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RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

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Computers, Materials & Continua 2024年第5期79卷 3175-3192页

作者： Farah mohammad saad Al-Ahmadi Jalal Al-Muhtadi Center of Excellence in Information Assurance(CoEIA) King Saud UniversityRiyadh11543Saudi Arabia College of Computer&Information Sciences King Saud UniversityRiyadh11543Saudi Arabia

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity *** hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by *** development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning ***,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive ***-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor *** this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious *** key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft *** advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for *** effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification *** performance evaluation of the proposed model revealed exemplary results across *** accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.

关键词： Electricity theft smart grid RoBERTa GRU transfer learning

Novel Adaptive Binarization Method for Degraded Document Images

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Computers, Materials & Continua 2021年第6期67卷 3815-3832页

作者： Siti Norul Huda Sheikh abdullah saad M.Ismail mohammad Kamrul Hasan Palaiahnakote Shivakumara Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaSelangor43600Malaysia The Faculty of Computer and Information Sciences Aljouf UniversitySaudi Arabia Faculty of Computer Systems and Information Technology University of MalayaMalaysia

Achieving a good recognition rate for degraded document images is difficult as degraded document images suffer from low contrast,bleedthrough,and nonuniform illumination *** the existing baseline thresholding techniques that use fixed thresholds and windows,the proposed method introduces a concept for obtaining dynamic windows according to the image content to achieve better *** enhance a low-contrast image,we proposed a new mean histogram stretching method for suppressing noisy pixels in the background and,simultaneously,increasing pixel contrast at edges or near edges,which results in an enhanced *** the enhanced image,we propose a new method for deriving adaptive local thresholds for dynamic *** dynamic window is derived by exploiting the advantage of Otsu *** assess the performance of the proposed method,we have used standard databases,namely,document image binarization contest(DIBCO),for *** comparative study on well-known existing methods indicates that the proposed method outperforms the existing methods in terms of quality and recognition rate.

关键词： Global and local thresholding adaptive binarization degraded document image image histogram document image binarization contest

Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI

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Computers, Materials & Continua 2023年第1期74卷 623-639页

作者： Farah mohammad saad Al-Ahmadi Department of Computer Science King Saud UniversityRiyadh11543Saudi Arabia

There exists various neurological disorder based diseases like tumor,sleep disorder,headache,dementia and *** these,epilepsy is the most common neurological illness in humans,comparable to *** is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging(MRI).Neurons are intricately coupled in order to communicate and generate signals from human *** to the complex nature of electroencephalogram(EEG)signals and MRI’s the epileptic seizures detection and brain related problems diagnosis becomes a challenging *** based techniques and machine learning models are continuously giving their contributions to diagnose all such diseases in a better way than the normal process of *** performancemay sometime degrade due to missing information,selection of poor classification model and unavailability of quality data that are used to train the models for better *** research work is an attempt to epileptic seizures detection by using amulti focus dataset based on EEG signals and *** key steps of this work are:feature extraction having two different streams i.e.,EEGusingwavelet transformation along with SVD-Entropy,and MRI using convolutional neural network(CNN),after extracting features fromboth streams,feature fusion is applied to generate feature vector used by support vector machine(SVM)to diagnose the epileptic *** the experimental evaluation and results comparison with the current state-of-the-art techniques,it has been concluded that the performance of the proposed scheme is better than the existing models.

关键词： Epilepsy EEG MRI CNN SVM

Deep Learning Based Cyber Event Detection from Open-Source Re-Emerging Social Data

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Computers, Materials & Continua 2023年第8期76卷 1423-1438页

Social media forums have emerged as the most popular form of communication in the modern technology era,allowing people to discuss and express their *** increases the amount of material being shared on social media *** is a wealth of information about the threat that may be found in such open data *** security of already-deployed software and systems relies heavily on the timely detection of newly-emerging threats to their safety that can be gleaned from such *** the fact that several models for detecting cybersecurity events have been presented,it remains challenging to extract security events from the vast amounts of unstructured text present in public data *** majority of the currently available methods concentrate on detecting events that have a high number of *** is because the unstructured text in open data sources typically contains a large number of ***,to react to attacks quicker than they can be launched,security analysts and information technology operators need to be aware of critical security events as soon as possible,regardless of how often they are *** research provides a unique event detection method that can swiftly identify significant security events from open forums such as *** proposed work identified new threats and the revival of an attack or related event,independent of the volume of mentions relating to those events on *** this research work,deep learning has been used to extract predictive features from open-source *** proposed model is composed of data collection,data transformation,feature extraction using deep learning,Latent Dirichlet Allocation(LDA)based medium-level cyber-event detection and final Google Trends-based high-level cyber-event *** proposed technique has been evaluated on numerous *** results show that the proposed method outperforms existing methods in detecting cyber events by giving 95.96

