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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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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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ROOM DIMENSION AND BOUNDARY CONDITION INFERENCE USING ROOM TRANSFER FUNCTIONS 10

ROOM DIMENSION AND BOUNDARY CONDITION INFERENCE USING ROOM T...

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10th Convention of the European Acoustics Association, EAA 2023

作者： Xia, Yuanxin Engsig-Karup, Allan P. Jeong, Cheol-Ho Acoustic Technology Department of Electrical and Photonics Engineering Technical University of Denmark Kgs. Lyngby2800 Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby2800 Denmark

ISBN: (纸本)9788888942674

The estimation of the absorption coefficients of the boundary surfaces in a room is important in room acoustic engineering. This research presents a machine learning method learns from simulated data to estimate the room dimensions and frequency-dependent absorption coefficients. We employ multi-task convolutional neural networks for inferring the frequency-dependent absorption coefficients and the dimensions of the room from transfer functions calculated by wave-based room acoustic methods. The proposed method provides reasonably accurate estimation of the boundary conditions and dimensions. © 2023 Xia et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Boundary conditions

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MNIST Digit Classification using Parallelised Convolutional Neural Network

MNIST Digit Classification using Parallelised Convolutional ...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： N. Aravindhraj V. Vilasini S Kiruthik Vishaal R Jaiprakash R Cibi Siddaarth Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

A program or piece of computer software is often built using serial computing techniques. In simple terms, a problem’s solution is created by breaking it down into smaller instructions, which are then each individually carried out by a computer’s Central Processing Unit (CPU). These modular instructions are first queued and then carried out one by one. Due to the fact that only one instruction was being performed at a time, this was a problem that could be plausibly argued to need to be solved in the computing industry. Hence, parallel computing is employed to benefit from serial computing. Using many processing elements simultaneously to solve a problem or carry out an instruction is known as parallel computing. Every operation is run or processed simultaneously, and problems are divided up into discrete instructions and solved that way. The number of Central Processing Units (CPU) in large-scale supercomputers is continually increasing, and parallelism is a key component of all modern supercomputer architectures. A deep learning technique called the Convolutional Neural Network (CNN) learns directly from the data. When dense layers are added, it transforms into a sizable polynomial time approximation technique. The Convolutional Neural Network (CNN) method will run faster and be greatly improved when it is parallelized. This strategy can be built on top of several convolution neural network -using applications. One such application that uses the convolution neural network technique to find numbers in images is MNIST classification.

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On Edge Irregular Reflexive Labeling of Categorical Product of Two Paths

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computer Systems science & Engineering 2021年第3期36卷 485-492页

作者： Muhammad Javed Azhar Khan Muhammad Ibrahim Ali Ahmad Centre for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakariya UniversityMultanPakistan College of Computer Science and Information Technology Jazan UniversityJazanSaudi Arabia

Among the huge diversity of ideas that show up while studying graph theory,one that has obtained a lot of popularity is the concept of labelings of *** labelings give valuable mathematical models for a wide scope of applications in high technologies(cryptography,astronomy,data security,various coding theory problems,communication networks,etc.).A labeling or a valuation of a graph is any mapping that sends a certain set of graph elements to a certain set of numbers subject to certain *** labeling is a mapping of elements of the graph,i.e.,vertex and for edges to a set of numbers(usually positive integers),called *** the domain is the vertex-set or the edge-set,the labelings are called vertex labelings or edge labelings ***,if the domain is V(G)[E(G)],then the labeling is called total labeling.A reflexive edge irregular k-labeling of graph introduced by Tanna et al.:A total labeling of graph such that for any two different edges ab and a'b'of the graph their weights has wt_(x)(ab)=x(a)+x(ab)+x(b) and wt_(x)(a'b')=x(a')+x(a'b')+x(b') are *** smallest value of k for which such labeling exist is called the reflexive edge strength of the graph and is denoted by res(G).In this paper we have found the exact value of the reflexive edge irregularity strength of the categorical product of two paths (P_(a)×P_(b))for any choice of a≥3 and b≥3.

