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5th International Conference on Advances in Computational Tools for Engineering Applications, ACTEA 2023

作者： Hamad, Martin Abou El Hajj, Antonio Daou, Roy Abi Zeid Hayek, Ali Haidar, Gaby Abou Lebanese German University Faculty of Public Health Biomedical Technologies Department Lebanon MART Learning Education and Research Center Lebanon University of Kassel Department of Computer Architecture and System Programming Kassel Germany American University of Science and Technology Engineering Faculty Zahle Lebanon

ISBN: (纸本)9798350309959

For several years, traffic congestion has been a major problem in big cities where the number of cars and different means of transportation has been increasing significantly. The problem of congestion is becoming more and more critical, and if not treated smartly this issue will negatively affect drivers by wasting time and fuel gas while waiting for hours in lanes. This paper presents a new and smart way to mitigate this issue in an affordable cost, minimum processing power, and low power consumption. This concept takes into consideration the majority of the cases that may cause congestion and presents a smart and accurate outputs to ease traffic flow leading to the prediction of the peak hours of traffic congestion for smarter control. A model is designed to study the case of a four lanes crossroad with two traffic lights and two LCD monitors. The strategy in reading data is divided into two parts: real data from sensors and pre collected data from google maps to create a kind of a predicted pattern over a certain time interval. The responsiveness of the system is analyzed thoroughly, and the accuracy of all possible cases is carefully considered and evaluated. Each part of the system was tested alone, and the overall system is still in an ongoing testing phase. The results have shown minimum faulty errors and accepted outputs that can lead to safe traffic control decisions. Finally, integrating more IoT devices and sensors between V2V, V2P, V2I with the help of artificial intelligence will definitely optimize this system with higher accuracy. © 2023 IEEE.

关键词： Traffic congestion

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Personalize Web Searching Strategies Classification and Comparison

arXiv

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arXiv 2022年

作者： Evtimova, Mariya Savova Momtchev, Ivan Momtchilov Faculty of Computer Systems and Control Programming Computer Technologies Department Technical University of Sofia Sofia1000 Bulgaria

Personalization is becoming very important direction in semantic web search for the users that needs to find appropriate information. In this paper, a classification of web personalization is proposed and semantic web search tools are investigated. Building user interest profile is essential for personalizing. Nowadays, semantic web tools use ontologies for personalization because of their advantages. It is important to mention that most of the semantic web search tools use agent technologies for implementation. © 2022, CC BY.

关键词： Search engines

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Analysis of Hardware and Software Tools for Implementation of Cognitive Radio Networks

Analysis of Hardware and Software Tools for Implementation o...

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2022 International Conference on Information Science and Communications technologies, ICISCT 2022

作者： Reypnazarov, Ernazar Khujamatov, Halimjon Khasanov, Doston Allamuratova, Zamira Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi The Computer Enjineering Faculty Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Data Communication Networks and Systems Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Information-Computer Technologies and Programming Tashkent Uzbekistan

ISBN: (纸本)9781665472296

This paper analyzes of the hardware and software tools for implementing a cognitive radio network. In particular, software-configurable radio modules, which are considered the main element of the cognitive network, were studied according to their main characteristics, among which the widely used ones were comparatively analyzed. As a result of the analysis, the HackRF One device was selected as the most preferred option for cognitive network research, and its working principles, structure, and characteristics were revealed in detail. It also provides information about the GNU Radio software environment and its capabilities, which are used to operate the HackRF One device. © 2022 IEEE.

关键词： Digital signal processing

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Research of Energy Detection Method in Spectrum Sensing in Cognitive Radio Networks

Research of Energy Detection Method in Spectrum Sensing in C...

