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 the Ankara Bilkent City Hospital by NewsRx editors, the research stated, “Erroneous and del ayed triage in an increasingly crowded emergency department (ED). ChatGPT is an artificial intelligence model developed by OpenAI® and is being trained for use in natural language processing tasks.” Our news journalists obtained a quote from the research from Ankara Bilkent City Hospital: “Our study aims to determine the accuracy of patient triage using Cha tGPT according to the emergency severity index (ESI) for triage in EDs. In our c ross-sectional study, 18 years and over patients who consecutively presented to our ED within 24 h were included. Age, gender, admission method, chief complaint , state of consciousness, and comorbidities were recorded on the case form, and the vital signs were detected at the triage desk. A five-member expert committee (EC) was formed from the fourth-year resident physicians. The investigators con verted real-time patient information into a standardized case format. The urgenc y status of the patients was evaluated simultaneously by EC and ChatGPT accordin g to ESI criteria. The median value of the EC decision was accepted as the gold standard. There was a statistically significant moderate agreement between EC an d ChatGPT assessments regarding urgency status (Cohen’s Kappa = 0.659; P <0.001). The accuracy between these two assessments was calculated as 76.6% . There was a high degree of agreement between EC and ChatGPT for the prediction of ESI-1 and 2, indicating high acuity (Cohen’s Kappa = 0.828). The diagnostic specificity, NPV, and accuracy of ChatGPT were determined as 95.63, 98.17 and 94 .90%, respectively, for ESI high acuity categories.”
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 Zhengzhou, People’s Re public of China, by NewsRx editors, the research stated, “With the development o f the manufacturing industry, precision grinding has become an indispensable par t of the high-end manufacturing field. As a key tool for precision grinding, the surface reshaping technology of diamond rollers is one of the critical technolo gies for making diamond rollers. Currently, the diamond grinding wheel grinding method is commonly used for diamond roller precision reshaping.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on Artificial Intelligence have bee n presented. According to news reporting originating from Beijing, People’s Repu blic of China, by NewsRx correspondents, research stated, “Magnetic tunneling ju nctions (MTJs) lie in the core of magnetic random access memory, holding promise in integrating memory and computing to reduce hardware complexity, transition l atency, and power consumption. However, traditional MTJs are insensitive to ligh t, limiting their functionality in in-memory sensing-a crucial component for mac hine vision systems in artificial intelligence applications.” Financial support for this research came from National Natural Science Foundatio n of China (NSFC).
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 robotics. According to news reporting from Jinzhong, People’s Republic of China, by NewsRx journalists, research stated, “The precise segmentation of * * Zantho xylum bungeanum* * clusters is crucial for developing picking robots.” Funders for this research include Key Research And Development Program of Shanxi Province. The news editors obtained a quote from the research from Shanxi Agricultural Uni versity: “An improved Mask R-CNN model was proposed in this study for the segmen tation of * * Zanthoxylum bungeanum* * clusters in natural environments. Firstly , the Swin-Transformer network was introduced into the model’s backbone as the f eature extraction network to enhance the model’s feature extraction capabilities . Then, the SK attention mechanism was utilized to fuse the detailed information into the mask branch from the low-level feature map of the feature pyramid netw ork (FPN), aiming to supplement the image detail features. Finally, the distance intersection over union (DIOU) loss function was adopted to replace the origina l bounding box loss function of Mask R-CNN. The model was trained and tested bas ed on a self-constructed * * Zanthoxylum bungeanum* * cluster dataset. Experimen ts showed that the improved Mask R-CNN model achieved 84.0% and 77 .2% in detection mAP 50 box and segmentation mAP 50 mask , respect ively, representing a 5.8% and 4.6% improvement over the baseline Mask R-CNN model.”
