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    New Machine Learning Study Findings Recently Were Reported by Researchers at Ind ian School of Mines (Estimation of Vertical Permeability of Hugin Sandstone From Petrophysical Well Logs Using Ensemble Methods: an Enhanced Machine Learning .. .)

    58-58页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Dhanba d, India, by NewsRx journalists, research stated, “Understanding the directional distribution of permeability is crucial for oil and gas exploration. In this st udy, well log and core data were used to predict continuous vertical permeabilit y (K-v) logs.” The news reporters obtained a quote from the research from the Indian School of Mines, “The predictions were performed for two wells drilled through the Hugin s andstone in the Volve field. Conventional well log and corresponding vertical co re permeability measurements were utilized for training machine learning (ML) mo dels. Data enhancement techniques, such as log smoothing, data transformation, a nd outlier removal were used. Thereafter, ensemble models, such as Random forest (RF), gradient boosting (GB), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost) were used with decision trees, which are the base learners. Multilinear regression models were used for comparison of K-v values from ML al gorithms and core measurements. Hyperparameter tuning was performed using grid s earch to obtain the best learning parameters for model optimization. The trained predictive models were used to predict K-v in two wells. Four metric scores, i. e., coefficient of determination (R-2), mean squared error (MSE), mean absolute error, and root mean square error were used for model evaluation. The obtained R -2 of RF and AdaBoost models are 0.94 and 0.92, respectively. Moreover, the R-2 of 0.99 for GB and XGBoost are overestimates confirmed by their high MSE values. The standard deviation of R-2’s obtained from four-fold cross-validation indica tes that GB and XGBoost are less stable compared to RF and AdaBoost. The RF ense mble model outperformed others.”
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    Pennsylvania State University (Penn State) Reports Findings in Machine Learning (Real-time machine learning-enhanced hyperspectropolarimetric imaging via an en coding metasurface)

    59-59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 th e subject of a report. According to news originating from University Park, Penns ylvania, by NewsRx correspondents, research stated, “Light fields carry a wealth of information, including intensity, spectrum, and polarization. However, stand ard cameras capture only the intensity, disregarding other valuable information. ” Our news journalists obtained a quote from the research from Pennsylvania State University (Penn State), “While hyperspectral and polarimetric imaging systems c apture spectral and polarization information, respectively, in addition to inten sity, they are often bulky, slow, and costly. Here, we have developed an encodin g metasurface paired with a neural network enabling a normal camera to acquire h yperspectro-polarimetric images from a single snapshot. Our experimental results demonstrate that this metasurface-enhanced camera can accurately resolve full-S tokes polarization across a broad spectral range (700 to 1150 nanometer) from a single snapshot, achieving a spectral sensitivity as high as 0.23 nanometer. In addition, our system captures full-Stokes hyperspectro-polarimetric video in rea l time at a rate of 28 frames per second, primarily limited by the camera’s read out rate.”
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    Yildiz Technical University Researcher Reports Recent Findings in Machine Learni ng (Toward Proactive Maintenance: A Multi-Tiered Architecture for Industrial Equ ipment Health Monitoring and Remaining Useful Life Prediction)

    60-60页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news reporting from Istanbul, Turkey, by NewsRx j ournalists, research stated, “This research paper introduces a comprehensive pro active maintenance architecture designed for large-scale industrial machinery sy stems. The proposed architectural framework integrates supervised and unsupervis ed machine learning business processes in order to enhance maintenance capabilit ies.” Our news correspondents obtained a quote from the research from Yildiz Technical University: “The primary objective of this architecture is to enhance operation al efficiency and reduce the occurrence of problems in industrial equipment. The collection of data on the state of industrial machinery is conducted through th e utilization of sensors that are attached to it. The recommended framework offe rs modules that might potentially implement capabilities such as immediate anoma ly detection, pre-failure status prediction, and assessment of remaining usable life. We offer a prototype implementation to verify the appropriateness of the p roposed framework for testing purposes. The prototype utilizes a simulation fram ework, Cooja, to model a sensor network. The concept entails the collection of s tatus data from industrial machinery by each sensor. The prototype utilizes a ma chine learning library for data streams, the MOA framework, to design and implem ent a business process for anomaly detection using unsupervised machine learning , as well as a business process for early machine fault prediction using supervi sed machine learning.”
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    University of Nottingham Reports Findings in Artificial Intelligence (Cost-Effec tiveness of AI for Risk-Stratified Breast Cancer Screening)

