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Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

computers, Materials & Continua 2022年第6期71卷 5837-5854页

作者： El-Sayed M.El-Kenawy Abdelhameed Ibrahim Nadjem Bailek Kada Bouchouicha Muhammed A.Hassan Basharat Jamil Nadhir Al-Ansari Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansouraEgypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of Tamanghasset10034TamanghassetAlgeria URERMS Centre de Developpement des Energies Renouvelables(CDER)01000AdrarAlgeria Mechanical Power Engineering Department Faculty of EngineeringCairo UniversityGiza12613GizaEgypt Department Computer Sciences Universidad Rey Juan CarlosMostoles28933MadridSpain Department of Civil Environmental and Natural Resources EngineeringLulea University of Technology97187LuleaSweden

In order to achieve a highly accurate estimation of solar energy resource potential,a novel hybrid ensemble-learning approach,hybridizing Advanced Squirrel-Search Optimization Algorithm(ASSOA)and support vector regression,is utilized to estimate the hourly tilted solar irradiation for selected arid regions in ***-term measured meteorological data,including mean-air temperature,relative humidity,wind speed,alongside global horizontal irradiation and extra-terrestrial horizontal irradiance,were obtained for the two cities of Tamanrasset-and-Adrar for two *** computational algorithms were considered and analyzed for the suitability of *** two new algorithms,namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization *** accuracy of the developed models was analyzed in terms of five statistical error metrics,as well as theWilcoxon rank-sum and ANOVA *** the previously selected algorithms,K Neighbors Regressor and support vector regression exhibited good ***,the newly proposed ensemble algorithms exhibited even better *** proposed model showed relative root mean square errors lower than 1.448%and correlation coefficients higher than *** was further verified by benchmarking the new ensemble against several popular swarm intelligence *** is concluded that the proposed algorithms are far superior to the commonly adopted ones.

关键词： Renewable energy resources hybrid modeling tilted solar irradiation arid region

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Edge-Cloud Computing systems for Unmanned Aerial Vehicles Capable of Optimal Work Offloading with Delay 2

Edge-Cloud Computing Systems for Unmanned Aerial Vehicles Ca...

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2nd International Conference on Electronics and Renewable systems, ICEARS 2023

作者： Narayana Tinnaluri, V.S. Vyankatesh Ghamande, Manasi Singh, Shashank Kuchipudi, Ramu Dattatraya Bhosale, Minakshi Dharani, R. Koneru Lakshmaiah Education Foundation Department of Computer Science Andhra Pradesh Vijayawada India Vishwakarma Institute of Information Technology Engineering and Applied Sciences Pune411037 India Integral University Lucknow Department of Computer Science and Engineering Uttar Pradesh India Chaitanya Bharathi Institute of Technology Department of Information Technology Telangana Hyderabad India Faculty of Mca Yashoda Technical Campus Satara Maharashtra Wadhe India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Tamilnadu Karur India

ISBN: (纸本)9798350346640

Mobile terminals' limited processing power and memory make it challenging to meet the needs of increasingly complex applications like autonomous vehicles and augmented reality. That's why there's been a rise in the demand for edge cloud computing power from endpoint gadgets. Due to its adaptability and proximity to the user, an unmanned aerial vehicle (UAV) may be used to support mobile edge computing (MEC) via job offloading, potentially relieving strain on edge clouds. Due to limitations in processing power and battery life, Unmanned Aerial Vehicles (UAV) devices face a huge issue with the rise of Applications and services for mobile devices that can't afford to be delayed and that need a lot of processing power. Task offloading is one way that mobile cloud computing may help you get around these restrictions. The biggest difficulties with this paradigm, however, are the significant latency and security concerns. The edge-cloud computing model was then proposed and has since seen widespread adoption as a means of dealing with these complications. However, the present task offloading models allow UAVs to perform their heavy jobs at the linked edge server, leading to unnecessary loads owing to the huge amount of UAVs and, in turn, increasing the latency. For this reason, this study has presented a delay-optimal task offloading strategy for multi-tier edge-cloud computing with many users. In this research, a system model has been proposed to assist an intelligent and perceptive agent in determining the best computational offloading strategy, minimizing both the time it takes to complete a job and the amount of power it needs to do so. The efficacy of an agent was shown, and simulation results revealed that its adoption may greatly cut delay and energy usage. © 2023 IEEE.

