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Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning 认领 引用 被引量：1
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作者 Ily s Abdullaev Natalia Prodanova +3 位作者 KAruna Bhaskar ELaxmi Lydia Seifedine Kadry Jungeun Kim 《Computers, Materials & Continua》 SCIE EI 2023年第8期1463-1477,共15页
Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-... Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data center.Smart city benefitted from offloading to edge point.Consider a mobile edge computing(MEC)network in multiple regions.They comprise N MDs and many access points,in which everyMDhasM independent real-time tasks.This study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)algorithm.The proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system cost.In addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted resources.The TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading decision-making.Finally,the SGO algorithm is used for the parameter tuning of the DBN model.The simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967. 展开更多
关键词 Mobile edge computing seagull optimization deep belief network resource management parameter tuning
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D2LFS2Net:Multi-class skin lesion diagnosis using deep learning and variance-controlled Marine Predator optimisation:An application for precision medicine 认领 引用 被引量：3
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作者 Veena Dillshad Muhammad Attique Khan +3 位作者 Muhammad Nazir Oumaima Saidani Nazik Alturki Seifedine Kadry 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第1期207-222,共16页
In computer vision applications like surveillance and remote sensing,to mention a few,deep learning has had considerable success.Medical imaging still faces a number of difficulties,including intra-class similarity,a ... In computer vision applications like surveillance and remote sensing,to mention a few,deep learning has had considerable success.Medical imaging still faces a number of difficulties,including intra-class similarity,a scarcity of training data,and poor contrast skin lesions,notably in the case of skin cancer.An optimisation-aided deep learningbased system is proposed for accurate multi-class skin lesion identification.The sequential procedures of the proposed system start with preprocessing and end with categorisation.The preprocessing step is where a hybrid contrast enhancement technique is initially proposed for lesion identification with healthy regions.Instead of flipping and rotating data,the outputs from the middle phases of the hybrid enhanced technique are employed for data augmentation in the next step.Next,two pre-trained deep learning models,MobileNetV2 and NasNet Mobile,are trained using deep transfer learning on the upgraded enriched dataset.Later,a dual-threshold serial approach is employed to obtain and combine the features of both models.The next step was the variance-controlled Marine Predator methodology,which the authors proposed as a superior optimisation method.The top features from the fused feature vector are classified using machine learning classifiers.The experimental strategy provided enhanced accuracy of 94.4%using the publicly available dataset HAM10000.Additionally,the proposed framework is evaluated compared to current approaches,with remarkable results. 展开更多
关键词 contrast enhancement deep learning dermoscopic images features optimization fusion skin cancer
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Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images 认领 引用
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作者 Shaik Mahaboob Basha Victor Hugo Cde Albuquerque +3 位作者 Samia Allaoua Chelloug Mohamed Abd Elaziz Shaik Hashmitha Mohisin Suhail Parvaze Pathan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第2期1981-2004,共24页
Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image a... Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented. 展开更多
关键词 Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis
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Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading 认领 引用 被引量：1
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作者 Zhuoqun Xia Hangyu Hu +4 位作者 Wenjing Li Qisheng Jiang Lan Pu Yicong Shu Arun Kumar Sangaiah 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第7期409-430,共22页
Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional ... Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064. 展开更多
关键词 DDR dataset diabetic retinopathy lesion localization multi-level patch attention mechanism
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Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm 认领 引用
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作者 Brij Bhooshan Gupta Akshat Gaurav +3 位作者 Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui 《Computers, Materials & Continua》 SCIE EI 2024年第9期4895-4916,共22页
Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection methods.Traditional machine learning approaches to phishing detec... Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection methods.Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 attributes.The WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation process.The results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection. 展开更多
关键词 Phishing detection Recurrent Neural Network(RNN) Whale Optimization Algorithm(WOA) cybersecurity machine learning optimization
