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Finger Vein Authentication Based on Wavelet Scattering Networks

Computers, Materials & Continua 2022年第8期72卷 3369-3383页

作者： Amjad Rehman Majid Harouni Maedeh Omidiravesh Suliman Mohamed Fati Saeed Ali Bahaj Artificial intelligence&Data Analytics Lab CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering Dolatabad BranchIslamic Azad UniversityIsfahanIran Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityIsfahanIran MiS Department College of business administration Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia

Biometric-based authentication systems have attracted more attention than traditional authentication techniques such as passwords in the last two *** biometrics such as fingerprint,palm,iris,palm vein and finger vein and other biometrics have been *** of the challenges in biometrics is physical *** of finger vein is of the biometrics least exposed to physical *** methods have been proposed for authentication with the help of this biometric that suffer from weaknesses such as high computational complexity and low identification *** paper presents a novel method of scattering wavelet-based identity *** wavelet extracts image features from Gabor wavelet filters in a structure similar to convolutional neural *** distinguishes this algorithm from other popular feature extraction methods such as deep learning methods,filter-based methods,statistical methods,etc.,is that this algorithm has very high skill and accuracy in differentiating similar images but belongs to different classes,even when the image is subject to serious damage such as noise,angle changes or pixel location,this descriptor still generates feature vectors in away thatminimizes classifier *** improves classification and *** proposed method has been evaluated using two databases Finger Vein USM(FV-USM)and Homologous Multimodal biometrics Traits(SDUMLA-HMT).in addition to having reasonable computational complexity,it has recorded excellent identification rates in noise,rotation,and transmission *** best,it has a 98.2%identification rate for the SDUMLA-HMT database and a 96.1%identification rate for the FV-USM database.

关键词： Biometrics finger veins wavelet scattering vein authentication disaster risk reduction

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An Analysis of Supply Chain Environment in Great Wall Motor Based on Life-Cycle Assessment

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Proceedings of business and Economic Studies 2024年第1期7卷 27-44页

作者： Zhenghui Pan Jian Li Zhifang Wang Yang Sun Haixing Huang Xiaoshan Kang Anqin Ma Zhisheng Zhang Ji Qin Jiehong Zhong Strategic business Management Research Areas Doctor of Business AdministrationRussian University of TransportMoscowRussia ioT Technology and Application Areas Doctorate in Computer ScienceSan Antonio Catholic University of MurciaMurciaSpain AioT Laboratory IoT Intelligent Link Technology and Application AreaGreat Wall Motor Holdings GroupShanghaiChina

Great Wall Motor(GWM),a leading automotive manufacturer,places a strong emphasis on environmental sustainability and social *** company focuses on comprehensively evaluating and enhancing its supply chain to align with these *** evaluation spans the entire product life cycle,encompassing design,manufacturing,packaging,distribution,usage,and recycling and disposal *** areas of focus include optimizing raw material selection,improving product recyclability,reducing energy consumption and waste emissions,and minimizing carbon emissions during *** these endeavors,GWM not only enhances its environmental performance by reducing carbon emissions and resource consumption but also bolsters its brand image and competitiveness in the ***’s dedication to environmental innovation and technological leadership serves as a driving force behind sustainable development and social responsibility within the industry.

关键词： Environmental assessment Sustainability Resource conservation Carbon emissions Social responsibility

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ioMT Enabled Melanoma Detection Using improved Region Growing Lesion Boundary Extraction

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Computers, Materials & Continua 2022年第6期71卷 6219-6237页

作者： Tanzila Saba Rabia Javed Mohd Shafry Mohd Rahim Amjad Rehman Saeed Ali Bahaj Artificial intelligence&Data Analytics Lab CCiS Prince Sultan UniversityRiyadh11586Saudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaSkudai81310Johor BahruMalaysia Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan MiS Department College of business administration Prince Sattam bin Abdulaziz UniversityAlkharj11942Saudi Arabia

The internet ofMedical Things(ioMT)and cloud-based healthcare applications,services are beneficial for better decision-making in recent *** is a deadly cancer with a highermortality rate than other skin cancer types such as basal cell,squamous cell,andMerkel ***,detection and treatment at an early stage can result in a higher chance of *** classical methods of detection are expensive and ***,they rely on a trained practitioner’s level,and the availability of the needed equipment is essential for the early detection of *** current improvement in computer-aided systems is providing very encouraging results in terms of precision and *** this article,we propose an improved region growing technique for efficient extraction of the lesion *** analysis and detection ofMelanoma are helpful for the expert *** CNN features are extracted using the pre-trained VGG-19 deep learning *** the end,the selected features are classified by *** proposed technique is gauged on openly accessible two datasets iSiC 2017 and *** the evaluation of our proposed framework,qualitative and quantitative experiments are *** suggested segmentation method has provided encouraging statistical results of Jaccard index 0.94,accuracy 95.7%on iSiC 2017,and Jaccard index 0.91,accuracy 93.3%on the PH2 *** results are notably better than the results of prevalent methods available on the same *** machine learning SVMclassifier executes significantly well on the suggested feature vector,and the comparative analysis is carried out with existing methods in terms of *** proposed method detects and classifies melanoma far better than other ***,our framework gained promising results in both segmentation and classification phases.

