期刊,Robotics & Machine Learning Daily News 2024年卷Feb.12期_国家学术搜索

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Artificial Intelligence tool designed to identify olive varieties based on photos of olive pits

1-1页

查看更多>>摘要：The development of an app capable of identifying olive varieties using photos of olive pits is the ultimate goal of ‘OliVaR,' a neural network trained with the largest photographic database of olive fruit endocarps, which has been generated by the partners of the GEN4OLIVE European project. The tool's development has been possible thanks to the cataloguing and documentation work of five germplasm banks in different countries and to advances in Artificial Intelligence systems. The University of Cordoba has played a fundamental role, as the institution having provided the most information, with data on 63 varieties from its Germplasm Bank. The initiative, which is part of the GEN4OLIVE European project to improve olive trees, coordinated by the Ucolivo group of the Maria de Maeztu Unit of Excellence - Department of Agronomy (DAUCO), involved the participation of olive germplasm banks from Morocco, Greece, Italy, and Turkey to gather more than 150,000 photos of 133 olive varieties from the Mediterranean basin. The Computer Science Department at Rome's Sapienza University was in charge of collecting the information and creating the algorithm for this tool, which proposes a new approach to identify varieties and automates the traditional morphological classification process.

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Study Findings on Artificial Intelligence Discussed by a Researcher at University of Alicante (Natural Gradient Boosting for Probabilistic Prediction of Soaked CBR Values Using an Explainable Artificial Intelligence Approach)

2-2页

查看更多>>摘要：New research on artificial intelligence isthe subject of a new report. According to news originating from Alicante, Spain, by NewsRx correspondents, research stated, "The California bearing ratio (CBR) value of subgrade is the most used parameter for dimensioning flexible and rigid pavements." The news correspondents obtained a quote from the research from University of Alicante: "The test for determining the CBR value is typically conducted under soaked conditions and is costly, labour-intensive, and time-consuming. Machine learning (ML) techniques have been recently implemented in engineering practice to predict the CBR value from the soil index properties with satisfactory results. However, they provide only deterministic predictions, which do not account for the aleatoric uncertainty linked to input variables and the epistemic uncertainty inherent in the model itself. This work addresses this limitation by introducing an ML model based on the natural gradient boosting (NGBoost) algorithm, becoming the first study to estimate the soaked CBR value from this probabilistic perspective. A database of 2130 soaked CBR tests was compiled for this study. The NGBoost model showcased robust predictive performance, establishing itself as a reliable and effective algorithm for predicting the soaked CBR value."

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Mdpi

Studies from Guangxi University Yield New Information about Machine Learning (Machine Learning-based Non-destructive Testing Model for High Precision and Stable Evaluation of Mechanical Properties In Bamboo-wood Composites)

3-4页

查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting from Guangxi, People's Republic of China, by NewsRx journalists, research stated, "The efficient evaluation of mechanical performance of bamboo-wood composites (BWCs) is an important part for their development and application. To address the issues of low efficiency, high consumables usage, and low accuracy in traditional BWC mechanical performance testing, a non-destructive testing method for BWC mechanical performance was proposed based on machine learning." Funders for this research include National Natural Science Foundation of China (NSFC), Guangxi Science and Technology Base and Talent Special Project, Sichuan University.

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Springer Nature

Fujian Medical University Reports Findings in Machine Learning (Bulk and single-cell transcriptome reveal the immuno-prognostic subtypes and tumour microenvironment heterogeneity in HCC)

