检索结果-内蒙古大学图书馆

Large-Scale Group Decision Making:A Systematic Review and a Critical Analysis

IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 949-966页

作者： Diego García-Zamora Alvaro Labella Weiping Ding Rosa M.Rodríguez Luis Martínez Department of Computer Science University of JaénJaén 23071Spain IEEE School of Information Science and Technology Nantong UniversityNantong 226019China

The society in the digital transformation era demands new decision schemes such as e-democracy or based on social *** novel decision schemes require the participation of many experts/decision makers/stakeholders in the decision *** a result,large-scale group decision making(LSGDM)has attracted the attention of many researchers in the last decade and many studies have been conducted in order to face the challenges associated with the ***,this paper aims at reviewing the most relevant studies about LSGDM,identifying the most profitable research trends and analyzing them from a critical point of *** do so,the Web of science database has been consulted by using different *** these results a total of 241 contributions were found and a selection process regarding language,type of contribution and actual relation with the studied topic was then carried *** 87 contributions finally selected for this review have been analyzed from four points of view that have been highly remarked in the topic,such as the preference structure in which decision-makers’opinions are modeled,the group decision rules used to define the decision making process,the techniques applied to verify the quality of these models and their applications to real world problems ***,a critical analysis of the main limitations of the existing proposals is ***,taking into account these limitations,new research lines for LSGDM are proposed and the main challenges are stressed out.

关键词： Challenges large-scale consensus models large-scale group decision making(LSGDM) systematic review

来源：评论

学校读者我要写书评

暂无评论

Analyzing Employee Retention Factors using Machine Learning 4

Analyzing Employee Retention Factors using Machine Learning

引用

4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rahman, Moshiur Islam, Md Rashedul Bala, Partho Sattar, Abdus Daffodil International University Department of Management Information System Daffodil Smart City Dhaka Bangladesh Daffodil Institute of Information and Technology Department of Computer Science and Engineering Dhanmondi Dhaka Bangladesh Bangabandhu Sheikh Mujibur Rahman Science and Technology University Department of Computer Science and Engineering Dhaka8100 Bangladesh Daffodil International University Department of Computer Science and Engineering Daffodil Smart City Dhaka Bangladesh

ISBN: (纸本)9798350343670

This research proposal aims to employ machine learning techniques to analyze employee retention factors in Software Companies, recognizing its crucial role in organizational success and the potential costs of high turnover rates. Through Watson Analytics' advanced analytics capabilities, the study seeks to identify key factors contributing to employee attrition and retention, culminating in the development of a predictive model using machine learning algorithms. The outcomes are expected to include actionable recommendations to improve retention strategies, insights into the relative importance of different factors, and the creation of a data- driven, proactive talent management approach for Software Companies. By empowering organizations to retain top talent and fostering a positive work environment, this research envisions a transformative impact on long-term success and performance. © 2024 IEEE.

关键词： Artificial Intelligence Data science Deep Learning Machine Learning Monitoring Smartphone Sensors

来源：评论

学校读者我要写书评

暂无评论

Prediction of material property using optimized augmented graph-attention layer in GNN

引用

Materials Today: Proceedings 2022年 69卷 1419-1424页

作者： Sathana, V. Mathumathi, M. Makanyadevi, K. Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur India

Research in materials science continues to focus on the development of ML models capable of accurately predicting material properties (MPP). Graph neural networks (GNNs) have also proven to be quite actual in extracting physicochemical properties from MPP, despite the fact that many other techniques have been offered in the past. Despite this, existing GNN models fail to distinguish between the contributions of various atoms adequately. To forecast the properties of inorganic materials, we have developed an upgraded graph neural network model dubbed IGAT-GNN. OAGAT and a global attention layer, where a meta-heuristic method is used to achieve optimal weight in OAGAT, are the distinguishing features of IGAT-GNN. Each of the two methods, OAGAT layers and global attention layers, is used to better understand the local relationships among nearby atoms and their overall influence to the material's stuff. This results in much improved prediction performance for various qualities that have been examined. As we have demonstrated through numerous experiments, the IGAT-GNN outperforms other currently available GNN models while also revealing new information about the relationship between atoms and their respective material properties. The proposed model alewives the accuracy of 99.95%, which is the high quality accuracy than other algorithm. © 2022

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Intelligent wearable vision systems for the visually impaired in Saudi Arabia

