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ABMRF:An Ensemble Model for Author Profiling Based on Stylistic Features Using Roman Urdu

Intelligent Automation & Soft Computing 2024年第2期39卷 301-317页

作者： Aiman Muhammad Arshad Bilal Khan Khalil Khan ali mustafa qamar Rehan Ullah Khan Department of Computer Science City University of Science and Information TechnologyPeshawarPakistan Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstanaKazakhstan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia

This study explores the area of Author Profiling(AP)and its importance in several industries,including forensics,security,marketing,and education.A key component of AP is the extraction of useful information from text,with an emphasis on the writers’ages and genders.To improve the accuracy of AP tasks,the study develops an ensemble model dubbed ABMRF that combines AdaBoostM1(ABM1)and Random Forest(RF).The work uses an extensive technique that involves textmessage dataset pretreatment,model training,and assessment.To evaluate the effectiveness of several machine learning(ML)algorithms in classifying age and gender,including Composite Hypercube on Random Projection(CHIRP),Decision Trees(J48),Na飗e Bayes(NB),K Nearest Neighbor,AdaboostM1,NB-Updatable,RF,andABMRF,they are compared.The findings demonstrate thatABMRFregularly beats the competition,with a gender classification accuracy of 71.14%and an age classification accuracy of 54.29%,respectively.Additional metrics like precision,recall,F-measure,Matthews Correlation Coefficient(MCC),and accuracy support ABMRF’s outstanding performance in age and gender profiling tasks.This study demonstrates the usefulness of ABMRF as an ensemble model for author profiling and highlights its possible uses in marketing,law enforcement,and education.The results emphasize the effectiveness of ensemble approaches in enhancing author profiling task accuracy,particularly when it comes to age and gender identification.

关键词： Machine learning author profiling AdaBoostM1 random forest ensemble learning text classification

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Deep Convolutional Neural Network Based Churn Prediction for Telecommunication Industry

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Computer Systems Science & Engineering 2022年第12期43卷 1255-1270页

作者： Nasebah Almufadi ali mustafa qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computing School of Electrical Engineering and Computer ScienceNational University of Sciences and Technology(NUST)IslamabadPakistan

Currently,mobile communication is one of the widely used means of communication.Nevertheless,it is quite challenging for a telecommunication company to attract new customers.The recent concept of mobile number portability has also aggravated the problem of customer churn.Companies need to identify beforehand the customers,who could potentially churn out to the competitors.In the telecommunication industry,such identification could be done based on call detail records.This research presents an extensive experimental study based on various deep learning models,such as the 1D convolutional neural network(CNN)model along with the recurrent neural network(RNN)and deep neural network(DNN)for churn prediction.We use the mobile telephony churn prediction dataset obtained from ***,containing the data for around 100,000 individuals,out of which 86,000 are non-churners,whereas 14,000 are churned customers.The imbalanced data are handled using undersampling and oversampling.The accuracy for CNN,RNN,and DNN is 91%,93%,and 96%,respectively.Furthermore,DNN got 99%for ROC.

关键词： Deep learning machine learning churn prediction convolutional neural network recurrent neural network

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Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

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Computer Systems Science & Engineering 2023年第3期44卷 1973-1988页

作者： Aishah Alrashidi ali mustafa qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia

Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting.The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh,Saudi Arabia.The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017.These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh.After applying the data pre-processing techniques to prepare the data,different machine learning algorithms,namely Artificial Neural Network and Support Vector Regression(SVR),are applied and compared to predict the factory load.In addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this study.The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process.Finally,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.

关键词： Electricity load forecasting meteorological data machine learning feature selection modeling real-world problems predictive analytics

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Novel Machine Learning–Based Approach for Arabic Text Classification Using Stylistic and Semantic Features

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Computers, Materials & Continua 2023年第6期75卷 5871-5886页

作者： Fethi Fkih Mohammed Alsuhaibani Delel Rhouma ali mustafa qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia MARS Research Lab LR17ES05 University of SousseSousseTunisia

Text classification is an essential task for many applications related to the Natural Language Processing domain.It can be applied in many fields,such as Information Retrieval,Knowledge Extraction,and Knowledge modeling.Even though the importance of this task,Arabic Text Classification tools still suffer from many problems and remain incapable of responding to the increasing volume of Arabic content that circulates on the web or resides in large databases.This paper introduces a novel machine learning-based approach that exclusively uses hybrid(stylistic and semantic)features.First,we clean the Arabic documents and translate them to English using translation tools.Consequently,the semantic features are automatically extracted from the translated documents using an existing database of English topics.Besides,the model automatically extracts from the textual content a set of stylistic features such as word and character frequencies and punctuation.Therefore,we obtain 3 types of features:semantic,stylistic and hybrid.Using each time,a different type of feature,we performed an in-depth comparison study of nine well-known Machine Learning models to evaluate our approach and used a standard Arabic corpus.The obtained results show that Neural Network outperforms other models and provides good performances using hybrid features(F1-score=0.88%).

