查看更多>>摘要：Data detailed on Machine Translation have been presented. According to news reporting originating from Shaanxi, People's Republic of China, by NewsRx correspondents, research stated, "The scale of parallel corpus plays an important role in training high-quality neural machine translation models. In order to expand the scale of parallel corpus in low-resource scenarios, researchers have proposed a series of data augmentation approaches, in which the most representative work is the back-translation." Funders for this research include National Natural Science Foundation of China (NSFC), Yunnan Fundamental Research Projects.
查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news reporting from Houston, Texas, by NewsRx journalists, research stated, "Day-ahead operation involves a complex and computationally intensive optimization process to determine the generator commitment schedule and dispatch. The optimization process is a mixed-integer linear program (MILP) also known as securityconstrained unit commitment (SCUC)." The news correspondents obtained a quote from the research from the University of Houston, "Independent system operators (ISOs) run SCUC daily and require state-of-the-art algorithms to speed up the process. Existing patterns in historical information can be leveraged for model reduction of SCUC, which can provide significant time savings. In this paper, machine learning (ML) based classification approaches, namely logistic regression, neural networks, random forest and K-nearest neighbor, were studied for model reduction of SCUC. The ML was then aided with a feasibility layer (FL) and post-process technique to ensure high quality solutions. The proposed approach is validated on several test systems namely, IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, South-Carolina (SC) synthetic grid 500-Bus system, and Polish 2383-Bus system. Moreover, model reduction of a stochastic SCUC (SSCUC) was demonstrated utilizing a modified IEEE 24-Bus system with renewable generation."
查看更多>>摘要：A new study on artificial intelligence is now available. According to news originating from Turin, Italy, by NewsRx correspondents, research stated, "Tea infusions are the most consumed beverages in the world after water; their pleasant yet peculiar flavor profile drives consumer choice and acceptance and becomes a fundamental benchmark for the industry." Funders for this research include Soremartec Italia Srl. The news correspondents obtained a quote from the research from University of Torino: "Any qualification method capable of objectifying the product's sensory features effectively supports industrial quality control laboratories in guaranteeing high sample throughputs even without human panel intervention. The current study presents an integrated analytical strategy acting as an Artificial Intelligence decision tool for black tea infusion aroma and taste blueprinting. Key markers validated by sensomics are accurately quantified in a wide dynamic range of concentrations. Thirteen key aromas are quantitatively assessed by standard addition with in-solution solid-phase microextraction sampling followed by GC-MS. On the other hand, nineteen key taste and quality markers are quantified by external standard calibration and LC-UV/DAD."
查看更多>>摘要：Researchers detail new data in Machine Learning. According to news reporting from Shenyang, People's Republic of China, by NewsRx journalists, research stated, "Tetragonal ratio (c/a) is a critical structural parameter that heavily decides plenty of performances of tetragonal martensitic materials. Nevertheless, the knowledge about c/a remains limited and there is still no definitive strategy to tailor it." Financial supporters for this research include National Natural Science Foundation of China (NSFC), Fundamental Research Funds for the Central Universities, Ministry of Education, China - 111 Project.
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating in Shenyang, People's Republic of China, by NewsRx journalists, research stated, "Machine learning performs well in many problems. However, the tendency to generate predictions that violate theoretical knowledge makes it difficult to apply to practical processing." The news reporters obtained a quote from the research from Northeastern University, "To resolve this situation, this paper combines domain knowledge with a data-driven model, proposes a theory-guided machine learning framework based on a parameter transfer strategy, and applies it to the width prediction of plates after multiple passes of hot rolling. The framework applies a swarm optimization algorithm to the original theoretical model and generates numerous highly-physical consistent samples. The established deep neural network (DNN) model is trained with simulated data, and the parameters are fine-tuned using a parameter transfer strategy combined with actual data to ensure excellent adaptation to the actual environment based on adequate learning of theoretical knowledge. In tests, the proposed model had the best overall prediction performance in this paper. Meanwhile, the developed model is consistent with the existing perception of rolling theory."
