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2024 IEEE International Conference on Big Data, BigData 2024

作者： Rehman, Abdul Heldal, Ilona Lin, Jerry Chun-Wei Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9798350362480

Self Supervised Representation Learning (SSRepL) can capture meaningful and robust representations of the Attention Deficit Hyperactivity Disorder (ADHD) data and have the potential to improve the model's performance on also downstream different types of Neurodevelopmental disorder (NDD) detection. In this paper, a novel SSRepL and Transfer Learning (TL)-based framework that incorporates a Long Short-Term Memory (LSTM) and a Gated Recurrent Units (GRU) model is proposed to detect children with potential symptoms of ADHD. This model uses Electroencephalogram (EEG) signals extracted during visual attention tasks to accurately detect ADHD by preprocessing EEG signal quality through normalization, filtering, and data balancing. For the experimental analysis, we use three different models: 1) SSRepL and TL-based LSTM-GRU model named as SSRepL-ADHD, which integrates LSTM and GRU layers to capture temporal dependencies in the data, 2) lightweight SSRepL-based DNN model (LSSRepL-DNN), and 3) Random Forest (RF). In the study, these models are thoroughly evaluated using well-known performance metrics (i.e., accuracy, precision, recall, and F1-score). The results show that the proposed SSRepL-ADHD model achieves the maximum accuracy of 81.11% while admitting the difficulties associated with dataset imbalance and feature selection. © 2024 IEEE.

关键词： Self-supervised learning

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Elite-guided equilibrium optimiser based on information enhancement:Algorithm and mobile edge computing applications

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1126-1171页

作者： Zong-Shan Wang Shi-Jin Li Hong-Wei Ding Gaurav Dhiman Peng Hou Ai-Shan Li Peng Hu Zhi-Jun Yang Jie Wang School of Information Science and Engineering Yunnan UniversityKunmingChina Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhenChina Academic Affairs Office Yunnan University of Finance and EconomicsKunmingChina Faculty of Management and Economics Kunming University of TechnologyKunmingChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirutLebanon Department of Computer Science and Engineering University Centre for Research and DevelopmentChandigarh UniversityChennaiIndia Department of Computer Science and Engineering Graphic Era Deemed to Be UniversityDehradunIndia School of Computer Science Fudan UniversityShanghaiChina Rackham Graduate School University of MichiganAnn ArborMIUSA Research and Development Department Youbei Technology Co.LTDKunmingChina School of Mechanical and Power Engineering Zhengzhou UniversityZhengzhouChina

The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation *** the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO *** alleviate these limitations,an EO variant named adaptive elite-guided Equilibrium Optimiser(AEEO)is ***,the adaptive elite-guided search mechanism enhances the balance between exploration and *** modified mutualism phase reinforces the information interaction among particles and local optima *** cooperation of these two mechanisms boosts the overall performance of the basic *** AEEO is subjected to competitive experiments with state-of-the-art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test *** results demonstrate that AEEO outperforms several well-performing EO variants,DE variants,PSO variants,SSA variants,and GWO variants in terms of convergence speed and *** addition,the AEEO algorithm is used for the edge server(ES)placement problem in mobile edge computing(MEC)*** experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.

关键词： ANT COLONY optimization CLOUD COMPUTING GENETIC ALGORITHMS SWARM intelligence

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Energy-saving Space Heaters by Employing the Internet of Things and Algorithms for Learning

Energy-saving Space Heaters by Employing the Internet of Thi...

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2023 IEEE Technology and engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

作者： Batra, Devyansh Sharma, Ashish Wasson, Vikas Chandigarh University Computer Science Department India Chandigarh University Electrical Engineering Department India

ISBN: (纸本)9798350384659

While utilizing gas heating systems, there is a substantial danger of hypoxia (sleep death). The high levels of carbon monoxide in the space stifle the flow of blood to the brain, which might cause hemorrhage and subsequently result in demise. To attempt to address the aforementioned issue, an entirely novel learning technique is developed in which Learning algorithms is going to be used in this endeavor to deliver an automated space heating effect. Whenever a room's requirements are met, the heating in that space is going to be turned on. (room, temperature, oxygen level, Carbon dioxide level, water vapours, foreign gases, humidity). The space is going to stay toasty until the atmospheric oxygen level drops below its limited threshold, at which point the heating system will turn itself to its power-saving mode. Additionally, the individual using it will have the ability to able to furnish the machine with every one of these variables in accordance to their preferences. Whenever there are more people in the room, the heating system will turn itself off. The warmth in the environment is unlikely to trigger a response on the skin of the person using it if the level of moisture in the space falls. The heating system won't turn on if it detects any noxious gas or smoke in the space. If the carbon monoxide level rises to the optimum level, the heating system will automatically turn itself down and switch to power-saving function to use a lesser amount of energy. individuals who have the condition This important development will make it possible for patients with respiratory issues to adjust their warming according to their oxygen in their blood levels. If the individual's arterial oxygen saturation suddenly lowers, the heating system will have to be halted immediately. Suffering the help of this groundbreaking advancement, individuals who have respiratory illnesses will eventually be able to modify their heating according to the amount of oxygenation that exists in their

关键词： Carbon monoxide

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OPTIMAL ACTIVATION FUNCTIONS FOR THE RANDOM FEATURES REGRESSION MODEL 11

OPTIMAL ACTIVATION FUNCTIONS FOR THE RANDOM FEATURES REGRESS...

