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Dragonfly Interaction Algorithm for Optimization of Queuing Delay in Industrial Wireless Networks

Journal of Bionic Engineering 2024年第1期21卷 447-485页

作者： Sanjay Bhardwaj Da-Hye Kim Dong-Seong Kim department of IT Convergence Engineering ICT Convergence Research CenterKumoh National Institute of TechnologyGumiSouth Korea

In industrial wireless networks,data transmitted from source to destination are highly *** often leads to the queuing of the data,and poor management of the queued data results in excessive delays,increased energy consumption,and packet ***,a nature-inspired-based Dragonfly Interaction Optimization Algorithm(DMOA)is proposed for optimization of the queue delay in industrial wireless *** term“interaction”herein used is the characterization of the“flying movement”of the dragonfly towards damselflies(female dragonflies)for *** a result,interaction is represented as the flow of transmitted data packets,or traffic,from the source to the base *** includes each and every feature of dragonfly movement as well as awareness of the rival dragonflies,predators,and damselflies for the desired optimization of the queue *** features are juxtaposed as noise and interference,which are further used in the calculation of industrial wireless metrics:latency,error rate(reliability),throughput,energy efficiency,and fairness for the optimization of the queue *** analysis,convergence analysis,the Wilcoxon test,the Friedman test,and the classical as well as the 2014 IEEE Congress of Evolutionary Computation(CEC)on the benchmark functions are also used for the evaluation of DMOA in terms of its robustness and *** results demonstrate the robustness of the proposed algorithm for both classical and benchmarking functions of the IEEE CEC ***,the accuracy and efficacy of DMOA were demonstrated by means of the convergence rate,Wilcoxon testing,and ***,fairness using Jain’s index in queue delay optimization in terms of throughput and latency,along with computational complexity,is also evaluated and compared with other *** results show that DMOA exceeds other bio-inspired optimization algorithms in terms of fairness in queue delay management and average packet *** proposed a

关键词： Dragonfly Damselfly Interaction Queuing delay Optimization Industrial wireless networks

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Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1729-1755页

作者： Awais Khan Chomyong Kim Jung-Yeon Kim Ahsan Aziz Yunyoung Nam department of ict Convergence Soonchunhyang UniversityAsan31538Korea ict Convergence Research Center Soonchunhyang UniversityAsan31538Korea Emotional and Intelligent Child Care Convergence Research Center Soonchunhyang UniversityAsan31538Korea

Sleep posture surveillance is crucial for patient comfort,yet current systems face difficulties in providing compre-hensive studies due to the obstruction caused by *** posture assessment remains challenging because of the complex nature of the human body and variations in sleep ***,this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification,thereby enhancing the analysis of body position and *** method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras,which depict six commonly adopted postures:supine,left log,right log,prone head,prone left,and prone *** study involves 10 participants under two conditions:with and without ***,the database is normalized into a video *** subsequent step entails training a fine-tuned,pretrained Visual Geometry Group(VGG16)and ResNet50 *** the third phase,the extracted features are utilized for *** fourth step of the proposed approach employs a serial fusion technique based on the normal distribution to merge the vectors derived from both the RGB and thermal ***,the fused vectors are passed to machine learning classifiers for final *** dataset,which includes human sleep postures used in this study’s experiments,achieved a 96.7%accuracy rate using the Quadratic Support Vector Machine(QSVM)without the ***,the Linear SVM,when utilized with a blanket,attained an accuracy of 96%.When normal distribution serial fusion was applied to the blanket features,it resulted in a remarkable average accuracy of 99%.

关键词： Human sleep posture VGG16 deep learning ResNet50 fusion machine learning

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Symmetric silicon microring resonator optical crossbar array for accelerated inference and training in deep learning

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Photonics Research 2024年第08期12卷 1681-1688页

作者： RUI TANG SHUHEI OHNO KEN TANIZAWA KAZUHIRO IKEDA MAKOTO OKANO KASIDIT TOPRASERTPONG SHINICHI TAKAGI MITSURU TAKENAKA department of Electrical Engineering and Information Systems The University of Tokyo Quantum ict Research Institute Tamagawa University National Institute of Advanced Industrial Science and Technology

Photonic integrated circuits are emerging as a promising platform for accelerating matrix multiplications in deep learning, leveraging the inherent parallel nature of light. Although various schemes have been proposed and demonstrated to realize such photonic matrix accelerators, the in situ training of artificial neural networks using photonic accelerators remains challenging due to the difficulty of direct on-chip backpropagation on a photonic chip. In this work, we propose a silicon microring resonator(MRR) optical crossbar array with a symmetric structure that allows for simple on-chip backpropagation, potentially enabling the acceleration of both the inference and training phases of deep learning. We demonstrate a 4 × 4 circuit on a Si-on-insulator platform and use it to perform inference tasks of a simple neural network for classifying iris flowers, achieving a classification accuracy of 93.3%. Subsequently, we train the neural network using simulated on-chip backpropagation and achieve an accuracy of 91.1% in the same inference task after training. Furthermore, we simulate a convolutional neural network for handwritten digit recognition, using a 9 × 9 MRR crossbar array to perform the convolution operations. This work contributes to the realization of compact and energy-efficient photonic accelerators for deep learning.

