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IEEE Industrial Electronics and Applications Conference (IEACon)

作者： Albe Bing Zhe Chai Bee Theng Lau Mark Kit Tsun Tee Chris McCarthy 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: (数字)9798350382976

ISBN: (纸本)9798350382983

The tremendous impact of road traffic accidents (RTAs) remains a global concern, especially in low- and middle-income countries (LMICs). While most LMICs have limited budget to afford advanced traffic monitoring systems, it is necessary to develop feasible solutions for enhancing road safety in these regions. Recent studies have extensively explored the applicability of machine learning (ML) models to predict RTA severity in different regions. However, there is limited exploration of training and evaluating the performance of ML model from a multi-region perspective. Further investigation is also necessary to formulate a cost-effective implementation strategy for LMICs to deploy ML models for real-time prediction. Hence, this paper contributes by proposing R-TAP, which deploys a TabNet-based model on the cloud for multi-region RTA severity predictions. A web application is also developed to receive inputs and visualise the prediction results. The TabNet model is trained on a multi-region dataset consisting of 42 features, produced by combining the individual RTA datasets collected from Australia, Brazil, Ethiopia, New Zealand, the UK, and the US. Evaluation with random forest and multinomial logistic regression showed that TabNet can achieve performance comparable to random forest. It also has the highest area under the receiver operating characteristics at 0.8857. R-TAP is also simulated using testing samples as real-time input, with an overall execution time of 4.104s per prediction. It is considered satisfactory and is expected to be useful for emergency responders to make informed decisions. This solution is expected to support LMICs in their roadmap towards enhanced road safety. Future research is essential to improve the predictive power and include datasets from more regions for a more inclusive RTA severity prediction.

关键词： Training Visualization Receivers Predictive models Road traffic Road safety Real-time systems Random forests Accidents Testing

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Automating Bad Smell Detection in Goal Refinement of Goal Models

Automating Bad Smell Detection in Goal Refinement of Goal Mo...

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作者： Hayashi, Shinpei Asano, Keisuke Saeki, Motoshi Department of Computer Science Tokyo Institute of Technology Tokyo152–8550 Japan Department of Software Engineering Faculty of Science and Technology Nanzan University Nagoya-shi466–8673 Japan

Goal refinement is a crucial step in goal-oriented requirements analysis to create a goal model of high quality. Poor goal refinement leads to missing requirements and eliciting incorrect requirements as well as less comprehensiveness of produced goal models. This paper proposes a technique to automate detecting bad smells of goal refinement, symptoms of poor goal refinement. At first, to clarify bad smells, we asked subjects to discover poor goal refinement concretely. Based on the classification of the specified poor refinement, we defined four types of bad smells of goal refinement: Low Semantic Relation, Many Siblings, Few Siblings, and Coarse Grained Leaf, and developed two types of measures to detect them: measures on the graph structure of a goal model and semantic similarity of goal descriptions. We have implemented a supporting tool to detect bad smells and assessed its usefulness by an experiment. Copyright © 2022 The Institute of Electronics, Information and Communication Engineers

关键词： Odors

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Light-weight Sketch Recognition with Knowledge Distillation 6

Light-weight Sketch Recognition with Knowledge Distillation

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6th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2023

作者： Huynh, Viet-Tham Nguyen, Tam.V Tran, Minh-Triet Software Engineering Laboratory Faculty of Information Technology University of Science VNU-HCM Viet Nam Vietnam National University Ho Chi Minh City Viet Nam University of Dayton Department of Computer Science United States

ISBN: (纸本)9798350327410

Recognizing hand-drawn sketches is a promising starting point for various applications, such as assisting artists in creating 3D environments for games or virtual environment scenes quickly and efficiently from concept arts. In addition, by understanding drawings, we can generate 3D models that can be used for further design and development. Thus, in this paper, we aim to develop a novel lightweight network that can accurately recognize sketch drawings. We propose a lightweight-yet-efficient neural network based on MobileNetV2 for sketch recognition and employ knowledge distillation to train the proposed model from EfficientNet-B4. To evaluate the accuracy of the proposed method, we collect a dataset of sketches comprising 1800 drawings in 12 categories, ranging from furniture to animals. The experimental results show that our network model achieves an accuracy of 96.7%, with 96.9% precision, 96.7% recall, and 96.7% F1-score. These results demonstrate that the proposed approach has great potential for practical sketch recognition applications, such as interior design or VR scene generation. © 2023 IEEE.

