Machine learning with applications最新文献

{"title":"Empirical loss weight optimization for PINN modeling laser bio-effects on human skin for the 1D heat equation","authors":"Jenny Farmer ,&nbsp;Chad A. Oian ,&nbsp;Brett A. Bowman ,&nbsp;Taufiquar Khan","doi":"10.1016/j.mlwa.2024.100563","DOIUrl":"https://doi.org/10.1016/j.mlwa.2024.100563","url":null,"abstract":"<div><p>The application of deep neural networks towards solving problems in science and engineering has demonstrated encouraging results with the recent formulation of physics-informed neural networks (PINNs). Through the development of refined machine learning techniques, the high computational cost of obtaining numerical solutions for partial differential equations governing complicated physical systems can be mitigated. However, solutions are not guaranteed to be unique, and are subject to uncertainty caused by the choice of network model parameters. For critical systems with significant consequences for errors, assessing and quantifying this model uncertainty is essential. In this paper, an application of PINN for laser bio-effects with limited training data is provided for uncertainty quantification analysis. Additionally, an efficacy study is performed to investigate the impact of the relative weights of the loss components of the PINN and how the uncertainty in the predictions depends on these weights. Network ensembles are constructed to empirically investigate the diversity of solutions across an extensive sweep of hyper-parameters to determine the model that consistently reproduces a high-fidelity numerical simulation.</p></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"16 ","pages":"Article 100563"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666827024000392/pdfft?md5=283004f05817debae277d850bbc84d0a&pid=1-s2.0-S2666827024000392-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141291144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applications of machine learning in surge prediction for vehicle turbochargers","authors":"Hiroki Saito ,&nbsp;Dai Kanzaki ,&nbsp;Kazuo Yonekura","doi":"10.1016/j.mlwa.2024.100560","DOIUrl":"10.1016/j.mlwa.2024.100560","url":null,"abstract":"<div><p>Surging in vehicle turbochargers is an important phenomenon that can damage the compressor and its peripheral equipment due to pressure fluctuations and vibration, so it is essential to understand the operating points where surging occurs. In this paper, we constructed a Neural Network (NN) that can predict these operating points, using as explanatory variables the geometry parameters of the vehicle turbocharger and one-dimensional predictions of the flow rates at surge. Our contribution is the use of machine learning to enable fast and low-cost prediction of surge points, which is usually only available through experiments or calculation-intensive Computational Fluid Dynamics (CFD). Evaluations conducted on the test data revealed that prediction accuracy was poor for some turbocharger geometries and operating conditions, and that this was associated with the relatively small data quantity included in the training data. Expanding the appropriate data offers some prospect of improving prediction accuracy.</p></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"16 ","pages":"Article 100560"},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666827024000367/pdfft?md5=7cefda1eb7f0f688680b98ed0f4e260c&pid=1-s2.0-S2666827024000367-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141026003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning feature importance selection for predicting aboveground biomass in African savannah with landsat 8 and ALOS PALSAR data","authors":"Sa'ad Ibrahim ,&nbsp;Heiko Balzter ,&nbsp;Kevin Tansey","doi":"10.1016/j.mlwa.2024.100561","DOIUrl":"10.1016/j.mlwa.2024.100561","url":null,"abstract":"<div><p>In remote sensing, multiple input bands are derived from various sensors covering different regions of the electromagnetic spectrum. Each spectral band plays a unique role in land use/land cover characterization. For example, while integrating multiple sensors for predicting aboveground biomass (AGB) is important for achieving high accuracy, reducing the dataset size by eliminating redundant and irrelevant spectral features is essential for enhancing the performance of machine learning algorithms. This accelerates the learning process, thereby developing simpler and more efficient models. Our results indicate that compared individual sensor datasets, the random forest (RF) classification approach using recursive feature elimination (RFE) increased the accuracy based on F score by 82.86 % and 26.19 respectively. The mutual information regression (MIR) method shows a slight increase in accuracy when considering individual sensor datasets, but its accuracy decreases when all features are taken into account for all models. Overall, the combination of features from the Landsat 8, ALOS PALSAR backscatter, and elevation data selected based on RFE provided the best AGB estimation for the RF and XGBoost models. In contrast to the k-nearest neighbors (KNN) and support vector machines (SVM), no significant improvement in AGB estimation was detected even when RFE and MIR were used. The effect of parameter optimization was found to be more significant for RF than for all the other methods. The AGB maps show patterns of AGB estimates consistent with those of the reference dataset. This study shows how prediction errors can be minimized based on feature selection using different ML classifiers.