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A Quantum Spatial Graph Convolutional Network for Text Classification

Computer Systems Science & Engineering 2021年第2期36卷 369-382页

作者： syed mustajar ahmad shah Hongwei Ge Sami Ahmed Haider Muhammad Irshad Sohail M.Noman Jehangir Arshad Asfandeyar ahmad Talha Younas College of Computer Science and Technology Dalian University of TechnologyDalian116024China Department of Computing University of WorcesterUK Department of Information Engineering Chang’an UniversityXi’an710054China Department of Cell Biology and Genetics Shantou University Medical CollegeShantou515041China Electrical and Computer Engineering Department COMSATS University IslamabadLahore CampusPakistan School of Information&Communication Engineering Dalian University of Technology Dalian116024China Electrical and Computer Engineering Department COMSATS University IslamabadSahiwal CampusPakistan

The data generated from non-Euclidean domains and its graphical representation(with complex-relationship object interdependence)applications has observed an exponential *** sophistication of graph data has posed consequential obstacles to the existing machine learning *** this study,we have considered a revamped version of a semi-supervised learning algorithm for graph-structured data to address the issue of expanding deep learning approaches to represent the graph ***,the quantum information theory has been applied through Graph Neural Networks(GNNs)to generate Riemannian metrics in closed-form of several graph *** further,to pre-process the adjacency matrix of graphs,a new formulation is established to incorporate high order *** proposed scheme has shown outstanding improvements to overcome the deficiencies in Graph Convolutional Network(GCN),particularly,the information loss and imprecise information representation with acceptable computational ***,the proposed Quantum Graph Convolutional Network(QGCN)has significantly strengthened the GCN on semi-supervised node classification *** parallel,it expands the generalization process with a significant difference by making small random perturbationsG of the graph during the training *** evaluation results are provided on three benchmark datasets,including Citeseer,Cora,and PubMed,that distinctly delineate the superiority of the proposed model in terms of computational accuracy against state-of-the-art GCN and three other methods based on the same algorithms in the existing literature.

关键词： Text classiﬁcation deep learning graph convolutional networks semi-supervised learning GPUs performance improvements

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基于图卷积网络的文本分类

基于图卷积网络的文本分类

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作者： syed mustajar ahmad shah 大连理工大学

学位级别：硕士

随着人机界面应用的指数式增长,文本分类已成为自然语言处理(NLP)研究的主要重点。在过去的几年里,深度学习已经彻底改变了许多机器学习任务,包括图像和视频处理、语音识别和NLP。这些任务中的数据用典型的欧几里德空间表示。然而,越来... 详细信息

随着人机界面应用的指数式增长,文本分类已成为自然语言处理(NLP)研究的主要重点。在过去的几年里,深度学习已经彻底改变了许多机器学习任务,包括图像和视频处理、语音识别和NLP。这些任务中的数据用典型的欧几里德空间表示。然而,越来越多的应用程序将数据从非欧几里德域中生成,并用具有复杂关系的图形表示,以实现对象相互依赖。图形数据的复杂性给现有的机器学习算法带来了严重的障碍。因此,近年来出现了许多研究来试图解决扩展基于图形数据的深度学习的问题。本文的研究主要是针对图结构数据的半监督学习的一个改进版本。为了克服图卷积网络(GCN)的不足,即信息丢失和不精确的信息表示以及可接受的计算开销,提出的量子图卷积网络(QGCN)可以增强GCN在半监督节点分类任务中的作用,并且扩大了推广范围在训练过程中,通过使图产生较小的随机扰动,使过程具有显着差异。Δ,在所提出的方法中,本文将量子信息论与GNN结合在一起,为了预处理图的邻接矩阵,建立了一种新的公式以合并高阶邻近度。对三个基准数据集(citeeser、Cora和PubMed)的评估结果表明,该模型在计算精度方面优于现有的GCN和其他三种基于相同算法的方法。本文共分为五个部分:第一部分介绍了文本分类和研究现状。第二部分考察了早期对文本分类所做的努力,并详细解释了预处理和多标签分类技术。第三部分介绍了一种新的方法,并用图解说明了所提出模型的处理过程,并用实证方法进行了说明。第四部分是利用所提出的量子QGCN得到的结果及其与以前的模型如GCN、GCN、FisherGCN和FisherGCN的比较。最后,在最后一节中得出我们的结论。

