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    Researchers from Henan Polytechnic University Publish New Stud- ies and Findings in the Area of Machine Learning (Carbon stock inversion study of a carbon peaking pilot urban combining machine learning and Landsat images)

    58-59页
    查看更多>>摘要：2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news reporting from Jiaozuo, People’s Republic of China, by NewsRx journalists, research stated, “Global warming is a significant challenge, and carbon stocks in terrestrial ecosystems are crucial for reducing the greenhouse effect and increasing sinks.” Financial supporters for this research include National Natural Science Foundation of China-guangdong Joint Fund.
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Delft University of Technology Researchers Yield New Data on Robotics (Meaningful human control and variable autonomy in human-robot teams for firefighting)

    59-59页
    查看更多>>摘要：2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on robotics. According to news reporting out of Delft, Netherlands, by NewsRx editors, research stated, “Humans and robots are increasingly collaborating on complex tasks such as firefighting. As robots are becoming more autonomous, collaboration in human-robot teams should be combined with meaningful human control.” Our news editors obtained a quote from the research from Delft University of Technology: “Variable autonomy approaches can ensure meaningful human control over robots by satisfying accountability, re- sponsibility, and transparency. To verify whether variable autonomy approaches truly ensure meaningful human control, the concept should be operationalized to allow its measurement. So far, designers of vari- able autonomy approaches lack metrics to systematically address meaningful human control. Therefore, this qualitative focus group (n = 5 experts) explored quantitative operationalizations of meaningful human control during dynamic task allocation using variable autonomy in human-robot teams for firefighting. This variable autonomy approach requires dynamic allocation of moral decisions to humans and non-moral decisions to robots, using robot identification of moral sensitivity. We analyzed the data of the focus group using reflexive thematic analysis. Results highlight the usefulness of quantifying the traceability requirement of meaningful human control, and how situation awareness and performance can be used to objectively measure aspects of the traceability requirement. Moreover, results emphasize that team and robot outcomes can be used to verify meaningful human control but that identifying reasons underlying these outcomes determines the level of meaningful human control.”
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa

    First Clinical Medical College of Lanzhou University Reports Find- ings in Prostate Cancer (Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer)

    60-61页
    查看更多>>摘要：2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Oncology - Prostate Cancer is the subject of a report. According to news reporting originating in Lanzhou, People’s Republic of China, by NewsRx journalists, research stated, “Bone metastasis is a significant contributor to morbidity and mortality in advanced prostate cancer, and early diagnosis is challenging due to its insidious onset. The use of machine learning to obtain prognostic information from pathological images has been highlighted.” Financial supporters for this research include Key Science and Technology Program in Gansu Province, The Natural Science Foundation of Gansu Province, Research Fund Project of Internal Medicine Depart- ment, Gansu Provincial Hospital.
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Study Results from University of Craiova Broaden Understanding of Artificial Intelligence (Exploring Empirical Correlations among Energy Efficiency, Energy Productivity, Energy Use and Economic Development in EU Countries)

    60-60页
    查看更多>>摘要：2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on artificial intelligence. According to news originat- ing from Craiova, Romania, by NewsRx editors, the research stated, “Energy is a crucial component of industrial development and the provision of essential services, as well as an essential element of economic development.” Our news journalists obtained a quote from the research from University of Craiova: “Therefore, improving the integration of environmental and energy-efficiency concerns into environmental, economic, and social policies is an essential task for all nations. During the last decades, the trends in energy consumption are determined by economic activities, demographics, lifestyle changes, and weather. In these contexts, the objective of the current research will be to analyse and evaluate the correlations between energy efficiency, energy productivity, energy use and economic development using the linear regression method.”
      原文链接:
    • NETL
    • NSTL

    Reports from AGH University of Science and Technology Advance Knowledge in Machine Learning (Differentiating Age and Sex In Vertebral Body Ct Scans - Texture Analysis Versus Deep Learning Approach)

    62-62页
    查看更多>>摘要：2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learning have been published. According to news originating from Krakow, Poland, by NewsRx correspondents, research stated, “The automated analysis of computed tomography (CT) scans of vertebrae, for the purpose of determining an individual’s age and sex constitutes a vital area of research. Accurate assessment of bone age in children facil-itates the monitoring of their growth and development.” Financial support for this research came from AGH University of Science and Technology, Faculty of EAIIB, KBIB.
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Indian Council of Agricultural Research (ICAR) Indian Agricultural Statistics Research Institute Researchers Highlight Recent Research in Machine Learning (Development and Assessment of SPM: A Sigmoid-Based Model for Probability Estimation in ...)

