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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

Emotion recognition using EEG signals has gained growing attention in recent years due to its promising applications in human-computer interaction, affective computing, and mental health assessment. This study investigates the performance of various deep learning architectures in classifying emotional states from EEG recordings. We utilize the Emotion EEG dataset as a benchmark and evaluate five prominent deep learning models. The classification task targets three emotion categories: neu-tral, positive, and negative. Experimental results reveal notable differences in model performance. Among the tested models, BiGRU achieves the highest classification accuracy of 97.70% (±0.69%), followed closely by BiLSTM with 97.37% (±0.17%). These bidirectional architectures significantly outperform the CNN model, which attains an accuracy of only 61.59% (±1.22%). The LSTM and GRU models yield intermediate results, nearing 92% accuracy. The findings highlight the effectiveness of bidirectional recurrent neural networks in modeling the temporal characteristics of EEG signals for emotion detection. This research contributes to advancing EEG-based emotion recognition by identifying the most suitable deep learning strategies, thus supporting the development of more intelligent and emotion-aware computing systems. © 2025 IEEE.

关键词： Recurrent neural networks

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Secure digital databases using watermarking based on english-character attributes

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Recent Advances in computer science and Communications 2020年第3期13卷 477-481页

作者： Khatatneh, Khalaf Odeh, Ashraf Mashaleh, Ashraf Hamadeen, Hind Department of Computer Science Al Balqa Applied University As-Salt Jordan Department of Science and Technology Computer Quality Society Jordan Computer Center Al Balqa Applied University As-Salt Jordan Department of Software Engineering Al-Balqa Applied University As-Salt Jordan

Introduction: The single space and the double space (DS). In this procedure, an image is used to watermark a digital database, where the image bytes are divided into binary strings that block the text attributes of the selected database, we proposed an algorithm to defend against four common database attacks. Objective: Perform the watermark is Embedding and makes extraction of the watermark. We also describe the principal of the Embedding and extraction the watermark. Methods: The procedure to extract the watermark does not require knowledge of the original database that does not carry the same watermark. This feature is extremely important because it allows the discovery of a watermark in a copy of the original database, regardless of the subsequent updates to the asset. The extraction procedure is a direct reflection of the procedure used to embed the watermark is six steps. Results: Using new algorithm ability to develop a database watermark that would make it difficult for an attacker to remove or change the watermark without discovering the value of the object. To be judged effective, the database algorithm had to be able to create a strong enough watermark that could sustain the security of the database in the face of the following four types of attack: deletion of a sub-dataset, addition of a sub-dataset. Conclusion: The performance of the proposed algorithm was assessed in respect of its ability to defend the database against four common attacks for all tuples selection. © 2020 Bentham science Publishers.

关键词： Database systems

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A Comparative Study of Validation Methods for Sensor-Based Human Activity Recognition Using Deep Learning Models 10

A Comparative Study of Validation Methods for Sensor-Based H...

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作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

With the rise of wearable sensors and smart devices, human activity recognition (HAR) has become a vital research area in ubiquitous computing. Although many studies report high accuracy using k-fold cross-validation, these results often do not reflect actual generalization interpretation due to subject-dependent data leakage, where models test on activities from the subjects they were trained. This study compares traditional k-fold cross-validation with leave-one-subject-out (LOSO) validation using the WISDM dataset, highlighting the importance of proper validation techniques in HAR systems. We implemented and evaluated five advanced deep learning models - convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU) - on windowed raw sensor data. Our experiments showed significant differences between validation methods. The BiGRU model achieved 97.91% accuracy with k-fold cross-validation and 98.02% with LOSO validation, while the CNN model achieved only 92.55% and 93.44%, respectively. These results underscore the impact of both model architecture and validation approach on performance. Our findings stress the need for subject-independent validation strategies like LOSO to develop truly generalizable HAR systems. © 2025 IEEE.

