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    Study Findings on Support Vector Machines Reported by Researchers at Carleton Un iversity (Novel comparative methodology of hybrid support vector machine with me ta-heuristic algorithms to develop an integrated candlestick technical analysis model)

    20-21页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on . According t o news reporting originating from Ottawa, Canada, by NewsRx correspondents, rese arch stated, “Purpose - The proposed model has been aimed to predict stock marke t signals by designing an accurate model. In this sense, the stock market is ana lysed by the technical analysis of Japanese Candlestick, which is combined by th e following meta heuristic algorithms: support vector machine (SVM), meta-heuris tic algorithms, particle swarm optimization (PSO), imperialist competition algor ithm (ICA) and genetic algorithm (GA).” The news editors obtained a quote from the research from Carleton University: “D esign/ methodology/approach - In addition, among the developed algorithms, the mo st effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand w ith the high speed of running. In terms of the second model, SVM and ICA are exa mined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feat ure selection agent. Findings - Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA mo dels have correctly predicted more sell signals, and the SCM-PSO model has corre ctly predicted more buy signals. However, SVM-ICA has shown better performance t han other models considering executing the implemented models. Research limitati ons/implications - In this study, the authors to analyze the data the long lengt h of time between the years 2013-2021, makes the input data analysis challenging .”
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    Findings from First People’s Hospital Yields New Findings on Medulloblastoma (Mu ltiparametric Mri-based Interpretable Radiomics Machine Learning Model Different iates Medulloblastoma and Ependymoma In Children: a Two-center Study)

    21-22页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on Oncology - Medulloblasto ma have been published. According to news originating from Xinjiang, People’s Re public of China, by NewsRx correspondents, research stated, “Medulloblastoma (MB ) and Ependymoma (EM) in children, share similarities in age group, tumor locati on, and clinical presentation. Distinguishing between them through clinical diag nosis is challenging.” Financial supporters for this research include National Key R&D Pro gram of China, Tianshan Innovation Team Program of Autonomous Region, Tianshan T alent Training Program: the Youth Support Talent Project.
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    Study Findings from Shanghai Ocean University Provide New Insights into Machine Learning (Machine Learning-Based Algorithm for SAR Wave Parameters Retrieval Dur ing a Tropical Cyclone)

    22-23页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New study results on artificial intelligence have been published. According to news reporting out of Shanghai, People’s Republic of China, by NewsRx editors, research stated, “The major objective of our resear ch is to retrieve wave parameters from synthetic aperture radar (SAR) images dur ing a tropical cyclone (TC) based on a machine learning method. In this study, m ore than 2000 Sentinel-1 images obtained in interferometric-wide and extra wide mode are collected during 200 TCs, which are collocated with hindcasted waves by a third-generation numeric model, namely WAVEWATCH-III (WW3).” Financial supporters for this research include National Natural Science Foundati on of China; Natural Science Foundation of Shanghai.
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    Soochow University Reports Findings in Bone Fractures (Fracture risk prediction in diabetes patients based on Lasso feature selection and Machine Learning)

    23-24页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Bone Diseases and Cond itions - Bone Fractures is the subject of a report. According to news reporting originating in Suzhou, People’s Republic of China, by NewsRx journalists, resear ch stated, “Fracture risk among individuals with diabetes poses significant clin ical challenges due to the multifaceted relationship between diabetes and bone h ealth. Diabetes not only affects bone density but also alters bone quality and s tructure, thereby increases the susceptibility to fractures.” The news reporters obtained a quote from the research from Soochow University, “ Given the rising prevalence of diabetes worldwide and its associated complicatio ns, accurate prediction of fracture risk in diabetic individuals has emerged as a pressing clinical need. This study aims to investigate the factors influencing fracture risk among diabetic patients. We propose a framework that combines Las so feature selection with eight classification algorithms. Initially, Lasso regr ession is employed to select 24 significant features. Subsequently, we utilize g rid search and 5-fold cross-validation to train and tune the selected classifica tion algorithms, including KNN, Naive Bayes, Decision Tree, Random Forest, AdaBo ost, XGBoost, Multi-layer Perceptron (MLP), and Support Vector Machine (SVM). Am ong models trained using these important features, Random Forest exhibits the hi ghest performance with a predictive accuracy of 93.87%. Comparative analysis across all features, important features, and remaining features demons trate the crucial role of features selected by Lasso regression in predicting fr acture risk among diabetic patients.”
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    Studies from Carnegie Mellon University Describe New Findings in Machine Learnin g (Investigating the Error Imbalance of Large-scale Machine Learning Potentials In Catalysis)