关键词： Arabic text classification machine learning stylistic features semantic features topics

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Managing Health Treatment by Optimizing Complex Lab-Developed Test Configurations: A Health Informatics Perspective

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Computers, Materials & Continua 2023年第6期75卷 6251-6267页

作者： Uzma Afzal Tariq Mahmood ali mustafa qamar Ayaz H.Khan Department of Computer Science Federal Urdu University of Arts Science and TechnologyKarachiPakistan Department of Computer Science Institute of Business AdministrationKarachiPakistan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Computer Engineering Department King Fahd University of Petroleum and MineralsDhahranSaudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence Dhahran31261Saudi Arabia

A complex Laboratory Developed Test(LDT)is a clinical test developed within a single laboratory.It is typically configured from many fea-ture constraints from clinical repositories,which are part of the existing Lab-oratory Information Management System(LIMS).Although these clinical repositories are automated,support for managing patient information with test results of an LDT is also integrated within the existing LIMS.Still,the support to configure LDTs design needs to be made available even in standard LIMS packages.The manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints between features can remain unsatisfied.It is a risky process and can lead patients to undergo unnecessary treatments.We proposed an optimized solution(opt-LDT)based on Genetic Algorithms to automate the configuration and resolve the inconsistencies in LDTs.Opt-LDT encodes LDT configuration as an optimization problem and generates a consistent configuration that satisfies the constraints of the features.We tested and validated opt-LDT for a local secondary care hospital in a real healthcare environment.Our results,averaged over ten runs,show that opt-LDT resolves 90%of inconsistencies while taking between 6 and 6.5 s for each configuration.Moreover,positive feedback based on a subjective questionnaire from clinicians regarding the performance,acceptability,and efficiency of opt-LDT motivates us to present our results for regulatory approval.

关键词： Artificial intelligence health informatics evolutionary algorithms genetic algorithms feature selection laboratory developed test

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The Impact of Check Bits on the Performance of Bloom Filter

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Computers, Materials & Continua 2022年第12期73卷 6037-6046页

作者： Rehan Ullah Khan ali mustafa qamar Suliman A.Alsuhibany Mohammed Alsuhaibani Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia

Bloom filter(BF)is a space-and-time efficient probabilistic technique that helps answermembership queries.However,BF faces several issues.The problems with traditional BF are generally two.Firstly,a large number of false positives can return wrong content when the data is queried.Secondly,the large size of BF is a bottleneck in the speed of querying and thus uses large memory.In order to solve the above two issues,in this article,we propose the check bits concept.From the implementation perspective,in the check bits approach,before saving the content value in the BF,we obtain the binary representation of the content value.Then,we take some bits of the content value,we call these the check bits.These bits are stored in a separate array such that they point to the same location as the BF.Finally,the content value(data)is stored in the BF based on the hash function values.Before retrieval of data from BF,the reverse process of the steps ensures that even if the same hash functions output has been generated for the content,the check bits make sure that the retrieval does not depend on the hash output alone.This thus helps in the reduction of false positives.In the experimental evaluation,we are able to reduce more than 50%of false positives.In our proposed approach,the false positives can still occur,however,false positives can only occur if the hash functions and check bits generate the same value for a particular content.The chances of such scenarios are less,therefore,we get a reduction of approximately more than 50%false positives in all cases.We believe that the proposed approach adds to the state of the art and opens new directions as such.

关键词： Bloom filter big data network processing optimization check bits

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Dates Fruit Recognition: From Classical Fusion to Deep Learning

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Computer Systems Science & Engineering 2022年第1期40卷 151-166页

作者： Khaled Marji Alresheedi Suliman Aladhadh Rehan Ullah Khan ali mustafa qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

There are over 200 different varieties of dates fruit in the world.Interestingly,every single type has some very specific features that differ from the others.In recent years,sorting,separating,and arranging in automated industries,in fruits businesses,and more specifically in dates businesses have inspired many research dimensions.In this regard,this paper focuses on the detection and recognition of dates using computer vision and machine learning.Our experimental setup is based on the classical machine learning approach and the deep learning approach for nine classes of dates fruit.Classical machine learning includes the Bayesian network,Support Vector Machine,Random Forest,and Multi-Layer Perceptron(MLP),while the Convolutional Neural Network is used for the deep learning set.The feature set includes Color Layout features,Fuzzy Color and Texture Histogram,Gabor filtering,and the Pyramid Histogram of the Oriented Gradients.The fusion of various features is also extensively explored in this paper.The MLP achieves the highest detection performance with an F-measure of 0.938.Moreover,deep learning shows better accuracy than the classical machine learning algorithms.In fact,deep learning got 2%more accurate results as compared to the MLP and the Random forest.We also show that classical machine learning could give increased classification performance which could get close to that provided by deep learning through the use of optimized tuning and a good feature set.

