检索结果-内蒙古大学图书馆

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Siluvainathan, Panneerselvam Padmakala, S. Selvakumar, A. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Applied Machine Learning Chennai India

ISBN: (纸本)9798331510022

Sustainable employee performance management (EPM) models are key to achieving the SDGs such as Promoting decent work and economic growth, reducing inequality, ensuring quality education, promoting gender equality, fostering innovation, sustainable practices like Green Human Resource Management, and Building Partnerships in work environments. Such performance management models would require data-driven insights to arrive at the right decisions in recruiting, assessing, rewarding, ensuring good employee health, and retaining employees for strategic advantages to institutions and countries alike. AI technologies could be employed for effective employee performance management with checks and balances as they have immense learning and predictive potential to avoid human errors and biases. This work presents EPM elements, an analysis of the EPM framework, EPM measurement techniques using AI techniques, Metrics for EPM, and finally, the future direction of research. © 2024 IEEE.

关键词： AI Employee Performance Management System Performance Metrics

来源：评论

学校读者我要写书评

暂无评论

Impact Assessment and Mitigation Strategies for Plug-in Electric Vehicles on Distribution Networks: A Probabilistic Approach 7

Impact Assessment and Mitigation Strategies for Plug-in Elec...

引用

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

作者： Raj, Vijilius Helena Reddy, M. Siva Swetha Thethi, H. Pal Ramesh, G. Mohammad, Q. Bhavana, Munugapati New Horizon College of Engineering Department of Applied Sciences Bangalore India Institute of Aeronautical Engineering Telangana Hyderabad50043 India Lovely Professional University Phagwara India Griet Department of Computer Science and Engineering Bachupally Hyderabad India Hilla University College Babylon Iraq Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

The rapid depletion of fossil fuel reserves coupled with the ever-increasing demand for electrical power has prompted the search for new energy sources. Electric vehicles (EVs) are one potential answer because of the technology that allows for more efficient transportation while simultaneously reducing fossil fuel usage and emissions of greenhouse chemicals. Depending on the power system's capability, structure, and architecture, several studies have shown that the distribution network may be negatively affected by the widespread interconnection of electric vehicles. Consequently, it is important to determine the effects of this additional load on the network so that measures can be taken to prevent threats to the system's stability, dependability, and security, especially because the Indian federal and state governments have introduced incentives to increase sales of this type of vehicle. © 2024 IEEE.

关键词： Power distribution networks

来源：评论

学校读者我要写书评

暂无评论

Heart Disease Prediction Using Novel Ensemble and Blending Based Cardiovascular Disease Detection Networks: EnsCVDD-Net and BlCVDD-Net

引用

IEEE Access 2024年 12卷 109230-109254页

作者： Khan, Hira Javaid, Nadeem Bashir, Tariq Akbar, Mariam Alrajeh, Nabil Aslam, Sheraz Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Islamabad44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia Cyprus University of Technology Limassol3036 Cyprus

Cardiovascular Diseases (CVDs) have emerged as a significant physiological condition, being a primary contributor to mortality. Timely and precise diagnosis of heart disease is crucial to safeguard patients from additional harm. Recent studies show that the usage of data driven approaches, such as Deep Learning (DL) and Machine Learning (ML) techniques, in the field of medical science is highly useful in accurately diagnosing heart disease in less time. However, statistical learning and traditional ML approaches require feature engineering to generate robust and effective features from data, which are then used in the prediction models. In the case of large complex data, both processes pose many challenges. Whereas, DL techniques are capable of learning features automatically from the data and are effective at handling large and intricate datasets while outperforming the ML models. This study focuses on the accurate prediction of CVDs, considering the patient’s health and socio-economic conditions while mitigating the challenges presented by imbalanced data. The Adaptive Synthetic Sampling Technique is used for data balancing, while the Point Biserial Correlation Coefficient is used as a feature selection technique. In this study, two DL models, Ensemble based Cardiovascular Disease Detection Network (EnsCVDD-Net) and Blending based Cardiovascular Disease Detection Network (BlCVDD-Net), are proposed for accurate prediction and classification of CVDs. EnsCVDD-Net is made by applying an ensemble technique to LeNet and Gated Recurrent Unit (GRU), and BlCVDD-Net is made by blending LeNet, GRU and Multilayer Perceptron. SHapley Additive exPlanations is used to provide a clear understanding of the influence different factors have on CVD diagnosis. The network’s performance is evaluated on the basis of various performance metrics. The results indicate that the EnsCVDD-Net outperforms all base models with 88% accuracy, 88% F1-score, 91% precision, 85% recall, and 777s execu

