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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Mironela, Pirnau Iustin, Priescu Mihaela, Priescu Catalina Claudiu, Pirnau Daniel, Ghiculescu Liviu 'Titu Maiorescu' University Faculty of Informatics Department of Informatics Bucharest Romania National University of Science and Technology Politehnica Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania National University of Science and Technology Politehnica Bucharest Faculty of Industrial Engineering and Robotics Manufacturing Engineering Department Bucharest Romania

ISBN: (纸本)9798350371154

This study proposes methods that can be used to examine and interpret comments that users have made after watching videos on YouTube on a particular topic. YouTube tutorials are very popular among young people. They have become an important pillar in informal education, thus contributing to the rapid acquisition of skills and knowledge. Under these circumstances, we were interested in analysing the YouTube videos comments, knowing that the platform has a continuous increase in popularity, which is also due to the opportunities of sharing them. Since ChatGPT-themed YouTube videos have seen a significant surge in popularity since 2022, we were interested to analyse some videos that approach the topics of 'ChatGPT, AI clone, AI robot and Deep Learning.' © 2024 IEEE.

关键词： Deep learning

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Novel replaceable EEG electrode system

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Current Directions in Biomedical engineering 2022年第2期8卷 249-252页

作者： Warsito, Indhika Fauzhan Fiedler, Patrique Komosar, Milana Haueisen, Jens Institute of Biomedical Engineering and Informatics Faculty of Computer Science and Automation Technische Universität Ilmenau Ilmenau Germany

Due to the direct contact between electrode and scalp, dry EEG electrodes are exposed to increased mechanical wear compared to conventional gel-based electrodes. However, state-of-the-art commercial cap systems commonly use permanently fixated electrodes which can lead to downtime of the EEG cap during professional repair and replacement as well as reduced overall lifetime. An easily replaceable EEG electrode would furthermore improve hygiene, especially for newborn and infant applications. We propose a novel replaceable electrode system, consisting of an electrode holder, a snap top, a contact ring fixated inside the electrode holder, and a replaceable electrode. The production process consists of 3D printing, silicone molding, resin casting, and electroless plating. The replaceable electrode system is integrated into a multichannel EEG cap system. A verification study is conducted with 30 volunteers. The operators experienced that the new electrode holder eases adjustment of the electrode to have proper contact with the scalp. During the study, defective electrodes can be replaced without a soldering process. Furthermore, all electrodes stayed in the holder and did not fall off the cap for the whole session. In conclusion, the novel replaceable electrode system is suitable for EEG measurements. © 2022 The Author(s), published by De Gruyter.

关键词： Electroencephalography

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EEG emotion recognition framework based on invariant wavelet scattering convolution network

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2181-2199页

作者： Elrefaiy, Ahmed Tawfik, Nahed Zayed, Nourhan Elhenawy, Ibrahim Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computers and Systems Department Electronics Research Institute Cairo Egypt Mechanical Engineering Department The British University in Egypt Cairo Egypt

EEG signals for real-time emotion identification are crucial for affective computing and human-computer interaction. The current emotion recognition models, which rely on a small number of emotion classes and stimuli like music and images in controlled lab conditions, have poor ecological validity. Furthermore, identifying relevant EEG signal features is crucial for efficient emotion identification. According to the complexity, non-stationarity, and variation nature of EEG signals, which make it challenging to identify relevant features to categorize and identify emotions, a novel approach for feature extraction and classification concerning EEG signals is suggested based on invariant wavelet scattering transform (WST) and support vector machine algorithm (SVM). The WST is a new time-frequency domain equivalent to a deep convolutional network. It produces scattering feature matrix representations that are stable against time-warping deformations, noise-resistant, and time-shift invariant existing in EEG signals. So, small, difficult-to-measure variations in the amplitude and duration of EEG signals can be captured. As a result, it addresses the limitations of the previous feature extraction approaches, which are unstable and sensitive to time-shift variations. In this paper, the zero, first, and second order features from DEAP datasets are obtained by performing the WST with two deep layers. Then, the PCA method is used for dimensionality reduction. Finally, the extracted features are fed as inputs for different classifiers. In the classification step, the SVM classifier is utilized with different classification algorithms such as k-nearest neighbours (KNN), random forest (RF), and AdaBoost classifier. This research employs a principal component analysis (PCA) approach to reduce the high dimensionality of scattering characteristics and increase the computational efficiency of our classifiers. The proposed method is performed across four different emotional classific

关键词： Support vector machines

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Emotion Recognition from Video Frame Sequence using Face Mesh and Pre-Trained Models of Convolutional Neural Network 24

Emotion Recognition from Video Frame Sequence using Face Mes...

