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Classification of sprott chaotic systems via projection of the attractors using deep learning methods

The European Physical Journal Special Topics 2024年 1-17页

作者： Akgul, Akif Deniz, Emre Emin, Berkay Çizmeci, Hüseyin Alaca, Yusuf Akmeşe, Ömer Faruk Özdem, Selim Department of Computer Engineering Faculty of Engineering Hitit University Corum Turkey Department of Electronics and Automation Osmancık Omer Derindere Vocational School Hitit University Corum Turkey Department of Computer Technologies Vocational School of Technical Sciences Hitit University Corum Turkey Department of Electronics and Automation Alaca Avni Celik Vocational School Hitit University Corum Turkey

This study uses deep learning methods to classify the projection of the attractor’s images of five different chaotic systems. The chaotic systems addressed in the research are Sprott C, Sprott F, Sprott G, Sprott H, and Sprott M. A dataset was created for classification using the projection of attractors of these five different chaotic systems. This dataset contains time series images, and the graphs are generated based on initial conditions, Runge–Kutta 4 step size, and time length. Deep learning methods such as ResNet50, ResNet50V2, VGG19, InceptionV3, MobileNetV2, and VGG16 have been utilized for classification. This study's classification accuracy varies between 91.6% and 99.9%, depending on the problem. Therefore, this research accurately determines which chaotic system a projection of the attractors graphic image belongs to. This high accuracy demonstrates the usability of this model in analyzing chaotic systems in real-world applications. Such accuracies can be considered a powerful tool in analyzing industrial systems or other systems with complex structures. This work successfully uses deep learning methods for classifying chaotic systems. Such research could be an important step toward understanding and managing complex systems.

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Synthesis, Analysis and Implementation of Partial Counters Using Integrated Circuits

Synthesis, Analysis and Implementation of Partial Counters U...

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International Scientific Conference electronics (ET)

作者： Adriana N. Borodzhieva Atila G. Pandzhar Martin N. Nedelchev Nikolay G. Genov Department of Telecommunications Faculty of Electrical Engineering Electronics and Automation University of Ruse "Angel Kanchev" Ruse Bulgaria Department of Computer Systems and Technologies Faculty of Electrical Engineering Electronics and Automation University of Ruse "Angel Kanchev" Ruse Bulgaria

The paper presents the processes of synthesis, analysis, and implementation of counters and frequency dividers using integrated circuits from the 74xx library. All the synthesized circuits are built and tested in Logisim. The work is related to the Teamwork Project in the course "Digital Circuits" for students-bachelors of the "computer Systems and Technologies" specialty at the University of Ruse.

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Classification of skin lesions using morpho-granulometry features derived from color clusters

Classification of skin lesions using morpho-granulometry fea...

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International Conference on System Theory, Control, and Computing (ICSTCC)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics "Dunarea de Jos" University of Galati Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics The Modelling & Simulation Laboratory "Dunarea de Jos" University of Galati Galati Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

The skin lesion can be thought of as a biological system, so the morpho-granulometry of significant color clusters found in skin lesions is one of the elements that reproduce in a natural way the structure of the lesion, this novelty is highlighted in this study. Important features of skin lesions can be modulated by fusing neural networks (NN) and machine learning (ML). By choosing the nevus and melanoma classes, the primary goal was accomplished, and three databases were used to test the methodology. The characteristics based on morpho-granulometry allowed for the identification of microstructure within the images, which can be very helpful in characterizing the biological system. Based on random forest (RF) and extreme gradient boosting (XGboost) classifiers, this work aimed to improve the classification performance of important feature selection. The selected features from three free image databases with three NNs were classified. In a binary classification of nevus vs. melanoma, the results showed that the pattern recognition neural network (PRNN), according to the PH2 database, provided an accuracy of 0.923 and an F1-score of 0.876. The classification is interpretable if it is not validated. In our study, the best results were verified with a logistic regression (LR) classifier.

关键词： Accuracy Recurrent neural networks Image color analysis Melanoma Biological systems Artificial neural networks Feature extraction Skin Lesions Classification tree analysis

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Analysis of the Performance Impact in Industrial Control Process of Wastewater Systems when using Distributed Ledger Technology Sensor Authentication

Analysis of the Performance Impact in Industrial Control Pro...

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International Symposium on Electrical and electronics engineering (ISEEE)

作者： Portasă Bogdan - Daniel Diaconu Larisa Faculty of Automation Computer Sciences Electronics and Electrical Engineering “Dunarea de Jos” University of Galati Galați Romania

In recent years, the integration of advanced technologies in industrial control processes has gained significant attention, particularly in the domain of wastewater systems. One emerging technology with promising potential is Distributed Ledger Technology (DLT), which offers secure and transparent data management through blockchain-based solutions. This paper presents an in-depth analysis of the performance impact that arises when incorporating DLT-based sensor authentication in industrial control processes of wastewater systems. The study aims to evaluate the benefits and challenges associated with this integration, providing insights into the effectiveness and efficiency of DLT-based sensor authentication in ensuring data integrity and enhancing the overall control process performance.

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology 33

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Bogacki, P. Dlugosz, M. Talaska, T. Dlugosz, R. Aptiv Services Poland Kraków30-399 Poland Agh University of Science and Technology Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications Kraków30-059 Poland Poznan University of Technology Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350347760

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock. © 2023 IEEE.

关键词： Pixels

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First Order Features Extraction from Thermal Images for Human State Recognition

First Order Features Extraction from Thermal Images for Huma...

