检索结果-内蒙古大学图书馆

2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Sharma, Pratyaksha Gupta, Sumita Bhavya, Boddu Asmitha Sharma, Khushi Dhir, Saru Gambhir, Sapna Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP India Department of Information Technology Amity School of Engineering & Technology Amity University UP India Dept. of Computer Science College of Engineering and Computing George Mason University United States

ISBN: (纸本)9798331530389

With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for individuals with physical disabilities or those requiring efficient documentation of spoken content. The goal of this study is to review recent advancements in this field and highlight gaps in current research, with a focus on accuracy, language adaptability, ease of use, and the integration of various technologies. We also suggest future directions for enhancing and developing this technology further. © 2025 IEEE.

关键词： Hidden Markov models

来源：评论

学校读者我要写书评

暂无评论

Leveraging AI for enhanced cybersecurity: a comprehensive review

引用

Discover Applied Sciences 2025年第6期7卷 1-27页

作者： Swetha, Thammisetty Kumaran, U. Meena, V.P. Hameed, Ibrahim A. Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical Engineering National Institute of Technology Jamshedpur Jharkhand Jamshedpur831014 India Department of ICT and Natural Sciences Norwegian University of Science and Technology Trondheim7034 Norway

Cybersecurity has become a serious concern in this digital age, with ever-increasing threats aimed at individuals, enterprises, and countries across the globe. AI has been rapidly emerging as an influential technology that possess enormous abilities to enhance data protection measures by automating threat recognition, vulnerability assessment, and incident response. This study focuses on comprehensively examining current literature works dedicated to AI along with its applications in cybersecurity and presents an innovative architecture to deal with challenges that pose enormous risks to worlds community. Explore advanced techniques, including Machine learning (ML) and deep learning (DL), and their implementation in threat intelligence, intrusion recognition, malware analysis, phishing detection, and user authentication. Furthermore, the propounded review deliberates on various limitations such as data scarcity, model interpretability, and vulnerability to adversarial attacks can hinder the effectiveness of existing models in real-world cybersecurity scenarios, leading to reduced accuracy, slower response times, and increased susceptibility to evasion techniques. The future advances, including the evolution of explainable AI, predictive analytics, quantum computing, and real-time management capabilities, are also emphasized. This review paper is intended to offer a systematic understanding of modern AI techniques and prospective solutions in enriching the cybersecurity domain, offering valuable insights for researcher scholars and professionals in diverse fields. © The Author(s) 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Development of Fast Prediction System of User Action for a Fully Autonomous Smart Home System 4

Development of Fast Prediction System of User Action for a F...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Grey Wolf Optimizer and Deep Neural Network based Feature Selection and Classification in Medical Image Analysis 18

Grey Wolf Optimizer and Deep Neural Network based Feature Se...

引用

18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Chandrashekhar, Katha Leela, P. Madhavi, K. Reddy Avanija, J. Tangudu, Naresh Indur Institute of Engineering and Technology Computer Science and Engineering Siddipet India Mohan Babu University School of Computing Dept. of Data Science Tirupati India Mohan Babu University School of Computing Tirupati India Aditya Institute of Technology and Management Tekkali India

ISBN: (纸本)9789380544519

Accurate and efficient medical image analysis is required for modern healthcare applications, facilitating timely disease diagnosis and treatment. Considering the importance of optimization algorithms and deep learning methodologies, this work presents a novel approach, Grey Wolf Optimizer-Assisted Deep Neural Networks (GWO-DNN), for feature selection and classification in medical image analysis. The proposed framework utilizes the power of the Grey Wolf Optimizer to intelligently select the most relevant features from complex medical images. The selected features are then integrated into Deep Neural Networks, to create accurate models for medical image classification tasks. This integrated approach is designed to improve diagnostic accuracy and enhance patient care. The experimentation and evaluation, reveal that the proposed GWO-DNN based model for image analysis is more efficient than the existing methods. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep Neural Networks (DNN) Disease Classification Feature Selection Grey Wolf Optimization (GWO) Medical Image Analysis

来源：评论

学校读者我要写书评

暂无评论

Data-Driven StrategicWorkforce Planning in UK SMEs: A Comparative Analysis

Data-Driven StrategicWorkforce Planning in UK SMEs: A Compar...

