检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Adonias, Geoflly L. Siljak, Harun Barros, Michael Taynnan Balasubramaniam, Sasitharan Telecommunications Software and Systems Group Waterford Institute of Technology Waterford Ireland Department of Electronic and Electrical Engineering Trinity College Dublin Dublin Ireland School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom

The COVID-19 pandemic has shaken the world unprecedentedly, where it has affected the vast global population both socially and economically. The pandemic has also opened our eyes to the many threats that novel virus infections can pose for humanity. While numerous unknowns are being investigated in terms of the distributed damage that the virus can do to the human body, recent studies have also shown that the infection can lead to lifelong sequelae that could affect other parts of the body, and one example is the brain. As part of this work, we investigate how viral infection can affect the brain by modelling and simulating a neuron’s behaviour under demyelination that is affected by the cytokine storm. We quantify the effects of cytokine-induced demyelination on the propagation of action potential signals within a neuron. We used information and communication theory analysis on the signal propagated through the axonal pathway under different intensity levels of demyelination to analyse these effects. Our simulations demonstrate that virus-induced degeneration can play a role in the signal power and spiking rate and the probability of releasing neurotransmitters and compromising the propagation and processing of information between the neurons. We also propose a transfer function that models these attenuation effects that degenerates the action potential, where this model has the potential to be used as a framework for the analysis of virus-induced neurodegeneration that can pave the way to improved understanding of virus-induced demyelination. Copyright © 2021, The Authors. All rights reserved.

关键词： Viruses

来源：评论

学校读者我要写书评

暂无评论

An Exploratory Study on the Introduction and Removal of Different Types of Technical Debt

arXiv

引用

arXiv 2021年

作者： Liu, Jiakun Huang, Qiao Xia, Xin Shihab, Emad Lo, David Li, Shanping College of Computer Science and Technology Zhejiang University China PengCheng Laboratory China College of Computer Science and Technology Zhejiang University China Faculty of Information Technology Monash University Melbourne Australia Department of Computer Science and Software Engineering Concordia University Canada School of Information Systems Singapore Management University Singapore

To complete tasks faster, developers often have to sacrifice the quality of the software. Such compromised practice results in the increasing burden to developers in future development. The metaphor, technical debt, describes such practice. Prior research has illustrated the negative impact of technical debt, and many researchers investigated how developers deal with a certain type of technical debt. However, few studies focused on the removal of different types of technical debt in practice. To fill this gap, we use the introduction and removal of different types of self-admitted technical debt (i.e., SATD) in 7 deep learning frameworks as an example. This is because deep learning frameworks are some of the most important software systems today due to their prevalent use in life-impacting deep learning applications. Moreover, the field of the development of different deep learning frameworks is the same, which enables us to find common behaviors on the removal of different types of technical debt across projects. By mining the file history of these frameworks, we find that design debt is introduced the most along the development process. As for the removal of technical debt, we find that requirement debt is removed the most, and design debt is removed the fastest. Most of test debt, design debt, and requirement debt are removed by the developers who introduced them. Based on the introduction and removal of different types of technical debt, we discuss the evolution of the frequencies of different types of technical debt to depict the unresolved sub-optimal trade-offs or decisions that are confronted by developers along the development process. We also discuss the removal patterns of different types of technical debt, highlight future research directions, and provide recommendations for practitioners. © 2021, CC BY.

关键词： Economic and social effects

来源：评论

学校读者我要写书评

暂无评论

Correction: Uncertainty Modelling in Performability Prediction for Safety-Critical systems

引用

Arabian Journal for science and engineering 2024年第2期50卷 1353-1353页

作者： Ahamad, Shakeel Gupta, Ratneshwer Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Software Quality Assurance Lab School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

来源：评论

学校读者我要写书评

暂无评论

Unified resilience model using deep learning for assessing power system performance

引用

Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

来源：评论

学校读者我要写书评

暂无评论

Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”

引用

IEEE Access 2025年 13卷 31659-31659页

作者： Khaoula Taji Ali Sohail Tariq Shahzad Bilal Shoaib Khan Muhammad Adnan Khan Khmaies Ouahada Electronic Systems Information Processing Mechanics and Energy Laboratory Kenitra Faculty of Sciences Ibn Tofail University Kenitra Morocco Department of Computer Science Minhaj University Lahore Lahore Pakistan Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg South Africa Department of Computer Science Green International University Lahore Pakistan Department of Software Faculty of Artificial Intelligence and Software Gachon University Seongnam-si Republic of Korea

Presents corrections to the paper, Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”.

关键词： Plant diseases Metaheuristics

来源：评论

学校读者我要写书评

暂无评论

Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging

arXiv

引用

arXiv 2025年

作者： Ayllón, Elena Mulero Mantegna, Massimiliano Shen, Linlin Soda, Paolo Guarrasi, Valerio Tortora, Matteo Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden Department of Naval Electrical Electronics and Telecommunications Engineering University of Genoa Genoa Italy

Accurate lung tumor segmentation is crucial for improving diagnosis, treatment planning, and patient outcomes in oncology. However, the complexity of tumor morphology, size, and location poses significant challenges for automated segmentation. This study presents a comprehensive benchmarking analysis of deep learning-based segmentation models, comparing traditional architectures such as U-Net and DeepLabV3, self-configuring models like nnUNet, and foundation models like MedSAM, and MedSAM 2. Evaluating performance across two lung tumor segmentation datasets, we assess segmentation accuracy and computational efficiency under various learning paradigms, including few-shot learning and fine-tuning. The results reveal that while traditional models struggle with tumor delineation, foundation models, particularly MedSAM 2, outperform them in both accuracy and computational efficiency. These findings underscore the potential of foundation models for lung tumor segmentation, highlighting their applicability in improving clinical workflows and patient outcomes. © 2025, CC BY-NC-SA.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