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

Multiview Orthonormalized Partial Least Squares: Regularizations and Deep Extensions

引用

IEEE Transactions on Neural Networks and Learning Systems 2023年第8期34卷 4371-4385页

作者： Wang, Li Li, Ren-Cang Lin, Wen-Wei The University of Texas at Arlington Department of Mathematics ArlingtonTX76019 United States The University of Texas at Arlington Department of Computer Science and Engineering ArlingtonTX76019 United States National Chiao Tung University Department of Applied Mathematics Hsinchu300 Taiwan

In this article, we establish a family of subspace-based learning methods for multiview learning using least squares as the fundamental basis. Specifically, we propose a novel unified multiview learning framework called multiview orthonormalized partial least squares (MvOPLSs) to learn a classifier over a common latent space shared by all views. The regularization technique is further leveraged to unleash the power of the proposed framework by providing three types of regularizers on its basic ingredients, including model parameters, decision values, and latent projected points. With a set of regularizers derived from various priors, we not only recast most existing multiview learning methods into the proposed framework with properly chosen regularizers but also propose two novel models. To further improve the performance of the proposed framework, we propose to learn nonlinear transformations parameterized by deep networks. Extensive experiments are conducted on multiview datasets in terms of both feature extraction and cross-modal retrieval. Results show that the subspace-based learning for a common latent space is effective and its nonlinear extension can further boost performance, and more importantly, one of two proposed methods with nonlinear extension can achieve better results than all compared methods. © 2012 IEEE.

关键词： mathematical transformations

来源：评论

学校读者我要写书评

暂无评论

Constructing the ChatGPT for PDF Files with Langchain - AI 7

Constructing the ChatGPT for PDF Files with Langchain - AI

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Prem Jacob, T. Bizotto, Beatriz Lucia Salvador Sathiyanarayanan, Mithileysh Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India University Center Unifacvest Department of Social And Applied Sciences Lages Brazil Research & Innovation MIT Square London United Kingdom

ISBN: (纸本)9798350359299

Queries in PDFs can be time-consuming and labor-intensive because of the unstructured nature of the PDF document type and the need for accurate and relevant search results. By applying cutting-edge algorithms for natural language processing to examine PDF documents and extract relevant data, LangChain solves these difficulties. It makes use of an easy search interface, adjustable filters, and efficient indexing and retrieval mechanisms to enhance the search experience. To efficiently retrieve relevant information from PDF documents, users can annotate critical portions, store queries, and create bookmarks with LangChain. The characteristics of LangChain improve overall productivity and greatly simplify PDF querying. Semantic search, driven by the latest Transformer language models, represents a significant evolution in information retrieval systems. This research work explores the capabilities of semantic search to efficiently retrieve documents from large collections in response to natural language queries. Unlike traditional keyword-based approaches, semantic search connects the power of Transformer models to discern meaning, providing users with more contextually relevant and accurate results within seconds. This technology not only enhances the user experience by delivering superior matches from document collections but also lays the foundation for tackling more intricate tasks, like text summarization and question-answering. The research investigates the impact of semantic search on information retrieval efficiency and accuracy, comparing its performance with conventional methods. The findings presented herein not only showcase the immediate benefits of semantic search but also open paths for future research and development in natural language processing and its applications. © 2024 IEEE.

关键词： Information retrieval systems

来源：评论

学校读者我要写书评

暂无评论

Developing an Artificial Intelligence Tool for Image Generation Using a Unique Dataset with Image-to-Image Functionality 24

Developing an Artificial Intelligence Tool for Image Generat...

引用

25th International Conference on computer Systems and Technologies, CompSysTech 2024

作者： Toktosunova, Akbiike Ergeshov, Alymbek Esenalieva, Gulzada Ermakov, Andrei Isaev, Ruslan Department of Applied Mathematics and Informatics Ala-Too International University Kyrgyzstan Department of Compute Science Ala-Too International University Kyrgyzstan Faculty of Engineering and Computer Science Ala-Too International University Kyrgyzstan

ISBN: (纸本)9798400716843

This article describes the application of a unique artificial intelligence model, trained on the basis of a special dataset, for drawing images. Artificial intelligence is very important in the modern world because it is a key driver of progress, offering innovative solutions to improve processes in various fields. A feature of this work is the use of the Stable Diffusion model and the creation of the LoRa model to create a specific object. The use of a special dataset, which is a classified set of images, to train models based on a stable propagation mechanism. The application of the image-to-image method, which consists in completely generating an image by having the model reproduce all the objects in the image, trying to preserve the maximum amount of detail in the image. As a result, a tool has been developed that can generate images of characters of a certain style. © 2024 ACM.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

