查看更多>>摘要：As you scroll through any social media feed, you are likely to be prompted to follow or friend another person, expanding your personal network and contributing to the growth of the app itself. The person suggested to you is a result of link prediction: a widespread machine learning (ML) task that evaluates the links in a network - your friends and everyone else’s - and tries to predict what the next links will be. Beyond being the engine that drives social media expansion, link prediction is also used in a wide range of scientific research, such as predicting the interaction between genes and proteins, and is used by researchers as a benchmark for testing the performance of new ML algorithms. New research from UC Santa Cruz Professor of Computer Science and Engineering C. “Sesh” Seshadhri published in the journal Proceedings of the National Academy of Sciences establishes that the metric used to measure link prediction performance is missing crucial information, and link prediction tasks are performing significantly worse than popular literature indicates.
查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news originating from Busan, South Korea, by NewsRx correspondents, research stated, “Specific humidity (SH) which means the amount of water vapor in 1kg of air, is used as an indicator of energy exchange between the atmosphere and the Earth’s surface.” Financial supporters for this research include Bk21 Four Project of The School of Integrated Science For Sustainable Earth & Environmental Disaster Pukyong National University. Our news reporters obtained a quote from the research from Pukyong National University: “SH is typically computed using microwave satellites. However, the spatial resolution of data for microwave satellite is too low. To overcome this disadvantage, we introduced new methods that applied data collected by the Landsat-8 satellite with high spatial resolution (30 m), a meteorological model, and observation data for South Korea in 2016-2017 to 4 machine learning techniques to develop an optimized technique for computing SH. Among the 4 machine learning techniques, the random forest-based method had the highest accuracy, with a coefficient of determination ® of 0.98, Root Mean Square Error (RMSE) of 0.001, bias of 0, and Relative Root Mean Square Error (RRMSE) of 11.16%.”
查看更多>>摘要：With a brain the size of a pinhead, insects perform fantastic navigational feats. They avoid obstacles and move through small openings. How do they do this, with their limited brain power? Understanding the inner workings of an insect’s brain can help us in our search towards energy-efficient computing, physicist Elisabetta Chicca of the University of Groningen demonstrates with her most recent result: a robot that acts like an insect. It’s not easy to make use of the images that come in through your eyes, when deciding what your feet or wings should do. A key aspect here is the apparent motion of things as you move. ‘Like when you’re on a train’, Chicca explains. ‘The trees nearby appear to move faster than the houses far away. Insects use this information to infer how far away things are. This works well when moving in a straight line, but reality is not that simple. Moving in curves makes the problem too complex for insects. To keep things manageable for their limited brainpower, they adjust their behaviour: they fly in a straight line, make a turn, then make another straight line. Chicca explains: ‘What we learn from this is: if you don’t have enough resources, you can simplify the problem with your behaviour.’
查看更多>>摘要：Researchers detail new data in Artificial Intelligence. According to news originating from Jingzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Artificial intelligence (AI) is revolutionizing several businesses across the world, and its implementation in drilling engineering has enhanced the performance of oil and gas companies. This paper reviews and analyzes the successful application of AI techniques to predict wellbore instabilities during drilling operations.” Financial supporters for this research include Open Fund of Hubei Key Laboratory of Oil and Gas Drilling and Production Engineering (Yangtze University), Post-doctoral innovation research fund in Hubei Province, National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Yangtze University, “First, a summary of the implementation of AI for the prediction of loss circulation, pipe stuck, and mud window is highlighted. Then, the recent innovations and challenges of the AI adoption in major drilling companies is pre-sented. Finally, recommendations are provided to improve the integration of AI in the drilling industry.”
