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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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Algorithm of Coordination of Swarm of Autonomous Robots

Algorithm of Coordination of Swarm of Autonomous Robots

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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Kyzyrkanov, Abzal Atanov, Sabyrzhan Aljawarneh, Shadi Tursynova, Nazira Kassymkhanov, Samat Astana It University L. N. Gumilyov Eurasian National University Department of Computer Engineering Astana Kazakhstan L. N. Gumilyov Eurasian National University Computer and Software Engineering Astana Kazakhstan Jordan University of Science and Technology Software Engineering Department Irbid Jordan L. N. Gumilyov Eurasian National University Department of Artificial Intelligence Technologies Astana Kazakhstan Astana It University Department of Computer Engineering Astana Kazakhstan

ISBN: (纸本)9798350335040

The task of keeping a specific geometric configuration while following a designated path is common in various fields that involve autonomous robots. When done correctly, this approach can result in several advantages, including a reduction in system costs and an increase in system reliability and efficiency, while maintaining the adaptability and flexibility of the system. The concept of maintaining a particular geometric pattern during motion is frequently utilized in different scenarios. For example, unmanned military vehicles must maintain a particular formation to explore and cover terrain during missions. Another example is in 'Smart Highways,' where the traffic system's capacity can be enhanced significantly by having vehicles move in groups at the same speed while maintaining a certain distance between them. This research presents an algorithm that allows a group of self-governing mobile robots to move while maintaining a specific geometric structure. A behavior-based control algorithm is advanced to regulate the movement of these systems. The coordination of the robots within the system is accomplished through the implementation of a leader-follower technique, with virtual leader. © 2023 IEEE.

关键词： Military vehicles

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Transforming Probabilistic Timed Automata to PRISM Model 26

Transforming Probabilistic Timed Automata to PRISM Model

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26th International computer Science and engineering Conference, ICSEC 2022

作者： Sutalungka, Thirawat Chariyathitipong, Patimakorn Vatanawood, Wiwat Chulalongkorn University Department of Computer Engineering Bangkok Thailand Sisaket Rajabhat University Department of Software Engineering Sisaket Thailand

ISBN: (纸本)9781665491983

The probabilistic timed automata are the models used to represent the probabilistic and time characteristics of systems to analyze the behavior of the system, including communication and multimedia. PRISM is a probabilistic model checker, a tool for formal modeling and probabilistic behavior analysis of systems. The PRISM language is used to construct the probabilistic timed automata to analyze and verify systems with PRISM. This paper proposes a set of probabilistic timed automata transform rules for transforming XML elements that represent probabilistic timed automata to PRISM model. The result can be used to analyze probabilistically and verify complex properties by the PRISM Model Checker. © 2022 IEEE.

关键词： Automata theory

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Enhancing Extreme Programming (XP) Adoption through SAMAM: A Scalable Agile Maturity Assessment Model Based on Industry Best Practices

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IEEE Access 2025年 13卷 97647-97680页

作者： Abrar, Muhammad Faisal Alferaidi, Ali Almurayziq, Tariq S. Saqib, Muhammad Khan, Wilayat Khan, Zawar Alsaffar, Mohammad University of Ha’il College of Computer Science and Engineering 55476 Saudi Arabia University of Engineering & Technology Department of Software Engineering Peshawar 25000 Pakistan

The adoption of Extreme Programming (XP), a widely recognized Agile methodology, faces numerous barriers that hinder its successful implementation in software development organizations. This research aims to develop a novel Scalable Agile Maturity Assessment Model (SAMAM) to address these barriers and facilitate the effective adoption of XP. The model is designed by leveraging established frameworks, including the Capability Maturity Model Integration (CMMI), software Outsourcing Vendor Readiness Model (SOVRM), and software Process Improvement Implementation Management Model (SPIIMM). Unlike traditional models that rely on predefined Key Process Areas (KPAs), SAMAM adopts 14 critical barriers (CBs) identified through a Systematic Literature Review (SLR) and corresponding practices as the foundation for its maturity levels. The study was conducted in four phases. First, an SLR was performed to identify 14 critical barriers to XP adoption and their respective mitigation practices. In the second phase, a survey questionnaire was administered within the software industry to validate the SLR findings and extract additional industry-relevant practices. The third phase involved the development of SAMAM, structured into five maturity levels using the identified barriers and practices instead of traditional KPAs. In the final phase, industrial case studies were conducted to evaluate the model’s effectiveness in real-world settings using the Motorola Assessment Tool. The findings demonstrate that SAMAM provides a comprehensive and scalable approach to assess and improve XP adoption maturity by systematically addressing critical adoption barriers. The model supports organizations in overcoming XP adoption challenges and achieving higher process maturity. The evaluation through case studies confirmed the practical applicability and effectiveness of the proposed model, contributing to the body of knowledge on agile methodologies and advancing XP adoption in the software developm

关键词： Agile maturity assessment mode Extreme Programming (XP) Adoption Scalable agile adoption (SAMAM) software process improvement

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Deepfakes Examiner: An End-to-End Deep Learning Model for Deepfakes Videos Detection 16

Deepfakes Examiner: An End-to-End Deep Learning Model for De...