    61-62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 Intelligenc e is the subject of a report. According to news reporting from Nottingham, Unite d Kingdom, by NewsRx journalists, research stated, “Previous research has shown good discrimination of short-term risk using an artificial intelligence (AI) ris k prediction model (Mirai). However, no studies have been undertaken to evaluate whether this might translate into economic gains.”The news correspondents obtained a quote from the research from the University o f Nottingham, “To assess the cost-effectiveness of incorporating risk-stratified screening using a breast cancer AI model into the United Kingdom (UK) National Breast Cancer Screening Program. This study, conducted from January 1, 2023, to January 31, 2024, involved the development of a decision analytical model to est imate health-related quality of life, cancer survival rates, and costs over the lifetime of the female population eligible for screening. The analysis took a UK payer perspective, and the simulated cohort consisted of women aged 50 to 70 ye ars at screening. Mammography screening at 1 to 6 yearly screening intervals bas ed on breast cancer risk and standard care (screening every 3 years). Incrementa l net monetary benefit based on quality-adjusted life-years (QALYs) and National Health Service (NHS) costs (given in pounds sterling; to convert to US dollars, multiply by 1.28). Artificial intelligence-based risk-stratified programs were estimated to be cost-saving and increase QALYs compared with the current screeni ng program. A screening schedule of every 6 years for lowest-risk individuals, b iannually and triennially for those below and above average risk, respectively, and annually for those at highest risk was estimated to give yearly net monetary benefits within the NHS of approximately £60.4 (US $77.3) million and £85.3 (US $109.2) million, with QALY values set at £20 000 (US $25 600) and £30 000 (US $38 400), respectively. Even in scenarios where decision-makers hesitate to allocate additional NHS resources toward screening, implementing the proposed strategies at a QALY value of £1 (U S $1.28) was estimated to generate a yearly monetary benefit of app roximately £10.6 (US $13.6) million. In this decision analytical mo del study of integrating risk-stratified screening with a breast cancer AI model into the UK National Breast Cancer Screening Program, risk-stratified screening was likely to be cost-effective, yielding added health benefits at reduced cost s. These results are particularly relevant for health care settings where resour ces are under pressure.”
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    Researcher at Fuzhou University Releases New Study Findings on Robotics (A Novel One-DOF Deployable Structure and Its Plate Form)

    61-61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 robotics are presented i n a new report. According to news reporting originating from Fuzhou University b y NewsRx correspondents, research stated, “Deployable structures are widely util ized in various fields due to their ability to switch between a deployed working state and a folded storage state.” Financial supporters for this research include National Natural Science Foundati on of China; Natural Science Foundation of Fujian Province. Our news journalists obtained a quote from the research from Fuzhou University: “This paper presents a new method for achieving plate forms in deployable struct ures, allowing the formation of closed surfaces suitable for covering purposes. Initially, a novel one-degree-of-freedom (one-DOF) deployable network is propose d, employing Bennett linkages and Bennett-based 6R linkages. Subsequently, the s hape of the links in the network is modified to obtain a plate form consisting o f equilateral triangular panels. The paper also conducts kinematic analysis, mot ion property examination, and bifurcation condition discussion to demonstrate th e folding properties of the proposed method.”
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    Research Results from Shaoxing University Update Knowledge of Machine Learning ( Optimization of Machine Learning-based Value Assessment Model in High Value Pate nt Cultivation in Universities)

    62-63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 intelligenc e is the subject of a new report. According to news originating from Zhejiang, P eople’s Republic of China, by NewsRx editors, the research stated, “This paper c onstructs a patent value assessment model for colleges and universities from two perspectives of: value identification and price prediction.” Our news correspondents obtained a quote from the research from Shaoxing Univers ity: “Firstly, 10 indicators are selected from 3 dimensions of technology, econo my, and law. Then it combines the artificial way of entropy weight TOPSIS model and the machine learning way of gradient boosting tree to realize the identifica tion of the value of university patents and the grading of the economic value of university patents. After analyzing, it can be seen that after pre-processing t he data, 10 feature items related to patent value and useful for evaluation are screened out, and the highest weights of the number of homologous patents and th e number of citations indicators are 0.1826 and 0.1274, respectively, which have the greatest influence on the economic value of high-value patents of colleges and universities. In the range of 4901-7071 of high-value patents, the assessmen t results fluctuated in the range of 1.3754-2.8395.”
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    Findings from Shanghai Jiao Tong University Provide New Insights into Machine Le arning (From Simulation To Reality: Cfd-ml-driven Structural Optimization and Ex perimental Analysis of Thermal Plasma Reactors)