关键词： Deep learning Neural networks Optical signals Photonic integrated circuits Silicon Stochastic gradient descent

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MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

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Computers, Materials & Continua 2024年第3期78卷 3489-3510页

作者： Rashmi Sharma Ashok Pal Nitin Mittal Lalit Kumar Sreypov Van Yunyoung Nam Mohamed Abouhawwash department of Mathematics Chandigarh UniversityGharuanMohali140413India Deaprtment of Industry 4.0 Shri Vishwakarma Skill UniversityPalwal121102India department of ict Convergence Soonchunhyang UniversityAsan31538Korea department of Computational Mathematics Science and Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI 48824USA department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt

This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated *** uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective *** hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge *** evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been *** proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and *** primary objective of developing this hybrid approach has been achieved *** proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.

关键词： Multi-objective optimization genetic algorithm ant lion optimizer metaheuristic

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The User Analysis of Amazon Using Artificial Intelligence at Customer Churn

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Journal of Data Analysis and Information Processing 2024年第1期12卷 40-48页

作者： Mohammed Ali Alzahrani Information Technology department Computing and ICT Faculty Taif University Taif Saudi Arabia

Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full analysis of the data set. Data-set is taken from the Kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, Amazon at customer churn Support Vector Machine performed better as in overall comparison.

关键词： Customer Churn Machine Learning Amazon Fine Food Reviews Data Science Artificial Intelligence

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Modelling of a WDM Network Using Graph Theory and Dijkstra Algorithm for Traffic Redirection

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Journal of Computer and Communications 2024年第7期12卷 78-93页

作者： Eric Michel Deussom Djomadji Ebude Carine Awasume Eloundou Boris Donald department of Electrical and Electronic Engineering College of Technology University of Buea Buea Cameroon Division of ict National Advanced School of Post Telecommunication and ICT University of Yaounde I Yaounde Cameroon

Optical transport networks are now the basic infrastructure of modern communications systems, including the SDH and WDM backbone network of local network operators, in the case of Cameroon. Given the colossal investments required to deploy these networks, particularly related to the cost of equipment (optical fibers, transponders and multiplexers), the optimization of bandwidth and dynamic allocation of resources is essential to control operating costs and ensure continuity of service. Automatic switching technology for optical networks brings intelligence to the control plane to fully facilitate bandwidth utilization, traffic redirection, and automatic configuration of end-to-end services. This paper considers a local network operator’s WDM network without the implementation of the automatic switching technology, develops a network modeling software platform called Graphic Networks and using graph theory integrates a particularity of the automatic switching technology, which is the automatic rerouting of traffic in case of incident in the network. The incidents considered here are those links or route failures and node failures.

关键词： Graph Theory Backbone Network WDM Djikstra Algorithm

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

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

作者： Syed Konain Abbas Muhammad Usman Ghani Khan Jia Zhu Raheem Sarwar Naif RAljohani Ibrahim AHameed Muhammad Umair Hassan department of Computer Science and Engineering University of Engineering and TechnologyLahorePakistan Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal UniversityJinhuaChina OTHEM Manchester Metropolitan UniversityManchesterUK Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia department of ict and Natural Sciences Norwegian University of Science and Technology(NTNU)ÅlesundNorway

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

关键词： access control artificial intelligence computer vision intelligent control

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The effect of slime accumulated in a long-term operating UASB using crude glycerol to treat S-rich wastewater

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Journal of Environmental Sciences 2024年第1期135卷 353-366页

作者： Xudong Zhou Eva Fernández-Palacios Antoni DDorado Javier Lafuente Xavier Gamisans David Gabriel Key Laboratory of Urban Pollutant Conversion Institute of Urban EnvironmentChinese Academy of SciencesXiamen 361021China GENOCOV Research Group Department of ChemicalBiological and Environmental EngineeringEscola d’EnginyeriaUniversitat Autònoma de Barcelona08193 BellaterraSpain department of Mining Industrial and ICT EngineeringUniversitat Politècnica de CatalunyaAvinguda de les Bases de Manresa 61-7308242 ManresaSpain