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16th International Conference on Open Source Systems and Technologies, ICOSST 2022

作者： Ilyas, Hafsa Irtaza, Aun Javed, Ali Malik, Khalid Mahmood University of Engineering and Technology Software Engineering Department Taxila Pakistan University of Engineering and Technology Computer Science Department Taxila Pakistan Oakland University Rochester Computer Science and Engineering Department MI United States

ISBN: (纸本)9781665464772

Deepfakes generation approaches have made it possible even for less technical users to generate fake videos using only the source and target images. Thus, the threats associated with deepfake video generation such as impersonating public figures, defamation, and spreading disinformation on media platforms have increased exponentially. The significant improvement in the deepfakes generation techniques necessitates the development of effective deepfakes detection methods to counter disinformation threats. Existing techniques do not provide reliable deepfakes detection particularly when the videos are generated using different deepfakes generation techniques and contain variations in illumination conditions and diverse ethnicities. Therefore, this paper proposes a novel hybrid deep learning framework, InceptionResNet-BiLSTM, that is robust to different ethnicities and varied illumination conditions, and able to detect deepfake videos generated using different techniques. The proposed InceptionResNet-BiLSTM consists of two components: customized InceptionResNetV2 and Bidirectional Long-Short Term Memory (BiLSTM). In our proposed framework, faces extracted from the videos are fed to our customized InceptionResNetV2 for extracting frame-level learnable features. The sequences of features are then used to train a temporally aware BiLSTM to classify between the real and fake video. We evaluated our proposed approach on the diverse, standard, and largescale FaceForensics++ (FF++) dataset containing videos manipulated using different techniques (i.e., DeepFakes, FaceSwap, Face2Face, FaceShifter, and NeuralTextures) and the FakeA VCeleb dataset. Our method achieved an accuracy greater than 90% on DeepFakes, FaceSwap, and Face2Face subsets. Performance and generalizability evaluation highlights the effectiveness of our method for detecting deepfake videos generated through different techniques on diverse FF++ and FakeA VCeleb datasets. © 2022 IEEE.

关键词： Long short-term memory

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Deep Reinforcement Learning for Joint Service Placement and Request Scheduling in Mobile Edge Computing Networks 28

Deep Reinforcement Learning for Joint Service Placement and ...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Deng, Yuxuan Li, Xiuhua Sun, Chuan Hao, Jinlong Wang, Xiaofei Leung, Victor C. M. School of Big Data and Software Engineering Chongqing University Chongqing China Chongqing Changan Automobile Software Technology Co. Ltd Chongqing China TKLAN College of Intelligence and Computing Tianjin University Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350300482

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-Tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-Term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS. © 2023 IEEE.

关键词： Reinforcement learning

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Towards Modeling Functional Requirements From Tacit Knowledge

Towards Modeling Functional Requirements From Tacit Knowledg...

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Jafar, Rameen Almufareh, Maram Fahaad Ashraf, Sundas Khan, Bushra Butt, Wasi Haider Humayun, Mamoona National University of Sciences and Technology Department of Computer and Software Engineering Pakistan College of Computer and Information Sciences Jouf University Department of Information Systems Sakakah Saudi Arabia

ISBN: (纸本)9798350327595

Requirements Elicitation is the process of identifying system needs by talking with stakeholders who have a direct or indirect effect on the requirements. Requirements may be derived from several sources and are one of the most important phases of requirement engineering. We need to perform different elicitation techniques to find out the user's needs. Pre-requirements tracing is the process of determining the origins of a specific demand. Typically, certain needs arise that have no apparent source, but stakeholders will testify to their importance. Such criteria, however, are most likely dependent on tacit or tacit-like information entrenched in the issue domain. Tacit knowledge is one of the most common challenges for requirement analysts. This study contributes to constructing a systematic literature review for exploring multiple ways to model functional requirements using tacit knowledge. We have identified and analyzed 10 studies. In these research papers, different authors have discussed different methods to gather functional requirements from tacit knowledge. © 2023 IEEE.

