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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

Accurate fall detection for orthopedic walker users is essential for timely medical intervention and enhancing safety for elderly and mobility-impaired individuals. This paper introduces a new method using a bidirectional gated recurrent unit (BiGRU) neural network to detect falls and classify motion patterns in walker usage. We use the Walker dataset, which includes inertial measurement unit sensor data for four motion classes: idle, motion, step, and fall. The dataset consists of 6-axis IMU readings (3-axis accelerometer and 3-axis gyroscope) from sensors mounted on orthopedic walkers, with around 620 samples per class. Our BiGRU model leverages temporal dependencies in both forward and backward directions to improve classification accuracy. We assess the model's performance in two scenarios: binary classification (fall vs. non-fall) and multi-class classification of all four motion types. The proposed architecture shows strong performance, achieving 99.79% accuracy and a 99.73% F1-score in binary classification, and 98.51% accuracy and a 98.50% F1-score in multi-class classification. It outperforms traditional deep learning models like CNN, LSTM, and GRU. The experimental results confirm the effectiveness of bidirectional processing in capturing temporal motion patterns, indicating significant potential for realtime fall detection in walker-assisted mobility. The high precision and recall values in both scenarios demonstrate the model's reliability for practical healthcare monitoring applications. © 2025 IEEE.

关键词： Recurrent neural networks

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Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

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computer Systems science & engineering 2023年第3期44卷 2395-2409页

作者： Ashit Kumar Dutta Basit Qureshi Yasser Albagory Majed Alsanea Manal Al Faraj Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Science Prince Sultan UniversityRiyadh11586Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake *** recently developed machine learning(ML)models can be employed for the detection and classification of fake *** study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity Fake News Detection and *** goal of the CAS-WELM technique is to discriminate news into fake and *** CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding ***,N-gram based feature extraction technique is derived to gen-erate feature ***,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS *** performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several *** experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.

关键词： Cybersecurity cybercrime fake news data classification machine learning metaheuristics

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Assessing Hallucination in Large Language Models Under Adversarial Attacks 9

Assessing Hallucination in Large Language Models Under Adver...

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9th International Conference on Mobile and Secure Services, MOBISECSERV 2024

作者： Tripathi, Shivangi Griffith, Henry Rathore, Heena Texas State University Department of Computer Science San MarcosTX United States San Antonio College Department of Math and Engineering San AntonioTX United States

ISBN: (纸本)9798350390896

Large Language Models (LLMs) are powerful tools capable of handling various tasks in natural language processing and beyond. Despite their advanced capabilities, LLMs are prone to generating hallucinations - responses that are fabricated from parameterized memorization rather than being factually accurate. Recent studies have showcased that hallucinations can not only naturally arise from issues related to the training data, training process, or inference stage, but can also result from adversarial attacks. This study extends the previous state-of-the-art by testing it on adversarial datasets. We conduct experiments using open-source models such as Vicuna-7B and LLaMA2-7B-chat and use performance metrics such as attack success rate and loss over epoch to evaluate the work on weak semantic and out-of-distribution attacks. The results showcase that the dataset selection plays a critical role in influencing the model susceptibility to attacks. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine Learning for Indoor Localization using CSI Technique 21

Machine Learning for Indoor Localization using CSI Technique

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Lee, Wilaiporn Suwanasri, Cattareeya Pipanmekaporn, Luepol Srisomboon, Kanabadee King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Sciences Bangkok Thailand

ISBN: (纸本)9798350377088

In this research, we propose a low-cost indoor localization technique using the CSI. By using CSI signal as input data, different locations and human activities are classified effectively using machine learning models. According to a low-cost constraint where a single transmitter (Tx) and receiver (Rx) are utilized, we investigate the appropriate transmitter location for achieving accurate position estimation in an indoor environment under two patterns of room environment. Moreover, an appropriate machine learning model for position estimation is examined where decision tree, KNN, Naïve bayes, and ANN are considered. The experimental results demonstrate that the highest accuracy can be achieved using the ANN technique. The shortest distance between a Tx and Rx provides the highest precision for determining the human location in an empty room, whereas placing the Tx and Rx in opposing positions is advantageous for a section with items positioned against the wall. When determining an average accuracy, placing the Tx and Rx in opposing positions shows the highest accuracy among others. © 2024 IEEE.

