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World Journal of Critical Care Medicine 2023年第3期12卷 153-164页

作者： muhammad k hayat syed Or Bruck Anupam kumar Salim Surani Pulmonary and Critical Care Medicine Baylor College of MedicineHoustonTX 77030United States Department of Medicine and Pharmacology Texas A&M UniversityCollege StationTX 77843United States

Interstitial lung disease(ILD)is typically managed on an outpatient *** care physicians manage patients with ILD in the setting of an acute exacerbation(ILD flare)causing severe *** principles of management of acute exacerbation of ILD are different from those used to manage patients with acute respiratory distress syndrome from sepsis,*** patients may be candidates for aggressive measures like extracorporeal membrane oxygenation and lung transplantation,while almost all patients will benefit from early palliative *** review focused on the types of ILD,diagnosis,and management pathways for this challenging condition.

关键词： Interstitial lung disease Pulmonary fibrosis Acute exacerbation of interstitial lung disease Extracorporeal membrane oxygenation Interstitial lung disease flare Immunosuppression

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

Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

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Computers, Materials & Continua 2024年第5期79卷 2795-2811页

作者： Yasir Maqsood syed muhammad Usman Musaed Alhussein khursheed Aurangzeb Shehzad khalid muhammad Zubair Department of Creative Technologies Faculty of Computing and Artificial IntelligenceAir UniversityIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Engineering Bahria School of Engineering and Applied SciencesBahria UniversityIslamabad44000Pakistan Laboratory for Neuro&Psychophysiology KU LeuvenLeuven3000Belgium

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating *** presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease *** advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a *** challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease *** this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data *** study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural *** paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate *** proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens *** future,more diseases can be included for detection along with some other modalities like pests and weed.

关键词： Wheat disease detection deep learning vision transformer graph neural network model agnostic meta learning

Vision based intelligent traffic light management system using Faster R‐CNN

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 932-947页

作者： syed konain Abbas muhammad Usman Ghani khan Jia Zhu Raheem Sarwar Naif RAljohani Ibrahim AHameed muhammad Umair Hassan Department of Computer Science and Engineering University of Engineering and TechnologyLahorePakistan Zhejiang key Laboratory of Intelligent Education Technology and Application Zhejiang Normal UniversityJinhuaChina OTHEM Manchester Metropolitan UniversityManchesterUK Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of ICT and Natural Sciences Norwegian University of Science and Technology(NTNU)ÅlesundNorway

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

关键词： access control artificial intelligence computer vision intelligent control

Deep Learning Based Efficient Crowd Counting System

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Computers, Materials & Continua 2024年第6期79卷 4001-4020页

作者： Waleed khalid Al-Ghanem Emad Ul Haq Qazi muhammad Hamza Faheem syed Shah Amanullah Quadri Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Centre of Excellence in Cybercrime and Digital Forensics Naif Arab University for Security SciencesRiyadh14812Saudi Arabia Center of Excellence for Information Assurance(COEIA) King Saud UniversityRiyadh12372Saudi Arabia

Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different *** is challenging to determine the approximate crowd size from an image of the crowd’s *** in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the *** employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s *** analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd *** results show the superiority of the proposed system.

关键词： Crowd counting EfficientNet multi-head attention convolutional neural network transfer learning

Mechanical performance of cold mix asphalt containing cup lump rubber as a sustainable bio-modifier

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Journal of Traffic and Transportation Engineering(English Edition) 2024年第3期11卷 424-440页

作者： syed Abdul Ghafar muhammad Naqiuddin Mohd Warid Norhidayah Abdul Hassan Zulfiqar Ali Jattak Ashraf Abdalla Mohammed Radwan Faculty of Civil Engineering Universiti Teknologi MalaysiaSkudai 81310Malaysia Department of Civil Engineering Balochistan University of Information TechnologyEngineering&Management SciencesQuettaPakistan Department of Civil Engineering Balochistan University of Engineering and TechnologyKhuzdarPakistan Department of Civil Engineering Faculty of EngineeringUniversity of TripoliTripoliLibya

The road construction industry aims to contribute to the protection of already compromised *** mix asphalt(CMA)is a measure initiated by the road industry to protect the environment and preserve *** having additional benefits,CMA has attracted little attention due to its inferior ***'s performance is enhanced using a sustainable binder bio-modifier,natural cup lump rubber(CLR)is one of *** study evaluated the tensile properties,rutting,moisture susceptibility,and adhesion properties of CLR-modified CMA(CMA-CR).The tensile property was enhanced by 26%due to CLR ***-CR had excellent rutting resistance of less than 2 mm rut depth at 10,000 load cycles,showing 70%improvement compared with conventional *** susceptibility evaluation indicated that CMA-CR had tensile strength ratio(TSR)value of 104%,satisfying the minimum 80%requirement of AASHTO *** also retained more than 96%bitumen *** moisture damage resistance was improved by 12%and 10%in terms of TSR and stripping,*** durability results revealed that the CMA-CR mixture prevented higher mass loss,representing 14%improvement compared with conventional CMA.

