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    University of Zilina Reports Findings in Machine Learning (Dataset of cattle biometrics through muzzle images)

    30-30页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news reporting out of Zilina, Slovakia, by NewsRx editors, research stated, “The Cattle Biometrics Dataset is the result of a rigorous process of data collecting, encompassing a wide range of cattle photographs obtained from publicly accessible cattle markets and farms. The dataset provided contains a comprehensive collection of more than 8,000 annotated samples derived from several cow breeds.” Our news journalists obtained a quote from the research from the University of Zilina, “This dataset represents a valuable asset for conducting research in the field of biometric recognition. The diversity of cattle in this context includes a range of ages, genders, breeds, and environmental conditions. Every photograph is taken from different quality cameras is thoroughly annotated, with special attention given to the muzzle of the cattle, which is considered an excellent biometric characteristic. In addition to its obvious practical benefits, this dataset possesses significant potential for extensive reuse. Within the domain of computer vision, it serves as a catalyst for algorithmic advancements, whereas in the agricultural sector, it augments practises related to cattle management. Machine learning aficionados highly value the use of machine learning for the construction and experimentation of models, especially in the context of transfer learning. Interdisciplinary collaboration is actively encouraged, facilitating the advancement of knowledge at the intersections of agriculture, computer science, and data science.” According to the news editors, the research concluded: “The Cattle Biometrics Dataset represents a valuable resource that has the potential to stimulate significant advancements in various academic disciplines, fostering ground breaking research and innovation.”
      原文链接:
    • NETL
    • NSTL

    Jinan University Reports Findings in Machine Learning (Prediction of Aureococcus anophageffens using machine learning and deep learning)

    31-31页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news originating from Guangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “The recurrent brown tide phenomenon, attributed to Aureococcus anophagefferens (A. anophagefferens), constitutes a significant threat to the Qinhuangdao sea area in China, leading to pronounced ecological degradation and substantial economic losses. This study utilized machine learning and deep learning techniques to predict A. anophagefferens population density, aiming to elucidate the occurrence mechanism and influencing factors of brown tide.” Our news journalists obtained a quote from the research from Jinan University, “Specifically, Random Forest (RF) algorithm was utilized to impute missing water quality data, facilitating its direct application in subsequent algal population prediction models. The results revealed that all four models-RF, Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN)-exhibited high accuracy in predicting A. anophagefferens population densities, with R values exceeding 0.75. RF, in particular, showed exceptional accuracy and reliability, with an R value surpassing 0.8.” According to the news editors, the research concluded: “Additionally, the study ascertained five critical factors influencing A. anophagefferens population density: ammonia nitrogen, pH, total nitrogen, temper- ature, and silicate.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL

    Investigators at Northwest University Report Findings in Support Vector Machines (Pliable Lasso for the Support Vector Machine)

    31-32页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Support Vector Machines are presented in a new report. According to news reporting originating in Xi’an, People’s Republic of China, by NewsRx journalists, research stated,“In this article, we study the support vector machine with interaction effects. The pliable lasso penalty, which allows for estimating the main effects of the covariates X and the interaction effects between the covariates and a set modifiers Z is implemented to handle the interaction effect.” The news reporters obtained a quote from the research from Northwest University, “Interaction variables are included in a hierarchical manner by first considering whether their corresponding main effect variables have been included in the model to avoid over-fitting. The loss function employed is the squared hinge loss, with the pliable lasso penalty and then, the block-wise coordinate descent approach is employed.” According to the news reporters, the research concluded: “The results from the simulation and real data show the effectiveness of the pliable lasso in building support vector machine models in situations where interaction effects are involved.” This research has been peer-reviewed. For more information on this research see: Pliable Lasso for the Support Vector Machine. Commu- nications in Statistics - Simulation and Computation, 2024;53(2):1-13. Communications in Statistics - Simulation and Computation can be contacted at: Taylor & Francis Inc, 530 Walnut Street, Ste 850, Philadelphia, PA 19106, USA.
      原文链接:
    • NETL
    • NSTL
    • Taylor & Francis

