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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Cai, Rongshen Tao, Qian Tang, Yufei Shi, Min School of Software South China University of Technology Guangzhou China Department of Computer and Electrical Engineering and Computer Science Florida Atlantic University Boca Raton United States Institute for Informatics Washington University School of Medicine in St. Louis St. Louis United States

ISBN: (纸本)9783030891879

Graph neural networks (GNNs) are widely used on graph-structured data, and its research has made substantial progress in recent years. However, given the various number of choices and combinations of components such as aggregator and activation function, designing GNNs for specific tasks is very heavy manual work. Recently, neural architecture search (NAS) was proposed with the aim of automating the GNN design process and generating task-dependent architectures. While existing approaches have achieved competitive performance, they are not well suited to practical application scenarios where the computational budget is limited. In this paper, we propose an auto-designed lightweight graph neural network (ALGNN) method to automatically design lightweight, task-dependent GNN architectures. ALGNN uses multi-objective optimization to optimize the architecture constrained by the computation cost and complexity of the model. We define, for the first time, an evaluation standard for consumption cost with the analysis of the message passing process in GNNs. Experiments on real-world datasets demonstrate that ALGNN can generate a lightweight GNN model that has much fewer parameters and GPU hours, meanwhile has comparable performance with state-of-the-art approaches. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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Cutting-Edge Predictive Methods for Enhancing Short-Term Load Prediction

Cutting-Edge Predictive Methods for Enhancing Short-Term Loa...

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2024 International Conference on Electrical and computer engineering Researches, ICECER 2024

作者： Ghazal, Taher M. Janjua, Jamshaid Iqbal Abushiba, Walid Ahmad, Munir Abbas, Tahir Ateeq, Karamath College of Arts & Science Applied Science University P.O.Box 5055 Manama Bahrain Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Selangor Malaysia Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul02841 Korea Republic of TIMES Institute Department of Computer Science Multan Pakistan School of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798331539733

Efficient management and cost-related factors in the power sector call for accurate short-term load forecasting as it enables better planning with the electric grid and achieving stability within it. This paper looks into the state-of-the-art forecasting strategies that enhance load forecasts made in shorter time frames. Such techniques include but are not limited to machine learning applications, effective data models, and multi-dimensional modeling leveraging historical, meteorological, and economic data. Their review is carried out in detail with their advantages and disadvantages and their application in practice. Furthermore, this study strives to understand the barriers that need to be overcome in the quest to increase the accuracy of the forecasting models especially concerning renewable energy sources. We illustrate how new trends relate to future developments of predictive models about supporting the decision-making in operating power systems and improving the reliability and sustainability of energy systems. © 2024 IEEE.

关键词： Prediction models

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Employing Deep Neural Networks for Real-Time Anomaly Detection and Mitigation in IoT-Based Smart Grid Cybersecurity Systems

Employing Deep Neural Networks for Real-Time Anomaly Detecti...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Aadam Quraishi Maher Ali Rusho Anurag Prasad Ismail Keshta Richard Rivera Mohammed Wasim Bhatt M.D. Research Intervention Treatment Institute Houston Texas US Department of Lockheed Martin Engineering Management University of Colorado Boulder Boulder Colorado IIT Kharagpur (Rajiv Gandhi School Of Intellectual Property Law) Gurugram India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador Model Institute of Engineering and Technology Jammu J&K India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

This research introduces a novel anomaly detection framework for IoT -based Smart Grid Cybersecurity Systems. Leveraging autoencoders, LSTM networks, GANs, SOMs, and transfer learning, our approach achieves superior precision, recall, and execution time compared to existing methods. Visualizations and an ablation study further validate the method's efficiency, emphasizing the critical roles of attention mechanisms and transfer learning. This comprehensive solution addresses the dynamic challenges of smart grid cybersecurity, offering a versatile and adaptive anomaly detection mechanism for real-world applications. This indicates the real-time efficacy of our anomaly detection method. Through our study of ablation and all aspects of computing, we discovered that attention processes and transfer learning facilitate faster problem solving in a dynamic smart grid. Our method is distinct and adaptable enough to address every problem arising from the discovery of anomalies in IoT-driven Smart Grid Cybersecurity Systems.