关键词：文本分类图卷积网络半监督学习

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Classification and Comprehension of Software Requirements Using Ensemble Learning

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Computers, Materials & Continua 2024年第8期80卷 2839-2855页

作者： Jalil Abbas Arshad ahmad syed Muqsit shaheed Rubia Fatima Sajid shah Mohammad Elaffendi Gauhar Ali School of Computer Science and Technology Anhui UniversityHefei230039China School of Computing Sciences Pak Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science and IT University of LahoreLahore55150Pakistan Department of Computer Science Emerson UniversityPunjabMultan60000Pakistan EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab Prince Sultan UniversityRiyad12435Saudi Arabia

The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional *** address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human ***,existing techniques often struggle with complex instructions and large-scale *** our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer *** results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT *** datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse *** findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

关键词： Ensemble learning machine learning non-functional requirements requirement engineering accuracy sliding window

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Automatic Finding of Brain-Tumour Group Using CNN Segmentation and Moth-Flame-Algorithm,Selected Deep and Handcrafted Features

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Computers, Materials & Continua 2024年第5期79卷 2585-2608页

作者： Imad Saud Al Naimi syed Alwee Aljunid syed Junid Muhammad lmran ahmad K.Suresh Manic Faculty of Electronic Engineering Technology University Malaysia Perils(UniMAP)Pauh Putra CampusArauPerlis02600Malaysia Department of Electrical and Communication Engineering National University of Science and TechnologyAL HailAL Seeb130Sultanate of Oman Department of Research and Innovation Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(SIMATS)ChennaiTamiNadu602105India

Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in ***’s clinical level screening is usually performed with Magnetic Resonance Imaging(MRI)due to its multi-modality *** overall aims of the study is to introduce,test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans,facilitating improved *** research intends to devise a reliable framework for detecting the BT region in the twodimensional(2D)MRI slice,and identifying its class with improved *** methodology for the devised framework comprises the phases of:(i)Collection and resizing of images,(ii)Implementation and Segmentation of Convolutional Neural Network(CNN),(iii)Deep feature extraction,(iv)Handcrafted feature extraction,(v)Moth-Flame-Algorithm(MFA)supported feature reduction,and(vi)Performance *** study utilized clinical-grade brain MRI of BRATS and TCIA datasets for the *** framework segments detected the glioma(low/high grade)and glioblastoma class *** work helped to get a segmentation accuracy of over 98%with VGG-UNet and a classification accuracy of over 98%with the VGG16 *** study has confirmed that the implemented framework is very efficient in detecting the BT in MRI slices with/without the skull section.

关键词： Brain tumour VGG-UNet VGG16 moth-flame-algorithm classification

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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Computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan ahmad Al Smadi Farah shahid shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi syed Aziz shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Financial inclusion and carbon emissions in Asia:Implications for environmental sustainability

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Economic and Political Studies 2024年第1期12卷 88-104页

作者： shahzad Hussain Tanveer ahmad Sabeeh Ullah Ajid Ur Rehmand syed Jawad Hussain shahzad Department of Business Administration Rawalpindi Women UniversityRawalpindiPakistan Institute of Business Studies Kohat University of Science and TechnologyKohatPakistan Institute of Business and Management Sciences Faculty of Management and Computer SciencesThe University of AgriculturePeshawarPakistan NUST Business School National University of Sciences and TechnologyIslamabadPakistan Finance Control and Law DepartmentMontpellier Business SchoolMontpellierFrance

This study explores how carbon emissions are affected by financial *** a balanced panel data set of 26 Asian countries,we compute a composite index,through the principal component analysis(PCA)technique,of financial inclusion based on a set of attributes related to financial *** main analysis also delineates the subsamples of developed and developing Asian *** results reveal a long(short)-run positive(negative)impact of financial inclusion on carbon emissions across the Asian *** finding is also true for the developed country subsample,implying nonlinearity in shortand long-run *** the developing countries,a more pronounced long-run positive impact compared to developed countries is ***,the pairwise causality test results indicate the existence of bi-directional causality between financial inclusion and carbon *** findings have important policy implications,especially in the context of the strategic integration of financial inclusion and climate change strategies.

