristu Jayanti College (Autonomous) Bengaluru India Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India Department of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Information Technology Department College of Computing and Information Sciences Univesity of Technology and Applied Sciences Ibri Sultanate of Oman

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medical field, particularly in detection and classification tasks. However, existing algorithms often exhibit suboptimal accuracy, necessitating improved methodologies. This research presents a novel approach using a Modified Support Vector Machine (MSVM) for breast cancer classification into benign and malignant categories. Leveraging the Wisconsin Breast Cancer Dataset (WBCD), preprocessing techniques are applied to enhance data quality. Principle Component Analysis (PCA) reduces dimensionality, while linear Discriminant Analysis (LDA) extracts discriminative features crucial for classification. The proposed MSVM classifier achieves exceptional performance, with a classification accuracy of 99.42%, outperforming other existing methods such as Deep Convolutional Neural Networks and Fuzzy Rule-based Systems. These results highlight the efficacy of the MSVM approach in accurately distinguishing between benign and malignant breast cancer cases, showcasing its potential as a reliable tool for medical image analysis and cancer diagnosis.

关键词： Support vector machines Accuracy Sociology Machine learning Feature extraction Breast cancer Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Leveraging Digital Twins and AI for Enhanced Clinical Decision Support in Endometrial Cancer Treatment

Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

引用

IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N. R Cibi. A S. J. Kavitha Steffi S College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Application Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Data & Analytics Visualizaation – Delivery Virtusa Consulting Service Private Limited Chennai India Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer.

关键词： Accuracy Cancer treatment Medical services Machine learning Digital twins Mirrors Artificial intelligence Wearable sensors Standards Cancer

来源：评论

学校读者我要写书评

暂无评论

A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging 46

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

引用

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Chang, Shun-Cheng Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350371499

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages. © 2024 IEEE.

关键词： Distribution transformers

来源：评论

学校读者我要写书评

暂无评论

Performance-Aligned LLMs for Generating Fast Code

arXiv

引用

arXiv 2024年

作者： Nichols, Daniel Polasam, Pranav Menon, Harshitha Marathe, Aniruddha Gamblin, Todd Bhatele, Abhinav Department of Computer Science University of Maryland College ParkMD United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States Livermore Computing Lawrence Livermore National Laboratory LivermoreCA United States

Optimizing scientific software is a difficult task because codebases are often large and complex, and performance can depend upon several factors including the algorithm, its implementation, and hardware among others. Causes of poor performance can originate from disparate sources and be difficult to diagnose. Recent years have seen a multitude of work that use large language models (LLMs) to assist in software development tasks. However, these tools are trained to model the distribution of code as text, and are not specifically designed to understand performance aspects of code. In this work, we introduce a reinforcement learning based methodology to align the outputs of code LLMs with performance. This allows us to build upon the current code modeling capabilities of LLMs and extend them to generate better performing code. We demonstrate that our finetuned model improves the expected speedup of generated code over base models for a set of benchmark tasks from 0.9 to 1.6 for serial code and 1.9 to 4.5 for OpenMP code. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence based Healthcare Knowledge Structure for Automatic Medical Report Generation 7

Artificial Intelligence based Healthcare Knowledge Structure...

引用

7th International Conference on Contemporary computing and Informatics, IC3I 2024

作者： Kumar, Pramod Alghayadh, Faisal Yousef Tamta, Vinay Prasad Anand, Monika Soni, Mukesh Chaudhary, Jitendra Kumar Ganga Institute of Technology and Management Bhadana- Bahadurgarh Road Kablana Haryana Jhajjar India AlMaarefa University Computer Science and Information Systems Department College of Applied Sciences Riyadh Saudi Arabia Department of Information Technology Uttarakhand Srinagar India Chandigarh University Department Cse Punjab Mohali India Lovely Professional University Division of Research and Development Punjab Phagwara India Graphic Era Hill University School of Computing Bhimtal Campus Uttarakhand India

ISBN: (纸本)9798350350067

Automatic medical report production is a key text summarization application. Traditional text summarizing systems cannot completely interpret and use medical texts' complicated vocabulary due to the disparities between medical consultation data and general domain data. The crucial knowledge in medical consultations has not been successfully harnessed. Traditional text summary algorithms cannot automatically identify, and filter key information based on medical reports' structure and generate structured text. An AI-based knowledge-assisted structured medical report creation solution addresses these difficulties. This method uses entity-guided previous domain knowledge and structure-guided task decoupling to fully utilize medical consultation data and medical report structure. The suggested strategy improves ROUGE scores by 2% to 3% compared to similar methods, resulting in more accurate medical reports, according to IMCS21 dataset experiments. © 2024 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

引用

2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Chang, Shun-Cheng Yu, Hsin-Pei Hsieh, Yi-Hsien Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan National Taiwan University Hospital Division of Gastroenterology and Hepatology Department of Internal Medicine Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350351552

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A multi-objective grey wolf optimizer for energy planning problem in smart home using renewable energy systems

