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16th Brazilian Symposium on Robotics and 15th Workshop on Robotics in Education, SBR-WRE 2024

作者： Garcia Gonçalves, Luiz Marcos Electrical and Computer Engineering Graduate Program Universidade Federal do Rio Grande do Norte Natal Brazil

ISBN: (纸本)9798350391084

We bring historical findings related to the most important Latin American and Brazilian Robotics Symposiums including their quality analytics regarding all papers published since their creation. For this quality analysis, we first provide a little of history and then we apply a methodology based on a systematic mapping of the literature using Scopus (from Elsevier) and Web of Science. The quality analysis includes variables as citations metrics, and also other historical variables as participation per countries, per states, per universities, and contributed areas among others. We use metadata and tools available in the most commonly used digital libraries, for a fair comparison. To the end, with basis on the results of our methodological approach, we point out that the community has followed the right path for establishing a high quality event, mainly regarding the decision of publication of the proceedings at IEEE Xplore, and recently in the Brazilian computer Society Library (Sol-SBC), all together. Also, our analysis corroborate to verify the modi operandi that have been used in the events execution, including joining in the Workshop of Robotics in Education (WRE) and the competitions (RCB, OBR, MNR, CBR, LARC). Besides happening jointly, each event keeps their original event structure and characteristics. ©2024 IEEE.

关键词： Taxonomies

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Application of a Voting-Based Ensemble Method for Recognizing Seven Basic Emotions in Real-Time Webcam Video Images 9

Application of a Voting-Based Ensemble Method for Recognizin...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Sanli, Ahmet Tunahan Saran, Murat Cankaya University Information Technology Program Ankara Turkey Cankaya University Computer Engineering Department Ankara Turkey

ISBN: (纸本)9798350394474

This study examines the automatic recognition of human emotions in real-time through facial expressions from webcams. Real-time emotion recognition is a crucial element in human-computer interaction and emotional computing. The study evaluates the effectiveness of various techniques in real-time facial emotion recognition using a custom CNN model, creating an ensemble with a voting mechanism, and integrating the system for real-time emotion recognition. The CNN model was trained on the FER2013 dataset, which consists of facial images labeled with different emotional states. It achieved a remarkable accuracy of 95%. In this study, we developed a dataset named ATS-FER2024, which consists of 184 images depicting seven distinct emotions. The tests conducted on this dataset yielded an accuracy rate of 89%. Despite its small size, the dataset's accuracy is noteworthy. The findings contribute to academic knowledge on developing emotion recognition systems, enhancing empathy, and creating context-sensitive interactions in real-world applications. © 2024 IEEE.

关键词： Emotion Recognition

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Using the CNN architecture based on the EfficientNetB4 model to efficiently detect Deepfake images 1

Using the CNN architecture based on the EfficientNetB4 model...

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IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Zhalgasbayev, Arman Aiteni, Tolegen Khaimuldin, Nursultan Sofrware Engineering Student Astana IT University Astana Kazakhstan Computer Science Student Astana IT University Astana Kazakhstan Senior lecturer of Computer Engineering Educational Program Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350364378

This study evaluates the effectiveness of using the EfficientNetB4 model architecture for deepfake image detection and identifies the main challenges in developing an accurate model for deepfake image detection. Deepfake image detection is a complex task that requires lots of data, advanced models and new approaches for handling features in face images which shows that image is real or fake. This research is based on the Deepfake Detection Challenge (DFDC), which took place in 2019-2020, where world experts showed new approaches to solving this problem. During the research and development of the model, the best practices and experience of experts were used to identify the deepfake images. ©2024 IEEE.

关键词： Deep learning

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Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits

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Information Processing in Agriculture 2023年第3期10卷 289-300页

作者： Inna Novianty Ringga Gilang Baskoro Muhammad Iqbal Nurulhaq Muhammad Achirul Nanda Computer Engineering Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Informatics Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Technology and Plantation Production Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Department of Agricultural and Biosystem Engineering Faculty of Agro-Industrial TechnologyUniversitas PadjadjaranJatinangor 45363Indonesia

Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production,starting from the upstream and *** content can be used to monitor the progress of the oil palm fresh fruit bunch(FFB)and be applied to identify product *** on the near-infrared(NIR)signals,this study proposes an empirical mode decomposition(EMD)technique to decompose signals and predict the oil content of palm ***,350 palm fruits with Tenera varieties(Elaeis guineensis ***),at various ages of maturity,were harvested from the Cikabayan Oil Palm Plantation(IPB University,Indonesia).Second,each sample was sent directly to the laboratory for NIR signal measurements and oil content ***,the EMD analysis and arti-ficial neural network(ANN)were employed to correlate the NIR signals and oil ***,a robust EMD-ANN model is generated by optimizing the lowest possible *** on performance evaluation,the proposed technique can predict oil content with a coefficient of determination(R2)of 0.933±0.015 and a root mean squared error(RMSE)of 1.446±*** results demonstrate that the model has a good predictive capacity and has the potential to predict the oil content of palm fruits directly,without neither solvents nor reagents,which makes it environmentally ***,the proposed technique has a promising potential to be applied in the oil palm *** like this will lead to the effective and efficient management of oil palm production.

关键词： Artificial neural network Empirical mode decomposition Oil palm Oil content prediction

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A ShuffleNet Architecture-Based Multi-Layer Feature Scaling and Fusion Approach for Underwater Net Cage Damage Detection in Aquaculture

A ShuffleNet Architecture-Based Multi-Layer Feature Scaling ...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Huang, Xiang-Rui Chen, Yi-Cheng Chen, Liang-Bi Hu, Wu-Chih National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

ISBN: (纸本)9798331530839

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection. © 2024 IEEE.

关键词： Aquaculture

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Simulation of Data Acquisition System Using SimulIDE, Virtual Serial Port and Python 9

Simulation of Data Acquisition System Using SimulIDE, Virtua...

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9th International Conference on Education and Technology, ICET 2023

作者： Puji, Muhammad Nurul Astuti, Winda Binus Aso School of Engineering Bina Nusantara University Automotive and Robotics Engineering Program Computer Engineering Department Jakarta Indonesia

ISBN: (纸本)9798350358292

Data acquisition systems are commonplace in the current era of digitalization and Industry 4.0. These systems are typically used to obtain data from sensors, whether analog or digital, which is then stored for processing in subsequent stages. However, the implementation of hardware for creating data acquisition systems can be challenging due to constraints such as laboratory facilities, budget limitations, and other factors. Therefore, in this research, SimulIDE simulation software was employed to simulate electronic components and the Arduino Uno. To make this virtual Arduino appear as a physical one to the host computer, PyVirtualSerialPorts was used as a virtual serial port. The data acquisition system was created using Python programming with several key features: it can store data in CSV format, present data plots and graphs, and display the frequency spectrum of signals. The system achieved a sampling speed of 1000 data points per second, with input signals limited to a maximum of 10 Hz. This study also shows that signal distortion can occur due to excessively frequent data plotting and Fast Fourier Transform (FFT) processing. Therefore, the use of SimulIDE simulator, virtual serial port, and Python can be utilized in the learning process of a simple data acquisition system. © 2023 IEEE.

关键词： Python

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Histogram of Oriented Gradients (HOG) and Haar Cascade with Convolutional Neural Network (CNN) Performance Comparison in the Application of Edge Home Security System 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Asy’ari, Muhammad Zacky Filbert, Sebastian Sukra, Zener Lie Automotive and Robotics Program Computer Engineering Department BINUS ASO of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

In recent years, security has played a significant role in our daily lives. House security has become more popular with the enhancement of the Internet of Things (IoT) in intelligent home automation. This paper aims to build a system that can detect faces and the presence of unknown people using computer vision methods combined with the Internet of things. Two algorithms, Histogram of Oriented Gradients (HOG) and Haar Cascade classifiers with the enhancement of Convolutional Neural Network (CNN), were used to compare their performance. The system utilizes Nvidia Jetson Nano as an edge device, HD camera, Light Emitting Diode (LED), and sound buzzer as peripheral hardware. Telegram messenger application was used to notify the homeowner when uninvited guests passed through the pre-defined security area. In the result, the HOG method has 100% accuracy compared to 75% Haar Cascade with 720p video quality in the proposed developed system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Internet of things

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Arabic text sentiment analysis and emotion classification using transformers 11

Arabic text sentiment analysis and emotion classification us...