关键词： Distillation

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MSKI-Net: Towards modality-specific knowledge interaction for glioma survival prediction

MSKI-Net: Towards modality-specific knowledge interaction fo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Xiao, Jin Cui, Hui Xuan, Ping Feng, Xikang Wei, Leyi Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science China Northwestern Polytechnical University School of Software China Macao Polytechnic University AIDD Faculty of Applied Science China Xiamen University School of Information Xiamen China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGAmm, which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer. © 2024 IEEE.

关键词： Gene expression

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Data Transfer for YOLOv5 Based on Selector Network and Partial Pseudo-Labels

Data Transfer for YOLOv5 Based on Selector Network and Parti...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Guoqiang Qu Jianchu Lin Haiyi Bian Haiping Zhou Muhammad Raees UL Haq Bo Jiang Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an China Faculty of Electronic Information Engineering Huaiyin Institute of Technology Huai’an China Jiangsu Eazytec Co. LTD Yixin China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

YOLOv5 is a widely used object detection method for its high-performance of balancing speed and accuracy. Data transfer for YOLOv5 is a challenging task, as it could involve domain shift between different datasets. We propose a data transfer method for YOLOv5 based on a Selector Network and Partial Pseudo-Labeling. The backbone network of YOLOv5 is used for feature extraction, a Selector Network is employed to filter source domain outlier samples to address the domain shift issue, and Partial Pseudo-Labels are leveraged to improve classification performance in the target domain. The results show that our method improves detection accuracy by an average of 2.4% on public datasets (COCO and VOC) and our datasets, with a notable improvement of 3.1% under complex backgrounds and varying lighting conditions. It demonstrates that our method can effectively enhances the accuracy and stability of YOLOv5.

关键词： YOLO Training Accuracy Filtering Lighting Data transfer Feature extraction Cognitive systems Faces Batch normalization

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Design and Simulation of a Network Infrastructure for a Multi-Branch Organization Using Cisco Packet Tracer 25

Design and Simulation of a Network Infrastructure for a Mult...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Aljamal, Osama S. Aljamal, Mahmoud Alsarhan, Ayoub Aljaidi, Mohammad Okour, Saif Al-Aiash, Issa Al-Qerem, Ahmad Daoud, Essam Al Elrashidi, Ali Al Hussein Technical University Department of Computer Science Amman Jordan Irbid National University Faculty of Science and Information Technology Department of Cybersecurity Irbid21110 Jordan The Hashemite University Department of Information Technology Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Irbid National University Department of Software Engineering Irbid Jordan Zarqa University Department of Cybersecurity Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Designing and simulating a network infrastructure for multi-branch organizations is essential for ensuring seamless connectivity and efficient operations. This paper addresses the challenges faced by organizations in maintaining consistent and reliable network connectivity across multiple locations, including managing network traffic, ensuring security, and optimizing resource allocation while keeping costs under control. The study presents a comprehensive methodology involving the conceptualization of network ideas, designing the network architecture, adding necessary devices and tools, configuring these devices according to the network topology, and troubleshooting to ensure optimal performance. Key elements such as VLANs, routers, switches, and routing protocols like RIP, OSPF, and EIGRP are incorporated, with the entire design and simulation conducted using Cisco Packet Tracer. The findings demonstrate that full connectivity between headquarters and branch sites can be achieved within a budget of $40,000. This paper provides a practical guide for implementing similar network projects, emphasizing the importance of detailed configurations and troubleshooting steps, and recommends adopting this methodology to enhance network infrastructure efficiently and cost-effectively. © 2024 IEEE.

关键词： Budget control

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Classification of Stakeholders of an Information System using Fuzzy based Adjusted Cosine Similarity Measure during Requirements Elicitation Process

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Procedia computer Science 2023年 230卷 620-628页

作者： Faiz Akram Tanvir Ahmad Mohd. Sadiq Department of Computer Engineering Faculty of Engineering and Technology Jamia Millia Islamia New Delhi-25 India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia New Delhi-25 India

Requirements elicitation process is employed for the identification of stakeholders of an information system. A large number of stakeholders from different domains across the globe participate during the requirements elicitation process. Based on our review, we found that existing methods for the analysis of stakeholders do not support how to classify the stakeholders based on the similarity measures. These measures play an important role for the recommendation of an item/user in a recommendation system. Thus, the objective of this paper is to classify the stakeholders of an information system using fuzzy-based adjusted cosine similarity measure. We have identified the stakeholders of library information system and their opinions for different requirements are recorded. The identified stakeholders have been classified and analysed based on the following similarity measures under fuzzy environment, i.e., Cosine similarity measure, Euclidean distance, Pearson coefficient correlation etc., so that stakeholders having similar requirements can be recommended to strengthen the requirements elicitation process.

