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5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Johora, Fatema Tuj Mahfuja, Israt Binte Masuqur Rahman, A.N.M. Rahman, Md Mosfikur Rahman, Md Sadekur Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Daffodil Bangladesh Department of Information Systems Kobe Institute of Computing Kobe Japan

ISBN: (纸本)9789819719228

The worldwide health catastrophe sparked by the COVID-19 epidemic continues, emphasizing the need for novel solutions in prediction, early diagnosis, and treatment. While vaccine development has progressed, the virus’s eradication remains questionable. This research presents a strong machine learning-driven approach to addressing this complicated problem. Rapid viral transmission and the lack of a single cure hamper illness diagnosis and treatment, particularly in highly populated developing countries like Bangladesh. Against this context, machine learning and artificial intelligence have emerged as critical instruments for the improvement of health care and medical research. This research investigates several machine learning algorithms and approaches that show potential for COVID-19 prediction. Among these is an algorithm that assesses individual vulnerability and environmental variables, allowing for self-diagnosis and early intervention, reducing the strain on healthcare institutions and government resources. This study demonstrates how machine learning may improve forecast accuracy and provide proactive treatment in pandemic management. Advanced techniques, such as K-nearest neighbor, Decision Tree, Random Forest, AdaBoost, XGBoost, Stochastic Gradient Descent, Linear SVC, Perceptron, Naive Bayes, Support Vector Machines, Logistic Regression, and Discriminant Analysis, are essential for achieving the best results. Finally, this research emphasizes the need for novel responses to global health challenges. Healthcare systems may improve their forecasting capacities and early intervention techniques by leveraging machine learning. This paper highlights technology’s revolutionary impact in redefining healthcare paradigms and encouraging resilience in the face of enormous difficulties. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Towards an ontology on project portfolio management

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Procedia Computer Science 2025年 256卷 1649-1657页

作者： Héctor Melo Oscar Avila María del Pilar Villamil Department of Systems and Computing Engineering School of Engineering Universidad de los Andes Bogotá Colombia

Project Portfolio Management (PPM) is essential for organizations aiming to align projects with strategic goals. Different organizations adopt diverse PPM frameworks and standards to manage project portfolios each employing its own terminology. This semantic heterogeneity leads to communication barriers, knowledge silos, and difficulty in integrating information across various platforms in an interorganizational context. This article proposes a PPM ontology to establish a common language encapsulating key concept, addressing this challenge. The methodology involves systematic revision of three prominent PPM standards - ISO 21504, PMI Standard for Portfolio Management, and AXELOS Management of Portfolios, and employs a novel algorithm to identify equivalences and containment relationships between terms across all three standards, resolving semantic ambiguities and enriching the ontology’s expressiveness. This contribution benefits researchers, academics, portfolio managers, project managers, and PPM practitioners by providing a common vocabulary and framework for understanding and improving PPM practices, facilitates knowledge representation, improves communication and collaboration among stakeholders, and lays the groundwork for developing intelligent PPM systems capable of leveraging shared semantic understanding.

关键词： Ontology portfolio project management

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Velocity control of a Stephenson III six-bar linkage-based gait rehabilitation robot using deep reinforcement learning

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Neural computing and Applications 2025年第7期37卷 5671-5682页

作者： Kapsalyamov, Akim Brown, Nicholas A. T. Goecke, Roland Jamwal, Prashant K. Hussain, Shahid School of Information Technology and Systems University of Canberra CanberraACT2617 Australia School of Clinical Sciences Queensland University of Technology BrisbaneQLD Australia School of Systems and Computing UNSW CanberraACT Australia Department of Electrical and Computer Engineering Nazarbayev University Nur-Sultan City Kazakhstan

Lower limb rehabilitation robots can help to improve the locomotor capabilities of patients experiencing gait impairments and help medical workers by reducing strain on them. However, since commercially available exoskeletons are expensive and there is a lack of number of physiotherapists many patients are still not able to get proper rehabilitation training. The closed-loop linkage mechanisms have recently drawn much attention in the realization of gait rehabilitation robots. Such mechanisms are affordable and capable of providing suitable trajectories for gait training therapy. In this work, we have proposed a fully operational one degree-of-freedom mechanism which can generate complex naturalistic lower limb trajectories. Although in theory, it is assumed that the constant speed applied at the input crank is sufficient to control the system, in reality, the external forces exerted by human legs and the inertia of the links can greatly alter the rotational velocity at the crank, which may negatively affect the training process. Therefore, we have explored the performance of a deep reinforcement learning-based control algorithm designed to regulate the speed of the input crank to reach satisfactory performance needed for gait rehabilitation training. Experimental evaluations with healthy human subjects were conducted to demonstrate that the mechanism is capable of directing lower limbs on naturalistic gait trajectories with a required walking speed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Exoskeleton (Robotics)

