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2023 International Conference on electrical engineering and informatics, ICEEI 2023

作者： Al Mursyidy Fadhlurrahman, Jalaluddin Herawati, Neng Ayu Widya Aulya, Hayyu Rachma Puspasari, Ira Utama, Nugraha Priya School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Dinamika University Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9798350381290

The Steam platform releases thousands of games almost every week, providing customers with many options, which require various considerations to make a purchase decision. Steam has features that can help consumers consider purchasing games, such as the help dashboard that provides information on game reviews and ratings. However, based on user input reviews, game ratings are still manual, which may cause human error and inaccurate results. To help gamers get the desired game information and increase customer purchasing power towards available games, sentiment analysis is one technique for classifying opinions. This study focuses on sentiment analysis of game reviews, which will result in classification into two classes, positive and negative. It will be measured for its performance based on the f1 score evaluation results. The proposed model that we created is to compare sentiment analysis models using the baseline Bi-directional Long-short Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and fine-tuning Bidirectional Encoder Representations from Transformer (BERT). We then propose another model, BiLSTM-CRF, by examining the effect of using the last four hidden states of BERT and comparing it with the last one hidden state of BERT to be used in the BiLSTM-CRF model. Based on the results of sentiment analysis models we developed for the ten most reviewed games on Steam, the BiLSTM model combined with CRF on the last four hidden states yielded the best performance, with an Fl score of 95.2 %. This score exceeds the baseline transformer-based model's Fl score of 88.0%. This is because combining BiLSTM and CRF allows the log-likelihood of the BiLSTM emission sample to be obtained more accurately so the model does not lose its context. © 2023 IEEE.

关键词： Sentiment analysis

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Big Five Personality Prediction Based on Indonesian Tweets and Personality Test

Big Five Personality Prediction Based on Indonesian Tweets a...

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2023 International Conference on electrical engineering and informatics, ICEEI 2023

作者： Parameswara, Made Arbi Mahendra, Farrel Satya Putra Mardika, Muhammad Randy Cahya Puspasari, Ira Utama, Nugraha Priya School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Dinamika University Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9798350381290

The integration of human resources and artificial intelligence has shown a growing interest in automatically predicting personality traits, particularly in recruitment. To contribute to this field, we propose a new method to assist human resource professionals in Indonesia to identify candidates' personality traits. Biases come from personality tests due to their question format and susceptibility to manipulation on this research, we propose an approach that combines personality tests using the BFI form, which measures OCEAN personality traits, with personality trait predictions derived from Twitter data. This research aims to create a personality prediction model and system that efficiently and effectively screens candidate personality traits in human resources. This research has developed a novel model that combines personality test results with tweets personality prediction with an impressive F1-macro performance of 98%. © 2023 IEEE.

关键词： Machine learning

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An Explainable Artificial Intelligence model in the assessment of Brain MRI Lesions in Multiple Sclerosis using Amplitude Modulation - Frequency Modulation multi-scale feature sets 24

An Explainable Artificial Intelligence model in the assessme...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Nicolaou, Andria Kakas, Antonis Pattichis, Constantinos S. Pattichis, Marios S. Fotso, Kevin Loizou, Christos P. Pantzaris, Marios Department of Computer Science University of Cyprus Nicosia Cyprus Department of Electrical Engineering and Computer Engineering University of New Mexico United States Department of Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Cyprus Institute of Neurology and Genetics Nicosia Cyprus

ISBN: (纸本)9798350339598

The objective of this study was to implement an explainable artificial intelligence (AI) model with embedded rules to assess Multiple Sclerosis (MS) disease evolution based on brain Magnetic Resonance Imaging (MRI) multi-scale lesion evaluation. Amplitude Modulation-Frequency Modulation (AM-FM) features were extracted from manually segmented brain MS lesions obtained using MRI and were labeled with the Expanded Disability Status Scale (EDSS). Machine learning models were used to classify the MS subjects with a benign course of the disease and subjects with advanced accumulating disability. Rules were extracted from the selected model with high accuracy and then were modified to perform argumentation-based reasoning. It is demonstrated that the proposed explainable AI modeling can distinguish MS subjects and give meaningful information to track the progression of the disease. Future research will examine more subjects and add new feature sets and models. © 2023 IEEE.

关键词： Frequency modulation

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Improving Information Freshness in Edge-Assisted Smart Grids: An AoI-Aware Routing Strategy for Neighborhood Area Networks 22

Improving Information Freshness in Edge-Assisted Smart Grids...

