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Fuzzy performance estimation of real-world driver’s stress recognition models based on physiological signals and deep learning approach

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Journal of Ambient Intelligence and Humanized Computing 2024年 1-16页

作者： Amin, Muhammad Ullah, Khalil Asif, Muhammad Shah, Habib Waheed, Abdul Din, Irfanud Department of Electronics University of Peshawar Peshawar25120 Pakistan Department of Physical & amp Numerical Sciences Qurtuba University of Science & ampIT Peshawar25100 Pakistan Department of Software Engineering University of Malakand Malakand23050 Pakistan Department of Computer Science King Khalid University Abha62529 Saudi Arabia Department of Computer Science Women University Swabi Swabi23430 Pakistan Department of Computer Science The Superior University Lahore Lahore54000 Pakistan

Driver’s mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in losses of humans, vehicles, and infrastructure. Likewise, persistent mental stress could develop mental, cardiovascular, and abdominal disorders. Preceding research in this domain mostly focuses on feature engineering and conventional machine learning (ML) approaches. These approaches recognize different stress levels based on handcrafted features extracted from various modalities including physiological, physical, and contextual data. Acquiring the good quality features from these modalities using feature engineering is often a difficult job. The recent developments in the form of deep learning (DL) algorithms have relieved feature engineering by automatically extracting and learning resilient features. Conventional DL models, however, frequently over-fit due to large number of parameters. Thus, large networks face gradient vanishing issues causing an increase in learning failure and generalization errors. Furthermore, it is often hard to acquire a large dataset for training a deep learning model from scratch. To overcome these problems for driver’s stress recognition domain, this paper proposes fast and computationally efficient deep transfer learning models based on Xception pre-trained neural networks. These models classify the driver’s Low, Medium, and High stress levels through electrocardiogram (ECG), heart rate (HR), galvanic skin response (GSR), electromyogram (EMG), and respiration (RESP) signals. Continuous Wavelet Transform (CWT) acquires the scalograms for ECG, HR, GSR, EMG, and RESP signals separately. Then unimodal Xception models are trained based on these scalograms to classify the three stress levels. The proposed Xception models have achieved 97.2%, 86.4%, 82.7%, 71.9%, and 68.9% average validation accuracies based on ECG, RESP, HR, GSR, and EMG signals, respectively. The fuzzy EDAS (evaluation based on distance from average solutio

关键词： Electrocardiograms

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RealDriftGenerator: A Novel Approach to Generate Concept Drift in Real World Scenario

RealDriftGenerator: A Novel Approach to Generate Concept Dri...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lin, Borong Huang, Chao Zhu, Xiaohui Jin, Nanlin School of Advance Technology Xi'an Jiaotong-Liverpool University Department of Computer Science and Software Engineering SuZhou China University of Liverpool Department of Computer Science Liverpool United Kingdom

ISBN: (纸本)9781665410205

Concept drift refers to the probability distribution of data generation changes over time in a data stream environment. In recent years, there has been an increasing interest in drift detection models. However, due to the lack of labeled concept drift datasets, most researchers tend to using synthetic drift data generators for model training. These generators only have relatively simple feature distributions, which fail to capture the complexity found in real-world scenarios. This paper introduces a real scenario concept drift label generator (RealDriftGenerator). This generator aims to preserve the complexity and temporal correlation of real-world scenario while generating concept drifts with user defined drift positions and drift widths. The validation result show that the temporal correlation coefficients of RealDriftGenerator is significantly higher than benchmark drift generators. Additionally, the ability of RealDriftGenerator to capture the complexity in real-world scenarios is 20% higher than benchmark drift generators(measured by model performance). The source code of RealDriftGenerator has been published on https://***/sniperrifle71/realDriftGenerator. © 2024 IEEE.

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Development of Classification Model based on Arabic Textual Analysis to Detect Fake News: Case Studies 1

Development of Classification Model based on Arabic Textual ...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Himdi, Hanen Tarik Assiri, Fatmah Yousef University of Jeddah College of Computer Science and Engineering Computer Science and Artificial Intelligence Department Jeddah Saudi Arabia University of Jeddah College of Computer Science and Engineering Software Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

The ease of communication that has been made possible by chat messaging platforms, such as WhatsApp and Line, and their increased use and ubiquity in society, have motivated purveyors of fake news to create and present their news as legitimate. Though different organisations continued their efforts to resolve this problem, many of the developed solutions rely on verifying the associated metadata, which are not always available. Several studies have attempted to identify fake news by analysing textual content;however, there is a lack of studies on Arabic language sources. Our work filled this gap;we proposed a machine learning model that classifies Arabic fake news based on textual analysis. This paper shows the feasibility of our approach to classify Arabic text in social media applications. © 2023 IEEE.