关键词： Deep features extraction lesion segmentation melanoma detection SVM VGG-19 healthcare ioMT public health

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Evolutionary intelligence and Deep Learning Enabled Diabetic Retinopathy Classification Model

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Computers, Materials & Continua 2022年第10期73卷 87-101页

作者： Bassam A.Y.Alqaralleh Fahad Aldhaban Anas Abukaraki Esam A.Al.Qaralleh MiS Department College of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia Department of Computer Science Faculty of Information TechnologyAl-Hussein Bin Talal UniversityMa’an71111Jordan School of Engineering Princess Sumaya University for TechnologyAmman11941Jordan

Diabetic Retinopathy(DR)has become a widespread illness among diabetics across the *** fundus images are generally used by physicians to detect and classify the stages of *** manual examination of DR images is a time-consuming process with the risks of biased results,automated tools using Artificial intelligence(Ai)to diagnose the disease have become *** this view,the current study develops an Optimal Deep Learning-enabled Fusion-based Diabetic Retinopathy Detection and Classification(ODL-FDRDC)*** intention of the proposed ODLFDRDC technique is to identify DR and categorize its different grades using retinal fundus *** addition,ODL-FDRDC technique involves region growing segmentation technique to determine the infected ***,the fusion of two DL models namely,CapsNet and MobileNet is used for feature ***,the hyperparameter tuning of these models is also performed via Coyote Optimization Algorithm(COA).Gated Recurrent Unit(GRU)is also utilized to identify *** experimental results of the analysis,accomplished by ODL-FDRDC technique against benchmark DR dataset,established the supremacy of the technique over existing methodologies under different measures.

关键词： Optimization algorithms medical images diabetic retinopathy deep learning fusion model

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Managing Health Treatment by Optimizing Complex Lab-Developed Test Configurations: A Health informatics Perspective

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Computers, Materials & Continua 2023年第6期75卷 6251-6267页

作者： Uzma Afzal Tariq Mahmood Ali Mustafa Qamar Ayaz H.Khan Department of Computer Science Federal Urdu University of Arts Science and TechnologyKarachiPakistan Department of Computer Science Institute of Business AdministrationKarachiPakistan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Computer Engineering Department King Fahd University of Petroleum and MineralsDhahranSaudi Arabia SDAiA-KFUPM Joint Research Center for Artificial intelligence Dhahran31261Saudi Arabia

A complex Laboratory Developed Test(LDT)is a clinical test developed within a single *** is typically configured from many fea-ture constraints from clinical repositories,which are part of the existing Lab-oratory information Management System(LiMS).Although these clinical repositories are automated,support for managing patient information with test results of an LDT is also integrated within the existing ***,the support to configure LDTs design needs to be made available even in standard LiMS *** manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints between features can remain *** is a risky process and can lead patients to undergo unnecessary *** proposed an optimized solution(opt-LDT)based on Genetic Algorithms to automate the configuration and resolve the inconsistencies in ***-LDT encodes LDT configuration as an optimization problem and generates a consistent configuration that satisfies the constraints of the *** tested and validated opt-LDT for a local secondary care hospital in a real healthcare *** results,averaged over ten runs,show that opt-LDT resolves 90%of inconsistencies while taking between 6 and 6.5 s for each ***,positive feedback based on a subjective questionnaire from clinicians regarding the performance,acceptability,and efficiency of opt-LDT motivates us to present our results for regulatory approval.

关键词： Artificial intelligence health informatics evolutionary algorithms genetic algorithms feature selection laboratory developed test

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Teamwork Optimization with Deep Learning Based Fall Detection for ioT-Enabled Smart Healthcare System

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Computers, Materials & Continua 2023年第4期75卷 1353-1369页

作者： Sarah B.Basahel Saleh Bajaba Mohammad Yamin Sachi Nandan Mohanty E.Laxmi Lydia Department of Management information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddahSaudi Arabia Department of business administration Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Management information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia School of Computer Science&Engineering(SCOPE) VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajam 532127India

The current advancement in cloud computing,Artificial intelligence(Ai),and the internet of Things(ioT)transformed the traditional healthcare system into smart *** services could be enhanced by incorporating key techniques like Ai and *** convergence of Ai and ioT provides distinct opportunities in the medical *** is regarded as a primary cause of death or post-traumatic complication for the ageing ***,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary ***,the emergence of ioT,Ai,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home *** article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for ioT Enabled Smart Healthcare Systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare ***,the presented TWODL-FDSHS technique exploits ioT devices for the data collection ***,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature *** last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS *** experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.

关键词： internet of things smart healthcare deep learning team work optimizer capsnet fall detection

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Multi-objective coordination optimal model for new power intelligence center based on hybrid algorithm

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Journal of Central South University 2009年第4期16卷 683-689页

作者：刘吉成牛东晓乞建勋 School of business administration North China Electric Power University

in order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PiC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PiC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPiC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of iEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPiC can effectively process the coordination and optimization problem of power network.

