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Optimizing Fully Convolutional Encoder-Decoder Network for Segmentation of Diabetic Eye Disease

Computers, Materials & Continua 2023年第11期77卷 2481-2504页

作者： Abdul Qadir Khan Guangmin Sun Yu Li Anas Bilal malik Abdul Manan Faculty of Information Technology Beijing University of TechnologyBeijing100124China College of Information Science Technology Hainan Normal UniversityHaikou571158China

In the emerging field of image segmentation,Fully Convolutional Networks(FCNs)have recently become ***,their effectiveness is intimately linked with the correct selection and fine-tuning of hyperparameters,which can often be a cumbersome manual *** main aim of this study is to propose a more efficient,less labour-intensive approach to hyperparameter optimization in FCNs for segmenting fundus *** this end,our research introduces a hyperparameter-optimized Fully Convolutional Encoder-Decoder Network(FCEDN).The optimization is handled by a novel Genetic Grey Wolf Optimization(G-GWO)*** algorithm employs the Genetic Algorithm(GA)to generate a diverse set of initial *** leverages Grey Wolf Optimization(GWO)to fine-tune these positions within the discrete search *** on the Indian Diabetic Retinopathy Image Dataset(IDRiD),Diabetic Retinopathy,Hypertension,Age-related macular degeneration and Glacuoma ImageS(DR-HAGIS),and Ocular Disease Intelligent Recognition(ODIR)datasets showed that the G-GWO method outperformed four other variants of GWO,GA,and PSO-based hyperparameter optimization *** proposed model achieved impressive segmentation results,with accuracy rates of 98.5%for IDRiD,98.7%for DR-HAGIS,and 98.4%,98.8%,and 98.5%for different sub-datasets within *** results suggest that the proposed hyperparameter-optimized FCEDN model,driven by the G-GWO algorithm,is more efficient than recent deep-learning models for image segmentation *** thereby presents the potential for increased automation and accuracy in the segmentation of fundus images,mitigating the need for extensive manual hyperparameter adjustments.

关键词： Diabetic eye disease image segmentation deep learning artificial intelligence grey wolf optimization FCN CNN

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Development of a monitoring and warning system based on optical fiber sensing technology for masonry retaining walls and trees

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Journal of Rock Mechanics and Geotechnical Engineering 2022年第4期14卷 1064-1076页

作者： Peichen Wu Daoyuan Tan Shaoqun Lin Wenbo Chen Jianhua Yin Numan malik An Li Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHong KongChina

Hong Kong has a long history of applying masonry retaining walls to provide horizontal platforms and stabilize man-made *** to the sub-tropical climate,some masonry retaining walls are colonized by *** weather,such as typhoons and heavy rains,may cause rupture or root failure of those trees,thus resulting in instability of the retaining walls.A monitoring and warning system for the movement of masonry retaining walls and sway of trees has been designed with the application of fiber Bragg grating(FBG)sensing *** monitoring system is also equipped with a solar power system and 4G data transmission *** key functions of the proposed monitoring system include remote sensing and data access,early warning,and real-time data *** setups and working principles of the monitoring systems and related transducers are *** feasibility,accuracy,serviceability and reliability of this monitoring system have been checked by in-site calibration tests and four-month ***,a two-level interface has been developed for data *** monitoring results show that the monitored masonry retaining wall had a reversible movement up to 2.5 mm during the monitoring ***,it is found that the locations of the maximum strain on trees depend on the crown spread of trees.

关键词： Masonry retaining walls Tree Monitoring Fiber Bragg grating(FBG)sensing Warning system

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Feature Enhanced Stacked Auto Encoder for Diseases Detection in Brain MRI

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Computers, Materials & Continua 2023年第8期76卷 2551-2570页

作者： Umair Muneer Butt Rimsha Arif Sukumar Letchmunan Babur Hayat malik Muhammad Adil Butt School of Computer Sciences Universiti Sains MalaysiaPenang1180Malaysia Department of Computer Science and IT The University of ChenabGujrat50700Pakistan

