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Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

Computer Modeling in Engineering & Sciences 2024年第8期140卷 1539-1562页

作者： Samar M.Alqhtani Toufique A.Soomro faisal bin ubaid Ahmed Ali Muhammad Irfan Abdullah A.Asiri Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Electronic Engineering The University of LarkanoLarkanaSindh75660Pakistan Computer Science Department Sukkur IBA UniversitySukkurSindh65200Pakistan Eletrical Engineering Department Sukkur IBA UniversitySukkurSindh65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various *** resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain *** plays a crucial role in the diagnosis of brain tumors and the examination of other brain ***,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely ***,early diagnosis of brain tumors is intricate,necessitating the use of computerized *** research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain *** proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third *** final step involves classification using the Support Vector Machine(SVM)*** classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)*** proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of ***,this method exhibits a shorter processing time of 0.44 s compared to existing *** performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and *** enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classi

关键词： Brain tumor magnetic resonance imaging principal component analysis fuzzy c-clustering support vector machine

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Construction of robust and durable Cu_(2)Se-V_(2)O_(5) nanosheet electrocatalyst for alkaline oxygen evolution reaction

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Frontiers of Chemical Science and Engineering 2024年第6期18卷 71-83页

作者： Tauseef Munawar Ambreen Bashir Khalid Mujasam Batoo Saman Fatima faisal Mukhtar Sajjad Hussain Sumaira Manzoor Muhammad Naeem Ashiq Shoukat Alim Khan Muammer Koc faisal Iqbal Institute of Physics The Islamia University of BahawalpurBahawalpur 63100Pakistan King Abdullah Institute for Nanotechnology King Saud UniversityRiyadh 11451Saudi Arabia Hybrid Materials Center(HMC) Sejong UniversitySeoul 05006Republic of Korea Department of Nanotechnology and Advanced Materials Engineering Sejong UniversitySeoul 05006Republic of Korea Institute of Chemical Sciences Bahauddin Zakariya UniversityMultan 60800Pakistan Division of Sustainable Development College of Science and EngineeringHamad Bin Khalifa UniversityQatar FoundationDoha 34110Qatar

Reducing the production costs of clean energy carriers such as hydrogen through scalable water electrolysis is a potential solution for advancing the hydrogen *** the various material candidates,our group demonstrated transition-metal-based materials with tunable electronic characteristics,various phases,and ***,electrochemical water oxidation using Cu_(2)Se-V_(2)O_(5) as a non-precious metallic electrocatalyst via a hydrothermal approach is *** water-splitting performance of all the fabricated electrocatalysts was evaluated after direct growth on a stainless-steel *** electrochemically tuned Cu_(2)Se-V_(2)O_(5) catalyst exhibited a reduced overpotential of 128 mV and provided a reduced Tafel slope of 57 mV·dec^(−1) to meet the maximum current density of 250 mA·cm^(−2).The optimized strategy for interfacial coupling of the fabricated Cu_(2)Se-V_(2)O_(5) catalyst resulted in a porous structure with accessible active sites,which enabled adsorption of the intermediates and afforded an effective charge transfer rate for promoting the oxygen evolution ***,the combined effect of the catalyst components provided long-term stability for over 110 h in an alkaline solution,which makes the catalyst promising for large-scale practical *** aforementioned advantages of the composite catalyst overcome the limitations of low conductivity,agglomeration,and poor stability of the pure catalysts(Cu2Se and V2O5).

关键词： oxygen evolution reaction Cu_(2)Se-V_(2)O_(5)composite nanosheets metal-diffused ions accessible adsorption sites

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DeBERTa-GRU: Sentiment Analysis for Large Language Model

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Computers, Materials & Continua 2024年第6期79卷 4219-4236页

作者： Adel Assiri Abdu Gumaei faisal Mehmood Touqeer Abbas Sami Ullah Department of Informatics for Business College of BusinessKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China Department of Computer Science and Technology Beijing University of Chemical TechnologyBeijing100029China Department of Computer Science Government College University FaisalabadFaisalabadPunjab38000Pakistan

Modern technological advancements have made social media an essential component of daily *** media allow individuals to share thoughts,emotions,and *** analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the *** analysis is essential in business and society because it impacts strategic *** analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance *** execution time increases due to the sequential processing of the sequence ***,the calculation times for the Transformer models are reduced because of the parallel *** study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their *** particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment *** the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics *** proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

