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Wide bandgap semiconductor-based integrated circuits

Chip 2023年第4期2卷 105-132页

作者： Saravanan Yuvaraja Vishal Khandelwal Xiao Tang Xiaohang Li Advanced Semiconductor Laboratory(ASL) Electrical and Computer Engineering ProgramComputerElectrical and Mathematical Science&Engineering(CEMSE)DivisionKing Abdullah University of Science andTechnology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia

Wide-bandgap semiconductors exhibit much larger energybandgaps than traditional semiconductors such as silicon,rendering them very promising to be applied in the fields of electronics and *** examples of semiconductors include SiC,GaN,ZnO,and diamond,which exhibitdistinctive characteristics such as elevated mobility and *** characteristics facilitate the operation of awide range of devices,including energy-efficient bipolar junctiontransistors(BJTs)and metal-oxide-semiconductor field-effecttransistors(MOSFETs),as well as high-frequency high-electronmobility transistors(HEMTs)and optoelectronic components suchas light-emitting diodes(LEDs)and *** semiconductorsare used in building integrated circuits(ICs)to facilitate theoperation of power electronics,computer devices,RF systems,andother optoelectronic *** breakthroughs includevarious applications such as imaging,optical communication,*** them,the field of power electronics has witnessedtremendous progress in recent years with the development of widebandgap(WBG)semiconductor devices,which is capable ofswitching large currents and voltages rapidly with low ***,it has been proven challenging to integrate these deviceswith silicon complementary metal oxide semiconductor(CMOS)logic circuits required for complex control *** monolithic integration of silicon CMOS with WBG devices increases thecomplexity of fabricating monolithically integrated smart integrated circuits(ICs).This review article proposes implementingCMOS logic directly on the WBG platform as a ***,achieving the CMOS functionalities with the adoption of WBGmaterials still remains a significant *** article summarizesthe research progress in the fabrication of integrated circuitsadopting various WBG materials ranging from SiC to diamond,with the goal of building future smart power ICs.

关键词： Wide bandgap semiconductors Integrated circuits Transistors Power electronics

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Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

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computers, Materials & Continua 2022年第4期71卷 1095-1109页

作者： R.Joshua Samuel Raj M.Varalatchoumy V.L.Helen Josephine A.Jegatheesan Seifedine Kadry Maytham N.Meqdad Yunyoung Nam Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India Department of Computer Science&Engineering Cambridge Institute of TechnologyBengaluru560036India Department of Computer Applications CMR Institute of TechnologyBengaluru560037India Department of Computer Science&Engineering Swarnandhra College of Engineering and TechnologyNarasapur534280India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Department of Computer Science and Engineering Soonchunhyang University31538Korea

Internet of Things (IoT) is transforming the technical setting ofconventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to theIoT enabled models are resource-limited and necessitate crisp responses, lowlatencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentionedchallenges. But the intrinsic high latency of CC makes it nonviable. The longerlatency degrades the outcome of IoT based smart systems. CC is an emergentdispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes theenergy utilization of the cloud infrastructure and rises the income of serviceproviders by the minimization of the processing time of the user job. Withthis motivation, this paper presents an intelligent Chaotic Artificial ImmuneOptimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabledcloud environment. The proposed CAIOA-RS algorithm solves the issue ofresource allocation in the IoT enabled cloud environment. It also satisfiesthe makespan by carrying out the optimum task scheduling process with thedistinct strategies of incoming tasks. The design of CAIOA-RS techniqueincorporates the concept of chaotic maps into the conventional AIOA toenhance its performance. A series of experiments were carried out on theCloudSim platform. The simulation results demonstrate that the CAIOA-RStechnique indicates that the proposed model outperforms the original version,as well as other heuristics and metaheuristics.

关键词： Internet of things cloud computing task scheduling metaheuristics resource allocation

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Modeling a Novel Hyper-Parameter Tuned Deep Learning Enabled Malaria Parasite Detection and Classification

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computers, Materials & Continua 2023年第12期77卷 3289-3304页

作者： Tamal Kumar Kundu Dinesh Kumar Anguraj S.V.Sudha Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationVaddeswaramAndhra PradeshIndia Department of Artificial Intelligence and Data Science KPR Institute of Engineering and TechnologyCoimbatoreTamil Nadu641407India

A theoretical methodology is suggested for finding the malaria parasites’presence with the help of an intelligent hyper-parameter tuned Deep Learning(DL)based malaria parasite detection and classification(HPTDL-MPDC)in the smear images of human peripheral *** existing approaches fail to predict the malaria parasitic features and reduce the prediction *** trained model initiated in the proposed system for classifying peripheral blood smear images into the non-parasite or parasite classes using the available online *** Adagrad optimizer is stacked with the suggested pre-trained Deep Neural Network(DNN)with the help of the contrastive divergence method to *** features are extracted from the images in the proposed system to train the DNN for initializing the visible *** smear images show the concatenated feature to be utilized as the feature vector in the proposed ***,hyper-parameters are used to fine-tune DNN to calculate the class labels’*** suggested system outperforms more modern methodologies with an accuracy of 91%,precision of 89%,recall of 93%and F1-score of 91%.The HPTDL-MPDC has the primary application in detecting the parasite of malaria in the smear images of human peripheral blood.