关键词： features Deep separating

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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 tasks.Video surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this domain.This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis.Managing theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic.The Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in Kaaba.The 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 flow.We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow.Our 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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Innovative Solutions for High-Performance Silicon Anodes in Lithium-Ion Batteries:Overcoming Challenges and Real-World Applications

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Nano-Micro Letters 2024年第9期16卷 341-384页

作者： mustafa Khan Suxia Yan Mujahid ali Faisal Mahmood Yang Zheng Guochun Li Junfeng Liu Xiaohui Song Yong Wang Institute for Energy Research Jiangsu UniversityZhenjiang 212013JiangsuPeople’s Republic of China School of Energy and Power Engineering Jiangsu UniversityZhenjiang 212013JiangsuPeople’s Republic of China School of Materials Science and Engineering Hefei University of TechnologyHefei 230009AnhuiPeople’s Republic of China

Silicon(Si)has emerged as a potent anode material for lithium-ion batteries(LIBs),but faces challenges like low electrical conductivity and significant volume changes during lithiation/delithiation,leading to material pulverization and capacity degradation.Recent research on nanostructured Si aims to mitigate volume expansion and enhance electrochemical performance,yet still grapples with issues like pulverization,unstable solid electrolyte interface(SEI)growth,and interparticle resistance.This review delves into innovative strategies for optimizing Si anodes’electrochemical performance via structural engineering,focusing on the synthesis of Si/C composites,engineering multidimensional nanostructures,and applying non-carbonaceous coatings.Forming a stable SEI is vital to prevent electrolyte decomposition and enhance Li^(+)transport,thereby stabilizing the Si anode interface and boosting cycling Coulombic efficiency.We also examine groundbreaking advancements such as self-healing polymers and advanced prelithiation methods to improve initial Coulombic efficiency and combat capacity loss.Our review uniquely provides a detailed examination of these strategies in real-world applications,moving beyond theoretical discussions.It offers a critical analysis of these approaches in terms of performance enhancement,scalability,and commercial feasibility.In conclusion,this review presents a comprehensive view and a forward-looking perspective on designing robust,high-performance Si-based anodes the next generation of LIBs.

关键词： Silicon anode Energy storage Nanostructure Prelithiation Binder

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An Automated and Real-time Approach of Depression Detection from Facial Micro-expressions

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Computers, Materials & Continua 2022年第11期73卷 2513-2528页

作者： Ghulam Gilanie Mahmood ul Hassan Mutyyba Asghar ali mustafa qamar Hafeez Ullah Rehan Ullah Khan Nida Aslam Irfan Ullah Khan Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurPakistan Department of Computer Skills Deanship of Preparatory YearNajran UniversityNajranSaudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Biophotonics Imaging Techniques Laboratory Institute of PhysicsThe Islamia University of BahawalpurPakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia

Depression is a mental psychological disorder that may cause a physical disorder or lead to death.It is highly impactful on the socialeconomical life of a person;therefore,its effective and timely detection is needful.Despite speech and gait,facial expressions have valuable clues to depression.This study proposes a depression detection system based on facial expression analysis.Facial features have been used for depression detection using Support Vector Machine(SVM)and Convolutional Neural Network(CNN).We extracted micro-expressions using Facial Action Coding System(FACS)as Action Units(AUs)correlated with the sad,disgust,and contempt features for depression detection.A CNN-based model is also proposed in this study to auto classify depressed subjects from images or videos in real-time.Experiments have been performed on the dataset obtained from Bahawal Victoria Hospital,Bahawalpur,Pakistan,as per the patient health questionnaire depression scale(PHQ-8);for inferring the mental condition of a patient.The experiments revealed 99.9%validation accuracy on the proposed CNN model,while extracted features obtained 100%accuracy on SVM.Moreover,the results proved the superiority of the reported approach over state-of-the-art methods.

关键词： Depression detection facial micro-expressions facial landmarked images

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