引用

2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Jaleh, Fatemeh Saadati, Parisa Husamaldin, Laden Aladesuru, Favour School of Business and Management London School of Science and Technology London United Kingdom School of Computing and Engineering University of West London London United Kingdom

ISBN: (纸本)9798350369106

Amidst escalating digital competition, small and medium-sized enterprises (SMEs) face a vital need to strategically manage human resource management (HRM) for sustainable growth. The adoption of data-driven strategic workforce planning is crucial for SMEs, facilitating informed decisions that enhance operational efficiency. Drawing from secondary data obtained through a governmental survey involving over 2000 SMEs across various sectors in the UK, this study endeavors to deepen our comprehension of the determinants and challenges associated with the adoption of data-driven workforce planning (DDWP) in SMEs and its consequential impact on organizational performance. The study endeavors to construct a framework anchored in the technology-organization-environment (TOE) model, subsequently validating this framework across three prominent industries within the UK SME landscape to substantiate hypotheses derived from the extant literature. The discussion encompasses an exploration of the challenges inherent in implementing DDWP within SMEs, followed by an assessment of how these challenges influence the adoption of DDWP. Subsequently, the study delves into an evaluation of the impact of DDWP on performance, concluding with an examination of the framework's applicability across industries. The results illuminate that drivers influencing the level of adoption of DDWP in SMEs similarly impact selected industries. Moreover, the findings underscore that the level of adoption of DDWP exerts a palpable impact on organizational performance, enhancing overall business efficiency. The outcomes of this study furnish valuable insights and practical recommendations for HR professionals, policymakers, and researchers, delineating strategies to elevate business performance and efficiency through the transformation of workforce planning into data-driven workforce planning. © 2024 IEEE.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Exploring the Impact of Deep Learning Models on Lane Detection Through Semantic Segmentation

引用

SN Computer Science 2024年第1期5卷 139页

作者： Kumar, Sunil Pandey, Ankur Varshney, Sudeep School of Computing Science and Engineering Galgotias University Greater Noida India Department of Computer Science & Engineering Manipal University Jaipur Jaipur India Department of Computer Science & Engineering Sharda School of Engineering & Technology Greater Noida India

Due to advancements in the deep learning technology, object detection has become significantly important for lane detection and vehicle detection. In recent times, lane detection has become more popular as it plays a significant role in traffic surveillance compared to other object detection technology. However, these strategies have several intrinsic flaws which need to be addressed. Traditional-based techniques still suffer from the challenges of the effectiveness and accuracy, whereas a complex convolutional layer is a challenge for deep learning-based strategies. A parameter selection issue affects the majority of the available lane detection algorithms, which further contributes to their unsatisfactory detection performance. In this study, we provide an effective lane detection method based on semantic segmentation to identify lane lines in a high-dimensional dataset by adding vertical spatial properties and contextual driving information. This paper employs two created frames—feature merging block and information exchange block—to identify unclear and obstructed lane lines more effectively. The simulations have been carried out for the proposed model on TUSimple and CULane datasets which resulted with 94.42% accuracy, 93.47% precision, 92.85% recall, and 93.17% F1-score. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Lane detection Occluded lane lines Semantic segmentation

来源：评论

学校读者我要写书评

暂无评论

Digital compass for multi-user beam access in mmWave cellular networks

引用

Digital Communications and Networks 2023年第4期9卷 879-886页

作者： Adel Aldalbahi Nazli Siasi Asim Mazin Mohammed A.Jasim Department of Electrical Engineering King Faisal UniversityAl-Ahsa34221Saudi Arabia School of Computing DePaul UniversityChicagoIL60604USA Department of Electrical Engineering University of South FloridaTampaFL33620USA School of Engineering and Technology University of WashingtonTacomaWA98402USA

Beamforming in stand-alone Millimeter-Wave(mmWave)communications results in prolonged access times and latencies due to the increased number of measurements required to determine the optimal beam directions at the Mobile Station(MS)and Base Station(BS),returning the highest received signal ***,dynamic and fast access schemes that meet the Third-Generation Partnership Project(3GPP)specifications are required ***,in this paper,a novel initial access scheme is proposed for multiple MS users by leveraging for the first time a digital compass in the access ***,when a new MS joins the footprint of a BS,it probes the channel for beacon signaling about the BS direction,i.e.,broadcasted by a neighboring MS that completed beam association at previous time ***,a digital compass is utilized to adjust the coordinates of the BS according to the location of the new *** proposed scheme is applied for a single and multi-user settings at various broadcasting *** includes a single associated MS user that broadcasts information to a single incoming MS user,a single user that broadcasts signals to multiple incoming users,or all multiple associated users broadcast to multiple incoming ***,the proposed schemes yield in notable efficiency in terms of the computational complexity,access times,power and energy consumption as compared to existing access ***,high success rates are achieved at the detriment of relatively higher cost.

