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Empowering Industrial Energy Management: Advancing Short-Term Load Forecasting with LSTM and CNN Deep Learning Models-Insights from a Moroccan Case Study 11

Empowering Industrial Energy Management: Advancing Short-Ter...

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11th IEEE Open Conference of Electrical, Electronic and Information sciences, eStream 2024

作者： Boumais, Khaoula Messaoudi, Faycal Lagnaoui, Saloua Fallah, Saad El Udris, Dainius ENSA Sidi Mohammed Ben Abdallah University AI Data Science and Emerging Systems Laboratory Fes Morocco ENSA Sidi Mohamed Ben Abdellah University Engineering Systems and Applications Laboratory Fes Morocco Systems and Sustainable Environment Laboratory Fes Morocco Vilnius Gediminas Technical University Faculty of Electronics Department of Electrical Engineering Vilnius Lithuania

ISBN: (纸本)9798350352412

Self-consumption of electricity plays an important role in the energy transition and using green, sustainable energy sources for industrial self-sufficiency and electricity bills, meeting part of their own energy needs and even generating 20% of the annual surplus that could be sold to the grid. This study aims to forecast short-Term load spanning between 2022 and 2023, employing deep learning models, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Various performance metrics have been used to evaluate and compare the accuracy of these two models, including mean squared error, root mean squared error, mean absolute error, and mean absolute deviation. Results reveal LSTM's superior performance over CNN, with LSTM demonstrating adeptness in capturing underlying patterns while CNN tends to learn noise, leading to a divergence in performance metrics between training and validation data. The findings underscore the significance of LSTM for accurate load forecasting and suggest the inclusion of additional hyperparameter optimization to enhance the reliability of short-Term load predictions, distinct from previous studies. © 2024 IEEE.

关键词： Long short-term memory

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Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

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computer systems science & engineering 2023年第5期45卷 1711-1726页

作者： Manar Ahmed Hamza Aisha Hassan Abdalla Hashim Dalia H.Elkamchouchi Nadhem Nemri Jaber S.Alzahrani Amira Sayed A.Aziz Mnahel Ahmed Ibrahim Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt College of Science and Humanities Studies in Alquwayiyah Shaqra UniversitySaudi Arabia

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the *** the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the *** node clustering is a popular approach for ***,the sensor nodes are grouped to form clusters in a cluster-based WSN *** battery performance of the sensor nodes is likewise *** a result,the energy efficiency of WSNs is *** specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in *** the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was *** selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed *** provides the best routing path and increases the lifetime and energy efficiency of the ***-to-end delay and packet loss rate have also been *** proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor *** to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

关键词： Wireless sensor networks energy-efficient load balancing energy consumption network’s lifetime cluster heads grey wolf optimization tabu search particle swarm optimization

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Adoption of Extended Reality as a Teaching Tool 1

Adoption of Extended Reality as a Teaching Tool

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1st International Conference on Computational science and Technology, ICCST 2022

作者： Mangay Arkarasi, T. Madhumita, S. Suretha, S. Harini, M. Ananthi, M. Hariharan, V. Sri Sairam Engineering College Department of Instrumentation and Control Chennai India Sri Sairam Engineering College Department of Computer Science and Business Systems Chennai India Founder Space Zee Technologies Private Limited Chennai India

ISBN: (纸本)9781665476553

Extended reality (XR) facets aid pupils in understanding difficult topics. Regular education has been reliant on 2D technology for a very long time. It no longer draws in pupils due to its lack of immersion and engagement assistance. The suggested remedy involves using AR to develop 3D models and educational components. The augmented reality (AR) application is more appealing to pupils than other approaches and can be utilized as an interactive tool to clarify difficult concepts in the classroom. The functions of the following human anatomy parts-the brain, heart, and pancreas-were supplied in a piece of note, and audio was added predominately. The 3d models were created using the unity3d application as a support system, and Sketchfab was used to import them. © 2022 IEEE.

关键词： Augmented reality

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Proposal of a novel peeling of nano-particle (PNP) process for localized material removal on a hard material surface by controllable magnetic fields 22

Proposal of a novel peeling of nano-particle (PNP) process f...

