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A New Framework for Software Vulnerability Detection Based on an Advanced Computing

Computers, Materials & Continua 2024年第6期79卷 3699-3723页

作者： Bui Van Cong Cho Do Xuan Department of Information Technology University of Economics and Technical IndustriesHanoi100000Vietnam faculty of Information Security Posts and Telecommunications Institute of TechnologyHanoi100000Vietnam

The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest *** paper proposes a new framework called DrCSE to improve software vulnerability *** uses an intelligent computation technique based on the combination of two methods:Rebalancing data and representation learning to analyze and evaluate the code property graph(CPG)of the source code for detecting abnormal behavior of software *** do that,DrCSE performs a combination of 3 main processing techniques:(i)building the source code feature profiles,(ii)rebalancing data,and(iii)contrastive *** which,the method(i)extracts the source code’s features based on the vertices and edges of the *** method of rebalancing data has the function of supporting the training process by balancing the experimental ***,contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers *** experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum *** a result,the method proposed in the article has brought a pretty good performance in all metrics,especially the Precision and Recall scores of 39.35%and 69.07%,respectively,proving the efficiency of the DrCSE *** performs better than other approaches,with a 5%boost in Precision and a 5%boost in ***,this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.

关键词： Source code vulnerability source code vulnerability detection code property graph feature profile contrastive learning data rebalancing

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Quantitative measures for assessment of the hydraulic excavator digging efficiency

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Journal of Zhejiang university-Science A(Applied Physics & Engineering) 2012年第12期13卷 926-942页

作者： Dragoslav JANOSEVIC Rosen MITREV Boban ANDJELKOVIC Plamen PETROV faculty of Mechanical Engineering University of Nis faculty of Mechanical Engineering Technical University of Sofia

In this paper,quantitative measures for the assessment of the hydraulic excavator digging efficiency are proposed and *** following factors are considered:(a) boundary digging forces allowed for by the stability of an excavator,(b) boundary digging forces enabled by the driving mechanisms of the excavator,(c) factors taking into consideration the digging position in the working range of an excavator,and(d) sign and direction of potential digging resistive force.A corrected digging force is defined and a mathematical model of kinematic chain and drive mechanisms of a five-member excavator configuration was developed comprising:an undercarriage,a rotational platform and an attachment with boom,stick,and *** the basis of the mathematical model of the excavator,software was developed for computation and detailed analysis of the digging forces in the entire workspace of the *** using the developed software,the analysis of boundary digging forces is conducted and the corrected digging force is determined for two models of hydraulic excavators of the same mass(around 17 000 kg) with identical kinematic chain parameters but with different parameters of manipulator driving *** results of the analysis show that the proposed set of quantitative measures can be used for assessment of the digging efficiency of existing excavator models and to serve as an optimization criterion in the synthesis of manipulator driving mechanisms of new excavator models.

关键词： Hydraulic excavators Digging efficiency Quantitative measures

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CONTINUOUS DEPENDENCE OF THE SOLUTIONS OF IMPULSIVE DIFFERENTIAL EQUATIONS ON THE INITIAL CONDITIONS AND BARRIER CURVES

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Acta Mathematica Scientia 2012年第3期32卷 1035-1052页

作者： K.G.Dishlieva faculty of Applied Mathematics and Informatics Technical University of SofiaSofia1756Bulgaria

The basic objects of investigation in this article are nonlinear impulsive dif- ferential equations. The impulsive moments coincide with the moments of meeting of the integral curve and some of the so-called barrier curves. For such type of equations, suf- ficient conditions are found under which the solutions are continuously dependent on the perturbations with respect to the initial conditions and barrier curves. The results are applied to a mathematical model of population dynamics.

关键词： Impulsive differential equations initial condition barrier curves continuous dependence population dynamics

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Optimal control of natural resources in mining industry

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International Journal of Mining Science and Technology 2015年第2期25卷 193-198页

作者： Petrov Nikolay Tanev Angel Electrical Engineering Electronics and AutomationTechnical University-Sofia faculty of Mathematics and Informatics Sofia University

The paper focuses on the optimal control of natural resources in mining industry. The purpose is to pro- pose an optimal extraction series of these resources during the lifetime of the Mine＇s maintenance. Fol- lowing the proposed optimal control model, a sensitivity analysis has been performed that includes the interest rate impact on the optimal solution. This study shows that the increasing of the interest rate sti- mulates faster extraction of the resources. The discounting factor induces that the resource has to be extracted faster hut this effect is counterbalanced by the diminishing returns of the annual cash flow. At higher parameters of ＂alpha＂ close to one of the power function about 80% from the whole resource will be extracted during the first 4 years of object/mine maintenance. An existence of unique positive root with respect to return of investment has been proposed and proved by two ways： by the ＂method of chords＂ and by using specialized software.

