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 originating in Hunan, People's Republic o f China, by NewsRx journalists, research stated, "Three-way decision (3WD) is a powerful tool for granular computing to deal with uncertain data, commonly used in information systems, decision -making, and medical care. Three-way decision g ets much research in traditional rough set models." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Changsha Municipal Natural Science Foundation, Guangdong Bas ic and Applied Basic Research Foundation, Natural Science Foundation of Hunan Pr ovince, Postgrad-uate Scientific Research Innovation Project of Hunan Province.
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 originating from Ural Federal University by NewsRx correspondents, research stated, "Risk and uncertainty pla y a vital role in almost every significant economic decision, and an individual' s propensity to make riskier decisions also depends on various circumstances." The news correspondents obtained a quote from the research from Ural Federal Uni versity: "This article aims to investigate the effects of social and economic co variates on an individual's willingness to take general risks and extends the sc ope of existing works by using quantitative measures of risk-taking from the GPS and Gallup datasets (in addition to the qualitative measures used in the litera ture). Based on the available observed risk-taking data for one year, this artic le proposes a semi-supervised machine learning-based approach that can efficient ly predict the observed risk index for those countries/individuals for years whe n the observed risk-taking index was not collected."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting from Philadelphia, Pennsylv ania, by NewsRx journalists, research stated, "In this study, we developed a mac hine learning model for automated seizure detection using system identification techniques on EEG recordings." The news journalists obtained a quote from the research from Drexel University: "System identification builds mathematical models from a time series signal and uses a small number of parameters to represent the entirety of time domain signa l epochs. Such parameters were used as features for the classifiers in our study . We analyzed 69 seizure and 55 non-seizure recordings and an additional 10 cont inuous recordings from Thomas Jefferson University Hospital, alongside a larger dataset from the CHB-MIT database. By dividing EEGs into epochs (1 s, 2 s, 5 s, and 10 s) and employing fifth-order state-space dynamic systems for feature extr action, we tested various classifiers, with the decision tree and 1 s epochs ach ieving the highest performance: 96.0% accuracy, 92.7% sensitivity, and 97.6% specificity based on the Jefferson dataset. Moreover, as the epoch length increased, the accuracy dropped to 94.9% , with a decrease in sensitivity to 91.5% and specificity to 96.7% . Accuracy for the CHB-MIT dataset was 94.1%, with 87.6 % sensitivity and 97.5% specificity."
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 Robotics have been published. According to news reporting out of Nanjing, People's Republic o f China, by NewsRx editors, research stated, "Currently, the acquisition of surf ace electromyographic (sEMG) signals commonly requires powerful and expensive eq uipment or systems. The signal analysis also requires additional equipment to pr ocess independently, which is timeconsuming and the results are difficult to un derstand and directly apply to wearable robots." Funders for this research include National Natural Science Foundation of China ( NSFC), Fundamental Research Funds for the Central Universities, Ph.D. Short-term Visiting Program of Nanjing University of Aeronautics and Astronautics.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating from Muttenz, Swi tzerland, by NewsRx correspondents, research stated, "Antimicrobial resistance ( AMR) is a major public health threat, reducing treatment options for infected pa tients. AMR is promoted by a lack of access to rapid antibiotic susceptibility t ests (ASTs)." Our news editors obtained a quote from the research, "Accelerated ASTs can ident ify effective antibiotics for treatment in a timely and informed manner. We desc ribe a rapid growth-independent phenotypic AST that uses a nanomotion technology platform to measure bacterial vibrations. Machine learning techniques are appli ed to analyze a large dataset encompassing 2762 individual nanomotion recordings from 1180 spiked positive blood culture samples covering 364 Escherichia coli and Klebsiella pneumoniae isolates exposed to cephalosporins and fluoroquinolones. The training performan ces of the different classification models achieve between 90.5 and 100% accuracy. Independent testing of the AST on 223 strains, including in clinical s etting, correctly predict susceptibility and resistance with accuracies between 89.5% and 98.9 %."
