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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Zhang, Zheng Xu, Liangliang Bao, Yida Baskiyar, Sanjeev Auburn University Dept. of Computer Science & Software Engineering Dept. of Mathematics & Statistics AuburnAL United States

ISBN: (纸本)9798350345346

Heart disease is one of the leading causes of death worldwide. Understanding the presence of heart disease is crucial for timely intervention and effective management. However, it is still a challenging task to accurately diagnose and treat heart disease. In this paper, we propose an ensemble machine learning model that combines the predictive power of three state-of-the-art machine learning algorithms: BERT (Bidirectional Encoder Representations from Transformers), FT-transformer, and XG-Boost. Through extensive training and evaluation, we assess the performance of our model using established metrics such as accuracy, precision, recall, F1-score, and receiver operating char-acteristic (ROC) curve analysis. The results obtained demonstrate the effectiveness and validity of our machine learning system in predicting heart disease accurately. This paper presents a promising approach that can assist healthcare professionals in making informed decisions and improving patient outcomes, enhances our understanding of heart disease patterns, and contributes to the development of effective diagnostic and treatment approaches. © 2023 IEEE.

关键词： Autoencoder BERT Heart Disease Machine Learning Transformer XG-Boost

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Evaluation and Validation of Design Metrics in Machine Learning for Depression Detection 18

Evaluation and Validation of Design Metrics in Machine Learn...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ajmal, Sahar Usman, Muhammad Javed, Abqa National University of Computer and Emerging Sciences Dept. of Software Engineering Chiniot Faisalabad Pakistan National University of Computer and Emerging Sciences Dept. of Computer Science Chiniot Faisalabad Pakistan Univ. of Engineering and Technology Dept. of Computer Science Lahore Pakistan

ISBN: (纸本)9798350328172

Depression is a medical syndrome that is often neglected and considered not as significant as physical illness due to lack of awareness and knowledge, but it should be treated in early stages to avoid extreme cases such as suicide. Therefore, Machine Learning Algorithms (MLA) are used to detect depression primitively to avoid extreme hazards. In-ternational Organization for Standardization (ISO) standards are essential for designing metrics in MLA as they ensure global consistency, quality assurance, and objective benchmark, enhancing credibility and reliability of the model. Although there is no proper standard design level quality metric is defined for MLA. Therefore, in this study, ISO 9126 standard is used to make as a quality standard considering functionality parameter of MLA design. These metrics are used to evaluate at the design level of MLA such as evaluation of dataset, preprocessing, MLA, and results. We have provide comparative analysis of MLA with partial functionality compliance with full ISO functionality conformance. In this study, MLA with ISO have significance compliance which is promising, where the raw data added to MLA to get the output and or the other hand all the steps are performed. The results clearly show that proposed model of MLA design metrics are out-performed provides 28% better results in terms of evaluations. Furthermore, the proposed design quality metrics can be used as the standard for the MLA domain. © 2023 IEEE.

关键词： Quality assurance

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Classifying white blood cells using combining different convolutional neural networks

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Multimedia Tools and Applications 2025年 1-24页

作者： Toptaş, Murat Toptaş, Buket Hanbay, Davut Software Eng. Dept. Engineering and Natural Science Faculty Bandırma Onyedi Eylül University Balıkesir Turkey Computer Eng. Dept. Engineering Faculty Inonu University Malatya44280 Turkey

White blood cells are warrior cells that protect the human body against external factors. Each of these warrior cells performs a distinct task, making every piece of information about them highly valuable in the medical field. In this article, a classification framework for the four known types of white blood cells is proposed. It is hoped that the classification of these types will contribute to the prediction of diseases such as AIDS, malaria, leukemia, and many others. In the proposed method, images of white blood cells from the Blood Cell Classification and Detection dataset were used as input to Convolutional Neural Networks. The feature vectors extracted using these Convolutional Neural Network architectures were combined into a single vector. A Minimum Redundancy Maximum Relevance algorithm was then employed to identify the most effective features within the feature vector. Experiments were conducted using these selected features, and the analysis of each experiment was reported in detail. The Support Vector Machines classifier achieved an accuracy of 98.63% in classifying white blood cell types by combining features from multiple deep learning architectures. The experimental results demonstrated that the features obtained from different layers of the Convolutional Neural Networks had varying impacts on the classification performance. © The Author(s) 2025.