    24-25页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting from Pittsburgh, Pennsylvania, by NewsRx journalists, research stated, “Machine learning potentials (MLPs) have greatly a ccelerated atomistic simulations for material discovery. The Open Catalyst 2020 (OC20) dataset is one of the largest datasets for training MLPs for heterogeneou s catalysis.” Financial support for this research came from European Union’s Horizon Europe.
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    New Findings from Villanova University in Machine Learning Provides New Insights (Physics-informed Machine Learning for Modeling Multidimensional Dynamics)

    25-26页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting originating in Villanova, Pennsylvan ia, by NewsRx journalists, research stated, “This study presents a hybrid modeli ng approach that integrates physics and machine learning for modeling multi-dime nsional dynamics of a coupled nonlinear dynamical system. This approach leverage s principles from classical mechanics, such as the Euler-Lagrange and Hamiltonia n formalisms, to facilitate the process of learning from data.” Financial support for this research came from Office of Naval Research. The news reporters obtained a quote from the research from Villanova University, “The hybrid model incorporates single or multiple artificial neural networks wi thin a customized computational graph designed based on the physics of the probl em. The customization minimizes the potential of violating the underlying physic s and maximizes the efficiency of information flow within the model. The capabil ities of this approach are investigated for various multidimensional modeling sc enarios using different configurations of a coupled nonlinear dynamical system. It is demonstrated that, in addition to improving modeling criteria such as accu racy and consistency with physics, this approach provides additional modeling be nefits. The hybrid model implements a physics-based architecture, enabling the d irect alteration of both conservative and non-conservative components of the dyn amics.”
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    Study Findings on Robotics Are Outlined in Reports from University of Extremadur a (Guessing Human Intentions to Avoid Dangerous Situations in Caregiving Robots)

    26-27页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on robotics. Acc ording to news reporting from the University of Extremadura by NewsRx journalist s, research stated, “The integration of robots into social environments necessit ates their ability to interpret human intentions and anticipate potential outcom es accurately. This capability is particularly crucial for social robots designe d for human care, as they may encounter situations that pose significant risks t o individuals, such as undetected obstacles in their path.” The news editors obtained a quote from the research from University of Extremadu ra: “These hazards must be identified and mitigated promptly to ensure human saf ety. This paper delves into the artificial theory of mind (ATM) approach to infe rring and interpreting human intentions within human-robot interaction. We propo se a novel algorithm that detects potentially hazardous situations for humans an d selects appropriate robotic actions to eliminate these dangers in real time. O ur methodology employs a simulation-based approach to ATM, incorporating a “like -me” policy to assign intentions and actions to human subjects. This strategy en ables the robot to detect risks and act with a high success rate, even under tim e-constrained circumstances. The algorithm was seamlessly integrated into an exi sting robotics cognitive architecture, enhancing its social interaction and risk mitigation capabilities. To evaluate the robustness, precision, and real-time r esponsiveness of our implementation, we conducted a series of three experiments: (i) A fully simulated scenario to assess the algorithm’s performance in a contr olled environment; (ii) A human-in-the-loop hybrid configuration to test the sys tem’s adaptability to realtime human input; and (iii) A real-world scenario to validate the algorithm’s effectiveness in practical applications.”
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    Data from University of Virginia Broaden Understanding of Machine Learning (Pred icting Yield Strength and Plastic Elongation in Body- Centered Cubic High-Entropy Alloys)

    27-27页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news originating from Charlottesville, Virg inia, by NewsRx editors, the research stated, “We employ machine learning (ML) t o predict the yield stress and plastic strain of body-centered cubic (BCC) high- entropy alloys (HEAs) in the compression test.” Funders for this research include University of Virginia Department of Physics F ellowship. Our news journalists obtained a quote from the research from University of Virgi nia: “Our machine learning model leverages currently available databases of BCC and BCC+B2 entropy alloys, using feature engineering to capture electronic facto rs, atomic ordering from mixing enthalpy, and the D parameter related to stackin g fault energy. The model achieves low Root Mean Square Errors (RMSE). Utilizing Random Forest Regression (RFR) and Genetic Algorithms for feature selection, ou r model excels in both predictive accuracy and interpretability. Rigorous 10-fol d cross-validation ensures robust generalization.”
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    University Hospital Bern Reports Findings in Artificial Intelligence (Artificial intelligence derived large language model in decisionmaking process in uveitis )

