期刊,Robotics & Machine Learning Daily News 2024年卷Feb.29期_国家学术搜索

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Robotics & Machine Learning Daily News

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Reports from Central Queensland University Describe Recent Advances in Machine Learning (Social Welfare Evaluation During Demand Response Programs Execution Considering Machine Learningbased Load Profile Clustering)

68-68页

查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting from Rockhampton, Australia, by NewsRx journalists, research stated, "In the last decade, with the introduction of smart meters to smart grids, demand response programs (DRPs) have been widely adopted to establish a generation and consumption balance. DRPs provide many benefits for efficient grid management." The news correspondents obtained a quote from the research from Central Queensland University, "However, these programs are conducive to higher levels of dissatisfaction by changing grid customers' consumption patterns. This paper aims to investigate the effects of DRPs on social welfare (SW). To this end, the paper presents a mathematical model for SW during the implementation of DRPs. In the proposed model, the level of customer satisfaction is assumed the main factor contributing to SW. This mathematical model considers different types of DRPs in terms of their impacts on SW. The paper also seeks to obtain linear and nonlinear models of DRPs and the coefficient of participation (CoP). CoP as an indicator shows the percentage of customers who actively participate in each DRP and plays a significant role in the assessment of the SW level. Moreover, owing to the sparsity and variety of distribution network customers, load patterns are classified into different clusters to take the load types into account. As a matter of fact, this process aims to identify similar patterns and thus, the same level of satisfaction for each separate cluster. The classification process is performed by using a machine learning-based clustering method known as the Affinity Propagation (AP) algorithm. Then, the model calculates the level of SW for the clusters based on the usage of electrical equipment and the time of day when they are turned on. The obtained levels of SW help operators select the best programs for every cluster in terms of customer satisfaction, and achieve the highest performance of DRPs."

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NSTL
Elsevier

Universidad Carlos Ⅲ de Madrid Reports Findings in Attention Deficit Hyperactivity Disorders (A systematic review on the application of machine learning models in psychometric questionnaires for the diagnosis of attention deficit …)

69-69页

查看更多>>摘要：New research on Developmental Diseases and Conditions - Attention Deficit Hyperactivity Disorders is the subject of a report. According to news reporting out of Getafe, Spain, by NewsRx editors, research stated, "Attention deficit hyperactivity disorder is one of the most prevalent neurodevelopmental disorders worldwide. Recent studies show that machine learning has great potential for the diagnosis of attention deficit hyperactivity disorder." Our news journalists obtained a quote from the research from Universidad Carlos Ⅲ de Madrid, "The aim of the present article is to systematically review the scientific literature on machine learning studies for the diagnosis of attention deficit hyperactivity disorder, focusing on psychometric questionnaire tools. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were adopted. The review protocol was registered in the PROSPERO database. A search was conducted in three databases-Web of Science Core Collection, Scopus and Pubmed-with the aim of identifying studies that apply ML techniques to support the diagnosis of attention deficit hyperactivity disorder. A total of 17 empirical studies were found that met the established inclusion criteria. The results showed that machine learning can be used to increase the accuracy of attention deficit hyperactivity disorder diagnosis." According to the news editors, the research concluded: "Machine learning techniques are useful and effective strategies that can complement traditional diagnostics in patients with attention deficit hyperactivity disorder."

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Studies from Akdeniz University Further Understanding of Machine Learning (Prediction of Leaf Break Resistance of Green and Dry Alfalfa Leaves by Machine Learning Methods)

