检索结果-内蒙古大学图书馆

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ghule, Vijaykumar R. Agrawal, Ankur Kumar Kumar, Jambi Ratna Raja Mondal, Dipannita Hemelatha, S. Preetha, M. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Management Aligarh India Genba Sopanrao Moze College of Engineering Balewadi Department of Computer Engineering Maharashtra Pune India Dr. D.Y. Patil College of Engineering and Innovation Artificial Intelligence and Data Science Department Talegaon India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319125

The emergence of synthetic intelligence (AI) has substantially revolutionized the way records is accessed and optimized. With AI-powered equipment and algorithms, groups are capable of efficiently method and manipulate large amounts of data, main to upgrades in overall performance and decision-making. This paper pursuits to evaluate the impact of AI on statistics get entry to optimization performance, discussing both the potential blessings and challenges that include its integration. One of the major blessings of using AI for information get entry to optimization is its capability to automate and streamline obligations. AI-powered structures can analyze and categorize massive datasets at a quicker and extra correct rate than human beings, liberating up time and assets for different obligations. This results in stronger efficiency and productivity in statistics control methods. Further, AI also can enhance the accuracy and best of facts access. thru system learning and predictive analytics, AI structures can identify styles and correlations in information, providing treasured insights for decision-making. This now not simplest improves the speed at which records is accessed, however also the reliability and relevance of the data. However, there are also demanding situations associated with the combination of AI in facts get admission to optimization. one of the fundamental worries is the potential bias in algorithms, which can result in erroneous or discriminatory selection-making. it's far vital for groups to carefully display and review the algorithms utilized by AI structures. © 2023 IEEE.

关键词： accuracy discriminatory predictive structures treasured

来源：评论

学校读者我要写书评

暂无评论

Rooted Tree Optimization for Wind Turbine Optimum Control Based on Energy Storage System

引用

Computers, Materials & Continua 2023年第2期74卷 3977-3996页

作者： Billel Meghni Afaf Benamor Oussama Hachana Ahmad Taher Azar Amira Boulmaiz Salah Saad El-Sayed M.El-kenawy Nashwa Ahmad Kamal Suliman Mohamed Fati Naglaa K.Bahgaat Department of Electrical Engineering Badji Mokhtar UniversityLSEM LaboratoryAnnaba23000Algeria Department of Electrical Engineering Biskra UniversityLGEB LaboratoryBiskra07000Algeria Department of Drilling and Rig Mechanics Ksadi Merbah UniversityOuargla30000Algeria College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt Department of Electronics University of Badji MokhtarLERICA LaboratoryAnnaba23000Algeria Delta Higher Institute for Engineering&Technology(DHIET) Mansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Faculty of Engineering Cairo UniversityGiza12613Egypt Department of Communications and Electronics Engineering Faculty of EngineeringCanadian International College(CIC)ElShiekh ZayedEgypt

The integration of wind turbines(WTs)in variable speed drive systems belongs to the main factors causing lowstability in electrical ***,in order to avoid this issue,WTs hybridization with a storage system is a *** paper investigates WT system operating at variable *** system contains of a permanent magnet synchronous generator(PMSG)supported by a battery storage system(BSS).To enhance the quality of active and reactive power injected into the network,direct power control(DPC)scheme utilizing space-vector modulation(SVM)technique based on proportional-integral(PI)control is ***,to improve the rendition of this method(DPC-SVM-PI),the rooted tree optimization technique(RTO)algorithm-based controller parameter identification is used to achieve PI optimal *** compare the performance ofRTO-based controllers,they were implemented and tested along with some other popular controllers under different working *** obtained results have shown the supremacy of the suggested PIRTO algorithm compared to competing controllers regarding total harmonic distortion(THD),overshoot percentage,settling time,rise time,average active power value,overall efficiency,and active power steadystate error.

关键词： Rooted tree optimization(RTO)method direct power control(DPC) wind turbine(WT) proportional integral(PI) PMSG

来源：评论

学校读者我要写书评

暂无评论

Multi-behavior aware recommendation with joint contrastive learning and reinforced negative sampling

引用

Complex & Intelligent Systems 2025年第8期11卷 1-17页

作者： Du, Yujia Yu, Zhengtao Wang, Hongbin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Key Laboratory of Artificial Intelligence in Yunnan Province Kunming University of Science and Technology Kunming China

Traditional recommendation systems usually rely on single user-item interaction information to capture the characteristics of users and items. However, in real-world applications, the interactions between users and items are far more diverse. Therefore, efficiently integrating multidimensional interaction data is crucial to improve the performance of recommendation systems. The existing methods still face challenges in terms of modeling behavioral dependencies, handling skewed data distributions, and optimizing the quality of negative samples. To address these obstacles, a joint contrastive learning and reinforced negative sampling model is proposed to multi-behavior aware recommendation. First, user interaction data concerning multiple behaviors are incorporated to automatically capture and encode behavioral context information and high-order dependencies, enabling a more comprehensive understanding of user behavior patterns. Second, contrastive learning is applied to the user data acquired under different behaviors to construct enhanced personalized user behavior representations. Additionally, a similarity-based strategy using the dot product operation is introduced to identify hard negative samples with high similarity to users, thereby strengthening the item-side representations. The model is optimized through multi-task learning. Extensive experiments conducted on three real datasets, i.e., IJCAI, Tmall, and Retail, demonstrate that the proposed method improves upon the performance of the existing multi-behavior baseline models by 14.5%, 6.0%, and 1.1% in terms of hit ratio and by 19.7%, 9.4%, and 2.3% in terms of the normalized discounted cumulative gain, respectively, verifying the effectiveness of the new approach.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhanced Route Optimization: Incorporating Road Safety Factors for Optimal Path Selection 4

Enhanced Route Optimization: Incorporating Road Safety Facto...

