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A Three-Dimensional Real-Time Gait-Based Age Detection System Using Machine Learning

Computers, Materials & Continua 2023年第4期75卷 165-182页

作者： Muhammad Azhar Sehat ullah khalil ullah Habib Shah Abdallah Namoun Khaliq Ur Rahman Department of Computer Science&IT University of MalakandChakdara18800Pakistan Department of Software Engineering University of MalakandChakdara18800Pakistan College of Computer Science King Khalid UniversityAbha62529Saudi Arabia Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Statistics Abdul Wali Khan UniversityMardan23200Pakistan

Human biometric analysis has gotten much attention due to itswidespread use in different research areas, such as security, surveillance,health, human identification, and classification. Human gait is one of the keyhuman traits that can identify and classify humans based on their age, gender,and ethnicity. Different approaches have been proposed for the estimation ofhuman age based on gait so far. However, challenges are there, for which anefficient, low-cost technique or algorithm is needed. In this paper, we proposea three-dimensional real-time gait-based age detection system using a machinelearning approach. The proposed system consists of training and testingphases. The proposed training phase consists of gait features extraction usingthe Microsoft Kinect (MS Kinect) controller, dataset generation based onjoints’ position, pre-processing of gait features, feature selection by calculatingthe Standard error and Standard deviation of the arithmetic mean and bestmodel selection using R2 and adjusted R2 techniques. T-test and ANOVAtechniques show that nine joints (right shoulder, right elbow, right hand, leftknee, right knee, right ankle, left ankle, left, and right foot) are statisticallysignificant at a 5% level of significance for age estimation. The proposedtesting phase correctly predicts the age of a walking person using the resultsobtained from the training phase. The proposed approach is evaluated on thedata that is experimentally recorded from the user in a real-time *** (50) volunteers of different ages participated in the experimental *** the limited features, the proposed method estimates the age with 98.0%accuracy on experimental images acquired in real-time via a classical generallinear regression model.

关键词： Age estimation gait biometrics classical linear regression model

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Ethnoveterinary medicines used against various livestock disorders in the flora of Shamozai Valley, Swat,KP Pakistan

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Traditional Medicine Research 2020年第5期5卷 377-388页

作者： Noor Muhammad Muhammad khalil ullah Khan Nisar Uddin Niaz Ali Shariat ullah Muhammad Umer College of Horticulture Hebei Agricultural UniversityBaoding 071000China Department of Botany Hazara UniversityMansehra 21300KPPakistan Department of Botany University of MalakandChakdara 18800KPPakistan College of Plant Protection Hebei Agricultural UniversityBaoding 071000China

Background:The Shamozai Valley of Swat District is remarkable with various plant *** information offers folk remedies for livestock,which are cheaper than standard treatment methods and are readily ***:Different trips were organized for gathering(harvesting)and recording medicinal plants in the area during 2018.A total of 140 local residents were ***,the obtained data were evaluated using used value,relative frequency citations,fidelity level,consensus index,and informant consensus ***:In this study,41 plants were presented,and these plants were used commonly as medication for treating various livestock *** therapeutic plants with most used value were Artemisia scoparia(0.607),Berberis lyceum Royle(0.610),Bromus japonicus(0.491),Avena sativa(0.482),Cannabis sativa L.(0.473),Capsicum annum L.(0.471),Cedrus deodara(0.462),and Chenopodium murale(0.453).On the basis of relative frequency citations values,the most quoted plant species by the indigenous people are Artemisia scoparia(0.760),Berberis lyceum(0.742),Bromus japonicus(0.731),Avena sativa(0.721),and Cannabis sativa L.(0.711).Consensus index percentage showed the highest for Artemisia scoparia(83.109%),followed by Berberis lyceum Royle(80.454%),whereas the ethnomedicinal plant species with most fidelity level were Artemisia scoparia(76.320%),Berberis lyceum Royle(73.403%),Bromus japonicus(72.013%),Avena sativa(71.024%),Cannabis sativa L.(69.322%),Capsicum annum L(68.344%),Cedrus deodara(67.215%),and Chenopodium murale(66.060%)for various *** consensus factor ranged from 0.947 to 1.000,whereas different ailments ***-causing agent,carminative treatment,eye diseases,mouth ulcers,myiasis,pediculosis,septicemia,and tick infestation had maximum informant consensus factor ***:The publics of Shamozai are deeply reliant on ethomedicinal plants for treating numerous livestock *** information always offers a

关键词： Ethnoveterinary practices Medicinal plants Folk knowledge Livestock ailments Shamozai Valley

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Automatic Detection of Outliers in Multi-Channel EMG Signals Using MFCC and SVM

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Intelligent Automation & Soft Computing 2023年第4期36卷 169-181页

作者： Muhammad Irfan khalil ullah Fazal Muhammad Salman Khan Faisal Althobiani Muhammad Usman Mohammed Alshareef Shadi Alghaffari Saifur Rahman Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Software Engineering University of MalakandDir LowerPakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan

