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2022 IEEE International Conference on Internet of Things and Intelligence systems, IoTaIS 2022

作者： Othman, Walaa Kashevnik, Alexey Itmo University Information Technology and Programming Faculty St. Petersburg Russia Laboratory of Computer-aided Integrated Systems Spc Ras St. Petersburg Russia

ISBN: (数字)9798350396454

ISBN: (纸本)9798350396454

Developing vehicles with the Internet of Thing technology including driver health monitoring systems, driver safety systems, and accident prevention has drawn the attention of researchers in the last few years. The monitoring system should prevent any dangerous situation and be comfortable for the driver inside the cabin. In this paper, we introduce a remote video-based method for detecting the heart rate in real-time using smartphone cameras, which can be used for the analysis of the driver's physiological parameters to enhance driving safety. We propose to use 3DDFA for automatic facial landmarks detection to extract the driver's face and a 3d-classification-based model for detecting the heart rate. The experiments showed good results with mean absolute error (MAE) equal to 6.8 on the LGI-PPGI dataset and 18.68 on our DriverMVT dataset that was recorded in the wild. © 2022 IEEE.

关键词： Smartphones

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Personalize Web Searching Strategies Classification and Comparison

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通讯和计算机（中英文版） 2016年第1期13卷 19-23页

作者： Mariya Savova Evtimova Ivan Momtchilov Momtchev Faculty of Computer Systems and Control Programming and Computer Technologies Department Technical University of SofiaSofia 1000 Bulgaria

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Information System for Administrative Services of Educational Process

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computer Technology and Application 2011年第5期2卷 354-357页

作者： Lyudmilla Yordanova Stoyanova Department of Programming and Computer Technology Faculty of Computer Systems and Control Technical University of SofiaSofia 1000 Bulgaria

The goal of this paper is to analyze and propose a reconstruction of functional possibilities and database requirements of a Web based Educational Information system. For the research the Information system of the Technology School ＂electronic systems＂（TUES） associated with the Technical University of Sofia is on focus. The modules for admission of students, specialty classification, and graduation of students, management of the student＇s work in computer classrooms and lecturers＇ data management have been analyzed, developed and implemented. The additionally developed modules concern mainly the management of educational process and do not affect the e-learning or the official part modules. That led to the idea that the functionality for managing educational process has to be organized in a separate module defined as module for administrative services of educational process. Such restructuring of the system will bring flexibility in further growth of the system--for example students＇ admission or selection for different purposes.

关键词： Information systems functional structure database education.

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Design and implementation of an embedded interactive timing system for medical treatment of patients with learning and attention issues 28

Design and implementation of an embedded interactive timing ...

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28th International Scientific Conference Electronics, ET 2019 - Proceedings

作者： Angelova, Maria A. Velchev, Yuliyan S. Dimitrov, Kalin L. Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies 8 Kliment Ohridski blvd Sofia1000 Bulgaria Department of Radio Communications and Video Technologies Faculty of Telecommunications Technical University of Sofia 8 Kliment Ohridski blvd Sofia1000 Bulgaria

ISBN: (纸本)9781728125749

This article describes the design and implementation of an Embedded Interactive Timing System. Such systems are used in the therapy of children who have learning problems. The matching of different beats can help the brain work more efficiently. A methodology and architecture of an interactive timing is proposed. Open-source software environment and microcontroller is used for the practical realization of the system. Some results of the program functioning are shown. © 2019 IEEE.

关键词： Microcontrollers

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An Experiment to Evaluate IoT Application Security Scheme (IoTGazePass)

An Experiment to Evaluate IoT Application Security Scheme (I...

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2022 International Conference on Engineering and MIS, ICEMIS 2022

作者： Keshlaf, Ayad A. Alfard, Faheemah M. Bouzid, Omar M. Sabratha University Faculty of Engineering Computer Eng. and It Department Sabratha Libya Libyan Center for Electronic Systems Programming and Aviation Research University of Zawia Computer Department Faculty of Engineering Zawia Libya University of Gharyan Faculty of Engineering Elec. and Electronic Eng. Dept Gharyan Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

IoTGazePass is a new password scheme that has been designed to tackle the weaknesses of existing types of passwords. It is intended to be suitable for IoT applications. An experiment was conducted to evaluate the strengths and weaknesses of the IoTGazePass scheme (e.g., security, usability and memorability). The evaluation aspects include a comparison with traditional textual and graphical based passwords in terms of memorability and security of the passwords. The results of the evaluation indicate that the IoTGazePass scheme is easy to use, memorable and flexible, as it can generate strong, secure and scalable passwords. The experiment results have also showed that the IoTGazePass scheme have good ability to deal with shoulder surfing attacks. In fact, the experiment results lead to some recommendations and suggestions to improve the planned performance. © 2022 IEEE.