关键词： DeBERTa GRU Naive Bayes LSTM sentiment analysis large language model

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Extracellular vesicles as tools and targets in therapy for diseases

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Signal Transduction and Targeted Therapy 2024年第3期9卷 872-912页

作者： Mudasir A.Kumar Sadaf K.Baba Hana Q.Sadida Sara Al.Marzooqi Jayakumar Jerobin faisal H.Altemani Naseh Algehainy Mohammad A.Alanazi Abdul-Badi Abou-Samra Rakesh Kumar Ammira S.Al-Shabeeb Akil Muzafar A.Macha Rashid Mir Ajaz A.Bhat Watson-Crick Centre for Molecular Medicine Islamic University of Science and TechnologyAwantiporaKashmir 192122India Department of Human Genetics-Precision Medicine in Diabetes Obesity and Cancer ProgramSidra MedicineDohaQatar Qatar Metabolic Institute Academic Health SystemHamad Medical CorporationDohaQatar Department of Medical Laboratory Technology Prince Fahad Bin Sultan Chair for Biomedical ResearchFaculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia School of Biotechnology Shri Mata Vaishno Devi UniversityKatraIndia

Extracellular vesicles(EVs)are nano-sized,membranous structures secreted into the extracellular *** exhibit diverse sizes,contents,and surface markers and are ubiquitously released from cells under normal and pathological *** serum is a rich source of these EVs,though their isolation from serum proteins and non-EV lipid particles poses *** vesicles transport various cellular components such as proteins,mRNAs,miRNAs,DNA,and lipids across distances,influencing numerous physiological and pathological events,including those within the tumor microenvironment(TME).Their pivotal roles in cellular communication make EVs promising candidates for therapeutic agents,drug delivery systems,and disease *** in cancer diagnostics,EV detection can pave the way for early identification and offers potential as diagnostic ***,various EV subtypes are emerging as targeted drug delivery tools,highlighting their potential clinical *** need for non-invasive biomarkers to monitor biological processes for diagnostic and therapeutic purposes remains *** into the unique composition of EVs could unlock advanced diagnostic and therapeutic avenues in the *** this review,we discuss in detail the roles of EVs across various conditions,including cancers(encompassing head and neck,lung,gastric,breast,and hepatocellular carcinoma),neurodegenerative disorders,diabetes,viral infections,autoimmune and renal diseases,emphasizing the potential advancements in molecular diagnostics and drug delivery.

关键词： diseases sized sizes

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Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment

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Intelligent Automation & Soft Computing 2023年第6期36卷 2897-2914页

作者： faisal SAlsubaei Haya Mesfer Alshahrani Khaled Tarmissi Abdelwahed Motwakel Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

Cybersecurity has become the most significant research area in the domain of the Internet of Things(IoT)owing to the ever-increasing number of *** rapid penetration of Android platforms in mobile devices has made the detection of malware attacks a challenging ***,Android malware is increasing on a daily ***,precise malware detection analytical techniques need a large number of hardware resources that are signifi-cantly resource-limited for mobile *** this research article,an optimal Graph Convolutional Neural Network-based Malware Detection and classification(OGCNN-MDC)model is introduced for an IoT-cloud *** pro-posed OGCNN-MDC model aims to recognize and categorize malware occur-rences in IoT-enabled cloud *** presented OGCNN-MDC model has three stages in total,such as data pre-processing,malware detection and para-meter *** detect and classify the malware,the GCNN model is exploited in this *** order to enhance the overall efficiency of the GCNN model,the Group Mean-based Optimizer(GMBO)algorithm is utilized to appropriately adjust the GCNN parameters,and this phenomenon shows the novelty of the cur-rent study.A widespread experimental analysis was conducted to establish the superiority of the proposed OGCNN-MDC model.A comprehensive comparison study was conducted,and the outcomes highlighted the supreme performance of the proposed OGCNN-MDC model over other recent approaches.