The automatic detection of noisy channels in surface Electromyogram(sEMG)signals,at the time of recording,is very critical in making a noise-free EMG *** an EMG signal contaminated by high-level noise is recorded,then it will be useless and can’t be used for any healthcare *** this research work,a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals.A modiﬁed version of mel fre-quency cepstral coefﬁcients(mMFCC)is proposed for the extraction of features from sEMG channels along with other statistical parameters i-e complexity coef-ﬁcient,hurst exponent,and root mean *** state-of-the-art classiﬁers such as Support Vector Machine(SVM),Ensemble Bagged Trees,Ensemble Sub-space Discriminant,and Logistic Regression are used to automatically identify an EMG channel either bad or good based on these extracted ***-based analyses of these classiﬁers have also been considered based on total classi-ﬁcation accuracy,prediction speed(observations/sec),and processing *** proposed method is tested on 320 simulated EMG channels as well as 640 experi-mental EMG *** is used as our main classiﬁer for the detection of noisy channels which gives a total classiﬁcation accuracy of 99.4%for simulated EMG channels whereas accuracy of 98.9%is achieved for experimental EMG channels.

关键词： Machine learning surface electromyography support vector machine classiﬁcation features

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Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

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Computers, Materials & Continua 2022年第12期73卷 5075-5089页

作者： Abdullah A Asiri Akhtar Badshah Fazal Muhammad Hassan A Alshamrani khalil ullah Khalaf A Alshamrani Samar Alqhtani Muhammad Irfan Hanan Talal Halawani Khlood M Mehdar Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Software Engineering University of MalakandDir LowerPakistan Department of Computer Science and Information Systems College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia

Electroencephalogram(EEG)is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain,measured and recorded chronologically the surface of the scalp from the *** recorded signals from the brain are rich with useful *** inference of this useful information is a challenging *** paper aims to process the EEG signals for the recognition of human emotions specifically happiness,anger,fear,sadness,and surprise in response to audiovisual *** EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp,in response to audiovisual stimuli for the mentioned *** a bandpass filter with a bandwidth of 1-100 Hz,recorded raw EEG signals are *** preprocessed signals then further analyzed and twelve selected features in different domains are *** Random forest(RF)and multilayer perceptron(MLP)algorithms are then used for the classification of the emotions through extracted *** proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80%and 88%usingMLP and RF classifiers respectively on hybrid features for experimental signals of different *** proposed model outperforms in terms of cost and accuracy.

关键词： Electroencephalography emotion classification signal processing multilayer perceptron random forest

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Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

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Computers, Materials & Continua 2024年第4期79卷 713-733页

作者： Aizaz Ali Maqbool Khan khalil Khan Rehan ullah Khan Abdulrahman Aloraini Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan Software Competence Center Hagenberg Softwarepark 32aHagenberg4232Austria Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstana010000Kazakhstan Department of Information Technology College of ComputerQassim UniversityP.O.Box 1162BuraydahSaudi Arabia

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse *** numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiv

关键词： Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks

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Race Classification Using Deep Learning

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Computers, Materials & Continua 2021年第9期68卷 3483-3498页

作者： khalil Khan Rehan ullah Khan Jehad Ali Irfan Uddin Sahib Khan Byeong-hee Roh Department of Information Technology and Computer Science Pak-Austria Institute of Applied Science and TechnologyHaripurPakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Engineering and Department of AI Convergence Network Ajou UniversitySuwonKorea Department of Computer Science Superior CollegeLahorePakistan Department of Electronics and Telecommunications Politecnico di TorinoTorinoItaly

Race classification is a long-standing challenge in the field of face image *** investigation of salient facial features is an important task to avoid processing all face *** segmentation strongly benefits several face analysis tasks,including ethnicity and race *** propose a race-classification algorithm using a prior face segmentation framework.A deep convolutional neural network(DCNN)was used to construct a face segmentation *** training the DCNN,we label face images according to seven different classes,that is,nose,skin,hair,eyes,brows,back,and *** DCNN model developed in the first phase was used to create segmentation *** probabilistic classification method is used,and probability maps(PMs)are created for each semantic *** investigated five salient facial features from among seven that help in race *** are extracted from the PMs of five classes,and a new model is trained based on the *** assessed the performance of the proposed race classification method on four standard face datasets,reporting superior results compared with previous studies.

