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Toward Drought Modeling in South Asia: Machine Learning Approaches, Challenges, and Opportunities

IEEE Access 2025年 13卷 87654-87671页

作者： Mana Saleh Al Reshan Muhammad Owais Raza Naeem Ahmed Mahoto Adel Rajab Asadullah Shaikh Mohamed A. Elmagzoub Khairan D. Rajab Department of Information Systems College of Computer Science and Information Systems Najran University Najran Saudi Arabia Emerging Technologies Research Laboratory (ETRL) College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Computer Engineering Istanbul Sabahattin Zaim University İstanbul Türkiye Department of Software Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Network and Communication Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia

Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and ecosystem sustainability. Machine all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools, which offer accurate and reliable drought detection. This review paper summarizes the drought modeling(Drought Prediction, Drought Detection and Drought Forecasting) approaches. This paper focuses on three main aspect. 1) The selection of the region for this study, for this study South Asia(SA) is selected as region of interest (ROI) that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture. 2) This paper focuses on the current and future trends, challenges, and advances of and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state-of-the-art in ML and DL for drought modeling. 3) This review encapsulates the indicator and metrics (Complex Machine learning and deep learning models) for drought modeling which are most relevant to the SA region. This study sum up as most common challenges in drought modeling are, highlighting current challenges such as incomplete and inconsistent datasets, lack of explainable and interpretable models, and unavailability of data for model uncertainty analysis. This study proposes that these problems can be solved with modern machine learning techniques such as explainable machine learning and federa

关键词： Droughts Machine learning Asia Deep learning Indexes Water resources Remote sensing Predictive models computer science Climate change Environmental monitoring Economics

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SubmergeStyleGAN: Synthetic Underwater Data Generation with Style Transfer for Domain Adaptation

SubmergeStyleGAN: Synthetic Underwater Data Generation with ...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Fathy, Mohamed E. Mohamed, Samer A. Awad, Mohammed I. Munim, Hossam E. Abd El Ain Shams University Mechatronics Engineering Department Faculty of Engineering Egypt Ain Shams University Computer and Systems Engineering Department Faculty of Engineering Egypt University of Bath Department of Electronic and Electrical Engineering Faculty of Engineering and Design United Kingdom

ISBN: (纸本)9798350382204

Underwater computer vision applications are challenged by limited access to annotated underwater datasets. Additionally, convolutional neural networks (CNNs) trained on in-air datasets do not perform well underwater due to the high domain variance caused by the degradation impact of the water column. This paper proposes an air-to-water dataset generator to create visually plausible underwater scenes out of existing in-air datasets. SubmergeStyleGAN, a generative adversarial network (GAN) designed to model attenuation, backscattering, and absorption, utilizes depth maps to apply range-dependent attenuation style transfer. In this work, the generated attenuated images and their corresponding original pairs are used to train an underwater image enhancement CNN. Real underwater datasets were used to validate the proposed approach by assessing various image quality metrics, including UCIQE, UIQM and CCF, as well as disparity estimation accuracy before and after enhancement. SubmergeStyleGAN exhibits a faster and more robust training procedure compared to existing methods in the literature. © 2023 IEEE.

关键词： Image enhancement

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FAULT TOLERANT REAL TIME MONITORED ELEVATOR SYSTEM DEVELOPMENT 14

FAULT TOLERANT REAL TIME MONITORED ELEVATOR SYSTEM DEVELOPME...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Vlachou, Vasileios I. Karakatsanis, Theoklitos S. Kladas, Antonios G. Laboratory of Electrical Machines and Power Electronics Department of Electrical and Computer Engineering National Technical University of Athens Iroon Polytechneiou 9 Athens15780 Greece Laboratory of Themodynamics and Thermal Machines Department of Production and Management Engineering Democritus University of Thrace Vas. Sofias 12 Xanthi67100 Greece