关键词： Convolutional neural networks

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IoT Security: A Deep Learning-Based Approach for Intrusion Detection and Prevention

IoT Security: A Deep Learning-Based Approach for Intrusion D...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kauhsik, Bhanu Nandanwar, Himanshu Katarya, Rahul Delhi Technological University Dept. of Software Engineering Delhi India Delhi Technological University Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350313413

The size and market worth of the Internet of Things (IoT) have expanded, but unfortunately, the likelihood of user data being compromised has also risen. This presents a notable danger that has the potential to create disorder. Consequently, the main aim of this paper is to recognize any weaknesses within existing security solutions and suggest an innovative approach to address them. In order to accomplish this, the paper puts forth four research inquiries concerning IoT security that leverage Machine Learning and Deep Learning techniques. Additionally, it conducts an extensive review of the latest publications and concludes that a combination of specific techniques can provide a potential solution to the security problems in IoT. Overall, the findings of this study suggest that this systematic literature review could help in significantly enhancing the security of IoT devices and applications, safeguarding user data from unauthorized access and potential misuse. © 2023 IEEE.

关键词： Cyberattack Deep Learning Internet of Things (IOT) Intrusion Detection System (IDS) Network Security

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Wildfire Spread Prediction Using Attention Mechanisms in U-NET

Wildfire Spread Prediction Using Attention Mechanisms in U-N...

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Shah, Kamen Pantoja, Maria California Polytechnic State University Dept. Computer Science and Software Engineering San Luis Obispo United States

ISBN: (纸本)9798350322972

Global warming has increased large-scale natural disasters at an alarmingly greater frequency. These natural disasters affect humans and the ecosystems that all species rely upon for food and shelter. A significant byproduct of global warming is the emergence of mega-fires, which are wildfires that burn more than 100,000 acres. Mega fires have exhibited the capacity to destroy entire ecosystems and urban areas. Researchers forecast a significant increase in wildfires during the next decade. This is a fundamentally growing problem that mandates modern solutions. Currently, simulation models are employed to predict wildfire propagation and aid firefighters in suppressing them. However, these methods are computationally expensive and often inaccurate. A recent advance in this area is the usage of neural networks (NN), which are systematically more accurate and computationally efficient. This paper focuses on predicting wildfire spread using NN with an attention mechanism to improve spatial recognition and overall performance in neural networks for wildfire spread prediction. We train different models on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. Results show that when augmenting segmentation models with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, we use ensemble modeling to reduce bias and variation, leading to more consistent and accurate predictions. The architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1% when inferencing on wildfire propagation at 30-minute intervals. © 2023 IEEE.

关键词： Forecasting

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Towards an integration management maturity 49

Towards an integration management maturity

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49th Euromicro Conference on software engineering and Advanced Applications, SEAA 2023

作者： Hyrynsalmi, Sonja M. Olsson, Helena Holmstrom Bosch, Jan Lut University Dept. Software Engineering Lahti Finland Malmö University Dept. Computer Science and Media Technology Malmö Sweden Chalmers University of Technology Dept. Computer Science and Engineering Göteborg Sweden

ISBN: (纸本)9798350342352

As industries continue to digitize at a rapid pace, the number of integrations, the connections between different digital systems, is increasing. As a consequence, integration management is becoming increasingly difficult, leading to scalability, stability, and system outage problems that directly affect company performance. For that reason the modern integration management platforms have emerged to offer efficient management of the growing number of integrations, as well as a new way to handle, enrich, and utilize data both inside and outside *** research involved interviews with 20 software vendor professionals and 20 clients who had recently undergone an integration platform project. The study identified key drivers behind integration management projects and recognized different maturity levels in integration management. The results revealed that more mature companies are utilizing modern integration platforms to support their digital transformation. The study also highlighted the growing importance of data management and utilization in integration *** address these findings, a maturity model was developed to help companies evaluate their current integration management status and find ways to progress in their integration management. © 2023 IEEE.

关键词： Integration

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Towards a Data Business Maturity Model for software-intensive Embedded System Companies 29

Towards a Data Business Maturity Model for Software-intensiv...

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29th International Conference on engineering, Technology, and Innovation, ICE 2023

作者： Hyrynsalmi, Sami Olsson, Helena Holmström Bosch, Jan Lut University Dept. Software Engineering Lahti Finland Malmö University Dept. Computer Science and Media Technology Malmö Sweden Chalmers University of Technology Dept. Computer Science and Engineering Göteborg Sweden

ISBN: (纸本)9798350315172

Data has been quickly becoming as the fuel, the new oil, of growth and prosperity of companies in the modern age. With useful data and sufficient tools, companies have the ability to enhance their current products, presents new innovations and services as well as generate new revenue streams with a secondary customer base. While there are ongoing efforts to develop machine learning and data science techniques, little attention has been paid to understanding and characterizing data-related business activities in software-intensive *** multiple-case study examines four large international embedded system companies to explore how they are utilizing data and how they have proceeded in their journey in the data business. This study identifies six distinct stages, each with unique challenges, that seems to be common for embedded system companies in their data business. As the result, this study presents an initial data business maturity model for software-intensive embedded system companies. Additionally, this research provides a foundation for future efforts to support software-intensive embedded system companies in establishing data businesses. © 2023 IEEE.

