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2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Bhatia, Sandeep Goel, Amit Kumar Naib, Bharat Bhushan Singh, Khushwant Yadav, Mohit Saini, Ashish Galgotias University Department of CSE Uttar Pradesh Greater Noida India Maharshi Dayanand University U.I.E.T Department of Computer Science & Engineering Rohtak124001 India Maharshi Dayanand University U.I.E.T Department of Applied Sciences Mathematics Rohtak124001 India Maharshi Dayanand University U.I.E.T Department of Computer Science & Applications Rohtak124001 India

ISBN: (纸本)9798350311204

Patient's Infirmity Assumption system is a psychological gauge structure which predicts an infection in light of the information or signs went into the system and gives the specific happens. We suggest a creative replacement for the traditional methodology that resolves the drawn-out issues with expert course of action arranging. This approach is the remedy if one isn't too certifiable and simply has to retain what kind of illness they are dealing with. It very well may be a structure that gives the client urging on the most proficient method to keep their prosperity system with everything looking good and offers a strategy to recognize disease using this expectation. The prosperity section is indispensable to the treatment of patients' diseases, so it is furthermore a casing of help to the medical services portion that the client will be taught and helped inside the event that the individual decides not to visit a center or other facility, allowing the client to remember around their condition by entering the incidental effects and some other fitting information. © 2023 IEEE.

关键词： Diseases

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Hyperparameter Tuning in Convolutional Neural Network for Face Touching Activity Recognition using Accelerometer Data

Hyperparameter Tuning in Convolutional Neural Network for Fa...

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2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao 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 Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

People have been encouraged to wear masks and avoid touching their faces in public as part of the new measures to prevent the spread of coronavirus 2019 (COVID-19). During the COVID-19 epidemic, few research have examined the effect of everyday living on the frequency of facial touch activity. To develop a face touching avoidance system, deep learning algorithms have been proposed and have demonstrated their amazing performance. However, an important drawback of deep learning is its extensive dependence on hyperparameters. The results of deep learning algorithms may vary depending on hyperparameters, such as the size of the filters, the number of filters, the batch size, the number of epochs, and the training optimization technique used. In this paper, we present an effective approach for hyperparameter tuning of convolutional neural networks (CNNs) for efficiently recognized face touching activities based on accelerometer data. Two hyperparameter tuning methods (Grid search and Bayesian optimization) were evaluated in order to construct the CNN with high performance. The experiment results show that Bayesian optimization can provide suitable hyperparameters for CNNs for face touching recognition with the highest accuracy of 96.61%. © 2022 IEEE.

关键词： Convolutional neural networks

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Heterogeneous Recognition of Human Activity with CNN and RNN-based Networks using Smartphone and Smartwatch Sensors 3

Heterogeneous Recognition of Human Activity with CNN and RNN...

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3rd International Conference on Big Data Analytics and Practices, IBDAP 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science North Bangkok Bangkok Thailand

ISBN: (数字)9781665469388

ISBN: (纸本)9781665469388

With ongoing advancements in remote sensor technologies, the concept of wearable sensors has gained widespread acceptance and grown ubiquitous due to its broad applicability in situations including ambient assisted living, innovative healthcare, and home appliances. In this context, the demand for Heterogeneous Human Activity Recognition (HHAR) has expanded considerably, as individuals desire their daily activities to be collected and analyzed to provide valuable information. This wearable sensing data is taken in the form of time-series data utilizing different wearable sensors, such as accelerometers, gyroscopes, and magnetometers, to offer other movement characteristics for each individual tracked from their smartphones and smartwatches. This data may be analyzed using different deep learning architectures to provide an additional platform for capturing and classifying the numerous sensor-based actions an individual could be undertaking. This research compares HHAR models based on deep learning techniques. We study the influence of smartphone and wristwatch sensor data on model performance and classification of human activities. We train and evaluate deep learning models to achieve our study objective using a publically available standard dataset (HHAR) with a 5-fold cross-validation technique. Using smartphone sensors, research findings demonstrate that the RNN-based model is superior to CNN-based models, with the most fantastic accuracy of 98.42%. © 2022 IEEE.

关键词： Smartphones

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A Deep Residual-based Model on Multi-Branch Aggregation for Stress and Emotion Recognition through Biosignals 19

A Deep Residual-based Model on Multi-Branch Aggregation for ...

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19th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Mekruksavanich, Sakorn Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao 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 Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9781665485845

Stress and emotion recognition (SER) is a rapidly growing field of study that has applications in various areas, including psychological wellbeing, rehabilitative services, athletic training, and human-computer interaction. Biological information such as the electrocardiogram (ECG), electromyography (EMG), and electrodermal activity (EDA) has been frequently utilized for the SER for learning-based approaches. This study introduces a convolutional neural network motivated by ResNeXt to facilitate multimodal awareness. The proposed model, named StressNeXt, can extract high-level insights from raw bio-signal signals and classify emotional expressions effectively. We undertake a series of investigations using a publicly released standard dataset (WESAD) to determine the optimal implementation of the proposed solution for recognizing stress and emotion. After incorporating preliminary fusion events, we examined deep learning models using 5-fold cross-validation. Our study demonstrates that the suggested technique can comprehend robust multimodal representations with an accuracy of 87.73% utilizing EDA. Additionally, the identification was designed to provide better to 99.92% by fusing with accelerometer sensor data. © 2022 IEEE.

