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    Radiation Oncology Unit Reports Findings in Artificial Intelligence (Efficacy of stereotactic body radiotherapy and response prediction using artificial intelligence in oligometastatic gynaecologic cancer)

    39-40页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting originating in Campobasso, Italy, by NewsRx journalists, research stated, “We present a large real-world multicentric dataset of ovarian, uterine and cervical oligometastatic lesions treated with SBRT exploring efficacy and clinical outcomes. In addition, an exploratory machine learning analysis was performed.” The news reporters obtained a quote from the research from Radiation Oncology Unit, “A pooled analysis of gynecological oligometastases in terms of efficacy and clinical outcomes as well an exploratory machine learning model to predict the CR to SBRT were carried out. The CR rate following radiotherapy (RT) was the study main endpoint. The secondary endpoints included the 2-year actuarial LC, DMFS, PFS, and OS. 501 patients from 21 radiation oncology institutions with 846 gynecological metastases were analyzed, mainly ovarian (53.1%) and uterine metastases(32.1%).Multiple fraction radiotherapy was used in 762 metastases(90.1%).The most frequent schedule was 24 Gy in 3 fractions(13.4%). CR was observed in 538(63.7%) lesions. The Machine learning analysis showed a poor ability to find covariates strong enough to predict CR in the whole series. Analyzing them separately, in uterine cancer, if RT dose 78.3Gy, the CR probability was 75.4%; if volume was ＜13.7 cc, the CR probability became 85.1%. In ovarian cancer, if the lesion was a lymph node, the CR probability was 71.4%; if volume was ＜17 cc, the CR probability rose to 78.4%. No covariate predicted the CR for cervical lesions. The overall 2-year actuarial LC was 79.2%, however it was 91.5% for CR and 52.5% for not CR lesions(p ＜0.001). The overall 2-year DMFS, PFS and OS rate were 27.3%, 24.8% and 71.0%, with significant differences between CR and not CR. CR was substantially associated to patient outcomes in our series of gynecological cancer oligometastatic lesions.” According to the news reporters, the research concluded: “The ability to predict a CR through artificial intelligence could also drive treatment choices in the context of personalized oncology.”
      原文链接:
    • NETL
    • NSTL

    Findings from Indian Institute of Technology (IIT) Gandhinagar Reveals New Findings on Machine Learning (Improving the Interpretability and Predictive Power of Hydrological Models: Applications for Daily Streamflow In Managed and Unmanaged ...)

    40-40页
    查看更多>>摘要：Current study results on Machine Learning have been published. According to news reporting originating from Gujarat, India, by NewsRx correspondents, research stated, “In recent years, Machine Learning (ML) techniques have gained the attention of the hydrological community for their better predictive skills. Specifically, ML models are widely applied for streamflow predictions.” Financial support for this research came from Scheme for Transformational and Advanced Research in Sciences of the Ministry of Education. Our news editors obtained a quote from the research from the Indian Institute of Technology (IIT) Gandhinagar, “However, limited interpretability in the ML models indicates space for improvement. Leveraging domain knowledge from conceptual models can aid in overcoming interpretability issues in ML models. Here, we have developed the Physics Informed Machine Learning (PIML) model at daily timestep, which accounts for memory in the hydrological processes and provides an interpretable model structure. We demonstrated three model cases, including lumped model and semi-distributed model structures with and without reservoir. We evaluate the first two model structures on three catchments in India, and the applicability of the third model structure is shown on the two United States catchments. Also, we compared the result of the PIML model with the conceptual model (SIMHYD), which is used as the parent model to derive contextual cues. Our results show that the PIML model outperforms simple ML model in target variable (streamflow) prediction and SIMHYD model in predicting target variable and intermediate variables (for example, evapotranspiration, reservoir storage) while being mindful of physical constraints. The water balance and runoff coefficient analysis reveals that the PIML model provides physically consistent outputs. The PIML modeling approach can make a conceptual model more modular such that it can be applied irrespective of the region for which it is developed.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Studies from York University in the Area of Machine Learning Published (Biooxidation of refractory sulfide-bearing ore using feroplasma acidophilum: Efficiency assessment and machine learning based prediction)