关键词： Machine learning

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Determining the Critical Factors of Antenatal Care Follow-Up Using Two-Phase Machine Learning 27

Determining the Critical Factors of Antenatal Care Follow-Up...

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27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Bessmertny, Igor A. Enkomaryam, Bidru C. Mamuye, Adane L. Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia University of Gondar Department of Computer Science Gondar Ethiopia

ISBN: (纸本)9798350363708

Determining the critical factors affecting antenatal visits will greatly contribute to reducing maternal and infant mortality. This study, thus attempted to construct a cluster-based predictive model to determine the critical factors of antenatal care follow-up among women in Ethiopia using two-step machine learning techniques. The 5,990 preprocessed obstetric dataset with 13 selected attributes were used that was collected from the Dabat Research Center at the University of Gondar, Ethiopia. To solve the study problem, six cluster models were developed using K-means, Expectation Maximization and Make Density clustering algorithms, which are well-suited for analyzing health datasets, providing scalability, flexibility, robustness to noise, and the ability to handle uneven cluster sizes. K-means out-formers Density based and Expectation Maximization clustering algorithms with minimum number of sum of square errors (17094) and number of iteration (3). Then, output of the K-means cluster model was used as input to segment the dataset and build a total of 21 predictive models using the tree-based J48 classifier, PART and JRip rule generators while adjusting their parameters. The model for predicting antenatal care follow-up is constructed by induction of the truncated PART rule (accuracy of 99.93%) and extracts attributes of interest such as antenatal care attendance, antenatal care location, antenatal care frequency, current vaccination, woman's age, and HIV testing to predict antenatal care follow-up in the study area. © 2024 IEEE.

关键词： Machine learning

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Stress Analysis in Social Media in Bengali language using BERT-based approaches

Stress Analysis in Social Media in Bengali language using BE...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Khanom, Afroza Akter, Nargis Maisha, Sabrina Jahan Masum, Abdul Kader M. International Islamic University Chittagong Department of Computer Science and Engineering Chattogram Bangladesh Daffodil International University Department of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Recent researches highlight a rapid increase in mental health issues, signaling a concerning rise in stress-related conditions such as high blood pressure, psoriasis, polycystic ovary syndrome (PCOS), etc., and these should have a sophisticated remedy that plays a big role in stress analysis. To proceed with that the ongoing connection between increased stress levels and social media has gained a lot of attention. This study focused on inspecting a complex language, Bengali by extracting comments from YouTube to analyze stress to provide a meaningful solution. We are aware of no research on stress analysis in the Bengali language, although many studies have been done on the same topic in other languages. Thus by paying closer attention to stress analysis in the Bengali language, our work suggested a BERT-based method that has shown good results on a range of NLP tasks. Our approach included Bangla-BERT-base and hybrid models that combine deep and transfer learning, like BERT-LSTM, BERT-GRU, and BERT-CNN-BiLSTM. Our task performed well with an accuracy of 90.36% in stress analysis utilizing the Bengali dataset. Our experiment also helped to get an understanding that hybrid models had an influence in increasing the performance of the BERT-base model. Though our research has made little contribution to the BNLP field considering its vast aspect, expanding the Bengali dataset and conducting further multi-class stress analysis work can have a remarkable contribution. © 2024 IEEE.

关键词： Blood pressure

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Utilizing Evidential Reasoning (ER) Approach for software Components Selection

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SN computer science 2025年第3期6卷 1-13页

作者： Ahmad, Nabot Al-Qerem, Ahmad Omar, Firas Jebreen, Issam Department of Software Engineering Al Zaytoonah University of Jordan Amman Jordan Computer Science Department Zarqa University Zarqa Jordan Business Intelligence Department Al Zaytoonah University of Jordan Amman Jordan Software Engineering Department Middle East University Amman Lebanon

software component selection is one of the most challenging problems for software developers to meet customer needs. Despite various methods researchers propose to aid in component selection, a gap exists in addressing the complexities of decision-making under uncertainty. This study utilizes the Evidential Reasoning (ER) approach, which effectively handles multi-criteria decision-making (MCDM) scenarios to evaluate software components for an online bookstore. The methodology involves assessing quality attributes using the ER approach incorporating evaluation grades, weights, and belief degrees to manage information uncertainty. The results demonstrate that the ER approach provides a robust framework for software component selection leading to informed decisions. This approach offers significant advantages in scenarios where traditional MCDM methods are insufficient, thereby enhancing the reliability of component selection in software engineering. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making Evidential reasoning Multi-criteria software component

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A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption

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International Journal of Information Technology (Singapore) 2024年 1-21页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare systems, their usage is relatively low among healthcare professionals in hospitals. Thus, a study on the healthcare professionals acceptance and adoption of CloudIoT-based healthcare is critical. This study explores factors influencing healthcare professionals adoption of CloudIoT-based healthcare solutions by extending the Unified Theory of Acceptance and Use of Technology 2 model. Data was collected from 300 healthcare professionals in Jordan through an online questionnaire developed using the Google^® form application. The Bayesian Network and Structural Equation Modeling techniques were used to analyze and validate the proposed model. The results revealed that the healthcare professional’s behavioral intention is directly affected by seven factors: performance expectancy, effort expectancy, facilitating conditions, habit, trust, privacy, and security. The results also indicate that trust mediates the influence of privacy, facilitating conditions, and performance expectancy on behavioral intention. The research results will aid CloudIoT service providers, healthcare organizations, designers, and developers by offering comprehensive knowledge of the significant factors of CloudIoT-based healthcare adoption. A better understanding of these factors will help these stakeholder groups enhance their services and speed up the adoption of CloudIoT in the healthcare area. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Bayesian network CloudIoT Healthcare professionals Structural equation modeling UTAUT2

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Unveiling the Power of Deep Learning: A Comparative Study of LSTM, BERT, and GRU for Disaster Tweet Classification

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IEIE Transactions on Smart Processing and Computing 2023年第6期12卷 526-534页

作者： Ullah, Ihsan Jamil, Anum Ul Hassan, Imtiaz Kim, Byung-Seo Department of Software and Communication Engineering Hongik University Korea Republic of Department of Physics NED University of Engineering & Technology Karachi Pakistan Department of Computer Science and Information Technology NED University of Engineering & Technology Karachi Pakistan

Disasters have serious effects on people's lives and buildings. Therefore, social media platforms, such as Twitter, have become more critical. They are crucial tools for responding to and managing disasters effectively. This study examined the effectiveness of various deep learning models, such as bidirectional encoder representations from transformers (BERT), gated recurrent units (GRU), and long short-term memory (LSTM) for classifying disaster-related tweets. Twitter data related to different disasters were collected using hashtags. The data were then cleaned, preprocessed, and manually annotated by a team. The annotated data were divided into training, validation, and testing sets. The data were used to train three models based on BERT, GRU, and LSTM for the categorical classification of disaster tweets. Finally, the three models were evaluated and compared using the test data. BERT achieved an accuracy of 96.2%, making it the most effective model. In contrast, the LSTM and GRU models achieved an accuracy of 93.2% and 88.4%, respectively. These findings underscore the potential effectiveness of deep learning models in classifying disaster-related tweets, offering insights that could enhance disaster management strategies, refine social media monitoring processes, bolster public safety, and provide directions for future research. © 2023 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Disasters

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Comparative Analysis of Camera Calibration Algorithms for Football Applications 4

Comparative Analysis of Camera Calibration Algorithms for Fo...

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4th International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2024

作者： Sorokivskyi, Oleksandr Hotovych, Volodymyr Nazarevych, Oleg Shymchuk, Grygorii Department of Computer Science Faculty of Computer Information Systems and Software Engineering Ternopil Ivan Puluj National Technical University Ternopil Ukraine

In solving the problem of automated analysis of football match video recordings, special video cameras are currently used. This work presents a comparative characterization of known algorithms and methods for video camera calibration, including those utilizing machine learning and neural networks, with the aim of identifying their shortcomings and forming a theoretical foundation for developing modern, more effective methods and algorithms. Specifically, it examines both algorithms that require more input data but operate quickly [1, 2] and more accurate algorithms using machine learning [3, 4, 5, 6, 5]. It is demonstrated that their main drawback is either accuracy or speed. More accurate algorithms using machine learning often do not specify the algorithm's operational speed, which precludes their use in real-time applications. The examined works that emphasize speed frequently lack the accuracy necessary for practical use in real-life scenarios. Copyright © 2024 for this paper by its authors.

关键词： Semantic Segmentation

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Generative AI — The End of Systematic Reviews in PhD Projects?

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ACM Inroads 2024年第2期15卷 48-50页

作者： Zamani, Sanaz Sinha, Roopak Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand School of Information Technology Deakin University Melbourne Australia

In this opinion piece, we question the efficacy of students conducting systematic reviews (SRs) at the very start of their PhDs, especially now that we are riding, or drowning in, the Generative AI wave. How would the ubiquitous availability of Generative AI tools shape PhD research and supervision, and how can we capitalize on such disruptions? We report our reflections about early-stage PhD research from 10+ PhD supervisions completed by Sinha, and a recently started PhD project with Zamani as the student. We conducted a systematic literature review following traditional methods for the first four months and then revisited the task using ChatGPT and other related tools. We opine that while SRs still have value for new students, Generative AI must be incorporated, with care, to significantly enhance student learning, reinvest time savings into other, often-ignored skills, and improve the quality (breadth and depth) of SRs. © 2024 Association for Computing Machinery. All rights reserved.

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