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Adi, Derry Pramono Yuniarno, Eko Mulyanto Wulandari, Diah Puspito Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Dept. of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Dept. of Electrical Engineering Dept. of Computer Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Dept. of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350313956

Emotions are a collection of subjective cognitive experiences and psychological and physiological characteristics that express a wide range of feelings, thoughts, and behaviors in human interaction. Emotions can be represented through several means, such as facial expressions, tone of voice, and behavior. Deep Learning (DL) research has focused on incorporating facial expressions. Images with facial expressions are commonly used as data input for the DL model. Unfortunately, most DL models in Facial Emotion Recognition (FER) use static images. This method does not take into consideration all conceivable facial expressions. The static image of facial expressions is insufficient for recognizing emotions, but a sequential image from a video is required. In this study, we extract MediaPipe's face mesh feature, the state-of-the-art multidimensional expression key points embedded in the video image sequence. Furthermore, we feed sequence image data into the pre-trained Convolutional Neural Network (CNN) model. The data we used is from The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) with the emotion classes of 'Anger,' 'Fearful,' 'Happy,' and 'Sad.' For this specific FER task, we found that the best pre-trained CNN model achieved 92.8% accuracy (using the VGG-19 model), with the fastest runtime of ∼2.3 seconds (achieved using the SqueezeNet model). © 2023 IEEE.

关键词： Emotion Recognition

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Enabling Cybersecurity Mechanisms in a SmartPatch-Based Emergency Response System 31

Enabling Cybersecurity Mechanisms in a SmartPatch-Based Emer...

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2023 31st National Conference with International Participation, TELECOM 2023

作者： Kostoska, Magdalena Trajkovik, Vladimir Koteska, Bojana Ackovska, Nevena Lehocki, Fedor Bogdanova, Ana Madevska Ss. Cyril and Methodius University Faculty of Computer Science and Engineering Skopje Macedonia Faculty of Informatics and Information Technologies Bratislava Slovakia

ISBN: (纸本)9798350303292

This paper elaborates on the cybersecurity challenges of a healthcare system that includes a chest-patch (SmartPatch) sensor, used for the process management of massive victims' events. Integrating biosensors into emergency communication strategies requires mechanisms for achieving trustful communication.A use case of usage Bluetooth LE communication between SmartPatch and other device is presented in the paper, and presents the security challenges and potential solutions. © 2023 IEEE.

关键词： Cybersecurity

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Advanced brain tumor segmentation using DeepLabV3Plus with Xception encoder on a multi-class MR image dataset

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Multimedia Tools and Applications 2025年 1-22页

作者： Saifullah, Shoffan Dreżewski, Rafal Yudhana, Anton Faculty of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia

Accurate segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans presents notable challenges. This particularly in differentiating tumors from surrounding tissues with similar intensity. This study utilizes the DeepLabV3Plus model with an Xception encoder to address these challenges. We evaluated the model on a dataset of 3064 MR images, which included meningioma, glioma, and pituitary tumors. The model achieved high segmentation performance, with an accuracy of 0.9991, Dice Similarity Coefficient (DSC) of 0.9529, and Jaccard Index (JI) of 0.9133. It also demonstrated substantial boundary precision, recording an Average Symmetric Surface Distance (ASSD) of 0.84 mm and a Hausdorff Distance (HD) of 2.3 mm. These metrics surpass those of existing state-of-the-art models. While the model performed well with well-defined tumor boundaries, it faced challenges segmenting tumors with ambiuous edges. These findings suggest significant potential for enhancing diagnostic accuracy in neuro-oncology. Future work will focus on enhancing the model’s sensitivity to subtle tumor features and expanding the dataset to include a broader range of tumor characteristics. Additionally, incorporating multimodal imaging data could further improve segmentation precision. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Magnetic resonance imaging

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The Impact of Weather Conditions on Renewable Energy Production: A Machine Learning Approach

The Impact of Weather Conditions on Renewable Energy Product...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Özbay, Recep Çelebi, Merve Yavanoǧlu, Uraz Gazi University Graduate School of Informatics Dept. Of Computer Forensics Ankara Turkey Hatay Mustafa Kemal University Faculty of Education Dept. Of Computer Education and Instructional Technologies Hatay Turkey Gazi University Faculty of Engineering Dept. of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350362480

In recent decades, obtaining energy from wind, solar, and hydro energy has brought hope for a sustainable future. Machine learning (ML) significantly impacts the field of energy systems. ML techniques have emerged as a powerful tool for analyzing and optimizing energy systems. This approach can potentially reduce the cost of analyzing a device by focusing solely on the data generated during its operation. This paper investigates the application of regression models to predict renewable energy production based on weather conditions, aiming to enhance the reliability and efficiency of renewable energy systems. Experiments are performed using Decision Tree (DT), k-nearest Neighbors (KNN), Multiple Linear (MLR), Polynomial (PR), Random Forest (RF), and Support Vector (SVR) Regression algorithms. The performances of ML models are compared based on the evaluations obtained through the Coefficient of Determination (R2), and error measures namely Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). RF is the most successful algorithm in predicting renewable energy production depending on weather conditions as it has the highest R2 value and the lowest error measures. © 2024 IEEE.

