查看更多>>摘要：Investigators discuss new findings in artificial intelligence. According to news reporting from Royal Oak, Michigan, by NewsRx journalists, research stated, "To determine how often ChatGPT is able to provide accurate and comprehensive information regarding clinical vitreoretinal scenarios. To assess the types of sources ChatGPT primarily utilizes and to determine if they are hallucinated." Our news correspondents obtained a quote from the research from Department of Ophthalmology: "A retrospective cross-sectional study. We designed 40 open-ended clinical scenarios across 4 main topics in vitreoretinal disease. Responses were graded on correctness and comprehensiveness by two blinded retina specialists. The primary outcome was the number of clinical scenarios that ChatGPT answered correctly and comprehensively. Secondary outcomes included: theoretical harm to patients, the distribution of the type of references utilized by the chatbot, and the frequency of hallucinated references. In June 2023, ChatGPT answered 83% (33/40) of clinical scenarios correctly but provided a comprehensive answer in only 52.5% (21/40) of cases. Subgroup analysis demonstrated an average correct score of 86.7% in nAMD, 100% in DR, 76.7% in retinal vascular disease and 70% in the surgical domain. There were 6 incorrect responses with 1 (16.7%) case of no harm, 3 (50%) cases of possible harm and 2 (33.3%) cases of definitive harm."
查看更多>>摘要：Researchers detail new data in Robotics - Field Robotics. According to news reporting originating from Starkville, Mississippi, by NewsRx correspondents, research stated, "This field report presents the results of a study of obstacle detection and avoidance (ODOA) by an autonomous ground vehicle (AGV) in off-road driving conditions. This study included both real and simulated testing of the AGV and served as the third and final phase of a 3-year research project studying the influence of environmental conditions over autonomous driving." Financial supporters for this research include U.S. Army Tank Automotive Research Development & Engineering Center (TARDEC), Automotive Research Center (ARC), US Army Tank Automotive Research, Development and Engineering Center (TARDEC) Warren, MI. Our news editors obtained a quote from the research from Mississippi State University, "We compare and contrast the results of the real and experimental field testing and report our findings on the influence of soft soil in ODOA performance by an AGV. We find that rutting in soft soil results in higher throttle effort but lower steering effort and speed."
查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting from Pune, India, by NewsRx journalists, research stated, "We present a machine learning (ML) framework HEART (HydrogEn storAge propeRty predicTor) for identifying suitable families of metal alloys for hydrogen storage under ambient conditions. Our framework includes two ML models that predict the hydrogen storage capacity (HYST) and the enthalpy of hydride formation (THOR) of multi-component metal alloys." Financial supporters for this research include Council of Scientific & Industrial Research (CSIR) - India, Department of Science & Technology (India).
查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Toronto, Canada, by NewsRx correspondents, research stated, "Adverse events refer to incidents with potential or actual harm to patients in hospitals. These events are typically documented through patient safety event (PSE) reports, which consist of detailed narratives providing contextual information on the occurrences." Our news journalists obtained a quote from the research from the University of Toronto, "Accurate classification of PSE reports is crucial for patient safety monitoring. However, this process faces challenges due to inconsistencies in classifications and the sheer volume of reports. Recent advancements in text representation, particularly contextual text representation derived from transformer-based language models, offer a promising solution for more precise PSE report classification. Integrating the machine learning (ML) classifier necessitates a balance between human expertise and artificial intelligence (AI). Central to this integration is the concept of explainability, which is crucial for building trust and ensuring effective human- AI collaboration. This study aims to investigate the efficacy of ML classifiers trained using contextual text representation in automatically classifying PSE reports. Furthermore, the study presents an interface that integrates the ML classifier with the explainability technique to facilitate human-AI collaboration for PSE report classification. This study used a data set of 861 PSE reports from a large academic hospital's maternity units in the Southeastern United States. Various ML classifiers were trained with both static and contextual text representations of PSE reports. The trained ML classifiers were evaluated with multiclass classification metrics and the confusion matrix. The local interpretable model-agnostic explanations (LIME) technique was used to provide the rationale for the ML classifier's predictions. An interface that integrates the ML classifier with the LIME technique was designed for incident reporting systems. The top-performing classifier using contextual representation was able to obtain an accuracy of 75.4% (95/126) compared to an accuracy of 66.7% (84/126) by the top-performing classifier trained using static text representation. A PSE reporting interface has been designed to facilitate human-AI collaboration in PSE report classification. In this design, the ML classifier recommends the top 2 most probable event types, along with the explanations for the prediction, enabling PSE reporters and patient safety analysts to choose the most suitable one. The LIME technique showed that the classifier occasionally relies on arbitrary words for classification, emphasizing the necessity of human oversight. This study demonstrates that training ML classifiers with contextual text representations can significantly enhance the accuracy of PSE report classification. The interface designed in this study lays the foundation for human-AI collaboration in the classification of PSE reports."
查看更多>>摘要：A new study on artificial intelligence is now available. According to news reporting originating from Khwaja Yunus Ali University by NewsRx correspondents, research stated, "This study presents a recently compiled dataset called ‘BDHusk,' which encompasses a wide range of husk images representing eight different husk species as a component of cattle feed sourced from different locales in Sirajganj, Bangladesh. The following are eight husk species: Oryza sativa, Zea mays, Triticum aestivum, Cicer arietinum, Lens culinaris, Glycine max, Lathyrus sativus, and Pisum sativum var. arvense L." The news journalists obtained a quote from the research from Khwaja Yunus Ali University: "Poiret. This dataset consists of a total of 2,400 original images and an additional 9,280 augmented images, all showcasing various husk species. Every single one of the original images was taken with the right backdrop and in enough amount of natural light. Every image was appropriately positioned into its respective subfolder, enabling a wide variety of machine learning and deep learning models to make the most effective use of the images. By utilizing this extensive dataset and employing various machine learning and deep learning techniques, researchers have the potential to achieve significant advancements in the fields of agriculture, food and nutrition science, environmental monitoring, and computer sciences. This dataset allows researchers to improve cattle feeding using data-driven methods."
