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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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ESTIMATION OF MOISTURE DISTRIBUTION IN SAWN TIMBER USING COMPUTED TOMOGRAPHY 13

ESTIMATION OF MOISTURE DISTRIBUTION IN SAWN TIMBER USING COM...

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13th World Conference on Timber Engineering: Timber for a Livable Future, WCTE 2023

作者： Poupet, Boris Couceiro, José Florisson, Sara Hansson, Lars Sandberg, Dick Division of Wood Science and Engineering Department of Engineering Sciences and Mathematics Luleå University of Technology Sweden Department of Materials Science and Engineering Division of Applied Mechanics Uppsala University Sweden Department of Ocean Operations and Civil Engineering Faculty of Engineering Norwegian University of Science and Technology Ålesund6025 Norway

ISBN: (纸本)9781713873297

Most of the physical, mechanical, and esthetic properties of wood products are affected by the drying of sawn timber. A better understanding of moisture transport in wood during kiln drying is necessary to obtain better quality products, shorter drying schedules, lower energy consumption, and a more sustainable process. Four-dimensional X-ray computed tomography (4DCT) (three-dimensional in space and one in time) with image processing techniques can be used to study the moisture content (MC) of sawn timber during kiln drying. The development of the technique is however made difficult by computational complexity and a lack of accurate experimental validation. In this study, a method relying on 4DCT has been developed using state-of-the-art image processing techniques. The method was validated by a regression analysis of the predicted MC against gravimetric measurements for different timber cross sections and different initial MC distributions on a significantly smaller scale than has previously been investigated. It is concluded that the MC can be estimated with an average uncertainty of ±4.8 percentage points on a 10 mm scale. Sawmills could ultimately benefit from a better understanding of wood-water interactions and dry sawn timber more efficiently. © (2023) WORLD CONFERENCE ON TIMBER ENGINEERING 2023 (WCTE 2023) All rights reserved.

关键词： Moisture

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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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Inventory Model for Imperfect Deteriorating Items Under Fuzzy Environment 11

Inventory Model for Imperfect Deteriorating Items Under Fuzz...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Mittal, Mandeep Pandey, Jayanti Tripathi Jain, Muskan School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Amity Institute of Applied Sciences Amity University Uttar Pradesh Department of Mathematics Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

The aim of this study is to introduce the application of fuzzy numbers in an Economic Order Quantity (EOQ) model for defective items under deterioration. In practice, the fuzziness of parameters tends to reduce over time compared to the theoretical assumptions, which justifies the use of fuzzy numbers. The proposed model starts with crisp approach which later involves the fuzzification of inventory parameters, such as demand rate, holding cost, deterioration rate, purchase cost, screening cost, and ordering cost, to account for uncertainty and imprecision. The triangular fuzzy numbers are utilized to represent these parameters, and a fuzzy model is developed accordingly. To defuzzify the model, Yager's ranking index is adopted, and a detailed comparison is made between crisp and general fuzzy solutions. The model is validated through numerical examples and graphical illustrations, followed by a thorough sensitivity analysis to gain a deeper comprehension of how modifications in parameters affect the optimal solution. © 2024 IEEE.

关键词： Deterioration

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Generating barcodes for nanopore sequencing data with PRO

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Fundamental Research 2024年第4期4卷 785-794页

作者： Ting Yu Zitong Ren Xin Gao Guojun Li Renmin Han Research Center for Mathematics and Interdisciplinary Sciences Frontiers Science Center for Nonlinear Expectations(Ministry of Education)Shandong UniversityShandong 266000China Computer Electrical and Mathematical Sciences and Engineering Division&Computational Bioscience Research CenterKing Abdullah University of Science and TechnologyThuwal 23955Saudi Arabia

DNA barcodes,short and unique DNA sequences,play a crucial role in sample identification when processing many samples simultaneously,which helps reduce experimental ***,the low quality of long-read sequencing makes it difficult to identify barcodes accurately,which poses significant challenges for the design of barcodes for large numbers of samples in a single sequencing ***,we present a comprehensive study of the generation of barcodes and develop a tool,PRO,that can be used for selecting optimal barcode sets and *** formulate the barcode design problem as a combinatorial problem and prove that finding the optimal largest barcode set in a given DNA sequence space in which all sequences have the same length is theoretically *** practical applications,we developed the novel method PRO by introducing the probability divergence between two DNA sequences to expand the capacity of barcode kits while ensuring demultiplexing ***,the maximum size of the barcode kits designed by PRO is 2,292,which keeps the length of barcodes the same as that of the official ones used by Oxford Nanopore Technologies(ONT).We validated the performance of PRO on a simulated nanopore dataset with high error *** demultiplexing accuracy of PRO reached 98.29%for a barcode kit of size 2,922,4.31%higher than that of Guppy,the official demultiplexing *** the size of the barcode kit generated by PRO is the same as the official size provided by ONT,both tools show superior and comparable demultiplexing accuracy.