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22nd International Conference of the European Society for Precision engineering and Nanotechnology, EUSPEN 2022

作者： Permpatdechakul, Thitipat Khajornrungruang, Panart Suzuki, Keisuke Kutomi, Shotaro Graduate School of Computer Science and System Engineering Mechanical Information Science and Technology Course Kyushu Institute of Technology Fukuoka Japan Department of Intelligent and Control Systems Avanced Mechanical Division Kyushu Institute of Technology Fukuoka Japan School of Computer Science and System Engineering Advanced Mechanical Course Kyushu Institute of Technology Fukuoka Japan

ISBN: (纸本)9781998999118

This article proposes a novel Peeling of Nano-Particle (PNP) process to locally remove material on a hard material surface using controllable magnetic fields. Fe3O4 particles in the size range of 50-100 nm in aqueous solution (pH value 11 adjusted by KOH) were magnetically controlled to adhere and remove from a 4H-SiC surface by two electromagnetic coils, which were sandwiched between the substrate. The particle controlling phenomenon on the surface was experimentally observed and verified by our developed apparatus applying an evanescent field. According to the results, particles could be controlled to approach and remove from the substrate surface with 12mT of magnetic field at the selective area on the substrate surface to realise the proposed PNP process. © European Society for Precision engineering and Nanotechnology, Conference Proceedings - 22nd International Conference and Exhibition, EUSPEN 2022. All rights reserved.

关键词： Magnetic fields

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Empirical Evaluation of Stock Market Prediction System using Intelligent Learning Scheme with Data Processing Logic 5

Empirical Evaluation of Stock Market Prediction System using...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Prabakar, S. Kumar, Ankit Jayakarthik, R. Venkatesh, D. Pratheeba, R.S. Khan, Bhola Isbr Business School Finance and Accounting Bangalore India Faculty of Science Swami Vivekanand Subharti University Department of Computer Application Meerut India Saveetha College of Liberal Arts and Sciences Simats Dept of Computer Science India Commerce School of Commerce and Management Mohan Babu University Tirupati India Velammal Institute of Technology Department of Computer Science and Engineering Panchetti601204 India Mjp Rohilkhand University Department of Regional Economics Bareilly India

ISBN: (纸本)9798350385649

The Stock Market Prediction System using Intelligent Learning Scheme (SMS-ILS) presented in this study integrates advanced methodologies to enhance the accuracy and reliability of stock market predictions. The system encompasses a preprocessing phase, addressing missing data and outliers, followed by feature extraction utilizing Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to capture intricate temporal dependencies. A Deep Belief Network (DBN) is employed for classification, categorizing stocks based on learned representations from historical and technical indicators. Grey Wolf Optimization (GWO) optimizes the system's parameters, ensuring adaptability to dynamic market conditions. Notably, the system achieves an impressive accuracy of 97%, emphasizing its efficacy in making correct predictions. This multi-model approach, integrating LSTM, RNN, DBN, and GWO, provides a comprehensive and adaptive framework for navigating the complexities of stock market prediction. The study emphasizes the importance of choosing metrics aligned with specific goals and conducting thorough analysis to ensure the robustness of the evaluation results in the challenging and dynamic context of financial markets. © 2024 IEEE.

关键词： Long short-term memory

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A Simplified Current Source Converter SPWM Online Mapping Scheme 7

A Simplified Current Source Converter SPWM Online Mapping Sc...

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7th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2025

作者： Alajmi, Bader N. Abdelsalam, Ibrahim Marei, Mostafa I. Ahmed, Nabil A. College of Technological Studies Electrical Engineering Technology Department Shuwaikh Kuwait College of Engineering & Technology Arab Academy for Science Technology & Maritime Transport Electrical and Control Engineering Dep. Cairo Egypt Ain Shams University Faculty of Engineering Electrical Power and Machines Department Cairo Egypt

ISBN: (纸本)9798331531836

This paper introduces an online mapping scheme for current source converters (CSC). This scheme is capable of transforming the gating signals from conventional voltage source converters (VSC) utilizing sinusoidal pulse width modulation (SPWM) techniques into signals suitable for CSC operation. The proposed mapping table is designed for straightforward implementation and follows to the operational constraints of CSCs, including the maintenance of continuous input current and the prevention of simultaneous activation of more than one top or bottom switches of the CSC circuit. The effectiveness of the proposed online CSC mapping table and the associated control strategy was evaluated using PSCAD/EMTDC simulation software. The simulation results confirm that the mapping scheme successfully meets the operational constraints of the CSC while producing a high-quality sinusoidal load current with low total harmonic distortion (THD). Furthermore, the proposed control strategy effectively tracks the reference output load voltage, representing satisfactory dynamic performance. © 2025 IEEE.