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 Nottingham, United Kingdom, b y NewsRx editors, research stated, “Customer service is an important and expensi ve aspect of business, often being the largest department in most companies. Wit h growing societal acceptance and increasing cost efficiency due to mass product ion, service robots are beginning to cross from the industrial domain to the soc ial domain.” Our news journalists obtained a quote from the research from Nottingham Trent Un iversity, “Currently, customer service robots tend to be digital and emulate soc ial interactions through on-screen text, but state-of-the-art research points to wards physical robots soon providing customer service in person. This article ex plores the feasibility of Transfer Learning different customer service domains t o improve chatbot models. In our proposed approach, transfer learning-based chat bot models are initially assigned to learn one domain from an initial random wei ght distribution. Each model is then tasked with learning another domain by tran sferring knowledge from the previous domains. To evaluate our approach, a range of 19 companies from domains such as e-Commerce, telecommunications, and technol ogy are selected through social interaction with X (formerly Twitter) customer s upport accounts. The results show that the majority of models are improved when transferring knowledge from at least one other domain, particularly those more d ata-scarce than others. General language transfer learning is observed, as well as higher-level transfer of similar domain knowledge. For each of the 19 domains , the Wilcoxon signed-rank test suggests that 16 have statistically significant distributions between transfer and non-transfer learning.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting originating from Zurich University of Applied Sciences by NewsRx correspondents, research stated, “Anomaly detection ( AD) tasks have been solved using machine learning algorithms in various domains and applications. The great majority of these algorithms use normal data to trai n a residual-based model, and assign anomaly scores to unseen samples based on t heir dissimilarity with the learned normal regime.” Our news editors obtained a quote from the research from Zurich University of Ap plied Sciences: “The underlying assumption of these approaches is that anomaly-f ree data is available for training. This is, however, often not the case in real -world operational settings, where the training data may be contaminated with a certain fraction of abnormal samples. Training with contaminated data, in turn, inevitably leads to a deteriorated AD performance of the residual-based algorith ms. In this paper we introduce a framework for a fully unsupervised refinement o f contaminated training data for AD tasks. The framework is generic and can be a pplied to any residual-based machine learning model. We demonstrate the applicat ion of the framework to two public datasets of multivariate time series machine data from different application fields. We show its clear superiority over the n aive approach of training with contaminated data without refinement.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Current study results on Machine Learning have be en published. According to news reporting originating from Guangzhou, People’s R epublic of China, by NewsRx correspondents, research stated, “The all-atomic ful l-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in lar ge realistic systems has received high research interest in recent years. Howeve r, such NAMD simulations normally generate an enormous amount of time-dependent high-dimensional data, leading to a significant challenge in result analyses.” Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Opening Project of the Key Laboratory of Optoelectronic Chem ical Materials and Devices of Ministry of Education, Jianghan University.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news originating from Pingdingshan, People’ s Republic of China, by NewsRx correspondents, research stated, “Providing couri ers with wearable artificial intelligence devices to prevent accidents is not on ly beneficial to the courier’s safety but will also save money in terms of insur ance premiums for express companies; therefore, it is worth investigating what f actors can influence the acceptance of wearable artificial intelligence devices by couriers.” The news correspondents obtained a quote from the research from Henan University of Urban Construction: “Push-pull-mooring (PPM) theory and affective event theo ry (AET) are integrated, to test couriers’ adoption of wearable safety detection devices. Social influence, perceived security, personal innovativeness, and aff ective event reaction are applied to the research model. Questionnaires are dist ributed among several listed express companies and 263 valid questionnaires are used for empirical testing. Empirical results indicated that social influence, p erceived safety, personal innovativeness and affective event reaction are positi vely related to usage with coefficients 0.218, 0.301, 0.698 and 0.309. Personal innovativeness has positive moderating effects on relationships between affectiv e event reaction, perceived security and usage, with coefficients 0.145 and 0.10 6; however, it has no significant moderating effect on the relationship between social influence and usage.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on have been presented. According to news reporting originating from Zaragoza, Spain, by NewsRx correspo ndents, research stated, “In this study, we evaluated the capability of an unman ned aerial vehicle with a LiDAR sensor (UAV-LiDAR) to classify and map fuel type s based on the Prometheus classification in Mediterranean environments. UAV data were collected across 73 forest plots located in NE of Spain.” Funders for this research include Spanish Ministry of Science, Innovation, And U niversities; Government of Aragon; University Institute For Research in Environm ental Sciences of Aragon (Iuca) of The University of Zaragoza.
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 from Christchurch, New Zealand, b y NewsRx journalists, research stated, “The use of depolarization lidar to measu re atmospheric volume depolarization ratio (VDR) is a common technique to classi fy cloud phase (liquid or ice).” The news journalists obtained a quote from the research from University of Cante rbury: “Previous work using a machine learning framework, applied to peak proper ties derived from co-polarized attenuated backscatter data, has been demonstrate d to effectively detect supercooled-liquid-water-containing clouds (SLCCs). Howe ver, the training data from Davis Station, Antarctica, include no warm liquid wa ter clouds (WLWCs), potentially limiting the model’s accuracy in regions where W LWCs are present. In this work, we apply the same framework used on the Davis da ta to a 9-month micro-pulse lidar dataset collected in Otautahi / Christchurch, Aotearoa / New Zealand, a location which includes WLWC. We then evaluate the res ults relative to a reference VDR cloud-phase mask. We found that the Davis model performed relatively poorly at detecting SLCC with a recall score of 0.18, ofte n misclassifying WLWC as SLCC. The performance of our new model, trained using d ata from Otautahi / Christchurch, displays recall scores as high as 0.88 for ide ntification of SLCC, although it generally underestimates SLCC occurrence.”
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