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    Study Findings on Machine Learning Are Outlined in Reports from School of Advanc ed Technology (Comparison of the Performance of Different Machine Learning Metho ds in Predicting VIX Volatility)

    119-119页
    查看更多>>摘要：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 originating from Suzhou, People’s Republic of China, by NewsRx correspondents, research stated, “As a matter of fact, index volatility has always been one of the key indicators of the state of an index a nd a reflection of investor confidence and expectations in the market.” Our news reporters obtained a quote from the research from School of Advanced Te chnology: “Among various indicators, the VIX, which is also known as the ‘Panic Index’, has always been viewed by the market as a barometer of the state of the economy. With this in mind, the purpose of this study is to investigate the proc ess of Random Forest, Support Vector Regression as well as XGBoost in predicting VIX volatility and to evaluate their performance. Based on the evaluations, exp eriments in this study show that XGBoost performs optimally for smaller, low-dim ensional time series data.”
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    New Machine Learning Study Findings Recently Were Published by a Researcher at S chool of Mechanical and Automotive Engineering (Application of Decision Tree and Machine Learning in New Energy Vehicle Maintenance Decision Making)

    120-120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 ar tificial intelligence. According to news originating from Anhui, People’s Republ ic of China, by NewsRx editors, the research stated, “Several incidents of elect ric vehicle combustion across various regions in China have brought the safety c oncerns associated with new energy vehicles into sharp focus within public disco urse.” The news correspondents obtained a quote from the research from School of Mechan ical and Automotive Engineering: “Addressing these concerns, this paper explores maintenance decision-making for new energy vehicles through the application of decision trees and machine learning techniques. Initially, the study analyzes ho w decision trees and machine learning are employed in crafting maintenance decis ions for these vehicles. It involves collecting data through internet searches, followed by statistical analyses and preprocessing to set the groundwork for fur ther inquiry. Furthermore, the research advances by developing and refining deci sion tree models, which facilitate the integration of fault diagnosis and mainte nance decision-making processes for new energy vehicles. This effort culminates in the establishment of a robust decision tree model specifically designed for t he maintenance of new energy vehicles, which is subsequently evaluated through a detailed case study.”
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    New Findings from Rochester Institute of Technology in the Area of Machine Learn ing Described (Machine Learning-Based Fatigue Level Prediction for Exoskeleton-A ssisted Trunk Flexion Tasks Using Wearable Sensors)

    121-121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 ar tificial intelligence. According to news reporting from Rochester, New York, by NewsRx journalists, research stated, “Monitoring physical demands during task ex ecution with exoskeletons can be instrumental in understanding their suitability for industrial tasks.” The news correspondents obtained a quote from the research from Rochester Instit ute of Technology: “This study aimed at developing a fatigue level prediction mo del for Back-Support Industrial Exoskeletons (BSIEs) using wearable sensors. Fou rteen participants performed a set of intermittent trunk-flexion task cycles con sisting of static, sustained, and dynamic activities, until they reached medium- high fatigue levels, while wearing BSIEs. Three classification algorithms, Suppo rt Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB), were implemented to predict perceived fatigue level in the back and leg regions using features f rom four wearable wireless Electromyography (EMG) sensors with integrated Inerti al Measurement Units (IMUs). We examined the best grouping and sensor combinatio ns by comparing prediction performance. The findings showed best performance in binary classification of leg and back fatigue with 95% (2 EMG + IM U sensors) and 82% (single IMU sensor) accuracy, respectively.”
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    Southeast University Reports Findings in Epilepsy (The Role of EEG microstates i n predicting oxcarbazepine treatment outcomes in patients with newly-diagnosed f ocal epilepsy)