    New Artificial Intelligence Study Results Reported from Oslo Metropolitan University [Navigating Uncertainties of Introducing Artificial Intelligence (Ai) In Healthcare: the Role of a Norwegian Network of Professionals]

    32-33页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Artificial Intelligence have been presented. According to news reporting from Oslo, Norway, by NewsRx editors, the research stated, “Artificial Intelligence (AI) technologies are expected to solve pressing challenges in healthcare services worldwide. However, the current state of introducing AI is characterised by several issues complicating and delaying their deployments.” The news correspondents obtained a quote from the research from Oslo Metropolitan University, “These issues concern topics such as ethics, regulations, data access, human trust, and limited evidence of AI technologies in real-world clinical settings. They further encompass uncertainties, for instance, whether AI technologies will ensure equal and safe patient treatment or whether the AI results will be accurate and transparent enough to establish user trust. Collective efforts by actors from different backgrounds and affiliations are required to navigate this complex landscape. This article explores the role of such collective efforts by investigating how an informally established network of professionals works to enable AI in the Norwegian public healthcare services. The study takes a qualitative longitudinal case study approach and is based on data from non-participant observations of digital meetings and interviews. The data are analysed by drawing on perspectives and concepts from Science and Technology Studies (STS) dealing with innovation and sociotechnical change, where collective efforts are conceptualised as actor mobilisation. The study finds that in the case of the ambiguous sociotechnical phenomenon of AI, some of the uncertainties related to the introduction of AI in healthcare may be reduced as more and more deployments occur, while others will prevail or emerge. Mobilising spokespersons representing actors not yet a part of the discussions, such as AI users or researchers studying AI technologies in use, can enable a ‘stronger’ hybrid knowledge production.” According to the news reporters, the research concluded: “This hybrid knowledge is essential to identify, mitigate and monitor existing and emerging uncertainties, thereby ensuring sustainable AI deployments.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Chinese Academy of Sciences Reports Findings in Epilepsy (Classification of self-limited epilepsy with centrotemporal spikes by classical machine learning and deep learning based on electroencephalogram data)

    33-34页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Central Nervous System Diseases and Conditions - Epilepsy is the subject of a report. According to news reporting originating in Xi’an, People’s Republic of China, by NewsRx journalists, research stated, “Electroencephalogram (EEG) has been widely utilized as a valuable assessment tool for diagnosing epilepsy in hospital settings. However, clinical diagnosis of patients with self-limited epilepsy with centrotemporal spikes (SeLECTS) is challenging due to the presence of similar abnormal discharges in EEG displays compared to other types of epilepsy (non-SeLECTS) patients.” The news reporters obtained a quote from the research from the Chinese Academy of Sciences, “To assist the diagnostic process of epilepsy, a comprehensive classification study utilizing machine learning or deep learning techniques is proposed. In this study, clinical EEG was collected from 33 patients diagnosed with either SeLECTS or non-SeLECTS, aged between 3 and 11 years. In the realm of classical machine learning, sharp wave features (including upslope, downslope, and width at half maximum) were extracted from the EEG data. These features were then combined with the random forest (RF) and extreme random forest (ERF) classifiers to differentiate between SeLECTS and non-SeLECTS. Additionally, deep learning was employed by directly inputting the EEG data into a deep residual network (ResNet) for classification. The classification results were evaluated based on accuracy, F1-score, area under the curve (AUC), and area under the precision-recall curve (AUPRC). Following a 10-fold cross-validation, the ERF classifier achieved an accuracy of 73.15 % when utilizing sharp wave feature extraction for classification. The F1- score obtained was 0.72, while the AUC and AUPRC values were 0.75 and 0.63, respectively. On the other hand, the ResNet model achieved a classification accuracy of 90.49 %, with an F1-score of 0.90. The AUC and AUPRC values for ResNet were found to be 0.96 and 0.92, respectively. These results highlighted the significant potential of deep learning methods in SeLECTS classification research, owing to their high accuracy.”
      原文链接:
    • NETL
    • NSTL