关键词： Adversarial machine learning

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System Approximation Using Pole Spectrum Analysis (PSA) and Modified Cauer Continued Fraction Method

System Approximation Using Pole Spectrum Analysis (PSA) and ...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Sengar, R.S. Awasthy, Neeta Singh, Bholey Singh, Ashish Pratap Kumar, Satender Kumar, Aadesh Gl Bajaj Group of Institutions Department of Electrical & Electronics Engineering Mathura India Gl Bajaj Group of Institutions Department of Computer Science and Engineering Mathura India Gl Bajaj Group of Institutions Department of Applied Science and Humanities Mathura India Gl Bajaj Group of Institutions Department of Electronics and Communications Engineering Mathura India Gl Bajaj Group of Institutions Department of Mechanical Engineering Mathura India

ISBN: (纸本)9798331527495

The manuscript presents an advanced approach for reducing large-scale Linear Time-Invariant (LTI) dynamic systems, utilizing a combined framework that integrates Dominant Pole Retention (DPR), Pole Spectrum Analysis (PSA), and Modified Cauer Continued Fraction (MCCF) method. The denominator dynamics of the reduced-order model are extracted via the PSA integrated with a pole dominance technique, while the numerator dynamics are obtained through MCCF method. Notably, for higher-order stable systems, the approach significantly guarantees the stability of the simplified system. To substantiate the efficacy and fidelity of the proposed technique, a set of numerical exemplars sourced from literature has been rigorously analyzed. Comparative evaluations have been conducted, leveraging performance indices and response congruence metrics to benchmark the alignment between the original and simplified system responses, thereby affirming the robustness and precision of the proposed reduction method. © 2025 IEEE.

关键词： Lyapunov methods

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Predicting Battery Levels of Sensor Nodes Using Reinforcement Learning in Harsh Underground Mining Environments 25

Predicting Battery Levels of Sensor Nodes Using Reinforcemen...

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40th Annual ACM Symposium on applied Computing, SAC 2025

作者： Yadav, Manish Anand Elmahallawy, Mohamed Madria, Sanjay Frimpong, Samuel Missouri S & T Rolla United States School of Engineering and Applied Science Washington State University TricitiesWA United States Department of Computer Science Missouri University of Science and Technology Rolla United States Department of Explosive & Mining Engineering Missouri University of Science and Technology United States

ISBN: (纸本)9798400706295

Underground mining is a hazardous environment, with frequent accidents leading to significant loss of life each year. To enhance safety, sensor nodes monitor key environmental factors such as temperature, toxic gases, and miners' locations, as well as transmit critical messages. Miners interact with these sensors, which track their movements, enabling their location to be determined even without GPS signals. Therefore, predicting the battery life of these sensors is essential for: (i) rerouting miners during emergencies, (ii) ensuring timely maintenance, and most importantly (iii) identifying sensors that need energy harvesting to maintain vital communication within the mine. In this work, we propose a deep reinforcement learning (DRL) approach, Proximal Policy Optimization-Long Short-Term Memory (PPO-LSTM), specifically tailored for the mining environment. This approach considers miners' movements and communication dynamics to predict sensor battery levels, facilitating timely "energy harvesting"for sensors nearing depletion at critical locations within the mine. Our PPO-LSTM framework integrates LSTM networks with PPO to leverage temporal data correlations, enabling better decision-making for energy management. Our extensive simulations demonstrate that the PPO-LSTM framework significantly outperforms current state-of-the-art methods, including Deep Deterministic Policy Gradient (DDPG) and Soft Actor-Critic (SAC). Specifically, it achieves improvements of approximately 4%, 1.07%, and 5-10% in Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), respectively. Copyright © 2025 held by the owner/author(s).

关键词： Risk perception

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Accurate and Fast Phase Compensation of Dynamic Samples in Digital Holographic Microscopy

Accurate and Fast Phase Compensation of Dynamic Samples in D...

