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Gradual divergence for seamless adaptation: a novel domain incremental learning method 24

Gradual divergence for seamless adaptation: a novel domain i...

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Proceedings of the 41st International Conference on Machine Learning

作者： Kishaan Jeeveswaran Elahe Arani Bahram Zonooz Dep. of Mathematics and Computer Science Eindhoven University of Technology NL Dep. of Mathematics and Computer Science Eindhoven University of Technology NL and Wayve Technologies Ltd London UK

Domain incremental learning (DIL) poses a significant challenge in real-world scenarios, as models need to be sequentially trained on diverse domains over time, all the while avoiding catastrophic forgetting. Mitigating representation drift, which refers to the phenomenon of learned representations undergoing changes as the model adapts to new tasks, can help alleviate catastrophic forgetting. In this study, we propose a novel DIL method named DARE, featuring a three-stage training process: Divergence, Adaptation, and REfinement. This process gradually adapts the representations associated with new tasks into the feature space spanned by samples from previous tasks, simultaneously integrating task-specific decision boundaries. Additionally, we introduce a novel strategy for buffer sampling and demonstrate the effectiveness of our proposed method, combined with this sampling strategy, in reducing representation drift within the feature encoder. This contribution effectively alleviates catastrophic forgetting across multiple DIL benchmarks. Furthermore, our approach prevents sudden representation drift at task boundaries, resulting in a well-calibrated DIL model that maintains the performance on previous tasks.

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Machine Learning Application to Transmission Quality Assessment in Optical Networks 30

Machine Learning Application to Transmission Quality Assessm...

引用

30th International Conference on Software, Telecommunications and computer Networks, SoftCOM 2022

作者： Kozdrowski, Stanislaw Cichosz, Pawel Sujecki, Slawomir Warsaw University of Technology Institute of Computer Science Warsaw Poland University of Science and Technology Telecommunications and Teleinformatics Dep. Wroclaw Poland

ISBN: (数字)9789532901177

ISBN: (纸本)9789532901177

This paper presents the application of 2 selected machine learning methods for transmission quality assessment in optical networks. The proposed solution uses real data to train classification models that predict whether a given optical channel meets transmission quality requirements. The training data is extracted directly from the running network through the control plane. Several attribute selection strategies have been analysed. The available attributes are divided into several subsets. The performance of the selected machine learning algorithms for each subset is compared using ROC and PR curves. The results are used to evaluate the predictive performance of the selected machine learning algorithms when applied to the optical channel classification task and to determine how this performance is related to the set of attributes used. © 2022 University of Split, FESB.

关键词： Machine learning

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The Role of the Internet of Things in Monitoring Air Pollution-A Survey 11

The Role of the Internet of Things in Monitoring Air Polluti...

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11th International Conference on Information technology, ICIT 2023

作者： Abusukhon, Ahmad Alghannam, Bareeq Al-Zaytoonah University of Jordan Faculty of Science and Information Technology Computer Science Dep. Amman Jordan College of Business Studies the Public Authority for Applied Education and Training Department of Computer Science and Information System Kuwait City Kuwait

ISBN: (纸本)9798350320060

Air pollution is one of the most common problems that the world is facing today. In fact, there are numerous causes of air pollution, including the large number of industries and automobiles that emit carbon dioxide (CO2) and carbon monoxide. Pollution is increasing on a daily basis, making air quality monitoring and management a major challenge for governments in both developed and developing countries. Air pollution affects human health as well as the atmosphere, causing climate change. One of the techniques used for monitoring air pollution is the Internet of Things (IoT). In this paper, we demonstrate various techniques for monitoring air quality using IoT and make a comparison based on various factors elicited from the previous work. © 2023 IEEE.

关键词： Internet of things

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Clinical Informatics System to Understand Lung Cancer Using Transfer Learning-Based computer-Aided Application 7

Clinical Informatics System to Understand Lung Cancer Using ...

