检索结果-内蒙古大学图书馆

Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

引用

Multimedia Tools and Applications 2025年第15期84卷 15123-15149页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

来源：评论

学校读者我要写书评

暂无评论

Algorithms for the Collaborative Delivery Problem with Monitored Constraints 19th

Algorithms for the Collaborative Delivery Problem with Mo...

引用

19th International Conference and Workshops on Algorithms and Computation, WALCOM 2025

作者： Blank, Lotte Huynh, Kien C. Luo, Kelin Naredla, Anurag Murty Institute of Computer Science University of Bonn Bonn Germany Communications and Transport Systems ITN Linköping University Linköping Sweden Department of Computer Science and Engineering University at Buffalo GetzvilleNY United States

ISBN: (纸本)9789819628445

We provide efficient algorithms to solve package delivery problems in which a sequence of drones work together to ‘optimally’ deliver a package from a source s to a target t. The package may be transferred from one drone to another only on a given line on which secure package transfers are ensured (we refer to this as monitored constraints). We allow the source and target to lie outside of this line. The drones have different starting locations, speeds, and rates of fuel consumption. Two notions of optimality are studied—fuel-efficient delivery (minimize total fuel consumption in a successful delivery), and fastest delivery (minimize total time for delivery). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Integrating GIS-Remote Sensing: A Comprehensive Approach to Predict Oceanographic Health and Coastal Dynamics

引用

Remote Sensing in Earth systems Sciences 2025年第1期8卷 200-212页

作者： Krishnamoorthy, R. Tanaka, Kazuaki Begum, M. Amina Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India Department of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Department of ECE Sri Venkateswaraa College of Technology Tamilnadu Chennai India

The monitoring of oceanographic and coastal dynamics is essential for understanding the effects of climate change, predicting natural disasters, and managing coastal resources. Remote sensing technology, particularly through satellite imagery, has revolutionized the ability to collect vast amounts of environmental data over large spatial and temporal scales. However, the complexity of these datasets, which often include intricate spatial patterns and long-term temporal trends, requires advanced machine learning models capable of handling both dimensions effectively. In this study, we present a Hybrid GIS-Transformer Network for analyzing oceanographic and coastal dynamics using remote sensing data. The proposed model integrates the spatial feature extraction capabilities of the GIS network with the temporal analysis strengths of the transformer model. This approach enables a comprehensive understanding of both spatial and temporal patterns in high-resolution satellite imagery, allowing for more accurate detection and prediction of environmental changes. Our methodology was tested across multiple tasks, including coastal erosion detection, sea surface temperature (SST) prediction, wave height prediction, and ocean currents detection. The Hybrid GIS-Transformer model consistently outperformed existing models such as CNN, ResNet50, MobileNet, EfficientNet, and LSTM. For coastal erosion detection, the hybrid model achieved an accuracy of 96.12%, while for SST prediction, the accuracy was 95.43%. In wave height prediction, the model achieved an accuracy of 93.02%, and for ocean currents detection, an accuracy of 91.88% was recorded. These results demonstrate the model’s superiority in capturing both fine spatial details and long-term temporal dynamics compared with individual models. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Coastal dynamics Deep learning Environmental monitoring Hybrid GIS-Transformer Machine learning Oceanographic analysis Remote sensing Sea surface temperature prediction Spatial feature extraction Temporal analysis Wave height prediction

来源：评论

学校读者我要写书评

暂无评论

Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

引用

computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

来源：评论

学校读者我要写书评

暂无评论

Single-Stage 48V-to-1V Regulator with a Half-Turn Transformer and Current-Doubler Rectifier 14

Single-Stage 48V-to-1V Regulator with a Half-Turn Transforme...

引用

14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Xu, Xinmiao Li, Qiang Virginia Polytechnic Institute and State University Center for Power Electronics Systems Department of Electrical and Computer Engineering BlacksburgVA24061 United States

ISBN: (纸本)9798331516116

Single-stage 48V-to-1V regulated solutions for high-performance processors become increasingly popular. This paper presents a full-bridge transformer-based buck converter with the current-doubler rectifier. The transformer adopts a half-turn winding structure for reduced resistance and leakage inductance. Operating principles of the converter with the half-turn transformer and the converter's optimized printed circuit board layout are illustrated. Synchronous rectifiers are integrated in high-frequency loops and are treated carefully to reduce parasitic-inductor-induced loss. With the help of recent advances in gallium nitride (GaN) and silicon transistors, a 48V-to-1.8V GaN-based prototype switching at 700 kHz is constructed, which achieves 95.34% peak efficiency, 92.79% full-load efficiency (including gating loss) and 576 W/in3 power density with 50-A current per phase. The same hardware achieves 94.00% peak efficiency and 89.75% full-load efficiency with 55-A current per phase in 48V-to-1.0V conversion. Experimental results with 54-V input are also included in this paper, demonstrating the great potential of the single-stage solution for future data centers. © 2025 IEEE.