关键词： Mobile edge computing

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The Parallelized Cuckoo Filter for Cold Data Representation 23

The Parallelized Cuckoo Filter for Cold Data Representation

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Sun, Bowen Luo, Lailong Li, Shangsen Chen, Yingwen Guo, Deke College of Computer National University of Defense Technology China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China College of Computer National University of Defense Technology National Lab for Parallel and Distributed Processing China

ISBN: (纸本)9781665494571

Cold data contributes a large portion of the big data today and is usually stored in secondary storage. Various sketch data structures are implemented to represent the stored elements and provide constant-time membership queries. Considering the large volume and infrequent access of cold data, we envision three rationales of cold data representation: 1) parallelized insertion, 2) accurate representation, and 3) constant-time query/deletion. The existing sketches, however, fail to achieve these rationales simultaneously. To this end, we propose a novel sketch named Parallelized cuckoo Filter (PCF) in this paper. PCF divides the standard cuckoo filter into multiple homogenous segments. An additional hash function is introduced to assign an element to a segment. The element will be inserted into the selected segment. With such a design, element insertions in PCF can be naturally parallelized. Besides, as a variant of the cuckoo filter, PCF naturally realizes the accurate representation and constant-time query/deletion requirements. Comprehensive experiments further indicate that PCF achieves the three rationales simultaneously. © 2021 IEEE.

关键词： Hash functions Smart cities Big Data Data structures Standards

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Bayesian Network Structure Discovery Using Antlion Optimization Algorithm.

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2022年第1期7卷 46-65页

作者： Awla, Hoshang Qasim Kareem, Shahab Wahhab Mohammed, Amin Salih Computer science department Faculty of Science Soran University Erbil Iraq Dep. of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Poly-technic University Erbil Iraq Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Erbil Iraq

Bayesian networks have recently been used for discovering an optimal learning structure in machine learning. Bayes networks can describe possible dependencies of explanatory variables. As a novel approach to studying the structure of a Bayesian network, the authors present the Antlion Optimization Algorithm (ALO). In the algorithm;deletion, rewind, insertion, and change are utilized to produce ALO to reach the best hull solution. Essentially, the technique used in the ALO algorithm imitates the antlions’ behaviors while hunting. The suggested approach is contrasted with simulated Annealing, Simulated Annealing Hybrid Bee, Greedy Search Hybrid Bee, optimization inspired by Pigeon, and greedy search using the BDe Score function. The researchers also studied the representation of the confusion matrix of these techniques using different reference data sets. The findings of the assessments reveal that the proposed algorithm works better than the other algorithms and has better consistency and score values. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values). © 2022. All Rights Reserved.

关键词： Antlion Optimization Bayesian Network Hunting Search Local & Global Search Metaheuristics Search and Score Structure Learning

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Discrepancies of remote techno-tolerance due to COVID-19 pandemic within Arab middle-east countries

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Journal of Umm Al-Qura University for engineering and Architecture 2023年第3期14卷 151-165页

作者： Abu-Hashem, Muhannad A. Gutub, Adnan Salem, Osama Shambour, Mohd Khaled Shambour, Qusai Shehab, Mohammad Izzat, Ahmad Alrawashdeh, Mufda J. Department of Geomatics Architecture and Planning Faculty King Abdulaziz University Jeddah Saudi Arabia Computer Engineering Department College of Computer and Information Systems Umm Al-Qura University Makkah Saudi Arabia Educational Technology and E-Learning Department College of Education Umm Al-Qura University Makkah Saudi Arabia The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura University Makkah Saudi Arabia Software Engineering Department Faculty of IT Al-Ahliyya Amman University Amman Jordan Artificial Intelligence & Software Engineering Departments Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Faculty of Engineering and Information Technology Israa University Gaza Palestine Department of Mathematics College of Sciences and Arts Qassim University Al-Ras Saudi Arabia

The coronavirus disease (COVID-19) changed the world’s lifestyle switching many techno-services to be provided remotely instead of direct usual physical interactions between people. This study focused on university students’ perceptions of this virtual technology-engineering change as discrepancies to be analyzed. The research surveyed 777 different students from four Arab middle-east neighboring countries, with related demographics and specifications, expressing full remarkable experiences of e-technology in virtual as well as traditional performances helping reveal overall tolerance possibilities. The study adopted examination technology via ANOVA to test discrepancies within students' perceptions for overall multi-factors deep analysis classification. The exploration highlighted an interesting range of pros and cons remarks including feminism and economic effect as well as other cultural and engineering interaction issues, raising signals to essentially consider and mutually benefit from adequate future generation e-techno adaptation within the region. © The Author(s) 2023.

关键词： ANOVA COVID-19 E-techno Students’ perceptions Traditional and virtual classes

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Privacy-Preserving information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

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computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

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A Study of Encapsulation Temperature Field of Dry-type Air-core Reactor with the Structure of Equivalent and Aluminum Wire-Insulation

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Distributed Generation and Alternative Energy Journal 2022年第5期37卷 1665-1680页

作者： Wan, Xiaorong Wang, Han Li, Yingna Li, Chuan Zhao, Zhengang Cui, Zhigang Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming650500 China Electric Power Research Institute of Yunnan Power Grid Co. Ltd Kunming650217 China

