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14th IEEE International Conference on Software engineering and Service science, ICSESS 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intclligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350336269

Wearable sensors and machine learning have opened new doors for innovative automated systems. Smart wearables like smartwatches and wristbands can efficiently record human movements due to their integrated, compact sensors. This data collection is vital for understanding human behavior, a field known as human activity recognition (HAR), dedicated to classifying actions. Deep learning methods, which autonomously extract intricate features, have shown promise in HAR. Sensor-based HAR is extensively studied in academia and applied across domains like wellness tracking, medical diagnosis, and mobility analysis. However, challenges persist in detecting complex activities. Our study focuses on recognizing aggressive behaviors through surface electromyography (sEMG) sensor data analysis. We introduce ResNeXt, a novel deep neural network, to improve recognition accuracy. Through a series of tests, we evaluate ResNeXt's recognition capabilities and compare its results to other deep learning models. Our experiments reveal that ResNeXt outperforms other models, achieving an impressive 94.06% overall accuracy and an exceptional 97.68% accuracy in distinguishing normal from aggressive actions. © 2023 IEEE.

关键词： Wearable sensors

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Position-aware Human Activity Recognition with Smartphone Sensors based on Deep Learning Approaches 46

Position-aware Human Activity Recognition with Smartphone Se...

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit Technology University of Phayao School of Information and Communication Department of Computer Engineering Phayao 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)9798350303964

Numerous research studies on human activity recognition (HAR) have utilized smartphone sensors, such as accelerometers, gyroscopes, and magnetometers. The accelerometer, in particular, has been considerably explored as the primary sensor in HAR research. Recently, researchers have integrated different smartphone sensors to enhance recognition interpretation. Nevertheless, it is still challenging to determine the optimal usage of these sensors, either individually or in combination, for better identification effectiveness in various motion situations. In this article, we explore how different motion sensors behave during the activity recognition process, using various deep learning (DL) approaches to classify physical actions based on smartphone sensor data. We evaluate the DL models using a publicly benchmarked dataset of data collected from ten participants performing eight movements while carrying smartphones in different situations. Our experimental results reveal that, except for the magnetometer, each sensor can play a leading role in HAR, depending on the recognized activity, body position, data features employed, and classification approach. Additionally, we find that the combination of sensors only improves overall recognition interpretation when their individual performances are low. © 2023 IEEE.

关键词： Smartphones

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Free-Weight Exercise Activity Recognition using Deep Residual Neural Network based on Sensor Data from In-Ear Wearable Devices 46

Free-Weight Exercise Activity Recognition using Deep Residua...

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao 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)9798350303964

Human activity recognition (HAR) is a popular study area in the current era of the Internet of Things and artificial intelligence. HAR approaches have been successfully applied in many real-world scenarios, such as sports tracking and assessment and remote healthcare for the elderly. Over the past decade, studies on HAR employing wearable sensors have been explored operating various wearable devices, including smartwatches, smart shoes, and other intelligent wearables. The advancement of sensor technology has directed the combining of inertial sensors into ear-worn devices, making it possible to record physical movements privately. In this study, we proposed a deep residual network named ResNeXt, for identifying free- weight exercises using sensor data from smart in-ear devices. To consider the performance of the proposed model and other deep learning models, we conducted experiments using a publicly available dataset called ERICA, which collected free-weight exercise activities from inertial sensors of both an in-ear device and a dumbbell. Our experimental results demonstrated that the suggested ResNeXt network surpassed other deep learning models, gaining the highest F1-score of 96.67% and 99.75% using inertial data from in-ear sensors and dumbbell sensors, respectively © 2023 IEEE.

关键词： Wearable sensors

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Breast Lesion Segmentation in DCE-MRI using Multi-Objective Clustering with NSGA-II

Breast Lesion Segmentation in DCE-MRI using Multi-Objective ...

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2022 International Conference on Innovative Trends in Information Technology, ICITIIT 2022

作者： Si, Tapas Patra, Dipak Kumar Mondal, Sukumar Mukherjee, Prakash Bankura Unnayani Institute of Engineering Department of Computer Science Engineering West Bengal Bankura722146 India Research Centre of Natural and Applied Sciences Department of Computer Science West Bengal Midnapore721102 India Department of Mathematics West Bengal Midnapore721102 India Hijli College Department of Mathematics West Bengal Kharagpur721306 India

ISBN: (纸本)9781665406703

Breast cancer causes the highest death among all types of cancers in women. Early detection and diagnosis leading to early treatment can save the life. The computer-assisted methodologies for breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) segmentation can help the radiologists/doctors in the diagnosis of the disease as well as further treatment planning. In this article, we propose a breast DCE-MRI segmentation method using a hard-clustering technique with a Non-dominated Sorting Genetic Algorithm (NSGA-II). The well-known cluster validity metrics namely DB-index and Dunn-index are utilized as objective functions in NSGA-II algorithm. The noise and intensity inhomogeneities in MRI are removed from MRI in the preprocessing step as these artifacts affect the segmentation process. After segmentation, the lesions are separated and finally, localized in the MRI. The devised method is applied to segment 10 Sagittal T2-Weighted fat-suppressed DCE-MRI of the breast. A comparative study has been conducted with the K-means algorithm and the devised method outperforms K-means both quantitatively and qualitatively. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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computer Systems science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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Visualization of Syntax and Semantics for Simple Functional Language of Natural Numbers and Boolean Values 10

Visualization of Syntax and Semantics for Simple Functional ...

