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A Vicenary Analysis of SARS-CoV-2 Genomes

computers, Materials & Continua 2021年第12期69卷 3477-3493页

作者： Sk Sarif Hassan Ranjeet Kumar Rout Kshira Sagar Sahoo Nz Jhanjhi Saiyed Umer Thamer A.Tabbakh Zahrah A.Almusaylim Department of Mathematics Pingla Thana MahavidyalayaPaschim Medinipur721140India Computer Science&Engineering National Institute of Technology SrinagarHazratbal190006J&KIndia Department of Computer Science and Engineering SRM UniversityAmaravatiAP522502India School of Computer Science and Engineering Taylor’s UniversitySubang Jaya47500Malaysia Department of Computer Science and Engineering Aliah UniversityKolkataIndia Materials Science Research Institute King Addulaziz City for Science and Technology(KACST)Riyad6086Kingdom of Saudi Arabia General Administration of Research and Development Laboratories King Abdulaziz City for Science and Technology(KACST)Riyad6086Kingdom of Saudi Arabia

Coronaviruses are responsible for various diseases ranging from the common cold to severe infections like the Middle East syndromes and the severe acute respiratory ***,a new coronavirus strain known as COVID-19 developed into a pandemic resulting in an ongoing global public health ***,there is a need to understand the genomic transformations that occur within this family of viruses in order to limit disease spread and develop new therapeutic *** nucleotide sequences of SARS-CoV-2 are consist of several *** bases can be classified into purines and pyrimidines according to their chemical *** include adenine(A)and guanine(G),while pyrimidines include cytosine(C)and tyrosine(T).There is a need to understand the spatial distribution of these bases on the nucleotide sequence to facilitate the development of antivirals(including neutralizing antibodies)and epitomes necessary for vaccine *** study aimed to evaluate all the purine and pyrimidine associations within the SARS-CoV-2 genome sequence by measuring mathematical parameters including;Shannon entropy,Hurst exponent,and the nucleotide guanine-cytosine *** Shannon entropy is used to identify closely associated *** Hurst exponent is used to identifying the auto-correlation of purine-pyrimidine bases even if their organization *** frequency patterns can be used to determine the distribution of all four proteins and the density of each *** GC-content is used to understand the stability of the *** relevant genome sequences were extracted from the National Center for Biotechnology Information(NCBI)virus ***,the phylogenetic properties of the COVID-19 virus were characterized to compare the closeness of the COVID-19 virus with other coronaviruses by evaluating the purine and pyrimidine distribution.

关键词： Fractal dimension shannon entropy hurst exponent GCcontent SARS-CoV-2

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Secure Remote Access IPSEC Virtual Private Network to University Network System

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Journal of computer science Research 2021年第1期3卷 16-27页

作者： Gajendra Sharma Department of Computer Science&Engineering Kathmandu UniversityDhulikhelKavre

With the popularity of the Internet and improvement of information technology,digital information sharing increasingly becomes the *** and More universities pay attention to the digital campus,and the construction of digital library has become the focus of digital campus.A set of manageable,authenticated and secure solutions are needed for remote access to make the campus network be a transit point for the outside *** Access IPSEC Virtual Private Network gives the solution of remote access to e-library resources,networks resources and so on very safely through a public *** establishes a safe and stable tunnel which encrypts the data passing through it with robust secured *** is to establish a virtual private network in Internet,so that the two long-distance network users can transmit data to each other in a dedicated network *** this technology,multi-network campus can communicate securely in the unreliable public internet.