关键词： Electric rectifiers

来源：评论

学校读者我要写书评

暂无评论

Leveraging Ensembles of Pre-trained CNNs for Improved Lung Cancer Detection and Classification

引用

Procedia computer Science 2025年 260卷 151-159页

作者： Dasari Bhulakshmi Dharmendra singh rajput Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore 632014 India

Lung cancer is a serious global health concern, highlighting the importance of early identification to improve patient survival rates. We explore the potential of deep learning(DL) models to improve lung cancer diagnosis through detection and classification models. The performance of pre-trained ResNet50, VGG19, and AlexNet models is evaluated on an augmented lung cancer image dataset to determine their suitability for lung cancer classification. The fine-tuned models are evaluated for their ability to identify and classify lung cancer, achieving high accuracy of 92.88%, 93.06%, and 95.23%. While promising, this approach has limitations. The efficacy of DL models is significantly influenced by both the quality and volume of the training data. Additionally, the ”black box” nature of DL models can make it challenging to understand their decision-making process. However, the results of this study suggest that DL ensembles hold significant potential for lung cancer diagnosis. Further research is necessary to address limitations and explore interpretability techniques for wider clinical acceptance.

关键词： Deep Learning Machine Learning convolutional neural networks Artificial Intelligence

来源：评论

学校读者我要写书评

暂无评论

Connectome graph attention network (CGAT): A multimodal approach for enhanced classification of Alzheimer's disease

Results in Engineering

引用

Results in engineering 2025年 26卷

作者： P, Udayakumar Subhashini, R. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

This study proposes a model for Alzheimer's Disease (AD) diagnosis called the Connectome Graph Attention Network (CGAT), designed to improve the classification of AD stages. By integrating multimodal neuroimaging data and clinical diagnosis information, CGAT constructs a connectome graph for each individual, with brain regions as nodes and the connections between them as edges. CGAT enhances classification by employing an attention mechanism that highlights critical brain region connections. A unique feature of the model is its capacity to merge features from diverse imaging modalities effectively, using modality-specific loss weighting to ensure balanced data contribution in the final classification. Developed in PyTorch Geometric (PyG), CGAT was optimized through the NNI AutoML toolkit. The model demonstrated a notable AUROC score of 94.00, outperforming other graph-based models like GCN, GraphSAGE, GGNN, and ChebNet, especially in differentiating EMCI from LMCI. Visualizations using t-SNE revealed CGAT's ability to distinctly separate cognitive states, emphasizing important connections among brain regions. Overall, CGAT efficiently combines multimodal neuroimaging data for improved, multi-class AD stage classification. © 2025 The Author(s)

关键词： Neurodegenerative diseases

来源：评论

学校读者我要写书评

暂无评论

Self-powered tactile sensor for real-time recognition of Morse code based on machine learning

引用

Nano Research 2025年第2期18卷 760-768页

作者： Shenxing Tan Yang Jiang Xujiang Chao Fei Liang Ripeng Li Tao Jiang Hai-Dong Yu Zhong Lin Wang Guangzhou Institute of Blue Energy Guangzhou 510555China Frontiers Science Center for Flexible Electronics Xi'an Institute of Flexible Electronics(IFE)and Xi'an Institute of Biomedical Materials&EngineeringNorthwestern Polytechnical UniversityXi'an 710072China Beijing Key Laboratory of Micro-Nano Energy and Sensor Center for High-Entropy Energy and SystemsBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing 101400China School of Nanoscience and Engineering University of Chinese Academy of SciencesBeijing 100049China School of Mechanical Engineering Northwestern Polytechnical UniversityXi’an 710072China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