4-5页

查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating in Fuzhou, People's Republic of China, by NewsRx journalists, research stated, "Accumulating evidences suggest tumour microenvironment (TME) profoundly influence clinical outcome in hepatocellular carcinoma (HCC). Existing immune subtypes are susceptible to batch effects, and integrative analysis of bulk and single-cell transcriptome is helpful to recognize immune subtypes and TME in HCC." Funders for this research include Natural Science Foundation of Fujian Province, National Natural Science Foundation of China. The news reporters obtained a quote from the research from Fujian Medical University, "Based on the relative expression ordering (REO) of 1259 immune-related genes, an immuno-prognostic signature was developed and validated in 907 HCC samples from five bulk transcriptomic cohorts, including 72 in-house samples. The machine learning models based on subtype-specific gene pairs with stable REOs were constructed to jointly predict immuno-prognostic subtypes in single-cell RNA-seq data and validated in another single-cell data. Then, cancer characteristics, immune landscape, underlying mechanism and therapeutic benefits between subtypes were analysed. An immune-related signature with 29 gene pairs stratified HCC samples individually into two risk subgroups (C1 and C2), which was an independent prognostic factor for overall survival. The machine learning models verified the immune subtypes from five bulk cohorts to two single-cell transcriptomic data. Integrative analysis revealed that C1 had poorer outcomes, higher CNV burden and malignant scores, higher sensitivity to sorafenib, and exhibited an immunosuppressive phenotype with more regulators, e.g., myeloid-derived suppressor cells (MDSCs), M?_SPP1, while C2 was characterized with better outcomes, higher metabolism, more benefit from immunotherapy, and displayed active immune with more effectors, e.g., tumour infiltrating lymphocyte and dendritic cell. Moreover, both two single-cell data revealed the crosstalk of SPP1-related L-R pairs between cancer and immune cells, especially SPP1-CD44, might lead to immunosuppression in C1. The REO-based immuno-prognostic subtypes were conducive to individualized prognosis prediction and treatment options for HCC."

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Wiley

G. d'Annunzio University Reports Findings in Bladder Cancer (Time to progression is the main predictor of survival in patients with highrisk nonmuscle invasive bladder cancer: Results from a machine learning-based analysis of a large ...)

5-6页

查看更多>>摘要：New research on Oncology - Bladder Cancer is the subject of a report. According to news reporting out of Chieti, Italy, by NewsRx editors, research stated, "In patients affected by high-risk nonmuscle invasive bladder cancer (HR-NMIBC) progression to muscle invasive status is considered as the main indicator of local treatment failure. We aimed to investigate the effect of progression and time to progression on overall survival (OS) and to investigate their validity as surrogate endpoints." Our news journalists obtained a quote from the research from G. d'Annunzio University, "A total of 1,510 patients from 18 different institutions treated for T1 high grade NMIBC, followed by a secondary transurethral resection and BCG intravesical instillation. We relied on random survival forest (RSF) to rank covariates based on OS prediction. Cox's regression models were used to quantify the effect of covariates on mortality. During a median follow-up of 49.0 months, 485 (32.1%) patients progressed to MIBC, while 163 (10.8%) patients died. The median time to progression was 82 (95%CI: 78.0-93.0) months. In RSF time-toprogression and age were the most predictive covariates of OS. The survival tree defined 5 groups of risk. In multivariable Cox's regression models accounting for progression status as time-dependent covariate, shorter time to progression (as continuous covariate) was associated with longer OS (HR: 9.0, 95%CI: 3.0-6.7; P＜0.001). Virtually same results after time to progression stratification (time to progression 10.5 months as reference). Time to progression is the main predictor of OS in patients with high risk NMIBC treated with BCG and might be considered a coprimary endpoint."

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Researcher at Shanghai University Describes Research in Machine Learning (Research on prediction of bitcoin price based on machine learning methods)

6-6页

查看更多>>摘要：Data detailed on artificial intelligence have been presented. According to news reporting originating from Shanghai University by NewsRx correspondents, research stated, "Bitcoin, a decentralized digital currency, has gained widespread acceptance and recognition in recent years." Our news reporters obtained a quote from the research from Shanghai University: "The prediction of Bitcoin prices is a challenging task due to its relatively young age and high volatility. Therefore, this study explores the accuracy of price prediction for Bitcoin using machine learning models and makes comparsion on the outcome of different models, Linear Regression, Long Short-Term Memory, and Recurrent Neural Network. This study utilizes the closing price of Bitcoin in USD from a Kaggle dataset as the independent variable. The study also adopts Mean Absolute Error (MAE) as the measurement indicators, and comparative performance analysis is conducted under various circumstances. The experimental results demonstrate that LR performs poorly in Bitcoin price prediction, while LSTM and RNN outperform LR. Further analysis reveals that LSTM performs better during price apexes, while RNN performs better during price recessions."