关键词： Cybersecurity IoT cloud malware detection graph convolution network

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Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

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Computers, Materials & Continua 2022年第2期70卷 4123-4145页

作者： Shahab Ul Islam Saifullah Jan Abdul Waheed Gulzar Mehmood Mahdi Zareei faisal Alanazi Department of Computer Science IQRA National UniversityPeshawar25124Pakistan Department of Computer Science City University of Science&Information TechnologyPeshawar25124Pakistan Department of Information Technology Hazara UniversityMansehra21120Pakistan School of Electrical and Computer Engineering Seoul National UniversitySeoul08826Korea Department of Computer Science and Information Technology University of Malakand23021Pakistan Tecnologico de Monterrey School of Engineering and SciencesZapopan45201Mexico Department of Electrical Engineering Prince Sattam Bin Abdulaziz UniversityCollege of EngineeringAl Kharj16278Saudi Arabia

Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management ***,a change in infrastructure has a direct impact on other land-cover and climatic *** study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature(LST)during the years 1996 and *** this purpose,firstly,satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper)plus and OLI(Operational Land Imager)of 30 m resolution were ***,for classification and image processing,remote sensing(RS)applications ENVI(Environment for Visualising Images)and GIS(Geographic Information System)were ***,for better visualization and more in-depth analysis of land sat images,pre-processing techniques were *** Land use and Land cover(LU/LC)four types of land cover areas were identified-vegetation area,water cover,urbanized area,and infertile land for the years under *** composition of red,green,and near infra-red bands was used for supervised *** images were extracted for analyzing the relative infrastructure change.A comparative analysis for the classification of images is performed for SVM(Support Vector Machine)and ANN(Artificial Neural Network).Based on analyzing these images,the result shows the rise in the average temperature from 30.04℃ to 45.25℃.This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km^(2) from 1996 to *** has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders.

关键词： Remote sensing temperature extraction urbanization satellite image classification artificial neural network support vector machine LU/LC land surface temperature

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Graphene-Coated Optical Fiber SPR Biosensor for BRCA1 and BRCA2 Breast Cancer Biomarker Detection:a Numerical Design-Based Analysis

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Photonic Sensors 2020年第1期10卷 67-79页

作者： Md.Biplob HOSSAIN Md.Muztahidul ISLAM Lway faisal ABDULRAZAK Md.Masud RANA Tarik bin Abdul AKIB Mehedi HASSAN Dept.of Electrical and Electronic Engineering Jashore University of Science and TechnologyJashore-7408Bangladesh Dept.of Electrical and Electronic Engineering Bangladesh Army University of Engineering and TechnologyNatore-6431Bangladesh Dept.of Computer Science Cihan University SlemaniSulaimaniya-46001Iraq Dept.of Electrical and Electronic Engineering Rajshahi University of Engineering&TechnologyRajshahi-6402Bangladesh

This paper provides a simple hybrid design and numerical analysis of the graphene-coated fiber-optic surface plasmon resonance(SPR)biosensor for breast cancer gene-1 early onset(BRCA1)and breast cancer gene-2 early onset(BRCA2)genetic breast cancer *** specific mutations named 916delTT and 6174delT in the BRCA1 and BRCA2 are selected for numerical detection of breast *** sensor is based on the technique of the attenuated total reflection(ATR)method to detect deoxyribonucleic acid(DNA)hybridization along with individual point mutations in BRCA1 and BRCA2 *** have numerically shown that momentous changes present in the SPR angle(minimum:135%more)and surface resonance frequency(SRF)(minimum:136%more)for probe DNA with various concentrations of target DNA corresponding to a mutation of the BRCA1 and BRCA2 *** variation of the SPR angle and SRF for mismatched DNA strands is quite negligible,whereas that for complementary DNA strands is considerable,which is essential for proper detection of genetic biomarkers(916delTT and 6174delT)for early breast *** last,the effect of electric field distribution in inserting graphene layer is analyzed incorporating the finite difference time domain(FDTD)technique by using Lumerical FDTD solution commercial *** the best of our knowledge,this is the first demonstration of such a highly efficient biosensor for detecting BRCA1 and BRCA2 breast ***,the proposed biosensor opens a new window toward the detection of breast cancers.

关键词： ATR BRCA1 BRCA2 cancer DNA hybridization graphene,SPR SRF 6174delT 916delTT

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Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks

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Computers, Materials & Continua 2022年第3期70卷 4675-4690页

作者： Muneeb Ur Rehman Fawad Ahmed Muhammad Attique Khan Usman Tariq faisal Abdulaziz Alfouzan Nouf M.Alzahrani Jawad Ahmad Department of Electrical Engineering HITEC University TaxilaPakistan Department of Computer Science HITEC University TaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Forensic Sciences College of Criminal JusticeNaif Arab University for Security SciencesRiyadhSaudi Arabia Department of Information Technology Albaha UniversityAlbahaSaudi Arabia School of Computing Edinburgh Napier UniversityUK

Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video *** researchers have been working on visionbased gesture recognition due to its various *** paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network(3D-CNN)and a Long Short-Term Memory(LSTM)*** proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive *** 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried *** proposed model is a light-weight architecture with only 3.7 million training *** model has been evaluated on 15 classes from the 20BN-jester dataset available *** model was trained on 2000 video-clips per class which were separated into 80%training and 20%validation *** accuracy of 99%and 97%was achieved on training and testing data,*** further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2+LSTM.