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11th International Conference on Learning Representations, ICLR 2023

作者： Wang, Jianxin Bento, José Department of Electrical and Computer Engineering Rice University United States Department of Computer Science Boston College United States

The asymptotic mean squared test error and sensitivity of the Random Features Regression model (RFR) have been recently studied. We build on this work and identify in closed-form the family of Activation Functions (AFs) that minimize a combination of the test error and sensitivity of the RFR under different notions of functional parsimony. We find scenarios under which the optimal AFs are linear, saturated linear functions, or expressible in terms of Hermite polynomials. Finally, we show how using optimal AFs impacts well established properties of the RFR model, such as its double descent curve, and the dependency of its optimal regularization parameter on the observation noise level. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Chemical activation

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Impact of Grid Strength on Sub-Synchronous Oscillations in AC Systems with Type-4 Wind Farms Integrated 50

Impact of Grid Strength on Sub-Synchronous Oscillations in A...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Zamzam, Tassneem Umar, Muhammad F. Qiblawey, Yazan Alassi, Abdulrahman Ghrayeb, Ali Abu-Rub, Haitham Department of Electrical and Computer Engineering Texas A&M University College Station United States Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Iberdrola Innovation Middle East Doha Qatar Department of Electrical and Computer Engineering College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar

ISBN: (纸本)9781665464543

The modern power system dominated by renewable energy resources such as Type-4 wind farms (WF) has recently seen significant increase in cases of sustained oscillations at the sub-synchronous frequency range that is typically referred to sub-synchronous oscillations (SSOs) in the existing literature. Although thorough studies have been conducted on prior types of WFs to understand the triggering factors for SSO, however, in the case of Type-4 WFs the triggering factors and causes still remain unclear. Therefore, this paper studies the SSO characteristics exhibited by Type-4 WFs operating within a weak grid. To emulate real-world grid conditions, an extensive AC network modeled after the IEEE 39-bus system is employed. Through simulations incorporating the integration of Type-4 WFs at various locations within the grid, the resulting effects on SSO triggering factors are analyzed. The investigation is structured around three distinct case studies, conducted at buses 31, 37, and 38. These studies involve varying network reactance to explore and establish the relationship between SSO events and the short-circuit ratio (SCR) of the network. The consistent findings across diverse case studies underscore the general relationship between SCR, grid strength, and SSOs. The analysis reaffirms the significant impact of increased reactance on SSO characteristics and confirms that weaker grids are more susceptible to SSOs. © 2024 IEEE.

关键词： Wind power

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Space-Time Graph Planner for Unsignalized Intersections with CAVs 16th

Space-Time Graph Planner for Unsignalized Intersections wit...

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16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Mutlu, Caner Cardei, Ionut Cardei, Mihaela Department of Electrical Engineering and Computer Science Florida Atlantic University Boca RatonFL33431 United States

ISBN: (纸本)9783031496103

Emerging autonomous intersection management systems control the entry order and trajectory for connected and autonomous vehicles ready to traverse a road intersection. They aim to compute trajectories that are safe and optimal in order to reduce congestion, environmental impact, and to cut travel time. We propose a novel approach for computing the fastest waypoint trajectory using search in a discretized space-time graph that produces collision-free paths with variable vehicle speeds complying with traffic rules and vehicle dynamics constraints. The resulting trajectories allow high levels of intersection sharing, high evacuation rate, with a low algorithm runtime even with large scenarios with 1200 vehicles (5.5 s on a laptop). © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Travel time

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A Conditional GAN and Dual-Channel Hybrid Deep Feature Framework for Robust Sensor Fault Detection in WSNs

A Conditional GAN and Dual-Channel Hybrid Deep Feature Frame...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Khan, Rehan Saeed, Umer Koo, Insoo University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of School of Computer Science and Technology University of Bedfordshire Luton United Kingdom

ISBN: (纸本)9798331510756

Sensor-generated data is vital to the operation of numerous systems and services in the rapidly growing field of the Internet of Things. Wireless Sensor Networks, as an essential setup for these systems, are frequently deployed in large, diverse, and often harsh environments. However, these networks are highly vulnerable to various faults, potentially leading to improper data transmission, reliability, and financial stability of the systems. To address these challenges, we propose a hybrid model for sensor fault detection that integrates a machine learning classifier with the deep learning (DL) model, specifically VGG-16 and ResNet-50. Synthetic samples are generated using a Conditional Generative Adversarial Network and common sensor faults, such as hardover, drift, spike, erratic, and stuck fault are introduced by leveraging a publicly available temperature sensor dataset. Time-series data is transformed into Gramian Angular Field images, from which deep features are extracted using VGG-16 and ResNet-50. These extracted features are then fused to form a hybrid feature pool. Our framework effectively addresses problems related to data imbalance and enhances accuracy. The proposed model outperforms the individual feature sets, VGG-16 (89.22%) and ResNet-50 (84.21%), achieving notable accuracy of 92.55% with the fused feature set, underscoring its potential for robust sensor fault detection. © 2025 IEEE.