关键词： Visualization Transfer learning Circuits Real-time systems Smart grids Problem-solving computer security

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Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients:A Multi-Center Retrospective Study in China

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engineering 2022年第1期8卷 116-121页

作者： Ye Yuan Chuan Sun Xiuchuan Tang Cheng Cheng Laurent Mombaerts Maolin Wang Tao Hu Chenyu Sun Yuqi Guo Xiuting Li Hui Xu Tongxin Ren Yang Xiao Yaru Xiao Hongling Zhu Honghan Wu Kezhi Li Chuming Chen Yingxia Liu Zhichao Liang Zhiguo Cao Hai-Tao Zhang Ioannis Ch.Paschaldis Quanying Liu Jorge Goncalves Qiang Zhong Li Yan School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Luxembourg Centre for Systems Biomedicine University of LuxembourgBelval L-4367Luxembourg Department of Plant Sciences University of CambridgeCambridge CB21TNUK Department of Emergency Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China AMITA Health Saint Joseph Hospital Chicago ChicagoIL 60657USA Department of Anesthesiology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Huazhong University of Science and Technology-Wuxi Research Institute Wuxi 214174China Division of Cardiology Department of Internal MedicineTongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Institute of Health Informatics University College LondonLondon NW12DAUK Department of Biomedical Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Infectious Diseases Shenzhen Key Laboratory of Pathogenic Microbiology and ImmunologyNational Clinical Research Center for Infectious DiseaseThe Third People’s Hospital of Shenzhen(Second Hospital Affiliated with the Southern University of Science and Technology)Shenzhen 518055China Department of Electrical and Computer Engineering&Division of Systems Engineering&Department of Biomedical Engineering Boston UniversityBostonMA 02215USA

Coronavirus disease 2019(COVID-19)has become a worldwide *** patients of COVID-19 suffer from a high mortality rate,motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk ***,we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital,Wuhan,China(development cohort)and externally validated with data from two other centers:141 inpatients from Jinyintan Hospital,Wuhan,China(validation cohort 1)and 432 inpatients from The Third People’s Hospital of Shenzhen,Shenzhen,China(validation cohort 2).The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of *** risk score can predict the mortality of individual patients more than 12 d in advance with more than 90%accuracy across all ***,the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low,intermediate,or high risk,with an area under the curve(AUC)score of *** summary,a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2);it has also been validated in independent cohorts.

关键词： COVID-19 Risk score Mortality risk prediction

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Preface and Acknowledgements

Preface and Acknowledgements

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作者： Favorskaya, Margarita N. Peng, Sheng-Lung Simic, Milan Alhadidi, Basim Pal, Souvik Institute of Informatics and Telecommunication Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Department of Creative Technologies and Product Design National Taipei University of Business Taipei City Taiwan School of Engineering RMIT University Bundoora East Campus MelbourneVIC Australia Department of Computer Information Systems Al-Balqa’ Applied University As-Salt Jordan Department of Computer Science and Engineering Global Institute of Management and Technology Kolkata India

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Stress Pattern Recognition Using Spatio Temporal Features Based on EEG Sport Simulation

Stress Pattern Recognition Using Spatio Temporal Features Ba...

