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How to Improve IT Specialists Training for Designing Cyber-Physical systems 10

How to Improve IT Specialists Training for Designing Cyber-P...

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10th Mediterranean Conference on Embedded Computing, MECO 2021

作者： Pinkevich, Vasiliy Platunov, Alexey Kluchev, Arkady ITMO University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia

ISBN: (纸本)9780738133614

Many universities today offer special courses or entire master programs for cyber-physical systems (CPS) developers training. A multi-aspect design route of the CPS creation is considered as the methodological vector, which should provide a parallel, balanced, and integrated movement from the task to the product. The most important part is knowledge and skills in computing, aimed at designing computing platforms and CPS application software. However, the ongoing segmentation in computing reduces the interdisciplinary interaction effectiveness. We consider the development of an integral perception of a computing system based on a multi-level and multi-domain representation model and a deep understanding of the fundamental universal mechanisms of its functioning as a way to improve the quality of design and the efficiency of interaction between computer specialists. This can be achieved by the development of microarchitectural descriptions of a computing system and its components, filling the gap between architectural representation and implementation-level documents. These principles should be implemented even during specialists training. The means of creating such models and descriptions, as well as the educational laboratory complex of distance learning, which supports the proposed approach, are demonstrated. © 2021 IEEE.

关键词： Embedded systems

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A Deep Learning Approach to Recognizing New Thai Currency Using Transfer Learning for Visually Impaired Individuals 17

A Deep Learning Approach to Recognizing New Thai Currency Us...

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17th International Conference on Signal Processing and Communication systems, ICSPCS 2024

作者： Ahmad, Irfan Sharif, Abubakar Azmat, Aneeka Jamil, Muhammad Usman Maynooth University Maynooth International Engineering College Faculty of Science and Engineering Ireland Maynooth University Maynooth International Engineering College Faculty of Science and Engineering Fuzhou China University of Glasgow James Watt School of Engineering United Kingdom National Tsinghua University College of Electrical Engineering and Computer Science Institute of Information Systems and Application Hsinchu Taiwan Chuzhou University School of Mechanical and Electrical Engineering Chuzhou China

ISBN: (纸本)9798350389630

Assistive mobile applications play a pivotal role for visually impaired individuals worldwide. These applications often face challenges in currency recognition due to varying perspectives, inconsistent illumination, and background clutter. This issue is especially pressing in developing countries like Thailand, where there is a notable gap in robust currency recognition systems, particularly for the new Thai currency notes. This study employs a deep learning approach using a convolutional neural network (CNN) to automate the recognition of the new Thai currency notes. Using transfer learning, we fine-tuned the CNN using the Xception model, renowned for its depth-wise separable convolution. The network trained on a meticulously curated dataset comprising 3600 images (without data augmentation) of five different denominations of the new Thai currency (20, 50, 100, 500, and 1000 baht) notes, captured under various conditions. The resulting model achieved an average training accuracy of 99.5% and a validation accuracy of 99.8%. Given its robustness and high accuracy, the model can be integrated into an Android application. Such an application would offer a user-friendly and reliable tool for visually impaired individuals to effortlessly identify the new Thai currency notes in their everyday transactions. © 2024 IEEE.

关键词： Convolutional neural networks

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Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Disease Prediction

Hybrid Deep Learning Algorithm For MRI Brain Alzheimer's Dis...

引用

2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Karthikeyan, D. Nivasini, R. Raja, Kiruba Thangam Madhivadhani, D. Sivakumar, B. Krishna Priya, A.T.R. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India Iitm Research Park Guvi Geek Networks Tamilnadu Chennai India Vit University School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Vellore India Kings Engineering College Department of Electronics and Communication Engineering Sriperumbudur Chennai India Rohini College of Engineering and Technology Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798350393378

Magnetic resonance imaging (MRI) has been used to study the structural makeup of the brain and analyse several neurological disorders and diseased areas. For the adoption of preventative measures, early recognition of Alzheimer's disease (AD) patients is essential. Here, a thorough inspection of the tissue arrangements obtained by MRI images of Outcome and Assessment information (OASIS) dataset enables an exact characterization of certain brain diseases. There have been a number of division techniques for diagnosing AD that range in complexity. Compassion has been tested by deep learning techniques used to segment the structure of the brain and classify AD because they have the potential to uncover important information from vast amounts of data. In this paper the deep learning technique of Hybrid Dragonfly based GWO convolutional Neural Network (CNN) is achieved promising result for the diagnosis of AD. At image preprocessing wiener filter is used for removing the additive noises and the Gray Level Co-Occurrence Matrix (GLCM) extraction is implemented for texture analyzing. As a result, hybrid deep learning methods of CNN has the accuracy as 90% and the result of the image prediction are presented in this paper. © 2023 IEEE.

