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    Wuhan University Researchers Detail Research in Machine Learning (Estimation of wheat kernel moisture content based on hyperspectral reflectance and satellite multispectral imagery)

    11-11页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting from Wuhan, People’s Republic of China, by NewsRx journalists, research stated, “The wheat kernel moisture content (KMC) plays a pivotal role in determining the quality, storage viability, and economic profitability of wheat. Monitoring wheat KMC in-field before harvest provides decision-making information to ensure harvested wheat meeting trade standards.” The news journalists obtained a quote from the research from Wuhan University: “The study aims to expand the feasibility of spectroscopy to estimate wheat KMC at multi-scales. In addition to using ground hyperspectral reflectance for single-point measurement of wheat KMC, satellite multispectral images were also utilized to monitor the field distribution of wheat KMC on large-scale. For each estimation task, we extracted sensitive spectral features and compared the accuracy of three machine learning methods including Ridge, Support Vector Regression (SVR) and Random Forest Regression (RFR). We also adopted Two-stage Tradaboost. R2 to address the issue of uneven distribution of samples corresponding to satellite imagery. Experimental results showed that, based on optimized spectral features, the R2 of RFR model outperformed other machine learning models (Ridge and SVR) for wheat KMC estimation (R2 ＞0.85). Among all spectral features, the B5 band and PSRI vegetation index from PlanetScope satellite, as well as the B11, and B12 bands and VARI vegetation index from Sentinel-2, proved to be effective features for wheat KMC estimation. Moreover, the incorporation of hyperspectral reflectance and multispectral imagery can improve the estimation accuracy on large-scale through the transfer learning algorithm of Two-stage Tradaboost. R2.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Researchers from East China Normal University Describe Findings in Robotics (Robust Motion Planning for Multi-robot Systems Against Position Deception Attacks)

    12-13页
    查看更多>>摘要：A new study on Robotics is now available. According to news originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “Deep reinforcement learning (DRL) is widely applied in motion planning for multi-robot systems as DRL leverages the offline compute an action for a robot based on the states of its surrounding obstacles, including other robots in the system.” Financial support for this research came from National Key Research and Development Program of China.
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New Artificial Intelligence Research from Universidad Internacional Discussed (Facial emotion recognition through artificial intelligence)

    12-12页
    查看更多>>摘要：Data detailed on artificial intelligence have been presented. According to news originating from Logrono, Spain, by NewsRx editors, the research stated, “This paper introduces a study employing artificial intelligence (AI) to utilize computer vision algorithms for detecting human emotions in video content during user interactions with diverse visual stimuli.” Financial supporters for this research include Fundacao Para A Ciencia E A Tecnologia. Our news correspondents obtained a quote from the research from Universidad Internacional: “The research aims to unveil the creation of software capable of emotion detection by leveraging AI algorithms and image processing pipelines to identify users’ facial expressions. The process involves assessing users through images and facilitating the implementation of computer vision algorithms aligned with psychological theories defining emotions and their recognizable features. The study demonstrates the feasibility of emotion recognition through convolutional neural networks (CNN) and software development and training based on facial expressions. The results highlight successful emotion identification; however, precision improvement necessitates further training for contexts with more diverse images and additional algorithms to distinguish closely related emotional patterns. The discussion and conclusions emphasize the potential of A.I. and computer vision algorithms in emotion detection, providing insights into software development, ongoing training, and the evolving landscape of emotion recognition technology.”
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa

    Amsterdam University Medical Centers Reports Findings in Cardiovascular Diseases and Conditions (Networks of gut bacteria relate to cardiovascular disease in a multi-ethnic population: the HELIUS study)

    13-14页
    查看更多>>摘要：New research on Cardiovascular Diseases and Conditions is the subject of a report. According to news originating from Amsterdam, Netherlands, by NewsRx correspondents, research stated, “Gut microbiota have been linked to blood lipid levels and cardiovascular diseases (CVD). The composition and abundance of gut microbiota trophic networks differ between ethnicities.” Our news journalists obtained a quote from the research from Amsterdam University Medical Centers, “We aim to evaluate the relationship between gut microbiotal trophic networks and CVD phenotypes. We included cross-sectional data from 3860 individuals without CVD history from six ethnicities living in the Amsterdam region participating in the prospective Healthy Life in Urban Setting (HELIUS) study. Genetic variants were genotyped, fecal gut microbiota were profiled and blood and anthropometric parameters were measured. A machine learning approach was used to assess the relationship between CVD risk (Framingham Score) and gut microbiota stratified by ethnicity. Potential causal relationships between gut microbiota composition and CVD were inferred by performing two sample Mendelian randomization with hard CVD events from the Pan-UK biobank and microbiome GWAS summary data from a subset of the HELIUS cohort (n = 4117). Microbial taxa identified to be associated with CVD by machine learning and Mendelian randomization were often ethnic specific, but some concordance across ethnicities was found. The microbes Akkermansia muciniphila and Ruminococcaceae UCG-002 were protective against ischemic heart disease in African Surinamese and Moroccans, respectively. We identified a strong inverse association between blood lipids, CVD risk and the combined abundance of the correlated microbes Christensenellaceae- Methanobrevibacter-Ruminococcaceae (CMR). The CMR cluster was also identified in two independent cohorts and the association with triglycerides was replicated. Certain gut microbes can have a potentially causal relationship with CVD events, with possible ethnic specific effects.”
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Findings from University of Algiers Provide New Insights into Artificial Intelligence (Competency Assessment Based on Fuzzy Logic and Artificial Intelligence Mechanism: A Study of Competency Assessment Document for the Algerian SEROR Company)