    Department of Cardiology Reports Findings in Artificial Intelligence (Single-lead electrocardiogram Artificial Intelligence model with risk factors detects atrial fibrillation during sinus rhythm)

    34-35页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligence is the subject of a report. According to news reporting from Roeselare, Belgium, by NewsRx journalists, research stated, “Guidelines recommend oppor- tunistic screening for atrial fibrillation (AF), using a 30 s single-lead electrocardiogram (ECG) recorded by a wearable device. Since many patients have paroxysmal AF, identification of patients at high risk presenting with sinus rhythm (SR) may increase the yield of subsequent long-term cardiac monitoring.” The news correspondents obtained a quote from the research from the Department of Cardiology, “The aim is to evaluate an AI-algorithm trained on 10 s single-lead ECG with or without risk factors to predict AF. This retrospective study used 13 479 ECGs from AF patients in SR around the time of diagnosis and 53 916 age- and sex-matched control ECGs, augmented with 17 risk factors extracted from electronic health records. AI models were trained and compared using 1- or 12-lead ECGs, with or without risk factors. Model bias was evaluated by age- and sex-stratification of results. Random forest models identified the most relevant risk factors. The single-lead model achieved an area under the curve of 0.74, which increased to 0.76 by adding six risk factors (95% confidence interval: 0.74-0.79). This model matched the performance of a 12-lead model. Results are stable for both sexes, over ages ranging from 40 to 90 years. Out of 17 clinical variables, 6 were sufficient for optimal accuracy of the model: hypertension, heart failure, valvular disease, history of myocardial infarction, age, and sex. An AI model using a single-lead SR ECG and six risk factors can identify patients with concurrent AF with similar accuracy as a 12-lead ECG-AI model.” According to the news reporters, the research concluded: “An age- and sex-matched data set leads to an unbiased model with consistent predictions across age groups.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Researchers' Work from Zhejiang University Focuses on Robotics (Modeling of Attractive Force of Magnetic Wheel Under Different Wall Structure and Attitude Used for Climbing Robot)

    35-36页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Robotics have been presented. According to news reporting out of Zhejiang, People’s Republic of China, by NewsRx editors, research stated, “Accurate dynamic modeling is the basis for achieving high-precision motion control of a wheeled wall-climbing robot. In a dynamic model, the magnetic attractive force is one of the important influencing forces.” Financial supporters for this research include The “Pioneer” and “Leading Goose” R&D Program of Zhejiang, China, Research and Development Project of the Ministry of Housing and Urban-Rural Develop- ment, China. Our news journalists obtained a quote from the research from Zhejiang University, “In this study, to quickly obtain the attractive force of the magnetic wheel under different wall structure and attitude, the equivalent magnetic circuit method is combined with an analytical approach to construct attractive force models on the flat wall, the 90 degrees concave corner, and the 90 degrees convex corner, respectively. By establishing the geometric relations of the air gap, the reluctance is calculated to analyze the effects of changes in wall structure and relative attitude. The formula for calculating the attractive force is obtained based on this analysis. The proposed model’s accuracy was confirmed by comparing it to finite element analysis (FEA) results.”
      原文链接:
    • NETL
    • NSTL
    • Korean Soc Mechanical Engineers

    New Findings Reported from Leeds Beckett University Describe Advances in Robotics (Intelligent Robotics Harvesting System Process for Fruits Grasping Prediction)

