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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 *** 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 *** 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 *** findings demonstrate thatABMRFregularly beats the competition,with a gender classification accuracy of 71.14%and an age classification accuracy of 54.29%,*** metrics like precision,recall,F-measure,Matthews Correlation Coefficient(MCC),and accuracy support ABMRF’s outstanding performance in age and gender profiling *** study demonstrates the usefulness of ABMRF as an ensemble model for author profiling and highlights its possible uses in marketing,law enforcement,and *** 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 ***,it is quite challenging for a telecommunication company to attract new *** recent concept of mobile number portability has also aggravated the problem of customer *** need to identify beforehand the customers,who could potentially churn out to the *** the telecommunication industry,such identification could be done based on call detail *** 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 *** 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 *** imbalanced data are handled using undersampling and *** accuracy for CNN,RNN,and DNN is 91%,93%,and 96%,***,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 *** 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 *** 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 *** factory load and meteorological data used in this study are recorded hourly between 2016 and *** data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in *** 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 *** addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this *** outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning ***,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 *** can be applied in many fields,such as Information Retrieval,Knowledge Extraction,and Knowledge *** 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 *** paper introduces a novel machine learning-based approach that exclusively uses hybrid(stylistic and semantic)***,we clean the Arabic documents and translate them to English using translation ***,the semantic features are automatically extracted from the translated documents using an existing database of English ***,the model automatically extracts from the textual content a set of stylistic features such as word and character frequencies and ***,we obtain 3 types of features:semantic,stylistic and *** 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 *** 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 *** 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 ***,the support to configure LDTs design needs to be made available even in standard LIMS *** manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints between features can remain *** is a risky process and can lead patients to undergo unnecessary *** proposed an optimized solution(opt-LDT)based on Genetic Algorithms to automate the configuration and resolve the inconsistencies in ***-LDT encodes LDT configuration as an optimization problem and generates a consistent configuration that satisfies the constraints of the *** tested and validated opt-LDT for a local secondary care hospital in a real healthcare *** results,averaged over ten runs,show that opt-LDT resolves 90%of inconsistencies while taking between 6 and 6.5 s for each ***,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 ***,BF faces several *** problems with traditional BF are generally ***,a large number of false positives can return wrong content when the data is ***,the large size of BF is a bottleneck in the speed of querying and thus uses large *** order to solve the above two issues,in this article,we propose the check bits *** the implementation perspective,in the check bits approach,before saving the content value in the BF,we obtain the binary representation of the content ***,we take some bits of the content value,we call these the check *** bits are stored in a separate array such that they point to the same location as the ***,the content value(data)is stored in the BF based on the hash function *** 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 *** thus helps in the reduction of false *** the experimental evaluation,we are able to reduce more than 50%of false *** 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 *** chances of such scenarios are less,therefore,we get a reduction of approximately more than 50%false positives in all *** 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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Recurrent Convolutional Neural Network MSER-Based Approach for Payable Document Processing

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Computers, Materials & Continua 2021年第12期69卷 3399-3411页

作者： Suliman Aladhadh Hidayat Ur Rehman ali mustafa qamar Rehan Ullah Khan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Ainfinity Algorythma Abu DhabiUnited Arab Emirates Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

A tremendous amount of vendor invoices is generated in the corporate *** automate the manual data entry in payable documents,highly accurate Optical Character Recognition(OCR)is *** paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash *** text localization,the maximally stable extremal region is used,which extracts a word or digit chunk from an *** chunk is later passed to the deep learning model,which performs text *** deep learning model utilizes both convolution neural networks and long short-term memory(LSTM).The convolution layer is used for extracting features,which are fed to the *** model integrates feature extraction,modeling sequence,and transcription into a unified *** handles the sequences of unconstrained lengths,independent of the character segmentation or horizontal scale ***,it applies to both the lexicon-free and lexicon-based text recognition,and finally,it produces a comparatively smaller model,which can be implemented in practical *** overall superior performance in the experimental evaluation demonstrates the usefulness of the proposed *** model is thus generic and can be used for other similar recognition scenarios.

关键词： Character recognition text spotting long short-term memory recurrent convolutional neural networks

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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 ***,every single type has some very specific features that differ from the *** recent years,sorting,separating,and arranging in automated industries,in fruits businesses,and more specifically in dates businesses have inspired many research *** this regard,this paper focuses on the detection and recognition of dates using computer vision and machine *** experimental setup is based on the classical machine learning approach and the deep learning approach for nine classes of dates *** 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 *** feature set includes Color Layout features,Fuzzy Color and Texture Histogram,Gabor filtering,and the Pyramid Histogram of the Oriented *** fusion of various features is also extensively explored in this *** MLP achieves the highest detection performance with an F-measure of ***,deep learning shows better accuracy than the classical machine learning *** fact,deep learning got 2%more accurate results as compared to the MLP and the Random *** 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 *** 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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TIE algorithm:a layer over clustering-based taxonomy generation for handling evolving data

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Frontiers of Information Technology & Electronic Engineering 2018年第6期19卷 763-782页

作者： Rabia IRFAN Sharifullah KHAN Kashif RAJPOOT ali mustafa qamar School of Electrical Engineering and Computer Science National University of Sciences and Technology School of Computer Science University of Birmingham Department of Computer Science College of Computer Qassim University

Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing automatic taxonomy generation techniques do not handle the evolution of data; therefore, the generated taxonomies do not truly represent the data. The evolution of data can be handled by either regenerating taxonomy from scratch, or allowing taxonomy to incrementally evolve whenever changes occur in the data. The former approach is not economical in terms of time and resources. A taxonomy incremental evolution（TIE） algorithm, as proposed, is a novel attempt to handle the data that evolve in time. It serves as a layer over an existing clustering-based taxonomy generation technique and allows an existing taxonomy to incrementally evolve. The algorithm was evaluated in research articles selected from the computing domain. It was found that the taxonomy using the algorithm that evolved with data needed considerably shorter time, and had better quality per unit time as compared to the taxonomy regenerated from scratch.

关键词： Taxonomy Clustering algorithms Information science Knowledge management Machine learning

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