关键词： Emotion Recognition

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Lassa hemorrhagic fever model using new generalized Caputo-type fractional derivative operator

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第6期12卷 129-149页

作者： Pushpendra Kumar Vedat Suat Erturk Abdullahi Yusuf Tukur Abdulkadir Sulaiman Department of Mathematics and Statistics School of Basic and Applied Sciences Central University of Punjab BathindaPunjab 151001India Department of Mathematics Ondokuz Mayis University Atakum 55200SamsunTurkey Department of Computer Engineering Biruni University Istanbul Turkey Department of Mathematics Science Faculty Federal University DutseJigawa 7156Nigeria

In some of the previous decades, we have observed that mathematical modeling hasbecome one of the most interesting research fields and has attracted many *** this regard, thousands of researchers have proposed different varieties of mathematicalmodels to study the dynamics of a number of real-world problems. This research workis framed to analyzing the structure of the well-known Lassa hemorrhagic epidemic;adangerous epidemic for pregnant women, via new generalized Caputo type nonintegerorder derivative with the help of a modified Predictor–Corrector scheme. Lassa hemorrhagic disease is an epidemical and biocidal fever, whose negative impacts were initiallyrecognized in the countries of Africa. This virus has killed many pregnant women ascompared to the Ebola epidemic. It was noticed that Lassa virus was isolated in Verocell cultures from a blood pattern, and after 12 days it was ejective, after the climb ofthe sickness. In this research study, necessary theorems and lemmas are reminded toprove the existence of a unique solution and stability of given fractional approximationscheme. All necessary results are reminded to confirm the effectiveness of the proposedapproximation algorithm by graphical observations for various fractional-order *** our practical calculations, we plotted the graphs for two different values of naturaldeath rate along with various values of given fractional-order operator. Our major target is to show the importance of the proposed modified version of the Predictor–Correctoralgorithm in epidemic studies by exploring the given Lassa hemorrhagic fever dynamics.

关键词： Lassa hemorrhagic fever epidemic mathematical model new generalized Caputo fractional derivatives predictor–corrector algorithm

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How we can use text classification in the Back-Office environment of a bank as 'business as usual' solution 17

How we can use text classification in the Back-Office enviro...

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17th IEEE International Symposium on applied Computational Intelligence and Informatics, SACI 2023

作者： Krutilla, Zsolt Kovari, Attila Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary Informatics Institution University of Dunaújváros Dunaújváros Hungary Institute of Engineering Alba Regia Technical Faculty buda University Budapest Hungary Institute of Digital Technology Faculty of Informatics Eszterházy Károly Catholic University Eger Hungary University of Dunaújváros Institute of Engineering Department of Natural Science Dunaújváros Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary

ISBN: (纸本)9798350321104

Natural Language Processing nowadays provides scientists with many research areas and opportunities, but as with most applied sciences, our goal in Natural Language Processing is to refine the underlying science and technology until it can be used reliably for business purposes. Transformer models, such as GPT or BERT, are currently showing outstanding results in the field of natural-language processing, but they require huge computational power and data to teach, but these conditions can only be met by larger research centers and large companies, and their inaccuracy makes them unsuitable for use as a 'business as usual' (BAU) solution. In this paper, we present a solution that is able to overcome these problems by focusing on accuracy and usability, and that can also bring a new perspective to the process of teaching deep learning models. © 2023 IEEE.

关键词： Information retrieval systems

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local properties and augmented lagrangians in fully nonconvex composite optimization

arXiv

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arXiv 2023年

作者： De Marchi, Alberto Mehlitz, Patrick University of the Bundeswehr Munich Department of Aerospace Engineering Institute of Applied Mathematics and Scientific Computing Neubiberg85577 Germany Philipps-Universität Marburg Department of Mathematics and Computer Science Marburg35032 Germany

A broad class of optimization problems can be cast in composite form, that is, considering the minimization of the composition of a lower semicontinuous function with a differentiable mapping. This paper investigates the versatile template of composite optimization without any convexity assumptions. First- and second-order optimality conditions are discussed. We highlight the difficulties that stem from the lack of convexity when dealing with necessary conditions in a Lagrangian framework and when considering error bounds. Building upon these characterizations, a local convergence analysis is delineated for a recently developed augmented Lagrangian method, deriving rates of convergence in the fully nonconvex *** Codes 49J52, 49J53, 65K10, 90C30, 90C33 © 2023, CC BY.