关键词： Magnetic resonance imaging

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An Internet of Things-based System Towards Detecting Oil Spills

An Internet of Things-based System Towards Detecting Oil Spi...

引用

2023 International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2023

作者： Khasawneh, Mahmoud Azab, Ahmad Alrabaee, Saed Al Ain University Computer Engineering Abu Dhabi United Arab Emirates The University of Sydney Faculty of Engineering Sydney Australia United Arab Emirates University Information Systems and Security AlAin United Arab Emirates

ISBN: (纸本)9798350339291

Oil fields are a crucial component of the global economy, particularly in the Gulf countries. Various units in these fields, including pipelines, require constant monitoring and maintenance. The trend towards automation using the Internet of Things (IoT) has led to the development of IoT oil field monitoring systems, which can greatly enhance the efficiency of the oil and gas industry by detecting and resolving issues such as oil spills and leakages. This paper proposes the use of an autonomous boat equipped with sensors and a microcontroller to detect oil spills on the water's surface. The boat is equipped with sensitive sensors and a high-resolution camera to locate any oil spill, and if detected, a message containing the image and location is sent to a cloud server. The cloud server is designed based on Blynk cloud and libraries, which analyzes the data received to determine the appropriate course of action. A small prototype of the system was constructed and tested, and the results demonstrated that it is fast and efficient in terms of detection and response times. © 2023 IEEE.

关键词： Oil spills

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Discussion Manipulation, Language and Domain Dependent Models: An Overview

Discussion Manipulation, Language and Domain Dependent Model...

引用

Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Filip Chalás Igor Stupavský Valentino Vranić Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava

The amount of antisocial online behavior (AOB) in the political field has been on the rise in recent years. In the research, we are dealing with the reason for confusing AOB with other forms of antisocial behavior. We specifically dealt with NLP in the political field in the Slovak language, with a specific number of prefixes and suffixes. In this paper, we focus on the area of detection of manipulation of political discussions. The data source consisted of previously collected data in the period from April 2018 with an update until November 19, 2022 realized within this research from the site ***, which we supplemented with new claims. We trained several classification models on preprocessed data and evaluated them. For multi-class classification, the best results were achieved using logistic regression and a support vector machine trained on the resampled dataset—both achieving an accuracy of 0.56 and a macro F1 score of 0.39. In the case of binary classification, the best results were achieved by logistic regression—accuracy 0.7 and macro F1 score 0.56. These models could help detect manipulation in online political discussions.

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Brain Sight: A Novel system for substituting Vision Sensor 3

Brain Sight: A Novel system for substituting Vision Sensor

引用

3rd International Conference on Distributed Sensing and Intelligent systems, ICDSIS 2022

作者： Elsaeed, Nadine Y. Alshaarawy, Mariam M. Esmaiel, Abd Elrhman S. El-Deen Elmahdy, Seif Abdelhameed, Omar Fakhry, Mariam M. Ogabih, Naglaa A. Ali, Nouran M. El-Deeb, Reem Software Engineering Department Faculty of Computers and Information Mansoura University Mansoura35516 Egypt Computer Science Department Faculty of Computers and Information Mansoura University Mansoura35516 Egypt The Department of Computer Science Faculty of Computers and Information Mansoura University P.O. Box: 35516 Mansoura Egypt

ISBN: (纸本)9781839538186

In a world where nearly everything we do depends on sight;it is ever more challenging for the unsighted to cope with it and lead a normal life without being reliant on the presence of a companion. Finding a mechanism to convey visual information to the brain is the most difficult part of the research as well as the development of efficient sensory replacement and augmentation systems. In this paper, we aim to propose Brain Sight as an innovative system to help blind people identifying and discovering things in front of them using vibro-tactile display for vision sensory substitution. Brain Sight is a wearable vest-like fabric containing small vibration motors that displays pulses to map the seen images on the Lower-back of the visually impaired. A deep labeling segmentation and edge detection techniques are applied to the captured image after preprocessing. A simple prototype is implemented and tested with volunteers. Thanks to neuroplasticity and the cognitive cortex inside humans' brain, the implemented vest showed promising results. Without the need for intensive training, Brain Sight enables blind to recognize different shapes and numbers. © IET Conference Proceedings 2022.

关键词： Semantics

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Synthesis Method for CGRA Processors based on Imitation Model 10

Synthesis Method for CGRA Processors based on Imitation Mode...