    28-28页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Bern, Switzerland , by NewsRx journalists, research stated, “Uveitis is the ophthalmic subfield de aling with a broad range of intraocular inflammatory diseases. With the raising importance of LLM such as ChatGPT and their potential use in the medical field, this research explores the strengths and weaknesses of its applicability in the subfield of uveitis.” The news correspondents obtained a quote from the research from University Hospi tal Bern, “A series of highly clinically relevant questions were asked three con secutive times (attempts 1, 2 and 3) of the LLM regarding current uveitis cases. The answers were classified on whether they were accurate and sufficient, parti ally accurate and sufficient or inaccurate and insufficient. Statistical analysi s included descriptive analysis, normality distribution, non-parametric test and reliability tests. References were checked for their correctness in different m edical databases. The data showed non-normal distribution. Data between subgroup s (attempts 1, 2 and 3) was comparable (Kruskal-Wallis H test, p-value = 0.7338) . There was a moderate agreement between attempt 1 and attempt 2 (Cohen’s kappa, q = 0.5172) as well as between attempt 2 and attempt 3 (Cohen’s kappa, q = 0.49 13). There was a fair agreement between attempt 1 and attempt 3 (Cohen’s kappa, q = 0.3647). The average agreement was moderate (Cohen’s kappa, q = 0.4577). Bet ween the three attempts together, there was a moderate agreement (Fleiss’ kappa, q = 0.4534). A total of 52 references were generated by the LLM. 22 references (42.3%) were found to be accurate and correctly cited. Another 22 r eferences (42.3%) could not be located in any of the searched datab ases. The remaining 8 references (15.4%) were found to exist, but w ere either misinterpreted or incorrectly cited by the LLM. Our results demonstra te the significant potential of LLMs in uveitis. However, their implementation r equires rigorous training and comprehensive testing for specific medical tasks. We also found out that the references made by ChatGPT 4.o were in most cases inc orrect.”
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    Nigerian Institute of Medical Research Reports Findings in Hyperglycemia (Machin e Learning-Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals)

    29-29页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Nutritional and Metabo lic Diseases and Conditions - Hyperglycemia is the subject of a report. Accordin g to news reporting from Lagos, Nigeria, by NewsRx journalists, research stated, “Noncommunicable diseases continue to pose a substantial health challenge globa lly, with hyperglycemia serving as a prominent indicator of diabetes. This study employed machine learning algorithms to predict hyperglycemia in a cohort of in dividuals who were asymptomatic and unraveled crucial predictors contributing to early risk identification.” The news correspondents obtained a quote from the research from the Nigerian Ins titute of Medical Research, “This dataset included an extensive array of clinica l and demographic data obtained from 195 adults who were asymptomatic and residi ng in a suburban community in Nigeria. The study conducted a thorough comparison of multiple machine learning algorithms to ascertain the most effective model f or predicting hyperglycemia. Moreover, we explored feature importance to pinpoin t correlates of high blood glucose levels within the cohort. Elevated blood pres sure and prehypertension were recorded in 8 (4.1 %) and 18 (9.2% ) of the 195 participants, respectively. A total of 41 (21%) partic ipants presented with hypertension, of which 34 (83%) were female. However, sex adjustment showed that 34 of 118 (28.8%) female partic ipants and 7 of 77 (9%) male participants had hypertension. Age-bas ed analysis revealed an inverse relationship between normotension and age (r=-0. 88; P=.02). Conversely, hypertension increased with age (r=0.53; P=.27), peaking between 50-59 years. Of the 195 participants, isolated systolic hypertension an d isolated diastolic hypertension were recorded in 16 (8.2%) and 15 (7.7%) participants, respectively, with female participants record ing a higher prevalence of isolated systolic hypertension (11/16, 69% ) and male participants reporting a higher prevalence of isolated diastolic hype rtension (11/15, 73 %). Following class rebalancing, the random fore st classifier gave the best performance (accuracy score 0.89; receiver operating characteristic-area under the curve score 0.89; F1-score 0.89) of the 26 model classifiers. The feature selection model identified uric acid and age as importa nt variables associated with hyperglycemia. The random forest classifier identif ied significant clinical correlates associated with hyperglycemia, offering valu able insights for the early detection of diabetes and informing the design and d eployment of therapeutic interventions.”
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