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2022 International Conference on Information Science and Communications technologies, ICISCT 2022

作者： Reypnazarov, Ernazar Khujamatov, Halimjon Khasanov, Doston Allamuratova, Zamira Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Tashkent Uzbekistan Computer Enjineering Faculty Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Data Communication Networks and Systems Tashkent Uzbekistan Department of Information-Computer Technologies and Programming Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9781665472296

This paper presents the threshold value determination model, its implementation algorithm, and the characteristics of the threshold value depending on the probability of false signal detection, developed for the purpose of researching the method of energy determination in spectrum sensing in cognitive radio networks. Also, the main methods of single-band spectrum sensing in cognitive radio networks were initially studied in the article, and the main spectrum detection algorithms were comparatively analyzed. © 2022 IEEE.

关键词： Cognitive radio

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Advantages of Applying a Multi-agent Approach in Simulation Modeling: a Data-to-Action Case Study Applied to Electricity Production

Advantages of Applying a Multi-agent Approach in Simulation ...

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National Conference with International Participation (TELECOM)

作者： Krassimir Stoyanov Varbinka Stefanova-Stoyanova Petko Danov Department of Computer Systems Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria

ISBN: (纸本)9781665482134

This article examines modern methods for analysis and dynamic process modeling with the context of the capabilities, characteristics and advantages of types of multiagent systems. A practical implementation of the “Data to Action” model is proposed through the research simulation modeling system “Photovoltaic (PV) to Smart Energy Storage (SES) Grid”, developed by the authors using a Multi-Agent System for simulating the process of generating electricity produced by a PV park, its storage with the possibility of Time-Shifting and sale of energy.

关键词： Photovoltaic systems Process modeling Production Data models Telecommunications Multi-agent systems Energy storage

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The Tale of Errors in Microservices: Extended Abstract 25

The Tale of Errors in Microservices: Extended Abstract

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Abstracts of the 2025 ACM SIGMETRICS International Conference on Measurement and Modeling of computer Systems

作者： I-Ting Angelina Lee Zhizhou Zhang Abhishek Parwal Milind Chabbi Department of Computer Science & Engineering Washington University in St. Louis St. Louis USA Programming Systems Group Uber Technologies Inc. Sunnyvale USA Marketplace Configurations Uber Technologies Inc. Sunnyvale USA

ISBN: (纸本)9798400715938

Microservice architectures have become the de facto paradigm for building scalable, service-oriented systems. Although their decentralized design promotes resilience and rapid development, the inherent complexity leads to subtle performance challenges. In particular, non-fatal errors - internal failures of remote procedure calls that do not cause top-level request failures - can accumulate along the critical path, inflating latency and wasting *** this work, we analyze over 11 billion RPCs across more than 6,000 microservices at Uber. Our study shows that nearly 29% of successful requests experience non-fatal errors that remain hidden in traditional monitoring. We propose a novel latency-reduction estimator (LR estimator) to quantify the potential benefit of eliminating these errors. Our contributions include a systematic study of RPC error patterns, a methodology to estimate latency reductions, and case studies demonstrating up to a 30% reduction in tail latency.

关键词： RPC errors

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Approach and Concept of Workflow for Animal Husbandry Data Integration and Analysis

Approach and Concept of Workflow for Animal Husbandry Data I...

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National Conference with International Participation (TELECOM)

作者： Veska Gancheva Stella Vetova Programming and Computer Technologies Technical University of Sofia Sofia Bulgaria Computer Systems Technical University of Sofia Sofia Bulgaria

ISBN: (纸本)9781665482134

A concept for data integration and risk analysis in animal husbandry production is presented in this paper. The proposed animal husbandry data integration and analysis model structure consists of three layers, each of which combines the tasks to be performed. The architecture for data integration and its components are described. The workflow organization is depicted in details including modules and their connections used for data exchange. On the base of the presented workflow experiments were implemented. To perform the experimental part of the study, statistical data on the population of domestic animals is used. The analysis of their results show the trend in the risk of animal husbandry production.