</p></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"16 ","pages":"Article 100561"},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666827024000379/pdfft?md5=eaa2c37c10a3e2753bcd07c6a3fa9373&pid=1-s2.0-S2666827024000379-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141054840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explaining vulnerabilities of heart rate biometric models securing IoT wearables","authors":"Chi-Wei Lien ,&nbsp;Sudip Vhaduri ,&nbsp;Sayanton V. Dibbo ,&nbsp;Maliha Shaheed","doi":"10.1016/j.mlwa.2024.100559","DOIUrl":"https://doi.org/10.1016/j.mlwa.2024.100559","url":null,"abstract":"<div><p>In the field of health informatics, extensive research has been conducted to predict diseases and extract valuable insights from patient data. However, a significant gap exists in addressing privacy concerns associated with data collection. Therefore, there is an urgent need to develop a machine-learning authentication model to secure the patients’ data seamlessly and continuously, as well as to find potential explanations when the model may fail. To address this challenge, we propose a unique approach to secure patients’ data using novel <em>eigenheart</em> features calculated from coarse-grained heart rate data. Various statistical and visualization techniques are utilized to explain the potential vulnerabilities of the model. Though it is feasible to develop continuous user authentication models from readily available heart rate data with reasonable performance, they are affected by factors such as age and Body Mass Index (BMI). These factors will be crucial for developing a more robust authentication model in the future.</p></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"16 ","pages":"Article 100559"},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666827024000355/pdfft?md5=49d6dff59b0bf14c46b5801d5d2b0451&pid=1-s2.0-S2666827024000355-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141066939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TeenyTinyLlama: Open-source tiny language models trained in Brazilian Portuguese","authors":"Nicholas Kluge Corrêa ,&nbsp;Sophia Falk ,&nbsp;Shiza Fatimah ,&nbsp;Aniket Sen ,&nbsp;Nythamar De Oliveira","doi":"10.1016/j.mlwa.2024.100558","DOIUrl":"https://doi.org/10.1016/j.mlwa.2024.100558","url":null,"abstract":"<div><p>Large language models (LLMs) have significantly advanced natural language processing, but their progress has yet to be equal across languages. While most LLMs are trained in high-resource languages like English, multilingual models generally underperform monolingual ones. Additionally, aspects of their multilingual foundation sometimes restrict the byproducts they produce, like computational demands and licensing regimes. In this study, we document the development of open-foundation models tailored for use in low-resource settings, their limitations, and their benefits. This is the <em>TeenyTinyLlama</em> pair: two compact models for Brazilian Portuguese text generation. We release them under the permissive Apache 2.0 license on <span>GitHub</span><svg><path></path></svg> and <span>Hugging Face</span><svg><path></path></svg> for community use and further development.</p></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"16 ","pages":"Article 100558"},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666827024000343/pdfft?md5=ca3df301a069c8298b65dcd69855e4ac&pid=1-s2.0-S2666827024000343-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141066938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using ChatGPT to annotate a dataset: A case study in intelligent tutoring systems","authors":"Aleksandar Vujinović,&nbsp;Nikola Luburić,&nbsp;Jelena Slivka,&nbsp;Aleksandar Kovačević","doi":"10.1016/j.mlwa.2024.100557","DOIUrl":"10.1016/j.mlwa.2024.100557","url":null,"abstract":"<div><p>Large language models like ChatGPT can learn in-context (ICL) from examples. Studies showed that, due to ICL, ChatGPT achieves impressive performance in various natural language processing tasks. However, to the best of our knowledge, this is the first study that assesses ChatGPT's effectiveness in annotating a dataset for training instructor models in intelligent tutoring systems (ITSs). The task of an ITS instructor model is to automatically provide effective tutoring instruction given a student's state, mimicking human instructors. These models are typically implemented as hardcoded rules, requiring expertise, and limiting their ability to generalize and personalize instructions. These problems could be mitigated by utilizing machine learning (ML). However, developing ML models requires a large dataset of student states annotated by corresponding tutoring instructions. Using human experts to annotate such a dataset is expensive, time-consuming, and requires pedagogical expertise. Thus, this study explores ChatGPT's potential to act as a pedagogy expert annotator. Using prompt engineering, we created a list of instructions a tutor could recommend to a student. We manually filtered this list and instructed ChatGPT to select the appropriate instruction from the list for the given student's state. We manually analyzed ChatGPT's responses that could be considered incorrectly annotated. Our results indicate that using ChatGPT as an annotator is an effective alternative to human experts. The contributions of our work are (1) a novel dataset annotation methodology for the ITS, (2) a publicly available dataset of student states annotated with tutoring instructions, and (3) a list of possible tutoring instructions.