关键词：混合算法优化模型多目标基础情报协调

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Dynamic individual Selection and Crossover Boosted Forensic-based investigation Algorithm for Global Optimization and Feature Selection

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Journal of Bionic Engineering 2023年第5期20卷 2416-2442页

作者： Hanyu Hu Weifeng Shan Jun Chen Lili Xing Ali Asghar Heidari Huiling Chen Xinxin He Maofa Wang School of Emergency Management Institute of Disaster PreventionSanhe065201China Earthquake administration of Anhui Province Hefei230031China Department of Computer Science and Artificial intelligence Wenzhou UniversityWenzhou325035China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China

The advent of Big Data has rendered Machine Learning tasks more intricate as they frequently involve higher-dimensional *** Selection(FS)methods can abate the complexity of the data and enhance the accuracy,generalizability,and interpretability of ***-heuristic algorithms are often utilized for FS tasks due to their low requirements and efficient *** paper introduces an augmented Forensic-Based investigation algorithm(DCFBi)that incorporates a Dynamic individual Selection(DiS)and crisscross(CC)mechanism to improve the pursuit phase of the ***,a binary version of DCFBi(BDCFBi)is applied to *** conducted on iEEE CEC 2017 with other metaheuristics demonstrate that DCFBi surpasses them in search *** influence of different mechanisms on the original FBi is analyzed on benchmark functions,while its scalability is verified by comparing it with the original FBi on benchmarks with varied *** is then applied to 18 real datasets from the UCi machine learning database and the Wieslaw dataset to select near-optimal features,which are then compared with six renowned binary *** results show that BDCFBi can be more competitive than similar methods and acquire a subset of features with superior classification accuracy.

关键词： Feature selection Forensic-based investigation algorithm Crisscross mechanism Global optimization Metaheuristic algorithms Bionic algorithm

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SCHEDULiNG iN THE SERViCE iNDUSTRiES:AN OVERViEW

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Journal of Systems Science and Systems Engineering 2015年第1期24卷 1-48页

作者： Michael Pinedo Christos Zacharias Ning Zhu Stern business School New York University School of business administration University of Miami College of Economics and Management Tianjin University

Scheduling plays an important role in many different service industries. in this paper we provide an overview of some of the more important scheduling problems that appear in the various service industries. We focus on the formulations of such problems as well as on the techniques used for solving those problems. We consider five areas of scheduling in service industries, namely （i） project scheduling, （ii） workforce scheduling, （iii） timetabling, reservations, and appointments, （iv） transportation scheduling, and （v） scheduling in entertainment. The first two areas are fairly general and have applications in many different service industries. The third, fourth and fifth areas are more related to some very specific service industries, namely the hospitality and health care industries, the transportation industries （of passengers as well as of cargo）, and the entertainment industries. in our conclusion section we discuss the similarities and the differences between the problem formulations and solution techniques used in the various different industries and we also discuss the design of the decision support systems that have been developed for scheduling in the service industries.

关键词： Artificial intelligence Decision support systems Problem solving

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Multi-strategies Boosted Mutative Crow Search Algorithm for Global Tasks:Cases of Continuous and Discrete Optimization

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Journal of Bionic Engineering 2022年第6期19卷 1830-1849页

作者： Weifeng Shan Hanyu Hu Zhennao Cai Huiling Chen Haijun Liu Maofa Wang Yuntian Teng School of Emergency Management Institute of Disaster PreventionLangfang065201China institute of Geophysics China Earthquake AdministrationBeijing100081China Department of Computer Science and Artificial intelligence Wenzhou UniversityWenzhou325035China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China

Crow Search Algorithm(CSA)is a swarm-based single-objective optimizer proposed in recent years,which tried to inspire the behavior of crows that hide foods in different locations and retrieve them when *** original version of the CSA has simple parameters and moderate ***,it often tends to converge slowly or get stuck in a locally optimal region due to a missed harmonizing strategy during the exploitation and exploration ***,strategies of mutation and crisscross are combined into CSA(CCMSCSA)in this paper to improve the performance and provide an efficient optimizer for various optimization *** verify the superiority of CCMSCSA,a set of comparisons has been performed reasonably with some well-established metaheuristics and advanced metaheuristics on 15 benchmark *** experimental results expose and verify that the proposed CCMSCSA has meaningfully improved the convergence speed and the ability to jump out of the local *** addition,the scalability of CCMSCSA is analyzed,and the algorithm is applied to several engineering problems in a constrained space and feature selection *** results show that the scalability of CCMSCSA has been significantly improved and can find better solutions than its competitors when dealing with combinatorial optimization *** proposed CCMSCSA performs well in almost all experimental ***,we hope the researchers can see it as an effective method for solving constrained and unconstrained optimization problems.

关键词： Crow search algorithm Feature selection Global optimization Metaheuristic algorithms Engineering problems Bionic algorithm

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