查看更多>>摘要：Data detailed on have been presented. According to news reporting originating from Hong Kong, People's Republic of China, by NewsRx correspondents, research stated, "We propose the total variation penalized sparse additive support vector machine (TVSAM) for performing classification in the high-dimensional settings, using a mixed $l_{1}$-type functional regularization scheme to induce sparsity and smoothness simultaneously." Nsfc; Cityu Shenzhen Research Institute; Nsf of Jiangxi Province; Hong Kong Rgc; Cityu. The news reporters obtained a quote from the research from City University of Hong Kong: "We establish a representer theorem for TVSAM, which turns the infinite-dimensional problem into a finitedimensional one, thereby providing computational feasibility. Even for the least squares loss, our result fills a gap in the literature when compared with the existing representer theorem. Theoretically, we derive some risk bounds for TVSAM under both exact sparsity and near sparsity, and with arbitrarily specified internal knots."
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news originating from Salzburg, Austria, by NewsRx correspondents, research stated, "Following the previous part of the narrative review on artificial intelligence (AI) applications in positron emission tomography (PET) using tracers rather than F-fluorodeoxyglucose ([F]F-FDG), in this part we review the impact of PETderived radiomics data on the diagnostic performance of other PET radiotracers, F-O-(2-fluoroethyl)-Ltyrosine ([F]F-FET), F-Fluorothymidine ([F]F-FLT) and C-Methionine ([C]C-MET). [F]F-FET-PET, using an artificial amino acid taken up into upregulated tumoral cells, showed potential in lesion detection and tumor characterization, especially with its ability to reflect glioma heterogeneity."
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting from Nanjing, People's Republic of China, by NewsRx journalists, research stated, "Distinguishing warfarin-related bleeding risk at the bedside remains challenging. Studies indicate that warfarin therapy should be suspended when international normalized ratio (INR) 4.5, or it may sharply increase the risk of bleeding." The news correspondents obtained a quote from the research from China Pharmaceutical University, "We aim to develop and validate a model to predict the high bleeding risk in valve replacement patients during hospitalization. Cardiac valve replacement patients from January 2016 to December 2021 across Nanjing First Hospital were collected. Five different machine-learning (ML) models were used to establish the prediction model. High bleeding risk was an INR 4.5. The area under the receiver operating characteristic curve (AUC) was used for evaluating the prediction performance of different models. The SHapley Additive exPlanations (SHAP) was used for interpreting the model. We also compared ML with ATRIA score and ORBIT score. A total of 2376 patients were finally enrolled in this model, 131 (5.5%) of whom experienced the high bleeding risk after anticoagulation therapy of warfarin during hospitalization. The extreme gradient boosting (XGBoost) exhibited the best overall prediction performance (AUC: 0.882, confidence interval [CI] 0.817-0.946, Brier score, 0.158) compared to other prediction models. It also shows superior performance compared with ATRIA score and ORBIT score. The top 5 most influential features in XGBoost model were platelet, thyroid stimulation hormone, body surface area, serum creatinine and white blood cell."
查看更多>>摘要：A new study on artificial intelligence is now available. According to news reporting from Seoul, South Korea, by NewsRx journalists, research stated, "With the rapid growth of digitalization and the increasing volume of data, the cybersecurity threat landscape is expanding at an alarming rate." Financial supporters for this research include National Research Foundation of Korea; Ministry of Education; Nrf; Ministry of Science And Ict. Our news editors obtained a quote from the research from Sejong University: "Intrusion Detection Systems (IDS) have been widely employed in conjunction with firewalls to safeguard networks. However, traditional IDS systems operate in a static manner, rendering them vulnerable to obsolescence and necessitating costly retraining efforts. As a result, the demand for dynamic models capable of handling continuous streams of network traffic has surged as they can learn from the incoming traffic without the need to old data and costly retraining. In response to this challenge, we have implemented an enhanced approach: an incremental majority voting IDS system, which utilizes existing tools and techniques to improve the robustness and adaptability of intrusion detection By leveraging the collective decision-making power of multiple machine learning models such as: KNN classifier, Softmax Regressor and Adaptive Random Forest classifier, our system aims to improve the accuracy, especially reducing false alarm rates, and effectiveness of intrusion detection in real-time scenarios. Through this research, we have managed to obtain a model which scores 96.43% of accuracy as well as giving 100% precision for majority type of attacks."
查看更多>>摘要：A new study on Artificial Intelligence is now available. According to news reporting originating from Toronto, Canada, by NewsRx editors, the research stated, "Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements." Financial support for this research came from RevivAb. Our news editors obtained a quote from the research from the Department of Medicinal Chemistry, "In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs."
查看更多>>摘要：Fresh data on robotics are presented in a new report. According to news reporting originating from Wuhan, People's Republic of China, by NewsRx correspondents, research stated, "Visual Simultaneous Localization and Mapping (VSLAM) estimates the robot's pose in three-dimensional space by analyzing the depth variations of inter-frame feature points."Financial supporters for this research include Hubei Provincial Science And Technology Innovative Talent Program Project; Hubei Provincial Department of Education Key Project; Open Fund of Hunan Provincial Key Laboratory of Intelligent Electricity-based Operation Technology And Equipment; National Natural Science Foundation of China; Hubei Provincial Natural Science Foundation Innovation Group Project; China Construction West Construction Science And Technology Research And Development Project; China State Construction Engineering Corporation 2022 Annual Science And Technology Research And Development Project.