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Xi'an Jiaotong University Reports Findings in Bioinformatics (Identification of potential crucial genes shared in psoriasis and ulcerative colitis by machine learning and integrated bioinformatics)

7-7页

查看更多>>摘要：New research on Biotechnology - Bioinformatics is the subject of a report. According to news reporting from Xi'an, People's Republic of China, by NewsRx journalists, research stated, "Mounting evidence suggest that there are an association between psoriasis and ulcerative colitis (UC), although the common pathogeneses are not fully understood. Our study aimed to find potential crucial genes in psoriasis and UC through machine learning and integrated bioinformatics." The news correspondents obtained a quote from the research from Xi'an Jiaotong University, "The overlapping differentially expressed genes (DEGs) of the datasets GSE13355 and GSE87466 were identified. Then the functional enrichment analysis was performed. The overlapping genes in LASSO, SVM-RFE and key module in WGCNA were considered as potential crucial genes. The receiver operator characteristic (ROC) curve was used to estimate their diagnostic confidence. The CIBERSORT was conducted to evaluate immune cell infiltration. Finally, the datasets GSE30999 and GSE107499 were retrieved to validate. 112 overlapping DEGs were identified in psoriasis and UC and the functional enrichment analysis revealed they were closely related to the inflammatory and immune response. Eight genes, including S100A9, PI3, KYNU, WNT5A, SERPINB3, CHI3L2, ARNTL2, and SLAMF7, were ultimately identified as potential crucial genes. ROC curves showed they all had high confidence in the test and validation datasets. CIBERSORT analysis indicated there was a correlation between infiltrating immune cells and potential crucial genes. In our study, we focused on the comprehensive understanding of pathogeneses in psoriasis and UC."

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Researchers' Work from Shandong University Focuses on Robotics (Hierarchical Perception-improving for Decentralized Multi-robot Motion Planning In Complex Scenarios)

8-9页

查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting out of Weihai, People's Republic of China, by NewsRx editors, research stated, "Multi-robot cooperative navigation is an important task, which has been widely studied in many fields like logistics, transportation, and disaster rescue. However, most of the existing methods either require some strong assumptions or are validated in simple scenarios, which greatly hinders their implementation in the real world." Financial support for this research came from National Natural Science Foundation of China (NSFC).

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IEEE

New Artificial Intelligence Study Findings Reported from Foshan University (Research on the Teaching Practice of the Course 'Foundation of Artificial Intelligence' in Universities Empowered by Digital Transformation)

8-8页

查看更多>>摘要：Current study results on artificial intelligence have been published. According to news originating from Foshan University by NewsRx correspondents, research stated, "Digital transformation can empower teachers to teach and students to learn, playing an important role in the reform of curriculum and teaching in universities." The news reporters obtained a quote from the research from Foshan University: "This article analyzed the background of the teaching reform of computer public course in Chinese universities. In the situation where digital talents are urgently needed in society, ‘Foundation of Artificial Intelligence' was proposed as a computer public course in universities. We have designed the course content, pre class teaching, in class teaching, post class teaching, and student learning evaluation of ‘Foundation of Artificial Intelligence', and deeply integrated information technology with education and teaching, carried out digital transformation teaching practice."

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Findings from University of Kufa Advance Knowledge in Machine Learning (Applying Machine Learning in CFD to Study the Impact of Thermal Characteristics on the Aerodynamic Characteristics of an Airfoil)

9-10页

查看更多>>摘要：A new study on artificial intelligence is now available. According to news originating from the University of Kufa by NewsRx correspondents, research stated, "A computational fluid dynamic (CFD) and machine learning approach is used to investigate heat transfer on NASA airfoils of type NACA 0012. Several different models have been developed to examine the effect of laminar flow, Spalart flow, and Allmaras flow on the NACA 0012 airfoil under varying aerodynamic conditions." The news editors obtained a quote from the research from University of Kufa: "Temperature conditions at high and low temperatures are discussed in this article for different airfoil modes, which are porous mode and non-porous mode. Specific parameters included permeability of 11.36 x 10-10 m2, porosity of 0.64, an inertia coefficient of 0.37, and a temperature range between 200 K and 400 K. The study revealed that a temperature increase can significantly increase lift-to-drag. Additionally, employing both a porous state and temperature differentials further contributes to enhancing the lift-to-drag coefficient. The neural network also successfully predicted outcomes when adjusting the temperature, particularly in scenarios with a greater number of cases. Nevertheless, this study assessed the accuracy of the system using a SMOTER model. It has been shown that the MSE, MAE, and R for the best performance validation of the testing case were 0.000314, 0.0008, and 0.998960, respectively, at K = 3.

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Isfahan Univ Technology