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The Application of Hybrid Krill Herd Artificial Hummingbird Algorithm for Scientific Workflow Scheduling in Fog Computing

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Journal of Bionic engineering 2023年第5期20卷 2443-2464页

作者： Aveen Othman Abdalrahman Daniel Pilevarzadeh Shafi Ghafouri Ali Ghaffari Department of Civil Engineering Garmian UniversityBardasureKalarIraq Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabriz***Iran Computer Engineering Department Faculty of Engineering and Natural SciencesIstinye UniversityIstanbulTurkey

Fog Computing(FC)provides processing and storage resources at the edge of the Internet of Things(IoT).By doing so,FC can help reduce latency and improve reliability of IoT *** energy consumption of servers and computing resources is one of the factors that directly affect conservation costs in fog *** consumption can be reduced by efficacious scheduling methods so that tasks are offloaded on the best possible *** deal with this problem,a binary model based on the combination of the Krill Herd Algorithm(KHA)and the Artificial Hummingbird Algorithm(AHA)is introduced as Binary KHA-AHA(BAHA-KHA).KHA is used to improve ***,the BAHA-KHA local optimal problem for task scheduling in FC environments is solved using the dynamic voltage and frequency scaling(DVFS)*** Heterogeneous Earliest Finish Time(HEFT)method is used to discover the order of task flow *** goal of the BAHA-KHA model is to minimize the number of resources,the communication between dependent tasks,and reduce energy *** this paper,the FC environment is considered to address the workflow scheduling issue to reduce energy consumption and minimize makespan on fog *** results were tested on five different workflows(Montage,CyberShake,LIGO,SIPHT,and Epigenomics).The evaluations show that the BAHA-KHA model has the best performance in comparison with the AHA,KHA,PSO and GA *** BAHA-KHA model has reduced the makespan rate by about 18%and the energy consumption by about 24%in comparison with *** is a preview of subscription content,log in via an institution to check access.

关键词： Workflow Scheduling Fog Computing Internet of Things Hummingbird Algorithm Krill Algorithm

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A Low Resource Multi-lingual Simultaneous Script Identification and Text Recognition Model

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SN computer Science 2024年第6期5卷 740页

作者： Mukherjee, Jayati Roy, Utpal Computer Science and Engineering Academy of Technology Department of computer and system sciences Visva-bharati

In this paper, we have proposed a multi-task learning model for multi-lingual Optical Character Recognition. Our model does the script identification and text recognition simultaneously of offline machine printed documents. We have extracted the spatial and temporal features of a line image by the combination of several CNN and BLSTM layers. The feature is shared between the script identification and text recognition modules. Fully connected layer and softmax identify the script. The identified script works as a case selector for the text recognizer which is a CTC layer. Finally, the text is identified by the text recognizer. The model is applied to two public datasets: ISIDDI, RETAS containing Bengali degraded, and English pages. We have created a dataset of Devnagari/Hindi and Tamil scripts to test our model. The model has achieved 99.2% accuracy for script recognition. The achieved text recognition accuracy on the scripts Bengali, English, Hindi, and Tamil are respectively 91.68%, 97.07%, 95.68% and 92.27%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Multi-task learning Optical character recognition

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Diagnosis of Autism Spectrum Disorder by Imperialistic Competitive Algorithm and Logistic Regression Classifier

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computers, Materials & Continua 2023年第11期77卷 1515-1534页

作者： Shabana R.Ziyad Liyakathunisa Eman Aljohani I.A.Saeed Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj16274Saudi Arabia Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah41411Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj16274Saudi Arabia

Autism spectrum disorder(ASD),classified as a developmental disability,is now more common in children than ever.A drastic increase in the rate of autism spectrum disorder in children worldwide demands early detection of autism in *** can seek professional help for a better prognosis of the child’s therapy when ASD is diagnosed under five *** research study aims to develop an automated tool for diagnosing autism in *** computer-aided diagnosis tool for ASD detection is designed and developed by a novel methodology that includes data acquisition,feature selection,and classification *** most deterministic features are selected from the self-acquired dataset by novel feature selection methods before *** Imperialistic competitive algorithm(ICA)based on empires conquering colonies performs feature selection in this *** performance of Logistic Regression(LR),Decision tree,K-Nearest Neighbor(KNN),and Random Forest(RF)classifiers are experimentally studied in this research *** experimental results prove that the Logistic regression classifier exhibits the highest accuracy for the self-acquired *** ASD detection is evaluated experimentally with the Least Absolute Shrinkage and Selection Operator(LASSO)feature selection method and different *** Exploratory Data Analysis(EDA)phase has uncovered crucial facts about the data,like the correlation of the features in the dataset with the class variable.

关键词： Autism spectrum disorder feature selection imperialist competitive algorithm LASSO logistic regression random forest

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A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

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computers, Materials & Continua 2024年第10期81卷 471-501页

作者： Chandraumakantham Om Kumar Sudhakaran Gajendran Suguna Marappan Mohammed Zakariah Abdulaziz S.Almazyad School of Computer Science&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India School of Electronics&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.

