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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Solomatin, A.N. Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Mathematical Methods of Regional Programming Moscow Russia

The concept of acceptable systems with a limited level of systemic pathologies and dysfunctions is considered. The main provisions of cognitive modeling are given and the tasks of single- and multi-criteria optimizati... 详细信息

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

关键词： Pathology

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Economic Analysis of the Situation Criminalization Production and Protection 15

Economic Analysis of the Situation Criminalization Productio...

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Troyanovskiy, V.M. Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Mathematical Methods of Regional Programming Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

The article analyzes criminal activity of an economic nature in case of penetration of crime into production and the system of protection against it. It is advisable to find out under what conditions it is unprofitable to steal, for which it is necessary to supplement legal studies with economic ones. The results of the analysis will help to find ways to rationally counteract crime. © 2022 IEEE.

关键词： Crime

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Pricing Policy with the Effect of Fairness Concern, Imprecise Greenness, and Prices in Imprecise Market for a Dual Channel

Pricing Policy with the Effect of Fairness Concern, Imprecis...

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International Conference on Nonlinear Applied Analysis and Optimization, ICNAAO-2021

作者： Ganguly, Sanchari Das, Pritha Maiti, Manoranjan Department of Mathematics Indian Institute of Engineering Science and Technology Shibpur WB Howrah711103 India Department of Applied Mathematics Oceanology and Computer Programming Vidyasagar University WB Midnapore721102 India

ISBN: (纸本)9789819905966

Fairness concern behavior, a well-known cognitive bias, refers to a person’s attitude of dissatisfaction for unequal pay-offs in someone’s favor. Against environmental pollution, many firms are focused on green manufacturing to maintain sustainable development. The adoption of online shopping by customers around the globe has altered the dynamics of competitiveness in the retail supply chain (SC). Considering the above facts, the effect of fairness concern on optimal prices and channel members’ profits in a two-level dual channel SC is investigated. Here, we consider an SC comprising a green manufacturer and a fair-minded retailer where the manufacturer produces and sells the green product through both offline and online channels to consumers. Due to uncertainty in the real world, the fuzziness is associated with market demand, price elasticity, and the coefficient of greenness. The fuzzy objectives and constraints are reduced to crisp ones using expectation and possibility measures, respectively. Both centralized and decentralized models (with and without cognitive bias) are formulated and solved by the Stackelberg game for the optimal prices and product greenness level. The models’ optimal solutions are analyzed and compared with the deterministic models numerically. The effects of the fairness concern coefficient on the optimal prices, product greenness level, and channel members’ profits are investigated. The sensitivity analyses are presented to study the effects of the fuzzy degree of customers’ sensitivity toward greenness on channel members’ profits. Retailer’s fairness is harmful to the manufacturer but beneficial for her own profit. Finally, some conclusions and managerial insights are presented. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Game theory

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Heterogeneous Ensemble Classifier in Medical Decision Support Systems

Heterogeneous Ensemble Classifier in Medical Decision Suppor...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Anatol y Povoroznyuk Oksana Povoroznyuk Anna Filatova Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

The work is devoted to solving the current scientific and technical problem of constructing a diagnostic decision support system in medicine based on a heterogeneous ensemble classifier model that implements two approaches to formulating a diagnostic conclusion: a probabilistic one based on the analysis of the training sample, and expert information on the structure of symptom complexes. The choice of prototype matching method as a probabilistic component is justified. Formalization of expert information on the structure of symptom complexes was carried out by representing symptom complexes of diseases with numerical intervals of linguistic variables. Options for taking into account expert assessments about the structure of symptom complexes in an ensemble classifier are considered. Test verification of the developed classifier was done on real medical data and confirmed the effectiveness of its work.

关键词： Decision support systems Training Analytical models Prototypes Linguistics Probabilistic logic Medical diagnostic imaging Diseases

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Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

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computer Systems Science & Engineering 2023年第7期46卷 883-896页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of System Programming South Ural State University454080ChelyabinskRussia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,*** world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi *** prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus ***:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)***:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),*** addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed *** achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base *** the other hand,a statistical analysis is performed to measure the significance of the proposed ***:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

关键词： COVID-19 prediction meta-heuristic optimization LSTM Al-Biruni earth radius algorithm

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Application-based spam detection with machine learning algorithms 27

Application-based spam detection with machine learning algor...

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27th International Conference on Information Society and University Studies, IVUS 2022

作者： Erbey, Ali Barışçı, Necaattin Uşak University Distance Education Vocational School Department of Computer Programming Uşak Turkey Gazi University Faculty of Technology Department of Computer Engineering Ankara Turkey

We investigated the process of unsupervised generative learning and the structure of informative generative representations of images of handwritten digits (MNIST dataset). Learning models with the architecture of sparse convolutional autoencoder with constraints to produce low-dimensional representations achieved successful generative learning demonstrated by high accuracy of generation of images. A well-defined, continuous and connected structure of generative representations was observed and described. Structured informative representations of unsupervised generative models can be an effective platform for investigation of origins of intelligent behaviors in artificial and biological learning systems. © 2022 Copyright for this paper by its authors.

