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

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

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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

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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

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Phishing Attacks Detection Using EnsembleMachine Learning Algorithms

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Computers, Materials & Continua 2024年第7期80卷 1325-1345页

作者： Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Marwan Abu-Zanona Bassam mohammad Elzaghmouri farah H.Zawaideh Mouiad Fadeil Alawneh Department of Computer Science and Information Systems College of Applied SciencesAlmaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26110Jordan Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjloun26767Jordan

Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based *** indicates a persistent rise in phishing ***,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to ***,it is imperative to utilize sophisticated algorithms to address this *** learning is a highly effective approach for identifying and uncovering these harmful *** learning(ML)approaches can identify common characteristics in most phishing *** this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing *** that,we used the normalization technique on the dataset to transform the range of all the features into the same *** findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best *** in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction ***,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.

关键词： Social engineering attacks phishing attacks machine learning security artificial intelligence

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Anomaly Detection in ICS Datasets with Machine Learning Algorithms

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Computer Systems Science & Engineering 2021年第4期37卷 33-46页

作者： Sinil Mubarak Mohamed Hadi Habaebi Md Rafiqul Islam farah Diyana Abdul Rahman mohammad Tahir International Islamic University Malaysia Jalan Gombak53100Malaysia Sunway University Selangor47500Malaysia

An Intrusion Detection System (IDS) provides a front-line defensemechanism for the Industrial Control System (ICS) dedicated to keeping the process operations running continuously for 24 hours in a day and 7 days in a week.A well-known ICS is the Supervisory Control and Data Acquisition (SCADA)system. It supervises the physical process from sensor data and performs remotemonitoring control and diagnostic functions in critical infrastructures. The ICScyber threats are growing at an alarming rate on industrial automation applications. Detection techniques with machine learning algorithms on public datasets,suitable for intrusion detection of cyber-attacks in SCADA systems, as the firstline of defense, have been detailed. The machine learning algorithms have beenperformed with labeled output for prediction classification. The activity trafficbetween ICS components is analyzed and packet inspection of the dataset is performed for the ICS network. The features of flow-based network traffic areextracted for behavior analysis with port-wise profiling based on the data baseline,and anomaly detection classification and prediction using machine learning algorithms are performed.

关键词： Industrial control system SCADA intrusion detection system machine learning anomaly detection

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Recovery of bird communities following the construction of a large-scale hydroelectric dam

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Ecological Processes 2021年第1期10卷 408-417页

作者： Affan Nasruddin-Roshidi mohammad Saiful Mansor Nor Adibah Ismail Ehwan Ngadi Mohd Izzat-Husna Shahril Mod Husin farah Shafawati Mohd-Taib Rahmah Illias Shukor Md Nor Department of Biological Sciences and Biotechnology Faculty of Science and TechnologyUniversiti Kebangsaan Malaysia43600 BangiSelangorMalaysia Kolej GENIUS Insan Universiti Sains Islam MalaysiaBandar Baru Nilai71800 NilaiNegeri SembilanMalaysia TNB Research Sdn Bhd No.1Kawasan Institusi PenyelidikanJalan Ayer Itam43000 KajangSelangorMalaysia Department of Wildlife and National Parks(PERHILITAN) Km.10Jln Cheras56100 Kuala LumpurWilayah PersekutuanMalaysia.

Background:Habitat degradation is known to have a major impact on the composition of bird communities,yet how these communities recover following such disturbance is less *** study examined bird community changes from pre-logging to recovery over the course of habitat disturbance caused by the development of the Hulu Terengganu Hydroelectric Dam Project in Peninsular Malaysia,between 2007 and *** surveys were conducted using point count observations and mist netting in the dam area from the prelogging phase until the operation ***:It was observed that while bird species significantly declined after large-scale habitat clearance between the pre-logging and construction phases,they showed positive signs of recovery from the inundation to operation *** findings indicate that the bird community is still recovering,and the permanent loss of habitat required by most of the original species has not occurred,as evidenced by *** species composition differed across phases due to varying habitat *** turnover rate and immigration rate(recolonisation and newly recorded species)were higher between the construction and operation *** was also observed that insectivorous birds were more heavily affected than others by changes to the landscape,which highlights the versatility,survivability and tolerance of certain species to extreme disturbance and habitat ***:Although the operation phase showed an incremental change in bird species richness due to recolonisation and newly recorded species,this value remains far from that of the pre-logging *** predict that bird species recovery will continue to increase for some time before reaching a plateau among the newly created islands,reservoir and catchment area of the dam.