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 Xi'an, People's Repub lic of China, by NewsRx journalists, research stated, "The samples representing abnormal situation is usually very few in the dataset, which makes it difficult to learn the features of abnormal samples by machine-learning-based methods. To improve the accuracy of anomaly detection, the number of abnormal samples should be expanded to ensure the balance of the dataset." Financial supporters for this research include National Key R&D Pro gram of China, National Natural Science Foundation of China (NSFC), Natural Scie nce Foundation of Shaanxi Province, Natural Science Foundation of Shaanxi Provin cial Department of Education, Key Laboratory of Complex System Intelligent Contr ol and Decision, Beijing Institute of Technology. The news reporters obtained a quote from the research from the Xi'an University of Technology, "In this paper, a discrete synthetic minority oversampling techni que (D-SMOTE) is proposed to generate new samples. A closed area is constructed using the three nearest abnormal samples in the dataset. The new samples are the n uniformly interpolated in a closed area. By this means, the problem of the imb alance for the original dataset is handled, thus improving the data quality. Bas ed on the expanded datasets, a two-dimensional convolutional neural network (2D CNN) is constructed to detect abnormal samples. In experiments, three cases and different machine learning methods are considered for comparison. Several indexe s including accuracy, precision, confusion matrix, F1-score, and Recall have bee n used to evaluate the detection effectiveness."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Robotics are presented i n a new report. According to news reporting out of Auckland, New Zealand, by New sRx editors, research stated, "This article presents a control strategy to carry out multiobject manipulation on a novel soft robotic table (SoTa), which is a n ew form of the planar distributed manipulator. Manipulating multiple delicate ob jects simultaneously is an attractive feature of SoTa." Financial support for this research came from China Scholarship Council. Our news journalists obtained a quote from the research from the University of A uckland, "The challenge here is to coordinate multiple objects in a confined pla nar space while avoiding interference with each other. The SoTa system adopts a manipulation strategy that includes a planning and a tracking stage for the purp ose of sorting objects. The planning stage consists of two phases: 1) discrete p ath planning to find a path for each object on a grid map with respect to time; 2) trajectory generation to optimize and produce workable trajectories for SoTa. In the discrete path planning phase, a hierarchical searching method based on t he time-varying potential field is proposed. Constraints of the SoTa system are modeled and incorporated into the path searching process. In the trajectory gene ration phase, a piecewise B-spline method is adopted to generate trajectories ba sed on previously found discrete paths. Next, in the tracking stage, the objects are led to their goals along the trajectories ensuring safety and SoTa's capabi lity. The performances of the proposed algorithm were simulated, analyzed, and c ompared with the conflict-based search method, which is optimal for multiagent p ath finding. A multiobject manipulation experiment of three objects on a 4 x 4 g rid was conducted on the SoTa."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning - Artificial Intelligence are discussed in a new report. According to news repor ting from Gunupur, India, by NewsRx journalists, research stated, "Bird species identification is becoming increasingly crucial for avian biodiversity conservat ion and assisting ornithologists in quantifying the presence of birds in a given area. Convolutional Neural Networks (CNNs) are advanced deep learning algorithm s that have proven to perform well in speech classification." The news correspondents obtained a quote from the research from the Department o f Computer Sciences and Engineering, "However, developing an accurate deep learn ing classifier requires a large amount of data. Such a large amount of data on e ndemic or endangered creatures is frequently difficult to gathered. Also, in som e other fields, such as bioinformatics and robotics, the high cost of data colle ction and expensive annotation limit their progress, so large, well-annotated da ta creating a set is also difficult. A transfer learning method can alleviate ov erfitting concerns in a deep learning model. This feature serves as the inspirat ion for transfer learning, which was created to deal with situations where the d ata are distributed across a variety of functional domains. In this study, the a bility of deep transfer models such as VGG16, VGG19 and InceptionV3 to effective ly extract and discriminate speech signals from different species of birds with high prediction accuracy is explored."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating from Samsun, Turk ey, by NewsRx correspondents, research stated, "Hydrological modelling can be co mplex in nonhomogeneous catchments with diverse geological, climatic, and topogr aphic conditions. In this study, an integrated conceptual model including the sn ow module with machine learning modelling approaches was implemented for daily r ainfall-runoff modelling in mostly karst Ljubljanica catchment, Slovenia, which has heterogeneous characteristics and is potentially exposed to extreme events t hat make the modelling process more challenging and crucial." Our news editors obtained a quote from the research from Ondokuz Mayis Universit y, "In this regard, the conceptual model CemaNeige Genie Rural a 6 parametres Jo urnalier (CemaNeige GR6J) was combined with machine learning models, namely wave let-based support vector regression (WSVR) and wavelet-based multivariate adapti ve regression spline (WMARS) to enhance modelling performance. In this study, th e performance of the models was comprehensively investigated, considering their ability to forecast daily extreme runoff. Although CemaNeige GR6J yielded a very good performance, it overestimated low flows. The WSVR and WMARS models yielded poorer performance than the conceptual and hybrid models. The hybrid model appr oach improved the performance of the machine learning models and the conceptual model by revealing the linkage between variables and runoff in the conceptual mo del, which provided more accurate results for extreme flows. Accordingly, the hy brid models improved the forecasting performance of the maximum flows up to 40 % and 61 %, and minimum flows up to 73 % and 72 % compared to the CemaNeige GR6J and stand-alone machine learning models."
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 Machine Learn ing have been published. According to news reporting from Tianjin, People's Repu blic of China, by NewsRx journalists, research stated, "The complexity of machin e learning (ML) systems increases each year. As these systems are widely utilize d, ensuring their reliable operation is becoming a design requirement." Financial support for this research came from FUN4DATE. The news correspondents obtained a quote from the research from Tianjin Universi ty, "Traditional error detection mechanisms introduce circuit or time redundancy that significantly impacts system performance. An alternative is the use of con current error detection (CED) schemes that operate in parallel with the system a nd exploit their properties to detect errors. CED is attractive for large ML sys tems because it can potentially reduce the cost of error detection. In this arti cle, we introduce concurrent classifier error detection (CCED), a scheme to impl ement CED in ML systems using a concurrent ML classifier to detect errors. CCED identifies a set of check signals in the main ML system and feed them to the con current ML classifier that is trained to detect errors. The proposed CCED scheme has been implemented and evaluated on two widely used large-scale ML models: Co ntrastive language-image pretraining (CLIP) used for image classification and bi directional encoder representations from transformers (BERT) used for natural la nguage applications."
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