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2020 2nd International Conference on science and Technology, ICoST 2020

作者： Riyantoko, P.A. Sugiarto Hindrayani, K.M. Data Science Department Faculty of Computer Science UPN "veteran" Jawa Timur Indonesia Informatics Department Faculty of Computer Science UPN "veteran" Jawa Timur Indonesia

Computer vision has the challenge to detect the facial emotions of humans. Recently, in computer vision and machine learning, it's possible to detect emotion from video or image accurate. In our research will propose to classify facial emotion using Haar-Cascade Classifier and Convolutional Neural Networks. The experiment uses the FER2013 dataset which was collected for the facial expression recognition dataset, and we proposed seven classified facial expression. The CNN model gain MSE and accuracy value based on epoch variety. The results showed that with the increase in the epoch value, the smaller MSE value would be obtained, likewise, the accuracy value would be increased. Thus, the proposed algorithm of CNN is proven to be effective for facial emotion detection. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Emotion Recognition

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3D Localization and data Quality Estimation with Marvelmind 2

3D Localization and Data Quality Estimation with Marvelmind

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2nd IEEE Conference on Information Technology and data science, CITDS 2022

作者： Vagner, Mate Palkovics, Denes Kovacs, Laszlo University of Debrecen Faculty of Informatics Debrecen Hungary Nvidia Deep Learning Institute Certified Instructor University of Debrecen Faculty of Informatics Department of Data Science and Visualization Debrecen Hungary

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

Localization and position estimation are crucial tasks in autonomous driving. In addition to the importance of positioning, with good quality position tracking, it is possible to implement sophisticated data collection procedures and use advanced machine learning methods such as reinforced learning. Global navigation satellite systems offer very accurate positioning, but their use is cumbersome or impossible under certain laboratory conditions. In our work, we applied an indoor positioning system, that is integrated into our 1:16 self-driving car. © 2022 IEEE.

关键词： Indoor positioning systems

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Efficient load profiling and forecasting in large electric power systems 19

Efficient load profiling and forecasting in large electric p...

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19th Conference Information Technologies - Applications and Theory, ITAT 2019

作者： Lendák, Imre Horváth, Tomáš Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Budapest Hungary

The goal of this paper is to present an efficient load forecasting algorithm for large electric power systems. It uses a combination of nearest neighbor-based load profile clustering and rule-based load forecasting. The load data was sliced into daily load curves, which were K-Means-clustered, thereby compressing data and simplifying the solution. K-Means was chosen in the proof of concept phase and will be substituted with more precise solutions later. In the forecasting phase the daily load profile is predicted based on the forecast date, day type (e.g. weekday or weekend) and historical consumption data for similar days in the past. The solution was tested on a large dataset consisting of one year-long, 5-minute measurement data in a 1900-power-line system. The solution showed excellent performance in both the training and forecast phases. It produced meaningful forecasts even when the input data contained significant amounts of anomalies. An additional advantage of the presented solution is that it can be used for medium and long-term forecasting with limited and/or missing input data. Copyright © 2019 for this paper by its authors.

关键词： Electric power plant loads

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Enhancing intrusion detection in IIoT: optimized CNN model with multi-class SMOTE balancing

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Neural Computing and Applications 2024年第24期36卷 14643-14659页

作者： Eid, Abdulrahman Mahmoud Soudan, Bassel Nassif, Ali Bou Injadat, MohammadNoor Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan

This work introduces an intrusion detection system (IDS) tailored for industrial internet of things (IIoT) environments based on an optimized convolutional neural network (CNN) model. The model is trained on a dataset that was balanced using a novel multi-class implementation of synthetic minority over-sampling technique (SMOTE) that ensures equal representation of all classes. Additionally, systematic optimization will be used to fine tune the hyperparameters of the CNN model and mitigate the effects of the increased size of the training dataset. Evaluation results will demonstrate substantial improvement in performance when the optimized CNN model is trained on the balanced dataset. The proposed IDS will be evaluated using the IIoT-specific WUSTL-IIOT-2021 dataset, and then its generalization capability will be verified using the non-domain specific UNSW_NB15 dataset. The model’s performance will be evaluated using accuracy, precision, recall, and F1-score metrics. The results will demonstrate that the proposed IDS is highly effective with performance exceeding 99.9% on all performance metrics. The IDS is also highly effective in detecting intrusion for generic IT networks achieving improvements in excess of 30% compared to the default baseline model. The results emphasize the versatility and effectiveness of the proposed IDS model, making it a reliable and adaptable solution for enhancing network security across diverse network environments. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neural network models

