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    University of Hohenheim Researcher Updates Current Data on Machine Learning (Using a Machine Learning Regression Approach to Predict the Aroma Partitioning in Dairy Matrices)

    105-105页
    查看更多>>摘要：A new study on artificial intelligence is now available. According to news originating from Stuttgart, Germany, by NewsRx editors, the research stated, “Aroma partitioning in food is a challenging area of research due to the contribution of several physical and chemical factors that affect the binding and release of aroma in food matrices.” Our news editors obtained a quote from the research from University of Hohenheim: “The partition coefficient measured by the Kmg value refers to the partition coefficient that describes how aroma compounds distribute themselves between matrices and a gas phase, such as between different components of a food matrix and air. This study introduces a regression approach to predict the Kmg value of aroma compounds of a wide range of physicochemical properties in dairy matrices representing products of different compositions and/or processing. The approach consists of data cleaning, grouping based on the temperature of Kmg analysis, pre-processing (log transformation and normalization), and, finally, the development and evaluation of prediction models with regression methods. We compared regression analysis with linear regression (LR) to five machine-learning-based regression algorithms: Random Forest Regressor (RFR), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGBoost, XGB), Support Vector Regression (SVR), and Artificial Neural Network Regression (NNR). Explainable AI (XAI) was used to calculate feature importance and therefore identify the features that mainly contribute to the prediction. The top three features that were identified are log P, specific gravity, and molecular weight.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Findings in Machine Learning Reported from Polytechnic University of Catalonia (Supervised Machine Learning-assisted Driving Stress Monitoring Mimo Radar System)

    106-106页
    查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news originating from Barcelona, Spain, by NewsRx correspondents, research stated, “Factors such as road traffic, challenging ambient temperature conditions, and extended periods of driving have detrimental effects on the physical and mental well-being of a driver. These factors can alter the stress levels, thereby diminishing his or her capacity to make effective decisions when faced with hazardous situations on the road.” Funders for this research include Consejo Interinstitucional de Ciencia y Tecnologia (CICYT), Metropolis, Catalan Research Group 2021, Ecuadorian Government. Our news journalists obtained a quote from the research from the Polytechnic University of Catalonia, “In this regard, this study presents a novel approach utilizing a multiple-input multiple-output (MIMO) radar system to accurately assess driver stress levels by measuring both physiological signals and driving behavior. The proposed method, assisted by a machine learning technique, provides comprehensive insights to classify the stress level of a driver into three states: drowsiness, awakeness, and anxiety. The MIMO radar system captures the elongation distance and velocity of six specific regions of the frontal torso of the driver in an advanced driving simulator based on virtual reality. This allows for the extraction of vital physiological parameters such as heart rate, respiratory rhythm, and breathing patterns over time, as well as the identification of changes in driving style determined by variations in their relative position in the seat and control of the steering wheel. Then, a fully connected neural network (FCNN) model is trained with the acquired data, and its performance is evaluated with volunteers submitted to different driving situations that induce stress in the driver.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New Machine Learning Research Reported from Sao Paulo State University (UNESP) (Carbon adsorption on waste biomass of passion fruit peel: A promising machine learning model for CO2 capture)

    107-107页
    查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news originating from Sao Paulo, Brazil, by NewsRx correspondents, research stated, “The alarming increase in the concentration of carbon dioxide (CO2) in the atmosphere, mainly due to human emissions, represents a significant threat to life. In this context, carbon capture and storage (CCS) technologies have emerged as promising solutions, such as adsorption on carbonaceous materials, standing out as a prominent approach.” Our news correspondents obtained a quote from the research from Sao Paulo State University (UNESP): “This study aims to quantify the maximum CO2 capture in the laboratory scale using functionalized activated carbon by passion fruit peel biomass (FACPFP) and to develop a simple and improved machine learning model to predict the capture of this greenhouse gas. FACPFP was successfully prepared through chemical activation with K2C2O4 and doping with ethylenediamine (EDA) at 700 ℃ and 1 h. The samples were thoroughly characterized by thermogravimetric analysis (TGA), scanning electron microscopy (SEM) with energy dispersive X-ray detector (EDX), Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). CO2 sorption was assessed using functional density theory (DFT). For predictive model, multiple linear regression with cross-validation was used. Under CO2 atmosphere conditions, the textural parameters allowed to see the probable presence of ultra-micropores, the BET surface area, the total pore and micropore volume were 105 m²/g, 0.03 cm³ /g and 0.06 cm³ /g, respectively. The maximum CO2 adsorption capacity in the FACPFP reached about 2.2 mmol/g at 0 ℃ and 1 bar.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    University of Waterloo Reports Findings in Cardiovascular Diseases and Conditions (Heart rate prediction with contactless active assisted living technology: a smart home approach for older adults)