关键词： Edge irregular reflexive labeling reflexive edge strength categorical product of two paths

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EnVoR: Multi-Granular Gold Price Prediction Using Ensemble Voting Regression

EnVoR: Multi-Granular Gold Price Prediction Using Ensemble V...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Rajkumar Kannan Samuel Giftson Durai Abdul Raouf Khan Kalaiarasi Sonai Muthu Department of Computer Science Bishop Heber College (Autonomous) Tiruchirappalli India Department of Information Technology University of Technology and Applied Sciences Muscat Oman Department of Computer Sciences King Faisal University Alhassa Saudi Arabia Faculty of Information Science and Technology Multimedia University Malaysia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Price prediction and forecasting have been important topics in the financial world. Among all commodities, such as gold, silver, platinum and others, gold has been the prime and forefront commodity which decides the price movements of other commodities. This paper proposes a unique multi-granular gold price prediction methodology, called EnVoR, by leveraging voting based regression tree models and utilizing simple, cumulative and exponential moving average values as features. Our proposed EnVoR predicts gold prices of next day, next week, next month and next quarter seasons. Extensive experiments are conducted on the gold price daily data from December 2011 to June 2024 to predict the future prices at various granularity levels. Experimental results on the gold price data have confirmed that the proposed EnVoR methodology has given superior performance in terms of highest regression accuracy and the lowest error values for MSE, RMSE, MAE and MAPE for predicting next day, next week, next month and next quarter price values. The proposed EnVoR model can be used by financial institutions and stock market analysts for providing buy, sell or hold recommendations to their clients.

关键词： Gold Silver Technological innovation Tracking Biological system modeling Market research Vectors Random forests Regression tree analysis Long short term memory

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Design and implementation of a scoring system for computer image recognition technology 12

Design and implementation of a scoring system for computer i...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Chang, Cheng Fanjiang, Yong-Yi Hung, Chi-Huang Wang, Li-Feng Chang, Ya-Jou Chang, Wen-Hsuan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering Dept. of Computer Science and Information Engineering New Taipei City Taiwan Fu Jen Catholic University Lee-Ming Institute of Technology Graduate Institute of Applied Science and Engineering Dept. of Information Technology New Taipei City Taiwan Tatung University Lee Ming Institute of Technology Graduate Institute of Design Science New Taipei City Taiwan Lee-Ming Institute of Technology Dept. of Applied Cosmetology New Taipei City Taiwan

ISBN: (纸本)9798350340181

This paper presents a scoring system for evaluating makeup paper pictures using computer vision feature recognition. In traditional makeup scoring systems, the assessment is conducted by human reviewers, leading to potential subjective biases and raising concerns about scoring fairness. To address this issue, the paper proposes a solution that utilizes computer vision feature recognition technology for scoring. By leveraging various computer vision techniques such as structural feature matching and image recognition retrieval, it develops a method for predicting makeup styles and scores based on an image learning model. An experimental platform is designed, utilizing paper drawings created by students and scores provided by multiple evaluators as makeup image data, to automatically generate grading results. This research demonstrates the potential of computer vision feature recognition technology to enhance scoring accuracy and objectivity in the future. © 2023 IEEE.

关键词： Grading

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Numerical Computation of SEIR Model for the Zika Virus Spreading

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computers, Materials & Continua 2023年第4期75卷 2155-2170页

作者： Suthep Suantai Zulqurnain Sabir Muhammad Asif Zahoor Raja Watcharaporn Cholamjiak Data Science Research Center Department of MathematicsFaculty of ScienceChiang Mai UniversityChiang Mai50200Thailand Research Group in Mathematics and Applied Mathematics Faculty of ScienceChiang Mai UniversityChiang Mai50200Thailand Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlinspan64002Taiwan School of Science University of PhayaoPhayao 56000Thailand

