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Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

computers, Materials & Continua 2024年第6期79卷 4449-4469页

作者： Weiming Huang Baisong Liu Zhaoliang Wang Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo315211China Inner Mongolia Metal Material Research Institute Baotou014000China

In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the ***,it uses co-occurrence techniques and tries to combine nodes’textual content for *** still do not,however,directly simulate many interactions in network *** order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling ***,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation ***,the Commuting Matrix for massive node pair paths is used to improve computational ***,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson *** addition,we also consider solving the model’s parameters by applying variational *** results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational *** on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.

关键词： Collaborative filtering citation networks variational inference poisson factorization tag recommendation

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Improving Badminton Action Recognition Using Spatio-Temporal Analysis and a Weighted Ensemble Learning Model

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computers, Materials & Continua 2024年第11期81卷 3079-3096页

作者： Farida Asriani Azhari Azhari Wahyono Wahyono Department of Computer Science and Electronics Universitas Gadjah MadaYogyakarta55281Indonesia Electrical Engineering Department Universitas Jenderal SoedirmanPurbalingga53371Indonesia

Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports ***,identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion *** learning techniques like convolutional neural networks(CNNs),long short-term memory(LSTM),and graph convolutional networks(GCNs)improve recognition in large datasets,while the traditional machine learning methods like SVM(support vector machines),RF(random forest),and LR(logistic regression),combined with handcrafted features and ensemble approaches,perform well but struggle with the complexity of fast-paced sports like *** proposed an ensemble learning model combining support vector machines(SVM),logistic regression(LR),random forest(RF),and adaptive boosting(AdaBoost)for badminton action *** data in this study consist of video recordings of badminton stroke techniques,which have been extracted into spatiotemporal *** three-dimensional distance between each skeleton point and the right hip represents the spatial *** temporal features are the results of Fast Dynamic Time Warping(FDTW)calculations applied to 15 frames of each video *** weighted ensemble model employs soft voting classifiers from SVM,LR,RF,and AdaBoost to enhance the accuracy of badminton action *** E2 ensemble model,which combines SVM,LR,and AdaBoost,achieves the highest accuracy of 95.38%.

关键词： Weighted ensemble learning badminton action soft voting classifier joint skeleton fast dynamic time warping spatiotemporal

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Trade Ease with Machine Learning and AWS

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IEEE Access 2023年 11卷 25893-25905页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Global trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by creating a machine learning trading strategy using a customized deep neural network (DNN). The strategy was then tested using a trading station before deployment on a cloud machine. The execution of the strategy resulted in a total profit of 42.74526 USD, with a test accuracy score of 87.45% and a training accuracy score of 89.15% over 6047 epochs. The roadmap provides a step-by-step overview of the entire process from strategy development to execution. In addition, this study offers insights into related issues and solutions that can enhance the effectiveness of trading strategies. Overall, this study contributes significantly to the field of cloud-based trading strategies and opens avenues for future research. © 2013 IEEE.

关键词： Deep neural networks

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Impact of STATCOM on Voltage Regulation and Total Harmonic Voltage Distortion in Low Load Conditions 13

Impact of STATCOM on Voltage Regulation and Total Harmonic V...

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13th International electrical engineering Congress, iEECON 2025

作者： Ratanabuntha, Tharin Sathiracheewin, Supalak Kasetsart University Faculty of Science and Engineering Department of Electrical and Computer Engineering Sakon Nakhon Thailand

ISBN: (纸本)9798331543952

The operation of a STATCOM in regulating voltage levels and filtering harmonics under different load conditions is investigated. Utilizing a three-phase bridge for voltage regulation at the point of common coupling (PCC), the STATCOM is tested with six resistive and inductive load combinations across five load levels. Experimental results show that the STATCOM maintains a voltage deviation within 5% of the 380 V reference for most loads and achieves a harmonic filtering efficiency ranging from 1.39% to 72.67%. However, at higher loads, the STATCOM's performance diminishes as the loads exceed its power compensation capacity, resulting in a slight increase in system harmonics distortion compared to the significant drop in voltage, which has a greater impact on the electrical system. The STATCOM's control system, based on feedback loops, optimizes active and reactive power flow, ensuring effective voltage regulation and harmonic suppression. As a Flexible AC Transmission System (FACTS) device, STATCOM enhances voltage stability while mitigating harmonic distortion in the electrical system. © 2025 IEEE.

关键词： Electric power utilization

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Short-Term Power Load Forecasting Method Based on GRU-Transformer Combined Neural Network Model

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Journal of Computing and Information Technology 2024年第1期32卷 1-14页

作者： Mao, Weiwei Yu, Suping Chen, Wenqing College of Computer and Information Engineering Luoyang Institute of Science and Technology Luoyang China School of Electrical Engineering and Automation Luoyang Institute of Technology Luoyang China

Load Forecast (LF) is an important task in the planning, control and application of public power systems. Accurate Short Term Load Forecast (STLF) is the premise of safe and economical operation of a power system. In the research of short-term power load forecasting, machine learning and deep learning are the most popular methods at present, but there still exists a problem that the single and simple structure of power load forecasting model leads to low accuracy of load forecasting. In order to improve the accuracy of STLF, a Gated Cycle Unit (GRU)-Transformer combined neural network model is proposed. Transformer encoder structure is used as feature extractor to mine the complex mapping relationships between the input features and load. The advantage of self-attention mechanism is used to solve the problem of information loss of long sequences in short-term power load forecasting. At the same time, the multivariate time series model of GRU is used for model training. The experimental results on the power load data set of a certain region in southwest China and Panama City show that the proposed combined model prediction method has higher accuracy than those proposed in other literatures, which further proves its feasibility and superiority. © 2024, University of Zagreb Faculty of electrical engineering and Computing. All rights reserved.

关键词： Neural network models

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A Smart CIoT with Secure Healthcare Framework Using Optimized Deep Recuperator Neural Network Long Short-Term Memory

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IEEE Internet of Things Journal 2024年第6期11卷 10551-10562页

作者： Narmatha, C. Manimurugan, S. Karthikeyan, P. University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia Oakland University Department of Electrical and Computer Engineering RochesterMI112345 United States National Chung Cheng University Department of Computer Science and Information Engineering Chiayi62102 Taiwan RV University School of Computer Science and Engineering Bengaluru560059 India

The healthcare system currently relies on the facility to store and process large amounts of health data, supported by efficient management. The Internet of Things (IoT) has driven the growth of Adroit Healthcare, which has vast data processing capabilities and extensive data collection. The consumer IoT (CIoT), also known as the IoT in the context of individual use cases, is dominated by personal healthcare applications. The remarkable expansion of the CIoT can be attributed to the extensive embrace of wearable technology, facilitating everyday monitoring of vital health indicators like blood pressure, heart rate, and respiration rate. However, the CIoT raises concerns about data security, confidentiality, and customer trust. This study proposes an integrating blockchain and deep learning (DL) approach to promote responsible use of CIoT. The study develops an Intelligent IoT (IIoT) and healthcare diagnostic model, named BT-PWO-DRNN-LSTM, using blockchain technology and Prairie Wolf optimization-based deep recuperator neural network (DRNN) and long short-term memory (LSTM). The BT-PWO-DRNN-LSTM approach includes three key processes: 1) encrypted transactions;2) cryptographic hash feature;and 3) medical diagnosis. The PWO technique is used for secure communication of medical images, the neighborhood catalogue disposition (NCD) technique is used in the cryptographic hash feature operation (CHFO), and the DL approach is employed as a classification algorithm for diagnosing disorders. The use of PWO methodology for secure healthcare communication and optimal parameter fine-tuning highlights the originality of the study. The BT-PWO-DRNN-LSTM prototype showed positive results with high sensitivity, specificity, and accuracy during the diagnostic process. © 2014 IEEE.

关键词： Blockchain

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Uncertainty quantification in large language models through convex hull analysis

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Discover Artificial Intelligence 2024年第1期4卷 1-14页

作者： Catak, Ferhat Ozgur Kuzlu, Murat Department of Electrical Engineering and Computer Science University of Stavanger Rogaland Norway Department of Engineering Technology Old Dominion University NorfolkVA United States

Uncertainty quantification approaches have been more critical in large language models (LLMs), particularly high-risk applications requiring reliable outputs. However, traditional methods for uncertainty quantification, such as probabilistic models and ensemble techniques, face challenges when applied to the complex and high-dimensional nature of LLM-generated outputs. This study proposes a novel geometric approach to uncertainty quantification using convex hull analysis. The proposed method leverages the spatial properties of response embeddings to measure the dispersion and variability of model outputs. The prompts are categorized into three types, i.e., ’easy’, ’moderate’, and ’confusing’, to generate multiple responses using different LLMs at varying temperature settings. The responses are transformed into high-dimensional embeddings via a BERT model and subsequently projected into a two-dimensional space using Principal Component Analysis (PCA), Isomap, Multidimensional Scaling (MDS). The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to cluster the embeddings and compute the convex hull for each selected cluster. The experimental results indicate that the uncertainty of the model for LLMs depends on the prompt complexity, the model, and the temperature setting. © The Author(s) 2024.

