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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 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

Dynamical study of varicella-zoster virus model in sense of Mittag-Leffler kernel

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International Journal of Biomathematics 2024年第3期17卷 153-183页

作者： Qura Tul Ain Aziz khan Thabet Abdeljawad J.f.Gomez-Aguilar Saleem Riaz Department of Mathematics Guizhou University GuiyangGuizhou 550025P.R.China Department of Mathematics and Sciences Prince Sultan University P.O.Bor 66833 11586 RiyadhSaudi Arabia Department of Medical Research China Medical University Taichung 40402Taiwan CONACyT-Tecnologico Nacional de Mécico/CENIDET Interior Internado Palmira S/N Col.Palmira C.P.62490CuernavacaMorelosMécico IConsejo Academico Universidad Virtual CNCIMonterreyMécico School of Automation Northwestern Polytechnical University 170072 Xian P.R.China

The primary varicella-zoster virus(VzV)infection that causes chickenpox(also known as varicella),spreads quickly among people and,in severe circumstances,can cause to fever and encephalitis.In this paper,the Mittag-Leffler fractional operator is used to examine the mathematical representation of the vzV.five fractional-order differential equations are created in terms of the disease's dynamical analysis such as S:Susceptible,V:Vaccinated,E:Exposed,I:Infectious and R:Recovered.We derive the existence criterion,positive solution,Hyers-Ulam stability,and boundedness of results in order to examine the suggested fractional-order model's wellposedness.finally,some numerical examples for the VzV model of various fractional orders are shown with the aid of the generalized Adams-Bashforth-Moulton approach to show the viability of the obtained results.

关键词： Varicella-zoster virus model Hyers-Ulam stability ABC fractional operator Schauder fix point theory numerical analysis

Computer vision-based six layered ConvNeural network to recognize sign language for both numeral and alphabet signs

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Biomimetic Intelligence & Robotics 2024年第1期4卷 45-58页

作者： Muhammad Aminur Rahaman Kabiratun Ummi Oyshe Prothoma khan Chowdhury Tanoy Debnath Anichur Rahman Md.Saikat Islam khan Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of Dhaka UniversityDhakaBangladesh Department of Computer Science and Engineering Mawlana Bhashani Science and Technology UniversityTangailBangladesh

People who have trouble communicating verbally are often dependent on sign language,which can be difficult for most people to understand,making interaction with them a difficult endeavor.The Sign Language Recognition(SLR)system takes an input expression from a hearing or speaking-impaired person and outputs it in the form of text or voice to a normal person.The existing study related to the Sign Language Recognition system has some drawbacks,such as a lack of large datasets and datasets with a range of backgrounds,skin tones,and ages.This research efficiently focuses on Sign Language Recognition to overcome previous limitations.Most importantly,we use our proposed Convolutional Neural Network(CNN)model,“ConvNeural”,in order to train our dataset.Additionally,we develop our own datasets,“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”,both of which have ambiguous backgrounds.“BdSL_OPSA22_STATIC1”and“BdSL_OPSA22_STATIC2”both include images of Bangla characters and numerals,a total of 24,615 and 8437 images,respectively.The“ConvNeural”model outperforms the pre-trained models with accuracy of 98.38%for“BdSL_OPSA22_STATIC1”and 92.78%for“BdSL_OPSA22_STATIC2”.for“BdSL_OPSA22_STATIC1”dataset,we get precision,recall,f1-score,sensitivity and specificity of 96%,95%,95%,99.31%,and 95.78%respectively.Moreover,in case of“BdSL_OPSA22_STATIC2”dataset,we achieve precision,recall,f1-score,sensitivity and specificity of 90%,88%,88%,100%,and 100%respectively.

