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Review of Stress Detection Techniques for IT Professionals Using Image Processing with Optimized Machine learning Classifiers 11

Review of Stress Detection Techniques for IT Professionals U...

引用

11th international conference on intelligent Systems and Embedded Design, ISED 2023

作者： Kallakuri, V Acharyulu Karthikeyan, S. Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350318159

Stress is an inevitable aspect of every human life and is going to remain for an extended period of time. Every individual is surely faced with a variety of stressful circumstances from birth. Nevertheless, stress is not always detrimental. Stress is constantly required to inspire and stimulate us. As a result, moderate stress is quite healthy and stress is a part of everyone's life, and IT professionals are no exception. It has become a major source of concern among IT professionals. Individuals working in information technology experience additional stress since they have to constantly improve their knowledge. Moreover, several development tools, including as Machine learning (ML) and optimisation approaches, are extremely effective in accurately identifying and anticipating stress. As a result, in this study, many forms of ML and optimisation strategies are examined in order to determine the stress levels of IT employees. In this case, IT management needs to take corrective action to minimize the detrimental effects of stress on their personnel. © 2023 IEEE.

关键词： Stresses

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Dual Deep learning for Sleep Classification 11

Dual Deep Learning for Sleep Classification

引用

11th international Japan-Africa conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Abdelmagid, Mohamed Yusuf, Marwa Elhalawany, Basem M. Fares, Ahmed Benha University Faculty of Engineering at Shoubra Electrical Engineering Department Cairo11629 Egypt Kuwait College of Science and Technology Electronics and Communication Engineering Department Doha District93004 Kuwait Alexandria21934 Egypt

ISBN: (纸本)9798350369328

Sleep stage classification based on EEG is a crucial tool for understanding sleep quality. It enables the identification of various stages of sleep, including REM and non-REM sleep, which exhibit distinct patterns of brain activity and physiological responses. this valuable information can be utilized to diagnose sleep disorders, monitor treatments' impact, and better understand the correlation between sleep and overall health. Over the years, various machine learning models have been proposed to help sleep specialists identify and distinguish different stages of sleep. However, most of these models have relied on multiple EEG signals or additional modalities such as EOG or ECG, which is impractical for real-time sleep stage classification at home or home monitoring. To address this issue, we propose a novel model that leverages the transformer network to classify sleep stages based on a single EEG channel automatically. In addition, we study the impact of combining the input EEG signal with its time-frequency representations on classification accuracy. Our proposed model achieves competitive results with currently available work on highly imbalanced data sets, namely edf-20. © 2023 IEEE.

关键词： Deep learning

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SIMPLIFIED STATE SPACE LAYERS FOR SEQUENCE MODELING 11

SIMPLIFIED STATE SPACE LAYERS FOR SEQUENCE MODELING

引用

11th international conference on learning Representations, ICLR 2023

作者： Smith, Jimmy T.H. Warrington, Andrew Linderman, Scott W. Institute for Computational and Mathematical Engineering Stanford University United States Wu Tsai Neurosciences Institute Stanford University United States Department of Statistics Stanford University United States

Models using structured state space sequence (S4) layers have achieved state-of-the-art performance on long-range sequence modeling tasks. An S4 layer combines linear state space models (SSMs), the HiPPO framework, and deep learning to achieve high performance. We build on the design of the S4 layer and introduce a new state space layer, the S5 layer. Whereas an S4 layer uses many independent single-input, single-output SSMs, the S5 layer uses one multi-input, multi-output SSM. We establish a connection between S5 and S4, and use this to develop the initialization and parameterization used by the S5 model. the result is a state space layer that can leverage efficient and widely implemented parallel scans, allowing S5 to match the computational efficiency of S4, while also achieving state-of-the-art performance on several long-range sequence modeling tasks. S5 averages 87.4% on the long range arena benchmark, and 98.5% on the most difficult Path-X task. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Deep learning

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Piecewise Modeling Algorithms Using Numerical data 1

引用

11th international conference on Mechatronics and Control engineering, ICMCE 2023

作者： Taniguchi, Tadanari Sugeno, Michio STEM Education Center Tokai University Kanagawa2591292 Japan Tokyo Institute of Technology Tokyo1528550 Japan

ISBN: (数字)9789819965236

ISBN: (纸本)9789819965229

In this study, a novel piecewise modeling method was devised using numerical data. the piecewise model was represented as a rectangular region divided into a state-space. the vertex values of the rectangular region were determined using the learning algorithm based on a simplified fuzzy inference model because the piecewise model was represented by a fuzzy if-then rule with singleton consequents. the proposed algorithm can be used to determine optimal vertex values and positions of segmented regions and with minimum modeling errors. three examples were considered to demonstrate the effectiveness of the proposed method using numerical simulations. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Numerical methods

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RUTGe: Realistic Urban Traffic Generator for Urban Environments Using Deep Reinforcement learning and SUMO Simulator 11

RUTGe: Realistic Urban Traffic Generator for Urban Environme...

