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Ion-acoustic wave structures in the fluid ions modeled by higher dimensional generalized Korteweg-de Vries-Zakharov-Kuznetsov equation

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Journal of Ocean Engineering and Science 2023年第6期8卷 623-635页

作者： u.younas J.Ren Muhammad Z.Baber Muhammad W.Yasin T.Shahzad State Key Laboratory of Power Grid Environmental Protection/Henan Academy of Big Data Zhengzhou UniversityZhengzhou 450001China Department of Mathematics and Statistics The University of LahoreLahorePakistan Department of mathematics University of NarowalNarowalPakistn Department of Basic Sciences and Humanities Narowal CampusUniversity of Engineering and Technology Lahore 54890Pakistan

In this paper,the higher dimensional generalized Korteweg-de-Varies-Zakharov-Kuznetsov(gKdV-ZK)equation is under *** model is used in the field of plasma physics which describes the effects of magnetic field on the weak ion-acoustic *** have applied two techniques,called asφ^(6)-model expansion method and the Hirota bilinear method(HBM)to explore the diversity of wave *** solutions are expressed in the form of hyperbolic,periodic and Jacobi elliptic function(JEF)***,the solitary wave solutions are also extracted.A comparison of our results to well-known results is made,and the study concludes that the solutions achieved here are ***,3-dimensional and contour profiles of achieved outcomes are drawn in order to study their dynamics as a function of parameter *** the basis of the obtained results,we can assert that the pro-posed computational methods are straightforward,dynamic,and well-organized,and will be useful for solving more complicated nonlinear problems in a variety of fields,particularly in nonlinear sciences,in conjunction with symbolic ***,our discoveries provide an important milestone in comprehending the structure and physical behavior of complex *** hope that our findings will contribute significantly to our understanding of ocean *** study,we hope,is appropriate and will be of significance to a broad range of experts involved in ocean engineering models.

关键词： Wave solutions gKdV-ZK equation Bilinear transformation φ^(6)-model expansion method

A new paradigm for diagnosis of neurodegenerative diseases: peripheral exosomes of brain origin

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Translational Neurodegeneration 2022年第1期11卷 532-546页

作者： Neelam younas Leticia Camila Fernandez Flores Franziska Hopfner Günter u.Höglinger Inga Zerr Prion Research Group Department of NeurologyNational Reference Center for Surveillance of TSEUniversitätsklinikum Göttingen:Universitätsmedizin GöttingenGeorg-August UniversityGöttingenGermanyRobert-Koch-Strasse 4037075 GöttingenGermany Hannover Medical School Medizinische Hochschule HannoverHannoverGermany German Center for Neurodegenerative Diseases(DZNE) MunichGermany German Center for Neurodegenerative Diseases(DZNE) GöttingenGermany.

Neurodegenerative diseases are a heterogeneous group of maladies, characterized by progressive loss of neurons. These diseases involve an intricate pattern of cross-talk between different types of cells to maintain specific signaling pathways. A component of such intercellular cross-talk is the exchange of various types of extracellular vesicles (EVs). Exosomes are a subset of EVs, which are increasingly being known for the role they play in the pathogenesis and progression of neurodegenerative diseases, e.g., synucleinopathies and tauopathies. The ability of the central nervous system exosomes to cross the blood-brain barrier into blood has generated enthusiasm in their study as potential biomarkers. However, the lack of standardized, efficient, and ultra-sensitive methods for the isolation and detection of brain-derived exosomes has hampered the development of effective biomarkers. Exosomes mirror heterogeneous biological changes that occur during the progression of these incurable illnesses, potentially offering a more comprehensive outlook of neurodegenerative disease diagnosis, progression and treatment. In this review, we aim to discuss the challenges and opportunities of peripheral biofluid-based brain-exosomes in the diagnosis and biomarker discovery of Alzheimer’s and Parkinson’s diseases. In the later part, we discuss the traditional and emerging methods used for the isolation of exosomes and compare their advantages and disadvantages in clinical settings.

关键词： Alzheimer’s disease Central nervous system Diagnosis Exosomes Blood-brain barrier Parkinson’s disease

An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity

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Computers, Materials & Continua 2021年第3期66卷 2409-2423页

作者： Muhammad Yahya Saeed Muhammad Awais Muhammad younas Muhammad Arif Shah Atif Khan M.Irfan uddin Marwan Mahmoud Department of Software Engineering Government College UniversityFaisalabadFaisalabad38000Pakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Department of Computer Science Islamia College PeshawarPeshawarPakistan Institute of Computing Kohat University of Science and TechnologyKohatPakistan Faculty of Applied Studies King Abdulaziz UniversityJeddahSaudi Arabia

Text Summarization is an essential area in text mining,which has procedures for text *** natural language processing,text summarization maps the documents to a representative set of descriptive ***,the objective of text extraction is to attain reduced expressive contents from the text *** summarization has two main areas such as abstractive,and extractive *** text summarization has further two approaches,in which the first approach applies the sentence score algorithm,and the second approach follows the word embedding *** such text extractions have limitations in providing the basic theme of the underlying *** this paper,we have employed text summarization by TF-IDF with PageRank keywords,sentence score algorithm,and Word2Vec word *** study compared these forms of the text summarizations with the actual text,by calculating cosine ***,TF-IDF based PageRank keywords are extracted from the other two extractive *** intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is *** technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each *** form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized *** also solves the issue of deciding the number of representative keywords for a specific text *** evaluate the technique,the study used a sample of more than eighteen hundred text *** abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text *** proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.

关键词： Metadata page rank sentence score word2vec cosine similarity This

Liver-Tumor Detection using CNN ResuNet

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Computers, Materials & Continua 2021年第5期67卷 1899-1914页

作者： Muhammad Sohaib Aslam Muhammad younas Muhammad umar Sarwar Muhammad Arif Shah Atif Khan MIrfan uddin Shafiq Ahmad Muhammad Firdausi Mazen Zaindin Department of Computer Science Government College University FaisalabadFaisalabad38000Pakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science Islamia College PeshawarPeshawarKPPakistan Institute of Computing Kohat University of Science and TechnologyKohatPakistan Department of Industrial Engineering College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia Department of Statistics and Operations Research College of ScienceKing Saud UniversityRiyadh11421Saudi Arabia

Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics *** are several imaging tests like Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the *** tests are costly and *** paper proposed that image processing through deep learning Convolutional Neural Network(CNNs)ResuNet model that can be helpful for the early diagnose of tumor instead of conventional *** existing studies have mainly used the two Cascaded CNNs for liver segmentation and evaluation of Region Of Interest(ROI).This study uses ResuNet,an updated version of u-Net and ResNet Models that utilize the service of Residential *** apply over method on the 3D-IRCADb01 dataset that is based on CT slices of liver tumor affected *** results showed the True Value Accuracy around 99%and F1 score performance around 95%.This method will be helpful for early and accurate diagnose of the Liver tumor to save the lives of many patients in the field of Biotechnology.

关键词： Liver tumor diagnose ResuNet CNNs segmentation

A Quantum Spatial Graph Convolutional Network for Text Classification

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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