    63-63页
    查看更多>>摘要：2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on artificial intelligence. According to news reporting out of New Delhi, India, by NewsRx editors, research stated, “Probability estimation plays a pivotal role across diverse domains, particularly in scenarios where the objective is to select non-repetitive units one at a time, with the option of replacement, from a predefined set of units.” Our news editors obtained a quote from the research from Indian Council of Agricultural Research (ICAR) Indian Agricultural Statistics Research Institute: “Traditional probability calculations in this scenario pose three challenges: the number of floating-point operations to be executed is directly proportional to the chosen set size, susceptibility to floating-point precision errors, and exponential growth in storage needs with increasing number of chosen units. In this scenario, the presented work aims to develop SPM: a sigmoid function-based model that estimates probabilities for such problems with a fixed number of calculations (independent of the input parameter), achieving a constant time complexity algorithm. The research methodology involves generating probability data points, selecting the optimal sigmoid function, augmenting additional data to enhance parameter estimation, identifying parameter estimation equations, and evaluating the model. Moreover, the study’s second objective includes training and comparing six established machine learning-based models (including Decision Tree, Random Forest, Support Vector, Linear Regression, Nearest Neighbour, and Artificial Neural Network) against the proposed SPM. The rigorous assessment of the model’s performance, utilising metrics including RMSE, MAE and $r∧{2}$ across a wide range of scenarios involving varying values of the total units, affirms the model’s accuracy and resilience.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Psychiatric Center Copenhagen Reports Findings in Bipolar Dis- orders (Using digital phenotyping to classify bipolar disorder and unipolar disorder - exploratory findings using machine learning mod- els)

    64-64页
    查看更多>>摘要：2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Mental Health Diseases and Conditions - Bipolar Disorders is the subject of a report. According to news reporting originating in Frederiksberg, Denmark, by NewsRx journalists, research stated, “The aims were to investigate 1) differences in smartphone-based data on phone usage between bipolar disorder (BD) and unipolar disorder (UD) and 2) by using machine learning models, the sensitivity, specificity, and AUC of the combined smartphone data in classifying BD and UD. Daily smartphone-based self-assessments of mood and same-time passively collected smartphone data on smartphone usage was available for six months.” The news reporters obtained a quote from the research from Psychiatric Center Copenhagen, “A total of 64 patients with BD and 74 patients with UD were included. Patients with BD during euthymic states compared with UD in euthymic states had a lower number of incoming phone calls/ day (B: -0.70, 95%CI: -1.37; -0.70, p=0.040). Patients with BD during depressive states had a lower number of incoming and outgoing phone calls/ day as compared with patients with UD in depressive states. In classification by using machine learning models, 1) overall (regardless of the affective state), patients with BD were classified with an AUC of 0.84, which reduced to 0.48 when using a leave-one-patient-out crossvalidation (LOOCV) approach; similarly 2) during a depressive state, patients with BD were classified with an AUC of 0.86, which reduced to 0.42 with LOOCV; 3) during a euthymic state, patients with BD were classified with an AUC of 0.87, which reduced to 0.46 with LOOCV. While digital phenotyping shows promise in differentiating between patients with BD and UD, it highlights the challenge of generalizing to unseen individuals.”
      原文链接:
    • NETL
    • NSTL

    Indian Institute of Information Technology Researchers Re- port Recent Findings in Machine Learning (Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness)

    65-65页
    查看更多>>摘要：2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Kerala, India, by NewsRx editors, research stated, “Aberrant alterations in any of the two dimensions of consciousness, namely awareness and arousal, can lead to the emergence of disorders of consciousness (DOC). The development of DOC may arise from more severe or targeted lesions in the brain, resulting in widespread functional abnormalities.” Our news reporters obtained a quote from the research from Indian Institute of Information Technol- ogy: “However, when it comes to classifying patients with disorders of consciousness, particularly utilizing resting-state electroencephalogram (EEG) signals through machine learning methods, several challenges surface. The non-stationarity and intricacy of EEG data present obstacles in understanding neuronal ac- tivities and achieving precise classification. To address these challenges, this study proposes variational mode decomposition (VMD) of EEG before feature extraction along with machine learning models. By decomposing preprocessed EEG signals into specified modes using VMD, features such as sample entropy, spectral entropy, kurtosis, and skewness are extracted across these modes. The study compares the perfor- mance of the features extracted from VMD-based approach with the frequency band-based approach and also the approach with features extracted from raw-EEG. The classification process involves binary classi- fication between unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), as well as multi-class classification (coma vs. UWS vs. MCS). Kruskal-Wallis test was applied to determine the statistical significance of the features and features with a significance of p ＜0.05 were chosen for a second round of classification experiments.”
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa

    Mansoura University Reports Findings in Artificial Intelligence (Dis- eases diagnosis based on artificial intelligence and ensemble classi- fication)

    66-66页
    查看更多>>摘要：2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligence is the subject of a report. According to news reporting out of Mansoura, Egypt, by NewsRx editors, research stated, “In recent years, Computer Aided Diagnosis (CAD) has become an important research area that attracted a lot of researchers. In medical diagnostic systems, several attempts have been made to build and enhance CAD applications to avoid errors that can cause dangerously misleading medical treatments.” Our news journalists obtained a quote from the research from Mansoura University, “The most exciting opportunity for promoting the performance of CAD system can be accomplished by integrating Artificial Intelligence (AI) in medicine. This allows the effective automation of traditional manual workflow, which is slow, inaccurate and affected by human errors. This paper aims to provide a complete Computer Aided Disease Diagnosis (CAD) strategy based on Machine Learning (ML) techniques that can help clinicians to make better medical decisions. The proposed CAD consists of three main sequential phases, namely; (i) Outlier Rejection Phase (ORP), (ⅱ) Feature Selection Phase (FSP), and (ⅲ) Classification Phase (CP). ORP is implemented to reject outliers using new Outlier Rejection Technique (ORT) that contains two sequential stages called Fast Outlier Rejection (FOR) and Accurate Outlier Rejection (AOR). The most informative features are selected through FSP using Hybrid Selection Technique (HST). HST includes two main stages called Quick Selection Stage (QS) using fisher score as a filter method and Precise Selection Stage (PS) using a Hybrid Bio-inspired Optimization (HBO) technique as a wrapper method. Finally, actual diagnose takes place through CP, which relies on Ensemble Classification Technique (ECT). The proposed CAD has been tested experimentally against recent disease diagnostic strategies using two different datasets in which the first contains several diseases, while the second includes data for Covid- 19 patients only. Experimental results have proven the high efficiency of the proposed CAD in terms of accuracy, error, precision, and recall compared with other competitors. Additionally, CAD strategy provides the best Wilcoxon signed rank test and Friedman test measurements against other strategies according to both datasets.”
      原文链接:
    • NETL
    • NSTL

    Data on Pattern Recognition and Artificial Intelligence Discussed by Researchers at University of Sriwijaya (Denoised Non-local Means With Bddu-net Architecture for Robust Retinal Blood Vessel Seg- mentation)

    67-67页
    查看更多>>摘要：2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning - Pattern Recognition and Artificial Intelligence have been presented. According to news reporting originating in Palembang, Indonesia, by NewsRx journalists, research stated, “Retinal blood vessels can be obtained by image segmentation. This study proposes combining image enhancement and segmentation to obtain retinal blood vessels.” The news reporters obtained a quote from the research from the University of Sriwijaya, “The image enhancement stages use CLAHE and Denoised Non-Local Means to increase contrast and reduce noise on the original image, and Bottom-Hat (BTH) filtering is used to lighten dark features in the image so the features become lighter and darken the bright features in the image. Bottom Hat is applied to make the features of the blood vessels in the retinal image more visible. The segmentation architecture proposes BDDU-Net architecture which combines U-Net in the encoder part, DenseNet in the bridge part, and Bi-ConvLSTM in the decoder part. Image enhancement performance results are PSNR and SSIM. The PSNR is more than 40 dB on both the DRIVE and STARE datasets. The SSIM results are close to 1 on the DRIVE and STARE datasets. These results show that the image enhancement stages in the proposed method can enhance the quality of the original image. The segmentation performance results of BDDU- Net architecture are measured based on accuracy, sensitivity, specificity, IoU, and F1-Score. The DRIVE dataset obtained 95.578% for accuracy, 85.75% for sensitivity, 96.75% for specificity, 67.407% for IoU, and 80.53% for F1-Score. The STARE dataset obtained 97.63% for accuracy, 84.33% for sensitivity, 98.66% for specificity, 75.67% for IoU, and 86.15% for F1-Score.”
      原文链接:
    • NETL
    • NSTL
    • World Scientific