关键词： Conv NeuralSign language CNN Static feature extraction Convolution2D fully connected layer Dropout

Understanding the catalytic performance and deactivation behaviour of second-promoter doped Pt/WO_(χ)/γ-Al_(2)O_(3) catalysts in the glycerol hydrogenolysis for selective and cleaner production of 1,3-propanediol

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Journal of Energy Chemistry 2024年第7期94卷 486-507页

作者： Rafik Rajjak Shaikh Sittichai Damruang Rais Ahmad khan Supareak Praserthdam Piyasan Praserthdam Center of Excellence on Catalysis and Catalytic Reaction Engineering(CECC) Department of Chemical EngineeringFaculty of EngineeringChulalongkorn UniversityBangkok 10330Thailand Department of Chemistry College of ScienceKing Saud UniversityP.O.Box 2455Riyadh 11451Saudi Arabia High-Performance Computing Unit(CECC-HCU) Centre of Excellence on Catalysis and Catalytic Reaction Engineering(CECC)Department of Chemical EngineeringFaculty of EngineeringChulalongkorn UniversityBangkok 10330Thailand

The selective aqueous-phase glycerol hydrogenolysis is a promising reaction to produce commercially useful 1,3-propanediol(1,3-PDO).The Pt-WOx bifunctional catalyst can catalyse the glycerol hydrogenol-ysis but the catalyst deactivation via sintering,metal leaching,and coking can predominantly occur in the aqueous phase reaction.In this work,the effect of reaction temperature,pressure and second promoter(Cu,fe,Rh,Mn,Re,Ru,Ir,Sn,B,and P)on catalytic performance and deactivation behaviour of Pt/WOx/-Al2O3 was investigated.When doped with Rh,Mn,Re,Ru,Ir,B,and P,the second promoter boosts catalytic activity by promoting great dispersion of Pt on support and increasing Pt surface area.The increased Bronsted acid sites lead to selective synthesis of 1,3-PDO than 1,2-propanediol(1,2-PDO).The characterization studies of fresh and spent catalysts reveal that the main cause of catalyst deactivation is the Pt sintering,as interpreted based on XRD,CO chemisorption,and TEM analyses.The Pt sintering is affected depending on the second promoter that can either or reduce the interaction between Pt,WO_(χ)/γ and Al_(2)O_(3).As an electron acceptor of Pt in Pt/WO_(χ)/γ-Al_(2)O_(3),Re and Mn as second promoters resulted in increased Pt^(2+) on the catalytic surface,which strengthens the contact between Pt andγ-Al_(2)O_(3) and WO_(χ),resulting in a decrease in Pt sintering.The metal leaching and coking are not affected by the presence of second promoter.The catalyst modified with a second promoter possesses improved catalytic activity and 1,3-PDO production,however the stability continues to remain a challenge.The present work unrav-elled the determining parameters of catalytic activity and deactivation,thus providing a promising pro-tocol toward effective catalysts for glycerol hydrogenolysis.

关键词： Glycerol hydrogenolysis 1,3-propanediol Metal-support interaction Second promoter Liquid phase Catalyst deactivation

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

Promotion of structural plasticity in area V2 of visual cortex prevents against object recognition memory deficits in aging and Alzheimer's disease rodents

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Neural Regeneration Research 2024年第8期19卷 1835-1841页

作者： Irene Navarro-Lobato Mariam Masmudi-Martín Manuel f.López-Aranda Juan f.López-Téllez Gloria Delgado Pablo Granados-Durán Celia Gaona-Romero Marta Carretero-Rey Sinforiano Posadas María E.Quiros-Ortega Zafar U.khan Laboratory of Neurobiology Centro de Investigaciones Médico Sanitarias(CIMES)University of MalagaMalagaSpain Department of Medicine Faculty of MedicineUniversity of MalagaMalagaSpain Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas(CIBERNED) Institute of Health Carlos IIIMadridSpain the Donders Institute for Brain Cognition and Behaviour Radboud UniversityNijmegenThe Netherlands the Brain Metastasis Group National Cancer Research Centre(CNIO)MadridSpain the Department of Neurobiology University of California-Los AngelesLos AngelesCAUSA