关键词： Social media twitter cyber events deep learning

Impedance learning adaptive super-twisting control of a robotic exoskeleton for physical human-robot interaction

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IET Cyber-Systems and Robotics 2023年第1期5卷 108-121页

作者： Brahim Brahmi mohammad Habibur Rahman Maarouf saad Department of Electrical Engineering College AhuntsicMontrealQuebecCanada Department of Mechanical Engineering University of Wisconsin-MilwaukeeMilwaukeeWIUSA Department of Electrical Engineering École de Technologie SupérieureMontrealQuebecCanada

This study addresses two issues about the interaction of the upper limb rehabilitation robot with individuals who have *** first step is to estimate the human's target position(also known as TPH).The second step is to develop a robust adaptive impedance control mechanism.A novel Non-singular Terminal Sliding Mode Control combined with an adaptive super-twisting controller is being developed to achieve this *** combination's purpose is to provide high reliability,continuous performance tracking of the system's *** proposed adaptive control strategy reduces matched dynamic uncertainty while also lowering chattering,which is the sliding mode's most glaring *** proposed TPH is coupled with adaptive impedance control with the use of a Radial Basis Function Neural Network,which allows a robotic exoskeleton to simply track the desired impedance *** validate the approach in real-time,an exoskeleton robot was deployed in controlled experimental circumstances.A comparison study has been set up to show how the adaptive impedance approach proposed is better than other traditional controllers.

关键词： adaptive control impedance model robust control super twisting controller upper limb rehabilitation robot

Geotechnical forensic investigation of a slope failure on silty clay soil--A case study

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Frontiers of Structural and Civil Engineering 2020年第2期14卷 501-517页

作者： mohammad Abubakar NAVEED Zulfiqar ALI Abdul QADIR Umar Naveed LATIF saad HAMID Umar SARWAR Military College of Engineering National University of Sciences and TechnologyRisalpur 24080Pakistan School of Civil Environmental and Mining EngineeringThe University of AdelaideAdelaibe 5096Australia

Qila Bala Hisar is one of the noteworthy places of Peshawar,Khyber *** fort was constructed on a flled ground during the 18th century and it was renovated several times by the occupants ever ***,due to an earthquake of magnitude 7.3,the upper part of the south-western wall of the fort *** collapse of the wall was attributed to the failure of the retained *** research was undertaken to characterize the slope material,study causal factors of failure and evaluate remedial *** investigation involved conventional field and laboratory testing and geophysical investigation using electrical resistivity technique to evaluate the nature of ***,X-ray Diffraction and Scanning Electron Microscopy was used to study the slope material at a molecular level to evaluate the existence of swelling *** analysis has shown that excessive seepage of water caused by the poor maintenance of runoff and sewage drains is the causal factor triggered by the seismic event.A remedial strategy involving soil nails,micro piles and improvement of the surface drainage is recommended.

关键词： forensic geotechnical investigation slope failure slope analysis finite element method numerical analysis seismic loading Qila Bala Hisar

Evaluation of the Antifibrillatory Drug Interactions between Amiodarone and Diltiazem in Isolated Perfused Rabbit Hearts

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Journal of Pharmacy and Pharmacology 2014年第6期2卷 366-370页

作者： Abdulrahman abdullah Almotrefi mohammad saad Alhumayyd Department of Pharmacology King Saud University Riyadh 11472 Saudi Arabia

The alarming reports of the proarrhythmic effects of several antiarrhythmic agents have stimulated our interest to study the antifibrillatory drug interactions that may occur when antiarrhythmic drugs from different classes are combined. Hearts isolated from New Zealand white rabbits of either sex weighing 1.5 to 2 kg were perfused by the Langendorff method with McEwen＇s solution. We used the serial-shock method of measuring VFT （ventricular fibrillation threshold）, a method that have proved reliable in quantitatively assessing the antifibrillatory potency of many antiarrhythmic drugs we have studied during the last three decades. Perfusion with either amiodarone or diltiazem produced significant, dose-dependent increase in VFT. In addition, there was significant difference in the increase in VFT produced by the combined infusion of 1 μmol of amiodarone and 0.02 μmol of diltiazem and the summation of the increases produced by the separate infusion of these two concentrations. This synergistic antifibrillatory interactions between amiodarone and diltiazem may suggest that catuion has to be taken when amiodarone and diltiazem are combined in the treatment of cardiac arrhythmias as this may lead to a proarrhythmic effect. However, further studies are required to establish this in the clinical setup.