引用

10th Mediterranean Conference on Embedded Computing, MECO 2021

作者： Penskoi, Aleksandr ITMO University Software Engineering and Computer Systems Faculty Saint Petersburg Russia

ISBN: (纸本)9780738133614

The article considers the problem of designing a synthesis method for Coarse-Grained Reconfigurable Array (CGRA) architectures. The author observes several general problems of such architecture and demonstrates how they can be solved using the NITTA project example. NITTA - the CAD for generating and programming CGRA processors for cyclic execution of control or signal/data processing algorithms. Target system work principles from hardware, algorithm, and transport points of view provided. The principles of building a target system imitation model and synthesis method based on interaction with that model in dialog form are described. All ideas were demonstrated on the simple algorithm example. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Hyper-Heuristic Strategy for Input-Output-Based Interaction Testing

Hyper-Heuristic Strategy for Input-Output-Based Interaction ...

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Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020

作者： Din, Fakhrud Zamli, Kamal Z. Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Pahang26300 Malaysia Department of Computer Science & IT University of Malakand Lower Dir District KPK Pakistan

ISBN: (纸本)9789813345966

software testing aims at exploring faults within software in order to ensure it meets all necessary specifications. Test case design strategies play key role in software testing. Classical test case design strategies, however, do not sufficiently include support for exploration of faults due to interaction between parameter values. New strategies known as t-way strategies (where t expresses interaction strength) have been developed for finding interaction faults. However, existing t-way strategies for input-output-based relationship (IOR) interaction testing mostly adopt greedy algorithms which often generate poor quality test data. Therefore, this paper presents the design of a new IOR test suite generation strategy called IOR_HH based on the exponential Monte Carlo with counter (EMCQ) hyper-heuristic. EMCQ is a parameter free hyper-heuristic which works as controller of the three implemented low-level meta-heuristic operators, namely crossover, peer learning and global pollination in the proposed IOR_HH strategy. Experimental results demonstrate the impact of the proposed strategy against existing computational strategies for IOR interaction testing. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： software testing

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Design of Deep CNN Model for Effective Traffic Signs Recognition

Design of Deep CNN Model for Effective Traffic Signs Recogni...

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2021 International Russian Automation Conference, RusAutoCon 2021

作者： Sichkar, Valentyn N. Lyamin, Andrey V. Itmo University Faculty of Software Engineering and Computer Systems Saint-Petersburg Russia

ISBN: (纸本)9780738131566

In this paper, the method to design deep Convolutional Neural Network (CNN) architecture for the problem of traffic signs classification is proposed. The approach incorporates five main stages followed by each other: selecting the deepness of the network via the number of convolutional-pooling pairs, choosing the needed number of feature maps in every convolutional layer, identifying the sufficient number of neurons in the hidden fully connected layer, estimating the needed percentage of dropout after every layer. The last stage is aimed to analyse if replacing max pooling by convolution with strides 2 or by average pooling improves designed architecture. At each stage, every designed model is trained and evaluated 100 times. The choice of the best model to continue with at the next stage is based on the highest of the most frequent accuracy. The obtained final architecture is trained and tested with the original German Traffic Sign Recognition Benchmark (GTSRB) and its augmented version via geometric transformations: rotation around the centre point and projection to different sides that imitates capturing of images by the camera with some skew angle. Experimental results showed testing accuracy of more than 99.9% that enables to use designed architecture in real-life applications. © 2021 IEEE.

关键词： Traffic signs

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Monkeypox Detection with Deep-Learning and Ensemble of Features: A Study 4

Monkeypox Detection with Deep-Learning and Ensemble of Featu...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Rajinikanth, Venkatesan Taniar, David Damaševičius, Robertas Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Division of Research and Innovation Department of Computer Science and Engineering TN Chennai602105 India Monash University Faculty of Information Technology MelbourneVIC3800 Australia Centre of Real Time Computer Systems Kaunas University of Technology Kaunas44249 Lithuania

ISBN: (纸本)9798350387490

There is a growing global prevalence of infectious diseases in the human population and it is imperative to have precise diagnosis and therapy in order to effectively cure these diseases. Monkeypox (Mpox) is an infectious disease that can cause varying degrees of skin infection, ranging from minor to severe. The recent spread of Mpox has been confirmed as Public Health Emergency of International Concern (PHEIC) due to its large infection rate. This study aims to develop a deep-learning (DL) scheme for Mpox identification. To enhance the detection accuracy, this study considered preprocessed skin images for the analysis and employed the binary classification with Ensemble of Deep-Features (EDF) using 5-fold cross validation. The outcome of this investigation validate that the suggested deep learning tool enables the attainment of a detection accuracy of 100% on the augmented images from the Monkeypox Skin Images Dataset (MSID). Compared to the existing results in the literature, this research provides a significant result on the chosen MSID database. © 2024 IEEE.

关键词： Classification Ensemble of deep-features Healthcare Illness Monkeypox

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