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11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Mossad, Nada Mohamed, Youssef Fares, A. Zaky, Ahmed B. Alexandria21934 Egypt Benha University The Computer Systems Engineering Program Faculty of Engineering at Shoubra The Department of Electrical Engineering Egypt

ISBN: (纸本)9798350369328

Sentiment analysis and emotion classification are two crucial components of natural language processing (NLP), which have been widely explored in recent years due to their broad applications. Sentiment analysis aims to identify the polarity of written texts, ranging from positive to negative. Meanwhile, emotion classification is focused on recognizing and categorizing the emotional states expressed in the text. To achieve a deeper understanding of sentiments and emotions, it's essential to utilize models like BERT transformers that can effectively interpret the context. The process begins with data preprocessing, including tokenization and noise removal, followed by fine-tuning techniques to adapt the BERT model to the proposed tasks. We employed the BERT model on four datasets obtained from various sources, including Twitter, news websites, and restaurant reviews, where each dataset represents a distinct Arabic dialect. Our proposed model outperforms commonly used techniques like LSTM and CNN, yielding superior results. Despite the progress made, there are still challenges to overcome, such as dealing with Arabic diacritics, the new Arabic Arabizi, which uses Latin characters, and handling Arabic idioms. Further research is required to address these challenges adequately. © 2023 IEEE.

关键词： Sentiment analysis

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Building a Brain computer Interface (BCI) Using Electroencephalogram (EEG) Signals' Classification 7

Building a Brain Computer Interface (BCI) Using Electroencep...

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7th International Conference on Advances in Biomedical engineering, ICABME 2023

作者： Younis, Mohammad Nabil Sleiman, Sary Haj Khadra, Salma Zaylaa, Amira Daher, Alaa Ayache, Mohammad Beirut Arab University Biomedical Program Depatment of Electrical and Computer Engineering Debbieh Lebanon

ISBN: (纸本)9798350325850

Development of Brain computer Interface (BCI) has been rapid since the mid 1990's. There are three criteria for BCI, (i) comfortability and possession of a suitable signal acquisition device, (ii) system validation and dissemination, and (iii) reliability and potentiality. As there are no BCI possessing the optimal criteria, it was essential to consider building a new one. Thereby, the paper investigates building BCI based on the utilization of EEG signals to translate brainwave patterns into actionable commands. The primary objective is to enhance communication capabilities for individuals afflicted with neurological disorders, empowering them to command external devices and engage more effectively with their surroundings. We built our model on EEG online dataset for the purpose of feature extraction and classification. Statistical features and Discrete Wavelet Transform (DWT) have been applied for feature selection. Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) were the classifiers involved. Results showed that the proposed architecture of MLP and RBF were able to classify the EEG signals into two classes (open eye and closed eye). Results also showed that the proposed approach, which is based on the combination of statistical features and DWT for features selection using AF3 and AF4 channels by the application of MLP, has 98% succession rate. BCI system based on Arduino circuit has been built after the classification Further algorithms and system evaluation need to be considered as future work. © 2023 IEEE.

关键词： Discrete wavelet transforms

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Security and Energy Efficiency Enhancement for the Internet of Things: Challenges, Architecture and Future Research

Security and Energy Efficiency Enhancement for the Internet ...

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International Conference on Data Science and Network engineering, ICDSNE 2023

作者： Dewan, Ritu Nagpal, Tapsi Ahmad, Sharik Rana, Arun Kumar Islam, Sardar M. N. Department of Computer Science and Engineering Lingayas Vidyapeeth Faridabad India Department of Computer Science and Engineering Galgotias College of Engineering & Technology Gr. Noida India SILC Decision Sciences and Modelling Program Victoria University Victoria Australia

ISBN: (纸本)9789819967544

Thanks to constantly advancing technology, the world is changing rapidly. One such idea that has contributed to the reality of automation is the Internet of Things (IoT). IoT links various non-living objects to the internet and enables them to communicate with their local network to automate processes and simplify people’s lives. The IoT’s potential needs to be completely realised despite the enormous efforts of standards, organisations, coalitions, businesses, academics and others. A number of problems remain. The enabling technology, applications and business models, as well as the social and environmental repercussions, should all be taken into consideration while analysing these difficulties. This article’s emphasis is on unresolved issues and challenges from a technological perspective. The main objective is to provide a thorough evaluation of IoT in terms of energy and security, as well as unresolved problems and obstacles that need further study. We provide some perspectives on some new concepts to help future research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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