关键词： Dam construction Extinction Habitat recovery Recolonisation Turnover

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A Comparison between Patients’ and Residents’ Perceptions of Patient Centeredness and Communication Skills among Physicians Working at Jordan University Hospital

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Open Journal of Nursing 2017年第6期7卷 698-706页

作者： Nada A. Yasein Farouq M. Shakhatreh Wejdan A. Shroukh mohammad S. farah Ruba M. Jaber Department of Family and Community Medicine Faculty of Medicine University of Jordan Amman Jordan Pharmacy Department University of Jordan Amman Jordan High Specialty in Ophthalmology Jordan University Amman Jordan

Aim: This research aims to evaluate patient-centeredness and communication skills from the patients’ point of view and that of the physicians’ point of view and compares the two outcomes. Methods: This was a cross-sectional study with a convenient sample of 418 patients and 94 residents. Instrument of the study was a structured questionnaire that aimed to evaluate patient centeredness and communication skills of the residents. Results: Residents gave themselves a significantly higher score than the score given to them by patients in most studied aspects such as the extent to which the doctor discussed the patient’s problem, the extent to which the doctor explained the problem, the doctor introduced himself, the doctor greeted the patient properly and others. The only aspect for which patients gave residents higher score than that residents gave themselves was the extent to which the doctor asked the patient about what is expected to be done (ECG, CT scan, giving antibiotics, …). Conclusion: A transformation from doctor centered approach to patient centered approach is needed.

关键词： Communication Skills Patient-Centeredness Residents

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Study of the effects of natural toothbrush (<i>Salva-dora persica</i>) in prevention of dental caries and plaque index

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Health 2012年第9期4卷 612-618页

作者： Fatemeh Ezoddini-Ardakani Mahmoud Nouri Shadkam Hasan Fotouhi farah Bolouri Kashani Mahin Abbassi Zahra Hashemian Hossein Dehghani mohammad Ali Jaffari Hossein Barzegar Faezeh Nazarinejhad mohammad Kazem Jafari Abas Heydaripour mohammad Javad Dashti Atefesadat Hedayati Masoud Masoumi Somaye Ebrahimi Faculty of Dentistry Shahid Sadoughi University of Medical Sciences Yazd Iran Faculty of Medicine Shahid Sadoughi University of Medical Sciences Yazd Iran Shahid Sadoughi University of Medical Sciences Yazd Iran

Background: Nowadays, though more attention is being paid to oral and dental hygiene all over the world, dental caries and periodontal diseases are increasing daily. We decided to study the effectiveness of natural toothbrush in prevention of dental caries and plaque formation. Methods: This analytic, semi-experimental clinical trial included 390 individuals. Subjects were divided into 3 groups: using natural toothbrush, using artificial toothbrush and using both natural and artificial toothbrush. The plaque index and DMFT was assessed at the beginning of the study until 1 year. ANOVA, Kruskal Wallis, LSD, Paired t-test, chi square, Fisher exact and Mc-Nemar tests were used for data analysis. Results: The plaque index in all three groups showed a mean reduction of 0.71. The mean DMFT in the samples before intervention was 8.38 ± 4.14 and increased to 9.1 ± 4.56. At the end of the study, the lowest plaque index was observed in the group using both the natural and artificial methods. During one year follow-up, the plaque index decreased 1.16, 0.83, and 0.21 degrees in the first, second and third groups. Conclusion: The increase in DMFT in the group that used artificial toothbrush was more than other groups. Thus, use of natural toothbrush leads to a decrease in growth rate of DMFT. Therefore natural toothbrush can be used for the cleaning of teeth and prevention of dental caries.

关键词： Salvadora persica Natural Toothbrush Toothbrush and Tooth Paste Caries Plaque Index

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