关键词： Cup lump rubber Cup lump modified cold mix asphalt Moisture damage Tensile property Rutting Digital imaging

An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

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Computers, Materials & Continua 2024年第5期79卷 2565-2583页

作者： syed Ayaz Ali Shah Aamir Shahzad Musaed Alhussein Chuan Meng Goh khursheed Aurangzeb Tong Boon Tang muhammad Awais Department of Electrical and Computer Engineering COMSATS University IslamabadAbbottabad CampusAbbottabad22060Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Information and Communication Technology University Tunku Abdul RehmanKampar CampusKampar31900Malaysia Centre for Intelligent Signal and Imaging Research University Teknologi PETRONASBandar Seri IskandarPerak32610Malaysia School of Computing Sciences University of East AngliaNorwichNR47TJUK

Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood *** task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different ***,the speed of the retinal vessel segmentation system is of utmost *** the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the *** address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical *** algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)*** experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE ***,our algorithmexhibits competitive performance with state-of-the-art ***,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE *** is worth noting that these results were achieved using Matlab scripts containing multiple *** suggests that the processing time can be further reduced by replacing loops with *** the proposed algorithm can be deployed in real time *** summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.

关键词： Line detector vessel detection localization mathematical morphology image processing

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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Computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi syed muhammad Saqlain Abdullah Alshahrani Adel Alshamran kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,k Nearest Neighbors(kNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes k Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

Plant Nitrogen Metabolism: Balancing Resilience to Nutritional Stress andAbiotic Challenges

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Phyton-International Journal of Experimental Botany 2024年第3期93卷 581-609页

作者： muhammad Farhan Manda Sathish Rafia kiran Aroosa Mushtaq Alaa Baazeem Ammarah Hasnain Fahad Hakim syed Atif Hasan Naqvi Mustansar Mubeen Yasir Iftikhar Aqleem Abbas muhammad Zeeshan Hassan Mahmoud Moustafa Department of Plant Pathology Faculty of Agricultural Sciences&TechnologyBahauddin Zakariya UniversityMultan60800Pakistan Centro de Investigación de Estudios Avanzados del Maule(CIEAM) Vicerrectoría de Investigación y PostgradoUniversidad Católica del MauleTalca3460000Chile College of Food Science and Nutritional Engineering China Agricultural UniversityBeijingChina Department of Biology College of ScienceTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Biotechnology Faculty of Biological SciencesLahore University of Biological and Applied SciencesLahorePakistan Department of Plant Pathology College of AgricultureUniversity of SargodhaSargodha40100Pakistan Department of Agriculture and Food Technology Karakorum International UniversityGilgitPakistan Department of Biology Faculty of ScienceKing Khalid UniversityAbhaSaudi Arabia

Plant growth and resilience to abiotic stresses,such as soil salinity and drought,depend intricately on nitrogen *** review explores nitrogen’s regulatory role in plant responses to these challenges,unveiling a dynamic interplay between nitrogen availability and abiotic *** the context of soil salinity,a nuanced rela-tionship emerges,featuring both antagonistic and synergistic interactions between salinity and nitrogen ***-induced chlorophyll depletion in plants can be alleviated by optimal nitrogen supplementation;however,excessive nitrogen can exacerbate salinity *** delve into the complexities of this interaction and its agri-cultural ***,a vital element within essential plant structures like chloroplasts,elicits diverse responses based on its *** review comprehensively examines manifestations of nitrogen deﬁciency and toxicity across various crop types,including cereals,vegetables,legumes,and ***,we explore the broader consequences of nitrogen products,such as N_(2)O,NO_(2),and ammonia,on human ***-ing the intricate relationship between nitrogen and salinity,especially chloride accumulation in nitrate-fed plants and sodium buildup in ammonium-fed plants,is pivotal for optimizing crop nitrogen ***,prudent nitrogen use is essential,as overapplication can exacerbate nitrogen-related *** Use Efﬁ-ciency(NUE)is of paramount importance in addressing salinity challenges and enhancing sustainable crop *** this goal requires advancements in crop varieties with efﬁcient nitrogen utilization,pre-cise timing and placement of nitrogen fertilizer application,and thoughtful nitrogen source selection to mitigate losses,particularly urea-based fertilizer *** review article delves into the multifaceted world of plant nitrogen metabolism and its pivotal role in enabling plant resilience to nutritional stress and abiotic chall

关键词： Synthetic nitrogen nitrogen signaling sustainable agriculture eutrophication ammonium nitrate

Systematic Review:Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms

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Intelligent Automation & Soft Computing 2024年第3期39卷 437-476页

作者： Darakhshan syed Ghulam muhammad Safdar Rizvi Department of Computer Science Bahria UniversityKarachi CampusKarachiPakistan

Cloud Computing has the ability to provide on-demand access to a shared resource *** has completely changed the way businesses are managed,implement applications,and provide *** rise in popularity has led to a significant increase in the user demand for ***,in cloud environments efficient load balancing is essential to ensure optimal performance and resource *** systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing ***,metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased *** a cloud-based context,this paper describes load balancing measurements,including the benefits and drawbacks associated with the selected load balancing *** also summarizes the algorithms based on implementation,time complexity,adaptability,associated issue(s),and targeted QoS ***,the analysis evaluates the tools and instruments utilized in each investigated ***,comparative analysis among static,traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also ***,the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed.

关键词： Cloud computing load balancing metaheuristic algorithm dynamic algorithm load balancer QoS