引用

7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Abugabah, Ahed Mehmood, Atif Shahid, Farah College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates Zhejiang Normal University Dep. of Computer Science and Technology Jinhua321002 China

ISBN: (纸本)9798350342154

Lung cancer is the second leading cause of death worldwide after cardiovascular disease. Due to late diagnosis, lung cancer patients have an extremely low survival rate when compared to other cancer patients. Hence, early lung cancer detection is critical for patients to undergo early therapies, boosting their chances of surviving or potentially becoming cancer-free. Over the past few years, researchers have developed several deep learning-based computer-aided diagnostic tools to detect lung cancer early. As a consequence, deep learning models are readily susceptible to over-fitting for fewer data samples and can be computationally time-consuming to pick hyper-parameters, which usually leads to a reduction in performance. Our proposed technique came up with a solution to this challenge of lung cancer classification tasks that used a layer-wise transfer learning strategy. In this work, three types of data samples such as Lung adenocarcinoma (Lung a), lung squamous cell carcinoma (Lung scc), and lung benign (Lung b) have been taken and implemented models, namely Group-I, II, III with frozen layers and baseline algorithms as VGG-19. To check the validity of the proposed models on lung samples and attain the best classification accuracy on Lung scc vs. Lung b 97.53%, also discriminate between Lung a vs. Lung b testing accuracy 92.59%. In contrast, the remaining Lung scc vs. Lung a attained 89.30%. Finally, a comparison to other studies demonstrating the suggested model superiority over state-of-the-art models in testing accuracy © 2023 IEEE.

关键词： Diseases

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Exploring Genetic Programming Models in computer-Aided Diagnosis of Skin Cancer Images 13

Exploring Genetic Programming Models in Computer-Aided Diagn...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ain, Qurrat Ui Al-Sahaf, Harith Xue, Bing Zhang, Mengjie School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington New Zealand College of Engineering and Info. Technology AlShaab University Dep. of Cybersecurity Engineering Baghdad Iraq

ISBN: (纸本)9798350308365

Extracting important information from complex skin lesion images is vital to effectively distinguish between different types of skin cancer images. In addition to providing high classification performance, such computer-aided diagnostic methods are needed where the models are interpretable and can provide knowledge about the discriminative features in skin lesion images. This underlying information can significantly assist dermatologists in identifying a particular stage or type of cancer. With its flexible representation and global search abilities, Genetic Programming (GP) is an ideal learning al-gorithm to evolve interpretable models and identify important features with significant information to discriminate between skin cancer classes. This paper provides an in-dep.h analysis of a recent GP-based feature learning method where different well-developed feature descriptors are integrated into the learning algorithms to extract high-level features for skin cancer image classification. The study explores the effectiveness of utilizing feature learning for this complex task and designing program structure to suit the problem domain as it has shown promising results compared to commonly used feature descriptors and an existing GP-based feature learning method developed for general image classification. This study analyzes the GP-evolved models to identify the prominent features and most effective feature descriptors important for the classification of these skin cancer images. The evolved models are interpretable, they provide knowledge that can assist dermatologists in making diagnoses in real-time clinical situations by identifying prominent skin cancer characteristics captured by the feature descriptors and learned during the evolutionary process. © 2024 IEEE.

关键词： Genetic programming

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Preventing Overfitting on Noisy Labels Through Adaptive Checkpointing

Preventing Overfitting on Noisy Labels Through Adaptive Chec...

引用

International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Aref Azizian Rouhollah Ahmadian Mehdi Ghatee Mostafa Shamsi Dep. of Math. & Computer Science Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

Overfitting and the presence of noisy labels are significant challenges in the training of machine learning models, particularly in complex datasets. This paper introduces a novel checkpointing method designed to mitigate overfitting while maintaining model calibration in the presence of noise. Our approach leverages the entropy of the two largest predicted probabilities from the model's output to determine when to save the model during training. This entropy-based criterion allows for effective checkpointing without necessitating a separate validation set. We evaluate our method across various datasets characterized by different noise levels and model architectures. The results demonstrate that our entropy-based checkpointing offers a more robust solution for managing model performance during training. This work contributes to the ongoing efforts to enhance model reliability in real-world applications where data imperfections are prevalent.