关键词： Financial inclusion CO2 emissions climate change sustainable development Asian countries

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Emerging bedaquiline resistance:A threat to the global fight against drug-resistant tuberculosis

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Journal of Biosafety and Biosecurity 2024年第1期6卷 13-15页

作者： Prakasini Satapathy Ramaiah Itumall ahmad Neyazi Abdul Mobin Nabizai Taraki Mahalaqua Nazli Khatib Shilpa Gaidhane Quazi syed Zahiruddin Sarvesh Rustagi Mehrab Neyazi Center for Global Health Research Saveetha Medical College and HospitalSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennaiIndia Medical Laboratories Techniques Department AL-Mustaqbal University51001 HillahBabilIraq School of Management The Apollo UniversityChittoorAndhra Pradesh 517127India Afghanistan Center for Epidemiological Studies Afghanistan Division of Evidence Synthesis Global Consortium of Public Health and ResearchDatta Meghe Institute of Higher EducationWardhaIndia One Health Centre(COHERD) Jawaharlal Nehru Medical CollegeDatta Meghe Institute of Higher EducationWardhaIndia South Asia Infant Feeding Research Network(SAIFRN) Division of Evidence SynthesisGlobal Consortium of Public Health and ResearchDatta Meghe Institute of Higher EducationWardhaIndia School of Applied and Life Sciences Uttaranchal UniversityDehradunUttarakhandIndia

Bedaquiline resistance is increasingly observed in the treatment of rifampicin-resistant tuberculosis(TB),yet standardized regimens for managing bedaquiline-resistant TB are *** indicate a high proportion of bedaquiline-resistant cases have previously been treated for TB,and often involve strains resistant to *** monitoring of the culture status in patients receiving bedaquiline resistance treatment is *** such as experimental evolution,protein modeling,genome sequencing,and phenotypic analysis have been instrumental in identifying the mechanisms of bedaquiline ***,variants in the Rv0678 transcriptional repressor of the MmpS5-MmpL5 efflux system are linked to this type of *** probability estimates show promise in determining the genotypic–phenotypic association for bedaquiline resistance,suggesting potential utility in clinical *** research should explore the practical application of Bayesian probabilities in managing bedaquiline ***-based technologies are anticipated to play a vital role in the early detection and management of drug-resistant TB strains.

关键词： Bedaquiline Resistance Drug-Resistant Tuberculosis Quinolone Resistance

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Unraveling the therapeutic mechanisms of myristic acid and luteolin 7-rutinoside in oral cancer: insights from network pharmacology and molecular docking analysis

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Pharmacology Discovery 2024年第2期4卷 1-9页

作者： Ansari Vikhar Danish ahmad Misba Ruhi syed Ayaz Ali Qazi Yasar Mohd.Mukhtar Khan Subur W Khan Mohammed Imran Anees Y.B.Chavan College of Pharmacy Dr.Babasaheb Ambedkar Marathwada UniversityAurangabad 431001India Maulana Azad College Dr.Babasaheb Ambedkar Marathwada UniversityAurangabad 431001India