关键词： Gallium nitride

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Noureldeen, Rana M. Elshshtawy, Rahma A. Elgohary, Omnia A. Wahba, Fatma Alzahraa A. Attia, Marco K. Abo Elazm, Zeinab B. Khattap, Mohamed G. Galal Eldeen Mohamed Ali Hassan, Hend Mostafa, Mariam Ibrahim, Nayra Ramadan, Reem Refaat, Yomna Khaled, Mennatallah Maher, Ali Abd Elaziz, Mohamed Gamal Abdellatif Ibrahim, Ahmed Faculty of Applied Health Sciences Galala University Suez435611 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Air Defense College Department of Electronics and Electrical Communications Engineering Alexandria Egypt

ISBN: (纸本)9798350351408

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %. © 2024 IEEE.

关键词： Convolutional neural networks

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A Universal Virtual Reality Training Platform for Microscale Robotic Cell Injection in Biomedical Research and Practice

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SN computer science 2024年第8期5卷 1-13页

作者： Hafeez, Rubab Amjad, Zohaib Waheed, Sadia Hasan, Osman Sharif, Muhammad Imran Ulhaq, Anwaar Department of Computer Science University of Wah Wah Cantt 47010 Pakistan School of Electrical Engineering and Computer Science NUST Islamabad 44320 Pakistan Department of Computer Science Kansas State University Manhattan 66506 United States Manna Institute School of Engineering and Technology Central Queensland University Brisbane QLD Australia

Micro-robotic cell injection is a widely used procedure in cell biology where a small quantity of biological material is inserted into a cell using an automated or semi-automated micro-robotic system. Given its micro-scale nature, cell injection using a semi-automated micro-robotic system requires extremely high precision, hand-eye coordination, and depth perception for maneuvering the micro-pipette. As the cell injection is a laborious work, there is a dire need of rigorous training and a great deal of experience using live specimens and high-end microscopes. To facilitate this training, we have presented a paper in the past about modular virtual reality (VR) with basic simulation modules, here, the present work deals with the modelling of advanced training modules and experimenting of VR simulator. The proposed simulator includes: (i) Advanced training modules to train users on complete injection/suction procedures;(ii) The performance evaluation of the users using three metrics, i.e., (i) time, (ii) trajectory, and (iii) positioning accuracy is also integrated within the simulator;(iii) An experimental set-up has been developed for the face, content, and construct validity of the VR simulator. Finally, the effectiveness of the proposed simulator is validated by experts from the relevant domains and can be used for various applications like drug delivery, injection procedures and in-vitro fertilization. The experimental results demonstrated that a Virtual cell injection training simulator is feasible for the training of cell injection procedures and depicted that the injection success rate is 100% after using VR simulator. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adherent cells Cell injection Collision modeling Micro-robotic Simulator validity VR simulation

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IoT Based Smart Framework Monitoring System for Power Station

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computers, Materials & Continua 2023年第3期74卷 6019-6037页

作者： Arodh Lal Karn Panneer Selvam Manickam R.Saravanan Roobaea Alroobaea Jasem Almotiri Sudhakar Sengan Department of Financial and Actuarial Mathematics School of ScienceXi’an Jiaotong-Liverpool UniversityChina Department of Electrical and Electronics Engineering Sona College of TechnologySalem636005Tamil NaduIndia Department of Computer Science and Engineering Vel Tech Multi Tech Dr RR Dr SR Engineering CollegeChennai600062Tamil NaduIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Power Station(PS)monitoring systems are becoming critical,ensuring electrical safety through early warning,and in the event of a PS fault,the power supply is quickly *** technologies are based on relays and don’t have a way to capture and store user data when there is a *** proposed framework is designed with the goal of providing smart environments for protecting electrical types of *** paper proposes an Internet of Things(IoT)-based Smart Framework(SF)for monitoring the Power Devices(PD)which are being used in power substations.A Real-Time Monitoring(RTM)system is proposed,and it uses a state-of-the-art smart IoT-based System on Chip(SoC)sensors,a Hybrid Prediction Model(HPM),and it is being used in Big Data Processing(BDP).The Cloud Server(CS)processes the data and does the data analytics by comparing it with the historical data already stored in the ***-Structural Query Language Mongo Data Base(MDB)is used to store Sensor Data(SD)from the *** proposed HPM combines the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)-algorithm for Outlier Detection(OD)and the Random Forest(RF)classification algorithm for removing the outlier SD and providing Fault Detection(FD)when the PD isn’t *** suggested work is assessed and tested under various fault circumstances that happened in *** simulation outcome proves that the proposed model is effective in monitoring the smooth functioning of the ***,the suggested HPM has a higher Fault Prediction(FP)*** means that faults can be found earlier,early warning signals can be sent,and the power supply can be turned off quickly to ensure electrical safety.A powerful RTM and event warning system can also be built into the system before faults happen.

关键词： System-on-chip cloud server apache kafka apache strom NoSQL MongoDB

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