关键词： Photomapping

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Ontological Model for Cohesive Smart Health Services Management

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computers, Materials & Continua 2023年第2期74卷 3679-3695页

作者： Muhammad Raza Naqvi Muhammad Waseem Iqbal Syed Khuram Shahzad M.Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Le Laboratoire Génie de Production de l’École Nationale d’Ingénieurs de Tarbes(LGP-INP-ENIT) Universitéde ToulouseCEDEXTarbes65016France Department of Software Engineering Superior UniversityLahore53700Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Computer Science University of Management and TechnologySialkot51310Pakistan Department of Computer Science King Abdul-Aziz UniversityJeddah21577Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21577Saudi Arabia

Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of *** massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human *** are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and *** applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service *** main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the *** ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare *** were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological *** proposed ontological model is derived from different classes,relationships,and constraints to integrate health care *** model is developed using Protégébased on each interrelated/correlated health care service having different *** querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge *** Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology *** to other smart health care services integration systems,the proposed ontological model provides more cohesiveness.

关键词： Ontology internet of things smart health services integration

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Adversarial Training with Maximal Coding Rate Reduction

Adversarial Training with Maximal Coding Rate Reduction

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Asilomar Conference on Signals, systems & computers

作者： Hsiang-Yu Chu Hongbo Zhao Markus Flierl Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Deep convolutional networks can solve various complex tasks in the field of image processing. However, adversarial attacks have been shown to have the ability of fooling deep learning models. Adversarial training is one commonly used strategy to improve the robustness of deep learning models against adversarial examples, which is performed by incorporating adversarial examples into the training process. Traditionally, during this process, cross-entropy loss is used as the loss function. In order to improve the robustness of deep learning models against adversarial examples, we propose in this paper two new methods of adversarial training by applying the principle of Maximal Coding Rate Reduction (MCR 2 ). We evaluate the performance of different adversarial training methods by comparing the clean accuracy and adversarial accuracy. It is shown that adversarial training with the MCR 2 loss function yields a more robust network than the traditional adversarial training method. In our experiments, adversarial accuracies are improved by up to 10%. The two loss functions are discussed by using a model.

关键词： Training Deep learning Hands Accuracy Image coding Computational modeling Robustness Data models Encoding Convolutional neural networks

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Suspicious Object Monitoring System Based on Multiview Model from Smart-camera

Suspicious Object Monitoring System Based on Multiview Model...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Huu, Phat Nguyen Nam, Khanh Nguyen Hoang Dang, Dinh Dang Thi, Trang Nguyen Manh, Hoang Tran Minh, Quang Tran Hanoi Viet Nam British International School Hanoi Viet Nam Department of Information Systems Faculty of Computer Science and Engineering Ho Chi Minh City Viet Nam Linh Trung Ward Thu Duc District Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350353983

The context of reporting on multiple cameras some-what overlaps. The paper solves three problems. The first is to detect pedestrian objects on each camera. The second is to capture and assign IDs to each object on each camera. Finally, unify IDs for objects on all cameras. For object recognition, through the survey and testing process, we chose the YOLOv4 model - a popular model used in object detection. The model was trained with new data, and matched to the pedestrian scene, achieving an accuracy of 95.6%. Continuing with the object tracking operation, we use the DeepSORT algorithm (tracking by detect algorithm) with the main input being the output of YOLOv4 achieving a MOTA of 40.85%. For the final problem, we bring all the objects from the cameras to a common coordinate system, then use the extracted positions and features of the objects to synchronize the IDs across all the cameras and achieve an IDF of 80.2. © 2024 IEEE.

关键词： Object recognition

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Skin Disease Detection Using Neural Networks

Skin Disease Detection Using Neural Networks

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Meenu Gupta Rakesh Kumar Nandan Anil Kumar Pradhan Ahmed J. Obaid Department of Computer Science & Engineering Chandigarh University Mohali Punjab India Dept of Computer Science Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq College of Engineering URUK University Baghdad Iraq

Skin diseases like acne, psoriasis, eczema, and dermatitis affect millions worldwide. Skin cancer and melanoma are diseases that happen due to exposure to UV radiation. The early detection of skin diseases is crucial for effective treatment and prevention of further complications. This work comprehensively reviews various image-based techniques for skin disease detection, including traditional computer vision methods such as Convolutional Neural Network (CNN), Random Forest (RF), Naïve Bayes (NB), k-nearest Neighbour (k-NN), and Support Vector Machine (SVM). ISIC and dermofit datasets comprised 26,150 images divided into three categories: training (20,150), testing (3000), and validation (2000), respectively. Receiver Operating Characteristics (ROC) and Mean Squared Error (MSE) are used to calculate the accuracy and precision of models. As a result, it concluded that CNN outperformed with 94.91% accuracy. The proposed system can aid dermatologists in making accurate diagnoses and improve patient care.

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