Background:The compound Luteolin-7-rutinoside(L7R)is a flavone derivative of luteolin,predominantly identified in plant species belonging to the families ***,Myristic acid is characterized by its structure as a 14-carbon,unsaturated fatty *** this investigation,we endeavor to elucidate the putative mechanisms underlying the therapeutic effects of Myristic Acid and Luteolin 7-rutinoside in the context of oral cancer treatment,employing network pharmacology coupled with molecular docking ***:The protein targets of Myristic Acid and Luteolin 7-rutinoside were identified through a search on the Swiss Target ***,a compound-target network was constructed using Cytoscape *** associated with OC were retrieved from the OMIM and GeneCards *** overlap between compound targets and OC-related targets was determined,and the resulting shared targets were subjected to protein-protein interaction(PPI)network analysis using the STRING ***,gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses were conducted on the identified *** docking were performed to investigate the interactions between the core target and the active ***:The component target network comprises 103 nodes and 102 *** the proteins in the protein-protein interaction(PPI)network,those with higher degrees are TNF,PPARG,and *** through Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways indicates that the treatment of OC with Myristic Acid and Luteolin 7-rutinoside primarily involves the regulation of miRNA transcription and inflammatory *** identified signaling pathways include Pathways in cancer,PPAR signaling pathway,EGFR signaling pathway,and TNF signaling *** docking studies reveal that Luteolin 7-rutinoside and Myristic acid exhibit higher affinity towards TNF,PPARG,TP53,and ***:This study

关键词： myristic acid luteolin 7-rutinoside network pharmacology oral cancer molecular docking

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Machine learning empowered COVID-19 patient monitoring using non-contact sensing:An extensive review

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Journal of Pharmaceutical Analysis 2022年第2期12卷 193-204页

作者： Umer Saeed syed Yaseen shah Jawad ahmad Muhammad Ali Imran Qammer H.Abbasi syed Aziz shah Research Centre for Intelligent Healthcare Coventry UniversityCoventryCV15FBUK School of Computing Engineering and Built EnvironmentGlasgow Caledonian UniversityGlasgowG40BAUK School of Computing Edinburgh Napier UniversityEdinburghEH114BNUK James Watt School of Engineering University of GlasgowGlasgowG128QQUK

The severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which caused the coronavirus disease 2019(COVID-19)pandemic,has affected more than 400 million people *** the recent rise of new Delta and Omicron variants,the efficacy of the vaccines has become an important *** goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions,particularly for healthcare *** this paper,we discuss the current literature on invasive/contact and non-invasive/noncontact technologies(including Wi-Fi,radar,and software-defined radio)that have been effectively used to detect,diagnose,and monitor human activities and COVID-19 related symptoms,such as irregular *** addition,we focused on cutting-edge machine learning algorithms(such as generative adversarial networks,random forest,multilayer perceptron,support vector machine,extremely randomized trees,and k-nearest neighbors)and their essential role in intelligent healthcare ***,this study highlights the limitations related to non-invasive techniques and prospective research directions.

关键词： Artificial intelligence Non-invasive healthcare Machine learning Non-contact sensing COVID-19

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Fractional Processing Based Adaptive Beamforming Algorithm

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Computers, Materials & Continua 2023年第7期76卷 1065-1084页

作者： syed Asghar Ali shah Tariqullah Jan syed Muslim shah Ruhul Amin Khalil ahmad Sawalmeh Muhammad Anan Department of Electrical Engineering University of Engineering and TechnologyPeshawar25120Pakistan Department of Electrical Engineering Capital University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science Northern Border UniversityArar91431Saudi Arabia Department of Software Engineering Alfaisal UniversityRiyadh11533Saudi Arabia

Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing *** goal of this work is to inves-tigate the use of fractional order algorithm in the field of adaptive beam-forming,with a focus on improving performance while keeping complexity *** effectiveness of the algorithm will be studied and evaluated in this *** this paper,a fractional order least mean square(FLMS)algorithm is proposed for adaptive beamforming in wireless applications for effective utilization of *** algorithm aims to improve upon existing beam-forming algorithms,which are inefficient in performance,by offering faster convergence,better accuracy,and comparable computational *** FLMS algorithm uses fractional order gradient in addition to the standard ordered gradient in weight *** derivation of the algorithm is provided and supported by mathematical convergence *** is evaluated through simulations using mean square error(MSE)minimization as a metric and compared with the standard LMS algorithm for various *** results,obtained through Matlab simulations,show that the FLMS algorithm outperforms the standard LMS in terms of convergence speed,beampattern accuracy and scatter *** outperforms LMS in terms of convergence speed by 34%.From this,it can be concluded that FLMS is a better candidate for adaptive beamforming and other signal processing applications.

关键词： Adaptive beamforming adaptive array fractional processing least mean square fractional least mean square

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