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22nd IEEE International Conference on Industrial informatics, INDIN 2024

作者： Farag, Hossam Kotb, Mostafa Stefanović, Čedomir Gidlund, Mikael Department of Electronic Systems Aalborg University Denmark Department of Informatics Universität Hamburg Germany Department of Electrical Engineering Aswan University Egypt Mathematics Department Aswan University Egypt Department of Computer and Electrical Engineering Mid Sweden University Sweden

ISBN: (纸本)9798331527471

The integration of edge controllers into smart grid infrastructures facilitates advanced functionalities and high responsiveness, thereby bolstering the overall efficiency of the energy grid. The freshness of the sensing information received at the edge controller, captured by the Age of Information (AoI) metric, is vital to maintain system stability, where outdated information may lead to incorrect responses to grid conditions, potentially causing inefficiencies or system disruptions. However, the timeliness of the transmitted updates is mainly compromised by the delay and congestion within the routing links in the Neighborhood Area Network (NAN). In this work, we develop an intelligent routing strategy to improve the AoI of the Routing Protocol for Low-power and lossy networks (RPL), which is the common routing protocol for smart grids. Our proposed method is based on an AoI-aware parent selection mechanism, by which a node becomes attached to the parent with the highest probability of delivering a packet within a predefined AoI threshold. The prediction is made based on a supervised machine learning model, trained using the collection of heterogeneous routing metrics. The performance of the proposed method is evaluated via extensive discrete-event simulations and the results show its potency to improve the peak AoI and the AoI violation probability compared to the standard RPL. © 2024 IEEE.

关键词： Edge computing

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Anomaly Detection Employing a 5G Core Data Analytics Framework 13

Anomaly Detection Employing a 5G Core Data Analytics Framewo...

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13th IEEE International Conference on Cloud Networking, CloudNet 2024

作者： Oliveira, Junia Maisa Almeida, Jonatan De Britto E Silva, Erik Rodrigues Moreira, Larissa Ferreira Moreira, Rodrigo Silva, Flavio O. MacEdo, Daniel F. Nogueira, Jose Marcos Universidade Federal de Minas Computer Science Department Gerais Brazil University of Bologna Department of Electrical Electronic and Information Engineering Italy Universidade Federal de Minas Electrical Engineering Department Gerais Brazil Brazil Department of Informatics Portugal

ISBN: (纸本)9798350376562

The Network Data Analytics Function (NWDAF) within the 5G core is not an inherent feature of open-source 5G cores, making its implementation necessary based on provider demands. However, for those seeking to integrate NWDAF into the core but lacking expertise in core architecture or application programming interface (API) integration, this task can be daunting, potentially resulting in the abandonment of data analytics implementation efforts. While the current version of NWDAF specifies certain use cases, it does not provide alternatives for developing new analytics scenarios. To fill this gap, this paper proposes a framework for 5G network data analytics designed to facilitate algorithmic modifications and the addition of new analytical contexts. The framework was developed considering the 3GPP technical specifications related to data analytics. Its evaluation was performed through a use case focused on cybersecurity anomaly detection applied to a 5G network core. The results demonstrated that the framework simplifies installation, increases flexibility for new algorithms, and integrates seamlessly into the 5G network core, effectively addressing previously identified shortcomings. © 2024 IEEE.

关键词： 3GPP 5G core 5G network anomaly detection data analytics network data analytics NWDAF

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AtheroRisk: An Integrated computer Software System for Stroke Risk Stratification Utilizing Carotid Plaque Analysis in Ultrasound Videos

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SN computer Science 2025年第2期6卷 1-16页

作者： Gemenaris, Michalis Liapi, Georgia D. Markides, Christos Constantinou, Kyriacos Loizou, Christos P. Neophytou, Michalis Kardoulas, Dimitrios Pattichis, Marios S. Griffin, Maura Nicolaides, Andrew Pattichis, Constantinos S. Kyriacou, Efthyvoulos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science and Engineering Frederick University Nicosia Cyprus Department of Computer Science University of Cyprus Nicosia Cyprus American Medical Center Nicosia Cyprus Vascular Surgery Clinic Attikon General University Hospital Athens Greece Department of Electrical and Comp. Engineering Univ. of New Mexico Albuquerque NM United States Vascular Screening and Diagnostic Center Nicosia Cyprus

In this study we propose and evaluate AtheroRisk, a standalone integrated computer software system for the analysis of carotid B-mode ultrasound (U/S) images and videos. Our goal was to provide a tool to help physicians stratify stroke risk. The presented system is based on outcomes from different research studies, as well as European guidelines for the evaluation and treatment of carotid artery disease. The goal is to enable reproducible analysis of atherosclerotic plaques in real time. AtheroRisk brings together analysis of U/S image and/or video of the carotid arteries facilitating anonymization, standardization, noise filtering and segmentation of plaques;followed by image and motion feature extraction to derive plaque-composition and stability. All the above steps are combined to determine the annual stroke risk rate and the 5-year stroke-free survival rate. The system is powered by an SQLite local database, in which the end user can save and manage data extracted from processing and analysis in an anonymized way. The first version of AtheroRisk was developed following an incremental software development process. The developed system was verified using 54 U/S videos (27 Asymptomatic, AS;27 Symptomatic, SY cases) while the clinician’s satisfaction and comments were collected through a questionnaire-based validation process. The results show that the integrated system proposed in this study can be successfully used for the automated image and video analysis of the CCA plaque in ultrasound videos. © The Author(s) 2025.