关键词： software Requirements Elicitation Information System Similarity Measures Recommendation System Stakeholders

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Systematic Review and Propose an Investment Type Recommender System Using Investor’s Demographic Using ANFIS 8th

Systematic Review and Propose an Investment Type Recommender...

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8th International Congress on Information and Communication technology, ICICT 2023

作者： Asemi, Asefeh Asemi, Adeleh Ko, Andrea Doctoral School of Economics Business and Informatics Corvinus University of Budapest Budapest Hungary Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Corvinus University of Budapest Budapest Hungary

ISBN: (纸本)9789819932429

The development of investment recommender systems (IRSs) has increased due to advancements in technology. This study aims to present a new model for IRSs based on potential investor’s demographic data and feedback, using fuzzy neural inference solutions. Both qualitative and quantitative methods were used in this research, including a review of past studies and analysis of data through Scopus analyze tool, Voyant, and VosViewer. The proposed model combines expert’s knowledge with demographic data to present the most suitable type of investment through an adaptive neuro-fuzzy inference recommender system. The model is processed in several steps, including clustering investment data types in JMP and proposing the results through MATLAB. This study provides a framework for IRSs that can give relevant and accurate recommendations for potential and actual investors, thus enhancing their investment experience. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Investments

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TOST: School Bus Monitoring and Management System for Schools and Parents

TOST: School Bus Monitoring and Management System for School...

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Computing (ICOCO), IEEE International Conference on

作者： Fahad Aqeel Thakur Muhammad Hafiz Maath Alani Muntadher Saadoon Chiew Thiam Kian Chiew Faculty of Computer Science & Information Technology University of Malaya Kuala Lumpur Malaysia Software Engineering Department Hillool Queensland Australia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

School bus contributes positively to reducing the number of cars on the road, thereby reducing the environmental impact of cars. It also dramatically relieves working parents, who do not have to pick up and drop off their children separately in their private vehicles. However, the irregularity of the bus arrival times often leads to an extended waiting time for students at the bus stops, potentially compromising the students' safety. The communication barrier between schools, parents and bus drivers is also a significant challenge, mainly when a delay occurs during the transit of the students. This paper presents TOST (to-school-together), a school bus monitoring and management system for schools and parents. The school administrators, parents, and bus drivers are connected in one comprehensive school bus system. The proposed system provides realtime updates on the bus status, allowing peace of mind and easing the burden of parents, school administrators, and bus drivers. The design of TOST is based on feedback from the system stakeholders collected via a survey. We draw a complete understanding of the existing issues by analyzing the feedback. Finally, we have implemented the system using state-of-the-art technologies to address the identified issues and fulfil the system requirements.

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ML Techniques for Employee Performance Prediction

ML Techniques for Employee Performance Prediction

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kumar, Brajesh Agrawal, Prateek Uike, Dipesh Lourens, Melanie Bhati, Indira Peter, M. Chandrakumar Dr. Apj Abdul Kalam Women's Institute of Technology Department of Computer Science & Engineering Bihar Darbhanga India India Dr. Ambedkar Institute of management studies and research Maharashtra Nagpur India Durban University of Technology Faculty of Management Sciences South Africa Department of Management India PeriyarManiammai Institute of Science and Technology Department of Software Engineering Tamilnadu Thanjavur India

ISBN: (纸本)9798331543617

When it comes to maximizing the effectiveness of a business and promoting professional growth, employee performance prediction is an extremely important factor. This research article investigates the use of machine learning (ML) approaches to forecast employee performance, with a particular emphasis on the incorporation of different algorithms from different sources to improve accuracy and dependability. Methods that have been used for a long time, such as linear regression and decision trees, are evaluated alongside more recent approaches, such as ensemble methods and deep learning. To refine the prediction models, our research makes use of approaches such as feature selection and dimensionality reduction. These techniques are utilized by utilizing previous performance data, demographic information, and behavioral indicators. It has been demonstrated through the analysis that sophisticated machine learning techniques, in particular ensemble and deep learning models, are superior to traditional methods when it comes to anticipating employee performance. Our findings offer HR departments concrete insights that can be used to adopt data-driven methods for performance management, which will eventually contribute to improved organizational outcomes and increased employee satisfaction. This paper offers opportunities for future development in predictive analytics within the field of human resources and emphasizes the potential of machine learning to alter performance evaluation systems. © 2024 IEEE.

关键词： Predictive analytics

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