    122-123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 Central Nervous System Diseases and Conditions - Epilepsy is the subject of a report. According to new s reporting from Jiangsu, People’s Republic of China, by NewsRx journalists, res earch stated, “Microstates represent the global and topographical distribution o f electrical brain activity from scalp-recorded EEG. This study aims to explore EEG microstates of patients with focal epilepsy prior to medication, and employ extracted microstate metrics for predicting treatment outcomes with Oxcarbazepin e monotherapy.” The news correspondents obtained a quote from the research from Southeast Univer sity, “This study involved 25 newly-diagnosed focal epilepsy patients (13 female s), aged 12 to 68, with various etiologies. Patients were categorized into Non-S eizure-Free (NSF) and Seizure-Free (SF) groups according to their first follow-u p outcomes. From pre-medication EEGs, four representative microstates were ident ified by using clustering. The temporal parameters and transition probabilities of microstates were extracted and analyzed to discern group differences. With ge nerating sample method, Support Vector Machine (SVM), Logistic Regression (LR), and Naive Bayes (NB) classifiers were employed for predicting treatment outcomes . In the NSF group, Microstate 1 (MS1) exhibited a significantly higher duration (mean±std. = 0.092±0.008 vs. 0.085±0.008, p = 0.047), occurrence (mean±std. = 2 .587±0.334 vs. 2.260±0.278, p = 0.014), and coverage (mean±std. = 0.240±0.046 vs . 0.194±0.040, p = 0.014) compared to the SF group. Additionally, the transition probabilities from Microstate 2 (MS2) and Microstate 3 (MS3) to MS1 were increa sed. In MS2, the NSF group displayed a stronger correlation (mean±std. = 0.618±0 .025 vs. 0.571±0.034, p<0.001) and a higher global explain ed variance (mean±std. = 0.083±0.035 vs. 0.055±0.023, p = 0.027) than the SF gro up. Conversely, Microstate 4 (MS4) in the SF group demonstrated significantly gr eater coverage (mean±std. = 0.388±0.074 vs. 0.334±0.052, p = 0.046) and more fre quent transitions from MS2 to MS4, indicating a distinct pattern. Temporal param eters contribute major predictive role in predicting treatment outcomes of Oxcar bazepine, with area under curves (AUCs) of 0.95, 0.70, and 0.86, achieved by LR, NB and SVM, respectively.”
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    Northwestern University in Qatar Researcher Advances Knowledge in Artificial Int elligence ['Technopian but lonely investors?': Comparison bet ween investors and non-investors of blockchain technologies, cryptocurrencies, a nd non-fungible tokens ...]

    123-123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 new report. According to news originating from Doha, Qatar , by NewsRx editors, the research stated, “Drawing from the literature on decent ralized finance (DeFi) and third-level digital (in)equality as well as from the emerging literature on artificial intelligence (AI), this study examined differe nces between investors and non-investors of blockchain technologies, cryptocurre ncies, and non-fungible tokens (NFTs).” Our news correspondents obtained a quote from the research from Northwestern Uni versity in Qatar: “Data from a cross-sectional survey among users of AI technolo gies (N = 502) indicate that investors of blockchain technologies, cryptocurrenc ies, and NFTs show higher blockchain transparency perception, greater trust in c ryptocurrencies, and higher perceived asset value of NFTs than non-investors. Re sults also indicate that investors show greater technopian views on AI, higher A I awareness, and higher thirdlevel AI equality than non-investors. Furthermore, the mediation effect of trust in cryptocurrencies on the relationship between i nvestment status and perceived asset value of NFTs and the moderation effect of AI awareness on the relationship between investment status and third-level AI eq uality were revealed. Differential psychographics of investors versus non-invest ors were also found such that investors feel lonelier, experience higher existen tial isolation, and indicate higher need to belong than non-investors.”
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    University of Texas MD Anderson Cancer Center Reports Findings in Personalized M edicine (Personalized Composite Dosimetric Score- Based Machine Learning Model of Severe Radiation-Induced Lymphopenia among Esophageal Cancer Patients)

    124-125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 Drugs and Therapies - Personalized Medicine is the subject of a report. According to news originating from Houston, Texas, by NewsRx correspondents, research stated, “Radiation-induc ed lymphopenia (RIL) is common among patients undergoing radiotherapy (RT), and severe RIL has been linked with adverse outcomes. The severity and risk of RIL c an be predicted from baseline clinical characteristics and dosimetric parameters .” Our news journalists obtained a quote from the research from the University of T exas MD Anderson Cancer Center, “However, dose-volume (DV) indices are highly co rrelated with one another and are only weakly associated with RIL. Here we intro duce the novel concept of ‘composite dosimetric score’ (CDS) as the index that c ondenses the dose distribution in immune tissues of interest to study the dosime tric dependence of RIL. We derived an improved multivariate classification schem e for risk of grade 4 (G4) RIL, based on this novel RT dosimetric feature, for p atients receiving chemoRT for esophageal cancer. DV indices were extracted for 7 34 patients who received chemoRT for biopsy-proven esophageal cancer. Non-negati ve matrix factorization was used to project the DV indices of lung, heart, and s pleen into a single CDS; XGBoost was employed to explore significant interaction s among predictors; and logistic regression was applied to combine the resultant CDS along with baseline clinical factors and interaction terms to facilitate in dividualized prediction of immunotoxicity. Five-fold cross-validation was applie d to evaluate the model performance. The CDS for selected immune organs at risk (OARs, i.e., heart, lung, and spleen) (1.791, 95 CI [1.350,2. 377]) was a statistically significant risk determinant for G4 RIL. Pearson correlation coefficients for CDS vs. G4RIL risk for individual immu ne OARs were greater than any single DV indices. Personalized prediction of G4RI L based on CDS and 4 clinical risk factors yielded an area under the curve value of 0.78. Interaction between age and CDS revealed that G4RIL risk increased mor e sharply with increasing CDS for patients 65. Risk of immunotoxicity for patien ts undergoing chemoRT for esophageal cancer can be predicted by CDS. The CDS con cept can be extended to immunotoxicity in other cancer types and in dose-respons e models currently based on DV indices.”
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    Researcher from School of Law Provides Details of New Studies and Findings in th e Area of Artificial Intelligence (A Study of Teaching Strategies Optimized with the Integration of Artificial Intelligence Technologies)

