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

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 languages.While 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 effectiveness of the models. The research findings revea

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

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

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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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Learning Patterns from COVID-19 Instances

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Computer Systems Science & Engineering 2022年第2期40卷 765-777页

作者： rehan ullah khan Waleed Albattah Suliman Aladhadh Shabana Habib Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia

Coronavirus disease,which resulted from the SARS-CoV-2 virus,has spread worldwide since early 2020 and has been declared a pandemic by the World Health Organization(WHO).Coronavirus disease is also termed COVID-19.It affects the human respiratory system and thus can be traced and tracked from the Chest X-Ray images.Therefore,Chest X-Ray alone may play a vital role in identifying COVID-19 cases.In this paper,we propose a Machine Learning(ML)approach that utilizes the X-Ray images to classify the healthy and affected patients based on the patterns found in these images.The article also explores traditional,and Deep Learning(DL)approaches for COVID-19 patterns from Chest X-Ray images to predict,analyze,and further understand this virus.The experimental evaluation of the proposed approach achieves 97.5% detection performance using the DL model for COVID-19 versus normal cases.In contrast,for COVID-19 versus Pneumonia Virus scenario,we achieve 94.5% accurate detections.Our extensive evaluation in the experimental section guides and helps in the selection of an appropriate model for similar tasks.Thus,the approach can be used for medical usages and is particularly pertinent in detecting COVID-19 positive patients using X-Ray images alone.

关键词： Coronavirus COVID-19 machine learning deep learning convolutional neural network

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Insight into the dynamics of non-Newtonian Carreau fluid when viscous dissipation,entropy generation,convective heating and diffusion are significant

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Applied Mathematics(A Journal of Chinese Universities) 2024年第1期39卷 34-46页

作者： ZHOU Shuang-shuang Muhammad Ijaz khan Sami ullah khan Sumaira Qayyum School of Science Hunan City UniversityYiyang 413000China Department of Mechanical Engineering Prince Mohammad Bin Fahd UniversityP.O.Box1664Al-Khobar 31952Kingdom of Saudi Arabia Department of Mathematics Namal UniversityMianwali 42250Pakistan Department of Mathematics Quaid-I-Azam University 45320Islamabad 44000Pakistan

The investigation endorsed the convective flow of Carreau nanofluid over a stretched surface in presence of entropy generation optimization.The novel dynamic of viscous dissipation is utilized to analyze the thermal mechanism of magnetized flow.The convective boundary assumptions are directed in order to examine the heat and mass transportation of nanofluid.The thermal concept of thermophoresis and Brownian movements has been re-called with the help of Buongiorno model.The problem formulated in dimensionless form is solved by NDSolve MATHEMATICA.The graphical analysis for parameters governed by the problem is performed with physical applications.The affiliation of entropy generation and Bejan number for different parameters is inspected in detail.The numerical data for illustrating skin friction,heat and mass transfer rate is also reported.The motion of the fluid is highest for the viscosity ratio parameter.The temperature of the fluid rises via thermal Biot number.Entropy generation rises for greater Brinkman number and diffusion parameter.

关键词： heat generation surface reaction CNTs based nano uid stretching/shrinking sheet thermal radiation

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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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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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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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Electronic and thermoelectric properties of YbMg_(2)X_(2)(X=P,As,Sb,Bi)zintl compounds by first-principles method

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Journal of Rare Earths 2024年第1期42卷 147-154,I0006页

作者： Sajid khan Dil Faraz khan R.Neffati Tariq Usman Muhammad Waqar Ahsraf Shamim khan Saeed ullah Jan G.Murtaza Department of Physics University of Science&Technology BannuBannu28100Pakistan Department of Physics Faculty of ScienceKing Khalid UniversityP.O.Box 9004AbhaSaudi Arabia Laboratoire de Physique de La Matiere Condensee Departement de PhysiqueFaculte des Sciences de TunisUniversite Tunis El ManarCampus Universitaire1060TunisTunisia Department of Physics Shaoxing UniversityShaoxing312000China Department of Mathematics&Natural Sciences Prince Mohammad Bin Fahd UniversityAlkhobar31952Saudi Arabia Materials Modeling Lab Department of PhysicsIslamia College Peshawar25120KhyberPakhtunkhwaPakistan