The detection of brain disease is an essential issue in medical and research *** learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)*** techniques involve training neural networks on large datasets of MRI images,allowing the networks to learn patterns and features indicative of different brain ***,several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these *** paper implements a Feature Enhanced Stacked Auto Encoder(FESAE)model to detect brain *** standard stack auto encoder’s results are trivial and not robust enough to boost the system’s ***,the standard Stack Auto Encoder(SAE)is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy froman *** proposed model consists of four ***,pre-processing is performed to remove noise,and the greyscale image is converted to Red,Green,and Blue(RGB)to enhance feature details for discriminative feature ***,feature Extraction is performed to extract significant features for classification using DiscreteWavelet Transform(DWT)and ***,classification is performed to classify MRI images into four major classes:Normal,Tumor,Brain Stroke,and Alzheimer’***,the FESAE model outperforms the state-of-theart,machine learning,and deep learning methods such as Artificial Neural Network(ANN),SAE,Random Forest(RF),and Logistic Regression(LR)by achieving a high accuracy of 98.61% on a dataset of 2000 MRI *** proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes.

关键词： Brain diseases deep learning feature enhanced stacked auto encoder stack auto encoder

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QoS Based Cloud Security Evaluation Using Neuro Fuzzy Model

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Computers, Materials & Continua 2022年第1期70卷 1127-1140页

作者： Nadia Tabassum Tahir Alyas Muhammad Hamid Muhammad Saleem Saadia malik Syeda Binish Zahra Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Statistics and Computer science University of veterinary and animal sciencesLahore54000Pakistan Department of Industrial Engineering Faculty of EngineeringRabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology-RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia

Cloud systems are tools and software for cloud computing that are deployed on the Internet or a cloud computing network,and users can use them at any *** assessing and choosing cloud providers,however,customers confront the variety and difficulty of quality of service(QoS).To increase customer retention and engagement success rates,it is critical to research and develops an accurate and objective evaluation *** is the emerging environment for distributed services at various *** to the benefits of this environment,globally cloud is being taken as a standard environment for individuals as well as for the corporate sector as it reduces capital expenditure and provides secure,accessible,and manageable services to all stakeholders but Cloud computing has security challenges,including vulnerability for clients and association acknowledgment,that delay the rapid adoption of computing *** of resources in the Cloud is difficult because resources provide numerous measures of quality of *** this paper,the proposed resource allocation approach is based on attribute QoS Scoring that takes into account parameters the reputation of the asset,task completion time,task completion ratio,and resource *** article is focused on the cloud service’s security,cloud reliability,and could *** this paper,the machine learning algorithm neuro-fuzzy has been used to address the cloud security issues to measure the parameter security and privacy,trust *** findings reveal that the ANFIS-dependent parameters are primarily designed to discern anomalies in cloud security and features output normally yields better results and guarantees data consistency and computational power.

关键词： Cloud computing performance quality of service machine learning neuro-fuzzy

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Review of sub-synchronous interaction in wind integrated power systems: classification, challenges, and mitigation techniques

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Protection and Control of Modern Power Systems 2023年第2期8卷 13-38页

作者： Neevatika Verma Narendra Kumar Saket Gupta Hasmat malik Fausto Pedro García Márquez Department of Electrical Engineering Delhi Technological UniversityDelhi 110042India Department of Electrical Power Engineering Faculty of Electrical EngineeringUniversiti Teknologi Malaysia(UTM)81310 JohorMalaysia Ingenium Research Group DepartmentUniversidad Castilla-La Mancha13071 Ciudad RealSpain Department of Electrical Engineering Graphic Era Deemed to be UniversityDehradun 248002India Instrumentation and Control Engineering Department Bharati Vidyapeeth’s College of EngineeringNew Delhi 110056India