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EEG Signal-Based Autism Spectrum Disorder Detection Through Normalized Mutual Information and Convolutional Neural Network 2nd

EEG Signal-Based Autism Spectrum Disorder Detection Through ...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Peya, Zahrul Jannat Maria, Mahfuza Akter Akhand, M.A.H. Siddique, Nazmul Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna9203 Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Northern Ireland Londonderry United Kingdom

ISBN: (纸本)9789819989362

Autism Spectrum Disorder (ASD) is a diverse neurological problem with several contributing factors involving both genetic and environmental variables. The diagnosis of ASD based on neural activity analysis of various signals from the brain, especially electroencephalography (EEG), has drawn attention. A technique for capturing an electrogram of the brain's spontaneous electrical activity is called EEG which is simple to use and non-intrusive. The aim of this study is to classify people with ASD and controls using the most efficient band by examining different sub-bands (such as alpha, beta, and gamma) of the EEG data. Normalized Mutual Information (NMI) is used to create Connectivity Feature Maps (CFMs). Classification is performed through Convolutional Neural Network (CNN). Gamma band is found effective for ASD detection with an accuracy of 96%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electroencephalography

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Automated BPMN Diagram Generation 6

Automated BPMN Diagram Generation

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6th International Conference on Advancements in computing, ICAC 2024

作者： Ajmal, Farhath Wijekoon, Poorna Dhanamina, Haritha Ravishan, Yasiru Nawinna, Dasuni Attanayaka, Buddhima Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

The automation of business process modelling has become crucial for organizations seeking to improve their operational efficiency. This research presents a novel methodology that leverages fine-tuned GPT models to automate the conversion of textual business process descriptions into Business Process Model and Notation (BPMN) diagrams. The study builds on previous work, addressing gaps related to the handling of unstructured text and the absence of anaphora resolution in our solution we use fine-tuned GPT models to accurately extract and structure business process activities from text input from end users, which are then converted into BPMN diagrams using a custom generation framework. The evaluation results demonstrate the system's accuracy with an average precision of 80%, recall of 76%, and F1 score of 78%. These findings outline the potential of using advanced language models to streamline business process modelling, offering significant improvements in accuracy and efficiency. The research concludes by suggesting enhancements for future iterations, including fine-tuning with advanced models and expanding support for complex BPMN elements, aiming to further optimize workflow for organizations. © 2024 IEEE.

关键词： Efficiency

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Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

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Multimedia Tools and Applications 2025年第15期84卷 15123-15149页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

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WIP: Examining Psychological Distance Perceptions Towards Advanced Technologies among University Students 54

WIP: Examining Psychological Distance Perceptions Towards Ad...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Xu, Xingchen Dai, Anjing Wei, Siqing Tan, Li Arizona State University Engineering Education Systems and Design MesaAZ United States University of Cincinnati Department of Engineering and Computing Education CincinnatiOH United States

ISBN: (纸本)9798350351507

The rapid development of advanced technologies, such as artificial intelligence (AI) and cloud computing, has led to their increasing integration into the daily lives of students, including educational settings. Yet, understanding the extent and depth of university students' engagement with these resources remains a significant gap. This work-in-progress study seeks to examine psychological distance perceptions towards advanced technologies among university students. To address this question, we created a quantitative survey guided by the Construal Level Theory (CLT) and the Technology Acceptance Model (TAM), which included two detailed psychological distance factors: the social distance factor and the hypothetical distance factor. We examined 37 university students from a Western university in the United States through linear regression analysis. We found that demographic factors, including age, race/ethnicity, and academic majors, were the primary predictors of students' psychological distance from advanced technologies. In detail, students who majored in Biomedical engineering negatively perceived these two technologies, which could be attributed to a perceived irrelevance of these technologies to their specific educational goals. These findings provide important insights into the associations of student backgrounds and psychological distance to advanced technologies in the university setting and suggest ways to create an inclusive, equitable, and future-oriented university learning environment. © 2024 IEEE.

