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Good error correcting output codes for adaptive multiclass learning

4th International Workshop on Multiple Classifier Systems, MCS 2003

作者： Tapia, Elizabeth González, José Carlos García-Villalba, Javier Department of Electrical Engineering National Univ. of Rosario Argentina Department of Telematics Engineering Technical Univ. of Madrid Spain Department of Computer Systems and Programming Complutense Univ. of Madrid Spain

ISBN: (纸本)3540403698

In recent work, we introduced a generalization of ECOC learning under the theory of recursive error correcting codes. We named it RECOC (Recursive ECOC) learning. If long output codewords are allowed, as in the case of problems involving a large number of classes, standard recursive codes such as LDPC or Turbo can be used. However, if the number of classes is moderated, neither good LDPC nor Turbo codes might exist due to the small block lengths involved. In this paper, RECOC learning based on the recently introduced ensemble of Product Accumulated codes is analyzed. Due to their native data storage oriented design, smaller block lengths are allowed. In addition, because of their simplicity, all key concepts regarding RECOC learning can be easily explained. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Digital storage

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

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computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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Unmanned vehicle-aided automated meter reading

Unmanned vehicle-aided automated meter reading

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6th International Conference on Broadband Communications and Biomedical Applications, IB2COM 2011

作者： Tuna, Gurkan Gungor, V. Cagri Gulez, Kayhan Trakya University Department of Computer Programming Edirne Turkey Bahçeşehir University Faculty of Engineering Department of Computer Engineering Istanbul Turkey Yildiz Technical University Electrical-Electronics Eng. Faculty Control and Automation Eng. Dept. Istanbul Turkey

ISBN: (纸本)9781467307680

This paper presents a novel approach of using unmanned vehicles for Automated Meter Reading (AMR) applications in rural areas where there are a few consumers scattered around a wide area. The proposed system does not require a fixed network infrastructure to transfer data to a central database, since data collection is carried out by unmanned vehicles. Compared to traditional meter reading systems, the use of unmanned vehicles for AMR brings several advantages, such as low cost operation, flexibility, and online system management. However, the realization of these potential gains directly depends on reliable communication capabilities of the deployed system and successful navigation of unmanned vehicles. Overall, in this paper, the design principles and challenges of using unmanned vehicles for AMR applications in rural areas have been presented. Also, the communication architecture of the proposed system has been explained and comparative simulation studies have been performed in terms of energy efficiency and navigation accuracy. © 2011 IEEE.

关键词： Wireless sensor networks

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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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Social engineering Attacks Detection Approach 13

Social Engineering Attacks Detection Approach

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13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Lysenko, Sergii Vorobiov, Volodymyr Bokhonko, Oleksandr Gaj, Piotr Savenko, Oleg Woloszyn, Jacek Khmelnitsky National University Department of Computer Engineering and Information Systems Khmelnitsky Ukraine Silesian University of Technology Gliwice Poland Khmelnitsky National University Department of Computer Engineering and System Programming Khmelnitsky Ukraine Kazimierz Pulaski University of Technology and Humanities in Radom Department of Informatics and Teleinformatics Radom Poland

ISBN: (纸本)9798350396119

The paper focuses on the problem of technical social engineering attacks that encompass the manipulation of individuals to reveal sensitive information, execute actions, or breach security systems. These exploits frequently capitalize on human psychology, trust, and a lack of vigilance to attain unauthorized entry to networks, systems, or data. In contrast to traditional social engineering tactics that center on psychological manipulation, technical social engineering attacks employ technological means and strategies to manipulate and beguile individuals. The paper presents an attempt to detect social engineering attacks. The approach utilized four machine learning algorithms (decision tree, random forest, K-nearest neighbor, and extreme gradient boosting). The analysis is centered on data collected from network hosts, which may serve as indicators of a potential social engineering attack. The empirical results demonstrated high detection accuracy. © 2023 IEEE.