引用

Neural Computing and Applications 2025年第14期37卷 8571-8591页

作者： Talaat, Fatma M. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Abd Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Navigating the world with visual impairments presents unique challenges, often limiting independence and safety. This research introduces SafeStride, a novel algorithm designed to empower visually impaired individuals through real-time obstacle detection and navigation assistance. SafeStride functions through five interconnected phases: sensor data acquisition, data preprocessing, obstacle detection, path planning, and feedback and guidance. A combination of ultrasonic sensors, cameras, and inertial measurement units (IMUs) feed comprehensive environmental data into the system, enabling accurate obstacle detection and safe path planning. Evaluating SafeStride's performance involved the MNIST dataset for image recognition and a dedicated indoor object detection dataset. Additionally, comparisons were made against three deep learning models—convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The results showcased SafeStride's high accuracy and effectiveness in both obstacle detection and classification. Beyond its impressive performance, SafeStride represents a significant leap forward in navigation aids for the visually impaired. By offering a comprehensive solution that enhances safety and independence in diverse environments, it holds immense potential to improve the lives of countless individuals. Future efforts will focus on further optimizing the algorithm and testing its real-world capabilities, paving the way for a future where safe and independent navigation becomes a reality for all. All three deep learning models (CNN, LSTM, GRU) excelled in the experiment, achieving high accuracy above 0.98, strong recall and F1-scores near 0.99, and exceptional AUC-ROC scores exceeding 0.9998. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Modelling a Learning-Based Dynamic Tree Routing Model for Wireless Mesh Access Networks

引用

computer Systems science & Engineering 2023年第2期44卷 1531-1549页

作者： N.Krishnammal C.Kalaiarasan A.Bharathi Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatoreIndia School of Engineering Presidency UniversityBengaluruIndia Department of Information Technology Bannari Amman Institute of TechnologySathyamangalamIndia

Link asymmetry in wireless mesh access networks(WMAN)of Mobile ad-hoc Networks(MANETs)is due mesh routers’transmission *** is depicted as signiﬁcant research challenges that pose during the design of network protocol in wireless *** on the extensive review,it is noted that the substantial link percentage is symmetric,i.e.,many links are *** is identiﬁed that the synchronous acknowledgement reliability is higher than the asynchronous ***,the process of establishing bidirectional link quality through asynchronous beacons underrates the link reliability of asym-metric *** paves the way to exploit an investigation on asymmetric links to enhance network functions through link ***,a novel Learning-based Dynamic Tree routing(LDTR)model is proposed to improve network performance and *** the evaluation of delay measures,asymmetric link,interference,probability of transmission failure is *** proportion of energy consumed is used for monitoring energy conditions based on the total energy *** learning model is a productive way for resolving the routing issues over the network model during *** asymmetric path is chosen to achieve exploitation and exploration *** learning-based Dynamic Tree routing model is utilized to resolve the multi-objective routing ***,the simulation is done with MATLAB 2020a simulation environment and path with energy-efﬁciency and lesser E2E delay is evaluated and compared with existing approaches like the Dyna-Q-network model(DQN),asymmetric MAC model(AMAC),and cooperative asymmetric MAC model(CAMAC)*** simulation outcomes demonstrate that the anticipated LDTR model attains superior network performance compared to *** average energy consump-tion is 250 J,packet energy consumption is 6.5 J,PRR is 50 bits/sec,95%PDR,average delay percentage is 20%.

关键词： Wireless mesh access networks mobile ad-hoc network reinforcement learning multi-objective constraint asymmetric link

来源：评论

学校读者我要写书评

暂无评论

Sign Language Translation and Voice Impairment Support System using Deep Learning 5

Sign Language Translation and Voice Impairment Support Syste...

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shajeena, J. Shiny, R.M. Mary Vespa, M. Kavitha, R. Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirapalli Campus India St Joseph's College of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

The Sign Language Translation and Voice Impairment Support System (SLT-VISS) represents a groundbreaking application of deep learning methodology aimed at facilitating communication for individuals with hearing impairments or speech disabilities. By utilizing Convolutional Neural Network (CNN), OpenCV for image processing, and Pyttsx3 for text-to-voice conversion, SLT-VISS offers real-time translation of sign language gestures into both text and speech output. Using a camera as input, the CNN algorithm accurately identifies and classifies sign language gestures displayed in front of it. OpenCV complements this process by preprocessing the input images for gesture segmentation and feature extraction, enhancing the accuracy of gesture recognition. Once a gesture is recognized, Pyttsx3 converts the translated text into synthesized speech, providing immediate auditory feedback to the user. SLT-VISS is designed to bridge the communication gap faced by individuals with communication disabilities, enabling them to express themselves effectively in various social and professional settings. By seamlessly translating sign language into both text and speech, this system promotes inclusivity and accessibility, empowering users to communicate with confidence and independence. The integration of CNN, OpenCV, and Pyttsx3 technologies within SLT-VISS signifies a significant advancement in assistive technology. As technology continues to evolve, further enhancements to SLT-VISS holds the potential to revolutionize communication accessibility and enable greater inclusivity in society. © 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

Virtual reality based Neural Sensors with Respirator for Cardiorespiratory Synchrony Training System 9

Virtual reality based Neural Sensors with Respirator for Car...