The purpose of this study is to present the numerical performancesand interpretations of the SEIR nonlinear system based on the Zika virusspreading by using the stochastic neural networks based intelligent computingsolver. The epidemic form of the nonlinear system represents the four dynamicsof the patients, susceptible patients S(y), exposed patients hospitalized inhospital E(y), infected patients I(y), and recovered patients R(y), i.e., SEIRmodel. The computing numerical outcomes and performances of the systemare examined by using the artificial neural networks (ANNs) and the scaledconjugate gradient (SCG) for the training of the networks, i.e., *** correctness of the ANNs-SCG scheme is observed by comparing theproposed and reference solutions for three cases of the SEIR model to solvethe nonlinear system based on the Zika virus spreading dynamics throughthe knacks of ANNs-SCG procedure based on exhaustive *** outcomes of the ANNs-SCG algorithm are found consistently in goodagreement with standard numerical solutions with negligible errors. Moreover,the procedure’s constancy, dependability, and exactness are perceived by usingthe values of state transitions, error histogram measures, correlation, andregression analysis.

关键词： SEIR nonlinear system Zika virus artificial neural networks scaled conjugate gradient statistical measures

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A Novel Methodology to Predict Flood and Landslide Disasters using Learning Assisted Artificial Intelligence (AI) Logic 3

A Novel Methodology to Predict Flood and Landslide Disasters...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Raj, G. Bhupal Patan, Muzeeb Khan Gupta, Ashwani Kumar Lakshmi, C. Gopinath, D. Yadav, Ajay Singh Sr University School of Agriculture Warangal India University of Technology and Applied Sciences College of Engineering and Technology Department of Eee Muscat Oman Chandigarh University Faculty of Business and Commerce Uttar Pradesh Unnao India Saveetha Engineering College Chennai India Department of Computer Science Karnataka Bengaluru India Srm Institute of Science and Technology Department of Mathematics Delhi-NCR Campus U.P Ghaziabad India

ISBN: (纸本)9798331509675

Comprehending the dynamics of flood and landslide prediction in current and adjacent regions is crucial for the formulation and advancement of effective predictive models, conservation, and management techniques. Conventional forecasting techniques, typically dependent on statistical models and empirical data, may prove inadequate because of the intrinsic complexities and variability of environmental elements. The complex interaction of climatic conditions, hydrological processes, geological attributes, and topographical aspects complicates precise forecasting. The constraints of traditional methods highlight the pressing necessity for more advanced and efficient predictive instruments. Artificial intelligence, especially machine learning (ML) and deep learning (DL), presents a robust paradigm that utilizes sophisticated algorithms to examine extensive datasets, discern intricate patterns, and produce more precise and prompt predictions. This proposed ViT-DL model prediction ability can greatly boost catastrophe preparedness, mitigation strategies, and ultimately, preserve lives and reduce economic damages. This study will comprehensively examine contemporary research trends, emphasizing the methodology used, model efficacy, related problems, and potential future research avenues in AI-based flood and landslide prediction. The experimental results of our proposed model produces 96.6% of Accuracy and 96.31% of Precision, comparision shows our model outperforms the overall accuracy to conventions model techniques. © 2025 IEEE.

关键词： Landslides

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Software Testing Issues Improvement in Quality Assurance 2

Software Testing Issues Improvement in Quality Assurance

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Salahat, Mohammed Said, Raed A. Hamid, Khalid Haseeb, Usama Abdel Maguid Abdel Ghani, Elsaid Abualkishik, Abedallah Iqbal, Muhammad Waseem Inairat, Mohammad University of Science and Technology of Fujairah College of Engineering and Technology Fujairah United Arab Emirates Canadian University Dubai Faculty of Management Dubai United Arab Emirates Superior University Department of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Superior University Department of Software Engineering Lahore Pakistan Canadian University Dubai Dubai United Arab Emirates American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates VICE CHANCELLOR Skyline University College Dubai United Arab Emirates

ISBN: (纸本)9798350335644

Quality assurance ensures that the project is carried out following the agreed-upon supplies, principles, and then purposes. The goal of type and its calibration investigation remains to better comprehend the multifaceted course of software analysis. Current software rehearses have some errors such as analysis techniques, user arrogances, and logistic culture. Altogether of these subjects, including such rushed testing, way to quickly times, totally inadequate supporting documents, and so on others must be addressed together. Describe a solution to the significant trouble of product testing in quality managing. We will discuss about the solutions of testing problems in quality management. propose a solution to the glitches mentioned above. © 2023 IEEE.

关键词： Quality control

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