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International Symposium on Electrical and electronics engineering (ISEEE)

作者： Simona Moldovanu Sorin Pavel Dorel Aiordachioaie Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania Department of Electronics and Telecommunications Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania

Due to the common limitation of the human visual system, internal features of thermal images cannot be fully discovered. To overcome these drawbacks, a lot of studies analyzed the facial expressions corroborating the features extracted from thermal images with machine learning tools. This paper proposed the first-order features extracted from the image histogram and a binary classification performed by the k-nearest neighbor algorithm (K-NN). The proposed method tests separately the performance of different input data, firstly, the features were extracted from raw images and the robustness of the K-NN algorithm led to an accuracy of 92.6%. Secondly, the features were extracted sequentially from corrupted images with noise. The novelty of the proposed method consists of verifying the quality classification when the images have bad quality, in this case, the accuracy decreased to 70.7%, as a result, the classification of facial expressions is sensitive to the quality of images.

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Diabetic Retinopathy Image Classification Using Machine Learning and Local Binary Patterns Features

Diabetic Retinopathy Image Classification Using Machine Lear...

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International Symposium on Electrical and electronics engineering (ISEEE)

作者： Mihaela Miron Anisia Culea-Florescu Simona Moldovanu Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania Department of Electronics and Telecommunications Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania

Diabetic Retinopathy (DR) is a condition caused by diabetes that affects the blood vessels in the retina. Detecting the disease early and providing appropriate treatment are crucial in slowing its progression. Therefore, there is great potential in utilizing Machine Learning (ML) to improve the identification and monitoring of DR development in patients. Our study aims to explore the performance of six ML algorithms, namely Random Forest (RF), Adaptive Boosting (AB), K-Nearest Neighbor (K-NN), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), and Quadratic Discriminant Analysis (QDA), in two binary classifications involving three classes: non-diabetic retinopathy (NoDR), moderate retinopathy (MR), and severe retinopathy (SV). These ML algorithms were applied to ten features extracted using local binary patterns (LBP). The first classification task involved distinguishing between NoDR and MR, while the second task involved differentiating between NoDR and SV. The RF technique achieved the highest classification accuracy, with 0.912 for the first task and 0.94 for the second task.

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Emotion Recognition in Human Thermal Images with Artificial Intelligence Technology

Emotion Recognition in Human Thermal Images with Artificial ...

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International Conference on Emerging Technologies and Factory automation (ETFA)

作者： Sorin Pavel Simona Moldovanu Dorel Aiordachioaie Department of Electronics and Telecommunications Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania

This paper presents a case study related to emotion recognition based on human thermal image processing. Three states are considered for human faces: normal, sad, and happy. The thermal images are pre-processed for important feature selection and extraction by using the random forest (RF) classifier. All and selected features set is used as input to a feedforward backpropagation neural network (FFBPN), with binary outputs. The input images are organized in pairs, i.e., normal with sad and normal with happy. The preliminary results show particularly good classification accuracy.

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Integrated Extended SIR-type Epidemiological Modeling Platform for Covid-19 10

Integrated Extended SIR-type Epidemiological Modeling Platfo...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Brezulianu, Adrian Popa, Iolanda Geman, Oana Chiuchisan, Iuliana Nastase, Carmen GREENSOFT S.R.L. Iasi Romania Computer Science Electronics and Automation Department Faculty of Electrical Engineering and Computer Science "Stefan cel Mare" University of Suceava Suceava Romania Faculty of Economics Administration and Business "Stefan cel Mare" University of Suceava Romania

ISBN: (纸本)9781665485579

In these conditions of Covid-19 pandemic, the design and implementation of an epidemiological modeling platform with an API-type integration with beneficiary entities and data providers is a priority for the healthcare system and other organizations. An epidemiological platform (PLIS), replicable and adaptable for managing an epidemic, based on an integrated extended SIR-type model, is presented in this paper. This solution has an open architecture, respecting the scalability and interoperability, requirements, and is based on international communication standards and protocols, and SOA technology. © 2022 IEEE.

关键词： COVID-19

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Effects of the Flatness Network Parameter Threshold on the Performance of the Rectified Linear Unit Memristor-Like Activation Function in Deep Learning

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SN computer Science 2024年第8期5卷 1-10页

作者： Tchepgoua Mbakop, Marcelle Mboupda Pone, Justin Roger Chassem Kamdem, Priva Kengne, Romanic Research Unit of Condensed Matter Electronics and Signal Processing (RU-MACETS) Department of Physics Faculty of Science University of Dschang P. O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer (RU-AIA) Electrical Engineering Department of IUT-FV University of Dschang P.O. Box 134 Bandjoun Cameroon Research Unit of Fundamental Computer Sciences and Artificial Intelligence (RU-FCSAI) Department of Computer Sciences Faculty of Science University of Dschang P. O. Box 67 Dschang Cameroon

In this contribution, we improve of the performance of the Rectified Linear Unit Memristor Like Activation Function with the implication to help training process of CNN without a lot of epochs by computing the best value of the flatness network parameter (p). In this regards the flatness network parameter threshold (p) has been investigated and a good performance of the activation function has been discovered at p = 4.5. We firstly used the MNIST and the CIFAR-10 datasets to trained and test the Alex-Net architecture model of convolutional neural network (CNN) and we showed better performances of Rectified Linear Unit Memristor-like Activation Function compared to those of the literature. We noticed that the performance of Alex-Net also improved and the better performance was recorded when p = 4.5 with 99.50%, 99.25%, 98.81% for training, validation and testing accuracy respectively when using the MNIST. These results open the outcome to reduce the training time of neural networks when this activation function is used. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Activation function Artificial intelligence Deep learning Flatness network parameter threshold Memristive windows function Neural network

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