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A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

Computers, Materials & Continua 2021年第9期68卷 2877-2893页

作者： D.venugopal T.Jayasankar Mohamed Yacin Sikkandar Mohamed Ibrahim Waly Irina v.Pustokhina denis A.Pustokhin K.Shankar Department of ECE KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Data fusion is one of the challenging issues,the healthcare sector is facing in the recent *** diagnosis from digital imagery and treatment are deemed to be the right *** Haemorrhage(ICH),a condition characterized by injury of blood vessels in brain tissues,is one of the important reasons for *** generated by X-rays and Computed Tomography(CT)are widely used for estimating the size and location of *** use manual planimetry,a time-consuming process for segmenting CT scan *** Learning(DL)is the most preferred method to increase the efficiency of diagnosing *** this paper,the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning(DL)model,abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification,also known as *** proposed FFEDL-ICH model has four stages namely,preprocessing,image segmentation,feature extraction,and *** input image is first preprocessed using the Gaussian Filtering(GF)technique to remove ***,the Density-based Fuzzy C-Means(DFCM)algorithm is used to segment the ***,the Fusion-based Feature Extraction model is implemented with handcrafted feature(Local Binary Patterns)and deep features(Residual Network-152)to extract useful ***,Deep Neural Network(DNN)is implemented as a classification technique to differentiate multiple classes of *** researchers,in the current study,used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic *** findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its *** future researches,the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.

关键词： Intracerebral hemorrhage fusion model feature extraction deep features classification

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AModified Search and Rescue Optimization Based Node Localization Technique inWSN

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Computers, Materials & Continua 2022年第1期70卷 1229-1245页

作者： Suma Sira Jacob K.Muthumayil M.Kavitha Lijo Jacob varghese M.Ilayaraja Irina v.Pustokhina denis A.Pustokhin Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Wireless sensor network(WSN)is an emerging technology which find useful in several application areas such as healthcare,environmentalmonitoring,border surveillance,*** issues that exist in the designing of WSN are node localization,coverage,energy efficiency,security,and so *** spite of the issues,node localization is considered an important issue,which intends to calculate the coordinate points of unknown nodes with the assistance of *** efficiency of the WSN can be considerably influenced by the node localization ***,this paper presents a modified search and rescue optimization based node localization technique(MSRONLT)*** major aim of theMSRO-NLT technique is to determine the positioning of the unknown nodes in *** the traditional search and rescue optimization(SRO)algorithm suffers from the local optima problemwith an increase in number of iterations,MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the *** application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO *** order to validate the effective node localization performance of the MSRO-NLT algorithm,a set of simulations were performed to highlight the supremacy of the presented model.A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.

关键词： Node localization WSN chaotic map search and rescue optimization algorithm localization error

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Enhanced efficiency and stability of 3.3 v Cu-Li batteries by tuning the cation-anion interaction in the electrolyte

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Journal of Energy Chemistry 2023年第11期86卷 208-216,I0005页

作者： Kaiming Xue Yu Zhao Huimin Wang denis Y.W.Yu School of Energy and Environment City University of Hong KongKowloonHong Kong 999077China Research Center for Energy and Environmental Materials(GREEN) National Institute for Materials ScienceTsukubaIbaraki 305-0044Japan

Cu-Li battery with Cu metal cathode and Li metal anode is a candidate for next-generation energy storage *** self-discharge of the battery can be suppressed with an anion exchange membrane,the voltage polarization depends strongly on the ***,when an electrolyte with 3 M LiTFSI(lithium bis(trifluoromethanesulfonyl)imide)in dimethyl carbonate(DMC)is used,overpotential increases with *** this work,we reveal why the voltage polarization changes,and reduce and stabilize it by replacing DMC solvent with a mixed solvent composed of dimethoxyethane(DME)and propylene carbonate(PC).The new electrolyte has higher ionic conductivity and stable solvation structure with more free TFSI-anions upon cycling,which also facilitates uniform plating of metal ions on the metal *** characteristics enable a stable Cu-Li battery with minimal change in overpotential for more than 1500 cycles at a current density of 2 m A cm^(-2).