关键词： IPSEC VPN Network Communication Data Encryption Integrity authentication Remote access University Security Server Client Peer

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Hydrogen peroxide-enhanced magnetic resonance imaging: A novel approach for diagnosing anorectal-fistula

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World Journal of Radiology 2025年第3期17卷 5-8页

作者： Riya Karmakar Devansh Gupta Arvind Mukundan Hsiang-Chen Wang Department of Mechanical Engineering National Chung Cheng UniversityChiayi 62102Taiwan Department of Computer Science&Engineering Thapar Institute of Engineering&TechnologyPatiala 147001PunjabIndia Department of Mechanical Engineering Center for Innovative Research on Aging SocietyNational Chung Cheng UniversityChiayi 621Taiwan

In this editorial,a commentary on the article by Chang et al has been provided,the course of treatment of anorectal fistulas,especially complex and recurring ones,require accurate diagnostic procedures for determining ideal surgical *** ways of imaging sometimes fall short,offering insufficient insights in aggravated *** this editorial,a novel application of hydrogen peroxide-enhanced magnetic resonance imaging(HP-MRI)that promises significant improvements in the imaging of anorectal *** is based on a retrospective investigation of 60 patients,contrasts the new HP-MRI with conventional diagnostic techniques such as physical examination,trans-perineal ultrasonography and poor spatial resolution *** findings demonstrate HP-MRI's incredible diagnostic performance,with sensitivity and specificity rates of 96.08%and 90.91%,respectively,and unparalleled interobserver agreement(Kappa values ranging from 0.80 to 0.89).It has been a significant advancement for assessment of anorectal fistulas providing a better roadmap for surgical planning,lowering recurrence rates as well as reduced personal and financial burden on patients by reducing the need for repeated treatment and extended hospital *** remaining funds can be utilized for treatment of other medical *** HP-MRI provides us a healthier&more efficient society by improvising patients well-being&optimized healthcare infrastructure.

关键词： Anorectal fistulas Magnetic resonance imaging Hydrogen peroxide Diagnostic imaging Fistula tract visualization Diagnostic accuracy Minimally invasive imaging Gadolinium contrast agent Retrospective analysis Perianal fistulas

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Improved DHOA-Fuzzy Based Load Scheduling in IoT Cloud Environment

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computers, Materials & Continua 2022年第5期71卷 4101-4114页

作者： R.Joshua Samuel Raj V.Ilango Prince Thomas V.R.Uma Fahd N.Al-Wesabi Radwa Marzouk Anwer Mustafa Hilal Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India Department of Computer Application CMR Institute of TechnologyBangalore560037India School of Computing Woldia Institute of TechnologyWoldia UniversityEthiopia Department of Commerce School of CommerceFinance and AccountancyChrist UniversityBangalore560029India Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Internet of things (IoT) has been significantly raised owing to thedevelopment of broadband access network, machine learning (ML), big dataanalytics (BDA), cloud computing (CC), and so on. The development of IoTtechnologies has resulted in a massive quantity of data due to the existenceof several people linking through distinct physical components, indicatingthe status of the CC environment. In the IoT, load scheduling is realistictechnique in distinct data center to guarantee the network suitability by fallingthe computer hardware and software catastrophe and with right utilize ofresource. The ideal load balancer improves many factors of Quality of Service(QoS) like resource performance, scalability, response time, error tolerance,and efficiency. The scholar is assumed as load scheduling a vital problem inIoT environment. There are many techniques accessible to load scheduling inIoT environments. With this motivation, this paper presents an improved deerhunting optimization algorithm with Type II fuzzy logic (IDHOA-T2F) modelfor load scheduling in IoT environment. The goal of the IDHOA-T2F is todiminish the energy utilization of integrated circuit of IoT node and enhancethe load scheduling in IoT environments. The IDHOA technique is derivedby integrating the concepts of Nelder Mead (NM) with the DHOA. Theproposed model also synthesized the T2L based on fuzzy logic (FL) systemsto counterbalance the load distribution. The proposed model finds usefulto improve the efficiency of IoT system. For validating the enhanced loadscheduling performance of the IDHOA-T2F technique, a series of simulationstake place to highlight the improved performance. The experimental outcomesdemonstrate the capable outcome of the IDHOA-T2F technique over therecent techniques.

