Machine learning with applications最新文献

{"title":"Artificial intelligence and sustainable development in Africa: A comprehensive review","authors":"Ibomoiye Domor Mienye ,&nbsp;Yanxia Sun ,&nbsp;Emmanuel Ileberi","doi":"10.1016/j.mlwa.2024.100591","DOIUrl":"10.1016/j.mlwa.2024.100591","url":null,"abstract":"<div><div>Artificial Intelligence (AI) techniques are transforming various sectors and hold significant potential to advance sustainable development in Africa. However, their effective integration is constrained by region-specific challenges, limiting widespread deployment. This study reviews the current state of sustainable development in Africa, highlighting the role AI can play in driving progress across key sectors, including healthcare, agriculture, education, environmental protection, and infrastructure. The paper outlines the challenges hindering AI adoption and presents strategic approaches to address these obstacles, specifically targeting Africa’s socio-economic and environmental needs. In addition, the study proposes a comprehensive framework for integrating AI into Africa’s sustainable development efforts, offering tailored AI-driven strategies that align with the continent’s unique context. This framework provides a valuable resource for AI researchers, policymakers, and practitioners working towards sustainable development in Africa.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100591"},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532255","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":"Detecting drug transfers via the drop-off method: A supervised model approach using AIS data","authors":"Britt van Leeuwen ,&nbsp;Maike Nutzel","doi":"10.1016/j.mlwa.2024.100590","DOIUrl":"10.1016/j.mlwa.2024.100590","url":null,"abstract":"<div><div>Maritime security is of tremendous importance in countering drug trafficking, particularly through sea-based routes. In this paper, we address the pressing need for effective detection methods by introducing a novel approach utilizing Automatic Identification System (AIS) data. Our focus lies on detecting the ‘drop-off’ method, a prevalent technique for contraband smuggling at sea. Unlike existing research, primarily employing unsupervised methods, we propose a supervised model specifically tailored to this illicit activity, with a particular emphasis on its application to fishing vessels.</div><div>Our model significantly reduces the number of data points requiring classification by the observer by 70% , thereby enhancing the efficiency of the drop-off detection process. By employing a Long Short-Term Memory (LSTM) model, our approach demonstrates a change from traditional methods and offers advantages in capturing complex temporal patterns inherent in ‘drop-off’ activities. The rationale behind choosing LSTM lies in its ability to effectively model sequential data, which is essential for detecting drug traffic activities at sea where patterns are subtle and dynamic.</div><div>Moreover, this model holds the potential for integration into real-time surveillance systems, thereby enhancing operational capabilities in detecting and preventing drug traffic. The generalizability of our model makes for considerable potential in enhancing maritime security efforts and providing assistance in countering drug traffic on a global scale. Importantly, our model outperforms both baseline models, underscoring its effectiveness and superiority in addressing the specific challenges posed by ‘drop-off’ detection. For more information and access to the code repository, please visit <span><span>this link</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100590"},"PeriodicalIF":0.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445582","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":"Noninvasive pressure monitoring using acoustic resonance spectroscopy and machine learning","authors":"M. Prisbrey,&nbsp;D. Pereira,&nbsp;J. Greenhall,&nbsp;E. Davis,&nbsp;P. Vakhlamov,&nbsp;C. Chavez,&nbsp;C. Pantea","doi":"10.1016/j.mlwa.2024.100589","DOIUrl":"10.1016/j.mlwa.2024.100589","url":null,"abstract":"<div><div>Monitoring pressure inside hermetically sealed vessels typically relies on devices that have direct contact with the fluid inside. Gaining this access requires a hole through the wall of the vessel, which creates potential for leaks, ruptures, and complete failures. To solve this, noninvasive solutions utilize external sensors that relate vessel-wall behavior to internal pressure. However, existing noninvasive techniques require permanently attaching sensors to a unique vessel and then monitoring for changes in the vessel. We present a noninvasive pressure monitoring technique based on acoustic resonance spectroscopy (ARS) and machine learning (ML) that enables estimating pressure in a vessel similar to those it was trained on and does not require sensors to be permanently attached. We train k-nearest neighbor (KNN) regressor models using experimentally gathered acoustic resonance spectra to estimate the pressure in six stainless-steel vessels. We demonstrate accurate estimation of the pressure inside the vessels when training and testing using spectra taken exclusively from an individual vessel, and when performing cross-validation between vessels. The acoustic technique presented in this paper finds broad applications across industry to monitor pressure in systems where having permanent sensors is undesirable, such as complicated pneumatic systems, vacuum sealed foods, and more.