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Subash, R. Prathaban, Banu Priya Ganeshkumar, N. Gopinathan, S. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Chennai India Gitam School of Technology Gandhi Institute of Technology and Management Department of Computer Science and Engineering Karnataka Bengaluru562163 India Sri Sai Ram Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331508845

Efficient route optimization is a crucial concern in modern transportation systems, influencing travel time, resource consumption, and user satisfaction. By empowering users to select their priority parameters, such as safety or efficiency, this project enables travellers to make eco-conscious choices and select routes that minimize their environmental footprint. Real-time data integration ensures users can bypass traffic jams and accidents, reducing emissions and fuel consumption. Furthermore, the focus on safety factors helps prevent accidents, thereby fostering safer road environments. Existing systems often fall short, providing limited customization and overlooking safety and sustainability priorities. To bridge these gaps, this project introduces an advanced route planning system that empowers users to select their priority parameters, integrates real-time data for up-to- the-minute updates, and prioritizes road safety. It introduces a pioneering approach that enhances the Dijkstra Algorithm by integrating real-time accident rate and travel time data into the route optimization process. By considering these additional factors, the algorithm produces routes that not only minimize distance but also prioritize safety and time efficiency in dynamic real-world conditions. The algorithm's multi-factor optimization considers distance, accident rates, and travel times, enabling the creation of routes that balance these criteria and offer improved decision-making for users through a user- friendly interface. The algorithm's dynamic adaptability, multi- factor optimization, and practical implementation showcase its potential to revolutionize how routes are planned, leading to safer, faster, and more informed travel experiences. © 2024 IEEE.

关键词： Travel time

来源：评论

学校读者我要写书评

暂无评论

Fetal Brain Component Segmentation Using 2-Way Ensemble U-Net 7th

Fetal Brain Component Segmentation Using 2-Way Ensemble U-Ne...

引用

7th International Conference on data Management, Analytics and Innovation, ICDMAI 2023

作者： Halder, Shinjini Gangopadhyay, Tuhinangshu Dasgupta, Paramik Chatterjee, Kingshuk Ganguly, Debayan Sarkar, Surjadeep Roy, Sudipta Government College of Engineering and Leather Technology Kolkata700106 India Asian Institute of Technology Khlong Nueng Thailand Government College of Engineering and Ceramic Technology Kolkata700010 India Artificial Intelligence and Data Science Jio Institute Navi Mumbai410206 India

ISBN: (纸本)9789819914135

Fetal brain segmentation has been a field of interest since a long time. However, it is a challenging task as well for reasons, like blurred images due to fetal motion. Recently, deep learning has been successful in performing this task with good accuracy. In this paper, we developed 2-way Ensemble U-Net model, a convolutional neural network architecture for performing segmentation on the fetal brain image to divide it into its seven components: intracranial space and extra-axial cerebrospinal fluid spaces, gray matter, white matter, ventricles, cerebellum, deep gray matter, and brainstem and spinal cord. The fetal brain image can be obtained by segmenting it from the fetal magnetic resonance images using any of the previous works on fetal brain segmentation, which presents our work as an extension of the already existing segmentation works. The Jaccard similarity and Dice score for this task are 83% and 88%, respectively. This is higher than that returned by any of the previous models, when trained for the same task, thus showing the potential of our model in segmentation related tasks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cerebrospinal fluid

来源：评论

学校读者我要写书评

暂无评论

Medical Imaging Analysis Using Machine Learning By Design Thinking Approach 4

Medical Imaging Analysis Using Machine Learning By Design Th...

引用

4th IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Devi, N. Ramya Revathi, K. Lekhaa, T.R. Aravindhan, K. Kavinkumar, K. Sarumathi, U. Sns College Of Engineering Department Of Information Technology Tamil Nadu Coimbatore India Reva University Department Of Computing And Information Technology Bangalore India Sns College Of Engineering Department Of Artificial Intelligence And Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384628

Breast cancer is a significant global health concern and a leading cause of cancer-related morbidity and mortality among women. Early detection and accurate prediction of breast cancer are crucial for improving outcomes and reducing the impact of this disease. The development of predictive models and risk assessment tools has become an important area of research, as they can help healthcare professionals identify individuals at a higher risk of developing breast cancer and tailor screening and prevention strategies accordingly. This introduction provides an overview of breast cancer prediction, highlighting the importance of the topic and the motivation behind predictive models. It sets the stage for a more comprehensive exploration of the subject and the various methods, technologies, and factors involved in breast cancer prediction. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Industry 5.0: Enhancing Human-Robot Collaboration through Collaborative Robots – A Review

Industry 5.0: Enhancing Human-Robot Collaboration through Co...