引用

Sustainable Operations and computers 2024年第1期5卷 88-101页

作者： Makhadmeh, Sharif Naser Al-Betar, Mohammed Azmi Al-Obeidat, Feras Alomari, Osama Ahmad Abasi, Ammar Kamal Tubishat, Mohammad Elgamal, Zenab Alomoush, Waleed Department of Data Science and Artificial Intelligence University of Petra Amman11196 Jordan College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University P.O. Box 50 Al-Huson Irbid Jordan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates School of Computing Skyline University College Sharjah United Arab Emirates School of Information Technology Skyline University College Sharjah P.O. Box 1797 United Arab Emirates Abu Dhabi United Arab Emirates

This paper presents the energy planning problem (EPP) as an optimization problem to find the optimal schedules to minimize energy consumption costs and demand and enhance users’ comfort levels. The grey wolf optimizer (GWO), One of the most powerful optimization methods, is adjusted and adapted to address EPP optimally and achieve its objectives efficiently. The GWO is adapted due to its high performance in addressing NP-complex hard problems like the EPP, where it contains efficient and dynamic parameters that enhance its exploration and exploitation capabilities, particularly for large search spaces. In addition, new energy and real-world resources based on solar renewable energy systems (RESs) are combined with the proposed GWO to enhance its performance and ensure the optimisation of EPP objectives. Furthermore, EPP is presented as a multi-objective planning problem to optimize all objectives simultaneously. To efficiently investigate the proposed method performance, the results obtained by the GWO with the RESs are compared in three stages: comparison with original methods without RESs, comparison with methods using RESs, and comparison with state-of-the-art. The obtained results proved the robust performance of the proposed method in handling EPP and optimizing its objectives. © 2024 The Author(s)

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

URL Spam Detection Using Machine Learning Classifiers

URL Spam Detection Using Machine Learning Classifiers

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Omar Almomani Adeeb Alsaaidah Mosleh M. Abualhaj Mohammed Amin Almaiah Ammar Almomani Shahzad Memon Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Computer Science King Abdullah the II IT School The University of Jordan Amman Jordan Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Department of Computer Information Science Higher Colleges of Technology Sharjah United Arab Emirates Department of Computer Science and Digital Technologies Faculty of Architecture Computing and Engineering University of East London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Cybersecurity has emerged as one of the most prevalent and significant challenges in recent years due to the advancement of technology. Among the most frequent and hazardous cybersecurity threats are spam URLs (Uniform Resource Locators), which are also one of the most popular methods for user fraud. Users are the victims of this attack, which also steals their data and infects their devices with harmful software. The detection of spam URLs has become very important in protecting the user. Therefore, this study aims to investigate the efficiency of machine learning classifiers in detecting spam URLs. The following machine learning classifiers were chosen: Random Forest, Decision Tree, and SVM. The evaluation was based on the ISCXURL2016 dataset, which is divided into three groups: All Features, BestFirst Features, and Infogain Features and evaluation matrices were the Accuracy, Precision, Sensitivity, and F-measure. The results obtained showed that Random Forest with All Features is superior to others with an accuracy of 99.75%, Precision of 99.74%, and Sensitivity of 99. 79%, and F-measure 99.76 %.

关键词： Uniform resource locators Support vector machines Sensitivity Accuracy Phishing Metaheuristics Feature extraction Malware Decision trees Random forests

来源：评论

学校读者我要写书评

暂无评论

Models of Battle Dynamics in Strategic computer Games 16

Models of Battle Dynamics in Strategic Computer Games

引用

16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Laryushin, Ivan Dmitrievich Pashin, Dmitrii Sergeevich Koltochenko, Yaroslav Alekseevich Ereshko, Felix Ivanovich Gaponov-Grekhov Institute of Applied Physics Ras Plasma Physics Department Nizhny Novgorod Russia State University of Nizhny Novgorod Faculty of Physics National Research Lobachevsky Nizhny Novgorod Russia Moscow Institute of Physics and Technology Department of Mathematical Modeling of Complex Systems and Optimization Moscow Russia Federal Research Center 'Computer Science and Control' of the Ras Department of Information and Computing Systems Moscow Russia

ISBN: (纸本)9798350337907

A set of new models that describe the dynamics of battles in strategic computer games is presented. These models not only have descriptive functions, but also allow solving problems of optimizing army control under given conditions, as well as calculating the most efficient composition of the army for the planned operation within a computer game. © 2023 IEEE.

关键词： computer games

来源：评论

学校读者我要写书评

暂无评论

Improved Attentive Recurrent Network for applied Linguistics-Based Offensive Speech Detection

引用

computer Systems science & Engineering 2023年第11期47卷 1691-1707页

作者： Manar Ahmed Hamza Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Amira Sayed A.Aziz Mohammad Mahzari Abu Sarwar Zamani Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech *** ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate *** the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the *** on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and *** these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or *** Learning(ML)techniques have recently been applied to recognize hate speech-related *** current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social *** GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate *** the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding ***,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and *** last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition *** depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was *** comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.

关键词： applied linguistics hate speech offensive language natural language processing deep learning grasshopper optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：