关键词： Checkpointing Training Adaptation models Prevention and mitigation Noise Machine learning Predictive models Robustness Noise measurement Overfitting

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A Proposed Approach for Risk Planning and Management 25

A Proposed Approach for Risk Planning and Management

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25th International Arab Conference on Information technology, ACIT 2024

作者： Al Refai, Mohammed N. Al-Smadi, Yazan Al-Momani, Adai Jamhawi, Zeyad M. Otoom, Ahmed Ali Atoum, Issa Yaseen, Ahid Ababneh, Jafar Kanan, Mohammad Zarqa University Software Engineering Dep. Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Applied Science Private University Department of Computer Science Amman Jordan Applied Science Private University Department of Cyber Security Amman Jordan Philadelphia University Faculty of Information Technology Software Engineering Department Jordan Zarqa University Department of Cyber Security Zarqa Jordan University of Business and Technelogy Dep. of Industry Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Project Risk management is the process of identifying, evaluating, avoiding, or reducing risks. Where there is no software project without risks existence are natural in the context of project planning and management. This study focuses on proposing an enhanced risk management approach where testing phases were added to make the risk management process more effective by proposing solutions to many mistakes and challenges that occur through dealing with risks. The proposed approach focuses on dealing with risks that cannot be easily defined, more accurate risk evaluation, planning, and managing risk coordinated with a contingency plan. © 2024 IEEE.

关键词： Project management

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Prediction of Electrical Characteristics of a-IGZO TFT Based on Transfer Learning-Based Variational Autoencoder

Prediction of Electrical Characteristics of a-IGZO TFT Based...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bea, Khean-Thye Wei-Hsuan Lin, Shih-Shin Hu Lin, Da-Zheng Hu, Hsin-Hui Chen, Xiu-Zhi Lin, Ting-Ru Chen, Yen-Lin Chen, Kun-Ming Cheng, Wai Khuen National Taipei University of Technology Dep. of Electronic Engineering Taipei106 Taiwan National Taipei University of Technology Dep. of Computer Science and Information Engineering Taipei106 Taiwan Taiwan Semiconductor Research Institute Hsinchu30078 Taiwan Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Kampar31900 Malaysia

ISBN: (纸本)9798350337020

In this study, we proposed a transfer-learning based variational autoencoder model for predicting the electrical characteristics in the parameter tuning process of a-IGZO TFT structure design. The result achieve a high R2 score of 0.9704 with a low-computing-power hardware-friendly method that reduced time consumption significantly compared to prior approaches. The findings have practical implications for mitigating the time-consuming nature of TCAD simulations, and the method can expand to various types of input data while ensuring high performance and generalization. We demonstrated significant improvement in generalization and accuracy through a k-fold validation. © 2023 IEEE.

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Real-Time Audio Analysis for Detection of Apnea Intervals in Health-Care System

Real-Time Audio Analysis for Detection of Apnea Intervals in...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Alireza Vahdani Mahdieh Rahmati Mehdi Sharifi Mehdi Ghatee Dep. Of Math. And Computer Science Amirkabir University of Technology Iran Tehran Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

Accurate detection of respiratory pauses is crucial in patients undergoing cataract surgery under sedation, as undetected pauses can lead to serious complications. Conventional monitoring techniques, such as pulse oximetry and capnography, often fail to detect respiratory pauses in a timely manner, limiting their effectiveness in high-stakes clinical settings. To address this gap, we propose an automated method leveraging tracheal sound analysis using a deep learning model based on ResNet18. Our approach incorporates signal processing techniques, noise reduction, Mel-spectrogram extraction, and data augmentation, to optimize the model's performance. The model was evaluated using multiple performance metrics, including AUC of the precision-recall curve, F1 score, etc. It achieved an AUC of 92.5% and an F1 score of 89% for the apnea class, demonstrating the system's potential for precise, real-time respiratory monitoring in clinical environments.

关键词： Time-frequency analysis Accuracy Smoothing methods Noise reduction Surgery Data augmentation Data models Real-time systems Data mining Monitoring

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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study 4

The Correlation Between Architecture Smells, Package Stabili...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Benidris, Mrwan Elfallah, Ehab A. Omar Cheema, Marwa Aslam Alsma, Mohammed Salah Addin Kadiki, Tareq Ziad Elbarsha, Abdelfattah University of Benghazi Faculty of Information Technology Dep. of Computer Science Libya Libyan International Medical University Faculty of Information Technology Dept. of Software Engineering Libya University of Benghazi Faculty of Public Health Health Informatics Department Libya

ISBN: (纸本)9798350372632

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-dep.h research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic dep.ndency and Unstable dep.ndency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable dep.ndency and reusability. Finally, we found a slight negative correlation between package stability and reusability. © 2024 IEEE.

关键词： Reusability

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