During the operation of the Dry-type Air-core Reactor (DAR), the insulation material deteriorates due to local overheating of the encapsulation, and the reactor is burned in severe conditions. Therefore, studying the temperature rise of the reactor plays an important role in the reliability of its operation and the economy of design and production. According to the structural characteristics of the reactor, the three-dimensional thermal field model of the aluminum wire-insulation structure of the reactor is established, and the encapsulation temperature field distribution of the reactor under steady state is obtained. Compare the simulation result with the encapsulation temperature field distribution of the equivalent structure. The results show that the highest temperature of the aluminum wire-insulation structure encapsulation rises to 73.51◦C, which is located in the middle of the reactor. Each encapsulation hotspot is located about 20% from the upper edge of the encapsulation. In the temperature rise test, a Fiber Bragg Grating (FGB) temperature sensor is installed about 20% from the upper edge of the encapsulation. The test shows that the most significant value of temperature rise of the reactor is 73◦C, which is located in the middle of the reactor. The temperature rise test results verify the accuracy of the simulated calculation value of the temperature field of the aluminum wire-insulated reactor and provide a reference for the temperature rise calculation and temperature rise monitoring of the DAR. © 2022 River Publishers.

关键词： Fiber Bragg gratings

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Contrast enhancement of optical videoscope images

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Iran Journal of computer Science 2023年第3期6卷 261-275页

作者： Ammar, Reda Aboshosha, Sahar El-Hag, Noha A. Saifeldeen, Reda El-Shafai, Walid El-Banby, Ghada M. Ashiba, Huda I. Khalaf, Ashraf A. M. Elazm, Atef Abou El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. El-Safrawey, Amir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Comm. Faculty of Engineering Minia University Minya Egypt Department of Management of Information Systems Higher Institute of Commercial Science El-Mahala El-Kobra Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Sharqia Bilbis Egypt Security Engineering Laboratory Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptive histogram equalization (CLAHE) with adaptive gamma correction. The second framework depends on homomorphic processing. The third framework is based on the homomorphic enhancement with histogram equalization. The first one is superior from the entropy perspective, while the third one is superior from the contrast perspective. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Fuzzy enhancement Histogram equalization Homomorphic enhancement Spectral entropy

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Predictive Analysis of Jordan Road Accidents Severity Using Machine Learning

Predictive Analysis of Jordan Road Accidents Severity Using ...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Dheya Mustafa Layan Sawaged Safaa M. Khabour Dept. of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan Dept. of Information Systems Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan

Jordan has a high and growing level of traffic accidents reaching 160,600) accidents in 2021, (11,241) of them had human injuries, (589) deaths, and (320) million JOD losses. Road traffic accidents are currently the 8th leading cause of death worldwide, accounting for about 1.35 million fatalities annually. Understanding the primary causes of these accidents and the circumstances in which they occur is essential if governments throughout the world are to put policies in place to limit the number of fatalities brought on by traffic accidents. This project has two objectives: (i) to learn more about Jordan's present road accident situation by performing an exploratory study on several road accident datasets, and (ii) to investigate the effectiveness of various machine-learning techniques in predicting the severity of road accidents in Jordan. The dataset entities were collected, collated, explored, and prepared for use in the model. To assess their predictive performance, five different classification algorithms were trained and tested. The findings of the present study demonstrate that the best algorithm was Logistic Regression, which had an accuracy of 98.1%.

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A Novel Transfer Learning Framework for Diagnosing Covid-19 from Chest X-Ray Images with Minimal Computational Resources

SSRN

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SSRN 2023年

作者： Abdelwahed, Ahmed Helmi, Ahmed M. Issa, Mohamed Computer and Systems Department Faculty of Engineering Zagazig University Egypt Computer Engineering Dept. Engineering and Information Technology College Buraydah Private Colleges Saudi Arabia Faculty of Computer Science and Information Technology Egypt−Japan University for Science and Technology Egypt

Deep learning proved effective in detecting COVID-19 from chest X-ray images. Although computing is the current bottleneck in training and deploying models, few studies focus on developing efficient frameworks while many aim only at performance. We propose a novel framework for detecting COVID-19 cases from chest X-ray images efficiently. Our study utilizes four high-performing architectures, including MobileNetV3, RegNetX, NasNetMobile, and EfficientNetV2, which use minimal resources, to extract features from X-ray images and classify COVID-19 cases using various statistical models and machine learning algorithms. We evaluated our proposed framework using metrics such as accuracy, precision, recall, specificity, f1-score, and AUC scores. The false negative rate for each feature extractor and classifier pair is reported, and the average inference time, to measure resource efficiency, is calculated. The results show that EfficientNetV2 was the best-performing feature extractor, while MobileNetV3 had the best average inference time and the lowest false negative rate. The Neural Network classifier performed the best, with state-of-the-art accuracy reaching 99.28%, followed by both RBF-SVM and XGBoost. The proposed framework is more robust, reliable, and resource-efficient for COVID-19 detection compared to existing literature. Our findings indicate the prospects of using the proposed framework in efficient clinical diagnosis. © 2023, The Authors. All rights reserved.

关键词： COVID-19

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