    36-37页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Robotics is the subject of a report. According to news originating from Leeds, United Kingdom, by NewsRx correspondents, research stated, “This paper proposes and executes an in-depth learning-based image processing approach for self-picking apples. The system includes a lightweight one-step detection network for fruit recognition.” Our news journalists obtained a quote from the research from Leeds Beckett University, “As well as computer vision to analyze the point class and anticipate a correct approach position for each fruit before grabbing. Using the raw inputs from a high-resolution camera, fruit recognition and instance segmentation are done on RGB photos. The computer vision classification and grasping systems are integrated and outcomes from tree-grown foods are provided as input information and output methodology poses for every apple and orange to robotic arm execution. Before RGB picture data is acquired from laboratory and plantation environments, the developed vision method will be evaluated. Robot harvest experiment is conducted in indoor as well as outdoor to evaluate the proposed harvesting system’s performance.” According to the news editors, the research concluded: “The research findings suggest that the pro- posed vision technique can control robotic harvesting effectively and precisely where the success rate of identification is increased above 95% in case of post prediction process with reattempts of less than 12%.”
      原文链接:
    • NETL
    • NSTL

    Future University Reports Findings in Machine Learning (Machine learning base models to predict the punching shear capacity of posttensioned UHPC flat slabs)

    37-38页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is the subject of a report. According to news originating from New Cairo, Egypt, by NewsRx correspondents, research stated, “The aim of this research is to present correction factors for the punching shear formulas of ACI-318 and EC2 design codes to adopt the punching capacity of post tensioned ultra-high-performance concrete (PT-UHPC) flat slabs. To achieve that goal, the results of previously tested PT-UHPC flat slabs were used to validate the developed finite element method (FEM) model in terms of punching shear capacity.” Financial support for this research came from Future University in Egypt. Our news journalists obtained a quote from the research from Future University, “Then, a parametric study was conducted using the validated FEM to generate two databases, each database included concrete compressive strength, strands layout, shear reinforcement capacity and the aspect ratio of the column besides the correction factor (the ratio between the FEM punching capacity and the design code punching capacity). The first considered design code in the first database was ACI-318 and in the second database was EC2. Finally, there different ‘Machine Learning’ (ML) techniques manly ‘Genetic programming’ (GP), ‘Artificial Neural Network’ (ANN) and ‘Evolutionary Polynomial Regression’ (EPR) were applied on the two generated databases to predict the correction factors as functions of the considered parameters.”
      原文链接:
    • NETL
    • NSTL

    New Findings Reported from National Institute of Technology Jamshedpur Describe Advances in Machine Learning (Stokes Shift Prediction of Fluorescent Organic Dyes Using Machine Learning Based Hybrid Cascade Models)

    38-39页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news reporting from Jharkhand, India, by NewsRx journalists, research stated, “Fluorescent organic dyes are widely used in various fields, including science and technology, research and development, medicine, and drug delivery. Multitudinous attempts have been made by experimentalists to develop such fluorescent organic dyes with the desired Stokes shift property at negligible cost and time.” Funders for this research include CSIR HRDG in India, CSIR NET-JRF fellowship. The news correspondents obtained a quote from the research from the National Institute of Technology Jamshedpur, “For quickly and accurately predicting the Stokes shift property of fluorescent organic dye, we proposed eight hybrid models based on the combination of nine single machine-learning models. To fulfill the objective, we considered a dataset of 3066 fluorescent organic materials and evaluated the performance of each model using three evaluation parameters: mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R2). The hybrid cascade model of Extreme Gradient Boosting Regression and Light Gradient Boosting Machine Regression (XGBR + LGBMR) performed best for Stokes shift prediction, with MAE of 13.83 nm, RMSE of 19.95 nm, and R2 of 86.18 %. The prediction performance of all the undertaken models was validated by the experimental data of four xanthene dyes (Rh- 19, Rh-B, Rh-6G, and Rh-110). In this regard, the XGBR + DTR (Extreme Gradient Boosting Regression + Decision Tree Regression) model was the best performer, with errors ranging from 5 to 13 nm for four dyes. The resultant errors are much smaller than the recently re-ported synthesized material with an error of 30 nm.” According to the news reporters, the research concluded: “The proposed models allow for rapid and cost-effective screening of a wide range of fluorescent organic dyes, which assists the researchers in gaining prior knowl-edge of materials and accelerates the discovery of new materials.” This research has been peer-reviewed.
      原文链接:
    • NETL
    • NSTL
    • Elsevier