    108-108页
    查看更多>>摘要：New research on Cardiovascular Diseases and Conditions is the subject of a report. According to news reporting out of Waterloo, Canada, by NewsRx editors, research stated, “As global demographics shift toward an aging population, monitoring their heart rate becomes essential, a key physiological metric for cardiovascular health. Traditional methods of heart rate monitoring are often invasive, while recent advancements in Active Assisted Living provide non-invasive alternatives.” Our news journalists obtained a quote from the research from the University of Waterloo, “This study aims to evaluate a novel heart rate prediction method that utilizes contactless smart home technology coupled with machine learning techniques for older adults. The study was conducted in a residential environment equipped with various contactless smart home sensors. We recruited 40 participants, each of whom was instructed to perform 23 types of predefined daily living activities across five phases. Concurrently, heart rate data were collected through Empatica E4 wristband as the benchmark. Analysis of data involved five prominent machine learning models: Support Vector Regression, K-nearest neighbor, Random Forest, Decision Tree, and Multilayer Perceptron. All machine learning models achieved commendable prediction performance, with an average Mean Absolute Error of 7.329. Particularly, Random Forest model outperformed the other models, achieving a Mean Absolute Error of 6.023 and a Scatter Index value of 9.72%. The Random Forest model also showed robust capabilities in capturing the relationship between individuals’ daily living activities and their corresponding heart rate responses, with the highest value of 0.782 observed during morning exercise activities. Environmental factors contribute the most to model prediction performance. The utilization of the proposed non-intrusive approach enabled an innovative method to observe heart rate fluctuations during different activities. The findings of this research have significant implications for public health. By predicting heart rate based on contactless smart home technologies for individuals’ daily living activities, healthcare providers and public health agencies can gain a comprehensive understanding of an individual’s cardiovascular health profile.”
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa

    University of Cambridge Reports Findings in Myelopathy (Machine Learning and Symptom Patterns in Degenerative Cervical Myelopathy: Web-Based Survey Study)

    109-110页
    查看更多>>摘要：New research on Spinal Cord Diseases and Conditions Myelopathy is the subject of a report. According to news originating from Cambridge, United Kingdom, by NewsRx correspondents, research stated, “Degenerative cervical myelopathy (DCM), a progressive spinal cord injury caused by spinal cord compression from degenerative pathology, often presents with neck pain, sensorimotor dysfunction in the upper or lower limbs, gait disturbance, and bladder or bowel dysfunction. Its symptomatology is very heterogeneous, making early detection as well as the measurement or understanding of the underlying factors and their consequences challenging.” Our news journalists obtained a quote from the research from the University of Cambridge, “Increasingly, evidence suggests that DCM may consist of subgroups of the disease, which are yet to be defined. This study aimed to explore whether machine learning can identify clinically meaningful groups of patients based solely on clinical features. A survey was conducted wherein participants were asked to specify the clinical features they had experienced, their principal presenting complaint, and time to diagnosis as well as demographic information, including disease severity, age, and sex. K-means clustering was used to divide respondents into clusters according to their clinical features using the Euclidean distance measure and the Hartigan-Wong algorithm. The clinical significance of groups was subsequently explored by comparing their time to presentation, time with disease severity, and other demographics. After a review of both ancillary and cluster data, it was determined by consensus that the optimal number of DCM response groups was 3. In Cluster 1, there were 40 respondents, and the ratio of male to female participants was 13:21. In Cluster 2, there were 92 respondents, with a male to female participant ratio of 27:65. Cluster 3 had 57 respondents, with a male to female participant ratio of 9:48. A total of 6 people did not report biological sex in Cluster 1. The mean age in this Cluster was 56.2 (SD 10.5) years; in Cluster 2, it was 54.7 (SD 9.63) years; and in Cluster 3, it was 51.8 (SD 8.4) years. Patients across clusters significantly differed in the total number of clinical features reported, with more clinical features in Cluster 3 and the least clinical features in Cluster 1 (Kruskal-Wallis rank sum test: ch=159.46; P<.001). There was no relationship between the pattern of clinical features and severity. There were also no differences between clusters regarding time since diagnosis and time with DCM. Using machine learning and patient-reported experience, 3 groups of patients with DCM were defined, which were different in the number of clinical features but not in the severity of DCM or time with DCM. Although a clearer biological basis for the clusters may have been missed, the findings are consistent with the emerging observation that DCM is a heterogeneous disease, difficult to diagnose or stratify. There is a place for machine learning methods to efficiently assist with pattern recognition.”
      原文链接:
    • NETL
    • NSTL
    • Jmir Publications, Inc