[Retracted] An Efficient Model for Lungs Nodule Classification Using Supervised Learning Technique

引用

Journal of Healthcare engineering 2023年第1期2023卷

作者： Fayez Eid Alazemi Babar Jehangir Muhammad Imran Oh-Young Song Tehmina Karamat Department of Computer Science and Information Systems College of Business Studies The Public Authority for Applied Education & Training Adailiyah 12062 Kuwait Department of Computer Science & Software Engineering Shaheed Zulfiqar Ali Bhutto Institute of Science & Technology Islamabad Pakistan Software Department Sejong University Seoul Republic of Koreasejong.ac.kr Department of Software Engineering Foundation University Islamabad Islamabad Pakistanfui.edu.pk

Lung cancer has the highest death rate of any other cancer in the world. Detecting lung cancer early can increase a patient’s survival rate. The corresponding work presents the method for improving the computer-aided detection (CAD) of nodules present in the lung area in computed tomography (CT) images. The main aim was to get an overview of the latest tools and technologies used: acquisition, storage, segmentation, classification, processing, and analysis of biomedical data. After the analysis, a model is proposed consisting of three main steps. In the first step, threshold values and component labeling of 3D components were used to segment the lung volume. In the second step, candidate nodules are identified and segmented with an optimal threshold value and rule-based trimming. It also selects 2D and 3D features from the candidate segmented node. In the final step, the selected features are used to train the SVM and classify the nodes and classify the non-nodes. To assess the performance of the proposed framework, experiments were performed on the LIDC data set. As a result, it was observed that the number of false positives in the nodule candidate was reduced to 4 FP per scan with a sensitivity of 95%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Review of Deep-Learning-Based Models for Afaan Oromo Fake News Detection on Social Media Networks 3rd

A Review of Deep-Learning-Based Models for Afaan Oromo Fake ...

引用

3rd International Conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Arega, Kedir Lemma Tune, Kula Kekeba Beyene, Asrat Mulatu Tariku, Wegderes Bune, Nurhussen Menza ICT Infrastructures and Administrator Team Leader Ambo University Ambo Ethiopia Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Department of Electrical and Computer Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Department of Information Systems Mizan Tepi University Tepi Ethiopia Project Management System Support Expert Ethio Telecom Addis Ababa Ethiopia

ISBN: (纸本)9783031862953

This study explores modern fake news detection techniques in the Afan Oromo language, aiming to contribute to advancing research in this field. It explores deep learning methods for transformers, including Bidirectional Encoder Representations, Attention, and Generative Adversarial Networks, and their applications in various industries such as machine translation, email spam detection, information extraction, summarization, medical, and question-answering. The paper investigates false news identification, its categories, characteristics, and taxonomy, and compares machine learning and deep learning algorithms for fake news, liars, and corpus datasets. It proposes that future studies use picture data and deeper learning network topologies to decrease erroneous outcomes. The research endeavor aims to illuminate and advance the understanding of the complex difficulties and possibilities that exist in this particular sector, thereby acting as a crucial resource for decision-makers, academics, and professionals, while also encouraging the creation of more effective and trustworthy methodologies that can adequately address and alleviate the dissemination of false information shared on various social media channels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques

arXiv

引用

arXiv 2025年

作者： Hasan, Mahade Yasmin, Farhana Hassan, Md Mehedi Yu, Xue Yeasmin, Soniya Joshi, Herat Islam, Sheikh Mohammed Shariful School of Software Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Technology Nanjing University of Information Science and Technology Nanjing China Computer Science and Engineering Discipline Khulna University Khulna Bangladesh Department of Computer Science and Engineering North Western University Khulna Bangladesh Great River Health Systems BurlingtonIA United States Institute for Physical Activity and Nutrition Deakin University MelbourneVIC Australia

Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. Our approach involved feature selection techniques to identify the most relevant predictors, aimed at refining the models to enhance both performance and interpretability. The models were trained, incorporating processes such as grid search hyperparameter tuning, and cross-validation to minimize overfitting. Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. Furthermore, we have evaluated the models using key performance metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). Among the models, XGBoost demonstrated exceptional performance, achieving 99% accuracy, precision, F1-Score, 98% recall, and 100% ROC AUC. This study offers a promising approach to early heart disease diagnosis and preventive healthcare. © 2025, CC BY.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

A regularized CNN approach for detecting cervical cancer

引用

AIP Conference Proceedings 2024年第1期3220卷

作者： Jesse Jeremiah Tanimu Mohamed Hamada Mohammed Hassan Tsentob Joy Samson Jane Simon Parah Department of computer Science Bayero Unoiversity Kano Kano Nigeria Division of information systems University of Aizu Aizu Japan Department of Software Engineering Bayero University Kano Kano Nigeria Department of computer Science Air Force Institute of Technology Kaduna Kaduna Nigeria Department of Mathematical and Physical Science Glorious Vision University Ogwa Benin Nigeria

This study delved into leveraging deep learning techniques to classify different types of cervical cancer images, recognizing the disease’s prevalence and criticality. We employed a dataset comprising cervical images and investigated the utilization of the VGG16 model as a foundational architecture. To enhance performance, we incorporated various improvements, such as L2 regularization and data augmentation. The outcomes underscored the efficacy of these enhancements, with the refined model achieving a remarkable accuracy of 94%, surpassing the VGG16 model’s baseline accuracy of 89%. These findings demonstrate the potential of deep learning to improve the classification of cervical cancer.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：