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2023 Digital Holography and Three-Dimensional Imaging, DH 2023 in Optica Imaging Congress - Part of Imaging and applied Optics Congress 2023

作者： Obando-Vásquez, Sofía Doblas, Ana Trujillo, Carlos Applied Optics Group School of Applied Science and Engineering Universidad EAFIT Medellín Colombia Department of Electrical and Computer Engineering The University of Memphis TN United States

We present a fast algorithm for accurate phase compensation of holographic videos of dynamic phenomena recorded in a digital holographic microscope. The proposal is tested with human sperm and red blood cell samples. ... 详细信息

ISBN: (纸本)9781957171289

关键词： Holography

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Performance Comparison of the Invasive Weed Optimization K-Means Algorithm With Particle Swarm Optimization K-Means for Banking Data 6

Performance Comparison of the Invasive Weed Optimization K-M...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Suwirmayanti, Ni Luh Gede Pivin Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Setyaningsih, Emy Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Computer Systems Engineering Faculty of Applied Science Universitas AKPRIND Indonesia Yogyakarta Indonesia

ISBN: (纸本)9798331539603

Banking produces extensive and diverse data, so a clustering process is needed to understand customer behavior patterns and transactions more effectively. This clustering has been widely utilized with the K-Means algorithm due to its simplicity and efficiency. However, this algorithm is limited in determining the optimal cluster center, affecting the accuracy of the clustering results. This study proposes applying two metaheuristic algorithms, Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO), as additional solutions combined with K-Means. These two algorithms were chosen because of their ability to find global solutions and convergence speed to handle large and complex banking data. The results show the superiority of IWOKM compared to PSOKM in terms of SSE and DBI. From the SSE value, IWOKM produces a lower value (3029.77), which indicates that this method is better at minimizing clustering errors and producing more accurate clustering than PSOKM (80007.09). From the DBI value, IWOKM also produces a better value (1.6684) than PSOKM (1.6782), which shows that the clusters produced by IWOKM are more compact and more separated. However, in terms of computation time, PSOKM is more efficient, with a faster average computation time. Although IWOKM produces better clustering quality, PSOKM offers advantages in terms of processing speed. This finding confirms that the selection of the IWO algorithm is more appropriate for use on datasets with characteristics such as the German Credit Dataset. © 2024 IEEE.

关键词： Invasive weed optimization

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Fluorescence Lifetime of NV Centers for Temperature-Insensitive All-Optical Magnetic Field Sensing 26

Fluorescence Lifetime of NV Centers for Temperature-Insensit...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Horsthemke, Ludwig Pogorzelski, Jens Stiegekotter, Dennis Hoffmann, Frederik Bulter, Ann-Sophie Langguth, Lutz Staacke, Robert Trinschek, Sarah Gregor, Markus Glosekotter, Peter FH Münster - University of Applied Sciences Department of Electrical Engineering and Computer Science Steinfurt48565 Germany Quantum Technologies GmbH Leipzig04103 Germany FH Münster - University of Applied Sciences Department of Engineering Physics Steinfurt48565 Germany

ISBN: (纸本)9798331532833

We investigate the combined temperature and magnetic field dependent behavior of high NV-density microdiamonds in an all-optical frequency-domain based setup, utilizing the change in fluorescence lifetime. We use these frequency-domain data to train neural networks for the prediction of temperatures and magnetic fields. At zero magnetic field, we show the ability to predict temperatures in the range of 0° C to 100°C, with a standard deviation of 1.24 °C. Furthermore, we show the ability to predict applied magnetic fields, while varying the ambient temperature. We achieve a higher accuracy, compared to the approach of observing of the fluorescence intensity at a single excitation frequency. © 2024 IEEE.

关键词： Magnetometry

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Spectrum Serenade: OPNET Expedition Unveiling WLAN 802.11e Performance Evaluation 9th

Spectrum Serenade: OPNET Expedition Unveiling WLAN 802.11e P...

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9th Future Technologies Conference, FTC 2024

作者： Ali, Ali Mohd Owida, Hamza Abu Al-Qerem, Ahmad Department of Electrical Engineering College of Engineering Technology Al-Balqa Applied University Amman Jordan Medical Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13132 Jordan

ISBN: (纸本)9783031731242

Interest in Wireless LAN 802.11 is climbing owing to its cost-effectiveness and simplicity of installation, yet its restricted Quality of Service (QoS) capabilities present obstacles for real-time apps. This endeavour aims to evaluate QoS characteristics and boosts by devising scenarios within the OPNET simulator. Through contrasting Wireless LAN 802.11e with the standard 802.11, our goal is to measure how advancements in allocated channel access can elevate performance levels. Findings show that the improved standard provides a more effective service mechanism, giving priority to traffic that is highly important while keeping latency within acceptable bounds, hence enhancing QoS support for contemporary applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Quality of service

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