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Elite-guided equilibrium optimiser based on information enhancement:Algorithm and mobile edge computing applications 认领 引用
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作者 Zong-Shan Wang Shi-Jin Li +6 位作者 Hong-Wei Ding Gaurav Dhiman Peng Hou Ai-Shan Li Peng Hu Zhi-Jun Yang Jie Wang 《CAAI Transactions on Intelligence Technology》 SCIE EI 2024年第5期1126-1171,共46页
The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation problems.Balancing the paradox between exploration and exploitation operation... The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation problems.Balancing the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO research.To alleviate these limitations,an EO variant named adaptive elite-guided Equilibrium Optimiser(AEEO)is introduced.Specifically,the adaptive elite-guided search mechanism enhances the balance between exploration and exploitation.The modified mutualism phase reinforces the information interaction among particles and local optima avoidance.The cooperation of these two mechanisms boosts the overall performance of the basic EO.The AEEO is subjected to competitive experiments with state-of-the-art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test suite.Experimental results demonstrate that AEEO outperforms several well-performing EO variants,DE variants,PSO variants,SSA variants,and GWO variants in terms of convergence speed and accuracy.In addition,the AEEO algorithm is used for the edge server(ES)placement problem in mobile edge computing(MEC)environments.The experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost. 展开更多
关键词 ANT COLONY optimization CLOUD COMPUTING GENETIC ALGORITHMS SWARM intelligence
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Sand Cat Swarm Optimization with Deep Transfer Learning for Skin Cancer Classification 认领 引用
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作者 C.S.S.Anupama Saud Yonbawi +3 位作者 G.Jose Moses E.Laxmi Lydia Seifedine Kadry Jungeun Kim 《Computer Systems Science & Engineering》 SCIE EI 2023年第11期2079-2095,共17页
Skin cancer is one of the most dangerous cancer.Because of the high melanoma death rate,skin cancer is divided into non-melanoma and melanoma.The dermatologist finds it difficult to identify skin cancer from dermoscop... Skin cancer is one of the most dangerous cancer.Because of the high melanoma death rate,skin cancer is divided into non-melanoma and melanoma.The dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin lesions.Sometimes,pathology and biopsy examinations are required for cancer diagnosis.Earlier studies have formulated computer-based systems for detecting skin cancer from skin lesion images.With recent advancements in hardware and software technologies,deep learning(DL)has developed as a potential technique for feature learning.Therefore,this study develops a new sand cat swarm optimization with a deep transfer learning method for skin cancer detection and classification(SCSODTL-SCC)technique.The major intention of the SCSODTL-SCC model lies in the recognition and classification of different types of skin cancer on dermoscopic images.Primarily,Dull razor approach-related hair removal and median filtering-based noise elimination are performed.Moreover,the U2Net segmentation approach is employed for detecting infected lesion regions in dermoscopic images.Furthermore,the NASNetLarge-based feature extractor with a hybrid deep belief network(DBN)model is used for classification.Finally,the classification performance can be improved by the SCSO algorithm for the hyperparameter tuning process,showing the novelty of the work.The simulation values of the SCSODTL-SCC model are scrutinized on the benchmark skin lesion dataset.The comparative results assured that the SCSODTL-SCC model had shown maximum skin cancer classification performance in different measures. 展开更多
关键词 Deep learning skin cancer dermoscopic images sand cat swarm optimization machine learning
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Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis 认领 引用
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作者 Kwok Tai Chui Brij B.Gupta +1 位作者 Varsha Arya Miguel Torres-Ruiz 《Computers, Materials & Continua》 SCIE EI 2024年第1期1363-1379,共17页
The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation fo... The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains. 展开更多
关键词 Deep learning incremental learning machine fault diagnosis negative transfer transfer learning
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Spatial and Contextual Path Network for Image Inpainting 认领 引用
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作者 Dengyong Zhang Yuting Zhao +1 位作者 Feng Li Arun Kumar Sangaiah 《Intelligent Automation & Soft Computing》 2024年第2期115-133,共19页