Emerging sub-synchronous interactions(SSI)in wind-integrated power systems have added intense attention after numerous incidents in the US and China due to the involvement of series compensated transmission lines and power electronics *** phenomenon occurs when two power system elements exchange energy below the synchro-nous *** phenomenon related to wind power plants is one of the most significant challenges to main-taining stability,while SSI phenomenon in practical wind farms,which has been observed recently,has not yet been described on the source of conventional SSI *** paper first explains the traditional development of SSI and its classification as given by the IEEE,and then it proposes a classification of SSI according to the current research status,reviews several mitigation techniques and challenges,and discusses analysis techniques for *** paper also describes the effect of the active damping controllers,control scheme parameters,degree of series compensation,and various techniques used in wind power plants(WPPs).In particular,a supplementary damping controller with converter controllers in Doubly Fed Induction Generator based WPPs is briefly *** paper provides a real-istic viewpoint and a potential outlook for the readers to properly deal with SSI and its mitigation techniques,which can help power engineers for the planning,economical operation,and future expansion of sustainable development.

关键词： Damping controller Doubly fed induction generator(DFIG) Series compensations Sub-synchronous interactions(SSI) Sub-synchronous resonance(SSR)

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Deep Learning Based Classification of Wrist Cracks from X-ray Imaging

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Computers, Materials & Continua 2022年第10期73卷 1827-1844页

作者： Jahangir Jabbar Muzammil Hussain Hassaan malik Abdullah Gani Ali Haider Khan Muhammad Shiraz Department of Computer Science National College of Business Administration&Economics LahoreMultan60000Pakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Faculty of Computing and Informatics University of Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Department of Computer Science Federal Urdu University of ArtsScience and TechnologyIslamabadPakistan

Wrist cracks are the most common sort of cracks with an excessive occurrence *** the routine detection of wrist cracks,conventional radiography(X-ray medical imaging)is used but periodically issues are presented by crack *** cracks often appear in the human arbitrary bone due to accidental injuries such as ***,many hospitals lack experienced clinicians to diagnose wrist ***,an automated system is required to reduce the burden on clinicians and identify *** this study,we have designed a novel residual network-based convolutional neural network(CNN)for the crack detection of the *** the classification of wrist cracks medical imaging,the diagnostics accuracy of the RN-21CNN model is compared with four well-known transfer learning(TL)models such as Inception V3,Vgg16,ResNet-50,and Vgg19,to assist the medical imaging technologist in identifying the cracks that occur due to wrist *** RN-21CNN model achieved an accuracy of 0.97 which is much better than its competitor`s *** results reveal that implementing a correct generalization that a computer-aided recognition system precisely designed for the assistance of clinician would limit the number of incorrect diagnoses and also saves a lot of time.

关键词： Wrist cracks fracture deep learning X-rays CNN

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Aquila Optimization with Machine Learning-Based Anomaly Detection Technique in Cyber-Physical Systems

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Computer Systems Science & Engineering 2023年第8期46卷 2177-2194页

作者： A.Ramachandran K.Gayathri Ahmed Alkhayyat Rami Q.malik Department of Computer Science and Engineering University College of EngineeringPanruti607106India Department of Electronics and Communication Engineering University College of EngineeringPanruti607106India College of Technical Engineering The Islamic UniversityNajafIraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University CollegeBabylonIraq

Cyber-physical system(CPS)is a concept that integrates every computer-driven system interacting closely with its physical ***-of-things(IoT)is a union of devices and technologies that provide universal interconnection mechanisms between the physical and digital *** the complexity level of the CPS increases,an adversary attack becomes possible in several *** security is a vital aspect of the CPS *** to the massive surge in the data size,the design of anomaly detection techniques becomes a challenging issue,and domain-specific knowledge can be applied to resolve *** article develops an Aquila Optimizer with Parameter Tuned Machine Learning Based Anomaly Detection(AOPTML-AD)technique in the CPS *** presented AOPTML-AD model intends to recognize and detect abnormal behaviour in the CPS *** presented AOPTML-AD framework initially pre-processes the network data by converting them into a compatible ***,the improved Aquila optimization algorithm-based feature selection(IAOA-FS)algorithm is designed to choose an optimal feature *** with that,the chimp optimization algorithm(ChOA)with an adaptive neuro-fuzzy inference system(ANFIS)model can be employed to recognise anomalies in the CPS *** ChOA is applied for optimal adjusting of the membership function(MF)indulged in the ANFIS *** performance validation of the AOPTML-AD algorithm is carried out using the benchmark *** extensive comparative study reported the better performance of the AOPTML-AD technique compared to recent models,with an accuracy of 99.37%.