关键词： Load scheduling internet of things cloud computing metaheuristics fuzzy logic

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LGNet:Local and global representation learning for fast biomedical image segmentation

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Journal of Innovative Optical Health sciences 2023年第4期16卷 29-39页

作者： Guoping Xu Xuan Zhang Wentao Liao Shangbin Chen Xinglong Wu School of Computer Science&Engineering Hubei Key Laboratory of Intelligent RobotWuhan Institute of TechnologyWuhanHubei 430205P.R.China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and TechnologyWuhanHubei 430074P.R.China

Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems,yet still faces many *** on convolutional neural networks(CNNs),medical image segmentation has achieved tremendous ***,owing to the locality of convolution operations,CNNs have the inherent limitation in learning global *** address the limitation in building global context relationship from CNNs,we propose LGNet,a semantic segmentation network aiming to learn local and global features for fast and accurate medical image segmentation in this ***,we employ a two-branch architecture consisting of convolution layers in one branch to learn local features and transformer layers in the other branch to learn global *** has two key insights:(1)We bridge two-branch to learn local and global features in an interactive way;(2)we present a novel multi-feature fusion model(MSFFM)to leverage the global contexture information from transformer and the local representational features from *** method achieves state-of-the-art trade-off in terms of accuracy and efficiency on several medical image segmentation benchmarks including Synapse,ACDC and ***,LGNet achieves the state-of-the-art performance with Dice's indexes of 80.15%on Synapse,of 91.70%on ACDC,and of 95.56%on ***,the inference speed attains at 172 frames per second with 224-224 input *** extensive experiments demonstrate the effectiveness of the proposed LGNet for fast and accurate for medical image segmentation.

关键词： CNNs transformers segmentation medical image contextual information

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Visualization Laboratory at University of Notre Dame

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Visual Informatics 2020年第3期4卷 63-68页

作者： Chaoli Wang Department of Computer Science&Engineering University of Notre DameNotre DameIN 46556United States

This article introduces the Visualization Laboratory at the Department of computer science&Engineering,the University of Notre Dame,including the lab’s overview,current research directions,facilities,and interna... 详细信息

关键词： directions collaboration facilities

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An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment

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computers, Materials & Continua 2021年第4期67卷 1191-1206页

作者： Ayesha Jabeen Sitara Afzal Muazzam Maqsood Irfan Mehmood Sadaf Yasmin Muhammad Tabish Niaz Yunyoung Nam Department of Computer Science COMSATS University IslamabadAttock CampusPakistan Department of Media Design and Technology Faculty of Engineering&InformaticsUniversity of BradfordBradfordBD71AZUK Department of Smart Device Engineering School of Intelligent Mechatronics EngineeringSejong UniversitySeoulKorea Department of Computer Science&Engineering Soonchunhyang UniversityAsan36538Korea

Stock market forecasting is an important research area,especially for better business decision *** stock predictions continue to be significant for business *** short-term stock market forecasting is usually based on historical market data analysis such as stock prices,moving averages,or daily ***,major events’news also contains significant information regarding market *** effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock *** research proposes an efficient model for stock market *** current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline,pharmaceutical,e-commerce,technology,and *** use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory(LSTM)model to improve stock *** LSTM has the advantage of analyzing relationship between time-series data through memory *** performance of the system is evaluated by Mean Absolute Error(MAE),Mean Squared Error(MSE),and Root Mean Squared Error(RMSE).The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model.