关键词： Elicitation Techniques Requirement engineering Tacit-knowledge

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Asymptotic Normality of and Normal Approximation to Interference Power in Wireless Passive Sensor Networks

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IEIE Transactions on Smart Processing and Computing 2021年第4期10卷 340-354页

作者： Seo, Heewon Park, Jin Kyung Choi, Cheon Won Department of Computer Engineering Dankook University Yongin Korea Republic of Department of Software and Computer Engineering Ajou University Suwon Korea Republic of

A wireless passive sensor network is distinguished from ordinary wireless sensor networks by radio frequency (RF) sources that radiate RF waves and supply energy to sensor nodes. Against theoretical expectations, such a network suffers from a scarcity of energy, which compels to employ a contending-type medium access control (MAC) scheme. Consequently, a collision may take place among the packets sent by some sensor nodes. Also, the interference power brought by the colliding packets may hinder a sink node from identifying any packet. Apparently, the interference power highly influences the performance of a wireless passive sensor network. Thus, it is essential to appropriately describe the interference power to evaluate the performance. In this paper, we characterize the interference power, occurring in a wireless passive sensor network, as a sum of random variables governed by log-normal distributions, which are mutually independent but are not necessarily identical. Then, we prove the asymptotic normality of the interference power and, in pursuit of tractability rather than accuracy, we propose a normal approximation to the interference power brought by a finite number of sensor nodes. To investigate the accuracy of the proposed normal approximation, we take the Kolmogorov-Smirnov distance and present an exact expression of the Berry-Esséen bound associated with the interference power. Also, to explore the acceptability of the normal approximation, we employ three well-known statistical tests for normality. Comparative studies of Berry-Esséen bounds reveal that the normal approximation to the interference power is moderately accurate. Furthermore, hypothesis testing for normality shows that the normal approximation is acceptable even in the case that the interference power is incurred by a relatively small number of packets (or, equivalently, sensor nodes) in a wireless passive sensor network. Copyrights © 2021 The Institute of Electronics and Information Enginee

关键词： Sensor nodes

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Road Traffic Accident Pattern Analysis with Data Mining 12

Road Traffic Accident Pattern Analysis with Data Mining

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12th International Conference on Awareness Science and Technology, iCAST 2023

作者： Zhe Chai, Albe Bing Lau, Bee Theng Tsun Tee, Mark Kit McCarthy, Chris Faculty of Engineering Computing & Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Department of Computer Science & Software Engineering Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9798350324693

The discovery of Road Traffic Accident (RTA) patterns is vital to formulate mitigation strategies based on the characteristics of RTA. Various studies have applied association rule mining for RTA pattern discovery. However, limited research has conducted a comparative analysis of RTA patterns in different regions. This work is necessary to identify the similarities and differences of RTA characteristics between different regions, especially the developed and developing countries with different RTA trends in fatality rates. This work explored the application of association rule mining (FP-Growth algorithm) to discover and compare the RTA patterns for developed and developing countries. The Orange Data Mining software is used to discover RTA patterns for Ethiopia (12,317 samples) and the UK (United Kingdom) (1,048,575 samples), representing developing and developed countries. Eventually, findings showed similarities and differences in RTA patterns between Ethiopia and the UK. Road characteristics and conditions are the common factors contributing to RTA severity levels, while other crucial factors include the light condition and speed limit. In conclusion, the discovery of RTA patterns in different regions is beneficial to assist in formulating strategies to reduce the risk of RTA. Future work is necessary to discover the RTA patterns from different perspectives, such as seasonal or periodical variations of RTA patterns. © 2023 IEEE.

关键词： Association rules

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Brain tumor localization and segmentation using mask RCNN

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Frontiers of computer Science 2021年第6期15卷 199-201页

作者： Momina MASOOD Tahira NAZIR Marriam NAWAZ Ali JAVED Munwar IQBAL Awais MEHMOOD Department of Computer Science University of Engineering and TechnologyTaxila 47050Pakistan Department of Software Engineering University of Engineering and TechnologyTaxila 47050Pakistan

1 Introduction Brain tumor is a lethal disease affecting millions of people around the globe and has a high mortality *** identification and segmentation of brain tumor helps to increase the survival chances of the patient and also saves them from complex surgical ***,the precise segmentation of brain tumors facilitates the surgeon for better clinical development and cure.

关键词： mortality clinical precise

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