ISBN: (纸本)9781837242689

The paper presents the development of a fault tolerant electromechanical elevator monitoring system aiming to achieve high safety during operation. The proposed methodology is based on a particular permanent magnet synchonous motor (PMSM) in conjunction with suitable driver system characteristics enabling improvements in overall system reliability. The motor design has been performed by using finite elements analysis (FEA) techniques providing detailed operating characteristics of the target motor. Then the motor has been manufactured and appropriately integrated into an existing elevator system in order to validate its operating performance through measurements. The main objective of the drive tuning is to achieve the best possible preventive maintenance of both the motor and the elevator, respectively. For this purpose, real time monitoring and data collection play a key role enabling fault prediction with appropriate techniques and timely information of the maintenance engineer. In this way, the problem can be repaired rapidly, reducing costs and increasing reliability. © The Institution of engineering & Technology 2024.

关键词： Permanent magnets

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Unsupervised High Impedance Fault Detection Using Autoencoder and Principal Component Analysis

Unsupervised High Impedance Fault Detection Using Autoencode...

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2023 IEEE Industry Applications Society Annual Meeting, IAS 2023

作者： Liu, Yingxiang Razeghi-Jahromi, Mohammad Stoupis, James University of Southern California Department of Electrical and Computer Engineering Los Angeles United States Abb Us Research Center Principal Scientist at Electrical Systems Department Raleigh United States Abb Us Research Center Electrical Systems Department Manager Raleigh United States

ISBN: (纸本)9798350320169

Detection of high impedance faults (HIFs) in power distribution networks remains a challenging task. Due to the low current magnitude and diverse characteristics of HIFs, conventional over-current relays are ineffective in detecting such faults. Machine learning-based methods have emerged as a promising solution, but most rely on supervised learning techniques to detect HIF by performing classifications that require a significant amount of HIF data that are difficult to acquire in the real world. As a result, the reliability and generalization of these models are limited when the load profiles and faults are not present in the training data. In this paper, we propose an unsupervised HIF detection framework that employs autoencoder and principal component analysis-based monitoring techniques to identify changes in the correlation structure within the current waveforms that deviate from normal loads. The performance of the proposed HIF detection method is tested using real data collected from a 4.16 kV distribution system and compared with results from a commercially available solution for HIF detection. The numerical results demonstrate the superiority of our proposed method over the commercially available HIF detection technique while ensuring high security by avoiding false detections during normal load conditions. © 2023 IEEE.

关键词： Principal component analysis

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BER Improvement of Cooperative Spectrum Sharing of NOMA in 5G Network 3

BER Improvement of Cooperative Spectrum Sharing of NOMA in 5...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Hassan, Mohamed Saeed, Rashid A. Mokhtar, Rania A. Hamid, Khalid Ali, Elmustafa Sayed Odeh, Nidhal Singh, Manwinder Khalifa, Othman O. Islam, Shayla Lovely Professional University Department of Wireless Communication Punjab Phagwara144001 India Taif University Department of Computer Engineering College of Computers and Information Technology Taif21944 Saudi Arabia University of Science & Technology Department of Communication Systems Engineering Omdurman Sudan Department of Electronics Engineering Khartoum Sudan University of Wollongong School of Electrical Computer and Telecommunications Engineering Australia International Islamic University Malaysia Department of Electrical and Computer Engineering Gombak Malaysia Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Institute of Computer Science and Digital Innovation Ucsi University Malaysia Department of Computer Science Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350319897

Non-orthogonal multiple access is one of the best methods for addressing the needs of 5G wireless services (NOMA). The proliferation of mobile devices in recent years has increased the importance of cooperative spectrum sharing and utilization in wireless communication due to increases in the bit error rate (BER) brought on by collisions and interference. In this study, MIMO and Massive MIMO (M-MIMO) in the downlink (DL) NOMA power domain (PD) in conjunction with the Cooperative Cognitive Radio Network are proposed as two new ways for improving and evaluating BER in the 5G network (CCRN). Customers of NOMA compete for available channels on the CCRN in this first strategy. The second approach creates a dedicated channel for NOMA users. Three scenarios with varying distances, power location coefficients, and transmission power are used to evaluate the proposed methods in the MATLAB software program. It is assumed that four users will share a 90 MHz BW using QPSK modulation in all three scenarios. When evaluating the performance of the suggested system under the presumption of frequency-selective Rayleigh fading, concurrent interference cancellation (SIC) and unstable channel conditions are also considered. © 2023 IEEE.