    15-15页
    查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news originating from the University of Algiers by NewsRx editors, the research stated, “Addressing the issue of how automating the quantitative assessment of competencies through a competency assessment document came to remove the assessment process from the descriptive side and an attempt to propose a new model aligned with modern management requirements.” The news correspondents obtained a quote from the research from University of Algiers: “Competency assessment is considered one of the most important indicators for managing competencies in organizations, as it offers valuable insights into the strengths and weaknesses of human resources, which is essential for strategic planning. Organizations are actively seeking a cost-effective and accurate system, aiming to minimize the impact of subjective biases in the evaluation process. Additionally, there is a need for a solution that facilitates swift assessment of a large workforce, ultimately reducing overall costs. To meet these requirements, the current study employs Fuzzy Logic and Artificial Intelligence mechanism to develop a contemporary and precise evaluation model. The study, which analyzed competency assessment data from the Algerian SEROR Company, showcased the possibility of creating a sophisticated quantitative model for competency evaluation using Fuzzy Logic and Artificial Intelligence Mechanism. The results imply that the institution has the potential to embrace a cutting-edge and forward-thinking approach, enhancing objectivity, particularly in dealing with complex systems.”
      原文链接:
    • NETL
    • NSTL

    Findings from Tufts University Update Understanding of Robotics and Automation (Correcting Motion Distortion for Lidar Scan-tomap Registration)

    16-17页
    查看更多>>摘要：Research findings on Robotics - Robotics and Automation are discussed in a new report. According to news reporting out of Somerville, Massachusetts, by NewsRx editors, research stated, “Because scanning-LIDAR sensors require finite time to create a point cloud, sensor motion during a scan warps the resulting image, a phenomenon known as motion distortion or rolling shutter. Motion-distortion correction methods exist, but they rely on external measurements or Bayesian filtering over multiple LIDAR scans.” Financial support for this research came from U.S. Department of Transportation Joint Program Office. Our news journalists obtained a quote from the research from Tufts University, “In this letter we propose a novel algorithm that performs snapshot processing to obtain a motion-distortion correction. Snapshot processing, which registers a current LIDAR scan to a reference image without using external sensors or Bayesian filtering, is particularly relevant for localization to a high-definition (HD) map. Our approach, which we call Velocity-corrected Iterative Compact Ellipsoidal Transformation (VICET), extends the wellknown Normal Distributions Transform (NDT) algorithm to solve jointly for both a 6 Degree-of-Freedom (DOF) rigid transform between a scan and a map and a set of 6DOF motion states that describe distortion within the current LIDAR scan.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Study Data from Meteorology Department Update Understanding of Machine Learning (Spatial rain probabilistic prediction performance using costsensitive learning algorithm)

    16-16页
    查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news reporting from the Meteorology Department by NewsRx journalists, research stated, “The use of machine learning in weather prediction is growing rapidly as an alternative to conventional numerical weather prediction.” Our news reporters obtained a quote from the research from Meteorology Department: “However, predictions using machine learning such as Long Short Term Memory (LSTM) based on neural networks have weaknesses in predicting extreme events with a high ratio of unbalanced data. This research examines the performance of using focal loss in LSTM to obtain a machine-learning model that is cost-sensitive. The model used the Global Forecasting System Data and the Global Satellite Measurement of Precipitation for the years 2017-2020. Testing the hyperparameter configuration was carried out using the hyperband method on the number of nodes and the number of iterations with 3 scenarios (2, 3, and 4 classes). The results showed an increased performance against noncost sensitive LSTM with an average increase of 25% accuracy and 11% F1-score on 2 classes scenario, 15% accuracy increase and 21% F1-score for scenario 3 classes, as well as an increase in accuracy of 15% and F1-score 26% for scenario 4 class.”
      原文链接:
    • NETL
    • NSTL

    Reports on Artificial Intelligence Findings from Ankara University Provide New Insights (A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions)