关键词： Lagrange multipliers

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TrumorGPT: Query Optimization and Semantic Reasoning over Networks for Automated Fact-Checking

TrumorGPT: Query Optimization and Semantic Reasoning over Ne...

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Annual Conference on Information sciences and Systems (CISS)

作者： Ching Nam Hang Pei-Duo Yu Chee Wei Tan Department of Computer Science City University of Hong Kong Hong Kong School of Computing and Information Sciences Saint Francis University Hong Kong Department of Applied Mathematics Chung Yuan Christian University Taiwan School of Computer Science and Engineering Nanyang Technological University Singapore

In the age of social media, the rapid spread of misinformation and rumors has led to the emergence of infodemics, where false information poses a significant threat to society. To combat this issue, we introduce TrumorGPT, a novel generative artificial intelligence solution designed for automated fact-checking. TrumorGPT aims to distinguish "trumors", which are rumors that turn out to be true, providing a crucial tool in differentiating between mere speculation and verified facts. This framework merges machine learning with natural language processing techniques, leveraging a large language model (LLM) with few-shot learning for knowledge graph construction and semantic reasoning. TrumorGPT addresses the "hallucination" issue common in LLMs and the limitations of static training data by incorporating retrieval-augmented generation. This approach involves accessing and utilizing information from regularly updated knowledge graphs that consist of the latest news and information, ensuring that fact-checking of TrumorGPT is based on the most recent data. Accessing updated knowledge graphs greatly enhances the proficiency of TrumorGPT in delivering accurate and reliable information promptly. Evaluating with extensive datasets, TrumorGPT demonstrates superior performance in automated fact-checking. Its ability to effectively conduct automated fact-checking across various platforms marks a critical step forward in the fight against misinformation, enhancing trust and accuracy in the digital information age.

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Malicious Code Detection Based on Convolutional Neural Network and Information Enhancement Algorithms 2

Malicious Code Detection Based on Convolutional Neural Netwo...

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2nd International Conference on applied mathematics, Modeling and computer Simulation, AMMCS 2022

作者： Yi, Fangzhou Lv, Shengkai He, Peng Hu, Yvhang Zeng, Cheng School of Computer Science & Information Engineering Hubei University Wuhan430062 China Hubei Key Laboratory of Applied Mathematics Hubei University Wuhan430062 China State Grid Yichang Electric Power Supply Company Yichang443000 China

ISBN: (纸本)9781643683522

With the increasing proliferation of malicious code, the camouflage of malicious code is more difficult to cope with. Traditional malicious code detection techniques based on byte comparison have limited accuracy. Detection techniques based on traditional machine learning are highly dependent on feature selection, and the quality of the classifier directly affects the detection results;this increases the difficulty of accurately distinguishing the types of malicious code. To address these problems, a deep neural network-based malicious code detection method is proposed in this work. First, the code binary file is transformed into a corresponding gray-scale image, and then the enhanced RGBA image is formed by using an image enhancement scheme based on information entropy and code file structure. Afterwards, a convolutional neural network is used. The network extracts high-dimensional features of the enhanced code image, detects the malicious code, and classifies the malicious code. The experimental results show that the proposed method distinguishes malicious code with 98.83% detection accuracy. Its classification accuracy is 97.74% (with positive samples) and 98.85% (without positive samples). These high levels of accuracy are suitable for current complex and changeable malicious code environments, and can provide a new solution for the current malicious code detection field. © 2022 The authors and IOS Press.

关键词： Image enhancement

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Deep Learning Approaches for Unobtrusive Human Activity Recognition using Insole-based and Smartwatch Sensors 3

Deep Learning Approaches for Unobtrusive Human Activity Reco...

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3rd International Conference on Big Data Analytics and Practices, IBDAP 2022

作者： Hnoohom, Narit Maitrichit, Nagorn Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakorn Pathom Thailand 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: (数字)9781665469388

ISBN: (纸本)9781665469388

In this digital age, human activity recognition (HAR) has been playing an increasingly important role in almost all aspects of life. Healthcare systems for monitoring the activities of daily living (ADL), security environments for automatically detecting abnormal activity and notifying the pertinent authorities, and increasing human contact with computers are all examples of HAR applications. The data collecting tools used in HAR research can also be categorized (sensors or cameras). In sensor-based HAR, various wearable sensors have been investigated for their usefulness in effectively detecting both simple and complex human activities. In this work, we studied HAR using signal data acquired from insole-based and smartwatch sensors. To achieve our research goal, we proposed a ResNeXt inspired deep learning (DL) network called HARNeXt. The proposed model was evaluated on a public data set named 19NonSense data that collected activity signal data recorded from insole-based and smartwatch sensors. In addition, we compared the proposed model to baseline DL models. The results showed that the HARNeXt outperformed other DL models and achieved the highest F1-score of 97.03%. © 2022 IEEE.

关键词： Wearable sensors

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