Developing lightweight,green,and flexible wearable electronics with high sensitivity and multifunctional sensing capabilities is of important significance in the field of outdoor sports,such as mountaineering,animal tracking and *** work proposes a silk fibroin fibers-based triboelectric nanogenerator(SF TENG)to harvest tiny energy from human fingertip tapping and act as a self-powered tactile *** SF-TENG adopts a green,efficient,and low-cost fabrication strategy,in which a breathable and electropositive silk fibroin fiber membrane and a silver conductive layer are prepared by electrostatic spinning and magnetron sputtering,and combined with a conductive cloth and a breathable tape to form a flexible sensor that can be attached to a human *** thin and soft portable TENG device,having a thickness of only 0.3 mm and a mass of 354 mg at the dimension of 4.5 cm×4.5 cm,can generate a maximum power density of 1.0 mW·m^(–2).Furthermore,the SF-TENG has excellent sensitivity of 1.767 mV·Pa^(–1) with good cyclic *** superior sensing characteristics provide new avenues for Morse code applications toward outdoor wearable autonomous *** proposed SF-TENG offers promising solutions in multi-scenario outdoor sport,human-machine interface interaction,and security systems.

关键词： flexible electronics triboelectric nanogenerator silk fibroin Morse code translation

来源：评论

学校读者我要写书评

暂无评论

Improved Single DC Source Hybrid Neutral Point Clamped Converter with 13 Levels

引用

IEEE Transactions on Power Electronics 2025年

作者： Zakzewski, Daniel Resalayyan, Rakesh Khaligh, Alireza University of Maryland Department of Electrical and Computer Engineering The Institute for Systems Research United States

In this paper, a 13-level circuit topology is proposed, which features a single dc source, extended linear modulation range, and a high ratio of output voltage levels to semiconductor devices. The proposed converter, which consists of a series-connected floating H-bridge (FHB) supplementing a five-level active neutral point clamped converter (5L-ANPC), can generate 13 pole voltage levels in the normal modulation region and 15 levels in the extended modulation region. The key performance improvements offered by the proposed topology are enhanced power quality, reduced losses, and increased power density. Whereas existing schemes with a similar circuit structure regulate the FHB to one-eighth of the dc-link voltage and only achieve nine levels, the proposed joint-phase redundancy balancing approach regulates the FHB stage to one-twelfth of the total dc-link voltage on a switching-frequency basis, thereby increasing output power quality while reducing the converter's cost and size without the need for multiple isolated supplies. Further, the proposed modulation scheme also allows for a switching frequency reduction of the higher voltage blocking 5L-ANPC stage by shifting the high-frequency switching to the low voltage blocking FHB stage, thus reducing losses. This work also evaluates operation in the topology's extended linear modulation range which is shown to be increased to 0.612 times the dc-link voltage (modulation index, M = 1.223) without limitations on load power factors, compared to the typical limit of to 0.577 times the dc-link voltage (M = 1.154) for non-boosting topologies such as 3L-ANPC and 5L-ANPC. The proposed converter uses boosting states enabled by the FHB in this extended modulation region, which increases the number of output levels to 15. The effectiveness of the proposed topology and capacitor balancing scheme is validated using experimental and analytical results. Based on experimental and analytical results, the proposed topology is shown to h

关键词： Model predictive control

来源：评论

学校读者我要写书评

暂无评论

An with effective ensemble classifier for network intrusion detection system

引用

Peer-to-Peer Networking and Applications 2025年第2期18卷

作者： Nandhini, U. Santhosh Kumar, S.V.N. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

The increasing frequency of network attacks poses a significant challenge in maintaining network security. In response to security vulnerabilities, the employment of Intrusion Detection systems (IDS) has become crucial. An IDS is a software application, which monitors network traffic to monitor and identify various malicious actions in the network. Network Intrusion Detection systems (NIDS) specifically examine network traffic to identify and monitor the various malicious activities identified by the network nodes. However, many existing IDS solutions encounter difficulties in achieving optimal feature selection, aiming for high classification accuracy while minimizing false alarm rates. In the proposed system, the Linear Discriminant Analysis (LDA) technique is employed to reduce the dataset dimensionality. Further, an Enhanced Whale Optimization Algorithm (EWOA) is utilized for efficient feature selection for global optimization tasks. To perform classification, an ensemble classifier is proposed which employs Random Forest (RF) and XGBoost. The main objective of the proposed approach is to improve classification accuracy while minimizing the false alarm rate by combining the benefits of RF and XGBoost. The proposed work is implemented by using the python jupyter framework. The experimental results show that the proposed system has an overall classification accuracy of 96.08%, a false alarm rate of 0.01%, and an overall detection rate of 94.03%, by using the NSL-KDD dataset when it is compared with their counterparts. © The Author(s).

关键词： Application programs

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：