关键词： Business intelligence decision making stock prediction long short-term memory COVID-19 event sentiment

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Addressing Economic Dispatch Problem with Multiple Fuels Using Oscillatory Particle Swarm Optimization

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computers, Materials & Continua 2021年第12期69卷 2863-2882页

作者： Jagannath Paramguru Subrat Kumar Barik Ajit Kumar Barisal Gaurav Dhiman Rutvij HJhaveri Mohammed Alkahtani Mustufa Haider Abidi KIIT University BhubaneswarOdishaIndia Department of Electrical Engineering CETBhubaneswarOdishaIndia Department of Computer Science Government Bikram College of CommercePatialaIndia Department of Computer Science&Engineering Pandit Deendayal Petroleum UniversityGandhinagarIndia Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

Economic dispatch has a significant effect on optimal economical operation in the power systems in industrial revolution 4.0 in terms of considerable savings in *** non-linearity are added to make the fossil fuel-based power systems more *** order to achieve an accurate economical schedule,valve point loading effect,ramp rate constraints,and prohibited operating zones are being considered for realistic *** this paper,an improved,and modified version of conventional particle swarm optimization(PSO),called Oscillatory PSO(OPSO),is devised to provide a cheaper schedule with optimum *** conventional PSO is improved by deriving a mechanism enabling the particle towards the trajectories of oscillatory motion to acquire the entire search space.A set of differential equations is implemented to expose the condition for trajectory motion in *** adaptive inertia weights,this OPSO method provides an optimized cost of generation as compared to the conventional particle swarm optimization and other new meta-heuristic approaches.

关键词： Economic load dispatch valve point loading industry 4.0 prohibited operating zones ramp rate limit oscillatory particle swarm optimization

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Hybrid Deep Learning Architecture to Forecast Maximum Load Duration Using Time-of-Use Pricing Plans

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computers, Materials & Continua 2021年第7期68卷 283-301页

作者： Jinseok Kim Babar Shah Ki-Il Kim Department of KDN Electric power IT Research Institute KEPCO KDNNaju58217Korea College of Technological Innovation Zayed UniversityAbu DhabiUAE Department of Computer Science&Engineering Chungnam National UniversityDaejeon34134Korea

Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning ***,we need the adequate model to forecast the maximum load duration based on time-of-use,which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power ***,the existing single machine learning or deep learning forecasting cannot easily avoid ***,a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum load duration based on *** overcome these limitations,we propose a hybrid deep learning architecture to forecast maximum load duration based on *** results indicate that this architecture could achieve the highest average of recall and accuracy(83.43%)compared to benchmark *** verify the effectiveness of the architecture,another experimental result shows that energy storage system(ESS)scheme in accordance with the forecast results of the proposed model(LSTM-MATO)in the architecture could provide peak load cost savings of 17,535,700 KRW each year comparing with original peak load costs without the ***,the proposed architecture could be utilized for practical applications such as peak load reduction in the grid.

关键词： Load forecasting deep learning hybrid architecture maximum load duration time-of-use

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Analysis of Finger Vein Recognition using Deep Learning Techniques: Finger Vein Recognition 2022

Analysis of Finger Vein Recognition using Deep Learning Tech...

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2022 7th International Conference on Machine Learning Technologies (ICMLT)

作者： Gururaj H L Nydile M N Francesco Flammini Vidyashree K P Soundarya B C Computer Science&Engineering Vidyavardhaka College of Engineering India Information Science&Engineering Vidyavardhaka College of Engineering India IDSIA USI-SUPSI Department of Innovative Technologies University of Applied Sciences and Arts of Southern Switzerland Switzerland

ISBN: (纸本)9781450395748

In recent years, security has become increasingly important. Because of its resilience, consistent accuracy, and outstanding performance, the Finger Vein Authentication System has piqued our interest. Other kinds of identification, such as fingerprint and iris biometrics, are less reliable. Because finger veins are unique even for identical twins, exist beneath the skin, and remain intact throughout a person's lifetime, finger vein authentication eliminates the danger of alteration. The identification of finger vein patterns has improved significantly using a variety of deep learning approaches. The main goal of this paper is to show different processes of finger vein authentication, as well as the deep learning methods utilized to construct the Finger Vein Recognition system.

关键词： Deep Learning Security Biometrics Finger vein authentication

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