Memory deficit,which is often associated with aging and many psychiatric,neurological,and neurodegenerative diseases,has been a challenging issue for treatment.Up till now,all potential drug candidates have failed to produce satisfa ctory effects.Therefore,in the search for a solution,we found that a treatment with the gene corresponding to the RGS14414protein in visual area V2,a brain area connected with brain circuits of the ventral stream and the medial temporal lobe,which is crucial for object recognition memory(ORM),can induce enhancement of ORM.In this study,we demonstrated that the same treatment with RGS14414in visual area V2,which is relatively unaffected in neurodegenerative diseases such as Alzheimer s disease,produced longlasting enhancement of ORM in young animals and prevent ORM deficits in rodent models of aging and Alzheimer’s disease.furthermore,we found that the prevention of memory deficits was mediated through the upregulation of neuronal arbo rization and spine density,as well as an increase in brain-derived neurotrophic factor(BDNf).A knockdown of BDNf gene in RGS14414-treated aging rats and Alzheimer s disease model mice caused complete loss in the upregulation of neuronal structural plasticity and in the prevention of ORM deficits.These findings suggest that BDNf-mediated neuronal structural plasticity in area V2 is crucial in the prevention of memory deficits in RGS14414-treated rodent models of aging and Alzheimer’s disease.Therefore,our findings of RGS14414gene-mediated activation of neuronal circuits in visual area V2 have therapeutic relevance in the treatment of memory deficits.

关键词： behavioral performance brain-derived neurotrophic factor cognitive dysfunction episodic memory memory circuit activation memory deficits memory enhancement object recognition memory prevention of memory loss regulator of G protein signaling

An unexpected cellular fountain of youth:platelets provide factors rejuvenating brain functions

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Signal Transduction and Targeted Therapy 2024年第1期9卷 16-18页

作者： Harald f.Langer Department of Cardiology AngiologyHaemostaseology and Medical Intensive CareUniversity Medical Centre MannheimMedical Faculty MannheimHeidelberg UniversityMannheimGermany German Centre for Cardiovascular Research(DZHK) Partner Site Heidelberg/MannheimMannheimGermany European Center for Angioscience Medical Faculty MannheimHeidelberg UniversityMannheimGermany Cardiovascular systems biology Medical Clinic IUniversity Heart Center LübeckLübeckGermany

In a recent manuscript published in Nature,Schroer et al.discovered that platelets and in particular platelet derived Pf-4(CXCL4)are a novel factor,which can rejuvenate the mammalian neuronal system.1 These findings have the potential to trigger an entire new field of research opening new platelet-derived candidate effector molecules for supporting regeneration.

关键词： supporting platelets finding

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

Premature axon-oligodendrocyte interaction contributes to stalling of experimental axon regeneration after injury to the white matter

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Neural Regeneration Research 2024年第3期19卷 469-470页

作者： Ephraim f.Trakhtenberg Department of Neuroscience University of Connecticut School of MedicineFarmingtonCTUSA

Studies from nearly 3 decades ago suggested that,in the central nervous system(CNS),myelination of axons by oligodendrocytes not only helps improve axonal conductivity but also stabilizes circuitry(Colello and Schwab,1994).Over the years,myelin sheaths produced by oligodendrocytes have been found to contain multiple molecules that are inhibitory to axonal growth(e.g.,MAG,NogoA,OMgp,Semaphorins)(Yiu and He,2006;Silver et al.,2014).After white matter injury in the adult CNS,myelin debris from damaged axons and dead oligodendrocytes accumulates in the forming glial scar and exposes these myelin-associated axon growth-inhibitory molecules to the injured axonal stumps,thereby contributing to the inhibition of axonal regrowth.During development,CNS axons reach their postsynaptic targets and stop growing before oligodendrocytes appear and myelinate them(foran and Peterson,1992;Dangata et al.,1996).Therefore,myelin-associated axon growth-inhibitory molecules interacting with already grown axons during myelination were thought to block axons from promiscuous sprouting and miswiring,thereby stabilizing neural circuitry in the CNS(Colello and Schwab,1994).

关键词： thereby white injury