关键词： 5G mobile communication systems

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USK-FEMO: A Face Emotion Dataset using Deep Learning for Effective Learning 2

USK-FEMO: A Face Emotion Dataset using Deep Learning for Eff...

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2nd International Conference on computer System, Information Technology, and electrical engineering, COSITE 2023

作者： Muhajir, Muhajir Oktiana, Maulisa Muchtar, Kahlil Fitria, Maya Akhyar, Akhyar Pratama, Muhammad Dandy Lin, Chih-Yang Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Selangor Malaysia Department of Mechanical Engineering National Central University Taoyuan City Taiwan

ISBN: (纸本)9798350343069

Emotions play an essential role in the learning process and have an impact on how the learning process is eventually carried out. Facial expressions can be used to visually identify a person's emotions. Along with the advancement of computer vision and deep learning techniques, the study of human-computer interaction is increasingly focusing on the recognition of facial expressions. One of the main issues is the availability of sufficient datasets, especially for students. This study examined the deep learning architecture for face emotion classification. In addition, this research also introduces a new emotional dataset acquired from the junior high school student at SMP Negeri 1 Darul Imarah, Aceh Besar Regency, Indonesia. This dataset contains five classes that include the emotions of happiness, sadness, anger, surprise, and boredom. The dataset was then tested using the Mobile-Net architecture, the highest accuracy was achieved with a learning rate of 0.0001% of 88.492%. %. The dataset can be explored via the link https://***/USK-FEMO-DATASET/. © 2023 IEEE.

关键词： Human computer interaction

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Multifractal Terrain Generation for Evaluating Autonomous Off-Road Ground Vehicles

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Journal of Autonomous Vehicles and systems 2025年第2期5卷 021003页

作者： Majhor, Casey D. Bos, Jeremy P. Robust Autonomous Systems Laboratory Department of Electricaland Computer Engineering Michigan Technological University Houghton MI 49931

We present a multifractal artificial terrain generation method that uses the 3D Weierstrass–Mandelbrot function to control roughness. By varying the fractal dimension used in terrain generation across three different values, we generate 60 unique off-road terrains. We use gradient maps to categorize the roughness of each terrain, consisting of low-, semi-, and high-roughness areas. To test how the fractal dimension affects the difficulty of vehicle traversals, we measure the success rates, vertical accelerations, pitch and roll rates, and traversal times of an autonomous ground vehicle traversing 20 randomized straight-line paths in each terrain. As we increase the fractal dimension from 2.3 to 2.45 and from 2.45 to 2.6, we find that the median area of low-roughness terrain decreases by 13.8% and 7.16%, the median area of semi-rough terrain increases by 11.7% and 5.63%, and the median area of high-roughness terrain increases by 1.54% and 3.33%, respectively. We find that the median success rate of the vehicle decreases by 22.5% and 25% as the fractal dimension increases from 2.3 to 2.45 and from 2.45 to 2.6, respectively. Successful traversal results show that the median root-mean-squared vertical accelerations, median root-mean-squared pitch and roll rates, and median traversal times all increase with the fractal dimension.

关键词： Roads Dimensions Testing Automated guided vehicles Discrete element methods Vehicles Simulation environment interactions autonomous mobility and maneuver vehicle autonomy Fractals Surface roughness environmental models perception and planning reliability and adaptability simulation and design

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Intent-Driven Distributed Applications Management Over Compute and Network Resources in the Computing Continuum 19

Intent-Driven Distributed Applications Management Over Compu...