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100589"},"PeriodicalIF":0.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425944","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":"VLFSE: Enhancing visual tracking through visual language fusion and state update evaluator","authors":"Fuchao Yang ,&nbsp;Mingkai Jiang ,&nbsp;Qiaohong Hao ,&nbsp;Xiaolei Zhao ,&nbsp;Qinghe Feng","doi":"10.1016/j.mlwa.2024.100588","DOIUrl":"10.1016/j.mlwa.2024.100588","url":null,"abstract":"<div><div>Recently, visual tracking algorithms have achieved impressive results by combining dynamic templates. However, the instability of visual images and the incorrect timing of template updates lead to decreased tracking accuracy and stability in intricate scenarios. To address these issues, we propose a visual tracking algorithm through visual language fusion and a state update evaluator (VLFSE). Specifically, our approach introduces a multimodal attention mechanism that uses self-attention to mine and integrate information from diverse sources effectively. This mechanism ensures a richer, context-aware representation of the target, enabling more accurate tracking even in complex scenes. Moreover, we recognize the critical need for precise template updates to maintain tracking accuracy over time. To this end, we develop a state update evaluator, a component trained online to assess the necessity and timing of template updates accurately. This evaluator acts as a safeguard, preventing erroneous updates and ensuring the tracker adapts optimally to changes in the target’s appearance. The experimental results on challenging visual language tracking datasets demonstrate our tracker’s superior performance, showcasing its adaptability and accuracy in complex tracking scenarios.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100588"},"PeriodicalIF":0.0,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425943","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":"InvarNet: Molecular property prediction via rotation invariant graph neural networks","authors":"Danyan Chen ,&nbsp;Gaoxiang Duan ,&nbsp;Dengbao Miao ,&nbsp;Xiaoying Zheng ,&nbsp;Yongxin Zhu","doi":"10.1016/j.mlwa.2024.100587","DOIUrl":"10.1016/j.mlwa.2024.100587","url":null,"abstract":"<div><div>Predicting molecular properties is crucial in drug synthesis and screening, but traditional molecular dynamics methods are time-consuming and costly. Recently, deep learning methods, particularly Graph Neural Networks (GNNs), have significantly improved efficiency by capturing molecular structures’ invariance under translation, rotation, and permutation. However, current GNN methods require complex data processing, increasing algorithmic complexity. This high complexity leads to several challenges, including increased computation time, higher computational resource demands, increased memory consumption. This paper introduces InvarNet, a GNN-based model trained with a composite loss function that bypasses intricate data processing while maintaining molecular property invariance. By pre-storing atomic feature attributes, InvarNet avoids repeated feature extraction during forward propagation. Experiments on three public datasets (Electronic Materials, QM9, and MD17) demonstrate that InvarNet achieves superior prediction accuracy, excellent stability, and convergence speed. It reaches state-of-the-art performance on the Electronic Materials dataset and outperforms existing models on the <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> and <span><math><mrow><mi>a</mi><mi>l</mi><mi>p</mi><mi>h</mi><mi>a</mi></mrow></math></span> properties of the QM9 dataset. On the MD17 dataset, InvarNet excels in energy prediction of benzene without atomic force. Additionally, InvarNet accelerates training time per epoch by 2.24 times compared to SphereNet on the QM9 dataset, simplifying data processing while maintaining acceptable accuracy.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100587"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359250","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":"Real-time monitoring and optimization of machine learning intelligent control system in power data modeling technology","authors":"Qiong Wang ,&nbsp;Zuohu Chen ,&nbsp;Yongbo Zhou ,&nbsp;Zhiyuan Liu ,&nbsp;Zhenguo Peng","doi":"10.1016/j.mlwa.2024.100584","DOIUrl":"10.1016/j.mlwa.2024.100584","url":null,"abstract":"<div><div>In response to the problems of insufficient model accuracy and poor real-time performance in real-time monitoring and optimization of data models in traditional power systems, this paper explored them based on machine learning. It used methods such as box plots and Kalman filters to preprocess power data and used Fourier transform and long short-term memory (LSTM) network to extract data features. Based on long-term and short-term memory networks, this paper constructed