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IEEE Industrial Electronics and Applications Conference (IEACon)

作者： Diah Risqiwati Hideyuki Ooshima Hiroki Sato Adhi Dharma Wibawa Evi Septiana Pane Mauridhi Hery Purnomo Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Universitas Muhammadiyah Malang Malang Indonesia Department of Systems Science and Engineering Shibaura Institute of Technology Saitama Japan Dept of Electrical Engineering Medical Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Industrial Training and Education of Surabaya Ministry of Industry RI Surabaya Indonesia Dept of Electrical Engineering Dept of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350382976

ISBN: (纸本)9798350382983

Several emotional factors that can induce stress affect participants performance during the competition, such as choking under pressure. This study aims to evaluate the performance of golf players under psychological pressure by creating a scenario in a golf putting simulation test. We forced each participant to compete by making a monetary reward scenario based on their winning score to increase each participant's stress level. Their winning score is classified as poor or good by measuring the distance to the target achievement. The objective is to detect the stress pattern by observing the prominent features and window segments in the alpha band. Our approach is processing the EEG data by wavelet transform and extracting the data into spatio temporal features in the central and occipital lobe. Our investigation found that the maximum amplitude and band power feature values were higher when the participants performed well, and this pattern is reversed when the participant's performance is poor. Meanwhile, based on brain area activity in a good performance, we found that central area activity is high from the first to the fourth segment. In contrast, occipital area activity is high only until the third segment. Following these parts, brain activity decreases compared to its previous state. This study also discovered that the significant segment window is located in the tenth segment, with a P-value < 0.05 between good and poor performance. Thus, this analysis's results can be used to recommend neurofeedback training for athletes to prevent choking under pressure.

关键词： Wavelet transforms Training Time-frequency analysis Psychology Human factors Feature extraction Electroencephalography Occipital Lobe Time-domain analysis Neurofeedback

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Advancing Fetal Surveillance with Physiological Sensing: Detecting Hypoxia in Fetal Sheep

Advancing Fetal Surveillance with Physiological Sensing: Det...

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IEEE SENSORS

作者： Weitao Tang Nhi Tran Nasim Katebi Reza Sameni Gari D. Clifford David Walker Vaishnavi Horlali Callum Taylor Robert Galinsky Faezeh Marzbanrad Electrical and Computer System Engineering Monash University Melbourne Australia The Ritchie Centre Hudson Institute of Medical Research Melbourne Australia Dept. of Biomedical Informatics Emory University Atlanta USA Dept. of Biomedical Engineering Georgia Institute of Technology Emory University Atlanta USA Department of Obstetrics and Gynaecology Monash University Melbourne Australia

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

Accurate detection of hypoxia during fetal monitoring is critical for timely intervention and prevention of brain injury. This study investigated hypoxia detection in fetal sheep by developing a classifier to differentiate between baseline, during acute hypoxia (umbilical cord occlusion; UCO), and post-hypoxia recovery (post-UCO), using physiological signals and an XGBoost machine learning model. A multi-modal approach integrating electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) signals was compared to a model based on the ECG signal only. We included recordings from 14 fetal sheep, each of which underwent acute hypoxia via a single UCO procedure. Performance of hypoxia detection was measured by Marco F1 score (F1 m ), Accuracy (Acc), and standard deviation of validation error (SDE). Our multi-modal model achieved F1 m = 0.88, Acc = 0.90, while the ECG-based approach achieved F1 m = 0.82, Acc = 0.85. These results indicate that the multi-modal approach was superior to the ECG-only model for overall classification. While ECG and multimodal models correctly classified over 92% and 97% of acute hypoxia intervals, respectively, the classification of post- UCO intervals proved more challenging. The complexity of interpreting physiological signals underscores the need to consider temporal dynamics and physiological context in fetal monitoring.