关键词： Students

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An Uncertainty-Driven ScaledYOLOv4 for Open-Pit Mining Helmet Detection 27th

An Uncertainty-Driven ScaledYOLOv4 for Open-Pit Mining Helme...

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27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2024

作者： Calle, Roger Aguilar, Eduardo Department of Computing and Systems Engineering Catholic University of the North Antofagasta Chile Departament de Matematiques i Informatica Universitat de Barcelona Barcelona Spain

ISBN: (纸本)9783031766039

Failure to use proper personal protective equipment can lead to fatal consequences or complex accidents. Binary helmet detection is a very active research topic that involves detecting whether a worker is wearing a safety helmet or not. Despite the great interest, almost all of the work focuses on the analysis of images acquired in construction environments. To support mining safety monitoring, we propose a new dataset collected from mining environments, consisting of 1,510 images labeled for helmet and head object detection. Furthermore, we propose an uncertainty-driven model aiming to improve the performance by reducing the false positive rate. The effectiveness of the proposed method was demonstrated on the proposed dataset, and also by a performance analysis between cross-datasets strategy. The proposed method provides 95.3% in terms of mAP and 91.4% in terms of F1, which is a promising performance for binary helmet detection in real solutions applied to mining environments. This work contributes to better control of the use of safety helmets to reduce the complexity of head injuries related to work-related accidents, thus protecting workers and guaranteeing the continuity of the operational process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Open pit mining

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Bridging the Gap: Secure Northbound Interface in SDN with gRPC 5

Bridging the Gap: Secure Northbound Interface in SDN with gR...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Rathnayake, R.M.K.G. Kalana, K.A.V. Dharmakeerthi, Uditha Senarathne, Amila Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Software Defined Networking (SDN) promises to introduce flexibility and programmability into networks by offering developers a northbound interface (NBI) to create SDN applications. However, current designs and implementations have drawbacks, including the lack of Proper Standardization, Controller-Lock-In Situation, and Loss of Compatibility with SDN applications. Using Google Remote Procedure Call (gRPC) Application Programming Interface (API), the SDN controller can offer network resources, such as statistics, flow information, or topology data, to registered SDN applications. Furthermore, the gRPC enables controller-agnostic and language-agnostic development to enable proactive and reactive applications and services to interact with controller services. Transport Layer Security (TLS) enables confidentiality with encryption, authenticity with mutual authentication, and integrity with Message Authentication Code (MAC). Finally, Attribute-based Access Control (ABAC) and Logging capability for controller services allow fine-grained access control and accountability enforcement to ensure the security goals of the NBI. This research paper provides a complete architecture to converge different Northbound API streams into one secure, language-agnostic, controller-agnostic API stream with the help of the gRPC framework. © 2023 IEEE.

关键词： Controllers

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Dynamic Android Malware Detection Using Temporal Convolutional Networks

Dynamic Android Malware Detection Using Temporal Convolution...

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2023 IEEE International Conference on computing, ICOCO 2023

作者： Joomye, Abdurraheem Ling, Mee Hong Yau, Kok-Lim Alvin Sunway University Department of Computing and Information Systems Malaysia Lee Kong Chian Faculty of Engineering & Science Malaysia

ISBN: (纸本)9798350302684

As new malware emerges and continues to grow in number with increasing complexity, user applications are foreseen to face greater security challenges. Hence, there is a need to leverage machine learning and deep learning (DL) approaches in Android malware detection. Temporal convolutional network (TCN), is a DL approach which uses causal and dilated 1-dimensional convolution layers to process sequential data, has not been implemented with features extracted through dynamic analysis (e.g. system calls) of Android malware applications. TCN has been shown to achieve higher accuracy in applications dealing with sequential data, with lower delay due to its parallelism and less computationally exhaustiveness. This paper implements TCN for dynamic Android malware detection. Compared to long short-term memory (LSTM), TCN is shown to achieve a similar validation accuracy and a higher steady convergence, with TCN achieving an accuracy of 89.25% and LSTM achieving an accuracy of 88.42% with 1000 system calls. Therefore, it is an effective model for Android malware detection. © 2023 IEEE.

关键词： Android malware

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