引用

11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Guillén, Alberto Bazán Barbecho Bautista, Pablo A. Igartua, Mónica Aguilar Barcelona Spain Cuenca Ecuador

ISBN: (纸本)9789897587450

We are witnessing a profound shift in societal and political attitudes, driven by the visible consequences of climate change in urban environments. Urban planners, public transport providers, and traffic managers are urgently reimagining cities to promote sustainable mobility and expand green spaces for pedestrians, bicycles, and scooters. To design more sustainable cities, urban planners require realistic simulation tools to optimize mobility, identify location for car chargers, convert streets to pedestrian zones, and evaluate the impact of alternative configurations. However, realistic traffic profiles are essential to produce meaningful simulation results. Addressing this need, we propose a traffic generator based on deep reinforcement learning integrated with the SUMO simulator. this tool learns to generate an instantaneous number of vehicles throughout the day, aligning closely with the target profiles observed at the traffic monitoring stations. Our approach generates accurate 24-hour traffic patterns for any city using minimal statistical data, achieving higher accuracy compared to existing alternatives. In particular, our proposal demonstrates a highly accurate 24-hour traffic adjustment, with the generated traffic deviating only by about 5% from the real target traffic. this performance significantly exceeds that of current SUMO tools like RouteSampler, which struggle to accurately follow the total daily traffic curve, especially during peak hours when severe traffic congestion occurs. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Traffic congestion

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Machine learning Based Agricultural Crop Yield Estimation in Yingcheng District, Hubei Province 11

Machine Learning Based Agricultural Crop Yield Estimation in...

引用

11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Sun, Yuchen Chen, Cheng Shi, Xulei Sun, Xu Fan, Hong Wuhan University School of Remote Sensing and Information Engineering Wuhan China Wuhan University School of Geodesy and Geomatics Wuhan China National University of Defense Technology Wuhan China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan China

ISBN: (纸本)9798350303513

Precision agricultural finance requires precise monitoring of agricultural conditions, including crop area and yield estimation. In this paper, Yingcheng City, Hubei Province, were selected to study the yield estimation based on the growth analysis combined with historical crop yield and meteorological data. there are many factors that affect crop yield, including meteorological conditions, geographical conditions, soil conditions, disaster situations, and the use of fertilizers. the artificial intelligence machine learning models based on multiple characteristic factors such as crop yield and temperature, precipitation, LAI, EVI, etc., as well as soil and meteorological conditions and other yield estimation features obtained from Google Earth Engine was constructed to conduct crops yield estimation so as to improve the precision of yield estimation in Yingcheng District, Hubei Province. the accuracy of the model was validated by the average yield estimation accuracy and goodness of fit compared to both the traditional regression model and the support vector machine model. Experiments show that machine learning model is better than regression model, and Random forest machine learning model is better than support vector machine model. © 2023 IEEE.

关键词： Crops

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intelligent Model-Free Control for Tendon-Driven Continuum Robotic Arms 11

Intelligent Model-Free Control for Tendon-Driven Continuum R...

引用

11th RSI international conference on Robotics and Mechatronics, ICRoM 2023

作者： Maghooli, Nima Mahdizadeh, Omid Moosavian, S. Ali A. Department of Mechanical Engineering Tehran Iran

ISBN: (纸本)9798350308105

Continuum manipulator modeling is always associated with structured and unstructured uncertainties. therefore, model-based control system design for this class of robotic systems will be very challenging. On the other hand, the performance quality of model-free controllers is completely dependent on their hyperparameters;they are also very sensitive to the considered trajectory for the system. As a result, using model-free controllers will require setting parameters for different scenarios. In this research, the design of a model-free controller with comparable performance to model-based control strategies is presented. To this end, various parameters are determined online by the gain adjustment system. the research innovation is to use a supervised machine learning method, fuzzy inference system (FIS), to implement the intelligent gain adaptation system to achieve this goal. the Modified Transpose Jacobian (MTJ) performs well in trajectory tracking due to its approximated feedback linearization tool. In addition, the PID controller structure makes it a locally robust control strategy. An adaptive gain adjustment system can greatly increase algorithm potential and establish the capability to follow trajectories in different work points in the system work space. this research aims to improve the performance of the MTJ model-free control strategy in tracking trajectories starting from arbitrary initial conditions in the system work space. this is achieved by the gain adjustment system design using a fuzzy inference system. Both simulation and experimental results reveal the merits of the proposed controller. © 2023 IEEE.