    63-64页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – A new study on Machine Learning is now available. According to news reporting out of Shanghai, People’s Republic of China, by New sRx editors, research stated, “Thermal plasma reactors offer an environment of h igh temperature, enthalpy, and reactivity, making them highly efficient for soli d waste treatment and promising for clean energy production from municipal and i ndustrial waste. Optimal geometrical parameters of the reactor can enhance waste treatment and reactor performance.” Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, “This study presents a comprehensive analysis of 11 key geometrical parameters of a thermal plasma reactor. Utilizing CFD Fluent software, numerical simulations were conducted to generate a dataset. Subsequently, a predictive mo del focusing on the average temperature in the core melt zone was trained using six Machine Learning (ML) algorithms. The Particle Swarm Optimisation (PSO) algo rithm optimized the hyperparameters of the Gradient Booster Regression (GBR) mod el, which was combined with a Genetic Algorithm (GA) to identify the reactor’s o ptimal geometrical parameters. A DC arc plasma torch-solid waste thermal plasma reactor treatment system was established on this basis. The study also explored the effects of gasification coefficient, reaction temperature, and thermal plasm a jet mode on system performance. Findings indicate that the PSO-GBR model achie ved the highest prediction accuracy, with the temperature in the core reaction z one reaching 3621 K. The deviation between numerical simulations and machine lea rning predictions was a mere 1.3%. Enhancing syngas yield and energ y efficiency is achievable by controlling reaction temperature and increasing th e gasification coefficient. A laminar plasma jet mode, at equal power, provides a more effective reaction environment.”
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    Recent Studies from Faculty of Agrobiotechnical Sciences Osijek Add New Data to Machine Learning (Machine Learning Methods for Evaluation of Technical Factors o f Spraying in Permanent Plantations)

    64-65页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting originating from Osijek, Croa tia, by NewsRx correspondents, research stated, “Considering the demand for the optimization of the technical factors of spraying for a greater area coverage an d minimal drift, field tests were carried out to determine the interaction betwe en the area coverage, number of droplets per cm2, droplet diameter, and drift.” Our news editors obtained a quote from the research from Faculty of Agrobiotechn ical Sciences Osijek: “The studies were conducted with two different types of sp rayers (axial and radial fan) in an apple orchard and a vineyard. The technical factors of the spraying interactions were nozzle type (ISO code 015, code 02, an d code 03), working speed (6 and 8 km h-1), and spraying norm (250-400 L h-1). T he airflow of both sprayers was adjusted to the plantation leaf mass and the wor king pressure was set for each repetition separately. A method using water-sensi tive paper and a digital image analysis was used to collect data on coverage fac tors. The data from the field research were processed using four machine learnin g models: quantile random forest (QRF), support vector regression with radial ba sis function kernel (SVR), Bayesian Regularization for Feed-Forward Neural Netwo rks (BRNN), and Ensemble Machine Learning (ENS). Nozzle type had the highest pre dictive value for the properties of number of droplets per cm2 (axial = 69.1% ;radial = 66.0%), droplet diameter (axial = 30.6%; ra dial = 38.2%), and area coverage (axial = 24.6%; radia l = 34.8%).”
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    Study Data from Oral Roberts University Provide New Insights into Machine Learni ng (Comparison of Denoising Methods in Improving V2V/V2X Communication)

    65-66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 artificial intelligence. According to news originating from Oral Roberts Univers ity by NewsRx editors, the research stated, “Vehicle-to-vehicle (V2V/V2X) commun ication is essential to our current transportation systems; it enables vehicles to exchange crucial data for better efficiency and safety.” The news reporters obtained a quote from the research from Oral Roberts Universi ty: “However, communication channels in these networks are susceptible to differ ent forms of interference and noise, which causes a deterioration in signal qual ity and communication reliability. This paper compares different signal denoisin g techniques for V2V communication channels, focusing on four prominent methods: Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT), machine learnin g, and deep residual networks. We evaluate the denoising performance of each met hod using simulated signals corrupted by different noises and interference. Our experimental results demonstrate the effectiveness of each approach in mitigatin g noise and possibly improving communication reliability. Specifically, we obser ve that FFT and DWT offer efficient frequency and time-frequency domain represen tations for denoising signals. Traditional machine learning methods and residual networks (ResNets) demonstrate superior denoising performance.”
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    New Machine Learning Research Reported from Faculty of Civil Engineering (Machin e Learning Model for Construction Time Prediction: A Case of Selected Public Bui lding Projects in Hosanna, Ethiopia)

    66-67页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news reporting from the Faculty of Civil Engineer ing by NewsRx journalists, research stated, “The duration of a construction proj ect is a vital factor to consider before the commencement of the new project.” The news journalists obtained a quote from the research from Faculty of Civil En gineering: “Nowadays, the common problem in the construction industry is time ov errun. The main reason for this is the poor prediction of construction contract durations. Therefore, the objective of this study is to evaluate and validate Br omilow’s time-cost model and Love et al.’s time-floor model to estimate early pr oject durations for public building construction projects in the Hadiya Zone. Th e study also suggested an alternative duration machine learning prediction model by considering possibly influential project influencing factors. A questionnair e survey is designed to collect data, and subsequently, the study was performed using the Python programming language for development and validation purposes wi th different libraries used. The study developed Bromilow’s time-cost model usin g a simple linear regression algorithm and Love et al.’s time-floor model using a multiple linear regression algorithm and proposed a parametric model using ran dom forest, XGBoost, decision tree, K-nearest neighbor, and polynomial regressio n algorithms.” According to the news reporters, the research concluded: “This study extends the body of knowledge related to construction time performance, and it contributes valuable insights that inform the implementation of machine learning model for c onstruction time prediction.”
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