Machine learning and knowledge extraction最新文献_第7页

Research on Forest Fire Detection Algorithm Based on Improved YOLOv5 基于改进YOLOv5的森林火灾检测算法研究

Machine learning and knowledge extraction Pub Date : 2023-06-28 DOI: 10.3390/make5030039

Jianfeng Li, Xiao-Feng Lian

{"title":"Research on Forest Fire Detection Algorithm Based on Improved YOLOv5","authors":"Jianfeng Li, Xiao-Feng Lian","doi":"10.3390/make5030039","DOIUrl":"https://doi.org/10.3390/make5030039","url":null,"abstract":"Forest fires are one of the world’s deadliest natural disasters. Early detection of forest fires can help minimize the damage to ecosystems and forest life. In this paper, we propose an improved fire detection method YOLOv5-IFFDM for YOLOv5. Firstly, the fire and smoke detection accuracy and the network perception accuracy of small targets are improved by adding an attention mechanism to the backbone network. Secondly, the loss function is improved and the SoftPool pyramid pooling structure is used to improve the regression accuracy and detection performance of the model and the robustness of the model. In addition, a random mosaic augmentation technique is used to enhance the data to increase the generalization ability of the model, and re-clustering of flame and smoke detection a priori frames are used to improve the accuracy and speed. Finally, the parameters of the convolutional and normalization layers of the trained model are homogeneously merged to further reduce the model processing load and to improve the detection speed. Experimental results on self-built forest-fire and smoke datasets show that this algorithm has high detection accuracy and fast detection speed, with average accuracy of fire up to 90.5% and smoke up to 84.3%, and detection speed up to 75 FPS (frames per second transmission), which can meet the requirements of real-time and efficient fire detection.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"10 1","pages":"725-745"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79529380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using Machine Learning with Eye-Tracking Data to Predict if a Recruiter Will Approve a Resume 使用机器学习和眼球追踪数据来预测招聘人员是否会批准一份简历

Machine learning and knowledge extraction Pub Date : 2023-06-28 DOI: 10.3390/make5030038

Angel Pina, Corbin Petersheim, Josh Cherian, J. Lahey, Gerianne Alexander, T. Hammond

{"title":"Using Machine Learning with Eye-Tracking Data to Predict if a Recruiter Will Approve a Resume","authors":"Angel Pina, Corbin Petersheim, Josh Cherian, J. Lahey, Gerianne Alexander, T. Hammond","doi":"10.3390/make5030038","DOIUrl":"https://doi.org/10.3390/make5030038","url":null,"abstract":"When job seekers are unsuccessful in getting a position, they often do not get feedback to inform them on how to develop a better application in the future. Therefore, there is a critical need to understand what qualifications recruiters value in order to help applicants. To address this need, we utilized eye-trackers to measure and record visual data of recruiters screening resumes to gain insight into which Areas of Interest (AOIs) influenced recruiters’ decisions the most. Using just this eye-tracking data, we trained a machine learning classifier to predict whether or not a recruiter would move a resume on to the next level of the hiring process with an AUC of 0.767. We found that features associated with recruiters looking outside the content of a resume were most predictive of their decision as well as total time viewing the resume and time spent on the Experience and Education sections. We hypothesize that this behavior is indicative of the recruiter reflecting on the content of the resume. These initial results show that applicants should focus on designing clear and concise resumes that are easy for recruiters to absorb and think about, with additional attention given to the Experience and Education sections.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"25 1","pages":"713-724"},"PeriodicalIF":0.0,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83289449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Drug-Drug Interaction Extraction from Biomedical Text Using Relation BioBERT with BLSTM 基于BioBERT和BLSTM的生物医学文本药物-药物相互作用提取

Machine learning and knowledge extraction Pub Date : 2023-06-10 DOI: 10.3390/make5020036

Maryam KafiKang, Abdeltawab Hendawi

{"title":"Drug-Drug Interaction Extraction from Biomedical Text Using Relation BioBERT with BLSTM","authors":"Maryam KafiKang, Abdeltawab Hendawi","doi":"10.3390/make5020036","DOIUrl":"https://doi.org/10.3390/make5020036","url":null,"abstract":"In the context of pharmaceuticals, drug-drug interactions (DDIs) occur when two or more drugs interact, potentially altering the intended effects of the drugs and resulting in adverse patient health outcomes. Therefore, it is essential to identify and comprehend these interactions. In recent years, an increasing number of novel compounds have been discovered, resulting in the discovery of numerous new DDIs. There is a need for effective methods to extract and analyze DDIs, as the majority of this information is still predominantly located in biomedical articles and sources. Despite the development of various techniques, accurately predicting DDIs remains a significant challenge. This paper proposes a novel solution to this problem by leveraging the power of Relation BioBERT (R-BioBERT) to detect and classify DDIs and the Bidirectional Long Short-Term Memory (BLSTM) to improve the accuracy of predictions. In addition to determining whether two drugs interact, the proposed method also identifies the specific types of interactions between them. Results show that the use of BLSTM leads to significantly higher F-scores compared to our baseline model, as demonstrated on three well-known DDI extraction datasets that includes SemEval 2013, TAC 2018, and TAC 2019.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135006472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Survey of Deep Learning for Alzheimer's Disease 深度学习治疗阿尔茨海默病的研究综述