关键词： Deep learning intrusion detection fuzzy rules feature selection false alarm rate accuracy wireless sensor networks

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GaitDONet: Gait Recognition Using Deep Features Optimization and Neural Network

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computers, Materials & Continua 2023年第6期75卷 5087-5103页

作者： Muhammad Attique Khan Awais Khan Majed Alhaisoni Abdullah Alqahtani Ammar Armghan Sara A.Althubiti Fayadh Alenezi Senghour Mey Yunyoung Nam Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakaka 72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of ICT Convergence Soonchunhyang UniversityKorea

Human gait recognition(HGR)is the process of identifying a sub-ject(human)based on their walking *** subject is a unique walking pattern and cannot be simulated by other ***,gait recognition is not easy and makes the system difficult if any object is carried by a subject,such as a bag or *** article proposes an automated architecture based on deep features optimization for *** our knowledge,it is the first architecture in which features are fused using multiset canonical correlation analysis(MCCA).In the proposed method,original video frames are processed for all 11 selected angles of the CASIA B dataset and utilized to train two fine-tuned deep learning models such as Squeezenet and *** transfer learning was used to train both fine-tuned models on selected angles,yielding two new targeted models that were later used for feature *** are extracted from the deep layer of both fine-tuned models and fused into one vector using *** improved manta ray foraging optimization algorithm is also proposed to select the best features from the fused feature matrix and classified using a narrow neural network *** experimental process was conducted on all 11 angles of the large multi-view gait dataset(CASIA B)dataset and obtained improved accuracy than the state-of-the-art ***,a detailed confidence interval based analysis also shows the effectiveness of the proposed architecture for HGR.

关键词： Human gait recognition biometric deep learning features fusion optimization neural network

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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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Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

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computers, Materials & Continua 2023年第5期75卷 4293-4315页

作者： Muhammad Hammad Waseem Malik Sajjad Ahmed Nadeem Ishtiaq Rasool Khan Seong-O-Shim Wajid Aziz Usman Habib Department of Computer Sciences and Information Technology University of Azad Jammu and KashmirMuzaffarabad13100Pakistan Department of Computer Science and AI College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah23890Saudi Arabia Department of AI&DS School of ComputingNational University of Computer and Emerging SciencesIslamabad44000Pakistan

Machine Learning(ML)-based prediction and classification systems employ data and learning algorithms to forecast target ***,improving predictive accuracy is a crucial step for informed *** the healthcare domain,data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or *** ML algorithms,Artificial Neural Networks(ANNs)are considered the most suitable framework for many classification *** network weights and the activation functions are the two crucial elements in the learning process of an *** weights affect the prediction ability and the convergence efficiency of the *** traditional settings,ANNs assign random weights to the *** research aims to develop a learning system for reliable cancer prediction by initializing more realistic weights computed using a supervised setting instead of random *** proposed learning system uses hybrid and traditional machine learning techniques such as Support Vector Machine(SVM),Linear Discriminant Analysis(LDA),Random Forest(RF),k-Nearest Neighbour(kNN),and ANN to achieve better accuracy in colon and breast cancer *** system computes the confusion matrix-based metrics for traditional and proposed *** proposed framework attains the highest accuracy of 89.24 percent using the colon cancer dataset and 72.20 percent using the breast cancer dataset,which outperforms the other *** results show that the proposed learning system has higher predictive accuracies than conventional classifiers for each dataset,overcoming previous research ***,the proposed framework is of use to predict and classify cancer patients ***,this will facilitate the effective management of cancer patients.

关键词： ANN decision support systems gene-expression data hybrid classification machine learning predictive analytics

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Feature Extraction From Single-Channel EEG Using Tsfresh and Stacked Ensemble Approach for Sleep Stage Classification

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International Journal of e-Collaboration 2023年第3期19卷 1-20页

作者： Radhakrishnan, B.L. Ezra, Kirubakaran Jebadurai, Immanuel Johnraja Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering GRACE College of Engineering Thoothukudi India

The smart world under Industry 4.0 is witnessing a notable spurt in sleep disorders and sleep-related issues in patients. Artificial intelligence and IoT are taking a giant leap in connecting sleep patients remotely with healthcare providers. The contemporary single-channel-based monitoring devices play a tremendous role in predicting sleep quality and related issues. Handcrafted feature extraction is a time-consuming job in machine learning-based automatic sleep classification. The proposed single-channel work uses Tsfresh to extract features from both the EEG channels (Pz-oz and Fpz-Cz) of the SEDFEx database individually to realise a single-channel EEG. The adopted mRMR feature selection approach selected 55 features from the extracted 787 features. A stacking ensemble classifier achieved 95%, 94%, 91%, and 88% accuracy using stratified 5-fold validation in 2, 3, 4, and 5 class classification employing healthy subjects data. The outcome of the experiments indicates that Tsfresh is an excellent tool to extract standard features from EEG signals. © 2023 IGI Global. All rights reserved.

关键词： Extraction

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Privacy-preserving explainable AI: a survey

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Science China(Information sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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Application of Physical Unclonable Function for Lightweight Authentication in Internet of Things

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computers, Materials & Continua 2023年第4期75卷 1901-1918页

作者： Ahmad O.Aseeri Sajjad Hussain Chauhdary Mohammed Saeed Alkatheiri Mohammed A.Alqarni Yu Zhuang Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science Texas Tech UniversityLubbockUSA

IoT devices rely on authentication mechanisms to render secure message *** data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted by IoT *** application of physical unclonable functions(PUFs)ensures secure data transmission among the internet of things(IoT)devices in a simplified network with an efficient time-stamped *** paper proposes a secure,lightweight,cost-efficient reinforcement machine learning framework(SLCR-MLF)to achieve decentralization and security,thus enabling scalability,data integrity,and optimized processing time in IoT *** has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device *** IoT network gathers information of interest from multiple cluster members selected by the proposed *** addition,the software-defined secured(SDS)technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT *** analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime,energy,secured data retrieval rate,and performance *** enabling the proposed framework,number of residual nodes is reduced to 16%,energy consumption is reduced by up to 50%,almost 30%improvement in data retrieval rate,and network lifetime is improved by up to 1000 msec.

关键词： Cyber-physical systems security data aggregation Internet of Things physical unclonable function swarm intelligences

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