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A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-ray Images

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2497-2517页

作者： Nasser Alalwan Ahmed I.Taloba Amr Abozeid Ahmed Ibrahim Alzahrani ali H.Al-Bayatti Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Information System Department Faculty of Computers and InformationAssiut UniversityAssiutEgypt Mathematics Department Faculty of ScienceAl-Azhar UniversityCairoEgypt Cyber Technology Institute(CTI) De Montfort UniversityLeicesterUK

An illness known as pneumonia causes inflammation in the *** there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven *** improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been *** date,several methods have been employed to identify *** Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and ***,these methods are complex,inefficient,and imprecise to analyze a big number of *** this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is *** proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and *** Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)*** features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of *** results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification *** proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.

关键词： African buffalo optimization convolutional neural network pneumonia X-ray

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Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid(MHAVH)Model

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Computers, Materials & Continua 2024年第5期79卷 2673-2696页

作者： Hina Naz Zuping Zhang Mohammed Al-Habib Fuad A.Awwad Emad A.A.Ismail Zaid ali Khan School of Computer Science and Engineering Central South UniversityChangsha410003China Department of Quantitative analysis College of Business AdministrationKing Saud UniversityP.O.Box 71115Riyadh11587Saudi Arabia Electrical and Computer Engineering University of VictoriaVictoriaV9A1B8Canada

Cardiovascular disease is the leading cause of death *** disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as ***,minimizing the death ratio can be achieved by early detection of heart attack(HA)*** the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four ***,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives *** objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their *** address these challenges,we employ deep learning *** have utilized a vision transformer(ViT)to address this ***,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product ***,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more *** response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as resNet-50 and VGG-16,renowned for their robust feature extraction *** incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising rGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack *** the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original dat

关键词： Image analysis posture of heart attack(PHA)detection hybrid features VGG-16 resNet-50 vision transformer advance multi-head attention layer

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QT AG-Modules whose h-Pure-S-High Submodules Have Closure

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Journal of Mathematical research with Applications 2024年第1期44卷 18-24页

作者： Mohd Noman ali Vinit Kumar SHArMA Ayazul HASAN Department of Mathematics Shri Venkateshwara University College of Applied Industrial Technology Jazan University

A right module M over an associative ring r with unity is a QTAG-module if every finitely generated submodule of any homomorphic image of M is a direct sum of uniserial *** article considers the closure of h-pure-S-high submodules of ***,we determine all submodules S of a QTAG-module M such that each closure of h-pure-S-high submodule of M is h-pure-■-high in■.A few results of this theme give a comparison of some elementary properties of h-pure-S-high and S-high submodules.

关键词： QTAG-modules closures h-pure-S-high submodules

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An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1051-1075页

作者： Shazia Shamas Surya Narayan Panda Ishu Sharma Kalpna Guleria Aman Singh Ahmad ali AlZubi Mallak Ahmad AlZubi Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpuraPunjab140401India Higher Polytechnic School Universidad Europea del AtlánticoC/Isabel Torres 21Santander39011Spain Department of Engineering Universidad Internacional IberoamericanaAreciboPR00613USA Uttaranchal Institute of Technology Uttaranchal UniversityDehradunUttarakhand248007India Computer Science Department King Saud UniversityRiyadhSaudi Arabia Faculty of Medicine Jordan University of Science and TechnologyIrbidJordan

The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and *** detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by resea

关键词： Lesion lung cancer segmentation medical imaging meta-heuristic Artificial Bee Colony(ABC) Cuckoo Search Algorithm(CSA) Particle Swarm Optimization(PSO) Firefly Algorithm(FFA) segmentation

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1455-1486页

作者： Kolli ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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Human Gait recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

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Computers, Materials & Continua 2024年第1期78卷 357-374页

作者： Ch Avais Hanif Muhammad ali Mughal Muhammad Attique Khan Nouf Abdullah Almujally Taerang Kim Jae-Hyuk Cha Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

The demand for a non-contact biometric approach for candidate identification has grown over the past ten *** on the most important biometric application,human gait analysis is a significant research topic in computer *** have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far *** recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access *** systems require a complex combination of technical,operational,and definitional *** employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked *** proposes a novel deep learning-based framework for human gait classification in video *** framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing *** proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer ***,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)***,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization *** algorithm chooses the best features,combined in a novel correlation-based fusion ***,the fused best features are categorized using medium,bi-layer,and tri-layered neural *** the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was *** achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this wor

关键词： Gait recognition covariant factors biometric deep learning fusion feature selection

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Ultrafast magneto-optical dynamics in nickel(111)single crystal studied by the integration of ultrafast reflectivity and polarimetry probes