70-70页

查看更多>>摘要：New study results on artificial intelligence have been published. According to news reporting originating from Akdeniz University by NewsRx correspondents, research stated, "Alfalfa holds an extremely significant place in animal nutrition when it comes to providing essential nutrients." Financial supporters for this research include National University of Science And Technology Politehnica Bucharest. The news journalists obtained a quote from the research from Akdeniz University: "The leaves of alfalfa specifically boast the highest nutritional value, containing a remarkable 70% of crude protein and an impressive 90% of essential vitamins. Due to this incredible nutritional profile, it becomes exceedingly important to ensure that the harvesting and threshing processes are executed with utmost care to minimize any potential loss of these invaluable nutrients present in the leaves. To minimize losses, it is essential to accurately determine the resistance of the leaves in both their green and dried forms. This study aimed to estimate the breaking resistance of green and dried alfalfa plants using machine learning methods. During the modeling phase, five different popular machine learning methods, Extra Trees (ET), Random Forest (RF), Gradient Boost (GB), Extreme Gradient Boosting (XGB), and CatBoost (CB), were used. The correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) metrics were used to evaluate the models."

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Mdpi

Fuzhou University Reports Findings in Machine Learning (PlantNh- Kcr: a deep learning model for predicting non-histone crotonylation sites in plants)

71-71页

查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Fuzhou, People's Republic of China, by NewsRx correspondents, research stated, "Lysine crotonylation (Kcr) is a crucial protein post-translational modification found in histone and non-histone proteins. It plays a pivotal role in regulating diverse biological processes in both animals and plants, including gene transcription and replication, cell metabolism and differentiation, as well as photosynthesis." Our news journalists obtained a quote from the research from Fuzhou University, "Despite the significance of Kcr, detection of Kcr sites through biological experiments is often time-consuming, expensive, and only a fraction of crotonylated peptides can be identified. This reality highlights the need for efficient and rapid prediction of Kcr sites through computational methods. Currently, several machine learning models exist for predicting Kcr sites in humans, yet models tailored for plants are rare. Furthermore, no downloadable Kcr site predictors or datasets have been developed specifically for plants. To address this gap, it is imperative to integrate existing Kcr sites detected in plant experiments and establish a dedicated computational model for plants. Most plant Kcr sites are located on non-histones. In this study, we collected non-histone Kcr sites from five plants, including wheat, tabacum, rice, peanut, and papaya. We then conducted a comprehensive analysis of the amino acid distribution surrounding these sites. To develop a predictive model for plant non-histone Kcr sites, we combined a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and attention mechanism to build a deep learning model called PlantNh-Kcr. On both five-fold cross-validation and independent tests, PlantNh-Kcr outperformed multiple conventional machine learning models and other deep learning models. Furthermore, we conducted an analysis of species-specific effect on the PlantNh-Kcr model and found that a general model trained using data from multiple species outperforms species-specific models. PlantNh-Kcr represents a valuable tool for predicting plant non-histone Kcr sites."

原文链接:

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Springer Nature

New Machine Learning Findings Reported from China University of Geosciences (Knn-gcn: a Deep Learning Approach for Slopeunit- based Landslide Susceptibility Mapping Incorporating Spatial Correlations)

72-73页

查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting out of Hubei, People's Republic of China, by NewsRx editors, research stated, "Landslides pose a significant risk to human life and property, making landslide susceptibility mapping (LSM) a crucial component of landslide risk assessment. However, spatial correlations among mapping units are often neglected in statistical or machine learning models proposed for LSM." Funders for this research include National Natural Science Foundation of China (NSFC), National Major Scientific Instruments and Equipment Development Projects of China, China Scholarship Council. Our news journalists obtained a quote from the research from the China University of Geosciences, "This study proposes KNN-GCN, a deep learning model for slope-unit-based LSM based on a graph convolutional network (GCN) and the K-nearest neighbor (KNN) algorithm. The model was experimentally applied to the Lueyang region and validated through the following steps. Firstly, we collected data for 15 landslide causal factors and from landslide inventories and established a slope unit map (SUM) through slope unit division. Next, we performed a multicollinearity analysis of landslide causal factors and divided the training and test sets at a 7∶3 ratio. We then constructed a GCN model based on a slope unit graph (SUG) generated from the SUM using the KNN algorithm. The proposed KNN-GCN model was tuned using a grid search with fivefold cross-validation on the training set, and then trained and validated on training and test sets separately. Finally, the performance of the KNN-GCN model was compared with that of six other models which were categorized into two groups: CG#1 was the traditional KNN, support vector regression (SVC), and automated machine learning (AutoML), and CG#2 included KNN-G, SVC-G and AutoML-G with additional spatial information. Our results demonstrate that the proposed model achieves superior performance (area under the curve [AUC] = 0.8351) and generates the most comprehensible susceptibility map with distinct boundaries between different susceptibility levels."