    17-18页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting originating from Ankara University by NewsRx correspondents, research stated, “Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), has become essential in the realm of cybersecurity. These techniques have proven to be effective in detecting and mitigating cyberattacks, which can cause significant harm to individuals, organizations, and even countries.” The news reporters obtained a quote from the research from Ankara University: “Machine learning algorithms use statistical methods to identify patterns and anomalies in large datasets, enabling security analysts to detect previously unknown threats. Deep learning, a subfield of ML, has shown great potential in improving the accuracy and efficiency of cybersecurity systems, particularly in image and speech recognition. On the other hand, RL is again a subfield of machine learning that trains algorithms to learn through trial and error, making it particularly effective in dynamic environments. We also evaluated the usage of ChatGPT-like AI tools in cyber-related problem domains on both sides, positive and negative. This article provides an overview of how ML, DL, and RL are applied in cybersecurity, including their usage in malware detection, intrusion detection, vulnerability assessment, and other areas. The paper also specifies several research questions to provide a more comprehensive framework to investigate the efficiency of AI and ML models in the cybersecurity domain. The state-of-the-art studies using ML, DL, and RL models are evaluated in each Section based on the main idea, techniques, and important findings. It also discusses these techniques’ challenges and limitations, including data quality, interpretability, and adversarial attacks. Overall, the use of ML, DL, and RL in cybersecurity holds great promise for improving the effectiveness of security systems and enhancing our ability to protect against cyberattacks. Therefore, it is essential to continue developing and refining these techniques to address the ever-evolving nature of cyber threats.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New Machine Learning Study Findings Have Been Reported by Researchers at University of Deusto (Quantum Machine Learning Revolution in Healthcare: A Systematic Review of Emerging Perspectives and Applications)

    18-19页
    查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news originating from Bilbao, Spain, by NewsRx editors, the research stated, “Quantum computing (QC) stands apart from traditional computing systems by employing revolutionary techniques for processing information. It leverages the power of quantum bits (qubits) and harnesses the unique properties exhibited by subatomic particles, such as superposition, entanglement, and interference.” Financial supporters for this research include European Union’s Horizon 2020 Research And Innovation Program Under The Marie Sklodowska-curie; Evida Research Group, University of Deusto, Bilbao, Spain, Through The Basque Government. The news reporters obtained a quote from the research from University of Deusto: “These quantum phenomena enable quantum computers to operate on an entirely different level, exponentially surpassing the computational capabilities of classical computers. By manipulating qubits and capitalising on their quantum states, QC holds the promise of solving complex problems that are currently intractable in the reaches into various critical sectors, including healthcare. Scientists and engineers are working diligently to overcome various challenges and limitations associated with QC technology. These include issues related to qubit stability, error correction, scalability, and noise reduction. In such a scenario, our proposed work provides a concise summary of the most recent state of the art based on articles published between 2018 and 2023 in the healthcare domain. Additionally, the approach follows the necessary guidelines for conducting a systematic literature review. This includes utilising research questions and evaluating the quality of the articles using specific metrics. Initially, a total of 2,038 records were acquired from multiple databases, with 468 duplicate records and 1,053 records unrelated to healthcare subsequently excluded. A further 258, 68, and 39 records were eliminated based on title, abstract, and full-text criteria, respectively.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New Machine Learning Study Findings Have Been Reported by Investigators at Czestochowa University of Technology (Forecasting Cryptocurrencies Volatility Using Statistical and Machine Learning Methods: a Comparative Study)

    19-20页
    查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting out of Czestochowa, Poland, by NewsRx editors, research stated, “Forecasting cryptocurrency volatility can help investors make better-informed investment decisions in order to minimize risks and maximize potential profits. Accurate forecasting of cryptocurrency price fluctuations is crucial for effective portfolio management and contributes to the stability of the financial system by identifying potential threats and developing risk management strategies.” Financial supporters for this research include National Science Centre, Poland, Czestochowa University of Technology, Ministry of Science and Higher Education, Poland. Our news journalists obtained a quote from the research from the Czestochowa University of Technology, “The objective of this paper is to provide a comprehensive study of statistical and machine learning methods for predicting daily and weekly volatility of the following four cryptocurrencies: Bitcoin, Ethereum, Litecoin, i.e., HAR (heterogeneous autoregressive), ARFIMA (autoregressive fractionally integrated moving average), GARCH (generalized autoregressive conditional heteroscedasticity), LASSO (least absolute shrinkage and selection operator), RR (ridge regression), SVR (support vector regression), MLP (multilayer perceptron), FNM (fuzzy neighbourhood model), RF (random forest), and LSTM (long short-term memory). The realized variance calculated from intraday returns is used as the input variable for the models. In order to assess the predictive power of the models considered, the model confidence set (MCS) procedure is applied. Our experimental results demonstrate that there is no single best method for forecasting volatility of each cryptocurrency, and different models may perform better depending on the specific cryptocurrency, choice of the error metric and forecast horizon. For daily forecasts, the method that is always found in a set of best models is linear SVR, while for weekly forecasts, there are two such methods, namely FNM and RR. Furthermore, we show that simple linear models such as HAR and ridge regression, perform not worse than more complex models like LSTM and RF.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier