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International Conference on Advanced Communication Technology (ICACT)

作者： Lan Dong Thi Ngoc Ngo-Thi-Thu-Trang Kyoung-Mok Kim Vung Pham Ha-Nam Nguyen Department of Economic Information System Academy of Finance Hanoi Viet Nam Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi CO Vietnam Department of Medical Information Sahmyook Health University Seoul Republic of Korea Computer Science Department Sam Houston State University Huntsville Texas TX USA Faculty of Information Technology Electric Power University Hanoi Viet Nam

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

This study explores the application of machine learning models in forecasting macro-economic indicators, including GDP, inflation rate, unemployment rate, and exchange rate across 11 Southeast Asian countries. The models used include Linear Regression, ARIMA, Random Forest, XGBoost, LSTM, and SVM. We conducted a performance comparison of each model based on MAE, RMSE, and R 2 metrics to evaluate the accuracy of the *** experimental results indicate that Random Forest and XGBoost models excel in predicting nonlinear and complex indicators such as GDP and unemployment rate, while ARIMA and Linear Regression models perform better in time series with clear regular patterns, like inflation rate. The LSTM model shows inconsistent effective-ness, requiring large data volumes and complex optimization processes. SVM demonstrates potential in handling nonlinear data but requires careful *** study concludes that using machine learning models presents significant potential for improving the accuracy of macroeconomic forecasting. However, model tuning and optimization are essential to match the characteristics of each type of economic indicator. Future research directions include developing hybrid models and integrating additional factors such as market sentiment, social and environmental indicators (ESG) to enhance forecasting outcomes.

关键词： Support vector machines Economic indicators Biological system modeling Predictive models Macroeconomics Forecasting Unemployment Random forests Optimization Long short term memory

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Synthesizing realistic images for improved early detection of novel pandemics: A GAN enabled approach

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Heliyon 2025年第6期11卷

作者： Rounaq, Sumera Shah, Shahid Munir Aljawarneh, Mahmoud Mohammad Khan, Sarah Muhammad, Ghulam Department of Computer Science DHA Suffa University Karachi Pakistan Faculty of Engineering Sciences and Technology Hamdard University Karachi Pakistan Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman Jordan System Architect Low Code Solutions London United Kingdom Department of Computer Science Bahria University Karachi Pakistan

Infectious diseases like the novel Coronavirus (COVID-19) affect millions of individuals if not managed well in time. Thus, to reduce the transmission rate, effective diagnostic techniques must be identified. Early detection and diagnosis may be aided by Artificial Intelligence (AI) based automated diagnostics;however, these algorithms need a lot of pertinent data to be trained. In the early stages of a pandemic, gathering a lot of data is never an easy task. This presents challenges for accurate automatic diagnosis of such infectious diseases. To address this issue, a customized Generative Adversarial Networks (GAN) based architecture i. e., CoroWGAN-GP is proposed to synthesize images and to expand datasets. A dataset of 588 images with COVID-19, Normal, and Viral Pneumonia categories (taken from Cohen's repository [1]) was expanded using the proposed CoroWGAN-GP approach and used to train a proposed customized deep CNN architecture i. e., DeepCNN-CovNet19. Experimental results demonstrate that the proposed model achieved a considerable detection accuracy. i. e., 98.0 % in detecting the disease. The effectiveness of the proposed method was further evaluated by comparing its results with the state-of-the-art methods in the literature. Comparative analyses indicate that the proposed approach offers a promising solution for the accurate diagnosis of COVID-19-like pandemics. © 2025 The Authors

关键词： Chest X-ray Convolutional neural networks COVID-19 CXR GAN Pandemic Wasserstein GAN

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Improved Power Transfer Efficiency Using a Dual Transmitter Receiver Design With Integrated Decoupling Coils for IPT EV Charging

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IEEE Access 2025年 13卷 91362-91373页

作者： Bilal Alam Saad Mekhilef Marizan Mubin Kok Soon Tey Addy Wahyudie Department of Electrical Engineering Power Electronics and Renewable Energy Research Laboratory University of Malaya Kuala Lumpur Malaysia School of Engineering Swinburne University of Technology Melbourne VIC Australia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Electrical and Communication Engineering United Arab Emirates University (UAE-U) Al Ain United Arab Emirates

In high-power inductive power transfer (IPT) systems, multiple coils are used to enhance efficiency, but interactions between coils on the same side can reduce overall performance. Traditionally, decoupling coils are added to minimize these interactions, however they don’t contribute to power transfer and may even hinder system efficiency. This article introduces a new IPT system and coil structure that integrates decoupling coils, which not only improve mutual interaction between same-side coils but also actively contribute to power transfer. The coils are optimized for high mutual inductance, strong coupling coefficient, reduced self-inductance, and minimized side-by-side mutual inductance, performing well even with X-axis, Y-axis, and angular misalignments. Using 3-D Finite Element Analysis (ANSYS-Maxwell), the magnetic coupler’s performance is thoroughly evaluated, and a downsized experimental setup demonstrates the system’s feasibility. The proposed system achieves a maximum dc-dc efficiency of 94.27%, successfully delivering 1 kW across a 120-mm air gap with zero-voltage switching, presenting a significant advancement for multi-transmitter and multi-receiver systems.

关键词： Coils Receivers Transmitters Mathematical models Couplings Inductance Costs Voltage Rectifiers Integrated circuit modeling

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Integrative Multi-Source Mapping of Coastal Environments Using Hy-perspectral Data Fusion Platforms

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Han, Huimin Por, Lip Yee Talpur, Bandeh Ali Hashim, Muhammad Yang, Jing Saidani, Taoufik Djumaniyazova, Mukhayya Xusinovna Madrakhimovich, Khudoynazarov Egambergan Talpur, Kazim Raza Hainan Vocational University of Science and Technology Mechanical and Electrical Engineering Col-lege Haikou571126 China Uni-versiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Trinity Col-lege Dublin School of Computer Science and Statistics Ireland National Textile Uni-versity Faisalabad Faisalabad Business School Pakistan Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur50603 Malaysia Northern Border University Center for Scientific Research and Entrepreneurship Arar73213 Saudi Arabia Urganch State University Pedagogy and Psychology Department Uzbekistan Mamun University Uzbekistan Jamshoro Institute of Health Management and Re-search Sciences Sindh Pakistan

Hyperspectral images (HSIs) not only have a large number of spectral features, but also show a comprehensive spa-tial distribution of land cover and offer substantial advantages in the fine classification of ground materials. However, due to the high dimensionality and redundancy of HSIs and class imbalance in hyperspectral datasets, improving classification performance is still a huge challenge. Recently, graph-based learning algorithms are proven to be better classifiers for high dimensional data. Learning graph data and modeling spatial topological links be-tween features are significantly more accessible and more effective with graph-based deep learning, which has been demonstrated to be a promising approach. In the past few years, convolutional neu-ral networks (CNNs) and graph convolutional networks (GCNs) have achieved good results in HSI classification, but CNNs strug-gle to achieve good accuracy in low samples, while GCNs have a substantial computational cost. To resolve these issues, this paper proposes a feature fusion model using a 3D-CNN and hybrid-GAT named FFCHG. The algorithm consists of two elements: the 3D-CNN, which produces good classification for 3D HSI cube data, and GAT-based encoder and decoder modules that help in im-proving the classification accuracy of the 3D-CNN. Finally, the multiple features are merged with the help of two neural network models. Experiments on three public HSI datasets show that the proposed methods perform better than other state-of-the-art methods using the limited training samples and in low classifica-tion time with accuracy of more than 90% in all datasets and kappa with more than 91% for each dataset. © 2025 IEEE.

关键词： Convolutional neural networks

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Blockchain technology and its impact on sustainable supply chain management in SMEs

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PeerJ computer science 2025年 11卷 e2466-e2466页

作者： Fang, Chao Ullah, Nazir Batumalay, M. Al-Rahmi, Waleed Mugahed Alblehai, Fahad School of Economics and Management Bengbu University Anhui Bengbu China School of Management Science and Engineering Nanjing University Nanjing China Faculty of Data Science & IT INTI International University Negeri Sembilan Nilai Malaysia Department of Management Information System College of Business Administration Dar Al Uloom University Al Falah Riyadh Saudi Arabia Computer Science Department Community College King Saud University Riyadh Saudi Arabia

The COVID-19 pandemic has had a significant impact on small and medium-sized enterprises (SMEs), leading to disruptions in supply chains, financial losses, and closures. To overcome these challenges, organizations, including those in developing economies like Malaysia, are turning to blockchain technology as a solution to enhance traditional supply chain management frameworks. This study aims to identify the factors that influence the acceptance of blockchain technology among SMEs. By drawing on established adoption theories such as the technology acceptance model (TAM), diffusion of innovation (DOI) theory, and theory of planned behavior (TPB), the researchers developed a research framework. They utilized partial least square structural equation modeling (PLS-SEM) to analyze the causal relationships between different constructs and test their hypotheses. The findings confirmed that the constructs of the technology acceptance model, specifically perceived usefulness, perceived ease of use and attitude were significantly associated with the intention to use blockchain technology. Additionally, the constructs of the diffusion of innovation theory, relative advantage and compatibility, showed significant associations with perceived ease of use, while complexity had a negligible relationship with perceived usefulness and perceived ease of use. The construct of subjective norms from the theory of planned behavior exhibited a significant relationship with perceived usefulness and an insignificant relationship with intention to use. Finally, perceived behavioral control demonstrated a positive relationship with intention to use. The study’s findings provide valuable insights for blockchain developers and organizations aiming to make informed decisions regarding the application of blockchain technology as a process innovation in SMEs. © 2025 Fang et al.

关键词： Blockchain

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qing Liu Qian Gao Jun Fan Zhiqiang Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Ltd Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN— a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model’s effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets.

关键词： Measurement Bridges Contrastive learning Signal processing Data processing Vectors Space exploration Resource management Speech processing Mutual information

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A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19

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computers, Materials & Continua 2022年第1期70卷 1381-1399页

作者： Ahmed Reda Sherif Barakat Amira Rezk Department of Information System Faculty of Computer and Information ScienceMansoura UniversityMansouraEgypt

Many respiratory infections around the world have been caused by ***-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival *** is a high need for computer-assisted diagnostics(CAD)in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud *** learning(ML)has been used to examine chest X-ray *** this paper,a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes,a pneumonia patient,a COVID-19 patient,or a normal ***,three different pre-trainedConvolutionalNeuralNetwork(CNN)models(resnet18,resnet25,densenet201)are employed for deep feature ***,each feature vector is passed through the binary Butterfly optimization algorithm(bBOA)to reduce the redundant features and extract the most representative ones,and enhance the performance of the CNN *** selective features are then passed to an improved Extreme learning machine(ELM)using a BOA to classify the chest X-ray *** proposed paradigm achieves a 99.48%accuracy in detecting covid-19 cases.

关键词： Butterfly optimization algorithm(BOA) covid-19 chest X-ray images convolutional neural network(CNN) extreme learning machine(ELM) feature selection

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Mathematical analysis and stability of thermally magnetized ternary hybrid nanofluid over porous moving wedge with suction/injection and entropy generation

Results in Engineering

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Results in Engineering 2025年 26卷

作者： Zeeshan Khan, Waris Kouki, Marouan Boukhris, Imed Arishi, Ali Garalleh, Hakim AL Safi, Mohammad Sediq Department of Mathematics and Statistics Bacha Khan University Charsadda KP Pakistan Department of Mathematics and Statistics Hazara University KP Mansehra21120 Pakistan Department of Information System Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Department of Physics Faculty of Science King Khalid University AbhaP.O. Box 960 Saudi Arabia Department of Industrial Engineering King Khalid University Abha61421 Saudi Arabia Department of Mathematical Science College of Engineering University of Business and Technology Jeddah21361 Saudi Arabia Department Computer Science Allamah University Kabul Afghanistan

Present study investigates the ternary hybrid nanofluid to analyze thermally magnetized flow over a moving wedge with wall permeability and convective boundary conditions. Using stability analysis and suction and injection give the novel contributions to the study. Water host fluid containing titania, silver, and graphene nanoparticles is used. A self-similarity approach is applied to convert the Partial Differential Equations into dimensionless Ordinary Differential Equations. Numerical solution has been obtained through bvp4c method. Duality has been observed due to suction and injection. From the stability analysis it is perceived that first solution is stable while the second solution is unstable. The impact of embedded factors like magnetic factor, wedge angle parameter Prandtl and Eckert numbers, Suction parameter, and thermal radiation on flow characteristics, temperature distribution, and the entropy generation are demonstrated. Entropy generation is compared for the convectional fluid, nanofluid, hybrid nanofluid, and THNF. Similarly, entropy generation for the downhill flow (m=−0.8), flat flow (m=0), Wedge flow (m=0.2), and stagnation flow (m=1) has been investigated for both suction (S=1) and injection (S=−1) and it has been observed that entropy generation is higher fir the THNF compared to other flows. Additionally, for confirmation, the present study is compared with published work. © 2025 The Author(s)

关键词： Heat radiation

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Fusion Strategy for Improving Medical Image Segmentation

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computers, Materials & Continua 2023年第2期74卷 3627-3646页

作者： Fahad Alraddady E.A.Zanaty Aida HAbu bakr Walaa M.Abd-Elhafiez Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Information System Department Faculty of Computers and InformationSohag UniversitySohagEgypt Mathematical and Computer Science Department Faculty of ScienceAswan UniversityAswanEgypt College of Computer Science&Information Technology Jazan UniversityJazanKingdom of Saudi Arabia Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohagEgypt

In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image *** proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)*** algorithms are used to compute the accuracy of each method while the outputs are passed to fusion *** order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these *** proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical *** is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.

关键词： Decision fusion particle swarmoptimization(PSO) cuckoo search algorithm CS McCulloch algorithm genetic algorithm CT and MRI

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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