关键词： Machine learning industry 4.0 cyber-physical systems anomaly detection aquila optimizer

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Robust Image Watermarking Using LWT and Stochastic Gradient Firefly Algorithm

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Computers, Materials & Continua 2023年第4期75卷 393-407页

作者： Sachin Sharma Meena malik Chander Prabha Amal Al-Rasheed Mona Alduailij Sultan Almakdi Department of CSE Koneru Lakshmaiah Education FoundationGunturAndhra Pradesh522302India Deparmtent of CSE Chandigarh UniversityMohaliPunjab140413India Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran55461Saudi Arabia

Watermarking of digital images is required in diversified applicationsranging from medical imaging to commercial images used over the ***, the copyright information is embossed over the image in the form ofa logo at the corner or diagonal text in the background. However, this formof visible watermarking is not suitable for a large class of applications. In allsuch cases, a hidden watermark is embedded inside the original image as proofof ownership. A large number of techniques and algorithms are proposedby researchers for invisible watermarking. In this paper, we focus on issuesthat are critical for security aspects in the most common domains like digitalphotography copyrighting, online image stores, etc. The requirements of thisclass of application include robustness (resistance to attack), blindness (directextraction without original image), high embedding capacity, high Peak Signalto Noise Ratio (PSNR), and high Structural Similarity Matrix (SSIM). Mostof these requirements are conflicting, which means that an attempt to maximizeone requirement harms the other. In this paper, a blind type of imagewatermarking scheme is proposed using Lifting Wavelet Transform (LWT)as the baseline. Using this technique, custom binary watermarks in the formof a binary string can be embedded. Hu’s Invariant moments’ coefficientsare used as a key to extract the watermark. A Stochastic variant of theFirefly algorithm (FA) is used for the optimization of the technique. Undera prespecified size of embedding data, high PSNR and SSIM are obtainedusing the Stochastic Gradient variant of the Firefly technique. The simulationis done using Matrix Laboratory (MATLAB) tool and it is shown that theproposed technique outperforms the benchmark techniques of watermarkingconsidering PSNR and SSIM as quality metrics.

关键词： Image watermarking lifting wavelet transform discrete wavelet transform(DWT) firefly technique invariant moments

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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

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Computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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Modified Buffalo Optimization with Big Data Analytics Assisted Intrusion Detection Model

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Computer Systems Science & Engineering 2023年第8期46卷 1415-1429页

作者： R.Sheeba R.Sharmila Ahmed Alkhayyat Rami Q.malik Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Applications Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India College of Technical Engineering The Islamic UniversityNajafIraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University CollegeBabylonIraq

Lately,the Internet of Things(IoT)application requires millions of structured and unstructured data since it has numerous problems,such as data organization,production,and *** address these shortcomings,big data analytics is the most superior technology that has to be *** though big data and IoT could make human life more convenient,those benefits come at the expense of *** manage these kinds of threats,the intrusion detection system has been extensively applied to identify malicious network traffic,particularly once the preventive technique fails at the level of endpoint IoT *** cyberattacks targeting IoT have gradually become stealthy and more sophisticated,intrusion detection systems(IDS)must continually emerge to manage evolving security *** study devises Big Data Analytics with the Internet of Things Assisted Intrusion Detection using Modified Buffalo Optimization Algorithm with Deep Learning(IDMBOA-DL)*** the presented IDMBOA-DL model,the Hadoop MapReduce tool is exploited for managing big *** MBOA algorithm is applied to derive an optimal subset of features from picking an optimum set of feature ***,the sine cosine algorithm(SCA)with convolutional autoencoder(CAE)mechanism is utilized to recognize and classify the intrusions in the IoT network.A wide range of simulations was conducted to demonstrate the enhanced results of the IDMBOA-DL *** comparison outcomes emphasized the better performance of the IDMBOA-DL model over other approaches.

关键词： Big data analytics internet of things security intrusion detection deep learning

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