关键词： Clean energy

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A Novel Drone-Station Matching model in Smart Cities based on Strict Preferences

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无人系统（英文） 2023年第3期11卷 261-271页

作者： Debolina Nath Anjan Bandyopadhyay Ankit Rana Tarek Gaber Aboul Ella Hassanien Department of Computer Science and Engineering Amity University Kolkata KolkataWest BengalIndia Department of Computer Science and Engineering Gitam University BangaloreKarnatakaIndia School of Science Engineeringand Environment University of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailiaEgypt Scientific Research Group in Egypt(SRGE) Egypt Faculty of Computer and Artificial Intelligence Cairo UniversityCairoEgypt Scientific Research Group in Egypt(SRGE) Egypt

There has been a considerable increase in the use of drones,or unmanned aerial vehicles(UAVs),in recent times,for a wide variety of purposes such as security,surveillance,delivery,search and rescue operations,penetration of inaccessible or unsafe areas,*** increasing number of drones working in an area poses a challenge to finding a suitable charging or resting station for each drone after completing its task or when it goes low on its *** classical methodology followed by drones is to return to their pre-assigned charging station every time it requires a *** approach is found to be inefficient as it leads to an unnecessary waste of time as well as power,which could be easily saved if the drone is allotted a nearby charging station that is ***,we propose a drone-allocation model based on a preference matching algorithm where the drones will be allotted the nearest available station to land if the station is *** problem is modeled as three entities:Drones,system controllers and charging *** matching algorithm was then used to design a Drone-Station Matching *** simulation results of our proposed model showed that there would be considerably less power consumption and more time saving over the conventional *** would save its travel time and power and ensure more efficient use of the drone.

关键词： Unmanned Aerial Vehicle(UAV) stable matching resource allocation smart city matching algorithm

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Ensemble based unsupervised anomaly detection with concept drift adaptation for time series data 10

Ensemble based unsupervised anomaly detection with concept d...

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10th IEEE Smart World Congress, SWC 2024

作者： Li, Danlei Nair, Nirmal Wang, Kevin I-Kai Sakurai, Kouichi University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Kyushu University Department of Informatics Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9798331520861

Anomaly detection is the identification of instances that substantially deviate from the majority of the data and do not conform to a well-defined normal behavior. Investigating time series anomalies has become increasingly popular in recent years due to widely deployed IoT devices and sensors. Although most existing methods apply image anomaly detection algorithms directly on time series and commonly assume the data is IID (independent and identically distributed). However, in most realworld applications, the underlying data distribution alters over time. Changes in data over time are referred to as concept drift and can significantly degrade the efficacy of an anomaly detection model. Existing research also indicates that the majority of anomaly detection frameworks ignored concept drift, resulting in poor long-term model performance. This paper presents an ensemble-based, unsupervised anomaly detection framework that is adaptive to the concept drift in time series data. Both distribution and performance-based techniques are used to detect concept drift and update the model to ensure the framework's longterm performance reliability. The proposed method is evaluated using a real-world univariate time series data that captures sea surface temperature data over a 2 -year period. By comparing our proposed framework to other state-of-the-art algorithms, we demonstrate our proposed method has obvious benefits in accuracy and long-term stability. © 2024 IEEE.

关键词： Anomaly detection Concept drift adaptation Ensemble method Time series Unsupervised method

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Usage of Machine Learning Methods for Cause-Effect Graph Feasibility Prediction 5

Usage of Machine Learning Methods for Cause-Effect Graph Fea...

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5th International Conference on Machine Learning and Intelligent Systems, MLIS 2023

作者： Krupalija, Ehlimana Cogo, Emir Pozderac, Damir Ali Al Zayat, Aya Bešić, Ingmar Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo71 000 Bosnia and Herzegovina

ISBN: (纸本)9781643684468

Cause-effect graphs (CEGs) are usually applied for black-box testing of complex industrial systems. The specification process is time-consuming and can result in many errors. In this work, machine learning methods were applied for predicting the feasibility of CEG elements. All information was extracted from graphs contained in CEGSet, a dataset of CEGs. The data was converted to two different formats. The Boolean features format represents relations as separate data rows, whereas the Term-Frequency times Inverse-Document-Frequency (TF-IDF) format represents graphs as data rows. Eight machine learning models were trained on this data. The results of testing by using the 80-20 holdout method indicate that important information is lost when converting the graphs to the TF-IDF format, whereas the Boolean feature format enables 100%-accurate predictions of ensemble methods. The achieved results indicate that pre-trained models can be used as help for domain experts during the CEG specification process. © 2023 The authors and IOS Press.

关键词： Machine learning

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