An up-flow anaerobic sludge blanket(UASB)reactor targeting sulfate reduction was operated under a constant TOC/S-SO2-4ratio of 1.5±0.3 g C/g S for 639 days using crude glycerol as carbon source.A filamentous and fluffy flocculant material,namely slime-like substances(SLS),was gradually accumulated in the bioreactor after the cease of methanogenic *** accumulation of SLS was followed by a decrease in the removal efficiencies and a deterioration in the *** characteristics of SLS were investigated to explore the causes of its formation and the effect of SLS on the UASB *** showed that glycerol fermentation and sulfate reduction processes taking place in the reactor were mainly accomplished in the bottom part of the UASB reactor,as the sludge concentration in the bottom was *** accumulation of SLS in the UASB reactor caused sludge flotation that further led to biomass washout,which decreased the sulfate and glycerol removal *** activity tests performed with granular sludge(GS),slime-covered granular sludge(SCGS)and SLS showed that there was no difference between GS and SLS in the mechanism of glycerol fermentation and sulfate ***,the specific sulfate reduction rate of GS was higher than that of SLS,while SLS showed a higher glycerol fermentation rate than that of *** different rates in GS and SLS were attributed to the higher relative abundances of fermentative microorganisms found in SLS and higher relative abundances of sulfate reducing bacteria(SRB)found in GS.

关键词： Sludge flotation Granular sludge Slime-like substances Glycerol fermentation Sulfate reduction Removal efficiency

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Identifying Malaria Hotspots Regions in Ghana Using Bayesian Spatial and Spatiotemporal Models

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Infectious Diseases & Immunity 2024年第2期4卷 69-78页

作者： Abdul-Karim Iddrisu Dominic Otoo Gordon Hinneh Yakubu Dekongmene Kanyiri Kanimam Yaaba Samuel Cecilia Kubio Francis Balungnaa Dhari Veriegh department of Mathematics and Statistics University of Energy and Natural ResourcesSunyani 233Ghana department of Statistical Service Ghana Statistical ServiceBono RegionSunyani 233Ghana department of Mathematics and ict Gambaga College of EducationSunyani 233Ghana Human Resources Division University of Energy and Natural ResourcesSunyani 233Ghana department of Biological Science University of Energy and Natural ResourcesSunyani 233Ghana.

Background:Malaria remains a significant public health concern in Ghana,with varying risk levels across different geographical *** affects millions of people each year and imposes a substantial burden on the health care system and *** risk estimation and mapping are crucial for effectively allocating resources and implementing targeted interventions to identify regions with disease *** study aimed to identify regions exhibiting elevated malaria risk so that public health interventions can be implemented,and to identify malaria risk predictors that can be controlled as part of public health interventions for malaria ***:The data on laboratory-confirmed malaria cases from 2015 to 2021 were obtained from the Ghana Health Service and Ghana Statistical *** studied the spatial and spatiotemporal patterns of the relative risk of malaria using Bayesian spatial and spatiotemporal *** malaria risk for each region was mapped to visually identify regions with malaria *** and heterogeneity of disease risks were established using correlated and uncorrelated structures via the conditional autoregressive and Gaussian models,*** estimates from the marginal posterior distribution were estimated within the Integrated Nested Laplace Approximation using the R ***:The spatial model indicated an increased risk of malaria in the North East,Bono East,Ahafo,Central,Upper West,Brong Ahafo,Ashanti,and Eastern *** spatiotemporal model results highlighted an elevated malaria risk in the North East,Upper West,Upper East,Savannah,Bono East,Central,Bono,and Ahafo *** spatial and spatiotemporal models identified the North East,Upper West,Bono East,Central,and Ahafo Regions as hotspots for malaria *** variations in risk were evident across regions(H=104.9,P<0.001).Although climatic and economic factors influenced malaria infection,statistical significance was not

关键词： Malaria Disease hotspot Bayesian modeling Conditional auto-regressive Integrated Nested Laplace Approximation Spatial and spatiotemporal models

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Dynamic Signature Verification Using Pattern Recognition

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Journal of Software Engineering and Applications 2024年第5期17卷 214-227页

作者： Emmanuel Nwabueze Ekwonwune Duroha Austin Ekekwe Chinyere Iheakachi Ubochi Henry Chinedu Oleribe department of Computer Science Imo State University Owerri Nigeria department of Computer Science Gregory University Abia Nigeria ict department Alvan Ikoku University of Education Owerri Nigeria

Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.

关键词： Verification Security Biometrics Signature Authentication Model Pattern Recognition Dynamic

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