关键词： Analytical models Animals Data integration Production Market research Information filters Data models

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Approach for Estimating Distances between Centers of Geometric Shapes based on Image Analysis and its Evaluation

Approach for Estimating Distances between Centers of Geometr...

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2021 International Conference on Information Science and Communications technologies, ICISCT 2021

作者： Takhirov, Shakhzod Kudratov, Sultan Kubantsev, Konstantin Engineering University of California - Berkeley Department of Civil and Environmental BerkeleyCA United States Technologies and Programming Tashkent University of Information Technologies Department of Information and Computer Tashkent Uzbekistan

ISBN: (纸本)9781665432580

A digital image is usually stored as a two-dimensional matrix consisting of numerical values of the intensity of each color;for example, Red-Green-Blue (RGB). This fact allows a possibility of manipulation of the image data, including making measurements based on the images of a moving object. This paper presents a new approach for estimating distances between the centers of geometric shapes based on image analysis. The algorithm is based on determining the parameters of the matrices - the number of rows and columns of graphic images of target plates and creating vectors of the sums of their values. As a result of such manipulations, peaks (maxima, minima) are formed that coincide with the centers of the squares and circles displayed on the target plates. These peaks are the coordinates of the target centers. © 2021 IEEE.

关键词： Image analysis

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Artificial Intelligence in Medicine for Chronic Disease Classification Using Machine Learning 16

Artificial Intelligence in Medicine for Chronic Disease Clas...

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16th IEEE International Conference on Application of Information and Communication technologies, AICT 2022

作者： Rakhimov, Mekhriddin Akhmadjonov, Ravshanjon Javliev, Shahzod Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Artificial Intelligence Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of System and Application Programming Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Computer System Tashkent Uzbekistan

ISBN: (纸本)9781665451628

Artificial intelligence (AI) systems in medicine are one of the most important modern trends in global healthcare. Artificial intelligence technologies are fundamentally changing the global healthcare system, making it possible to radically rebuild the system of medical diagnostics while reducing healthcare costs. AI is actively used in research to develop methods for diagnosing coronary heart disease (CHD). There are different types of CHD. Before treating a disease, it is necessary to determine which class of diseases it belongs to. Based on the feature space of the disease, it is possible to classify the type of CHD. Machine learning algorithms can solve this problem. The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve classification problems. The dataset is the more important part of the supervised machine learning algorithm for training. Gathering data is the most important step in solving any supervised machine learning problem. But choosing more important part from the collected data is one of the tasks to be solved. The main purpose of this study is to select more useful parametric attributes from the dataset to obtain a high F1-score of CHD classification. © 2022 IEEE.

关键词： Diseases

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Method of Detection of Intrusions Based on Stacking Meta-Algorithm

Method of Detection of Intrusions Based on Stacking Meta-Alg...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

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

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

An analysis of modern computer network intrusion detection systems was carried out. The application of machine and deep learning methods for classification problems has been investigated. The UNSW-NB15 dataset, developed at the Australian Cyber Security Center's (ACCS) Cyber Range Laboratory, contains data on normal network operations and synthetic intrusions. Data pre-processing was performed, including class balancing using the SMOTEENN method and selection of informative features using the Recursive Feature Elimination method. The possibility of using the stacking meta-algorithm to detect intrusions into computer networks has been investigated. A new algorithm for generating packets of raw data is proposed, which generates two sets of training data: one for training basic models, the other for a meta-model. A study of the effectiveness of using Random Forest, ANN, K Nearest Neighbor methods and Support Vector Machine and Random Forest as a decision-making meta-model was conducted. The use of the stacking meta-algorithm with the proposed algorithm for forming packets of output data, as well as basic models and a meta-model, led to a significant improvement in the quality of the model. It was found that, on average, recall and f1 score increased by 55.6% and 37.4%, respectively, compared to raw data and other models.

关键词： Training Support vector machines Computational modeling Stacking Training data Intrusion detection Predictive models Nearest neighbor methods Data models Random forests

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