Image inpainting is a kind of use known area of information technology to repair the loss or damage to the area.Image feature extraction is the core of image restoration.Getting enough space for information and a larg... Image inpainting is a kind of use known area of information technology to repair the loss or damage to the area.Image feature extraction is the core of image restoration.Getting enough space for information and a larger receptive field is very important to realize high-precision image inpainting.However,in the process of feature extraction,it is difficult to meet the two requirements of obtaining sufficient spatial information and large receptive fields at the same time.In order to obtain more spatial information and a larger receptive field at the same time,we put forward a kind of image restoration based on space path and context path network.For the space path,we stack three convolution layers for 1/8 of the figure,the figure retained the rich spatial details.For the context path,we use the global average pooling layer,where the accept field is the maximum of the backbone network,and the pooling module can provide global context information for the maximum accept field.In order to better integrate the features extracted from the spatial and contextual paths,we study the fusion module of the two paths.Features fusionmodule first path output of the space and context path,and then through themass normalization to balance the scale of the characteristics,finally the characteristics of the pool will be connected into a feature vector and calculate the weight vector.Features of images in order to extract context information,we add attention to the context path refinement module.Attention modules respectively from channel dimension and space dimension to weighted images,in order to obtain more effective information.Experiments show that our method is better than the existing technology in the quality and quantity of themethod,and further to expand our network to other inpainting networks,in order to achieve consistent performance improvements. 展开更多
关键词 Image inpainting attention deep learning convolutional network
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An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces 认领 引用 被引量：1
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作者 Sheetal Sharma Kamali Gupta +2 位作者 DeepaliGupta Shalli Rani Gaurav Dhiman 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第3期2029-2059,共31页
The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness... The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments.To address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed analysis.This analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things. 展开更多
关键词 Error fault detection techniques sensor faults outliers Internet of Things
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A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment 认领 引用 被引量：1
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作者 Salma Khan Muhammad Gulistan +2 位作者 NasreenKausar Seifedine Kadry Jungeun Kim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第2期1951-1979,共29页
Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathema... Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology. 展开更多
关键词 q−Rung orthopair fuzzy hypersoft set decision-making tourism carrying capacity aggregation operator
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CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification 认领 引用
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作者 Mehwish Zafar JaveriaAmin +3 位作者 Muhammad Sharif Muhammad Almas Anjum Seifedine Kadry Jungeun Kim 《Computers, Materials & Continua》 SCIE EI 2023年第9期2779-2793,共15页
Worldwide cotton is the most profitable cash crop.Each year the production of this crop suffers because of several diseases.At an early stage,computerized methods are used for disease detection that may reduce the los... Worldwide cotton is the most profitable cash crop.Each year the production of this crop suffers because of several diseases.At an early stage,computerized methods are used for disease detection that may reduce the loss in the production of cotton.Although several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex background.Due to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease classification.Therefore in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×1000.After that,the extracted features are serially concatenated having a feature vector lengthN×2000.Themost prominent features are selected usingEmperor PenguinOptimizer(EPO)method.The method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle cotton-leaf-infection-II.The EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,respectively.The classification is performed using 5,7,and 10 folds cross-validation.The Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II. 展开更多
关键词 Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN
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Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets 认领 引用
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作者 Murugan Palanikumar Nasreen Kausar +3 位作者 Dragan Pamucar Seifedine Kadry Chomyong Kim Yunyoung Nam 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第6期3353-3385,共33页
In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung n... In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed. 展开更多
关键词 Vague set aggregating operators euclidean distance hamming distance decision making
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A Novel Framework for Learning and Classifying the Imbalanced Multi-Label Data 认领 引用
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作者 P.K.A.Chitra S.Appavu alias Balamurugan +3 位作者 S.Geetha Seifedine Kadry Jungeun Kim Keejun Han 《Computer Systems Science & Engineering》 2024年第5期1367-1385,共19页