引用

9th IEEE International Conference on Smart Cloud, SmartCloud 2024

作者： Lu, Yun-En Yeh, Shih-Ching Wu, Eric Hsiao-Kuang Liu, Lizheng Lin, Sheng Kai National Chen-Kung University Department of Computer Science and Information Engineering Taipei Taiwan National Central University Department of Computer Science and Information Engineering Taipei Taiwan Fudan University Academy for Engineering and Technology Shanghai China

ISBN: (纸本)9798350389500

Cardiorespiratory synchronization training (CRST) uses breathing to increase balance in the autonomic nervous system and reduce negative emotions. Virtual Reality (VR) makes the patients immersive in the virtual environment for CRST training system. All the participants were trained to reduce their breathing rates through slow and deep breathing to achieve their target resonance frequency. Our proposed to design the new system based on virtual reality technology, and combined with a variety of neural sensors, such as Looxid EEG sensor, BMD101 ECG sensor, and Arduino SFM3400 Respirator, not only embedded on CRST training system, but also machine learning method to stimulate a real relaxing environment. Develop the innovative features of clinical psychological intervention, user convenience and provide the therapist with real-time display of heart rate features(SDNN, LF, LnLf, HF, LF/HF, Mhr), detect emotion and flow data on the monitor. During the process, the computer calculates LnLf value and change VR scenes at any time, we provide an instant interactive system. Our showed the two modules of CRST training system including both of passive CRST module and direct CRST module, that heart rate variability (HRV) variables and EEG have significant difference between the first one minute and last one minute in CRST VR training system. © 2024 IEEE.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

Silver nano-particles' size prediction using deep learning model with green method

引用

Materials Today: Proceedings 2022年 69卷 1193-1199页

作者： Mathumathi, M. Vetriselvi, T. Matheswaran, P. Nandhini, R. Selvi, A. Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur India

When it comes to casting technologies and other production processes such as welding, DL models minimise the need for costly experimental inquiry. One of the most important criteria in nanotechnology is the measurement of particle size. They've received a lot of interest for their chemical, physical, and clinical uses because of their outstanding qualities. By adopting a green chemical reduction process, the nano-silver crystals are generated in the biopolymer interface without any agglomeration. Nano-metal production relies on precise control of particle size, and this approach provides that. For the prediction size of Ag-NPs via the green approach, a hybrid ideal of Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and model is presented in this research. Temperature, starch stabiliser, and AgNO3 concentration are among the experimental variables fed into the CNN-LSTM model. The size of a nano particle is the output of the CNN-LSTM model. Making three subsets of the experimental data for training, validation, and testing was one of the initial steps in creating an adapting model. The silver nanoparticles were identified using X-ray diffraction and transmission electron microscopy. Ag-NPs' crystallographic airplanes were found to be face-centered cubic, according to XRD analysis (fcc).When comparing with other existing classifiers, DNN achieved high performance on precision, i.e. 89.87%, and this is due to the presence of simple structure. The CNN and auto encoder achieved nearly 82% and 80% of accuracy, where LSTM achieved 77.25% of accuracy and proposed CNN-LSTM achieved 83.50% of recall. © 2022

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Quantum Best Arm Identification

引用

Performance Evaluation Review 2023年第2期51卷 72-74页

作者： Wang, Xuchuang Janice Chen, Yu-Zhen De Andrade, Matheus Guedes Hajiesmaili, Mohammad Lui, John C.S. Towsley, Don Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong College of Information and Computer Sciences University of Massachusetts Amherst Amherst United States

来源：评论

学校读者我要写书评

暂无评论

Dual Cross-Stage Partial Learning for Detecting Objects in Dehazed Images 24

Dual Cross-Stage Partial Learning for Detecting Objects in D...

引用

24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhao, Jinbiao Zhang, Zhao Ren, Jiahuan Zhang, Haijun Zhao, Zhongqiu Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China Harbin Institute of Technology Department of Computer Science Shenzhen China

ISBN: (纸本)9798331506681

Performing an object detection task after the restoration of a hazy image, or rather detecting with the network backbone directly, will result in the inclusion of information mixed with dehazing, which tends to interfere with detection performance. To address these issues, we propose a novel framework for detecting objects in dehazed images via Dual Cross-Stage Partial Learning (DCSP). Specifically, we introduce a Cross-Stage Partial (CSP) module for extracting clean feature information after dehazing. Secondly, to enhance data integrity, we employ a skip-input strategy to supplement information related to object detection features that may be lost during the dehazing task, while avoiding the gradient vanishing problem. In addition, CSP is also introduced to facilitate comprehensive learning of multiple feature representations. Finally, to avoid the inclusion of irrelevant dehazing information in detection, we apply a Ground-Truth Flow at detection network (at dark3), for fine feature information calibration. Additionally, we created a synthetic fog dataset to expand the training data for DCSP. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness and accuracy of the proposed method. The code is available at https://***/zhaojinbiao/DCSP. © 2024 IEEE.

关键词： Demulsification

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：