a deep neural network model to process data. Recursive feature elimination method can be used for feature selection in the model, and multi-model integration method can be used for feature fusion. The experiment trained and tested the model on two datasets, IEEE 39-Bus and Pecan Street. The experimental results show that the accuracy of the paper's model in both the training and testing sets is higher than that of the convolutional neural network, decision tree, and support vector machine models in the comparative experiments, reaching 93 % and 94 %, respectively. The average response times in three different testing methods were 139.8 ms, 151 ms, and 140.6 ms, respectively. The lowest daily failure rate for 30 consecutive days of work was 0 %, which reflects the stability of the model; in the satisfaction survey of practitioners, the proportion of high recognition answers to each question was higher than 80 %. The experimental results show that the machine learning intelligent control system has achieved good application in power data modeling technology, providing a reference for similar research in the future.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100584"},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666827024000604/pdfft?md5=e8c69d1fc05c37ebcbb461a56873cfe6&pid=1-s2.0-S2666827024000604-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312046","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":"Recurrent neural networks for anomaly detection in magnet power supplies of particle accelerators","authors":"Ihar Lobach,&nbsp;Michael Borland","doi":"10.1016/j.mlwa.2024.100585","DOIUrl":"10.1016/j.mlwa.2024.100585","url":null,"abstract":"<div><p>This research illustrates how time-series forecasting employing recurrent neural networks (RNNs) can be used for anomaly detection in particle accelerators—complex machines that accelerate elementary particles to high speeds for various scientific and industrial applications. Our approach utilizes an RNN to predict temperatures of key components of magnet power supplies (PSs), which can number up to thousands in an accelerator. An anomaly is declared when the predicted temperature deviates significantly from observation. Our method can help identify a PS requiring maintenance before it fails and leads to costly downtime of an entire billion-dollar accelerator facility. We demonstrate that the RNN outperforms a reasonably complex physics-based model at predicting the PS temperatures and at anomaly detection. We conclude that for practical applications it can be beneficial to use RNNs instead of increasing the complexity of the physics-based model. We chose the long short-term memory (LSTM) as opposed to other RNN cell structures due to its widespread use in time-series forecasting and its relative simplicity. However, we demonstrate that the LSTM’s precision of predicting PS temperatures is nearly on par with measurement precision, making more complex or custom architectures unnecessary. Lastly, we dedicate a section of this paper to presenting a proof-of-concept for using infrared cameras for spatially-resolved anomaly detection inside power supplies, which will be a subject of future research.</p></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100585"},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666827024000616/pdfft?md5=356acb04faf4b179c151d8c9721c75e7&pid=1-s2.0-S2666827024000616-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161595","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":"Robust image classification system via cloud computing, aligned multimodal embeddings, centroids and neighbours","authors":"Wei Lun Koh,&nbsp;James Boon Yong Koh,&nbsp;Bing Tian Dai","doi":"10.1016/j.mlwa.2024.100583","DOIUrl":"10.1016/j.mlwa.2024.100583","url":null,"abstract":"<div><p>We propose a framework for a cloud-based application of an image classification system that is highly accessible, maintains data confidentiality, and robust to incorrect training labels. The end-to-end system is implemented using Amazon Web Services (AWS), with a detailed guide provided for replication, enhancing the ways which researchers can collaborate with a community of users for mutual benefits. A front-end web application allows users across the world to securely log in, contribute labelled training images conveniently via a drag-and-drop approach, and use that same application to query an up-to-date model that has knowledge of images from the community of users. This resulting system demonstrates that theory can be effectively interlaced with practice, with various considerations addressed by our architecture. Users will have access to an image classification model that can be updated and automatically deployed within minutes, gaining benefits from and at the same time providing benefits to the community of users. At the same time, researchers, who will act as administrators, will be able to conveniently and securely engage a large number of users with their respective machine learning models and build up a labelled database over time, paying only variable costs that is proportional to utilization.