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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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Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning

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Journal of Magnesium and Alloys 2024年第4期12卷 1518-1528页

作者： Annayath maqbool Abdul Khalad Noor Zaman khan Department of Mechanical Engineering National Institute of Technology SrinagarJ&K 190006India Department of Mechanical Engineering Indian Institute of TechnologyHyderabadSangareddyTelangana 502284India School of Engineering Deakin UniversityVictoria 3216Australia

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.

关键词： Corrosion rate Friction stir processing Virtual sample generation Particle swarm optimization Machine learning Graphical user interface

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Autologous hematopoietic stem cell transplantation conditioning regimens and chimeric antigen receptor T cell therapy in various diseases

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World Journal of Transplantation 2024年第1期14卷 32-41页

作者： Shahzaib maqbool Maryam Farhan Baloch Muhammad Abdul Khaliq khan Azeem Khalid Kiran Naimat Department of Medicine Holy Family HospitalRawalpindi 46000Pakistan Department of Community Medicine Allama Iqbal Medical CollegeLahore 45000Pakistan Department of Community Dentistry Baqai Medical UniversityKarachi 43000Pakistan Department of Medicine Allama lqbal Medical CollegeLahore 45000Pakistan Department of MedicineLiaquat University of Medical and Health Sciences Karachi 43000Pakistan

Conditioning regimens employed in autologous stem cell transplantation have been proven useful in various hematological disorders and underlying malignancies;however,despite being efficacious in various instances,negative consequences have also been recorded.Multiple conditioning regimens were extracted from various literature searches from databases like PubMed,Google scholar,EMBASE,and Cochrane.Conditioning regimens for each disease were compared by using various end points such as overall survival(OS),progression free survival(PFS),and leukemia free survival(LFS).Variables were presented on graphs and analyzed to conclude a more efficacious conditioning regimen.In multiple myeloma,the most effective regimen was high dose melphalan(MEL)given at a dose of 200/mg/m2.The comparative results of acute myeloid leukemia were presented and the regimens that proved to be at an admirable position were busulfan(BU)+MEL regarding OS and BU+VP16 regarding LFS.In case of acute lymphoblastic leukemia(ALL),BU,fludarabine,and etoposide(BuFluVP)conferred good disease control not only with a paramount improvement in survival rate but also low risk of recurrence.However,for ALL,chimeric antigen receptor(CAR)T cell therapy was preferred in the context of better OS and LFS.With respect to Hodgkin’s lymphoma,mitoxantrone(MITO)/MEL overtook carmustine,VP16,cytarabine,and MEL in view of PFS and vice versa regarding OS.Non-Hodgkin’s lymphoma patients were administered MITO(60 mg/m2)and MEL(180 mg/m2)which showed promising results.Lastly,amyloidosis was considered,and the regimen that proved to be competent was MEL 200(200 mg/m2).This review article demonstrates a comparison between various conditioning regimens employed in different diseases.

关键词： Conditioning regimens Multiple myeloma Lymphoma Hodgkin Non-Hodgkin Acute leukemia

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Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

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Computers, Materials & Continua 2023年第9期76卷 3345-3361页

作者： Asif khan Huaping Zhang Nada Boudjellal Arshad Ahmad Maqbool Khan School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various topics.In recent years,the rise of social media platforms(SMPs)has provided a rich source of data for analyzing public opinions,particularly in the context of election-related conversations.Nevertheless,sentiment analysis of electionrelated tweets presents unique challenges due to the complex language used,including figurative expressions,sarcasm,and the spread of misinformation.To address these challenges,this paper proposes Election-focused Bidirectional Encoder Representations from Transformers(ElecBERT),a new model for sentiment analysis in the context of election-related tweets.Election-related tweets pose unique challenges for sentiment analysis due to their complex language,sarcasm,andmisinformation.ElecBERT is based on the Bidirectional Encoder Representations from Transformers(BERT)language model and is fine-tuned on two datasets:Election-Related Sentiment-Annotated Tweets(ElecSent)-Multi-Languages,containing 5.31 million labeled tweets in multiple languages,and ElecSent-English,containing 4.75million labeled tweets in English.Themodel outperforms othermachine learning models such as Support Vector Machines(SVM),Na飗e Bayes(NB),and eXtreme Gradient Boosting(XGBoost),with an accuracy of 0.9905 and F1-score of 0.9816 on ElecSent-Multi-Languages,and an accuracy of 0.9930 and F1-score of 0.9899 on ElecSent-English.The performance of differentmodels was compared using the 2020 United States(US)Presidential Election as a case study.The ElecBERT-English and ElecBERT-Multi-Languages models outperformed BERTweet,with the ElecBERT-English model achieving aMean Absolute Error(MAE)of 6.13.This paper presents a valuable contribution to sentiment analysis in the context of election-related tweets,with potential applications in political analysis,social media management,and policymaking.