    40-41页
    查看更多>>摘要：A new study on artificial intelligence is now available. According to news reporting out of Toronto, Canada, by NewsRx editors, research stated, “The adhesive properties of microorganisms on the surface of minerals play an important role in the biooxidation efficiency of sulfidic refractory gold ores.” Financial supporters for this research include York University; Mitacs; Natural Sciences And Engineering Research Council of Canada; National Research Council Canada. Our news editors obtained a quote from the research from York University: “In this research, the simultaneous effects of monosaccharides, ore content, pyrite content, and time on the activity and growth rate of Ferroplasma acidiphilum-from native Acid Mine Drainage (AMD)- was investigated during biooxidization alongside finding the best machine learning approach for the prediction of process efficiency using the independent variables. The results revealed that the optimum condition for reaching the highest pyrite dissolution ( 75 %) is 15 days of operating time, pyrite content of 7.2 wt%, and ore content of 5 wt%, pH of 1.47, and D-+-sucrose, D-+-galactose, and D-+-fructose concentrations of 0.52, 0.09, and 0.12 wt%, respectively. The results of the model comparison indicated that the Artificial Neural Network (ANN) model was able to predict the experimental results of this study with acceptable accuracy and better than Genetic Programming (GP) and Polynomial Regression informed by Response Surface Methodology (PR-RSM) from experimental data.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Report Summarizes Machine Learning Study Findings from University of Murcia (Studying Drowsiness Detection Performance While Driving Through Scalable Machine Learning Models Using Electroencephalography)

    41-42页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting out of Murcia, Spain, by NewsRx editors, research stated, “Driver drowsiness is a significant concern and one of the leading causes of traffic accidents. Advances in cognitive neuroscience and computer science have enabled the detection of drivers’ drowsiness using Brain-Computer Interfaces (BCIs) and Machine Learning (ML).” Financial support for this research came from Fundacin Sneca. Our news journalists obtained a quote from the research from the University of Murcia, “However, the literature lacks a comprehensive evaluation of drowsiness detection performance using a heterogeneous set of ML algorithms, being also necessary to study the performance of scalable ML models suitable for groups of subjects. To address these limitations, this work presents an intelligent framework employing BCIs and features based on electroencephalography for detecting drowsiness in driving scenarios. The SEED-VIG dataset is used to evaluate the best-performing models for individual subjects and groups. Random Forest (RF) outperformed other models used in the literature, such as Support Vector Machine (SVM), with a 78% f1-score for individual models. Regarding scalable models, RF reached a 79% f1-score, demonstrating the effectiveness of these approaches. This publication highlights the relevance of exploring a diverse set of ML algorithms and scalable approaches suitable for groups of subjects to improve drowsiness detection systems and ultimately reduce the number of accidents caused by driver fatigue. The lessons learned from this study show that not only SVM but also other models not sufficiently explored in the literature are relevant for drowsiness detection. Additionally, scalable approaches are effective in detecting drowsiness, even when new subjects are evaluated.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Investigators at PSG College of Technology Detail Findings in Machine Learning (Prediction of Compressive Strength and Tensile Strain of Engineered Cementitious Composite Using Machine Learning)

    42-43页
    查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting out of Coimbatore, India, by NewsRx editors, research stated, “This research extensively used different progressive machine learning (ML) techniques to predict the compressive strength (CS) and tensile strain (TSt) of engineered cementitious composites (ECC) with 14 input variables and six algorithms. Specifically, random forest (RF), support vector machine, extreme gradient boosting (XGBoost), light gradient boosting machine, categorical gradient boosting (CatBoost), and natural gradient boosting techniques were used in the present study, to understand mechanical properties of ECC meanwhile these properties are crucial for design codes and developing new reliable models for mixtures.” Our news journalists obtained a quote from the research from the PSG College of Technology, “The discrepancy between the ML technique and specific ECC expected outputs is novel in this study and will aid researchers in better understanding of ECC features. To estimate the CS and TSt of the ECC, 2535 and 1469 input data points, respectively, were incorporated based on the material ratio, W/B, and different properties of the fibers. In addition, hyperparameter optimization techniques have also been used in ML to improve over fitting and make the model more accurate and robust. Moreover, an error analysis was highlighted between the actual and predicted CS and TSt of the ECC with each ML technique. Also, the significance and influence of the variable inputs that affect the CS and TSt were explained using the Shapley additive explanation (SHAP) approach. Among all approaches, CatBoost and XGBoost predicted the CS and TSt of ECC with greater accuracy than other techniques in terms of the coefficient of determination (R2), mean square error, mean absolute error, root mean square error, and symmetric mean absolute percentage error. The training and testing R2 values of CatBoost and XGBoost for predicting the CS and TSt of ECC were 0.96, 0.89, 0.89, and 0.76, respectively.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Civil Aviation Flight University of China Researchers Update Knowledge of Support Vector Machines (Modeling and detection of lowaltitude flight conflict network based on SVM)