关键词： Cu-Li battery Metal cathode Electrolyte engineering Solvation structure

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AI Based Traffic Flow Prediction Model for Connected and Autonomous Electric vehicles

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Computers, Materials & Continua 2022年第2期70卷 3333-3347页

作者： P.Thamizhazhagan M.Sujatha S.Umadevi K.Priyadarshini velmurugan Subbiah Parvathy Irina v.Pustokhina denis A.Pustokhin Department of Electrical and Electronics Engineering University College of EngineeringPanruti607106India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationVijayawada522502India Centre for Nanoelectronics and vLSI Design SENSEVIT UniversityChennai CampusChennai600127India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential *** and Autonomous Electric vehicle(CAEv)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and ***,Traffic Flow Prediction(TFP)is identified as a major issue in CAEv technologies which needs to be addressed with the help of Deep Learning(DL)*** this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFP,abbreviated as AIPAE-TFP model in *** presented model involves two major processes namely,feature engineering and *** feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature *** addition to the above,a Support vector Data Description(SvDD)model is also used in the filtration of anomaly points and smoothen the raw ***,AIPAE model is applied to determine the predictive values of traffic *** order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct *** experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.

关键词： Autonomous electric vehicle traffic flow predictive automation industry connected vehicles seep learning

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An Optimal Deep Learning for Cooperative Intelligent Transportation System

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Computers, Materials & Continua 2022年第7期72卷 19-35页

作者： K.Lakshmi Srinivas Nagineni E.Laxmi Lydia A.Francis Saviour Devaraj Sachi Nandan Mohanty Irina v.Pustokhina denis A.Pustokhin Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyTelangana500075India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Engineering College of Engineering PunePuneMaharashtra411005India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related *** the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS *** the same time,prediction of short-term traffic flow is highly essential to manage the traffic *** to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow *** paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so *** presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow *** IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count *** addition,deep belief network(DBN)model is applied for the prediction of short-term traffic *** further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of *** results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

关键词： Cooperative intelligent transportation systems traffic flow prediction deep belief network deep learning vehicle counting

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Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

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Computers, Materials & Continua 2022年第11期73卷 2541-2554页

作者： K.vijaya Kumar E.Laxmi Lydia Ashit Kumar Dutta velmurugan Subbiah Parvathy Gobi Ramasamy Irina v.Pustokhina denis A.Pustokhin Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Christ UniversityBangalore560029India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of ManagementMoscow109542Russia

Recently,big data becomes evitable due to massive increase in the generation of data in real time ***,object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation,augmented reality,surveillance,*** paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN(AIA-IFRCNN)model in big data *** AIA-IFRCNN model annotates the images by Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR),named DCF-CSRT *** AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,which comprises region proposal network(RPN)and Fast *** addition,inception v2 model is applied as a shared convolution neural network(CNN)to generate the feature ***,softmax layer is applied to perform classification *** effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.

关键词： Object detection tracking convolutional neural network inception v2 image annotation

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Poly(Ionic Liquid) as an Anion Exchange Membrane for a 3.3 v Copper–Lithium Battery

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Energy & Environmental Materials 2023年第4期6卷 89-97页

作者： Kaiming Xue Yu Zhao Pui-Kit Lee denis Y.W.Yu School of Energy and Environment City University of Hong Kong83 Tat Chee AveKowloonHong KongChina

Metal–metal battery bears great potential for next-generation large-scale energy storage system because of its simple manufacture process and low production ***,the cross-over of metal cations from the cathode to the anode causes a loss in capacity and influences battery ***,a coating of poly(ionic liquid)(PIL)with poly(diallyldimethylammonium bis(trifluoromethanesulfonyl)imide)(PDADMA^(+)TFSI^(−))on a commercial polypropylene(PP)separator serves as an anion exchange membrane for a 3.3 v copper–lithium *** PIL has a positively charged polymer backbone that can block the migration of copper ions,thus improving Coulombic efficiency,long-term cycling stability and inhibiting self-discharge of the *** can also facilitate the conduction of anions through the membrane and reduce polarization,especially for fast charging/***-vincent method gives the transport number in the electrolyte to be 0.25 and 0.04 for PP separator without and with PIL coating,*** suggests that the PIL layer reduces the contribution of the internal current due to cation *** use of PIL as a coating layer for commercial PP separator is a cost-effective way to improve overall electrochemical performance of copper–lithium *** to PP and polyacrylic acid(PAA)/PP separators,the PIL/PP membrane raises the Coulombic efficiency to 99%and decreases the average discharge voltage drop to about 0.09 v when the current density is increased from 0.1 to 1 mA cm^(−2).