关键词： Convolutional neural networks

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A Comparative Study of Validation Methods for Sensor-Based Human Activity Recognition Using Deep Learning Models

A Comparative Study of Validation Methods for Sensor-Based H...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Narit Hnoohom Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Computer Engineering Faculty of Engineering Image Information and Intelligence Laboratory Mahidol University Nakhon Pathom Thailand Department of Applied Statistics Faculty of Applied Science Research Group in Statistical Learning and Inference King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

With the rise of wearable sensors and smart devices, human activity recognition (HAR) has become a vital research area in ubiquitous computing. Although many studies report high accuracy using k-fold cross-validation, these results often do not reflect actual generalization interpretation due to subject-dependent data leakage, where models test on activities from the subjects they were trained. This study compares traditional k-fold cross-validation with leave-one-subject-out (LOSO) validation using the WISDM dataset, highlighting the importance of proper validation techniques in HAR systems. We implemented and evaluated five advanced deep learning models – convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU) – on windowed raw sensor data. Our experiments showed significant differences between validation methods. The BiGRU model achieved 97.91% accuracy with k-fold cross-validation and 98.02% with LOSO validation, while the CNN model achieved only 92.55% and 93.44%, respectively. These results underscore the impact of both model architecture and validation approach on performance. Our findings stress the need for subject-independent validation strategies like LOSO to develop truly generalizable HAR systems.

关键词： Deep learning Accuracy Bidirectional long short term memory Ubiquitous computing Data models Human activity recognition Convolutional neural networks Smart devices Wearable sensors Stress

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Electrically Programmable Non-Volatile Silicon Photonic Content Addressable Memory (CAM) cell

Electrically Programmable Non-Volatile Silicon Photonic Cont...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Lian, Chuanyu Sun, Hongyi Huang, Yi-Siou Vitale, Steven A. Hu, Juejun Takeuchi, Ichiro Youngblood, Nathan Vagionas, Christos Ríos Ocampo, Carlos A. Department of Materials Science & Engineering University of Maryland College ParkMD20742 United States Institute for Research in Electronics and Applied Physics University of Maryland College ParkMD20742 United States Advanced Materials and Microsystems Group MIT Lincoln Laboratory LexingtonMA02139 United States Department of Materials Science & Engineering MIT CambridgeMA02139 United States Quantum Materials Center Department of Physics University of Maryland College ParkMD20742 United States Electrical & Computer Engineering Department The University of Pittsburgh PittsburghPA15213 United States Department of Informatics Center for Interdisciplinary Research and Innovation Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9781957171395

We experimentally demonstrate the first electrically programmable, non-volatile silicon photonic content addressable memory cell using Sb2Se3 phase change material on microring resonators, opening the path for light-based search operations in zero-power look-up tables. CLEO 2024 © Optica Publishing group 2024 © 2024 The Author(s)

关键词： Silicon photonics

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Disjoint Compatibility via graph classes

arXiv

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arXiv 2024年

作者： Aichholzer, Oswin Obmann, Julia Paták, Pavel Perz, Daniel Tkadlec, Josef Vogtenhuber, Birgit Graz University of Technology Institute of Software Technology Austria Czech Technical University in Prague Department of Applied Mathematics Czech Republic University of Perugia Department of Engineering Italy Charles University Prague Computer Science Institute Czech Republic

Two plane drawings of graphs on the same set of points are called disjoint compatible if their union is plane and they do not have an edge in common. Let S be a convex point set of 2n ≥ 10 points and let H be a family of plane drawings on S. Two plane perfect matchings M1 and M2 on S (which do not need to be disjoint nor compatible) are disjoint H-compatible if there exists a drawing in H which is disjoint compatible to both M1 and M2. In this work, we consider the graph which has all plane perfect matchings as vertices and where two vertices are connected by an edge if the matchings are disjoint H-compatible. We study the diameter of this graph when H is the family of all plane spanning trees, caterpillars or paths. We show that in the first two cases the graph is connected with constant and linear diameter, respectively, while in the third case it is disconnected. © 2024, CC BY-NC-ND.

关键词： Graphic methods

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Proof of Location through a Blockchain Agnostic Smart Contract Language

Proof of Location through a Blockchain Agnostic Smart Contra...