查看更多>>摘要：New research on Oncology - Liver Cancer is the subject of a report. According to news reporting originating from Guangdong, People’s Republic of China, by NewsRx correspondents, research stated, “To explore the potential of pre-therapy computed tomography (CT) parameters in predicting the treatment response to initial conventional TACE (cTACE) in intermediate-stage hepatocellular carcinoma (HCC) and develop an interpretable machine learning model. This retrospective study included 367 patients with intermediate-stage HCC who received cTACE as first-line therapy from three centers.” Our news editors obtained a quote from the research from the First Affiliated Hospital of Jinan University, “We measured the mean attenuation values of target lesions on multi-phase contrast-enhanced CT and further calculated three CT parameters, including arterial (AER), portal venous (PER), and arterial portal venous (APR) enhancement ratios. We used logistic regression analysis to select discriminative features and trained three machine learning models via 5-fold cross-validation. The performance in predicting treatment response was evaluated in terms of discrimination, calibration, and clinical utility. Afterward, a Shapley additive explanation (SHAP) algorithm was leveraged to interpret the outputs of the best-performing model. The mean diameter, ECOG performance status, and cirrhosis were the important clinical predictors of cTACE treatment response, by multiple logistic regression. Adding the CT parameters to clinical variables showed significant improvement in performance (net reclassification index, 0.318, P＜0.001). The Random Forest model (hereafter, RF-combined model) integrating CT parameters and clinical variables demonstrated the highest performance on external validation dataset (AUC of 0.800). The decision curve analysis illustrated the optimal clinical benefits of RF-combined model. This model could successfully stratify patients into responders and non-responders with distinct survival (P = 0.001).”
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news originating from Marseille, France, by NewsRx correspondents, research stated, “Spatiotemporal dispersionguided ablation is a tailored approach for patients in persistent atrial fibrillation (PsAF). The characterization of dispersion extent and distribution and its association with common clinical descriptors of PsAF patients has not been studied.” Our news journalists obtained a quote from the research from St. Joseph Hospital, “Artificial intelligence-adjudicated dispersion extent and distribution (AI-DED) was obtained with a machine/deep learning classifier (VX1 Software, Volta Medical) in PsAF patients undergoing ablation. The purpose of this study was to test the hypothesis that AI-DED is unique to each patient and independent of common procedural and clinical parameters. In a subanalysis of the Ev-AIFib study (NCT03434964), spatiotemporal dispersion maps were built with VX1 software in 78 consecutive persistent and long-standing PsAF patients. AI-DED was quantified using 2 distinct approaches (visual regional characterization or automated global quantification of AI-DED). AI-DED paired-subregion Euclidean distance measurements between 78 patients (average distance 5.07 ± 0.60; min 2.23; max 9.75) demonstrate that AI-DED is a patient-unique characteristic of PsAF. Importantly, both AF type and AF history do not correlate with AI-DED levels (R = 0.006, P = .53; and R = 0.03, P = .25, respectively). The most extensive AI-DED levels are not associated with poorer procedural (83%, 81%, and 83% of AF termination in low, medium, and high dispersion groups, respectively; P = .954) and long-term (88%, 75%, and 91% of freedom from AF/atrial tachycardia after multiple procedures; P = .517) outcomes. The atrial distribution and extent of multipolar electrogram spatiotemporal dispersion follow a nonrandom, albeit patient-unique, distribution in PsAF patients.”
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news originating from Boston, United States, by NewsRx correspondents, research stated, “Objective This proofof- concept study assessed how confidently an artificial intelligence (AI) model can determine the sex of a fetus from an ultrasound image. Analysis was performed using 19,212 ultrasound image slices from a high-volume fetal sex determination practice.” Our news journalists obtained a quote from the research from Northeastern University, “This dataset was split into a training set (11,769) and test set (7,443). A computer vision model was trained using a transfer learning approach with EfficientNetB4 architecture as base. The performance of the computer vision model was evaluated on the hold out test set. Accuracy, Cohen’s Kappa and Multiclass Receiver Operating Characteristic AUC were used to evaluate the performance of the model. The AI model achieved an Accuracy of 88.27% on the holdout test set and a Cohen’s Kappa score 0.843. The ROC AUC score for Male was calculated to be 0.896, for Female a score of 0.897, Unable to Assess a score of 0.916 and for Text Added score of 0.981 was achieved.”