    43-43页
    查看更多>>摘要：Investigators discuss new findings in . According to news reporting originating from Sichuan, People’s Republic of China, by NewsRx correspondents, research stated, “With the continuous increase in low altitude flight density, the issue of low altitude navigation safety has attracted widespread attention.” Our news editors obtained a quote from the research from Civil Aviation Flight University of China: “Due to the complex low altitude environment, low altitude flight is more susceptible to ground obstacles and weather effects than commercial aviation. In order to ensure the flight safety of helicopters in low altitude airspace, this paper proposes an improved support vector machine based flight conflict detection model. By modeling the conflict network in low altitude flight areas and utilizing Support Vector Machine (SVM) classification features, the safety discrimination of low altitude flight was achieved, ultimately achieving the safety of aircraft in low altitude flight. This article adopts a protected area model that considers the shape of the aircraft as a conflict zone. In order to reduce the complexity of the conflict detection model, an improved ID3 decision tree algorithm and random forest are used to reduce the complexity of the classifier. The study solved the saturation problem of S-type functions in conflict detection models by using more sensitive functions for probability mapping.”
      原文链接:
    • NETL
    • NSTL

    University of Malaya Reports Findings in HIV/AIDS (Testing the Feasibility and Acceptability of Using an Artificial Intelligence Chatbot to Promote HIV Testing and Pre-Exposure Prophylaxis in Malaysia: Mixed Methods Study)

    44-45页
    查看更多>>摘要：New research on Immune System Diseases and Conditions - HIV/AIDS is the subject of a report. According to news reporting originating from Kuala Lumpur, Malaysia, by NewsRx correspondents, research stated, “The HIV epidemic continues to grow fastest among men who have sex with men (MSM) in Malaysia in the presence of stigma and discrimination. Engaging MSM on the internet using chatbots supported through artificial intelligence (AI) can potentially help HIV prevention efforts.” Our news editors obtained a quote from the research from the University of Malaya, “We previously identified the benefits, limitations, and preferred features of HIV prevention AI chatbots and developed an AI chatbot prototype that is now tested for feasibility and acceptability. This study aims to test the feasibility and acceptability of an AI chatbot in promoting the uptake of HIV testing and pre-exposure prophylaxis (PrEP) in MSM. We conducted beta testing with 14 MSM from February to April 2022 using Zoom (Zoom Video Communications, Inc). Beta testing involved 3 steps: a 45-minute human-chatbot interaction using the think-aloud method, a 35-minute semistructured interview, and a 10-minute web-based survey. The first 2 steps were recorded, transcribed verbatim, and analyzed using the Unified Theory of Acceptance and Use of Technology. Emerging themes from the qualitative data were mapped on the 4 domains of the Unified Theory of Acceptance and Use of Technology: performance expectancy, effort expectancy, facilitating conditions, and social influence. Most participants (13/14, 93%) perceived the chatbot to be useful because it provided comprehensive information on HIV testing and PrEP (performance expectancy). All participants indicated that the chatbot was easy to use because of its simple, straightforward design and quick, friendly responses (effort expectancy). Moreover, 93% (13/14) of the participants rated the overall chatbot quality as high, and all participants perceived the chatbot as a helpful tool and would refer it to others. Approximately 79% (11/14) of the participants agreed they would continue using the chatbot. They suggested adding a local language (ie, Bahasa Malaysia) to customize the chatbot to the Malaysian context (facilitating condition) and suggested that the chatbot should also incorporate more information on mental health, HIV risk assessment, and consequences of HIV. In terms of social influence, all participants perceived the chatbot as helpful in avoiding stigma-inducing interactions and thus could increase the frequency of HIV testing and PrEP uptake among MSM. The current AI chatbot is feasible and acceptable to promote the uptake of HIV testing and PrEP.”
      原文链接:
    • NETL
    • NSTL
    • Jmir Publications, Inc

    North China University of Water Resources and Electric Power Reports Findings in Machine Learning (Hazards and influence factors of arsenic in the upper pleistocene aquifer, Hetao region, using machine learning modeling)