Machine learning and knowledge extraction Pub Date : 2023-06-09 DOI: 10.3390/make5020035

Qinghua Zhou, Jiaji Wang, Xiang Yu, Shuihua Wang, Yudong Zhang

引用次数: 3

A Mathematical Framework for Enriching Human-Machine Interactions 丰富人机交互的数学框架

Machine learning and knowledge extraction Pub Date : 2023-06-06 DOI: 10.3390/make5020034

A. Ehresmann, Mathias Béjean, J. Vanbremeersch

{"title":"A Mathematical Framework for Enriching Human-Machine Interactions","authors":"A. Ehresmann, Mathias Béjean, J. Vanbremeersch","doi":"10.3390/make5020034","DOIUrl":"https://doi.org/10.3390/make5020034","url":null,"abstract":"This paper presents a conceptual mathematical framework for developing rich human–machine interactions in order to improve decision-making in a social organisation, S. The idea is to model how S can create a “multi-level artificial cognitive system”, called a data analyser (DA), to collaborate with humans in collecting and learning how to analyse data, to anticipate situations, and to develop new responses, thus improving decision-making. In this model, the DA is “processed” to not only gather data and extend existing knowledge, but also to learn how to act autonomously with its own specific procedures or even to create new ones. An application is given in cases where such rich human–machine interactions are expected to allow the DA+S partnership to acquire deep anticipation capabilities for possible future changes, e.g., to prevent risks or seize opportunities. The way the social organization S operates over time, including the construction of DA, is described using the conceptual framework comprising “memory evolutive systems” (MES), a mathematical theoretical approach introduced by Ehresmann and Vanbremeersch for evolutionary multi-scale, multi-agent and multi-temporality systems. This leads to the definition of a “data analyser–MES”.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"613 1","pages":"597-610"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86676769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Systematic Review of Recommendation Systems for Course Selection 课程选择推荐系统的系统回顾

Machine learning and knowledge extraction Pub Date : 2023-06-06 DOI: 10.3390/make5020033

Shrooq Algarni, Frederick T. Sheldon

引用次数: 0

What about the Latent Space? The Need for Latent Feature Saliency Detection in Deep Time Series Classification 潜伏空间呢?深度时间序列分类中潜在特征显著性检测的必要性

Machine learning and knowledge extraction Pub Date : 2023-05-18 DOI: 10.3390/make5020032

Maresa Schröder, Alireza Zamanian, N. Ahmidi

{"title":"What about the Latent Space? The Need for Latent Feature Saliency Detection in Deep Time Series Classification","authors":"Maresa Schröder, Alireza Zamanian, N. Ahmidi","doi":"10.3390/make5020032","DOIUrl":"https://doi.org/10.3390/make5020032","url":null,"abstract":"Saliency methods are designed to provide explainability for deep image processing models by assigning feature-wise importance scores and thus detecting informative regions in the input images. Recently, these methods have been widely adapted to the time series domain, aiming to identify important temporal regions in a time series. This paper extends our former work on identifying the systematic failure of such methods in the time series domain to produce relevant results when informative patterns are based on underlying latent information rather than temporal regions. First, we both visually and quantitatively assess the quality of explanations provided by multiple state-of-the-art saliency methods, including Integrated Gradients, Deep-Lift, Kernel SHAP, and Lime using univariate simulated time series data with temporal or latent patterns. In addition, to emphasize the severity of the latent feature saliency detection problem, we also run experiments on a real-world predictive maintenance dataset with known latent patterns. We identify Integrated Gradients, Deep-Lift, and the input-cell attention mechanism as potential candidates for refinement to yield latent saliency scores. Finally, we provide recommendations on using saliency methods for time series classification and suggest a guideline for developing latent saliency methods for time series.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"23 1","pages":"539-559"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90893026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Alzheimer's Disease Detection from Fused PET and MRI Modalities Using an Ensemble Classifier 使用集成分类器从融合PET和MRI模式检测阿尔茨海默病

Machine learning and knowledge extraction Pub Date : 2023-05-18 DOI: 10.3390/make5020031