查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news reporting out of Knoxville, Tennessee, by NewsRx editors, research stated, "Machine learning (ML) methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition." The news reporters obtained a quote from the research from University of Tennessee: "The introduction of the application programming interfaces (APIs) by major instrument manufacturers now allows the deployment of ML workflows in microscopes, not only for data analytics but also for real-time decisionmaking and feedback for microscope operation. However, the number of use cases for real-time ML remains remarkably small."
查看更多>>摘要：Researchers detail new data in robotics. According to news reporting out of Chaohu University by NewsRx editors, research stated, "In order to effectively improve the efficiency of surface garbage cleaning robot, an intelligent control algorithm was applied to plan the robot path." Funders for this research include Excellent Young Talents Fund Program of Higher Education Institutions of Anhui Province; University Natural Science Research Project of Anhui Province; China University Students' Innovation And Entrepreneurship Project. Our news correspondents obtained a quote from the research from Chaohu University: "To do so, an improved immune particle swarm algorithm was developed based on the robot model. This algorithm introduced the adaptive information dynamic adjustment strategy to dynamically adjust the main link indices, which improved the global searchability and convergence of particles and facilitated the quick identification of the optimal path by the robot. Through comparative simulation experiments with the particle swarm optimization algorithm, genetic algorithm, and immune particle swarm optimization algorithm, it was found that the robot based on the Adaptive Immune Particle Swarm Optimization (AIPSO) algorithm had the shortest planning path and search time, the lowest energy consumption, and the highest efficiency. A robot prototype platform was built. Compared to other algorithms, the efficiency of the robot space search based on the AIPSO algorithm was the highest, the search time was the shortest, and the energy consumption was also the lowest."
查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting from Montreal, Canada, by NewsRx journalists, research stated, "Artificial intelligence (AI)-based optical diagnosis systems (CADx) have been developed to allow pathology prediction of colorectal polyps during colonoscopies. However, CADx systems have not yet been validated for autonomous performance." The news correspondents obtained a quote from the research from Montreal University Hospital, "Therefore, we conducted a trial comparing Autonomous AI to AI assisted human (AI-H) optical diagnosis. We performed a randomized non-inferiority trial of patients undergoing elective colonoscopies in one academic institution. Patients were randomized int: 1) Autonomous AI-based CADx optical diagnosis of diminutive polyps without human input; 2) endoscopists performed optical diagnosis of diminutive polyps after seeing the real-time CADx diagnosis. Primary outcome was accuracy in optical diagnosis in both arms using pathology as gold standard. Secondary outcomes included agreement with pathology for surveillance intervals. 467 patients were randomized (238 patients/158 polyps in the Autonomous AI group; 229 patients/ 179 polyps in the AI-H group). Accuracy for optical diagnosis was 77.2% (95%Confidence Interval 69.7-84.7) in the Autonomous AI group and 72.1% (95%CI 65.5-78.6) in the AI-H group (p=0.86). For high confidence diagnoses, accuracy for optical diagnosis was 77.2% (95%CI 69.7-84.7) in the Autonomous AI group and 75.5% (95%CI 67.9-82.0) in the AI-H group. Autonomous AI had statistically significantly higher agreement with pathology-based surveillance intervals compared to AI-H (91.5% [95%CI 86.9-96.1] vs 82.1% [95%CI 76.5-87.7]; p=0.016). Autonomous AI-based optical diagnosis exhibits non-inferior accuracy to endoscopist-based diagnosis."
查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting from Dublin, Ireland, by NewsRx journalists, research stated, "The prediction of phase diagrams in the search for new phases is a complex and computationally intensive task. Density functional theory provides, in many situations, the desired accuracy, but its throughput becomes prohibitively limited as the number of species involved grows, even when used with local and semilocal functionals." Financial supporters for this research include Irish Research Council for Science, Engineering and Technology, Irish Research Council for Science, Engineering and Technology. The news correspondents obtained a quote from the research from Trinity College Dublin, "Here, we explore the possibility of integrating machine-learning models in the workflow for the construction of ternary convex hull diagrams. In particular, we train a set of spectral neighbor-analysis potentials (SNAPs) over readily available binary phases, and we establish whether this is good enough to predict the energies of novel ternaries. Such a strategy does not require any new calculations specific for the construction of the model, but just avails of data stored in binary-phase-diagram repositories. We find that a so-constructed SNAP is capable of accurate total-energy estimates for ternary phases close to the equilibrium geometry but, in general, is not able to perform atomic relaxation. This is because during a typical relaxation path, a given phase traverses regions in the parameter space poorly represented by the training set. Different metrics are then investigated to assess how well an unknown structure is described by a given SNAP model, and we find that the standard deviation of an ensemble of SNAPs provides a fast and non-specie-specific metric."
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