A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning.The main objective of this wor... A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning.The main objective of this work is to create a novel framework for learning and classifying imbalancedmulti-label data.This work proposes a framework of two phases.The imbalanced distribution of themulti-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase 1.Later,an adaptive weighted l21 norm regularized(Elastic-net)multilabel logistic regression is used to predict unseen samples in phase 2.The proposed Borderline MLSMOTE resampling method focuses on samples with concurrent high labels in contrast to conventional MLSMOTE.The minority labels in these samples are called difficult minority labels and are more prone to penalize classification performance.The concurrentmeasure is considered borderline,and labels associated with samples are regarded as borderline labels in the decision boundary.In phase II,a novel adaptive l21 norm regularized weighted multi-label logistic regression is used to handle balanced data with different weighted synthetic samples.Experimentation on various benchmark datasets shows the outperformance of the proposed method and its powerful predictive performances over existing conventional state-of-the-art multi-label methods. 展开更多
关键词 Multi-label imbalanced data multi-label learning Borderline MLSMOTE concurrent multi-label adaptive weighted multi-label elastic net difficult minority label
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Analyzing the barriers to blockchain adoption in supply chain finance using an integrated interval‑valued Fermatean fuzzy RAFSI model 认领 引用
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作者 Weizhong Wang Yu Chen +3 位作者 Yi Wang Muhammet Deveci Amer Al‑Hinai Seifedine Kadry 《Financial Innovation》 2025年第1期213-251,共39页
Many attempts have been made to identify barriers to blockchain adoption in supply chain;however,barriers to blockchain adoption in supply chain finance(SCF)are underexplored.This study prioritizes barriers to blockch... Many attempts have been made to identify barriers to blockchain adoption in supply chain;however,barriers to blockchain adoption in supply chain finance(SCF)are underexplored.This study prioritizes barriers to blockchain adoption in SCF and evaluates the barrier level of each alternative participant.We propose an integrated decision model to prioritize the barriers and evaluate their levels of alternative participants.To determine the barriers,we conducted a literature review.We then introduce an integrated weight calculation method by combining interval-valued Fermatean fuzzy(IVFF)-optimistic-pessimistic-utility values-based and IVFF-RS(ranking sum)methods to determine the barrier weights.To evaluate the barrier level of each alternative participant in SCF,the integrated IVFF-RAFSI(Ranking of Alternatives through Functional Mapping of Criterion Subintervals into a Single Interval)model is presented to rank the barrier,which uses a power-weighted aggregation operator to fuse experts’opinions.A case study demonstrates the practicality of the integrated IVFF-RAFSI model.The results show that uncertain and competitive markets(weighted at 0.0676)are the most significant barriers.This finding also suggests that small and medium-sized processing enterprises have the highest barriers to blockchain adoption.Sensitivity and comparative analyses validate the steadiness and competency of the proposed model.These results indicate that the proposed methodology provides a systematic technique for analyzing barriers to blockchain applications in SCF. 展开更多
关键词 Blockchain RAFSI model Supply chain finance Interval-valued Fermatean fuzzy
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Quantum-Resistant Cryptographic Primitives Using Modular Hash Learning Algorithms for Enhanced SCADA System Security 认领 引用
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作者 Sunil K.Singh Sudhakar Kumar +5 位作者 Manraj Singh Savita Gupta Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Brij B.Gupta 《Computers, Materials & Continua》 SCIE EI 2025年第8期3927-3941,共15页
As quantum computing continues to advance,traditional cryptographic methods are increasingly challenged,particularly when it comes to securing critical systems like Supervisory Control andData Acquisition(SCADA)system... As quantum computing continues to advance,traditional cryptographic methods are increasingly challenged,particularly when it comes to securing critical systems like Supervisory Control andData Acquisition(SCADA)systems.These systems are essential for monitoring and controlling industrial operations,making their security paramount.A key threat arises from Shor’s algorithm,a powerful quantum computing tool that can compromise current hash functions,leading to significant concerns about data integrity and confidentiality.To tackle these issues,this article introduces a novel Quantum-Resistant Hash Algorithm(QRHA)known as the Modular Hash Learning Algorithm(MHLA).This algorithm is meticulously crafted to withstand potential quantum attacks by incorporating advanced mathematical and algorithmic techniques,enhancing its overall security framework.Our research delves into the effectiveness ofMHLA in defending against both traditional and quantum-based threats,with a particular emphasis on its resilience to Shor’s algorithm.The findings from our study demonstrate that MHLA significantly enhances the security of SCADA systems in the context of quantum technology.By ensuring that sensitive data remains protected and confidential,MHLA not only fortifies individual systems but also contributes to the broader efforts of safeguarding industrial and infrastructure control systems against future quantumthreats.Our evaluation demonstrates that MHLA improves security by 38%against quantumattack simulations compared to traditional hash functionswhilemaintaining a computational efficiency ofO(m⋅n⋅k+v+n).The algorithm achieved a 98%success rate in detecting data tampering during integrity testing.These findings underline MHLA’s effectiveness in enhancing SCADA system security amidst evolving quantum technologies.This research represents a crucial step toward developing more secure cryptographic systems that can adapt to the rapidly changing technological landscape,ultimately ensuring the reliability and integrity of critical infrastructure in an era where quantum computing poses a growing risk. 展开更多