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Springer Nature

Study Data from Indiana University Update Understanding of Artificial Intelligence (Influence of Exposure Protocol, Voxel Size, and Artifact Removal Algorithm On the Trueness of Segmentation Utilizing an Artificial-intelligence-based System)

73-74页

查看更多>>摘要：Investigators discuss new findings in Artificial Intelligence. According to news originating from Indianapolis, Indiana, by NewsRx correspondents, research stated, "To evaluate the effects of exposure protocol, voxel sizes, and artifact removal algorithms on the trueness of segmentation in various mandible regions using an artificial intelligence (AI)-based system. Eleven dry human mandibles were scanned using a cone beam computed tomography (CBCT) scanner under differing exposure protocols (standard and ultra-low), voxel sizes (0.15 mm, 0.3 mm, and 0.45 mm), and with or without artifact removal algorithm." Financial supporters for this research include Analyst Programmer at the UITS RT Advanced Visualization Lab, Indiana University Information Technology Services. Our news journalists obtained a quote from the research from Indiana University, "The resulting datasets were segmented using an AI-based system, exported as 3D models, and compared to reference files derived from a white-light laboratory scanner. Deviation measurement was performed using a computer-aided design (CAD) program and recorded as root mean square (RMS). The RMS values were used as a representation of the trueness of the AI-segmented 3D models. A 4-way ANOVA was used to assess the impact of voxel size, exposure protocol, artifact removal algorithm, and location on RMS values (alpha = 0.05). Significant effects were found with voxel size (p ＜0.001) and location (p ＜0.001), but not with exposure protocol (p = 0.259) or artifact removal algorithm (p = 0.752). Standard exposure groups had significantly lower RMS values than the ultra-low exposure groups in the mandible body with 0.3 mm (p = 0.014) or 0.45 mm (p ＜0.001) voxel sizes, the symphysis with a 0.45 mm voxel size (p = 0.011), and the whole mandible with a 0.45 mm voxel size (p = 0.001). Exposure protocol did not affect RMS values at teeth and alveolar bone (p = 0.544), mandible angles (p = 0.380), condyles (p = 0.114), and coronoids (p = 0.806) locations. This study informs optimal exposure protocol and voxel size choices in CBCT imaging for true AI-based automatic segmentation with minimal radiation. The artifact removal algorithm did not influence the trueness of AI segmentation."

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Wiley

New Findings on Support Vector Machines from Shaanxi Railway Institute Summarized (Fresh State and Strength Performance Evaluation of Slag-based Alkali-activated Concrete Using Soft-computing Methods)

74-75页

查看更多>>摘要：A new study on Machine Learning - Support Vector Machines is now available. According to news reporting originating in Weinan, People's Republic of China, by NewsRx journalists, research stated, "In this study, machine learning prediction models for the slump (SL) and compressive strength (CS) of alkaliactivated concrete (AAC) were developed. Extreme gradient boosting (XGB) as an ensemble and support vector machine (SVM) as individual methods were chosen." Financial supporters for this research include Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia, Deanship of Scientific Research at Najran University, Shaanxi Railway Institute Scientific Research Fund Project: Application Research of Locked Steel Pipe Pile Cofferdam in Underwater Bridge Pier Construction. The news reporters obtained a quote from the research from Shaanxi Railway Institute, "To evaluate the performance of the models, the Taylor diagram, k-fold validation, and statistical tests were performed. Moreover, to determine the significance of features, a SHapley Additive exPlanations (SHAP) study was carried out. XGB outperformed SVM considerably in predicting the SL and CS of AAC. XGB outperformed SVM in terms of R2 (0.94 for SL and 0.97 for CS), which was 0.86 and 0.88, respectively. Precursor content had the greatest effect on the SL of AAC, followed by blast furnace slag ratio, test time, SiO2/Na2O, and quantities of NaOH, aggregate, and water, according to the results of the SHAP study. The SHAP investigation revealed that curing time had the greatest effect on the CS of AAC, followed by SiO2/Na2O, NaOH quantity, precursor content, aggregate quantity, blast furnace slag ratio, and water quantity."