关键词： Embedded systems

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Exploring the Potential of Llama Models in Automated Code Refinement: A Replication Study

Exploring the Potential of Llama Models in Automated Code Re...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Genevieve Caumartin Qiaolin Qin Sharon Chatragadda Janmitsinh Panjrolia Heng Li Diego Elias Costa Dept of Computer Science and Software Engineering Concordia University Montreal Canada Dept of Computer Engineering and Software Engineering Polytechnique Montreal Montreal Canada

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Code reviews are an integral part of software development and have been recognized as a crucial practice for minimizing bugs and favouring higher code quality. They serve as an important checkpoint before committing code and play an essential role in knowledge transfer between developers. However, code reviews can be time-consuming and can stale the development of large software projects. In a recent study, Guo et al. assessed how ChatGPT3.5 can help the code review process. They evaluated the effectiveness of ChatGPT in automating the code refinement tasks, where developers recommend small changes in the submitted code. While Guo et al.'s study showed promising results, proprietary models like ChatGPT pose risks to data privacy and incur extra costs for software projects. In this study, we explore alternatives to ChatGPT in code refinement tasks by including two open-source, smaller-scale large language models: CodeLlama and Llama 2 (7B parameters). Our results show that, if properly tuned, the Llama models, particularly CodeLlama, can achieve reasonable performance, often comparable to ChatGPT in auto-mated code refinement. However, not all code refinement tasks are equally successful: tasks that require changing existing code (e.g., refactoring) are more manageable for models to automate than tasks that demand new code. Our study highlights the potential of open-source models for code refinement, offering cost-effective, privacy-conscious solutions for real-world software development.

关键词： Temperature measurement Data privacy Codes Reviews Large language models Documentation Chatbots software Data models software development management

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Analyzing Different Protocols of Information Granularity Distribution to Improve Consistency of Fuzzy Preference Relations in Decision-Making

Analyzing Different Protocols of Information Granularity Dis...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Gonzalez-Quesada, Juan Carlos Perez, Ignacio Javier Morente-Molinera, Juan Antonio Alonso, Sergio Herrera-Viedma, Enrique Cabrerizo, Francisco Javier University of Granada Dept. of Computer Science and Ai Granada Spain University of Granada Dept. of Software Engineering Granada Spain

ISBN: (纸本)9781665430654

A fundamental principle of Granular Computing is that of an information granularity distribution and its optimization process. It has been used in system modelling to elevate a numerical model to its granular counterpart, which is more in rapport with reality. For example, in decision-making with fuzzy preference relations, it has been applied to enhance the existing numerical consistency improvement procedures. However, even though different protocols of information granularity distribution have been proposed, only the one based on a uniform and symmetric distribution and the one based on a symmetric but non-uniform distribution have been considered. Given that there exist others, this study aims to analyze how we can take advantage of all of them to improve the consistency of the fuzzy preference relations. Some numerical experiments are also completed to show the performance of these protocols. © 2023 IEEE.

关键词： consistency decision-making fuzzy preference relation information granularity

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Using Deep Learning depth Maps to Improve Monocular SLAM 38

Using Deep Learning Depth Maps to Improve Monocular SLAM

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38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023

作者： Hollows, Simon Green, Richard University of Canterbury Dept. Mechatronics Engineering Christchurch New Zealand University of Canterbury Dept. Computer Science and Software Engineering Christchurch New Zealand

ISBN: (纸本)9798350370515

This paper proposes the use of a neural network to generate depth maps from 2D frames, offering an alternative to depth cameras. The goal is to address the accuracy limitations of monocular SLAM by gaining more accurate depth data through deep learning. This approach combines the simplicity and cost-effectiveness of a single camera setup with the enhanced accuracy of RGB-D ***-SLAM running with RGB-D input was used as the SLAM framework for this method. This allowed for accurate comparisons between depth sensor depth maps in test datasets and the generated depth maps. The MiDaS neural network was used to generate estimated depth maps. These were then scaled to create consistency over the entire test sequence. The TUM RGB-D dataset was used for *** generated depth maps were able to compare with the TUM RGB-D data in accuracy. On one dataset, the TUM depth maps had a root mean square error (RMSE) or 0.24 m while the generated depth maps had a RMSE of 0.18 m. In general, the proposed method lacks consistency and the lack of good fusion between depth maps causes this approach to fail in slow-moving scenes. © 2023 IEEE.

关键词： Cost effectiveness

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