    Sun Yat-sen University Reports Findings in Liver Cancer (Prognosis prediction and risk stratification of transarterial chemoembolization or intraarterial chemotherapy for unresectable hepatocellular carcinoma based on machine learning)

    110-111页
    查看更多>>摘要：New research on Oncology Liver Cancer is the subject of a report. According to news reporting from Guangdong, People’s Republic of China, by NewsRx journalists, research stated, “To develop and validate a risk scoring scale model (RSSM) for stratifying prognostic risk after intra-arterial therapies (IATs) for hepatocellular carcinoma (HCC). Between February 2014 and October 2022, 2338 patients with HCC who underwent initial IATs were consecutively enrolled.” The news correspondents obtained a quote from the research from Sun Yat-sen University, “These patients were divided into training datasets (TD, n = 1700), internal validation datasets (ITD, n = 428), and external validation datasets (ETD, n = 200). Five-years death was used to predict outcome. Thirty-four clinical information were input and five supervised machine learning (ML) algorithms, including eXtreme Gradient Boosting (XGBoost), Categorical Gradient Boosting (CatBoost), Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LGBT), and Random Forest (RF), were compared using the areas under the receiver operating characteristic (AUC) with DeLong test. The variables with top important ML scores were used to build the RSSM by stepwise Cox regression. The CatBoost model achieved the best discrimination when 12 top variables were input, with the AUC of 0.851 (95% confidence intervals (CI), 0.833-0.868) for TD, 0.817 (95%CI, 0.759-0.857) for ITD, and 0.791 (95%CI, 0.748-0.834) for ETD. The RSSM was developed based on the immune checkpoint inhibitors (ICI) (hazard ratios (HR), 0.678; 95%CI 0.549, 0.837), tyrosine kinase inhibitors (TKI) (HR, 0.702; 95%CI 0.605, 0.814), local therapy (HR, 0.104; 95%CI 0.014, 0.747), response to the first IAT (HR, 4.221; 95%CI 2.229, 7.994), tumor size (HR, 1.054; 95%CI 1.038, 1.070), and BCLC grade (HR, 2.375; 95%CI 1.950, 2.894). Kaplan-Meier analysis confirmed the role of RSSM in risk stratification (p <0.001). The RSSM can stratify accurately prognostic risk for HCC patients received IAT. On the basis, an online calculator permits easy implementation of this model.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Data on Robotics Reported by Researchers at Carnegie Mellon University (Onboard Dynamic-object Detection and Tracking for Autonomous Robot Navigation With Rgb-d Camera)

    111-112页
    查看更多>>摘要：Current study results on Robotics have been published. According to news reporting from Pittsburgh, Pennsylvania, by NewsRx journalists, research stated, “Deploying autonomous robots in crowded indoor environments usually requires them to have accurate dynamic obstacle perception. Although plenty of previous works in the autonomous driving field have investigated the 3D object detection problem, the usage of dense point clouds from a heavy Light Detection and Ranging (LiDAR) sensor and their high computation cost for learning-based data processing make those methods not applicable to small robots, such as vision-based UAVs with small onboard computers.” The news correspondents obtained a quote from the research from Carnegie Mellon University, “To address this issue, we propose a lightweight 3D dynamic obstacle detection and tracking (DODT) method based on an RGB-D camera, which is designed for low-power robots with limited computing power. Our method adopts a novel ensemble detection strategy, combining multiple computationally efficient but low-accuracy detectors to achieve real-time high-accuracy obstacle detection. Besides, we introduce a new feature-based data association and tracking method to prevent mismatches utilizing point clouds’ statistical features. In addition, our system includes an optional and auxiliary learning-based module to enhance the obstacle detection range and dynamic obstacle identification. The proposed method is implemented in a small quadcopter, and the results show that our method can achieve the lowest position error (0.11 m) and a comparable velocity error (0.23 m/s) across the benchmarking algorithms running on the robot’s onboard computer. The flight experiments prove that the tracking results from the proposed method can make the robot efficiently alter its trajectory for navigating dynamic environments.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New Machine Translation Data Have Been Reported by Researchers at Beijing Institute of Technology (Alleviating Repetitive Tokens In Non-autoregressive Machine Translation With Unlikelihood Training)