原文链接:

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Elsevier

Department of Computer Science Researcher Provides New Insights into Robotics (Inverse Firefly-Based Search Algorithms for Multi- Target Search Problem)

75-75页

查看更多>>摘要：Current study results on robotics have been published. According to news originating from Guelma, Algeria, by NewsRx correspondents, research stated, "Efficiently searching for multiple targets in complex environments with limited perception and computational capabilities is challenging for multiple robots, which can coordinate their actions indirectly through their environment." The news correspondents obtained a quote from the research from Department of Computer Science: "In this context, swarm intelligence has been a source of inspiration for addressing multi-target search problems in the literature. So far, several algorithms have been proposed for solving such a problem, and in this study, we propose two novel multi-target search algorithms inspired by the Firefly algorithm. Unlike the conventional Firefly algorithm, where light is an attractor, light represents a negative effect in our proposed algorithms. Upon discovering targets, robots emit light to repel other robots from that region. This repulsive behavior is intended to achieve several objectives: (1) partitioning the search space among different robots, (2) expanding the search region by avoiding areas already explored, and (3) preventing congestion among robots." According to the news reporters, the research concluded: "The proposed algorithms, named Global Lawnmower Firefly Algorithm (GLFA) and Random Bounce Firefly Algorithm (RBFA), integrate inverse light-based behavior with two random walks: random bounce and global lawnmower. These algorithms were implemented and evaluated using the ArGOS simulator, demonstrating promising performance compared to existing approaches."

原文链接:

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Mdpi

New Findings on Machine Learning Described by Investigators at Machine Learning Group (Scaling Up Machine Learning-based Chemical Plant Simulation: a Method for Fine-tuning a Model To Induce Stable Fixed Points)

76-76页

查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting originating in Berlin, Germany, by NewsRx journalists, research stated, "Idealized first-principles models of chemical plants can be inaccurate. An alternative is to fit a Machine Learning (ML) model directly to plant sensor data." Financial supporters for this research include TU Berlin, Germany, BASF. The news reporters obtained a quote from the research from Machine Learning Group, "We use a structured approach: Each unit within the plant gets represented by one ML model. After fitting the models to the data, the models are connected into a flowsheet-like directed graph. We find that for smaller plants, this approach works well, but for larger plants, the complex dynamics arising from large and nested cycles in the flowsheet lead to instabilities in the solver during model initialization. We show that a high accuracy of the single -unit models is not enough: The gradient can point in unexpected directions, which prevents the solver from converging to the correct stationary state." According to the news reporters, the research concluded: "To address this problem, we present a way to fine -tune ML models such that initialization, even with very simple solvers, becomes robust." This research has been peer-reviewed.

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NSTL
Elsevier

Reports Outline Machine Learning Findings from University of Basel (Annotations As Knowledge Practices In Image Archives: Application of Linked Open Usable Data and Machine Learning)

76-77页

查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting out of Basel, Switzerland, by NewsRx editors, research stated, "We reflect on some of the preliminary findings of the Participatory Knowledge Practices in Analogue and Digital Image Archives (PIA) research project around annotations of photographic archives from the Swiss Society for Folklore Studies (SSFS) as knowledge practices, the underlying technological decisions, and their impact. The aim is not only to seek more information but to find new approaches of understanding the way in which people's memory relate to the collective, public form of archival memory and ultimately how users figure in and shape the digital archive." Financial support for this research came from Swiss National Science Foundation.

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Assoc Computing Machinery