引用

Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Raffik R Rithish Ramamoorthy Sathya Vaishali V Balavedhaa S Jyothi Lakshmi N Mechatronics Engineering Kumaraguru College of Technology Coimbatore India Artificial Intelligence and Data Science Kumaraguru College of Technology Coimbatore India Artificial Intelligence and Data Science Engineering Kumaraguru College of Technology Coimbatore India

Modern production systems are currently changing, moving from mass production to mass customization logic, as described in Industry 5.0. The aim of Industry 5.0 is to outperform conventional manufacturing techniques by creating interconnected systems that include everything from machinery, robots, and workers to goods and consumers. This is accomplished by fully integrating the physical and virtual worlds. It focuses on building intelligent hybrid workspaces where robots share labour-intensive duties to increase productivity and safety in a range of industrial sectors. Cobots, or collaborative robots, are introduced in this. Cobots are becoming more prevalent in modern industrial systems because they combine the adaptability and competence of manual labour with the efficiency of automated systems. To maintain consistent human safety and effective collaborative productivity, communication between a human operator and the robot in these collaborative contexts must be precise and seamless. In more specific words, the framework can be used to operate an industrial robot securely and remotely in real-time while still protecting the safety of the technician. The objective of this article is to offer a clear overview of Industry 5.0's major ideas and prospective applications. It focuses on the supporting technologies of Industry 5.0 such as the Internet of Things (IoT), 6G systems, Blockchain technology (BT), Digital Twin (DT), artificial intelligence (AI), Cyber-physical systems, and Extended Reality (XR) technologies, for seamless connectivity and data exchange. Furthermore, it also elaborates on the application of Cobots in various industrial fields.

关键词：

来源：评论

学校读者我要写书评

暂无评论

The Utilization of Google Cloud in the Healthcare Industry

The Utilization of Google Cloud in the Healthcare Industry

引用

Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Rishikesh Kapse Palash Gourshettiwar Dept. of Artificial Intelligence and Data Science Faculty of Engineering and Technology DMIHER (DU) Sawangi(M) Wardha Maharashtra Dept. of Computer Science And Medical Engineering Faculty of Engineering and Technology DMIHER (DU) Sawangi(M) Wardha Maharashtra

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This essay's primary focus is on Google Cloud's use in healthcare and how it affects data management, teamwork, and cost-effectiveness. With a focus on Google Cloud, which improves the processing, storing, and accessibility of medical data, the study examines the trends, advantages, and persistent challenges that come with the increasing usage of cloud computing technology. These consist of remote patient monitoring, “artificial intelligence,” and “machine learning” applications in diagnostics, diagnostics, and personalized medicine. data privacy, security issues, and concerns with the HIPAA incident are among the problems that are discussed along with all the significant elements that could facilitate the cloud migration of healthcare.

关键词： Industries Cloud computing data privacy Technological innovation Medical services Machine learning Teamwork Security Predictive analytics Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

FLODA: Harnessing Vision-Language Models for Deepfake Assessment

FLODA: Harnessing Vision-Language Models for Deepfake Assess...

引用

2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Park, Seunghyeon Bae, Youngho Han, Gunhui Olson, Alexander W. School of Integrated Technology Yonsei University Incheon Korea Republic of Dept. Data Science Hanyang University Seoul Korea Republic of Dept. Mechanical Engineering Yonsei University Seoul Korea Republic of Centre for Analytics and Artificial Intelligence Engineering University of Toronto Toronto Canada

ISBN: (纸本)9798331521165

The proliferation of advanced generative AI models has ushered in a new era of image creation, where the distinction between real and synthetic content has become increasingly blurred. This phenomenon poses significant challenges to the integrity and trustworthiness of digital content and AI systems. Recently, Vision-Language Models (VLMs) have shown great potential in addressing these challenges due to their superior performance in vision-language tasks. In this paper, we propose FLODA (FLorence-2 Optimized for Deepfake Assessment), an advanced method designed to surpass existing deepfake detection models. FLODA leverages the VLM model, Florence-2, and extends it by integrating image captioning and authenticity assessment into a single end-to-end architecture. By utilizing caption information, FLODA enhances visual analysis with contextual details, streamlining both caption generation and deepfake detection. To ensure FLODA's efficacy, we performed an ablation study to identify the optimal model configuration and demonstrated its contributions to performance improvements. We also compared our best FLODA model against existing benchmarks through rigorous evaluation. Notably, FLODA demonstrates strong generalization, showcasing its robustness across diverse scenarios with an average accuracy of 97.14%. This research advances digital content integrity and AI trustworthiness, providing a promising direction for future VLM applications. Code and models can be found at https://***/byh711/FLODA. © 2025 IEEE.

关键词： Photointerpretation

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

引用

Computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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：