    112-113页
    查看更多>>摘要：Investigators publish new report on Machine Translation. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “In recent years, significant progress has been made in the field of non-autoregressive machine translations. However, the accuracy of non-autoregressive models still lags behind their autoregressive counterparts.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from the Beijing Institute of Technology, “This discrepancy can be attributed to the abundance of repetitive tokens in the target sequences generated by non-autoregressive models. In this study, we delve into this phenomenon and propose a novel approach to train a non-autoregressive model using unlikelihood loss. We evaluate our method on three widely used benchmark tasks. The experimental results demonstrating that our proposed approach significantly reduces the number of repetitive tokens while improving the overall performance of non-autoregressive machine translations.” According to the news editors, the research concluded: “Compared to the baseline model ‘MaskPredict’, the average number of repetitions on IWSLT 14 DE ->EN valid set is reduced from 0.48 to 0.17, resulting in a remarkable 62% decrease.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Liverpool John Moores University Reports Findings in Alzheimer Disease (An explainable machine learning approach for Alzheimer's disease classification)

    113-114页
    查看更多>>摘要：New research on Neurodegenerative Diseases and Conditions Alzheimer Disease is the subject of a report. According to news reporting from Liverpool, United Kingdom, by NewsRx journalists, research stated, “The early diagnosis of Alzheimer’s disease (AD) presents a significant challenge due to the subtle biomarker changes often overlooked. Machine learning (ML) models offer a promising tool for identifying individuals at risk of AD.” The news correspondents obtained a quote from the research from Liverpool John Moores University, “However, current research tends to prioritize ML accuracy while neglecting the crucial aspect of model explainability. The diverse nature of AD data and the limited dataset size introduce additional challenges, primarily related to high dimensionality. In this study, we leveraged a dataset obtained from the National Alzheimer’s Coordinating Center, comprising 169,408 records and 1024 features. After applying various steps to reduce the feature space. Notably, support vector machine (SVM) models trained on the selected features exhibited high performance when tested on an external dataset. SVM achieved a high F1 score of 98.9% for binary classification (distinguishing between NC and AD) and 90.7% for multiclass classification. Furthermore, SVM was able to predict AD progression over a 4-year period, with F1 scores reached 88% for binary task and 72.8% for multiclass task. To enhance model explainability, we employed two ruleextraction approaches: class rule mining and stable and interpretable rule set for classification model. These approaches generated human-understandable rules to assist domain experts in comprehending the key factors involved in AD development. We further validated these rules using SHAP and LIME models, underscoring the significance of factors such as MEMORY, JUDGMENT, COMMUN, and ORIENT in determining AD risk.”
      原文链接:
    • NETL
    • NSTL

    Patent Application Titled 'Intelligent Speech Or Dialogue Enhancement' Published Online (USPTO 20240029755)

    114-118页
    查看更多>>摘要：According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventors CARVAJAL, Santiago (Ashland, MA, US); ILIEV, Stoyan I. (Framingham, MA, US); ISRAELOWITZ, Miriam (Natick, MA, US); JULIEN, Isaac Keir (Cambridge, MA, US); LIU, Yang (Boston, MA, US); MANIET, Edward (Auburn, MA, US); MCHUGH, James Michael (Jamaica Plain, MA, US); QUERZE, III, Elio Dante (Arlington, MA, US); STARK, Michael W. (Acton, MA, US); ZHANG, Shuo (Cambridge, MA, US), filed on July 19, 2022, was made available online on January 25, 2024. No assignee for this patent application has been made. Reporters obtained the following quote from the background information supplied by the inventors: “In sound recording and reproduction, an equalizer may perform equalization to adjust magnitudes of different frequency bands in an audio signal. For example, the equalizer may use filters to adjust bass and treble to enhance listening experience. The equalization may be dynamically adjusted by a user in real time, or include one or more preset profiles for different genres of audio input (e.g., jazz, classical, pop, etc.).
      原文链接:
    • NETL
    • NSTL