查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting out of Rajasthan, India, by NewsRx editors, research stated, “This study proposes novel hybrid methodology that combines machine learning (ML) techniques with experi-mental strategies to analyse microbial growthkinetics of acetic acid-producing bacteria isolated from fruit waste. This work employs ML algorithms to create different models such as multivariate linear regression (MLR), partial least square regression (PLSR), Kernel ridge regression (KRR), support vector regression (SVR), Gradient boosting regression (GBR) that captures time-dependent patterns of bacterial growth dynamics.” Our news journalists obtained a quote from the research from the Malaviya National Institute of Technology Jaipur, “Experiments for microbial growth kinetic analysis were conducted on best isolate of acid producing bacteria with different glucose con-centrations (1-5 %) at predefined operating conditions. It is found significant growth rate (mu) was obtained at 4 % and 5 % concentration of glucose from experimental work. 0.0588 h-1 and 0.0571 h-1 are the specific growth rate obtained at 4 % and 5 % glucose concentration respectively. Proposed ML models employed to predict growth rate kinetics theoretically at varied glucose concentrations. Comparative results indicate that GBR model exhibits superior performance in predicting growth kinetics than other models. GBR model fits the experimental results approximately with lower RMSE (0.004) than other models. This enables more accurate representation of growth patterns that is difficult to discernible through conventional analytical methods.”
查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news originating from Nanchang, People’s Republic of China, by NewsRx correspondents, research stated, “In the existing landslide susceptibility prediction (LSP) models, the influences of random errors in landslide conditioning factors on LSP are not considered, instead the original conditioning factors are directly taken as the model inputs, which brings uncertainties to LSP results. This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP uncertainties, and further explore a method which can effectively reduce the random errors in conditioning factors.” Financial supporters for this research include National Natural Science Foundation of China; China National Funds For Distinguished Young Scientists.
查看更多>>摘要：New research on Computational Intelligence is the subject of a report. According to news reporting out of Turin, Italy, by NewsRx editors, research stated, “Energy management in smart homes is one of the most critical problems for the Quality of Life (QoL) and preserving energy resources. One of the relevant issues in this subject is environmental contamination, which threatens the world’s future.” Our news journalists obtained a quote from the research from Polytechnic University Torino, “Green computing-enabled Artificial Intelligence (AI) algorithms can provide impactful solutions to this topic. This research proposes using one of the Recurrent Neural Network (RNN) algorithms known as Long Short-Term Memory (LSTM) to comprehend how it is feasible to perform the cloud/fog/edge-enabled prediction of the building’s energy. Four parameters of power electricity, power heating, power cooling, and total power in an office/home in cold-climate cities are considered as our features in the study. Based on the collected data, we evaluate the LSTM approach for forecasting parameters for the next year to predict energy consumption and online monitoring of the model’s performance under various conditions. Towards implementing the AI predictive algorithm, several existing tools are studied.”
查看更多>>摘要：A new study on artificial intelligence is now available. According to news reporting from Islamabad, Pakistan, by NewsRx journalists, research stated, “With the emergence of intelligent services and applications powered by artificial intelligence (AI), the Internet of Things (IoT) affects many aspects of our daily lives.” Financial supporters for this research include Technische Universit?T Wien Bibliothek. The news reporters obtained a quote from the research from Quaid-i-Azam University: “Traditional approaches to machine learning (ML) relied on centralized data collection and processing, where data was collected and analyzed in one place. However, with the development of Deep Federated Learning (DFL), models can now be trained on decentralized data, reducing the need for centralized data storage and processing. In this work, we provide a detailed analysis of DFL and its benefits, followed by an extensive survey of the use of DFL in various IoT services and applications. We have studied the impact of DFL and how to preserve security and privacy by ensuring compliance in machine learning-enabled IoT systems. In addition, we present a generic architecture for a GDPR-compliant DFL-based framework.”
查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting originating from Umea, Sweden, by NewsRx correspondents, research stated, “In this research, we develop three statistical based control charts: the Hotelling’s T2, MEWMA (multivariate exponentially weighted moving average), and LRT (likelihood ratio test) as well as three machine learning (ML) based control charts: the ANN (artificial neural network), SVR (support vector regression), and RFR (random forest regression), for monitoring generalized linear model (GLM) profiles. We train these ML models with two different training methods to get a linear (regression) output and then apply our classification technique to see if the process is in-control or out-of-control, at each sampling time.” Our news editors obtained a quote from the research from Umea University, “In addition to developing the FP (fixed parameters) schemes, for the first time in GLM profiles, we design an adaptive VP (variable parameters) scheme for each control chart as well to increase the charts’ sensitivity in detecting shifts. We develop some algorithms with which the values of the control chart parameters in both FP and VP schemes can be obtained. Then, we develop two algorithms to measure the charts’ performance in both FP and VP schemes, by using the run-length and timeto-signal based performance measures. This is also the first control chart-related research that develops an algorithm to compute the performance measures that applies to any VP adaptive control scheme. After designing the control charts as well as performance measures, we perform extensive simulation studies and evaluate and compare all our control charts under different shift sizes and scenarios, and in three different simulation environments.”
查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news reporting originating from Xi’an, People’s Republic of China, by NewsRx correspondents, research stated, “The difficulty in determining the bearing capacity of pile foundations in saline soil environments in cold regions can pose a challenge when developing a bearing capacity prediction model. To address this, the study uses data from the construction of the De Xiang Expressway project in Qinghai Province, China, and considers pile length, pile diameter, corrosion depth, and spalling thickness as influential parameters.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Qinghai Provincial Highway and Traffic Science and Technology Research Project.