关键词： Optimal control Natural resources Interest rate Sensitivity analysis Method of chords

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Impulsive efects on global stability of models based on impulsive diferential equations with “supremum” and variable impulsive perturbations

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Applied Mathematics and Mechanics(English Edition) 2014年第1期35卷 85-96页

作者： I.M.STAMOVA T.G.STAMOV Department of Mathematics The University of Texas at San Antonio Department of Engineering Design Faculty of Mechanical EngineeringTechnical University of Sofia

Sufficient conditions are investigated for the global stability of the solu tions to models based on nonlinear impulsive differential equations with ＂supremum＂ and variable impulsive perturbations. The main tools are the Lyapunov functions and Razu mikhin technique. Two illustrative examples are given to demonstrate the effectiveness of the obtained results.

关键词： global stability Lyapunov-Razumikhin function nonlinear impulsive dif-ferential equation with "supremum" variable impulsive perturbation

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Large power analysis of switched reluctance machine system for coal mine

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Mining Science and Technology 2009年第5期19卷 657-659页

作者： CHEN Hao PAVLITOV Constantin School of Chemical Engineering and Technology China University of Mining ＆ Technology Xuzhou Jiangsu 221116 China Department of Automation of Electrical Drives Faculty of Automation Technical University of Sofia Sofia Bulgaria

The conventional structures in the Switched Reluctance machines are introduced, such as three-phase 12/8 structure Switched Reluctance machine, three-phase 6/4 structure Switched Reluctance machine, four-phase 16/12 structure Switched Reluctance machine, and four-phase 8/6 structure Switched Reluctance machine. Three-phase 12/8 structure Switched Reluctance machine is the best choice for the large power Switched Reluctance machine system in coal mines. The asymmetric bridge power converter main circuit and the bifilar winding power converter main circuit are also introduced. Three-phase asymmetric bridge power converter main circuit is the best choice for the large power Switched Reluctance machine system in coal mines. The magnetic paths of the designed large power motor are given with one phase excitation and double phases excitation. The phase current waveforms are also given.

关键词： switched reluctance large power simulation

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Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition:The Possibilities of Next Generation 5G Data Networks

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Computers, Materials & Continua 2022年第6期71卷 4659-4676页

作者： Radek Martinek Jan Baros Rene Jaros Lukas Danys Jan Nedoma VSB-technical university of Ostrava Faculty of Electrical Engineering and Computer ScienceDepartment of Cybernetics and Biomedical Engineering70800Ostrava-PorubaCzechia VSB technical university of Ostrava Faculty of Electrical Engineering and Computer ScienceDepartment of Telecommunications70800Ostrava-PorubaCzechia

This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise *** vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART *** speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground *** article presents the new concept of a hybrid system which is implemented as a virtual *** highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise *** study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual *** average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.

关键词： 5G noise reduction hybrid algorithms speech recognition 5G data networks in-vehicle background noise

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Energy Scheduling for Island Microgrid Applications

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通讯和计算机（中英文版） 2016年第6期13卷 281-290页

作者： Galia Marinova Vassil Guliashki faculty of telecommunications at technical university of sofia Sofia 1000 Bulgaria Institute of lnformation and Communication Technologies -BAS Sofia 1113 Bulgaria

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Noise Reduction in Industry Based on Virtual Instrumentation

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Computers, Materials & Continua 2021年第10期69卷 1073-1096页

作者： Radek Martinek Rene Jaros Jan Baros Lukas Danys Aleksandra Kawala-Sterniuk Jan Nedoma Zdenek Machacek Jiri Koziorek faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical EngineeringVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia faculty of Electrical Engineering Automatic Control and InformaticsOpole University of TechnologyOpolePoland faculty of Electrical Engineering and Computer Science Department of TelecommunicationsVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia

This paper discusses the reduction of background noise in an industrial environment to extend *** the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G *** study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a *** experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from *** tested a hybrid algorithm for noise reduction and its impact on voice command recognition *** virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested *** welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+*** workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

关键词： 5G hybrid algorithms signal processing speech recognition

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