关键词： Embeddings

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Hybrid Fuzzy Data Aggregation and Optimization-Based Routing for Energy Efficiency in Heterogeneous Wireless Sensor Networks

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第20期48卷 95-106页

作者： Alhijaj, Asaad A. Marzook, Ali K. Hussein, Dheyaa Mezaal Alkenani, Jawad Department of Computer Information Systems College of Computer Science and Information Technology University of Basrah Basrah Iraq University of Basrah College of Engineering Department of Electrical Engineering Basrah Iraq University of Technology Office of the President of the University Baghdad Iraq Department of Computer Science Shatt Al-Arab University College Basra Iraq

Wireless sensor networks (WSNs) are networks with many sensor nodes that are utilized for various purposes, including the military and medical. In hazardous circumstances, precise data aggregation and routing are essential, and the energy consumption of the sensors needs to be closely controlled. Nonetheless, there is a significant chance of redundant data because of external factors and nearby sensors. A multitude of information can be found in large datasets, some of it unnecessary and others useful. This redundancy negatively impacts performance in terms of redundant transmission and computing costs. However, data aggregation might help a network get rid of unnecessary data. In this work, we present a hybrid protocol called fuzzy data aggregation with fuzzy spider monkey optimization routing protocol (FDA-FSMORP) that represents an intelligent approach to collecting sensor data in HWSNs considering energy consumption. The results indicated that the suggested method beat in minimizing data latency our approach reduced energy consumption by 73% using energy more effectively when compared to our simulated outcomes. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Sensor nodes

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TLP Balancer: Predictive Thread Allocation for Multitenant Inference in Embedded GPUs

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IEEE Embedded Systems Letters 2025年第3期17卷 180-183页

作者： Gil, Minseong Jeon, Jaebeom Kim, Junsu Choi, Sangun Koo, Gunjae Yoon, Myung Kuk Oh, Yunho Korea University School of Electrical Engineering Seoul02841 Korea Republic of Korea University Department of Computer Science Seoul02841 Korea Republic of Ewha Womans University Department of Computer Science Seoul03760 Korea Republic of

This letter introduces a novel software technique to optimize thread allocation for merged and fused kernels in multitenant inference systems on embedded graphics processing units (GPUs). Embedded systems equipped with GPUs face challenges in managing diverse deep learning workloads while adhering to quality-of-service (QoS) standards, primarily due to limited hardware resources and the varied nature of deep learning models. Prior work has relied on static thread allocation strategies, often leading to suboptimal hardware utilization. To address these challenges, we propose a new software technique called thread-level parallelism (TLP) Balancer. TLP Balancer automatically identifies the best-performing number of threads based on performance modeling. This approach significantly enhances hardware utilization and ensures QoS compliance, outperforming traditional fixed-thread allocation methods. Our evaluation shows that TLP Balancer improves throughput by 40% compared to the state-of-the-art automated kernel merge and fusion techniques. © 2009-2012 IEEE.

关键词： Quality of service

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An arrhythmia classification using a deep learning and optimisation-based methodology

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Journal of Medical engineering and Technology 2024年第7期48卷 253-261页

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Longowal India

The work proposes a methodology for five different classes of ECG signals. The methodology utilises moving average filter and discrete wavelet transformation for the remove of baseline wandering and powerline interference. The preprocessed signals are segmented by R peak detection process. Thereafter, the greyscale and scalograms images have been formed. The features of the images are extracted using the EfficientNet-B0 deep learning model. These features are normalised using z-score normalisation method and then optimal features are selected using the hybrid feature selection method. The hybrid feature selection is constructed utilising two filter methods and Self Adaptive Bald Eagle Search (SABES) optimisation algorithm. The proposed methodology has been applied to the ECG signals for the classification of the five types of beats. The methodology acquired 99.31% of accuracy. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Contrastive Learning

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