    125-125页
    查看更多>>摘要：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 out of Guangdong, People’s Republ ic of China, by NewsRx editors, research stated, “The integration of artificial intelligence and education is a general trend, and it has become necessary to ut ilize artificial intelligence for teaching.” The news reporters obtained a quote from the research from School of Law: “This study takes the application of artificial intelligence technology in teaching st rategy as the starting point, constructs an intelligent classroom model for reco gnizing students’ faces and emotions, and better understands their learning stat e. The method first carries out emotion recognition based on a support vector ma chine, then realizes the fusion of the decision layer through a posteriori proba bility, completes the construction of a multimodal emotion recognition model, an d finally applies the PAD scale to quantify the emotion and analyze the emotion and state of the sample students in various stages of the classroom. The artific ial intelligence teaching strategy that integrates multimodal emotion recognitio n is constructed and its application effectiveness is examined. More than 60% of the students were in a calm state in the 10 minutes before and after the clas s, and 78% of the students were in a positive learning mood in the 25 minutes of the class, so the multimodal emotion recognition model has a good effect of recognizing students’ emotions. After the teaching practice is carrie d out, the student’s performance in the experimental class is 12.29% higher than that in the control class.”
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    Research Study Findings from Edith Cowan University Update Understanding of Arti ficial Intelligence (LLM potentiality and awareness: a position paper from the p erspective of trustworthy and responsible AI modeling)

    126-126页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on artificial in telligence have been published. According to news originating from Edith Cowan U niversity by NewsRx correspondents, research stated, “Large language models (LLM s) are an exciting breakthrough in the rapidly growing field of artificial intel ligence (AI), offering unparalleled potential in a variety of application domain s such as finance, business, healthcare, cybersecurity, and so on.” Our news reporters obtained a quote from the research from Edith Cowan Universit y: “However, concerns regarding their trustworthiness and ethical implications h ave become increasingly prominent as these models are considered black-box and c ontinue to progress. This position paper explores the potentiality of LLM from d iverse perspectives as well as the associated risk factors with awareness. Towar ds this, we highlight not only the technical challenges but also the ethical imp lications and societal impacts associated with LLM deployment emphasizing fairne ss, transparency, explainability, trust and accountability. We conclude this pap er by summarizing potential research scopes with direction.”
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    Asian Institute of Technology Researchers Broaden Understanding of Machine Learn ing (Integrating Remote Sensing and Ground- Based Data for Enhanced Spatial-Tempo ral Analysis of Heatwaves: A Machine Learning Approach)

    126-127页
    查看更多>>摘要：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 originating from Pathum Thani, Thailand, by NewsRx editors, the research stated, “In response to the urgent glo bal threat posed by human-induced extreme climate hazards, heatwaves are still s ystematically under-reported and under-researched in Thailand. This region is co nfronting a significant rise in heat-related mortality, which has resulted in hu ndreds of deaths, underscoring a pressing issue that needs to be addressed.”
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    Investigators from University of London Zero in on Machine Learning (Factor Corr elation and the Cross Section of Asset Returns: a Correlation-robust Machine Lea rning Approach)

    127-128页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news originating from London, United Kingdo m, by NewsRx editors, the research stated, “This paper inves-tigates high -dimens ional factor models for cross-sectional asset returns, with a specific focus on robust estimation in the presence of (highly) correlated factors. Factor correla tions can significantly compromise the robustness and credibility of commonly em ployed analytical methods.” Funders for this research include Pump Priming Fund, City University of London.
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