    45-46页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Zhengzhou, People’s Republic of China, by NewsRx correspondents, research stated, “The Hetao region is one of the regions with the most serious problem of the greatest measured arsenic concentrations in China. The enrichment of arsenic in groundwater may poses a great risk to the health of local residents.” Our news editors obtained a quote from the research from the North China University of Water Resources and Electric Power, “A comprehensive understanding of the groundwater quality, spatial distribution characteristics and hazard of the high arsenic in groundwater is indispensable for the sustainable utilization of groundwater resources and resident health. This study selected six environmental factors, climate, human activity, sedimentary environment, hydrogeology, soil, and others, as the independent input variables to the model, compared three machine learning algorithms (support vector machine, extreme gradient boosting, and random forest), and mapped unsafe arsenic to estimate the population that may be exposed to unhealthy conditions in the Hetao region. The results show that nearly half the number of the 605 sampling wells for arsenic exceeded the WHO provisional guide value for drinking water, the water chemistry of groundwater are mainly Na-HCO-Cl or Na-Mg-HCO-Cl type water, and the groundwater with excessive arsenic concentration is mainly concentrated in the ancient stream channel influence zone and the Yellow River crevasse splay. The results of factor importance explanation revealed that the sedimentary environment was the key factor affecting the primary high arsenic groundwater concentration, followed by climate and human activities. The random forest algorithm produced the probability distribution of high arsenic groundwater that is consistent with the observed results. The estimated area of groundwater with excessive arsenic reached 38.81 %.”
      原文链接:
    • NETL
    • NSTL

    Florida Polytechnic University Researchers Publish New Data on Machine Learning [Benchmarking Automated Machine Learning (AutoML) Frameworks for Object Detection]

    46-47页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news originating from Lakeland, Florida, by NewsRx correspondents, research stated, “Automated Machine Learning (AutoML) is a subdomain of machine learning that seeks to expand the usability of traditional machine learning methods to non-expert users by automating various tasks which normally require manual configuration. Prior benchmarking studies on AutoML systems-whose aim is to compare and evaluate their capabilities-have mostly focused on tabular or structured data.” Our news journalists obtained a quote from the research from Florida Polytechnic University: “In this study, we evaluate AutoML systems on the task of object detection by curating three commonly used object detection datasets (Open Images V7, Microsoft COCO 2017, and Pascal VOC2012) in order to benchmark three different AutoML frameworks-namely, Google’s Vertex AI, NVIDIA’s TAO, and AutoGluon. We reduced the datasets to only include images with a single object instance in order to understand the effect of class imbalance, as well as dataset and object size. We used the metrics of the average precision (AP) and mean average precision (mAP). Solely in terms of accuracy, our results indicate AutoGluon as the best-performing framework, with a mAP of 0.8901, 0.8972, and 0.8644 for the Pascal VOC2012, COCO 2017, and Open Images V7 datasets, respectively. NVIDIA TAO achieved a mAP of 0.8254, 0.8165, and 0.7754 for those same datasets, while Google’s VertexAI scored 0.855, 0.793, and 0.761. We found the dataset size had an inverse relationship to mAP across all the frameworks, and there was no relationship between class size or imbalance and accuracy. Furthermore, we discuss each framework’s relative benefits and drawbacks from the standpoint of ease of use. This study also points out the issues found as we examined the labels of a subset of each dataset.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    University of Diyala Researchers Describe Recent Advances in Robotics (Design and deployment of a voice activated for intelligent robot)

    47-47页
    查看更多>>摘要：New study results on robotics have been published. According to news reporting out of the University of Diyala by NewsRx editors, research stated, “The development of smart robots has been advancing rapidly in recent years.” Our news journalists obtained a quote from the research from University of Diyala: “One of the key areas of focus in this field has been the integration of voice command control, which enables users to interact with robots using natural language. By integrating voice recognition technology with advanced artificial intelligence algorithms, smart robots can perform a wide range of tasks, from simple household chores to complex industrial operations. Voice command control provides a more intuitive way to interact with smart robots, making them more accessible to users of all ages and skill levels. With voice recognition technology, users can easily give instructions to smart robots, which can then interpret and execute those commands. This approach has the potential to revolutionize the way we interact with machines, allowing us to leverage their advanced capabilities without requiring specialized training. Overall, smart robots based on voice command control represent a significant step forward in the development of intelligent machines.”
      原文链接:
    • NETL
    • NSTL