In this work the structural,electronic and thermoelectric properties of YbMg_(2)X_(2)(X=P,As,Sb,Bi)zintl compounds were investigated comprehensively using first principles study.The electronic properties were studied using PBE GGA,TB-mBJ and hybrid(YS-PBE0)potentials.All the structural parameters of optimized structures are in harmonious agreement to the available data.The band structure study illustrates that the titled materials manifest metallic and semi metallic nature using PBE and TB-mBJ potentials while they show wide band gap semiconducting behavior by following hybrid(YS-PBE0)potential.Total density of states(TDOS)and partial density of states(PDOS)were also calculated to glimpse the contribution of orbitals of atoms in the formation of bands.Transport properties were studied by using BoltzTraP2 code employed to WIEN2k.We get enormous values of Seebeck coefficient(S),power factor(PF)and thermoelectric figure of merit(ZT)for all the samples YbMg_(2)X_(2)(X=P,As,Sb,Bi),Moreover,the overwhelming transport properties for the titled compounds indicate the optimum level of carriers’concentration which pinpoints these materials to be better thermoelectrics in the 1-2-2 zintl family.

关键词： Zintl compounds Hybrid functional Band structure Thermoelectrics Rare earths

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A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting

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Computers, Materials & Continua 2024年第5期79卷 3373-3395页

作者： Farhan ullah Xuexia Zhang Mansoor khan Muhammad Abid Abdullah Mohamed School of Electrical Engineering Southwest Jiaotong UniversityChengdu611756China College of Intelligent Manufacturing and Control Engineering Qilu Institute of TechnologyJinan250200China College of Aerospace and Civil Engineering Harbin Engineering UniversityHarbin150001China Research Centre Future University in EgyptNew Cairo11835Egypt

Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows.Traditional approaches frequently struggle with complex data and non-linear connections. This article presentsa novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts.The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-EraRetrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms usingin-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model,while a temporal convolutional network handles time-series complexities and data gaps. The ensemble-temporalneural network is enhanced by providing different input parameters including training layers, hidden and dropoutlayers along with activation and loss functions. The proposed framework is further analyzed by comparing stateof-the-art forecasting models in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE),respectively. The energy efficiency performance indicators showed that the proposed model demonstrates errorreduction percentages of approximately 16.67%, 28.57%, and 81.92% for MAE, and 38.46%, 17.65%, and 90.78%for RMSE for MERRAWind farms 1, 2, and 3, respectively, compared to other existingmethods. These quantitativeresults show the effectiveness of our proposed model with MAE values ranging from 0.0010 to 0.0156 and RMSEvalues ranging from 0.0014 to 0.0174. This work highlights the effectiveness of requirements engineering in windpower forecasting, leading to enhanced forecast accuracy and grid stability, ultimately paving the way for moresustainable energy solutions.

关键词： Ensemble learning machine learning real-time data analysis stakeholder analysis temporal convolutional network wind power forecasting

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Leveraging Augmented Reality,Semantic-Segmentation,and VANETs for Enhanced Driver’s Safety Assistance

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Computers, Materials & Continua 2024年第1期78卷 1443-1460页

作者： Sitara Afzal Imran ullah khan Irfan Mehmood Jong Weon Lee Mixed Reality and Interaction Lab Department of SoftwareSejong UniversitySeoul05006Korea Faculty of Engineering and Digital Technologies School of Computer ScienceAI and ElectronicsUniversity of BradfordBradfordUK

Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ahead.However,limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers,leading to accidents and fatalities.In this paper,we consider atrous convolution,a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image segmentation.This article explores the potential of seeing-through vehicles as a solution to enhance overtaking safety.See-through vehicles leverage advanced technologies such as cameras,sensors,and displays to provide drivers with a real-time view of the vehicle ahead,including the areas hidden from their direct line of sight.To address the problems of safe passing and occlusion by huge vehicles,we designed a see-through vehicle system in this study,we employed a windshield display in the back car together with cameras in both cars.The server within the back car was used to segment the car,and the segmented portion of the car displayed the video from the front car.Our see-through system improves the driver’s field of vision and helps him change lanes,cross a large car that is blocking their view,and safely overtake other vehicles.Our network was trained and tested on the Cityscape dataset using semantic segmentation.This transparent technique will instruct the driver on the concealed traffic situation that the front vehicle has obscured.For our findings,we have achieved 97.1% F1-score.The article also discusses the challenges and opportunities of implementing see-through vehicles in real-world scenarios,including technical,regulatory,and user acceptance factors.

关键词： Overtaking safety augmented reality VANET V2V deep learning

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