关键词： Malaria parasite classification hyper-parameter deep neural network the feature vector

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An Unsupervised Writer Identification Based on Generating Clusterable Embeddings

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computer Systems science & engineering 2023年第8期46卷 2059-2073页

作者： M.F.Mridha Zabir Mohammad Muhammad Mohsin Kabir Aklima Akter Lima Sujoy Chandra Das Md Rashedul Islam Yutaka Watanobe Department of Computer Science and Engineering American International University BangladeshDhaka1229Bangladesh Department of Computer Science&Engineering Bangladesh University of Business&TechnologyDhaka1216Bangladesh Department of Computer Science and Engineering University of Asia PacificDhaka1216Bangladesh Department of Computer Science and Engineering University of AizuAizu-Wakamatsu965-8580Japan

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification *** to its importance,numerous studies have been conducted in various *** have established several learning methods for writer identification including supervised and unsupervised ***,supervised methods require a large amount of annotation data,which is impossible in most *** the other hand,unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be *** paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features.A pairwise architecturebased Autoembedder was applied to generate clusterable embeddings for handwritten text ***,the trained baseline architecture generates the embedding of the data image,and the K-means algorithm is used to distinguish the embedding of individual *** proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification *** addition,traditional evaluation metrics are used in the proposed ***,the proposed model is compared with a few unsupervised models,and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data.

关键词： Writer identification pairwise architecture clusterable embeddings convolutional neural network

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Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

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computer Systems science & engineering 2023年第9期46卷 2667-2685页

作者： I.Mettildha Mary K.Karuppasamy Department of Information Technology Sri Ramakrishna Engineering CollegeCoimbatoreTamilnaduIndia Department of Computer Science&Engineering RVS College of Engineering and TechnologyCoimbatoreTamilnaduIndia

CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose ***(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance ***,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution ***(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between *** problem can be overcome by the use of Wrappers as they select better features by accounting for test and train *** aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between *** proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)*** methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.

关键词： Cloud analytics machine learning ensemble learning distributed learning clustering classification auto selection auto tuning decision feedback cloud DevOps feature selection wrapper feature selection Adaptive Kernel Firefly Algorithm(AKFA) Q learning

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Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

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computers, Materials & Continua 2024年第8期80卷 2557-2578页

作者： Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng Department of Computer Science&Engineering Ramaiah Institute of TechnologyBangalore560054India Department of Artificial Intelligence&Data Science Ramaiah Institute of TechnologyBangalore560054India School of Information Engineering Changchun Sci-Tech UniversityChangchun130600China Department of Computer Science and Information Engineering Chaoyang University of TechnologyTaichung41349Taiwan Department of Information Science and Engineering Malnad College of EngineeringHassan573202India

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

关键词： Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation

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Hybrid Color Texture Features Classification Through ANN for Melanoma

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Intelligent Automation & Soft Computing 2023年第2期35卷 2205-2218页

作者： Saleem Mustafa Arfan Jaffar Muhammad Waseem Iqbal Asma Abubakar Abdullah S.Alshahrani Ahmed Alghamdi Faculty of Computer Science&Information Technology Superior UniversityLahore54600Pakistan Department of Software Engineering Superior UniversityLahore54600Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of Jeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia

Melanoma is of the lethal and rare types of skin *** is curable at an initial stage and the patient can survive *** is very difﬁcult to screen all skin lesion patients due to costly *** are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders,pigment networks,and the color of *** challenges are required an automated system to classify the clinical features of melanoma and non-melanoma *** trained clinicians can overcome the issues such as low contrast,lesions varying in size,color,and the existence of several objects like hair,reﬂections,air bubbles,and oils on almost all *** contour is one of the suitable methods with some drawbacks for the segmentation of irre-gular *** entropy and morphology-based automated mask selection is pro-posed for the active contour *** proposed method can improve the overall segmentation along with the boundary of melanoma *** this study,features have been extracted to perform the classiﬁcation on different texture scales like Gray level co-occurrence matrix(GLCM)and Local binary pattern(LBP).When four different moments pull out in six different color spaces like HSV,Lin RGB,YIQ,YCbCr,XYZ,and CIE L*a*b then global information from different colors channels have been ***,hybrid fused texture features;such as local,color feature as global,shape features,and Artiﬁcial neural network(ANN)as classiﬁers have been proposed for the categorization of the malignant and *** had been carried out on datasets Dermis,DermQuest,and *** results of our advanced method showed super-iority and contrast with the existing state-of-the-art techniques.