A. Shukla, Rajeev Tiwari, Shamik Tiwari

{"title":"Alzheimer's Disease Detection from Fused PET and MRI Modalities Using an Ensemble Classifier","authors":"A. Shukla, Rajeev Tiwari, Shamik Tiwari","doi":"10.3390/make5020031","DOIUrl":"https://doi.org/10.3390/make5020031","url":null,"abstract":"Alzheimer’s disease (AD) is an old-age disease that comes in different stages and directly affects the different regions of the brain. The research into the detection of AD and its stages has new advancements in terms of single-modality and multimodality approaches. However, sustainable techniques for the detection of AD and its stages still require a greater extent of research. In this study, a multimodal image-fusion method is initially proposed for the fusion of two different modalities, i.e., PET (Positron Emission Tomography) and MRI (Magnetic Resonance Imaging). Further, the features obtained from fused and non-fused biomarkers are passed to the ensemble classifier with a Random Forest-based feature selection strategy. Three classes of Alzheimer’s disease are used in this work, namely AD, MCI (Mild Cognitive Impairment) and CN (Cognitive Normal). In the resulting analysis, the Binary classifications, i.e., AD vs. CN and MCI vs. CN, attained an accuracy (Acc) of 99% in both cases. The class AD vs. MCI detection achieved an adequate accuracy (Acc) of 91%. Furthermore, the Multi Class classification, i.e., AD vs. MCI vs. CN, achieved 96% (Acc).","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"8 1","pages":"512-538"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80634773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Probabilistic Transformation of Distance-Based Outliers 基于距离的离群值的概率变换

Machine learning and knowledge extraction Pub Date : 2023-05-16 DOI: 10.3390/make5030042

David Muhr, M. Affenzeller, Josef Küng

{"title":"A Probabilistic Transformation of Distance-Based Outliers","authors":"David Muhr, M. Affenzeller, Josef Küng","doi":"10.3390/make5030042","DOIUrl":"https://doi.org/10.3390/make5030042","url":null,"abstract":"The scores of distance-based outlier detection methods are difficult to interpret, and it is challenging to determine a suitable cut-off threshold between normal and outlier data points without additional context. We describe a generic transformation of distance-based outlier scores into interpretable, probabilistic estimates. The transformation is ranking-stable and increases the contrast between normal and outlier data points. Determining distance relationships between data points is necessary to identify the nearest-neighbor relationships in the data, yet most of the computed distances are typically discarded. We show that the distances to other data points can be used to model distance probability distributions and, subsequently, use the distributions to turn distance-based outlier scores into outlier probabilities. Over a variety of tabular and image benchmark datasets, we show that the probabilistic transformation does not impact outlier ranking (ROC AUC) or detection performance (AP, F1), and increases the contrast between normal and outlier score distributions (statistical distance). The experimental findings indicate that it is possible to transform distance-based outlier scores into interpretable probabilities with increased contrast between normal and outlier samples. Our work generalizes to a wide range of distance-based outlier detection methods, and, because existing distance computations are used, it adds no significant computational overhead.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"569 1","pages":"782-802"},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83496234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Biologically Inspired Self-Organizing Computational Model to Mimic Infant Learning 模仿婴儿学习的生物学启发自组织计算模型

Machine learning and knowledge extraction Pub Date : 2023-05-15 DOI: 10.3390/make5020030

Karthik Santhanaraj, Dinakaran Devaraj, Ramya Mm, J. Dhanraj, K. Ramanathan

{"title":"Biologically Inspired Self-Organizing Computational Model to Mimic Infant Learning","authors":"Karthik Santhanaraj, Dinakaran Devaraj, Ramya Mm, J. Dhanraj, K. Ramanathan","doi":"10.3390/make5020030","DOIUrl":"https://doi.org/10.3390/make5020030","url":null,"abstract":"Recent technological advancements have fostered human–robot coexistence in work and residential environments. The assistive robot must exhibit humane behavior and consistent care to become an integral part of the human habitat. Furthermore, the robot requires an adaptive unsupervised learning model to explore unfamiliar conditions and collaborate seamlessly. This paper introduces variants of the growing hierarchical self-organizing map (GHSOM)-based computational models for assistive robots, which constructs knowledge from unsupervised exploration-based learning. Traditional self-organizing map (SOM) algorithms have shortcomings, including finite neuron structure, user-defined parameters, and non-hierarchical adaptive architecture. The proposed models overcome these limitations and dynamically grow to form problem-dependent hierarchical feature clusters, thereby allowing associative learning and symbol grounding. Infants can learn from their surroundings through exploration and experience, developing new neuronal connections as they learn. They can also apply their prior knowledge to solve unfamiliar problems. With infant-like emergent behavior, the presented models can operate on different problems without modifications, producing new patterns not present in the input vectors and allowing interactive result visualization. The proposed models are applied to the color, handwritten digits clustering, finger identification, and image classification problems to evaluate their adaptiveness and infant-like knowledge building. The results show that the proposed models are the preferred generalized models for assistive robots.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"46 1","pages":"491-511"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76167857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0