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International Conference on Distributed Computing Systems Workshop

作者： Michele Bonini Mirko Zichichi Stefano Ferrettiv Gabriele D’Angelo Department of Computer Science and Engineering University of Bologna Italy Ontology Engineering Group Universidad Politécnica de Madrid Spain Department of Pure and Applied Sciences University of Urbino Carlo Bo Italy

Location-based services are at the heart of many applications that individuals use every day. However, there is often no guarantee of the truthfulness of users’ location data, since this information can be easily spoofed without a proof mechanism. In distributed system applications, preventing users from submitting counterfeit locations becomes even more challenging because of the lack of a central authority that monitors data provenance. In this work, we propose a decentralized architecture based on blockchains and decentralized technologies, offering a transparent solution for Proof of Location (PoL). We specifically address two main challenges, i.e., the issuing process of the PoL and the proof verification. We describe a smart contract based implementation in Reach, a blockchain-agnostic smart contract language, and the tests we conducted on different blockchains, i.e. Ethereum, Polygon, and Algorand, measuring latency and costs due to the payment of fees. Results confirm the viability of the proposal.

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Numerical Analysis of Soliton Propagation in a Tapered Waveguide

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Journal of Optical Communications 2023年第4期44卷 535-538页

作者： Raja, M.A. Ranathive, S. Sivaram, M. Krishna Kumar, L. Vinoth Kumar, K. Amiri, Iraj S Department of Biomedical Engineering Dr. N.G.P. Institute of Technology Coimbatore India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Networking Lebanese French University Kurdistan Region Erbil Iraq Department of CSE Nehru Institute of Engineering & Technology Coimbatore India Department of EEE Karunya Institute of Technology and Sciences Coimbatore India Computational Optics Research Group Advanced Institute of Materials Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Viet Nam

In this paper, dispersion decreased profiled tapered fiber is designed whose dispersion characteristics and soliton propagation is investigated numerically using Darboux transformation. The result reveals that solitons pulse gets compression as it propagates along the length of the tapered region. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Dispersion (waves)

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A Two-stage Anomaly Detection System for Fog Computing Services 5

A Two-stage Anomaly Detection System for Fog Computing Servi...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Ogundoyin, Sunday Oyinlola Adeniyi Kamil, Ismaila Ojedokun, Isaac Adewale Babalola, John Oluwaseun Nyangaresi, Vincent Omollo Bowen University Electrical and Electronic Engineering Programme Iwo Nigeria University of Ibadan Department of Electrical and Electronic Engineering Ibadan Nigeria Jaramogi Oginga Odinga University of Science and Technology Department of Computer Science and Software Engineering Bando40601 Kenya Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Applied Electronics Chennai602105 India

ISBN: (纸本)9798331542559

Fog computing enhances traditional cloud computing by bringing resources closer to edge devices, enabling faster response times and supporting mission-critical applications. However, its distributed nature makes it vulnerable to cyber-attacks like distributed denial-of-service (DDoS), resulting in service disruption and performance degradation. To mitigate this attack, a two-stage anomaly detection system (ADS) is proposed in this paper. The first stage uses AdaBoost (AB) ensemble machine learning to classify events as obstructive or non-obstructive. Obstructive events are offloaded to the cloud where they are classified into normal or specific attack categories using Long Short-term Memory Encoder-Decoder (LSTM-ED). The results demonstrate that the AB-based model achieves exceptional performance, with nearly 100% in accuracy, precision, recall, and a minimal mean squared error (MSE) of 8.34 × 10-5. The LSTM-ED model also performs well, with accuracy and MSE at 99.50% and 0.02686, respectively. © 2024 IEEE.

关键词： Fog computing

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An Anomaly Detection System Based on Clustering and Fuzzy Set Theory in VANETs 8th

An Anomaly Detection System Based on Clustering and Fuzzy Se...

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8th International Workshop on Soft Computing Applications, SOFA 2018

作者： Kuchaki Rafsanjani, Marjan Fatemidokht, Hamideh Balas, Valentina Emilia Batth, Ranbir Singh Department of Computer Science Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Department of Applied Mathematics Young Research Society Shahid Bahonar University of Kerman Kerman Iran Department of Automatics and Applied Software Faculty of Engineering Aurel Vlaicu University of Arad Arad Romania School of Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (纸本)9783030521899

In vehicular ad hoc network (VANET), which is considered as a type of mobile ad hoc network (MANET), communication occurs between neighbor vehicles and also roadside equipment and vehicles. Due to the characteristics of VANET, there are chances of a number possible attack in this network. In this paper, an anomaly detection system that uses clustering and fuzzy set theory to defend the Denial of Service (DoS) attack is presented. Results of simulation represent that the proposed algorithm arrives to the high detection rate. © 2021, Springer Nature Switzerland AG.

关键词： Fuzzy set theory

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