关键词： anion exchange membrane copper cathode cycle stability ion transport metal-metal battery poly(ionic liquid)

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Self-Dual Hadamard Bent Sequences

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Journal of Systems Science & Complexity 2023年第2期36卷 894-908页

作者： SHI Minjia LI Yaya CHENG Wei CRNKOvIC Dean KROTOv denis SOLéPatrick Key Laboratory of Intelligent Computing Signal Processing Ministry of EducationSchool of Mathematical SciencesAnhui UniversityHefei 230601China State Key Laboratory of Information Security(Institute of Information Engineering) Chinese Academy of SciencesBeijing 100093China. LTCI Télécom ParisParis91120 PalaiseauFrance Secure-IC S.A.S. 104 Bd du Montparnasse75014 Paris Faculty of Mathematics University of RijekaNovosibirsk 630090Russia Sobolev Institute of Mathematics Novosibirsk 630090Russia Aix Marseille Univ CNRSCentrale MarseilleI2MMarseilleFrance

A new notion of bent sequence related to Hadamard matrices was introduced recently,motivated by a security application(Solé,et al.,2021).The authors study the self-dual class in length at most *** authors use three competing methods of generation:Exhaustion,Linear Algebra and Gr?bner *** Hadamard matrices and Bush-type Hadamard matrices provide many *** authors conjecture that if v is an even perfect square,a self-dual bent sequence of length v always *** authors introduce the strong automorphism group of Hadamard matrices,which acts on their associated self-dual bent *** authors give an efficient algorithm to compute that group.

关键词： Bent sequences bush-type Hadamard matrices Hadanard matrices PUF functions regular Hadamard matrices

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3,6-bis(2,2,2-trinitroethylnitramino)-1,2,4,*** and energy abilities as a component of solid composite propellants

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Defence Technology（防务技术） 2022年第7期18卷 1148-1155页

作者： Anatoly G.Korepin Natalia M.Glushakova David B.Lempert Anatoly I.Kazakov Gennady v.Shilov denis v.Korchagin vadim M.volokhov Elena S.Amosova Sergey M.Aldoshin Institute of Problems of Chemical Physics Ac.Semenov Avenue 1Chernogolovka142432Russian Federation

The work addresses to the study of the molecular and crystal structure and properties of a new energyintensive compound 3,6-bis(2,2,2-trinitroethylnitramino)-1,2,4,5-tetrazine(NBTAT),first obtained by the authors in *** compound crystallizes in the monoclinic system,space group P2(1)/n,density at room temperature 1.939 g/cm^(3).The energies of crystal packing and pairwise intermolecular interactions in NBTAT and its unnitrated analogue BTAT were calculated,and their comparative analysis was carried *** enthalpy of formation of NBTAT molecules was calculated by quantum-chemical methods using Gaussian 09,and the enthalpy of formation of NBTAT in the solid phase(618 kJ/mol)was *** energy capabilities of NBTAT as an oxidizer of solid composite propellants are *** is shown that in metal-free compositions NBTAT is significantly superior to ammonium perchlorate(AP),dinitramide ammonium salt(ADN),HMX,BTAT at all stages of rocket systems,and is comparable to the superdense CL-20 yielding to the latter at the lower stages and slightly winning at the upper stages.

关键词： Nitrogen heterocycles Quantum chemistry Enthalpy of formation 3,6-bis(2,2,2-trinitroethylnitramino)-1,2,4,5-tetrazine Crystal structure Energetic ability

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Development of a methodology for assessing the adequacy of electric power systems

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Global Energy Interconnection 2022年第5期5卷 543-550页

作者： Dmitry S.Krupenev denis A.Boyarkin Dmitrii v.Iakubovskii Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences Lermontov str.130Irkutsk664033Russia

This study presents the results of a research into the developing a methodology for assessing the adequacy of advanced electric power systems characterized by the integration of various innovative technologies,which complicates their *** methodology development is aimed at solving two main problems:(1)increase the adequacy of modeling the processes that occur in the electric power system and (2)enhance the computational efficiency of the adequacy assessment *** study proposes a new mathematical model to minimize the power shortage and enhance the adequacy of modeling the *** model considers quadratic power transmission losses and network *** computational efficiency of the adequacy assessment methodology is enhanced using efficient random-number generators to form the calculated states of electric power systems and machine learning methods to assess power shortages and other reliability characteristics in the calculated states.

关键词： Electric power systems Adequacy Power shortage minimization Pseudo-and quasi-random number generation Machine learning methods.

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