关键词： Gray level co-occurrence matrix local binary pattern artiﬁcial neural networks support vector machines color skin cancer dermoscopic

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Combined Linear Multi-Model for Reliable Route Recommender in Next Generation Network

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Intelligent Automation & Soft Computing 2023年第4期36卷 39-56页

作者： S.Kalavathi R.Nedunchelian Department of Information Technology Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science&Engineering Excel Engineering CollegeKomarapalayamNamakkal638183India

Network analysis is a promisingﬁeld in the area of network applications as different types of trafﬁc grow enormously and *** route prediction is a challenging task in the Large Scale Networks(LSN).Various non-self-learning and self-learning approaches have been adopted to predict reliable *** protocols decide how to send all the packets from source to the destination addresses across the network through their *** the current era,dynamic protocols are preferred as they network self-learning internally using an algorithm and may not entail being updated physically more than the static protocols.A novel method named Reliable Route Prediction Model(RRPM)is proposed toﬁnd the best routes in the given hefty gage network to balance the load of the entire network to advance the network *** task is carried out in two *** theﬁrst phase,Network Embedding(NE)based node classiﬁcation is carried *** second phase involves the network analysis to predict the route of the *** experiment is carried out for average data transmission and rerouting time is measured between RRPM and Routing Information Protocol(RIP)protocol models with before and after failure *** was observed that average transmission time for RIP protocol has measured as 18.5 ms and RRPM protocol has measured as 18.2 *** the proposed RRPM model outperforms well than the traditional routeﬁnding protocols such as RIP and Open Shortest Path First(OSPF).

关键词： Network embedding node classiﬁcation link prediction routing protocols novel RRPM

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SRC:Superior Robustness of COVID-19 Detection from Noisy Cough Data Using GFCC

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computer Systems science & engineering 2023年第8期46卷 2337-2349页

作者： Basanta Kumar Swain Mohammad Zubair Khan Chiranji Lal Chowdhary Abdullah Alsaeedi Department of Computer Science&Engineering Government College of EngineeringKalahandiBhawanipatna766002India Department of Computer Science and Information Taibah UniversityMedina42353Saudi Arabia School of Information Technology and Engineering VIT University Vellore632014Tamil NaduIndia Computer Science Department College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia

This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients(GFCC)for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hunting optimization and artificial neural network(DHO-ANN).The noisy crowdsourced cough datasets were collected from the public *** research work claimed that the GFCC yielded better results in terms of COVID-19 detection as compared to the widely used Mel-frequency cepstral coefficient in noisy crowdsourced speech *** proposed algorithm's performance for detecting COVID-19 disease is rigorously validated using statistical measures,F1 score,confusion matrix,specificity,and sensitivity ***,it is found that the proposed algorithm using GFCC performs well in terms of detecting the COVID-19 disease from the noisy crowdsourced cough dataset,***,the proposed algorithm and undertaken feature parameters have improved the detection of COVID-19 by 5%compared to the existing methods.

关键词： COVID-19 GFCC DHO-ANN cough data

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Hybrid Image Compression-Encryption Scheme Based on Multilayer Stacked Autoencoder and Logistic Map

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China Communications 2022年第1期19卷 238-252页

作者： Neetu Gupta Ritu Vijay Computer Science&Engineering Department Banasthali VidyapithBanasthaliIndia Department of Electronics Engineering Banasthali VidyapithBanasthaliIndia.

Secure transmission of images over a communication channel, with limited data transfer capacity, possesses compression and encryption schemes. A deep learning based hybrid image compression-encryption scheme is proposed by combining stacked auto-encoder with the logistic map. The proposed structure of stacked autoencoder has seven multiple layers, and back propagation algorithm is intended to extend vector portrayal of information into lower vector space. The randomly generated key is used to set initial conditions and control parameters of logistic map. Subsequently, compressed image is encrypted by substituting and scrambling of pixel sequences using key stream sequences generated from logistic *** proposed algorithms are experimentally tested over five standard grayscale images. Compression and encryption efficiency of proposed algorithms are evaluated and analyzed based on peak signal to noise ratio(PSNR), mean square error(MSE), structural similarity index metrics(SSIM) and statistical,differential, entropy analysis respectively. Simulation results show that proposed algorithms provide high quality reconstructed images with excellent levels of security during transmission..

关键词： compression-encryption stacked autoencoder chaotic system back propagation algorithm logistic map

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