关键词 Hash functions post-quantum cryptography quantum-resistant hash functions network security supervisory control and data acquisition(SCADA)
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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study 认领 引用 被引量：2
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作者 Adel Got Djaafar Zouache +2 位作者 Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第1期409-425,共17页
Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning thes... Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification tasks.However,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good performance.On the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of datasets.In this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset simultaneously.The proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking datasets.Additionally,it is applied to a disease Covid-19 dataset.The experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem. 展开更多
关键词 Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer
DeepCNN:Spectro-temporal feature representation for speech emotion recognition 认领 引用 被引量：4
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作者 Nasir Saleem Jiechao Gao +4 位作者 Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第2期401-417,共17页
Speech emotion recognition(SER)is an important research problem in human-computer interaction systems.The representation and extraction of features are significant challenges in SER systems.Despite the promising resul... Speech emotion recognition(SER)is an important research problem in human-computer interaction systems.The representation and extraction of features are significant challenges in SER systems.Despite the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive fields.To mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based transformer.Two parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel CNNs.The learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification layer.This fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature map.The Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech emotions.With efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset respectively.The proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets. 展开更多
关键词 decision making deep learning
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Modified Elite Opposition-Based Artificial Hummingbird Algorithm for Designing FOPID Controlled Cruise Control System 认领 引用 被引量：2
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作者 Laith Abualigah Serdar Ekinci +1 位作者 Davut Izci Raed Abu Zitar 《Intelligent Automation & Soft Computing》 SCIE 2023年第11期169-183,共15页
Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-... Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-derivative(FOPID)controller that utilizes a modified elite opposition-based artificial hummingbird algorithm(m-AHA)for optimal parameter tuning.Our approach outperforms existing optimization techniques on benchmark functions,and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision.Our study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and reliability.We highlight the significance of our findings by demonstrating how our approach can improve the performance,safety,and reliability of autonomous vehicles.This study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe operation.Our research provides a promising avenue for further research and development in this area. 展开更多
关键词 Cruise control system FOPID controller artificial hummingbird algorithm elite opposition-based learning
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Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs 认领 引用
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作者 Mohamed Yacin Sikkandar K.Hemalatha +4 位作者 M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han 《Computer Systems Science & Engineering》 SCIE EI 2023年第10期873-889,共17页
Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its treatment.The researchers are making persistent efforts ... Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its treatment.The researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their areas.One of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)scans.At the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical imaging.Particularly,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest radiographs.While the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly CNN.This study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and classification.The major intention of the IFFO-DLEF model is to investigate the presence or absence of COVID-19.To do so,the presented IFFODLEF model applies image pre-processing at the initial stage.In addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature extraction.Moreover,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify COVID-19.The parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification performance.The experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches. 展开更多
关键词 COVID-19 computer vision deep learning image classification fusion model
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