关键词： Authentication

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences 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 Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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Neuromorphic Neurons and Networks for Artificial Intelligence Built Using Temporal Space Calculations 32

Neuromorphic Neurons and Networks for Artificial Intelligenc...

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32nd International Scientific Conference Electronics, ET 2023

作者： Trendafilov, Ianislav Gancheva, Veska Neuromorphica Ltd. 8 Kliment Ohridski blvd. Sofia1335 Bulgaria Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350302004

We deigned a spiking neural network that computes network weights in the temporal dimension. Such a network can be used for artificial intelligence and deep learning. We demonstrate circuits implementing blocks for building such a network and then a training model. This enables creation of efficient Hessenstein-Reichardt detectors observed in motion detection networks in the nature. The proposed network allows reconfiguration across network layers algorithmically. © 2023 IEEE.

关键词： Neural networks

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Software Anomaly Detection Method Based on Artificial Neural Network

Software Anomaly Detection Method Based on Artificial Neural...

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IEEE International Conference on e-Business Engineering (ICEBE)

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

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

A growing range of data sets have been created in recent years; these are used by platforms and software applications and kept in remote access repositories. Datasets are therefore more susceptible to harmful attacks. As a result, network security in data transmission is becoming a more important area of study. One well-known method for safeguarding computer systems is the deployment of intrusion detection systems. This study proposes an artificial intelligence based method for data analysis-based anomaly detection. Methods based on machine learning and rules are mixed together. The right rules are created via a genetic algorithm. Relevant features are extracted using principal component analysis with the goal of enhancing performance. The KDD Cup 1999 dataset is used to empirically validate the suggested procedure, satisfying the criterion of using appropriate data. Using the well-known benchmark dataset, the suggested approach is used to identify and examine four different kinds of attacks: Neptune, Ipsweep, Pod, and Teardrop. During the machine learning phase, the data is categorized into categories of attacks and normal behavior after the features set during the training phase are tested. For the purpose of data analysis, the input data is divided into training and testing sets for an artificial neural network. The first 80% of the data are used to train the neural network, and the remaining 20% are used for testing. The estimated accuracy improves with the number of epochs and is higher for training data and lower for validation test data, according to experimental results. Consequently, the trained model can be retained and used to detect discrepancies. The learnt model is used to the system to compute new input parameters that are not used during training or validation.

关键词： Training Accuracy Predictive models Network security Feature extraction Data structures Data models Software Anomaly detection Principal component analysis

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Healthcare Data Analytics Based on Machine Learning

Healthcare Data Analytics Based on Machine Learning

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Communications, Computing, Cybersecurity, and Informatics (CCCI), International Conference on

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

ISBN: (数字)9798350349832

ISBN: (纸本)9798350349849

Technology advancements have transformed medical science and practice, leading to the vast gathering of a wide range of medical data. Medical researchers use artificial intelligence techniques extensively because they enable the identification and creation of models of complicated datasets and the interactions between them. This, in turn, enables the successful prediction of future outcomes associated with a specific sickness type. An artificial intelligence-based approach to healthcare data analytics is presented, which leverages data to build a desired model and solve a particular issue. The suggested approach for healthcare data analytics uses a random forest and feedforward artificial neural network with two hidden layers as its basis to get the best model.

关键词： Analytical models Data analysis Computational modeling Medical services Artificial neural networks Learning (artificial intelligence) Data models Telecommunication computing Informatics Random forests

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Information theoretical approach to minimization of AND/EXOR expressions of switching functions

Information theoretical approach to minimization of AND/EXOR...

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4th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services, ITELSIKS 1999

作者： Shmerko, V. Popel, D. Stanković, R.S. Cheushev, V. Yanushkevich, S. Department of Computer Aided Design Systems Techinal University of Szczecin Poland Department of Computer Sicience State University of Informatics and Radioelectronics Minks Belarus Department of Computer Science Faculty of Electronic Engineering University of Nis Serbia Department of Programming Technologies State University Minsk Belarus

ISBN: (纸本)078035768X

In modern circuit design, the Shannon decomposition of switching functions is widely used. On the other hand, in information theory of telecommunication, the Shannon entropy used as a measure to represent the information in numerical values, is a key notion. In this paper, we relate these two concepts, belonging to different areas, into an approach to the minimisation of exclusive-or sum-of-products (AND/EXOR) expressions for switching functions. The Shannon decomposition, and the closely related Davio decomposition for AND/EXOR expressions are interpreted in terms of basic concepts in the information theory. Thanks to that, we have proposed an entropy based strategy for minimization of AND/EXOR expressions. We have provided a comparison and an experimental verification of this strategy with some known heuristic minimization strategies using benchmarks. In some cases, our program Info-EXOR produces extremely better results. Moreover, the information theory interpretation of classical decomposition of switching functions offers a new point of view to the existing design styles. © 1999 IEEE.

关键词： Information theory

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