</p></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"16 ","pages":"Article 100557"},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666827024000331/pdfft?md5=3322a1226bc15e9303a8f45ef791c421&pid=1-s2.0-S2666827024000331-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141051011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep learning approach for Maize Lethal Necrosis and Maize Streak Virus disease detection","authors":"Tony O’Halloran ,&nbsp;George Obaido ,&nbsp;Bunmi Otegbade ,&nbsp;Ibomoiye Domor Mienye","doi":"10.1016/j.mlwa.2024.100556","DOIUrl":"https://doi.org/10.1016/j.mlwa.2024.100556","url":null,"abstract":"<div><p>Maize is an important crop cultivated in Sub-Saharan Africa, essential for food security. However, its cultivation faces significant challenges due to debilitating diseases such as Maize Lethal Necrosis (MLN) and Maize Streak Virus (MSV), which can lead to severe yield losses. Traditional plant disease diagnosis methods are often time-consuming and prone to errors, necessitating more efficient approaches. This study explores the application of deep learning, specifically Convolutional Neural Networks (CNNs), in the automatic detection and classification of maize diseases. We investigate six architectures: Basic CNN, EfficientNet V2 B0 and B1, LeNet-5, VGG-16, and ResNet50, using a dataset of 15344 images comprising MSV, MLN, and healthy maize leaves. Additionally, We performed hyperparameter tuning to improve the performance of the models and Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability. Our results show that the EfficientNet V2 B0 model demonstrated an accuracy of 99.99% in distinguishing between healthy and disease-infected plants. The results of this study contribute to the advancement of AI applications in agriculture, particularly in diagnosing maize diseases within Sub-Saharan Africa.</p></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"16 ","pages":"Article 100556"},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266682702400032X/pdfft?md5=63e258fda2023d11e907699e71790fd7&pid=1-s2.0-S266682702400032X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140901864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing breast cancer segmentation and classification: An Ensemble Deep Convolutional Neural Network and U-net approach on ultrasound images","authors":"Md Rakibul Islam ,&nbsp;Md Mahbubur Rahman ,&nbsp;Md Shahin Ali ,&nbsp;Abdullah Al Nomaan Nafi ,&nbsp;Md Shahariar Alam ,&nbsp;Tapan Kumar Godder ,&nbsp;Md Sipon Miah ,&nbsp;Md Khairul Islam","doi":"10.1016/j.mlwa.2024.100555","DOIUrl":"https://doi.org/10.1016/j.mlwa.2024.100555","url":null,"abstract":"<div><p>Breast cancer is a condition where the irregular growth of breast cells occurs uncontrollably, leading to the formation of tumors. It poses a significant threat to women’s lives globally, emphasizing the need for enhanced methods of detecting and categorizing the disease. In this work, we propose an Ensemble Deep Convolutional Neural Network (EDCNN) model that exhibits superior accuracy compared to several transfer learning models and the Vision Transformer model. Our EDCNN model integrates the strengths of the MobileNet and Xception models to improve its performance in breast cancer detection and classification. We employ various preprocessing techniques, including image resizing, data normalization, and data augmentation, to prepare the data for analysis. By following these measures, the formatting is optimized, and the model’s capacity to make generalizations is improved. We trained and evaluated our proposed EDCNN model using ultrasound images, a widely available modality for breast cancer imaging. The outcomes of our experiments illustrate that the EDCNN model attains an exceptional accuracy of 87.82% on Dataset 1 and 85.69% on Dataset 2, surpassing the performance of several well-known transfer learning models and the Vision Transformer model. Furthermore, an AUC value of 0.91 on Dataset 1 highlights the robustness and effectiveness of our proposed model. Moreover, we highlight the incorporation of the Grad-CAM Explainable Artificial Intelligence (XAI) technique to improve the interpretability and transparency of our proposed model. Additionally, we performed image segmentation using the U-Net segmentation technique on the input ultrasound images. This segmentation process allowed for the identification and isolation of specific regions of interest, facilitating a more comprehensive analysis of breast cancer characteristics. In conclusion, the study presents a creative approach to detecting and categorizing breast cancer, demonstrating the superior performance of the EDCNN model compared to well-established transfer learning models. Through advanced deep learning techniques and image segmentation, this study contributes to improving diagnosis and treatment outcomes in breast cancer.