查看更多>>摘要：Fresh data on intelligent systems are presented in a new report. According to news reporting originating from the University Teknologi MARA by NewsRx correspondents, research stated, “Vision is the main sensory organ for human beings to contact and understand the objective world. The results of various statistical data show that more than 60% of all ways for human beings to obtain external information are through the visual system. Vision is of great significance for human beings to obtain all kinds of information needed for survival, which is the most important sense of human beings.” Our news reporters obtained a quote from the research from University Teknologi MARA: “The rapid growth of computer technology, image processing, pattern recognition, and other disciplines have been widely applied. Traditional image processing algorithms have some limitations when dealing with complex images. To solve these problems, some scholars have proposed various new methods. Most of these methods are based on statistical models or artificial neural networks. Although they meet the requirements of modern computer vision systems for feature extraction algorithms with high accuracy, high speed, and low complexity, these algorithms still have many shortcomings. For example, many researchers have used different methods for feature extraction and segmentation to get better segmentation results. Scaleinvariant feature transform (SIFT) is a description used in the field of image processing. This description has scale invariance and can detect key points in the image. It is a local feature descriptor. A sparse coding algorithm is an unsupervised learning method, which is used to find a set of “super complete” basis vectors to represent sample data more efficiently. Therefore, combining SIFT and sparse coding, this article proposed an image feature extraction algorithm based on visual information to extract image features. The results showed that the feature extraction time of X algorithm for different targets was within 0.5 s when the other conditions were the same. The feature matching time was within 1 s, and the correct matching rate was more than 90%.”
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting from Guangdong, People’s Republic of China, by NewsRx journalists, research stated, “To investigate the clinical value of a novel deep-learning based CT reconstruction algorithm, artificial intelligence iterative reconstruction (AIIR), in diagnostic imaging of colorectal cancer (CRC). This study retrospectively enrolled 217 patients with pathologically confirmed CRC.” The news correspondents obtained a quote from the research from Sun Yat-sen University, “CT images were reconstructed with the AIIR algorithm and compared with those originally obtained with hybrid iterative reconstruction (HIR). Objective image quality was evaluated in terms of the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Subjective image quality was graded on the conspicuity of tumor margin and enhancement pattern as well as the certainty in diagnosing organ invasion and regional lymphadenopathy. In patients with surgical pathology (n = 116), the performance of diagnosing visceral peritoneum invasion was characterized using receiver operating characteristic (ROC) analysis. Changes of diagnostic thinking in diagnosing hepatic metastases were assessed through lesion classification confidence. The SNRs and CNRs on AIIR images were significantly higher than those on HIR images (all p＜0.001). The AIIR was scored higher for all subjective metrics (all p＜0.001) except for the certainty of diagnosing regional lymphadenopathy (p = 0.467). In diagnosing visceral peritoneum invasion, higher area under curve (AUC) of the ROC was found for AIIR than HIR (0.87 vs 0.77, p = 0.001). In assessing hepatic metastases, AIIR was found capable of correcting the misdiagnosis and improving the diagnostic confidence provided by HIR (p = 0.01).”
查看更多>>摘要：Current study results on Machine Learning have been published. According to news originating from Selangor, Malaysia, by NewsRx correspondents, research stated, “Predicting concrete strength is complex due to the high non-linearity involved in strength development, especially when using supplementary cementitious materials (SCMs) such as fly ash, silica fume, and GGBS. In this paper, an artificial neural network has been used to predict the compressive strength of concrete for four cases, namely concrete without cement replacement, and binary, ternary, and quaternary cement concretes corresponding to one, two and three different SCMs in the mix.” Our news journalists obtained a quote from the research from the University of Putra Malaysia, “To predict the strength accurately, a total of 1013 data were collected from 37 literature and trained using two training algorithms namely Levenberg-Marquardt (LM) and Bayesian Regularization (BR). The best predictions were achieved using one hidden layer with 14 and 15 neurons for LM and BR algorithms respectively. A high accuracy has been achieved with a correlation factor of 0.97 and 0.966 using the BR and LM algorithms respectively, with a20-index of 83%. Generally, the BR algorithm gives a better overall performance, while underestimating the compressive strength compared to LM. Sensitivity analysis has also been investigated using linear and quadratic regressions.”
查看更多>>摘要：Researchers detail new data in Machine Learning. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Thermally activated delayed fluorescence (TADF) materials have attracted much attention due to their high performance in organic light-emitting diodes (OLEDs). However, progress in developing novel TADF materials is limited by the low-efficiency traditional trial-and-error experimental approach.” Financial supporters for this research include Chinese Academy of Sciences, National Natural Science Foundation of China (NSFC), National Key R&D Program of China, Information Plan of Chinese Academy of Sciences, Fundamental Research Funds for the Central Universities. Our news journalists obtained a quote from the research from the University of the Chinese Academy of Sciences, “In this work, by integrating machine learning (ML) and quantum mechanics (QM) calculations, we have achieved fast prediction of high-efficiency TADF emitters. 44470 molecules are first generated by virtually combining common 49 donor and 50 acceptor fragments. The ML model is trained for predicting molecular properties by using the QM results of 5136 molecules. Finally, by applying it, 384 molecules are rapidly screened out of 39334 molecules as potential TADF emitters. To validate these results, the photophysical parameters of 18 out of 384 molecules are calculated from first-principles calculations, and the obtained TADF rates are greater than 10(4) s(-1), even up to 106 s(-1), suggesting excellent TADF properties. Moreover, the TADF performance of the predicted candidates overwhelms that of existing TADF emitters with similar chemical structures.”
NETL
NSTL
Elsevier
Tsinghua Univ Press
IEEE
Walter de Gruyter
Korean Society Of Civil Engineers-Ksce
Amer Chemical Soc