关键词： Cybersecurity

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Modeling of Memory Dependent Derivative Under Three-Phase Lag in Generalized Thermo-Viscoelasticity

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International Journal of Applied and Computational Mathematics 2021年第6期7卷 1-20页

作者： Singh, Biswajit Sarkar, Smita Pal Barman, Krishnendu Department of Mathematics Indian Institute of Engineering Science and Technology Shibpur Howrah 711103 West Bengal India Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Paschim Medinipur 721102 West Bengal India

In this study, a new generalized model of thermo-viscoelasticity with three phase-lag (TPL) theory concerning memory-dependent derivative (MDD) theory is emphasized. The governing combined equations of the novel model associated with kernel function and time delay are considered in a two-dimensional semi-infinite space. The bounding surface of the medium is assumed to be free of traction and subjected to time-dependent thermal loading. Using Laplace and Fourier Transform techniques, the problem is transformed from the space–time domain and then solved numerically. Suitable numerical technique is used to find the inversion of Fourier and Laplace transforms. In the simulation, the effects of the time-delay parameter and kernel function on the distributions of the displacement components, stresses and temperature field are studied and represented graphically. The results shows that the presence of TPL, the time-delay and kernel function extensively affect all the distributions. © 2021, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Laplace and Fourier transform Memory dependent derivative Thermo-viscoelasticity Three phase-lag Vector matrix differential equation

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Approximation of the Objective Functional in a Partially Defined Optimization Problem 3

Approximation of the Objective Functional in a Partially Def...

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3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Kalinin, Yevhen Kuchuk, Nina Lysytsia, Dmytro National University of Life and Environmental Sciences of Ukraine Tractors Cars and Bioenergy Resources Department Kyiv Ukraine National Technical University «kharkiv Polytechnic Institute» Department of Computer Engineering and Programming Kharkiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

The reliable operation of today's complex technical systems often relies on remote software solutions for predicting their performance. To solve these tasks, client-server architectures, based on the database server, used to store the obtained data, as well as server-based data analyzers are taken advantage of. Due to the increasing complexity of tasks of diagnosing the state of complex technical systems, associated with the generation of higher volumes of data for assessing the current state of the diagnostic objects, the use of classical relational storage resulted in a significant increase in the requirements for cluster architectures. With the increasing use of technological solutions based on the 'Big Data' architecture, there is a contradiction associated with the need to store large amounts of unstructured data for further structuring by methods of database schema development. The mission of Data Mining is the implementation of search operations applied to the laws of behavior and overall functioning of the system under study, consisting of numerical data. An obligatory condition that arises when using Data Mining methodologies is to interpret the resulting patterns formed as a consequence of determining the practical utility of the data. In this regard, there is a need to develop a method for optimizing large amounts of data, especially in partially defined tasks, allowing the distribution of the readings of the state of complex technical systems. Thus, the paper considers the problem of objective function approximation by the linear functional in a partially defined optimization problem, when the objective function is partially defined by comparing its values on some set of vectors from the acceptance region. Necessary and sufficient conditions for the existence of the approximating linear functional and the method of recovery of its coefficients through a linear correction procedure are formulated. © 2022 IEEE.

关键词： Data mining

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Models and algorithms for determining the probability of occurrence of a critical combination of events for three-element minimum sections in intelligent decision support systems 4

Models and algorithms for determining the probability of occ...

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

作者： Kolomiitsev, Oleksii Osiievskyi, Serhii Zakharchenko, Iryna Rybalchenko, Alina Tretiak, Viacheslav Heiko, Hennadii Computer Engineering and Programming Department National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine Department of Mathematics and Software of ACS Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine

A method for determining the probability of occurrence of a critical combination of events for the three-element minimum cross-sections of attributes of the characteristics of the software quality model of intelligent... 详细信息

ISBN: (纸本)9798350395532

关键词： Decision support systems

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Sensing Metric-Based Hybrid Underlay/Overlay Transmission Framework for CR-IoT Networks for Smart City Applications 7

Sensing Metric-Based Hybrid Underlay/Overlay Transmission Fr...

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7th IET Smart Cities Symposium, SCS 2023

作者： Bala, Indu Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed School of Electrical and Electronics Engineering Lovely Professional University Punjab India Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results. © The Institution of engineering & Technology 2023.

关键词： Smart city

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