</p></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"16 ","pages":"Article 100555"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666827024000318/pdfft?md5=bd8495c4192aeafbc922477585a1e7f6&pid=1-s2.0-S2666827024000318-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140843003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anomaly detection in log-event sequences: A federated deep learning approach and open challenges","authors":"Patrick Himler,&nbsp;Max Landauer,&nbsp;Florian Skopik,&nbsp;Markus Wurzenberger","doi":"10.1016/j.mlwa.2024.100554","DOIUrl":"https://doi.org/10.1016/j.mlwa.2024.100554","url":null,"abstract":"<div><p>Anomaly Detection (AD) is an important area to reliably detect malicious behavior and attacks on computer systems. Log data is a rich source of information about systems and thus provides a suitable input for AD. With the sheer amount of log data available today, for years Machine Learning (ML) and more recently Deep Learning (DL) have been applied to create models for AD. Especially when processing complex log data, DL has shown some promising results in recent research to spot anomalies. It is necessary to group these log lines into log-event sequences, to detect anomalous patterns that span over multiple log lines. This work uses a centralized approach using a Long Short-Term Memory (LSTM) model for AD as its basis which is one of the most important approaches to represent long-range temporal dependencies in log-event sequences of arbitrary length. Therefore, we use past information to predict whether future events are normal or anomalous. For the LSTM model we adapt a state of the art open source implementation called LogDeep. For the evaluation, we use a Hadoop Distributed File System (HDFS) data set, which is well studied in current research. In this paper we show that without padding, which is a commonly used preprocessing step that strongly influences the AD process and artificially improves detection results and thus accuracy in lab testing, it is not possible to achieve the same high quality of results shown in literature. With the large quantity of log data, issues arise with the transfer of log data to a central entity where model computation can be done. Federated Learning (FL) tries to overcome this problem, by learning local models simultaneously on edge devices and overcome biases due to a lack of heterogeneity in training data through exchange of model parameters and finally arrive at a converging global model. Processing log data locally takes privacy and legal concerns into account, which could improve coordination and collaboration between researchers, cyber security companies, etc., in the future. Currently, there are only few scientific publications on log-based AD which use FL. Implementing FL gives the advantage of converging models even if the log data are heterogeneously distributed among participants as our results show. Furthermore, by varying individual LSTM model parameters, the results can be greatly improved. Further scientific research will be necessary to optimize FL approaches.</p></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"16 ","pages":"Article 100554"},"PeriodicalIF":0.0,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666827024000306/pdfft?md5=fc8d0afe652c7146979d5889ecbf2afa&pid=1-s2.0-S2666827024000306-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140825198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel variational mode decomposition based convolutional neural network for the identification of freezing of gait intervals for patients with Parkinson's disease","authors":"Mohamed Shaban","doi":"10.1016/j.mlwa.2024.100553","DOIUrl":"https://doi.org/10.1016/j.mlwa.2024.100553","url":null,"abstract":"<div><p>Freezing of gait (FoG) is a debilitating and serious motor system complication of Parkinson's disease (PD) that may expose patients to frequent falls and life-threating injuries. Several artificial and machine learning methods have been proposed for the prediction of FoG based upon a limited time-duration of sensory data, However, most of the related work has been insufficiently trained and tested on smaller datasets compromising the generalizability of the models. Further, the proposed models provided a prediction at a lower rate (e.g., every 7.8 s). In response to the above shortcomings, we propose a novel variational mode decomposition (VMD) based deep learning that is capable of efficiently inferring the occurrence of FoG at a higher time-resolution (i.e., every sampling period of 7.8 ms) and with a subject-independent accuracy up to 98.8 % outperforming the state-of-the-art architectures and the standard LSTM models. The proposed model will enable the prompt detection of FoG episodes and support PD sufferers reducing the likelihood of falls.</p></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"16 ","pages":"Article 100553"},"PeriodicalIF":0.0,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266682702400029X/pdfft?md5=2bda73c87a2dab2da0303eef45731096&pid=1-s2.0-S266682702400029X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140818139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}