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Chinese Physics B 2024年第3期33卷 65-69页

作者：匡皓余军潇陈洁 H.E.Elsayed-ali 李润泽 Peter M.rentzepis School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China Center for Transformative Science ShanghaiTech UniversityShanghai 201210China Center for Ultrafast Science and Technology Key Laboratory for Laser Plasmas (Ministry of Education)Collaborative Innovation Center of IFSA (CICIFSA)School of Physics and AstronomyShanghai Jiao Tong UniversityShanghai 200240China Department of Electrical and Computer Engineering Old Dominion UniversityNorfolkVirginia 23529USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTexas 77843USA

With the integration of ultrafast reflectivity and polarimetry probes,we observed carrier relaxation and spin dynamics induced by ultrafast laser excitation of Ni(111)single *** carrier relaxation time within the linear excitation range reveals that electron-phonon coupling and dissipation of photon energy into the bulk of the crystal take tens of *** the other hand,the observed spin dynamics indicate a longer time of about 120 *** further understand how the lattice degree of freedom is coupled with these dynamics may require the integration of an ultrafast diffraction probe.

关键词： ultrafast spin dynamics non-equilibrium dynamics multi-probe

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Complement-dependent neuroinflammation in spinal cord injury:from pathology to therapeutic implications

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Neural regeneration research 2025年第5期20卷 1324-1335页

作者： Hassan Saad Bachar El Baba ali Tfaily Firas Kobeissy Juanmarco Gutierrez Gonzalez Daniel refai Gerald r.rodts Christian Mustroph David Gimbel Jonathan Grossberg Daniel L.Barrow Matthew F.Gary ali M.Alawieh Department of Neurosurgery Emory University School of MedicineAtlantaGAUSA Department of Neurology Yale UniversityNew HavenCTUSA Center for Neurotrauma Multiomics&Biomarkers(CNMB)Morehouse School of MedicineAtlantaGAUSA

Spinal cord injury remains a major cause of disability in young adults,and beyond acute decompression and rehabilitation,there are no pharmacological treatments to limit the progression of injury and optimize recovery in this *** the thorough investigation of the complement system in triggering and propagating cerebral neuroinflammation,a similar role for complement in spinal neuroinflammation is a focus of ongoing *** this work,we survey the current literature investigating the role of complement in spinal cord injury including the sources of complement proteins,triggers of complement activation,and role of effector functions in the *** study relevant data demonstrating the different triggers of complement activation after spinal cord injury including direct binding to cellular debris,and or activation via antibody binding to damage-associated molecular *** effector functions of complement have been implicated in spinal cord injury,and we critically evaluate recent studies on the dual role of complement anaphylatoxins in spinal cord injury while emphasizing the lack of pathophysiological understanding of the role of opsonins in spinal cord *** this pathophysiological review,we systematically review the different translational approaches used in preclinical models of spinal cord injury and discuss the challenges for future translation into human *** review emphasizes the need for future studies to dissect the roles of different complement pathways in the pathology of spinal cord injury,to evaluate the phases of involvement of opsonins and anaphylatoxins,and to study the role of complement in white matter degeneration and regeneration using translational strategies to supplement genetic models.

关键词： complement neuroinflammation neuroplasticity regeneration spinal cord injury targeted therapy

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Identification of Software Bugs by Analyzing Natural Language-Based requirements Using Optimized Deep Learning Features

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Computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed ali Khan Saddaf rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the receiver Operating Characteristic Curve(AUC-rOC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Modelling of debris-flow susceptibility and propagation: a case study from Northwest Himalaya

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Journal of Mountain Science 2024年第1期21卷 200-217页

作者： Hamza DAUD Javed Iqbal TANOLI Sardar Muhammad ASIF Muhammad QASIM Muhammad ali Junaid KHAN Zahid Imran BHATTI Ishtiaq Ahmad Khan JADOON Badong National Observation and research Station of Geohazards China University of GeosciencesWuhan 430074China Department of Earth Sciences COMSATS University IslamabadAbbottabad Campus 22010Pakistan Department of Earth Sciences Sultan Qaboos UniversityAl-KhoudMascat 123Oman Faculty of Engineering China University of Geosciences(Wuhan)Wuhan 430074China Department of Earth Sciences Abbottabad University of Science and TechnologyHavelian 22500Pakistan

The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-r technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic roughness Index(TrI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). results were validated through the receiver Operator Characteristics(rOC) curve. results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-r propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-r validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure.

关键词： North Pakistan Debris flow Flow-r Propagation Susceptibility mapping Debris-flow inventory Weighted Overlay Method

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