</p></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"17 ","pages":"Article 100583"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666827024000598/pdfft?md5=e2b0096db158ce87d429b6d8deb1da6e&pid=1-s2.0-S2666827024000598-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129217","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":"Prediction of bike-sharing station demand using explainable artificial intelligence","authors":"Frank Ngeni ,&nbsp;Boniphace Kutela ,&nbsp;Tumlumbe Juliana Chengula ,&nbsp;Cuthbert Ruseruka ,&nbsp;Hannah Musau ,&nbsp;Norris Novat ,&nbsp;Debbie Aisiana Indah ,&nbsp;Sarah Kasomi","doi":"10.1016/j.mlwa.2024.100582","DOIUrl":"10.1016/j.mlwa.2024.100582","url":null,"abstract":"<div><p>Bike-sharing systems have grown in popularity in metropolitan areas, providing a handy and environmentally friendly transportation choice for commuters and visitors alike. As demand for bike-sharing programs grows, efficient capacity planning becomes critical to ensuring good user experience and system sustainability in terms of demand. The random forest model was used in this study to predict bike-sharing station demand and is considered a strong ensemble learning approach that can successfully capture complicated nonlinear correlations and interactions between input variables. This study employed data from the Smart Location Database (SLD) to test the model accuracy in estimating station demand and used a form of explainable artificial intelligence (XAI) function to further understand machine learning (ML) prediction outcomes owing to the blackbox tendencies of ML models. Vehicle Miles of Travel (VMT) and Greenhouse Gas (GHG) emissions were the most important features in predicting docking station demand individually but not holistically based on the datasets. The percentage of zero-car households, gross residential density, road network density, aggregate frequency of transit service, and gross activity density were found to have a moderate influence on the prediction model. Further, there may be a better prediction model generating sensible results for every type of explanatory variable, but their contributions are minimum to the prediction outcome. By measuring each feature's contribution to demand prediction in feature engineering, bike-sharing operators can acquire a better understanding of the bike-sharing station capacity and forecast future demands during planning. At the same time, ML models will need further assessment before a holistic conclusion.</p></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"17 ","pages":"Article 100582"},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666827024000586/pdfft?md5=bf46aecfa5d4b69f24c5a8d196610032&pid=1-s2.0-S2666827024000586-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012918","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 advanced driver assistance systems through explainable artificial intelligence for driver anomaly detection","authors":"Tumlumbe Juliana Chengula ,&nbsp;Judith Mwakalonge ,&nbsp;Gurcan Comert ,&nbsp;Methusela Sulle ,&nbsp;Saidi Siuhi ,&nbsp;Eric Osei","doi":"10.1016/j.mlwa.2024.100580","DOIUrl":"10.1016/j.mlwa.2024.100580","url":null,"abstract":"<div><p>The recent advancements in Advanced Driver Assistance Systems (ADAS) have significantly contributed to road safety and driving comfort. An integral aspect of these systems is the detection of driver anomalies such as drowsiness, distraction, and impairment, which are crucial for preventing accidents. Building upon previous studies that utilized ensemble model learning (XGBoost) with deep learning models (ResNet50, DenseNet201, and InceptionV3) for anomaly detection, this study introduces a comprehensive feature importance analysis using the SHAP (SHapley Additive exPlanations) technique. The technique is implemented through explainable artificial intelligence (XAI). The primary objective is to unravel the complex decision-making process of the ensemble model, which has previously demonstrated near-perfect performance metrics in classifying driver behaviors using in-vehicle cameras. By applying SHAP, the study aims to identify and quantify the contribution of each feature – such as facial expressions, head position, yawning, and sleeping – in predicting driver states. This analysis offers insights into the model’s inner workings and guides the enhancement of feature engineering for more precise and reliable anomaly detection. The findings of this study are expected to impact the development of future ADAS technologies significantly. By pinpointing the most influential features and understanding their dynamics, a model can be optimized for various driving scenarios, ensuring that ADAS systems are robust, accurate, and tailored to real-world conditions. Ultimately, this study contributes to the overarching goal of enhancing road safety through technologically advanced, data-driven approaches.</p></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"17 ","pages":"Article 100580"},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666827024000562/pdfft?md5=f80e9f1f7d3e0f04778d0e4fbebf27c0&pid=1-s2.0-S2666827024000562-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953205","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}