关键词： Carotid plaque analysis Motion analysis Plaque features Stroke risk stratification Ultrasound video

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Enhancing Solar Farm Operations: Machine Learning for Equipment Fault Detection and Classification

Enhancing Solar Farm Operations: Machine Learning for Equipm...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Hamza, Ali Ali, Zunaib Dudley, Sandra Saleem, Komal Christofides, Nicholas School of Engineering LondonSE1 0AA United Kingdom Department of Engineering and Construction London United Kingdom Frederick University Department of Electrical Engineering Computer Engineering and Informatics Cyprus

ISBN: (纸本)9798350376067

Photovoltaic (PV) energy is considered one of the most widespread renewable sources. In this study, the vulnerability of solar PV systems to various faults, leading to potential performance degradation, has been addressed. A robust fault detection and classification strategy is proposed, employing Gradient Boosting Machine Learning (ML) algorithms (Light Gradient Boosting Method (LGBM), Categorical Boosting (Cat-boost), and AdaBoost). The methodology involved formulating a comprehensive PV system to create a synthetic fault database, utilizing diverse features. To simulate normal conditions and various fault scenarios, a MATLAB/Simulink-based PV System is developed. The optimization of hyperparameters of ML algorithms has been achieved by grid search optimization technique, resulting in enhanced performance, and reduced computational cost/time. The study involved multiple independent runs on ML algorithms and the application of Principal Component Analysis (PCA) for dimensionality reduction in the context of fault classification to assess the accuracy and consistency. Cross-validation is implemented to ensure the generalization capability of the ML algorithms to unseen data. Comparison has been established with Random Forest (RF) algorithms to show the performance accuracy of ML in fault diagnosis of PV systems. © 2024 IEEE.

关键词： Dimensionality reduction

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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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Developing Energy Autonomous and Cable-less Multi-gateway LoRa Networks

Developing Energy Autonomous and Cable-less Multi-gateway Lo...

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2023 IEEE International Conference on Communications, Computing, Cybersecurity and informatics, CCCI 2023

作者： Valkanis, Anastasios Beletsioti, Georgia A. Kantelis, Konstantinos Nicopolitidis, Petros Papadimitriou, Georgios Louta, Malamati Aristotle University of Thessaloniki Department of Informatics Thessaloniki Greece University of Western Macedonia Faculty of Engineering Department of Electrical and Computer Engineering Kozani Greece

ISBN: (纸本)9798350302561

Long Range (LoRa) networks are a reliable and efficient solution to serve Internet of Things (IoT) applications in a long range rural and forest areas. The capability of the gateways, both for wireless connection with the backbone network and be powered by renewable energy resources, makes possible the development of energy autonomous and without the need of cabling LoRa networks. This perspective greatly facilitates the network installation in hard-to-reach areas, as well as reducing their operating costs. An important obstacle, for the operation of energy autonomous LoRa networks, is the need for continuous operation of the gateways defined by the LoRaWAN protocol, leading to increased power consumption. For this reason, it is necessary the creation of a protocol that will reduce the energy consumption of the gateways, while maintaining the quality of the services provided in the IoT applications. In this paper we present a new hybrid protocol for energy autonomous and cable-less multi-gateway LoRa networks. The simulation results showed that the proposed protocol ensures a more than 50% reduced energy consumption for the gateways, as well as increased efficiency and reliability compared to the existing protocol, at the cost of slightly increased delay in data transmission. © 2023 IEEE.

关键词： Energy efficiency

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Decision Making in Precision Agriculture - The Case of VEL OS Intelligent Decision Support System 14

Decision Making in Precision Agriculture - The Case of VEL O...

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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Theodorou, Dimitrios Mantas, Nikolaos Karampelia, Ioanna Dimokas, Nikos Kyriakidis, Thomas Louta, Malamati University of Western Macedonia Faculty of Engineering Department of Electrical and Computer Engineering Kozani Greece University of Western Macedonia Department of Informatics Kastoria Greece

ISBN: (纸本)9798350318067

Precision agriculture includes Integrated Pest Management (IPM), which focuses on preventing and controlling pests and diseases through a combination of biological, traditional, and chemical practices. An Intelligent Decision Support System (VELOS) was developed for plant protection in bean cultivations in the Prespa region of Greece. VELOS utilizes remote sensing, GPS mapping, and sensor technologies to assist in collecting and generating massive amounts of high-quality data. This information is then used to monitor crop development, detect pests and diseases, and assess the efficacy of pest management strategies. The collected information is exploited by the Decision Support Systems (DSS) at the core of IPM. The DSS assists in saving resources and minimizing the overall cost while improving overall pest management efficiency. It also provides an intelligent decision support mechanism for pest management methods, such as which pesticides to apply and when to use them. A DSS can also help pest control practitioners monitor the effectiveness of pest management measures and modify them appropriately. This feedback loop can provide effective disease management, avoiding the danger of serious crop losses or property damage. The proposed Intelligent Decision Support System (VELOS) is responsible for generating suitable plant protection recommendations as well as scheduling their implementation. © 2023 IEEE.

关键词： Precision agriculture

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