查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting from Riyadh, Saudi Arabia, by NewsRx journalists, research stated, "Previously, we introduced photomagnetic imaging (PMI) that synergistically utilizes laser light to slightly elevate the tissue temperature and magnetic resonance thermometry (MRT) to measure the induced temperature." Funders for this research include Nih Grants; Uci Cancer Center. Our news reporters obtained a quote from the research from Imam Mohammad Ibn Saud Islamic University: "The MRT temperature maps are then converted into absorption maps using a dedicated PMI image reconstruction algorithm. In the MRT maps, the presence of abnormalities such as tumors would create a notable high contrast due to their higher hemoglobin levels. In this study, we present a new artificial intelligence-based image reconstruction algorithm that improves the accuracy and spatial resolution of the recovered absorption maps while reducing the recovery time. Technically, a supervised machine learning approach was used to detect and delineate the boundary of tumors directly from the MRT maps based on their temperature contrast to the background. This information was further utilized as a soft functional a priori in the standard PMI algorithm to enhance the absorption recovery."
查看更多>>摘要：Current study results on Robotics have been published. According to news reporting out of Xining, People's Republic of China, by NewsRx editors, research stated, "The multi-point dynamic aggregation problem (MPDAP) that arises in practical applications is characterized by a group of robots that have to cooperate in executing a set of tasks distributed over multiple locations, in which the demand for each task grows over time. To minimize the completion time of all tasks, one needs to schedule the robots and plan the routes." Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Qinghai Normal University, "Hence, the problem is essentially a combinatorial optimization problem. The manuscript presented a new MPDAP in which the priority of the task was considered that is to say, some tasks must be first completed before others begin to be executed. When the tasks were located at different priority levels, some additional constraints were added to express the priorities of tasks. Since route selection of robots depends on the priorities of tasks, these additional constraints caused the presented MPDAP to be more complex than ever. To efficiently solve this problem, an improved optimization algorithm, called the multi-strategy genetic algorithm (MSGA), was developed. First of all, a two-stage hybrid matrix coding scheme was proposed based on the priorities of tasks, then to generate more route combinations, a hybrid crossover operator based on 0-1 matrix operations was proposed. Furthermore, to improve the feasibility of individuals, a repair schedule was designed based on constraints. Meanwhile, a q-tournament selection operator was adopted so that better individuals can be kept into the next generation."
查看更多>>摘要：Current study results on Robotics have been published. According to news reporting out of Beijing, People's Republic of China, by NewsRx editors, research stated, "In this paper, a novel self-adaptive underactuated robot hand with rigid-flexible coupling fingers (SAU-RFC hand) is proposed. The seven degrees of freedom (DOFs) SAU-RFC hand is driven by four servomotors, consists of three fingers, including two side-turning (ST) fingers and one non-side-turning finger." Financial supporters for this research include National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC), Beijing Municipal Science & Technology Commission, Beijing Natural Science Foundation, Youth Innovation Promotion Association CAS.
查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting from Zurich, Switzerland, by NewsRx journalists, research stated, "Mitigating the energy requirements of artificial intelligence requires novel physical substrates for computation. Phononic metamaterials have vanishingly low power dissipation and hence are a prime candidate for green, always-on computers." Funders for this research include European Research Council (ERC), European Research Council (ERC), Horizon Europe Programme. The news correspondents obtained a quote from the research from the Swiss Federal Institute of Technology, "However, their use in machine learning applications has not been explored due to the complexity of their design process. Current phononic metamaterials are restricted to simple geometries (e.g., periodic and tapered) and hence do not possess sufficient expressivity to encode machine learning tasks. A nonperiodic phononic metamaterial, directly from data samples, that can distinguish between pairs of spoken words in the presence of a simple readout nonlinearity is designed and fabricated, hence demonstrating that phononic metamaterials are a viable avenue towards zero-power smart devices. Elastic neural networks composed of phononic metamaterials respond differently to different spoken commands, passively solving a speech classification problem. Their design harnesses the vanishingly low power dissipation of elastic waves, combined with the high expressivity and efficient simulation of metamaterials."
查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting originating from Changchun, People's Republic of China, by NewsRx correspondents, research stated, "The real-time light curve classification of transients is helpful in searching for rare transients." Financial supporters for this research include National Natural Science Foundation of China; Chinese Academy of Sciences And Local Government Cooperation Project; Strategic Priority Research Program of The Chinese Academy of Sciences; Satural Science Foundation of Jilin Province; Svom Project. The news editors obtained a quote from the research from National Astronomical Observatories: "We propose a new algorithm based on machine learning, namely the Temporary Convective Network and Light Gradient Boosting Machine Combined with Weight Module Algorithm (TLW). The TLW algorithm can classify the photometric simulation transients data in g, r, i bands provided via PLAsTiCC, typing Tidal Disruption Event (TDE), Kilonova (KN), Type Ia supernova (SNIa), and Type I Super-luminous supernova (SLSN-I). When comparing the real-time classification results of the TLW algorithm and six other algorithms, such as Rapid, we found that the TLW algorithm has the best comprehensive performance indexes and has the advantages of high precision and high efficiency. The average accuracy of TLW is 84.54%. The average implementation timings of the TLW algorithm for classifying four types of transients is 123.09 s, which is based on TensorFlow's architecture in windows and python. We use three indicators to prove that the TLW algorithm is superior to the classical Rapid algorithm, including Confusion Matrix, PR curve, and ROC curve."
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