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47th International Conference on Telecommunications and Signal Processing, TSP 2024

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

ISBN: (纸本)9798350365597

This study introduces a novel approach to identifying human activities using wearable sensors, particularly smart-phones and smartwatches. By leveraging deep learning neural networks and data from the HHAR dataset, which includes accelerometer and gyroscope data from individuals engaged in various activities, our method, centered around the HAR-Res NeXt model, accurately detects six activities. Utilizing residual connections and multi-kernel blocks, our approach effectively captures temporal and spatial relationships in sensor data. Experimental results demonstrate superior performance to standard machine learning algorithms and other deep learning approaches for human activity recognition. HAR-ResNeXt achieves high accuracy rates, particularly in classifying smartphone sensor data, underscoring its adaptability across diverse scenarios. Comparative analysis reveals the effectiveness of smartphone sensors and emphasizes the importance of multi-modal sensor fusion for accurate activity detection. © 2024 IEEE.

关键词： Smartphones

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Privacy Vulnerabilities in Marginals-based Synthetic Data

Privacy Vulnerabilities in Marginals-based Synthetic Data

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Secure and Trustworthy Machine Learning (SaTML), IEEE Conference on

作者： Steven Golob Sikha Pentyala Anuar Maratkhan Martine De Cock School of Engineering and Technology University of Washington Tacoma USA Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

ISBN: (数字)9798331517113

ISBN: (纸本)9798331517120

When acting as a privacy-enhancing technology, synthetic data generation (SDG) aims to maintain a resemblance to the real data while excluding personally-identifiable information. Many SDG algorithms provide robust differential privacy (DP) guarantees to this end. However, we show that the strongest class of SDG algorithms-those that preserve marginal probabilities, or similar statistics, from the underlying data-leak information about individuals that can be recovered more efficiently than previously understood. We demonstrate this by presenting a novel membership inference attack, MAMA-MIA, and evaluate it against three seminal DP SDG algorithms: MST, PrivBayes, and Private-GSD. MAMA-MIA leverages knowledge of which SDG algorithm was used, allowing it to learn information about the hidden data more accurately, and orders-of-magnitude faster, than other leading attacks. We use MAMA-MIA to lend insight into existing SDG vulnerabilities. Our approach went on to win the first SNAKE (SaNitization Algorithm under attacK … ε) competition.

关键词： Differential privacy Machine learning algorithms Technology Machine learning Probability Inference algorithms Synthetic data Identification of persons

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A computational supervised neural network procedure for the fractional SIQ mathematical model

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European Physical Journal: Special Topics 2023年第5期232卷 535-546页

作者： Mukdasai, Kanit Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Singkibud, Peerapongpat Sadat, R. Ali, Mohamed R. Department of Mathematics Faculty of Science Khon Kaen University Khon Kaen 40002 Thailand Department of Mathematics and Statistics Hazara University Mansehra Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Applied Mathematics and Statistics Faculty of Science and Liberal Arts Rajamangala University of Technology Isan Nakhon Ratchasima 30000 Thailand Department of Mathematics Faculty of Engineering Zagazig University Zagazig Egypt Faculty of Engineering and Technology Future University in Egypt New Cairo 11835 Egypt Basic Engineering Science Department Benha Faculty of Engineering Benha University Benha Egypt Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

The purpose of the current work is to provide the numerical solutions of the fractional mathematical system of the susceptible, infected and quarantine (SIQ) system based on the lockdown effects of the coronavirus disease. These investigations provide more accurateness by using the fractional SIQ system. The investigations based on the nonlinear, integer and mathematical form of the SIQ model together with the effects of lockdown are also presented in this work. The impact of the lockdown is classified into the susceptible/infection/quarantine categories, which is based on the system of differential models. The fractional study is provided to find the accurate as well as realistic solutions of the SIQ model using the artificial intelligence (AI) performances along with the scale conjugate gradient (SCG) design, i.e., AI-SCG. The fractional-order derivatives have been used to solve three different cases of the nonlinear SIQ differential model. The statics to perform the numerical results of the fractional SIQ dynamical system are 7% for validation, 82% for training and 11% for testing. To observe the exactness of the AI-SCG procedure, the comparison of the numerical attained performances of the results is presented with the reference Adam solutions. For the validation, authentication, aptitude, consistency and validity of the AI-SCG solver, the computing numerical results have been provided based on the error histograms, state transition measures, correlation/regression values and mean square error. © 2022, The Author(s), under exclusive licence to EDP sciences, Springer-Verlag GmbH Germany, part of Springer Nature.