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Enhancing CNN-based network intrusion detection through hyperparameter optimization

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Intelligent Systems with Applications 2025年 26卷

作者： Kaissar, Antanios Nassif, Ali Bou Soudan, Bassel Injadat, MohammadNoor College of Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Data Science & AI Faculty of Information Technology Zarqa University Zarqa Jordan

This research investigates the optimization of hyperparameters in Convolutional Neural Networks (CNNs) to enhance the performance of Network Intrusion Detection Systems (NIDS). Four distinct optimization techniques, including Grid Search (GS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), are rigorously examined. Comprehensive experiments employ the UNSW-NB15 and CSE-CIC-IDS2018 datasets to determine the optimal hyperparameter configurations for each technique. Subsequently, detailed experiments evaluate the efficiency of the optimized models in detecting network attacks. The findings consistently reveal that optimized CNN models outperform their non-optimized counterparts across all optimization techniques. Notably, GWO emerges as the top-performing technique, achieving remarkable detection performance on both datasets, with 97.08 % accuracy, 96.95 % precision, 97.21 % recall, and 97.08 % F1 score on the UNSW-NB15 dataset, and 96.37 % accuracy, 96.13 % precision, 96.59 % recall, and 96.36 % F1 score on the CSE-CIC-IDS2018 dataset. Furthermore, hyperparameter optimization significantly reduces training and testing times. The GWO-optimized model achieved a reduction of >11 % in training time and 6.14 % in testing time on the UNSW-NB15 dataset. On the CSE-CIC-IDS2018 dataset, the GA-optimized model provided the best improvements, reducing training and testing times by 9.63 % and 8.61 %, respectively. In comparison to existing CNN models trained on the same datasets, the GWO-optimized CNN model consistently excels in all performance metrics, without the need for complex hybrid Deep Learning models. These results underscore the value of systematic hyperparameter optimization in enhancing CNN-based NIDS, with GWO standing out as a compelling technique for achieving optimal model configurations. © 2025 The Author(s)

关键词： Particle swarm optimization (PSO)

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Neuroprotective effect of Spilanthes acmella Murr. on pesticide-induced neuronal cells death

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Asian Pacific Journal of Tropical Medicine 2017年第1期10卷 35-41页

作者： Wilasinee Suwanjang Bongkot Khongniam Sujittra Srisung Supaluk Prachayasittikul Virapong Prachayasittikul Center for Research and Innovation Faculty of Medical TechnologyMahidol University Department of Chemistry Faculty of ScienceSrinakharinwirot University Center of Data Mining and Biomedical Informatics Faculty of Medical TechnologyMahidol University Department of Clinical Microbiology and Applied Technology Faculty of Medical TechnologyMahidol University

Objective: To investigate protective effects of Spilanthes acmella(S,acmella) Murr,extracts against pesticide-induced neuronal cells death and to elucidate the underlying molecular mechanism in dopaminergic(SH-SY5Y) cells lines,Methods: Cell viability of SH-SY5 Y cells was studied by treating the cells with various concentration of pirimicarb for 24 hr,Neuroprotective effect of S,acmella Murr,extracts was investigated by adding the plant extracts to the medium for 24 hr prior to the incubation with 100 μM H_2O_2 or with pirimicarb for 24 hr,Control-untreated cells were incubated with the culture medium,Cell viability was measured by MTT assay,calpain and calpastatin expressions were analyzed by Western blotting and immunocytochemistry,Results: Pretreatment of SH-SY5 Y cells with S,acmella Murr,extracts(1 μg/m L) for 24 hr significantly increased the dopaminergic neurons in pirimicarb-induced neurotoxicity,In addition,pretreatment with the S,acmella Murr,extracts led to decreased calpain but increased calpastatin protein levels,Conclusion: S,acmella Murr,extracts exerted neuroprotective effect,via an alteration of calcium homeostasis,against pirimicarb induced neurotoxicity,The S,acmella Murr,might be a potential natural candidate with neuroprotective activity.

关键词： Spilanthes acmella Murr. Pirimicarb Pesticide Neuronal cell Calpain Calpastatin

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Towards conceptually novel oscillating agent-based simulation of the relationship between cultural participation and social capital 1st

Towards conceptually novel oscillating agent-based simulatio...