关键词： Deep learning facial feature face analysis learning race race classification

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An Eigenspace Method for Detecting Space-Time Disease Clusters with Unknown Population-Data

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Computers, Materials & Continua 2022年第1期70卷 1945-1953页

作者： Sami ullah Nurul Hidayah Mohd Nor Hanita Daud Nooraini Zainuddin Hadi Fanaee-T Alamgir khalil Department of Fundamental&Applied Sciences Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Center for Applied Intelligent Systems Research(CAISR) Halmstad UniversityHalmstadSE-30118Sweden Department of Statistics University of PeshawarPakistan

Space-time disease cluster detection assists in conducting disease surveillance and implementing control *** state-of-the-art method for this kind of problem is the Space-time Scan Statistics(SaTScan)which has limitations for non-traditional/non-clinical data sources due to its parametric model assumptions such as Poisson orGaussian *** this problem,an Eigenspace-based method called Multi-EigenSpot has recently been proposed as a nonparametric ***,it is based on the population counts data which are not always available in the least developed *** addition,the population counts are difficult to approximate for some surveillance data such as emergency department visits and over-the-counter drug sales,where the catchment area for each hospital/pharmacy is *** extend the population-based Multi-EigenSpot method to approximate the potential disease clusters from the observed/reported disease counts only with no need for the population *** proposed adaptation uses an estimator of expected disease count that does not depend on the population *** proposed method was evaluated on the real-world dataset and the results were compared with the population-based methods:Multi-EigenSpot and *** result shows that the proposed adaptation is effective in approximating the important outputs of the population-based methods.

关键词： Space-time disease clusters Eigenspace method nontraditional data sources nonparametric methods

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Space-Time Cluster Analysis of Accidental Oil Spills in Rivers State, Nigeria, 2011–2019

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Computers, Materials & Continua 2021年第3期66卷 3065-3074页

作者： Sami ullah Hanita Daud Nooraini Zainuddin Sarat CDass Alamgir khalil Hadi Fanaee-T Ilyas Khan Department of Fundamental&Applied Sciences Universiti Teknologi PETRONAS32610Seri IskandarPerakMalaysia School of Mathematical and Computer Sciences Heriot-Watt University Malaysia62200PutrajayaMalaysia Department of Statistics University of PeshawarPakistan Center for Applied Intelligent Systems Research(CAISR) Halmstad UniversityHalmstadSweden Faculty of Mathematics and Statistics Ton Duc Thang UniversityHo Chi Minh City72915Vietnam

Oil spills cause environmental pollution with a serious threat to local communities and sustainable *** oil spills can be modelled as a stochastic process where each oil spill event is described by its spatial locations and incidence-time and hence allow for space-time cluster *** cluster analysis can detect space-time pattern distribution of oil spills which can be useful for implementing preventive measures and evidence-based decision *** study aims to detect the space-time clusters of accidental oil spills in Rivers state,Nigeria through the Space-time Scan *** Space-time Scan Statistic was applied under the permutation model to the oil spill data(each for sabotage and operational oil spills)collected at Local Government Area(LGA)-level during the period from 2011 to *** results show that the sabotage oil spill clusters have covered most of the LGAs in the southern part of the state at the start of the study period and then in 2018–2019,it moved to the west covering a single *** operational oil spill clusters covered two neighboring LGAs in the *** addition,the temporal cluster of sabotage oil spills was seen in 2019 and operational oil spills in 2011–*** sabotage oil spills show an increasing trend with the maximum in 2019 while the operational oil spills show a decreasing trend with the minimum in *** findings assist in more effective decision-making for combating the environmental problems and controlling the future spill incidence in the cluster-regions.

关键词： Oil spills space-time clusters SaTScan^(™) permutation model Nigeria

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Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

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Computers, Materials & Continua 2022年第6期71卷 5581-5601页

作者： Durr-e-Nayab Ali Mustafa Qamar Rehan ullah Khan Waleed Albattah khalil Khan Shabana Habib Muhammad Islam Department of Computer Systems Engineering University of Engineering and TechnologyPeshawarPakistan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology and Computer Science Pak-Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripurPakistan Department of Electrical Engineering College of Engineering and Information TechnologyOnaizah CollegesAl-QassimSaudi Arabia

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related *** surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this *** research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd *** theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the *** Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in *** crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major *** have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority *** algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.

关键词： Computer vision COVID sparse crowd crowd analysis flow analysis sparse crowd management tawaaf video analysis video processing

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Robust Stabilization of Non-minimum Phase Switched Nonlinear Systems with Uncertainty

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Journal of Systems Science & Complexity 2020年第2期33卷 289-311页

作者： khalil Jouili Laboratory of Advanced Systems Polytechnic School of Tunisia(EPT) B.P.7432078 MarsaTunisia

This paper investigates the problem of robust stabilization of a class of switched nonlinear system with uncertain dynamics where each subsystem represents a non-minimum *** authors first construct a stabilizing sliding mode controller for each subsystem to stabilize individually its own unstable internal ***,a switching strategy is introduced to select the most appropriate diffeomorphism through an infinity of *** conditions are specifically given for the exponential stability and the exponential upper bound of the trajectory of the switched subsystem,which guarantees the global asymptotical stability of the resulting switched ***,the proposed control approach can improvemore the transient state,compared to a feedback linearization based on only one *** studies illustrate the effectiveness of the suggested approach.

关键词： Exponential stability sliding mode control switching control uncertain non-minimum phase systems

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