关键词： Convolutional neural networks 3D-CNN LSTM spatiotemporal jester real-time hand gesture recognition

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Highly active Pd-Fe/α-Al_(2)O_(3) catalyst with the bayberry tannin as chelating promoter for CO oxidative coupling to diethyl oxalate

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Chinese Chemical Letters 2021年第2期32卷 796-800页

作者： Wei-Chao Xing Ji-Min An Jing Lv faisal Irshad Yu-Jun Zhao Sheng-Ping Wang Xin-bin Ma Key Laboratory for Green Chemical Technology School of Chemical Engineering and TechnologyTianjin UniversityCollaborative Innovation Center of Chemical Science and EngineeringTianjin 300072China

A novel Pd-Fe/α-Al_(2)O_(3) catalyst was synthesized by incipient-wetness impregnation method with bayberry tannin as chelating promoter and commercial hollow column Raschig ring a-Al_(2)O_(3) as support for the synthesis of diethyl oxalate from CO and ethyl nitrite.A variety of characterization techniques including N2 physical adsorption,optical microscopy,scanning electron microscopy and energy dispersive system(SEM-EDS),inductively coupled plasma optical emission spectroscopy(ICP-OES),X-ray diffraction(XRD),X-ray photoelectron spectroscopy(XPS),transmission electron microscopy(TEM),were employed to explore the relationship between the physicochemical properties and activity of *** indicated that a large number of phenolic hydroxyl groups in bayberry tannin can efficiently anchor the active component Pd,reduce the particle size and make the active Pd as a multi-ring distribution on the commercial a-Al2O3 suppo rt,which we re beneficial to improve the catalytic activity for the production of diethyl oxalate from CO and ethyl nitrite.0.3 wts Pd-Fe/α-Al_(2)O_(3) showed excelle nt catalytic activity and selectivity in a continuous flow,fixed-bed reactor with the loading amount of 10 mL catalysts,Under the mild reaction conditions,the space-time yield of diethyl oxalate was 978 g L^(-1) h^(-1) and CO conversion was 44% with the selectivity to diethyl oxalate of 95.5%.

关键词： Bayberry tannin Diethyl oxalate Phenolic hydroxyl groups Ethyl nitrite Pd-Fe/α-Al_(2)O_(3)

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Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

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Computers, Materials & Continua 2023年第2期74卷 3063-3083页

作者： Sukhendra Singh Sur Singh Rawat Manoj Gupta B.K.Tripathi faisal Alanzi Arnab Majumdar Pattaraporn Khuwuthyakorn Orawit Thinnukool Department of Information Technology JSS Academy of Technical EducationNoidaIndia JSS Academy of Technical Education NoidaIndia Department of Electronics and Communication Engineering JECRC University JaipurRajasthanIndia Harcourt Butler Technological University Kanpur India Department of Electrical Engineering Prince Sattam Bin Abdulaziz UniversityCollege of EngineeringAl Kharj16278Saudi Arabia Faculty of Engineering Imperial College LondonLondonSW72AZUK College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Currently,breast cancer has been amajor cause of deaths in women worldwide and the World Health Organization(WHO)has confirmed *** severity of this disease can be minimized to the large extend,if it is diagnosed properly at an early stage of the ***,the proper treatment of a patient having cancer can be processed in better way,if it can be diagnosed properly as early as possible using the better ***,it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological images of breast *** address the above said issues,this paper presents a hybrid model using the transfer learning to study the histopathological images,which help in detection and rectification of the disease at a low *** dataset experiments were carried out to validate the suggested hybrid model in this *** experimental results show that the proposed model outperformed the baseline methods,with F-scores of 0.81 for DenseNet+Logistic Regression hybrid model,(F-score:0.73)for Visual Geometry Group(VGG)+Logistic Regression hybrid model,(F-score:0.74)for VGG+Random Forest,(F-score:0.79)for DenseNet+Random Forest,and(F-score:0.79)for VGG+Densenet+Logistic Regression hybrid model on the dataset of histopathological images.

关键词： Histopathological deep neural network machine learning breast cancer binary classification transfer learning

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