引用

computers, Materials & Continua 2022年第11期73卷 2541-2554页

作者： K.Vijaya Kumar E.Laxmi Lydia Ashit Kumar Dutta Velmurugan Subbiah Parvathy Gobi Ramasamy Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Christ UniversityBangalore560029India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of ManagementMoscow109542Russia

Recently,big data becomes evitable due to massive increase in the generation of data in real time ***,object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation,augmented reality,surveillance,*** paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN(AIA-IFRCNN)model in big data *** AIA-IFRCNN model annotates the images by Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR),named DCF-CSRT *** AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,which comprises region proposal network(RPN)and Fast *** addition,inception v2 model is applied as a shared convolution neural network(CNN)to generate the feature ***,softmax layer is applied to perform classification *** effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.

关键词： Object detection tracking convolutional neural network inception v2 image annotation

来源：评论

学校读者我要写书评

暂无评论

Optimal Sparse Autoencoder Based Sleep Stage Classification Using Biomedical Signals

引用

computer Systems science & engineering 2023年第2期44卷 1517-1529页

作者： Ashit Kumar Dutta Yasser Albagory Manal Al Faraj Yasir A.M.Eltahir Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Respiratory Care College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

The recently developed machine learning(ML)models have the ability to obtain high detection rate using biomedical ***,this article develops an Optimal Sparse Autoencoder based Sleep Stage Classiﬁcation Model on Electroencephalography(EEG)Biomedical Signals,named OSAE-SSCEEG *** major intention of the OSAE-SSCEEG technique is toﬁnd the sleep stage disorders using the EEG biomedical *** OSAE-SSCEEG technique primarily undergoes preprocessing using min-max data normalization ***,the classiﬁcation of sleep stages takes place using the Sparse Autoencoder with Smoothed Regularization(SAE-SR)with softmax(SM)***,the parameter optimization of the SAE-SR technique is carried out by the use of Coyote Optimization Algorithm(COA)and it leads to boosted classiﬁcation efﬁ*** order to ensure the enhanced performance of the OSAE-SSCEEG technique,a wide ranging simulation analysis is performed and the obtained results demonstrate the betterment of the OSAE-SSCEEG tech-nique over the recent methods.

关键词： Biomedical signals EEG sleep stage classiﬁcation machine learning autoencoder softmax parameter tuning

来源：评论

学校读者我要写书评

暂无评论

Hamiltonian learning using machine-learning models trained with continuous measurements

引用

Physical Review applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

来源：评论

学校读者我要写书评

暂无评论

Modeling Hot Rolling Process Forces Using Bio-Inspired Metaheuristic Search Algorithm 2

Modeling Hot Rolling Process Forces Using Bio-Inspired Metah...

引用

2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Sheta, Alaa Braik, Malik Oznergiz, Ertan Elashmawi, Walaa H. Rausch, Peter Othman, Emad S. Southern Connecticut State University Department of Computer Science New HavenCT United States Al-Balqa Applied University Computer Science Department Salt Jordan Istanbul Technical University Mechanical Engineering Faculty Istanbul Turkey Misr International University Dept of Computer Science Cairo Egypt George Simon Ohm Univ. of Applied Sciences Computer Science Department Nuremberg Germany AL-Shorouk Academy Management Information System Dept. Cairo Egypt

ISBN: (纸本)9798350362633

This research introduces the application of an innovative bio-inspired metaheuristic technique, termed the Crow Search Algorithm (CSA), to model a crucial industrial process - hot rolling manufacturing. Inspired by the foraging patterns of crows, the CSA algorithm has demonstrated its prowess in solving diverse optimization challenges. In the context of this study, the CSA algorithm is harnessed to fine-tune the parameters of a simulation model focused on predicting the force exerted during a hot rolling procedure. The proposed model takes into consideration a range of influential factors, including the initial temperature (Ti), width (Ws), carbon equivalent (Ce), gauge (hi), draft (i), and roll diameter (R). The findings underscore the CSA's capability to deliver an exceptional modeling performance characterized by swift convergence and high solution quality. By getting along very well with the proposed model with the CSA algorithm, a robust and efficient avenue to optimize the hot rolling process emerges, with the potential for expansion into other manufacturing domains. The computational and simulation results demonstrated that the proposed approach-based CSA outperformed different meta-heuristic search algorithms, such as the Salp Swarm Algorithm (SSA), Dandelion Optimizer (DO), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), and Moth-Flame Optimization (MFO), in all test cases. The CSA has achieved the highest coefficient of determination (R2), equal to 0.97244, and the lowest mean squared error (MSE), equal to 1904.97, compared to its opponent algorithms. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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：