关键词： Trajectories

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Ocular Artefact Removal from Electroencephalogram Signals: A Review 4

Ocular Artefact Removal from Electroencephalogram Signals: A...

引用

4th IEEE international conference on Computing, Communication, and intelligent Systems, ICCCIS 2023

作者： Babeetha, S. Sridhar, S.S. College of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Tamilnadu India

ISBN: (纸本)9798350306118

Electroencephalogram (EEG) is widely utilized since it provides information on brain activity without causing any harm to the subject. EEG's great sensitivity makes it vulnerable to artefacts from both the environment and the brain. Blinks and eye movement, head movement, pulse, and so on may all generate artefacts in EEG recordings, which is the primary source of poor signal quality. Artefacts complicate the analysis of EEG data and may lead to erroneous conclusions about brain activity because they overlap with the signal collected from the brain. the creation of algorithms to detect and eliminate artefacts from EEG signals is crucial for obtaining such precise and trustworthy signal information. In this study, we take a look at the state of the art in removing artefacts caused by blinks and eye movements from EEG data. However, conventional artefact removal methods cannot eradicate all noise and will result in data loss due to the indiscriminate elimination of contaminated parts. the research demonstrates that combining conventional with other approaches is widely utilized because it may enhance artefact removal. the most up-To-date method for eliminating artefacts is an automated one that uses machine learning and deep neural networks. © 2023 IEEE.

关键词： Electroencephalography

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Improved automated Classification of Sentences in data Science Exercises 11th

Improved Automated Classification of Sentences in Data Scien...

引用

11th international conference on Methodologies and intelligent Systems for Technology-Enhanced learning (MIS4TEL)

作者： Angelone, Anna Maria Galassi, Alessandra Vittorini, Pierpaolo Univ Aquila Dept Life Hlth & Environm Sci I-67100 Laquila Italy

ISBN: (纸本)9783030866181;9783030866174

the use of artificial intelligence proved to be useful to automating the grading process, especially when the assessment involves a large number of students. the general problem we are addressing is the automated grading of assignments, which solutions are composed of a list of commands, their outputs, and possible comments. In this paper, we focus on the automated classification of the comments, as "right" or "wrong". In particular, we investigated the effect of different features (i.e., fastText, BERT, distance-based and custom features), fed to several classifiers (i.e., Logistic Regression, Support Vector Machines, Random Forest, Multi-Layer Perceptron - MLP), to select the best one in terms of best balanced accuracy. In the experiment carried out, the best result was obtained by the MLP classifier using the fastText embeddings. When instead fed with BERT embeddings, MLP obtained a slightly lower accuracy and F1 score, even if it remains the best option with respect to the other classifiers. Furthermore, we tested the classifier with comments given to different assignments (of the same structure), given by different students and evaluated by a different professor. Also in this case, we achieved a relatively good accuracy and F1 score.

关键词： TEL automated grading NLP ML fastText BERT Multi-layer perceptron

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11th international conference on Model and data engineering, MEDI 2022

11th International Conference on Model and Data Engineering,...

引用

11th international conference on Model and data engineering, MEDI 2022

ISBN: (纸本)9783031215940

the proceedings contain 18 papers. the special focus in this conference is on Model and data engineering. the topics include: Efficient Checking of Timed Ordered Anti-patterns over Graph-Encoded Event Logs;trans-Compiler-Based database Code Conversion Model for Native Platforms and Languages;MDMSD4IoT a Model Driven Microservice Development for IoT Systems;parallel Skyline Query Processing of Massive Incomplete Activity-Trajectories data;compact data Structures for efficient Processing of Distance-Based Join Queries;towards a Complete Direct Mapping from Relational databases to Property Graphs;a Matching Approach to Confer Semantics over Tabular data Based on Knowledge Graphs;τ JUpdate: A Temporal Update Language for JSON data;rice Plant Disease Detection and Diagnosis Using Deep Convolutional Neural Networks and Multispectral Imaging;a Novel Diagnostic Model for Early Detection of Alzheimer’s Disease Based on Clinical and Neuroimaging Features;benchmarking Concept Drift Detectors for Online Machine learning;computational Microarray Gene Selection Model Using Metaheuristic Optimization Algorithm for Imbalanced Microarrays Based on Bagging and Boosting Techniques;fuzzing-Based Grammar Inference;in the Identification of Arabic Dialects: A Loss Function Ensemble learning Based-Approach;emotion Recognition System for Arabic Speech: Case Study Egyptian Accent;towards the Strengthening of Capella Modeling Semantics by Integrating Event-B: A Rigorous Model-Based Approach for Safety-Critical Systems.

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