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1st International Euro Mini Conference on Modeling and Simulation of Social-Behavioral Phenomena in Creative Societies, MSBC 2019

作者： Laužikas, Rimvydas Plikynas, Darius Faculty of Communication Vilnius University Vilnius Lithuania Institute of Data Science and Digital Technologies Faculty of Mathematics and Informatics Vilnius University Vilnius Lithuania

ISBN: (纸本)9783030298616

Effective simulation and prediction of the social impact of culture is one of the most important questions in contemporary social science and formative cultural policy. After a comprehensive review of the current simulation approaches, we found an evident lack of systematic conceptual models, however. It gave an impetus to investigate some novel conceptual approaches. In general, we admit that cultural events take part in the formation of social capital via the ability to communicate behavioral information in social networks. Following the bottom-up approach, implications of the social impact of cultural events are taking place on the individual (agent or actor) level first. Consequently, the aggregated effect can be simulated and predicted for the group or society (multiagent) level as well. For several reasons, we used CIDOC-CRM cultural ontology, which gives a structured framework of main cultural entities. We discovered that relations between them are not trivial and require fundamentally different viewpoints and simulation frameworks, which would better conform to the emergent complexity of social networks. For this reason, we analyzed in more detail Youri Lotman‘s semiosphere concept and OSIMAS (an oscillations-based multiagent system) paradigm. Consequently, in the proposed agent-based conceptual model, there is employed not only classical pair-to-pair based Axelrod’s neighborhood interaction model but also a one-to-many information broadcasting model. Such conceptual approach is able to provide simulation models for the complex emergent relations between cultural participation and social capital. © Springer Nature Switzerland AG 2019.

关键词： Multi agent systems

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Evaluating data sources for crawling events from the Web 17

Evaluating data sources for crawling events from the Web

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17th Conference on Information Technologies - Applications and Theory, ITAT 2017

作者： Horváth, Balázs Horváth, Tomáš ELTE - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Budapest Hungary

The bottleneck of event recommender systems is the availability of actual, up-to-date information on events. Usually, there is no single data feed, thus information on events must be crawled from numerous sources. Ranking these sources helps the system to decide which sources to crawl and how often. In this paper, a model for event source evaluation and ranking is proposed based on well-known centrality measures from social network analysis. Experiments made on real data, crawled from Budapest event sources, shows interesting results for further research. © 2017 B. Horváth, T. Horváth.

关键词： Web crawler

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A Plastic Waste Hotspot Detection System and Its Application to Measuring Environmental Improvement 1

A Plastic Waste Hotspot Detection System and Its Application...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Sasaki, Shiori Nakamura, Ryota Wibowo, Prasetyo Musashino University Department of Data Science Tokyo Japan Musashino University Faculty of Data Science Tokyo Japan Politeknik Elektronika Negeri Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350365191

This paper describes a Plastic Waste Hotspot Detection System which has been developed in an international collaborative research project to realize an 'Environmental AI-Human Actions integration' with marine plastic waste reduction actions. In this project, various local conditions are obtained to the system to realize an environmentally resilient society. The nature conservation activities such as marine plastic waste collection and reduction actions are implemented based on these conditions. In the process of integration of Environmental AI and Human Actions, the effects of these activities and activists' contribution to environmental improvement and global-scale knowledge are accumulated, shared, analyzed, and visualized through global-scale analysis. This paper describes the Plastic Waste Hotspot Detection system as one of the systems that integrate AI systems and human actions. This paper examines the feasibility and effectiveness of the system through the fieldwork tests in Phuket, Thailand and Surabaya, Indonesia. © 2024 IEEE.

关键词： Plastic recycling

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BlaBoO: A lightweight black box optimizer framework 1

BlaBoO: A lightweight black box optimizer framework

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1st IEEE World Symposium on Digital Intelligence for Systems and Machines, DISA 2018

作者： Kiss, Peter Fonyo, David Horvath, Tomas Department of Data Science and Engineering Faculty of Informatics ELTE - Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9781538651025

BlaBoO is an open source software initiative, that aims to help find optimal solution for the widest possible range of optimization problems such as, amongst others, hyperparameter tuning of machine learning algorithms. The software is able to optimize the parameters of any black-box function runnable in command line. It needs no cumbersome installation, launching the application requires running a Java executable file because of what the application runs under various platforms. BlaBoO provides a GUI as well as a batch mode processing abilities. It is easy to extend it by new optimization algorithms. BlaBoO is intended not only for managing black-box optimization experiments but also for education purposes. Its features make it a valuable tool for (not only beginner) machine learning practitioners, students and teachers as well. © 2018 IEEE.

关键词： Open source software

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