关键词： Arrhythmia amiodarone deltiazem fibrillation.

Deep Learning Control for Autonomous Robot

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Computers, Materials & Continua 2022年第8期72卷 2811-2824页

作者： Rihem Farkh saad Alhuwaimel Sultan Alzahrani Khaled Al Jaloud mohammad Tabrez Quasim College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Laboratory for Analysis Conception and Control of SystemsLR-11-ES20Department of Electrical Engineering National Engineering School of TunisTunis El Manar University1002Tunisia King Abdulaziz City for Science and Technology Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

Several applications of machine learning and artificial intelligence,have acquired importance and come to the fore as a result of recent advances and improvements in these *** cars are one such *** is expected to have a significant and revolutionary influence on *** with smart cities,new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving *** autonomous automobile,often known as selfdriving systems or driverless vehicles,is a vehicle that can perceive its surroundings and navigate predetermined routes without human *** are on the verge of evolving into autonomous robots,thanks to significant breakthroughs in artificial intelligence and related technologies,and this will have a wide range of socio-economic ***,in order for these automobiles to become a reality,they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate *** majority of self-driving car technologies are based on computer systems that automate vehicle control *** forward-collision warning and antilock brakes to lane-keeping and adaptive drive control,to fully automated driving,these technological components have a wide range of capabilities.A self-driving car combines a wide range of sensors,actuators,and *** researches on computer vision and deep learning are used to control autonomous driving *** self-driving automobiles,lane-keeping is *** study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the *** propose an advanced control for a selfdriving robot by using two controllers *** neural networks(CNNs)are employed,to predict the car’and a proportionalintegral-derivative(PID)controller is designed for speed and steering *** stu

关键词： Autonomous car cascade PID control deep learning convolutional neural network differential drive system raspberry PI road lane detector

Threefold Optimized Forecasting of Electricity Consumption in Higher Education Institutions

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Computers, Materials & Continua 2022年第11期73卷 2351-2370页

作者： Majida Kazmi Hashim Raza Khan Lubaba mohammad Hashir Bin Khalid saad Ahmed Qazi Faculty of Electrical and Computer Engineering NED University of Engineering and TechnologyKarachi75270Pakistan Neurocomputation Lab National Center of Artificial IntelligenceKarachi75270Pakistan

Energy management benefits both consumers and utility companiesalike. Utility companies remain interested in identifying and reducing energywaste and theft, whereas consumers’ interest remain in lowering their energyexpenses. A large supply-demand gap of over 6 GW exists in Pakistan asreported in 2018. Reducing this gap from the supply side is an expensiveand complex task. However, efficient energy management and distributionon demand side has potential to reduce this gap economically. Electricityload forecasting models are increasingly used by energy managers in takingreal-time tactical decisions to ensure efficient use of resources. Advancementin Machine-learning (ML) technology has enabled accurate forecasting ofelectricity consumption. However, the impact of computation cost affordedby these ML models is often ignored in favour of accuracy. This studyconsiders both accuracy and computation cost as concurrently significantfactors because together they shape the technology environment as well ascreate economic impact. Thus, a three-fold optimized load forecasting modelis proposed which includes (1) application specific parameters selection, (2)impact of different dataset granularities and (3) implementation of specificdata preparation. It deploys and compares the widely used back-propagationArtificial Neural Network (ANN) and Random Forest (RF) models for theprediction of electricity consumption of buildings within a university. In addition to the temporal and historical power consumption date as input parameters, the study also embeds weather data as well as university operationalcalendars resulting in improved performance. The outcomes are indicativethat the granularity i.e. the scale of details in data, and set of reduced and fullinput parameters impact performance accuracies differently for ANN and RFmodels. Experimental results show that overall RF model performed betterboth in terms of accuracy as well as computational time for a 1-min, 15-minand 1-h dataset

关键词： Electricity forecasting short term higher educational institution artificial neural network random forest accuracy computational time