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting from New Haven, Connecticut, by NewsRx journalists, research stated, “Appendicitis is an inflammatory condition that requires timely and effective intervention. Despite being one of the most common surgically treated diseases, the condition is difficult to diagnose because of atypical presentations.” Financial support for this research came from Presented at the Connecticut Chapter of the American College of Surgeons Annual Meeting, Trumbull. The news correspondents obtained a quote from the research from Yale University, “Ultrasound and computed tomography (CT) imaging improve the sensitivity and specificity of diagnoses, yet these tools bear the drawbacks of high operator dependency and radiation exposure, respectively. However, new artificial intelligence tools (such as machine learning) may be able to address these shortcomings. We conducted a state-of-the-art review to delineate the various use cases of emerging machine learning algorithms for diagnosing and managing appendicitis in recent literature. The query (‘Appendectomy’ OR ‘Appendicitis’) AND (‘Machine Learning’ OR ‘Artificial Intelligence’) was searched across three databases for publications ranging from 2012 to 2022. Upon filtering for duplicates and based on our predefined inclusion criteria, 39 relevant studies were identified. The algorithms used in these studies performed with an average accuracy of 86% (18/39), a sensitivity of 81% (16/39), a specificity of 75% (16/39), and area under the receiver operating characteristic curves (AUROCs) of 0.82 (15/39) where reported. Based on accuracy alone, the optimal model was logistic regression in 18% of studies, an artificial neural network in 15%, a random forest in 13%, and a support vector machine in 10%. The identified studies suggest that machine learning may provide a novel solution for diagnosing appendicitis and preparing for patient-specific post-operative complications.”
查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Liaoning, People’s Republic of China, by NewsRx correspondents, research stated, “Molecular models were developed to evaluate the adsorption behavior between NH3 and MgCl2. These models were utilized to assess the diffusion coefficient and adsorption energy at different temperatures and pressures through the application of molecular dynamics (MD) simulations.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Liaoning Application demonstration of new generation light. Our news editors obtained a quote from the research from the Shenyang University of Technology, “Subsequently, a comprehensive dataset comprising the diffusion coefficient and adsorption energy was established. The analysis of the relative diffusion and adsorption mechanisms involved calculating the radius distribution function, coordination numbers, and energy values within the primary solvent layer. The molecular simulation results revealed that the highest values for the diffusion coefficient and adsorption energy of NH3 in MgCl2 were observed at a temperature of 348 K and a pressure of 0.2 MPa. Moreover, the experimental findings exhibited good agreement with the computational simulation conclusions. The preparation of Magnesium hydroxide (MH) under the aforementioned temperature and pressure conditions resulted in a concentrated particle size distribution, effective dispersion, and a complete hexagonal sheet morphology. Furthermore, machine learning predictions were performed using significant features (i.e., molarity(M), temperature(T), pressure(P), volume(Ⅴ), density(D), and total energy(E)).”
查看更多>>摘要：A new study on Artificial Intelligence is now available. According to news reporting from Diepenbeek, Belgium, by NewsRx journalists, research stated, “Artificial intelligence and machine learning are revolutionising fields of science and engineering. In recent years, process engineering has widely benefited from this novel modelling and optimisation approach.” Financial supporters for this research include VLAIO, DAP2CHEM: Real-time data- assisted process development and production in chemical applications. The news correspondents obtained a quote from the research from the University of Leuven (KU Leuven), “The open literature can offer several examples of their applications to chemical engineering problems. Increasing investments are devoted to these techniques from different industrial areas, but insufficient information on a structured course covering these topics in a chemical engineering curriculum could be found. The course in this paper intends to reduce this gap. We introduce one of the first courses on artificial intelligence applications in a chemical engineering curriculum. The course targets Master’s students with a chemical engineering background and insufficient knowledge of statistical approaches. It covers the main aspects by utilising frontal lectures and hands-on exercises with active learning methods. This paper shows the methodology we adapted to introduce students to machine learning techniques and how they responded to each class. The student performances for each test are shown, as well as the survey results based on student feedback and suggestions.”
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Mary Ann Liebert, Inc
Elsevier