关键词： Sentiment analysis social media election prediction machine learning transformers

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From Social Media to Ballot Box:Leveraging Location-Aware Sentiment Analysis for Election Predictions

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Computers, Materials & Continua 2023年第12期77卷 3037-3055页

作者： Asif khan Nada Boudjellal Huaping Zhang Arshad Ahmad Maqbool Khan School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

Predicting election outcomes is a crucial undertaking,and various methods are employed for this purpose,such as traditional opinion polling,and social media analysis.However,traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels,resulting in a limited depth of analysis and understanding.In light of this challenge,this study focuses on predicting elections at the state/regional level along with the country level,intending to offer a comprehensive analysis and deeper insights into the electoral process.To achieve this,the study introduces the Location-Based Election Prediction Model(LEPM),which utilizes social media data,specifically Twitter,and integrates location-aware sentiment analysis techniques at both the state/region and country levels.LEPM predicts the support and opposing strength of each political party/candidate.To determine the location of users/voters who have not disclosed their location information in tweets,the model utilizes a Voter Location Detection(VotLocaDetect)approach,which leverages recent tweets/posts.The sentiment analysis techniques employed in this study include rule-based sentiment analysis,Valence Aware Dictionary and Sentiment Reasoner(VADER)as well as transformers-based sentiment analysis such as Bidirectional Encoder Representations from Transformers(BERT),BERTweet,and Election based BERT(ElecBERT).This study uses the 2020 United States(US)Presidential Election as a case study.By applying the LEPM model to the election,the study demonstrates its ability to accurately predict outcomes in forty-one states,achieving an 0.84 accuracy rate at the state level.Moreover,at the country level,the LEPM model outperforms traditional polling results.With a low Mean Absolute Error(MAE)of 0.87,the model exhibits more precise predictions and serves as a successful alternative to conventional polls and other methodologies.Leveraging the extensive social media data,the LEPM model provides nuanced insights into voter behavior,enabling policym

关键词： Sentiment analysis big data machine learning election predictions social media analysis

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Can NogoA Be a Suitable Target to Treat Ischemic Stroke?

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Neuroscience Bulletin 2024年第4期40卷 557-560页

作者： Suliman khan Abdul Nasir Medical Research Center The Second Affiliated Hospital of Zhengzhou UniversityZhengzhou540014China

Treating stroke or restoring brain function after stroke remains challenging for clinicians[1,2].Researchers have largely focused on developing neuroprotective strategies and translating these discoveries from rodent experiments to the clinical setting;however,these have been typified by several failures[2].It is known that astrocytes play a crucial role in the pathogenesis of ischemic stroke[3,4].They are activated in response to stimuli arising from injury,characterized by drastic changes to their morphology and function-associated gene expression.Reactive astrocytes exacerbate secondary neurodegeneration(repair of nervous tissue by the generation of new neurons,glial cells,axons,myelin,or synapses)[5]as well as provide neuroprotection during CNS pathologies[6].It is worth noting that myelin-related neurite growth inhibitors induce the suppression of axon regeneration after stroke[7].

关键词： protective NogoA clinical

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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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Performance of Text-Independent Automatic Speaker Recognition on a Multicore System

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Tsinghua Science and Technology 2024年第2期29卷 447-456页

作者： Rand Kouatly Talha Ali khan Faculty of Tech and Software Engineering University of Europe for Applied SciencesPotsdam 14469Germany

This paper studies a high-speed text-independent Automatic Speaker Recognition(ASR)algorithm based on a multicore system's Gaussian Mixture Model(GMM).The high speech is achieved using parallel implementation of the feature's extraction and aggregation methods during training and testing procedures.Shared memory parallel programming techniques using both OpenMP and PThreads libraries are developed to accelerate the code and improve the performance of the ASR algorithm.The experimental results show speed-up improvements of around 3.2 on a personal laptop with Intel i5-6300HQ(2.3 GHz,four cores without hyper-threading,and 8 GB of RAM).In addition,a remarkable 100%speaker recognition accuracy is achieved.

关键词： Automatic Speaker Recognition(ASR) Gaussian Mixture Model(GMM) shared memory parallel programming PThreads OpenMP

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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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Adipose-derived regenerative therapies for the treatment of knee osteoarthritis

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World Journal of Stem Cells 2024年第4期16卷 324-333页

作者： Ilias E Epanomeritakis Wasim S khan Division of Trauma and Orthopaedic Surgery Addenbrooke’s HospitalCambridge University Hospitals NHS Foundation TrustCambridge CB20QQUnited Kingdom Division of Trauma and Orthopaedic Surgery Department of SurgeryUniversity of CambridgeCambridge CB20QQUnited Kingdom

Knee osteoarthritis is a degenerative condition with a significant disease burden and no disease-modifying therapy.Definitive treatment ultimately requires joint replacement.Therapies capable of regenerating cartilage could significantly reduce financial and clinical costs.The regenerative potential of mesenchymal stromal cells(MSCs)has been extensively studied in the context of knee osteoarthritis.This has yielded promising results in human studies,and is likely a product of immunomodulatory and chondroprotective biomolecules produced by MSCs in response to inflammation.Adipose-derived MSCs(ASCs)are becoming increasingly popular owing to their relative ease of isolation and high proliferative capacity.Stromal vascular fraction(SVF)and micro-fragmented adipose tissue(MFAT)are produced by the enzymatic and mechanical disruption of adipose tissue,respectively.This avoids expansion of isolated ASCs ex vivo and their composition of heterogeneous cell populations,including immune cells,may potentiate the reparative function of ASCs.In this editorial,we comment on a multicenter randomized trial regarding the efficacy of MFAT in treating knee osteoarthritis.We discuss the study’s findings in the context of emerging evidence regarding adipose-derived regenerative therapies.An underlying mechanism of action of ASCs is proposed while drawing important distinctions between the properties of isolated ASCs,SVF,and MFAT.

关键词： Knee osteoarthritis Mesenchymal stromal cells Adipose tissue Stromal vascular fraction Micro-fragmented adipose tissue Regeneration

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