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19th Annual International Conference on Distributed Computing in Smart systems and the Internet of Things, DCOSS-IoT 2023

作者： Zafeiropoulos, Anastasios Fotopoulou, Eleni Vassilakis, Constantinos Tzanettis, Ioannis Lombardo, Chiara Carrega, Alesssandro Bruschi, Roberto National Technical University of Athens Institute of Communication and Computer Systems Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Cnit S2N National Laboratory Genoa Italy

ISBN: (纸本)9798350346497

Intent-driven orchestration enables organizations to achieve greater automation, integration, and intelligence of their compute and network resources by defining their objectives and policies in a more abstract and holistic way, while letting the orchestration system handle the coordination, optimization, and adaptation of the resources in response to changing needs and conditions. The trend for the development of intent-driven orchestration mechanisms has been growing in recent years as the deployment of distributed applications in the computing continuum has become more complex and diverse. In this manuscript we detail a novel approach for the management of distributed applications in the computing continuum. A hierarchical decision making scheme is proposed where an entity in a specific level of the hierarchy has the responsibility for the management of the overall application or a part of it, without having the control of all the management actions. The control is distributed across various entities that have to collaborate towards some joint objectives. Both compute and network resources management is considered that may be provided by the same or different infrastructure providers. © 2023 IEEE.

关键词： Decision making

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Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy systems

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computers, Materials & Continua 2022年第8期72卷 3999-4017页

作者： Md.Mottahir Alam Ahteshamul Haque Mohammed Ali Khan Nebras M.Sobahi Ibrahim Mustafa Mehedi Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew Delhi 110025India Department of Electrical Power Engineering Faculty of Electrical Engineering and CommunicationBrno University of TechnologyBrno 61600Czech Republic Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of *** with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow,monitoring and maintenance are a few of the prime *** problems were addressed widely in the literature,but most of the research has drawbacks due to long detection time,and high misclassification *** to overcome these drawbacks,and to develop an accurate monitoring approach,this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic(PV)system and highlighted along with a brief overview on existing fault detection *** on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance ***,the system was tested with a 4 kWp grid-connected PV system,and a decision tree-based algorithm was developed for the identification of a *** results identified 94.7%training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.

关键词： Fault detection machine learning fault ride through

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Machine Learning-Based Multivariate Classification of Cardiac Autonomic Neuropathy Via Imbalanced ECG Recordings

Machine Learning-Based Multivariate Classification of Cardia...

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2024 Advances in Science and engineering Technology International Conferences, ASET 2024

作者： Al Radi, Muaz Ahmad, Abdelfatah Ahmad, Abdulrahman Boudiaf, Abderrahmene Alkaabi, Nouf Malouf, Maher Jelinek, Herbert Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University Department of Industrial and Systems Engineering Abu Dhabi United Arab Emirates Khalifa University Department of Biomedical Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350344134

Cardiac Autonomic Neuropathy (CAN) is a frequent complication of diabetes that presents a diagnostic challenge in clinical settings. Ewing tests, comprising five assessments of blood pressure and heart rate, have traditionally been used to categorize the severity of CAN. This paper investigates the application of a machine learning (ML) approach that leverages patient demographic information and electrocardiogram (ECG) recordings to automatically classify CAN dysfunction without any of the Ewing tests. Additionally, the ML model being proposed addresses the challenges posed by imbalanced datasets both at the data level, through the utilization of synthetic minority over-sampling technique (SMOTE), and at the algorithmic level, by employing the focal loss function. The effectiveness of the proposed techniques is reflected in the ROC curve achieving an area under curve (AUC) value of 92%. Moreover, the model is able to produce 80% accurate prediction of the development of CAN. However, for enhancing system's classification performance, two Ewing tests are supported as additional features. A higher performance is achieved as indicated by accuracy and F1-score values of 88% and 88%, respectively. © 2024 IEEE.

关键词： Blood pressure

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