关键词： Third-generation sequencing Nanopore sequencing DNA barcode Farthest point sampling algorithm High throughput

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STRONG CONVERGENCE OF AN INERTIAL EXTRAGRADIENT METHOD WITH AN ADAPTIVE NONDECREASING STEP SIZE FOR SOLVING VARIATIONAL INEQUALITIES

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Acta Mathematica Scientia 2022年第2期42卷 795-812页

作者： Nguyen Xuan LINH Duong Viet THONG Prasit CHOLAMJIAK Pham Anh TUAN Luong Van LONG Department of Mathematics Mathematics Falcuty of Information TechnologyNational University of Civil Engineering Division of Applied Mathematics Thu Dau Mot UniversityBinh Duong ProvinceVietnam School of Science University of PhayaoPhayao 56000Thailand Paculty of Mathematical Economics National Economics UniversityHanoi CityVietnam

In this work,we investigate a classical pseudomonotone and Lipschitz continuous variational inequality in the setting of Hilbert space,and present a projection-type approximation method for solving this *** method requires only to compute one projection onto the feasible set per iteration and without any linesearch procedure or additional projections as well as does not need to the prior knowledge of the Lipschitz constant and the sequentially weakly continuity of the variational inequality mapping.A strong convergence is established for the proposed method to a solution of a variational inequality problem under certain mild ***,we give some numerical experiments illustrating the performance of the proposed method for variational inequality problems.

关键词： Inertial method Tseng’s extragradient viscosity method variational inequality problem pseudomonotone mapping strong convergence

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Optimizing Error Detection in Generated Code Using Metaheuristic Optimized Natural Language Processing 6th

Optimizing Error Detection in Generated Code Using Metaheur...

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6th International Conference on Soft Computing and its Engineering Applications, icSoftComp 2024

作者： Villoth, Saramma John Villoth, John Philipose Jovanovic, Luka Mani, Joseph Zivkovic, Miodrag Zivkovic, Tamara Bacanin, Nebojsa Antonijevic, Milos Milovanovic, Marina University of Technology and Applied Sciences Nizwa Oman Singidunum University Danijelova 32 Belgrade Serbia Mathematics and Computer Science Modern College of Business and Science 3 Bawshar Street Muscat Oman

ISBN: (纸本)9783031880384

This paper tackles the issue of software defect identification by employing advanced classification methods, leveraging Term Frequency-Inverse Document Frequency encoding to process software source code akin to natural language processing. A semi-synthetic dataset is constructed using the mostly basic Python problems dataset. A code generator is trained to produce potential solutions based on specific prompts. Incorrect answers that do not meet the established test criteria are retained as defective code samples. The study evaluates the efficacy of the AdaBoost classifier in detecting such invalid code. Recognizing the critical influence of hyperparameter settings on classifier performance, several contemporary optimization techniques are employed to fine-tune these settings. Additionally, a modified version of the particle swarm optimization (PSO) algorithm is introduced, which demonstrates the highest performance among the tested methods, achieving an accuracy exceeding 0.983208. This approach underscores the potential of integrating advanced classification techniques with optimized hyperparameter tuning for effective software defect detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fault detection Metaheuristics Natural language processing Optimization Software testing

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Coherence Awareness in Diffractive Neural Networks

Coherence Awareness in Diffractive Neural Networks

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Kleiner, Matan Michaeli, Lior Michaeli, Tomer Faculty of Electrical and Computer Engineering Technion Haifa32000 Israel Division of Engineering and Applied Science California Institute of Technology 1200 E. California Avenue PasadenaCA91125 United States

We demonstrate the significant i nfluence of th e il lumination co herence on diffractive networks, and propose a framework for network optimization with any prescribed degree of spatial and temporal coherence. We ana... 详细信息

ISBN: (纸本)9781957171395

关键词： Diffractive optics

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Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

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2022 Saudi Arabia Smart Grid Conference, SASG 2022

作者： Kamarianakis, Yiannis Pantazis, Yannis Kalligiannaki, Evangelia Katsaounis, Theodoros D. Kotsovos, Konstantinos Gereige, Issam Abdullah, Marwan Jamal, Aqil Tzavaras, Athanasios Foundation for Research and Technology-Hellas Institute of Applied and Computational Mathematics Data Science Group Greece University of Crete Dept. of Mathematics and Applied Mathematics Greece Saudi Aramco RandD Center Carbon Management Division Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9781665475891

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average. © 2022 IEEE.

关键词： Forecasting

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Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems

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Mathematical Biosciences and Engineering 2024年第7期21卷 6471-6492页

作者： Ali, Ahmed Salah, Ahmad Bekhit, Mahmoud Fathalla, Ahmed Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044 Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ad-Dhahirah Ibri Oman Zagazig University Department of Computer Science Faculty of Computers and Informatics Sharkeya Zagazig Egypt Sydney Australia Kaplan Business School Sydney Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user’s data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user’s age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest. © 2024 the Author(s).

关键词： Forecasting

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