关键词： Accuracy Machine learning Electrocardiography Hypoxia Brain modeling Feature extraction Electromyography Electroencephalography Complexity theory Biomedical monitoring

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PND-Net: plant nutrition deficiency and disease classification using graph convolutional network (vol 14, 15537, 2024)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Bera, Asish Bhattacharjee, Debotosh Krejcar, Ondrej Department of Computer Science and Information Systems BITS Pilani Pilani Campus Pilani Rajasthan 333031 India Department of Computer Science and Engineering Jadavpur University Kolkata West Bengal 700032 India Faculty of Informatics and Management University of Hradec Kralove Hradec Kralove Czech Republic Skoda Auto University Na Karmeli 1457 293 01 Mlada Boleslav Czech Republic Malaysia Japan International Institute of Technology (MJIIT) Universiti Teknologi Malaysia Kuala Lumpur Malaysia

Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, calledPlantNutrition Deficiency andDiseaseNetwork (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40\(\times \): 95.50%, and BreakHis 100\(\times \): 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy).

关键词： Agriculture Cancer classification Convolutional neural network Graph convolutional network Nutrition deficiency Plant disease Spatial pyramid pooling

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Adaptive latent fingerprint image segmentation and matching using Chan-vese technique based on EDTV model 2

Adaptive latent fingerprint image segmentation and matching ...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Altrad, Abdullah Mahmoud Miaikil, Othman A.M. El-Ebiary, Yousef A. Baker Contreras, Jennifer Hilles, Mohanad M Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia FEU Institute of Technology Department of Information Technology Manila Philippines University College of Applied Science Information Technology Department Gaza Palestine

ISBN: (纸本)9781665432221

Biometrics such as face, fingerprint, iris, voice and palm prints are the most widely used, and as well the fingerprints are one of the most frequently used biometrics to identify individuals and authenticate their identity. commonly categorized into three different categories which are rolled, plain and latent fingerprints. The reliability of image segmentation for latent fingerprint which is used in criminal issues still challenges, The difficulty of latent fingerprint image segmentation mainly lies in the poor quality of fingerprint patterns and the presence of the noise in the background, This research has investigated the fingerprint segmentation and matching based on EDTV and presented Chan-vese active contour segmentation technique, in addition, presented NIST SD27 for grayscale dataset of latent fingerprint which is standard by National institute of Standard and technology, where is dataset have varieties of fingerprint image samples, a total about 258 of latent fingerprint, those samples collected from crime scenes and matching fingerprint and shown the performance of matching accuracy ROC and CMC curves, To evaluate the performance of the matching ROC and CMC curves has been deployed, The area under curve (AUC) of the ROC of the good images performance is 72% with CMC rank1-idnetification of 42% and rank-20 identification of 79%. the result shows that the latent fingerprint method performance is better for good latent fingerprint images compare to bad and ugly images, while there is no much difference for bad and ugly image. © 2021 IEEE.

关键词： Biometrics

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DRL-Based Green Task Offloading for Content Distribution in NOMA-Enabled Cloud-Edge-End Cooperation Environments

DRL-Based Green Task Offloading for Content Distribution in ...

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IEEE International Conference on Communications (ICC)

作者： Chao Fang Xiangheng Meng Zhaoming Hu Xiaoping Yang Fangmin Xu Peng Li Mianxiong Dong Faculty of Information Technology Beijing University of Technology Beijing China Purple Mountain Laboratories Nanjing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Engineering University of Aizu Japan Department of Sciences and Informatics Muroran Institute of Technology Muroran Japan

With the widespread utilization of intelligent devices, massive mobile users' needs for rich multimedia services bring serious challenges in the aspects of network traffic, energy consumption and carbon emission. How to realize green content distribution by optimizing resource allocation is an urgent problem to solve in complex and dynamic networks. In this paper, we design a cross-layer cooperative scheme to promote energy efficiency in non-orthogonal multiple access (NOMA)-assisted cloud-edge-side environments. To be specific, we formulate the joint optimization issue of computation, caching and communication resources as an energy minimization model while considering request aggregation. Next, we propose a new deep reinforcement learning (DRL)-based task offloading strategy to minimize energy consumption by making optimal resource allocation decisions according to content request history and resource availability. Simulation results show that the proposed solution has better performance than current typical strategies in cloud-edge-end collaboration environments.

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