查看更多>>摘要：New research on Oncology - Gliomas is the subject of a report. According to news reporting out of Beijing, People's Republic of China, by NewsRx editors, research stated, "Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive. This study aimed to develop and validate a diagnostic prediction model for glioma by employing multiple machine learning algorithms to identify risk factors associated with high-grade glioma, facilitating the prediction of glioma grading."
查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news reporting originating from the Madanapalle Institute of Technology & Science by NewsRx correspondents, research stated, "Embracing Artificial Intelligence (AI) is becoming more common in a variety of areas, including healthcare, banking, and transportation, and it is based on substantial data analysis." Our news journalists obtained a quote from the research from Madanapalle Institute of Technology & Science: "However, utilizing data for AI raises a number of obstacles. This extensive article examines the challenges connected with using data for AI, including data quality, volume, privacy and security, bias and fairness, interpretability and ethical considerations, and the required technical knowledge. The investigation delves into each obstacle, providing insightful solutions for businesses and organizations to properly handle these complexities. Organizations may effectively harness AI's capabilities to make educated decisions by understanding and proactively tackling these difficulties, obtaining a competitive edge in the digital era."
查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news originating from the University Gadjah Mada by NewsRx editors, the research stated, "Volcanic eruptions pose a significant risk to communities located near active volcanoes. Disaster mitigation and risk reduction efforts rely on detecting and monitoring volcanic activity as early as possible." Our news editors obtained a quote from the research from University Gadjah Mada: "This article introduces VEVCC, a MATLAB-based application designed to precisely identify and extract volcanic seismic events from continuous data streams. VEVCC's primary objective is to facilitate the creation of an Excel file containing the arrival times of detected events, which can then be used for various purposes, such as early warning disaster mitigation and automated event identification via machine learning techniques. VEVCC utilizes cross-correlation algorithms to identify volcanic seismic events. It separates these events from background noise and other sources of seismicity, allowing for the construction of a clean and informative dataset. The extracted data is a valuable resource for estimating the frequency of volcanic events and evaluating patterns of volcanic activity. VEVCC's time-stamped event data is indispensable for improving early warning systems, real-time surveillance, and automated event identification."
查看更多>>摘要：Research findings on pattern recognition and artificial intelligence are discussed in a new report. According to news reporting originating from Chongqing, People's Republic of China, by NewsRx correspondents, research stated, "In this paper, a parallel decoder and a word group prediction module are proposed to speed up decoding and improve the effect of captions." Financial supporters for this research include Nsfc. The news journalists obtained a quote from the research from Chongqing University: "The features of the image extracted by the encoder are linearly projected to different word groups, and then a unique relaxed mask matrix is designed to improve the decoding speed and the caption effect. First, since image captioning is composed of many words, sentences can also be broken down into word groups or words according to their syntactic structure, and we achieve this function through constituency parsing. Second, we make full use of the extracted features to predict the size of word groups. Then, a new embedding representing the information of the word is proposed based on word embedding."
查看更多>>摘要：Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from the Institute Technology Bandung by NewsRx correspondents, research stated, "Using a virtual ATC tower is one aspect of the airport digitization initiative, which aims to cut costs." Our news correspondents obtained a quote from the research from Institute Technology Bandung: "Technology for tracking and detecting aircraft is required to guarantee the security of the deployment of virtual ATC towers. The invention of visual artificial intelligence that can recognize and follow aircraft ground movement in an airport is presented in this study. One feature that should be developed in this study is the capacity to issue a warning if two aircraft are in close proximity to one another. The techniques consist of a coordinate system conversion from pixels to meters for aircraft separation computation, the Deep SORT object tracking algorithm, and the YOLOv4 object recognition algorithm that has been trained using Image Dehazing Filter. Next, a fair-condition recorded airport video is used to validate the model. With a mean average precision score of 95.92%, the trained YOLOv4 model was able to track every aircraft in the video, and with an error of 5.09%, the aircraft separation warning system functioned as expected." According to the news reporters, the research concluded: "An airport's possible use of an aircraft ground movement tracker was demonstrated by the constructed model."
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