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10th IEEE Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2022

作者： Perháč, Ján Ferencsik, Michal Zhukovskyy, Viktor Zhukovska, Nataliia Shatnyi, Serhii Technical University of Košice Faculty of Electrical Engineering and Informatics Department of Computers and Informatics Košice Slovakia National University of Water and Environmental Engineering Department of Computer Science and Applied Mathematics Rivne Ukraine

ISBN: (数字)9781665481779

ISBN: (纸本)9781665481779

Building an abstract syntax tree is an integral part of compilers. We analyze a few of the popular techniques providing such analysis in the web environment. Based on the analysis of these solutions, we deal with the implementation of our parser solution, which also provides a graphical representation of abstract syntax trees as well as proof trees. This solution is then used in our application designed to interpret the expressions of the simple teaching functional language of natural numbers and boolean values. In addition, the application supports the evaluation (semantics) of expressions (step by step), and also their individual properties: the depth of the syntax tree, syntax tree nodes count, and the constants used in the expression. © 2022 IEEE.

关键词： Semantics

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A novel finite element formulation for static bending analysis of functionally graded porous sandwich plates

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Frontiers of Structural and Civil engineering 2022年第12期16卷 1599-1620页

作者： Van Chinh NGUYEN Trung Thanh TRAN Trung NGUYEN-THOI Quoc-Hoa PHAM Faculty of Mechanical Engineering Le Quy Don Technical UniversityHanoiVietnam Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityVietnam Faculty of Mechanical-Electrical and Computer Engineering Van Lang UniversityHo Chi Minh CityVietnam Faculty of Engineering and Technology Nguyen Tat Thanh UniversityHo Chi Minh CityVietnam

This article aims to propose a finite element formulation based on Quasi-3D theory for the static bending analysis of functionally graded porous(FGP)sandwich *** FGP sandwich plates consist of three layers including the bottom skin of homogeneous metal,the top skin of fully ceramic and the FGP core layer with uneven porosity distribution.A quadrilateral(Q4)element with nine degrees of freedom(DOFs)per node is derived and employed in analyzing the static bending response of the plate under uniform and/or sinusoidally distributed *** accuracy of the present finite element formulation is verified by comparing the obtained numerical results with the published results in the ***,some numerical examples are performed to examine the effects of the parameters including power-law index k and porosity coefficient on the static bending response of rectangular FGP sandwich *** addition,a problem with a complicated L-shape model is conducted to illustrate the superiority of the proposed finite element method.

关键词： sandwich plates functionally graded porous static bending Quasi-3D theory Q4 element

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Optimizing Reliability in Total Artificial Heart Design Through Redundancy Strategies and Swarm-Inspired Optimization

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International Journal of applied and Computational mathematics 2023年第6期9卷 1-20页

作者： Bhandari, Ashok Singh Rawat, Suhani Sharma, Yashaswi Ram, Mangey Kumar, Akshay Department of Mathematics School of Basic and Applied Sciences Shri Guru Ram Rai University Uttarakhand Dehradun India Department of Mathematics Graphic Era Hill University Uttarakhand Dehradun India Department of Mathematics Computer Science and Engineering Graphic Era (Deemed to be University) Dehardun India Institute of Advanced Manufacturing Technologies Peter the Great St. Petersburg Polytechnic University Saint Petersburg 195251 Russian Federation

With noticeable advancements in medical technology, the seemingly impossible procedures have now come to practical implementation. Artificial Heart is one such device which is developed to replace ineffectual human hearts in order to provide a better quality of life for heart failure patients, making it crucial to hold high reliability. In the proposed work, the TAH is considered for the reliability redundancy allocation problem with an objective of maximizing system reliability. The non-linear mixed integer programming problem is solved by using the recently developed meta-heuristic—whale optimization algorithm. A comparative study is presented for optimized reliability of the system using active and cold standby redundancy strategies. © 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Active redundancy Artificial heart Cold-standby redundancy Reliability redundancy allocation problem Whale optimization algorithm

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Steganography Algorithm Based on Transparent Display Principle of Executable Program Icon 8th

Steganography Algorithm Based on Transparent Display Princip...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Tian, Zuwei Gao, Zhichen College of Computer Science Hunan First Normal University Changsha410205 China Department of Applied Mathematics and Statistics College of Engineering and Applied Sciences Stony Brook University Stony BrookNY11794-2300 United States

ISBN: (纸本)9783031067631

Icon resource is a very important resource in PE file. In order to get the best visual effect when displaying icons under different screen backgrounds and different operating system environments, PE files often store multiple icons of different sizes and different color depths. Because the size of the icon is irregular, the number of bytes filled is often large. These filled byte spaces are redundant and can be used to hide information without affecting the execution result and performance of the program. This paper presents a steganography algorithm based on transparent display principle of executable program icon, which hides secret information in transparent areas of icons. The algorithm has good concealment. The display effect of the embedded icon is the same as that of the original icon. It does not cause any visual distortion neither change the functionality of the executable. The size of the executable and the performance of the executable will not change. It can be widely used in covert data communication. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Steganography

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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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