关键词： Neural networks

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Customs Fraud Detection Method Based on Artificial Intelligence and Data Analytics 4

Customs Fraud Detection Method Based on Artificial Intellige...

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4th International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Gancheva, Veska Popov, George Nakov, Ognyan Raynova, Kamelia Popova, Antoaneta Technical University of Sofia Dept. of Programming and Computer Technologies Sofia Bulgaria Technical University of Sofia Dept. of Cybersecurity Sofia Bulgaria University of Sofia Dept. of Computer Systems Technical Sofia Bulgaria Technical University of Sofia Dept. of Information Technologies in Industry Sofia Bulgaria

ISBN: (纸本)9798350391183

Because of the increased market opening and massive data collection brought about by globalization, the need of maintaining control over customs procedures has increased. However, the integration and processing of customs data are complicated by its extreme imbalance. Finding autonomous, computationally sophisticated solutions for customs handling is therefore crucial. The study described in this paper aims to propose an artificial intelligence-based method for customs fraud detection through data analytics. A machine learning-based approach for detecting customs fraud is proposed. The research methodology includes data preprocessing, data analytics, combining results, and deriving precise solutions from results obtained. The aim is to extract meaningful information from a dataset. For the purpose of classifying customs fraud, seven algorithms for machine-learning-kNN, SGD, Constant, Logistic Regression, Random Forest, Neural Network, and Naive Bayes-Are chosen and empirically assessed. © 2024 IEEE.

关键词： Adversarial machine learning

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Application of Machine Learning Techniques for Software Anomaly Detection

Application of Machine Learning Techniques for Software Anom...

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International Conference on Applied Mathematics & computer Science (ICAMCS)

作者： Veska Gancheva Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria

A rising variety of platforms and software programs have leveraged repository-stored datasets and remote access in recent years. As a result, datasets are more vulnerable to malicious attacks. As a result, network security has grown in importance as a research topic. The usage of intrusion detection systems is a well-known strategy for safeguarding computer networks. This paper proposes an anomaly detection method that blends rule-based and machine-learning-based methods. In order to construct the appropriate rules, a genetic algorithm is utilized. Principal component analysis is used to extract the relevant features aimed to improve the performance. The suggested method is validated experimentally using the KDD Cup 1999 dataset, which meets the requirement of using appropriate data. The proposed method is applied to detect and analyze four types of attacks in a well-known benchmark dataset: Neptune, Ipsweep, Pod, and Teardrop, utilizing Support Vector Machine, Decision Tree, and Naive Bayes algorithms. After testing the characteristics specified in the training phase, the data is classified into attack categories and normal behavior during the machine learning phase.

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Workflow for Medical Data Classification and Analysis 6

Workflow for Medical Data Classification and Analysis

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6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022

作者： Gancheva, Veska Todorova, Violeta Technical University of Sofia Department of Programming and Computer Technologies Sofia Bulgaria Technical University of Sofia Department of Teoretical Electrotechnics Sofia Bulgaria

ISBN: (数字)9781665470131

ISBN: (纸本)9781665470131

An approach for automated knowledge extraction and decision-making from medical images through a workflow for preprocessing of incoming X-ray images, analysis, classification and evaluation of the results is presented in this paper. The designed algorithm for analysis of medical X-rays images is based on machine learning and consists of three main phases: preprocessing of training and validation datasets, medical images classification utilizing Logistic Regression, Naive Bayes, SVM methods, evaluation of the model. A workflow was developed to process and analyze datasets of lung X-ray images containing four classes, and determine classification accuracy by examining performance evaluation parameters. © 2022 IEEE.

关键词： Image analysis

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METHODS FOR IMPROVING THE QUALITY OF CLASSIFICATION ON IMBALANCED DATA 4

METHODS FOR IMPROVING THE QUALITY OF CLASSIFICATION ON IMBAL...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Gavrylenko, Svitlana Vladislav, Zozulia Khatsko, Nataliia Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine Department of Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

ISBN: (纸本)9798350395532

The subject of the study is methods of balancing raw data. The purpose of the article is to improve the quality of intrusion detection in computer networks by using class balancing methods. Task: to investigate methods of balancing classes and to develop a classification method on imbalanced data to increase the level of network security. The methods used are: methods of artificial intelligence, machine learning. The following results were obtained: Class balancing methods based on Undersampling, Oversampling and their combinations were studied. The following methods were chosen for further research: SMOTEENN, SVMSMOTE, BorderlineSMOTE, ADASYN, SMOTE, KMeansSMOTE. The UNSW-NB 15 set was used as the source data, which contains information about the normal functioning of the network and during intrusions. A decision tree based on the CART (Classification And Regression Tree) algorithm was used as the basic classifier. According to the research results, it was found that the use of the SMOTEENN method provides an opportunity to improve the quality of detection of intrusions in the functioning of the network. Conclusions. The scientific novelty of the obtained results lies in the complex use of data balancing methods and the method of data classification based on decision trees to detect intrusions into computer networks, which made it possible to reduce the number of Type II errors. © 2023 IEEE.

关键词： Classification (of information)

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