关键词： Millimeter wave Initial beam access Electronic compass Beamforming Access times Energy consumption

来源：评论

学校读者我要写书评

暂无评论

Enhancing Image Resolution with Achromatic and Optical Spatial Filtering in Image Processing 7th

Enhancing Image Resolution with Achromatic and Optical Spati...

引用

7th International Conference on Soft computing and Signal Processing, ICSCSP 2024

作者： Premanand, V. Arulalan, V. Balamurugan, G. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9789819609239

Once the design had been completed for a common optical system, the variables in the image processing technique were modified to produce accurate image reproduction. The researchers suggest collaborative planning of an optical system and the characteristics of the reconstructed image as an alternative to this consecutive design technique. On a large set of photos, researchers jointly optimize optical and algorithmic variables for image processing to reduce the difference between the original and reconstructed images. Researchers use automatic segmentation to execute our collaborative optimization approach and quickly estimate the variable gradient in a stochastic optimization technique. The results of a theoretical and numerical study on a new technique for filtering optical images from this dual optical filtering of the spatial spectra of the images were provided. Examining the shape of the optical cell transfer function and therefore its dependence on crystal cutting, optical contact form, and ultrasonic frequencies seems to be the main areas of interest. The instance of an item in the form of an amplitude gradient serves as an illustration of the results of the computer model of optical extraction features. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

CPIE: A Spatiotemporal Visual Analytic Tool to Explore the Impact of Coal Pollution

CPIE: A Spatiotemporal Visual Analytic Tool to Explore the I...

引用

2024 IEEE Workshop on Energy Data Visualization, EnergyVis 2024

作者： Jin, Sichen Henneman, Lucas Roberts, Jessica Georgia Institute of Technology School of Interactive Computing United States George Mason University College of Engineering and Computing Sid and Reba Dewberry Department of Civil Environmental and Infrastructure Engineering United States

ISBN: (纸本)9798350379235

This paper introduces CPIE (Coal Pollution Impact Explorer), a spatiotemporal visual analytic tool developed for interactive visualization of coal pollution impacts. CPIE visualizes electricitygenerating units (EGUs) and their contributions to statewide Medicare deaths related to coal P M2.5 emissions. The tool is designed to make scientific findings on the impacts of coal pollution more accessible to the general public and to raise awareness of the associated health risks. We present three use cases for CPIE: 1) the overall spatial distribution of all 480 facilities in the United States, their statewide impact on excess deaths, and the overall decreasing trend in deaths associated with coal pollution from 1999 to 2020;2) the influence of pollution transport, where most deaths associated with the facilities located within the same state and neighboring states but some deaths occur far away;and 3) the effectiveness of intervention regulations, such as installing emissions control devices and shutting down coal facilities, in significantly reducing the number of deaths associated with coal pollution. © 2024 IEEE.

关键词： Coal transportation

来源：评论

学校读者我要写书评

暂无评论

IMGWOFS: A Feature Selector With Trade-Off Between Conflict Objectives for EEG-Based Emotion Recognition

引用

IEEE Transactions on Affective computing 2025年第2期16卷 598-610页

作者： Luo, Gang Sun, Shuting Yan, Chang Qu, Shanshan Wang, Dixin Chu, Na Liu, Xuesong Tian, Fuze Qian, Kun Li, Xiaowei Hu, Bin Ministry of Education Beijing Institute of Technology Key Laboratory of Brain Health Intelligent Evaluation and Intervention Beijing100081 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou730000 China

Feature selection is a crucial step in EEG emotion recognition. However, it was often used as a single objective problem to either reduce the number of features or maximize classification accuracy, while neglecting their balance. To address the issue, we proposed Improved Multi-objective Grey Wolf Optimization Feature Selection (IMGWOFS). First, we designed a population initialization operator via discriminability and independence of features to accelerate search speed. Second, we employed a twostage update strategy to improve the global search capabilities of the EEG feature subsets. Finally, we incorporated an adaptive mutation operator to escape the local *** conducted experiments on SEED and DEAP datasets, and the accuracy were 86.87 ± 1.62% and 60.65 ± 1.51% in the beta band using a smaller number of EEG features. In addition, the frontal lobe was related to emotion processing. In conclusion, IMGWOFS is an effective and feasible feature selection method for EEG-based emotion recognition. © 2010-2012 IEEE.

关键词： Multiobjective optimization

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：