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Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

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computer Modeling in engineering & sciences 2024年第6期139卷 3353-3385页

作者： Murugan Palanikumar Nasreen Kausar Dragan Pamucar Seifedine Kadry Chomyong Kim Yunyoung Nam Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenlerIstanbul34220Turkey Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Yuan Ze UniversityTaoyuan City320315TaiwanChina Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos1102-2801Lebanon ICT Convergence Research Center Soonchunhyang UniversityAsan31538South Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538South Korea

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this *** is provided of the scoring function,accuracy function and operational laws of the log q-rung *** algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming ***,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify *** chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile *** numbers q are used to express the results of the *** demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

关键词： Vague set aggregating operators euclidean distance hamming distance decision making

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U-Net Implementation for High Throughput Grain Boundary Detection in Bright Field TEM Micrographs: Toward In Situ Grain Growth Studies

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1581-1582页

作者： Patrick, Matthew J. Eckstein, James K. Lopez, Javier Ma, Alan J. Toderas, Silvia Levine, Stacey Barmak, Katayun Department of Applied Physics and Applied Mathematics Columbia University New York NY USA Department of Physics University of Illinois -- Urbana-Champaign Champaign IL United States Department of Mechanical Engineering Columbia University New York NY USA Department of Mathematics Columbia University New York NY USA Department of Mathematics and Computer Science Duquesne University Pittsburgh PA United States

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A Deep Multi-Task Learning Network for Activity Recognition and User Identification Using Smartphone Sensors 16

A Deep Multi-Task Learning Network for Activity Recognition ...

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16th International Conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand

Human activity recognition (HAR) and user identification utilizing smartphone sensors are crucial in domains such as wellness tracking, tailored services, and security. Conventional approaches handle these activities as distinct entities, leading to less-Than-ideal performance and restricted applicability. This research presents a novel deep multi-Task learning network that simultaneously performs activity recognition and user identification using smartphone sensor data. By leveraging shared representations and task-specific information, the network enhances accuracy. It combines a shared convolutional neural network (CNN) with bidirectional long short-Term memory (BiLSTM) layers to extract features from raw sensor data. The CNN identifies patterns, while the BiLSTM captures sequential dependencies and contextual information. These features are processed by fully connected layers for both activity recognition and user identification. A multi-Task loss function is proposed to optimize both tasks based on their complexity and importance. The approach is tested on the UCI-HAR dataset, containing diverse activities from multiple users. Experimental results show that the proposed CNN-BiLSTM model achieves 97.64% accuracy in activity recognition and 82.59% in user identification, surpassing current state-of-The-Art methods. This demonstrates the effectiveness of the multi-Task learning framework and the advantage of using the shared CNN-BiLSTM architecture for improving both tasks. © 2025 The Author(s).

关键词： Convolutional neural networks

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LowResIR-Net: Lightweight Residual Network for ADL Recognition Using Low-Resolution Infrared Sensor Data 16

LowResIR-Net: Lightweight Residual Network for ADL Recogniti...

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Detecting activities of daily living (ADL) is crucial for supported living and medical monitoring. Traditional approaches rely on high-resolution sensors and computationally intensive algorithms, limiting their scalability and practical use in real-world settings. This research introduces LowResIR-Net, a compact deep residual network architecture specifically tailored for recognizing ADL utilizing low-resolution infrared sensor data. LowResIR-Net efficiently extracts key features from low-dimensional data by leveraging the advantages of infrared sensing, such as privacy protection and light resilience. Its architecture combines residual connections with depth-wise convolutions, enhancing feature learning while maintaining a compact model size. The model’s performance is evaluated using the Coventry-2018 dataset, containing low-resolution infrared data from three residential sensor locations. Our tests have shown that in all three cases, LowResIR-Net consistently achieves superior performance compared to other cutting-edge deep learning models, such as CNN, LSTM, BiLSTM, GRU, and BiGRU. LowResIR-Net attains the most excellent accuracies of 90.18%, 93.75%, and 93.30%, and F1-scores of 81.81%, 89.55%, and 88.92% for scenarios I, II, and III, respectively. Furthermore, it achieves these results with fewer parameters than most other models. LowResIR-Net’s strong performance highlights its ability to accurately capture complex patterns in low-resolution infrared data for ADL detection. This study demonstrates the potential of lightweight deep learning and low-resolution infrared sensors in developing privacy-focused and computationally efficient wellness monitoring systems. © 2025 The Author(s).

关键词： Infrared detectors

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Negative Binomial Regression Model for Predicting the Number of Tourists in National Parks of Thailand 10

Negative Binomial Regression Model for Predicting the Number...

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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： Thepsumritporn, Sthaporn Jitpattanakul, Anuchit Mekruksavanich, Sakorn Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand University of Phayao School of Information and Communication Department of Computer Engineering Phayao56000 Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798331543273

This article examines the use of count data models to predict the number of tourists visiting Thailand's national parks. The dataset, encompassing observations from 2016 to 2022 across 146 national parks, exhibits characteristics such as discreteness, over dispersion, and non-negative integer values. Two models - Poisson (POI), and Negative Binomial (NB) - were compared to identify the most effective approach for modeling and prediction. Model evaluation metrics, including the Akaike Information Criterion (AIC) and log-likelihood, were employed. Results demonstrate that the Negative Binomial regression model best captures the data's overdispersion compared to the POI model. The NB model achieved the lowest AIC and highest log-likelihood, affirming its superior performance for this application. Our findings underscore the NB model's utility in predicting tourism-related count data and suggest its broader applicability to other domains characterized by overdispersed count data. © 2025 IEEE.

关键词： Logistic regression

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Two-Step Register Allocation for Implementing Single-Path Code

Two-Step Register Allocation for Implementing Single-Path Co...

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International Symposium on Object-Oriented Real-Time Distributed Computing

作者： Emad Jacob Maroun Martin Schoeberl Peter Puschner Institute of Computer Engineering TU Wien Vienna Austria Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark

ISBN: (数字)9798350371284

ISBN: (纸本)9798350371291

Register allocation is a crucial step in the compilation pipeline that decides what program values occupy which physical registers. Single-path code’s use of predicated instructions instead of branching control-flow means register allocation must also allocate predicate registers. In this paper, we improve the original single-path transformation to allow generic register allocators to allocate predicate registers. Our improved transformation splits register allocation into two. First, the general-purpose registers are allocated as usual using a generic register allocator. Then, the main steps of the single-path transformation are performed while still using virtual predicate registers. Lastly, register allocation is rerun using the generic allocator to allocate the predicate registers. Our results show the improved single-path transformation increasing performance by up to 80 % and reducing code size by up to 43 % compared to the original transformation that uses a custom predicate allocator.

关键词： Codes Pipelines Real-time systems Registers Resource management Distributed computing

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SSABA:Search Step Adjustment Based Algorithm

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computers, Materials & Continua 2022年第6期71卷 4237-4256页

作者： Fatemeh Ahmadi Zeidabadi Ali Dehghani Mohammad Dehghani Zeinab Montazeri Stepán Hubálovsky Pavel Trojovsky Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Civil Engineering Islamic Azad UniversityEstahban BranchEstahbanIran Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Electrical and Electronics Engineering Shiraz University of TechnologyShirazIran Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

Finding the suitable solution to optimization problems is a fundamental challenge in various *** algorithms are one of the effective stochastic methods in solving optimization *** this paper,a new stochastic optimization algorithm called Search StepAdjustment Based Algorithm(SSABA)is presented to provide quasi-optimal solutions to various optimization *** the initial iterations of the algorithm,the step index is set to the highest value for a comprehensive search of the search ***,with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal,the step index is reduced to reach the minimum value at the end of the algorithm *** is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal *** results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed *** addition,the performance of the proposed SSABA is compared with the performance of eight well-known algorithms,including Particle Swarm Optimization(PSO),Genetic Algorithm(GA),Teaching-Learning Based Optimization(TLBO),Gravitational Search Algorithm(GSA),Grey Wolf Optimization(GWO),Whale Optimization Algorithm(WOA),Marine Predators Algorithm(MPA),and Tunicate Swarm Algorithm(TSA).The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.

关键词： Optimization population-based optimization problem search step optimization algorithm minimization maximization

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