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 artific ial intelligence. According to news reporting from Irbid, Jordan, by NewsRx jour nalists, research stated, "This study employs machine learning (ML) techniques t o predict pedestrian compliance at crosswalks in urban settings in Jordan, aimin g to enhance pedestrian safety and traffic management." The news correspondents obtained a quote from the research from Yarmouk Universi ty: "Utilizing data from 2437 pedestrians at signalized intersections in Amman, Irbid, and Zarqa, four models based on different ML algorithms were developed: a n artificial neural network (ANN), a support vector machine (SVM), a decision tr ee (ID3), and a random forest (RF). The results have shown that local infrastruc ture and traffic conditions influence pedestrian behavior. The RF model, with it s excellent accuracy and precision, has proven to be an excellent choice for acc urately predicting pedestrian behavior. This research provides valuable insights into the demographic and spatial aspects that influence pedestrian compliance w ith laws and regulations in the local environment. Additionally, this work highl ights the ability of ML algorithms to improve urban traffic dynamics."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on human-centric intelli gent systems have been presented. According to news originating from North South University by NewsRx editors, the research stated, "Parkinson's disease (PD) is a neurological condition characterized by the disruption of both motor and non- motor functions. Given the absence of a definitive diagnostic method, it is cruc ial to uncover its root causes." The news editors obtained a quote from the research from North South University: "Consequently, individuals displaying symptoms of Parkinson's disease can promp tly receive treatment and comprehensive care. To address this, our study aims to develop an AI-powered system capable of detecting Parkinson's disease and subse quently evaluating the primary factors influencing its development. We collected 12 distinct datasets from the well-known PPMI database, covering various medica l assessments such as motor abilities, olfaction, cognition, sleep patterns, and depressive symptoms. Subsequently, we refined this raw data using advanced sear ch techniques to tailor it to our model's requirements. Moreover, we introduced a novel labeling approach known as the majority voting algorithm. Following data preparation, we conducted Single and Multi-Modality analyses, focusing on singl e-treatment approaches and integrating multiple treatments for a comprehensive t herapeutic strategy. To analyze these both, we employed five distinct Machine Le arning algorithms. Notably, the Support Vector Machine (linear) emerged as the t op performer, reaching an accuracy of 100% in both single and mult imodality analysis."
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 report. According to news originating from Shanghai, Peopl e's Republic of China, by NewsRx correspondents, research stated, "Stroke is a m ajor threat to life and health in modern society, especially in the aging popula tion. Stroke may cause sudden death or severe sequela-like hemiplegia." Financial supporters for this research include National Natural Science Foundati on of China, Ministry of Science and Technology of the People's Republic of Chin a. Our news journalists obtained a quote from the research from the Shanghai Jiao T ong University School of Medicine, "Although computed tomography (CT) and magnet ic resonance imaging (MRI) are standard diagnosis methods, and artificial intell igence models have been built based on these images, shortage in medical resourc es and the time and cost of CT/MRI imaging hamper fast detection, thus increasin g the severity of stroke. Here, we developed a convolutional neural network mode l by integrating four networks, Xception, ResNet50, VGG19, and EfficientNetb1, t o recognize stroke based on 2D facial images with a cross-validation area under curve (AUC) of 0.91 within the training set of 185 acute ischemic stroke patient s and 551 age- and sex-matched controls, and AUC of 0.82 in an independent data set regardless of age and sex. The model computed stroke probability was quantit atively associated with facial features, various clinical parameters of blood cl otting indicators and leukocyte counts, and, more importantly, stroke incidence in the near future."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Central Nervous System Diseases and Conditions-Cerebral Hemorrhage is the subject of a report. Accor ding to news reporting originating from Chengdu, People's Republic of China, by NewsRx correspondents, research stated, "Pneumonia is one of the most common com plications after spontaneous intracerebral hemorrhage (sICH), namely stroke asso ciated pneumonia (SAP). Timely identification of targeted patients is beneficial to reduce poor prognosis." Our news editors obtained a quote from the research from Sichuan University, "So far, there is no consensus on SAP prediction, and application of existing predi ctors is limited. The aim of the study is to develop a machine learning model to predict SAP after sICH. We retrospectively reviewed 748 patients diagnosed with sICH and collected their data from four dimensions including demographic featur es, clinical features, medical history, and laboratory tests. Five machine learn ing algorithms including logistic regres-sion, gradient boosting decision tree, r andom forest, extreme gradient boosting, and category boosting were used to buil d and validate the predictive model. And we applied recursive feature eliminatio n with cross-validation to obtain the best feature combination for each model. T he predictive performance was evaluated by the areas under the receiver operatin g characteristic curves (AUC). A total of 237 patients were diagnosed as SAP. Th e model developed by category boosting yielded the most satisfied outcomes overa ll with its AUC in training set and test set were 0.8307 and 0.8178, respectivel y. The incidence of SAP after sICH in our center was 31.68%."
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 originating from Madurai, India, by N ewsRx correspondents, research stated, "Selecting designs that efficiently optim ize multiple objectives simultaneously is an important problem in several distin ct industries. Typically, there is not a single ideal design; rather, there are several Pareto-optimal designs that provide the best possible trade-offs between the objectives." Financial supporters for this research include Science and Engineering Research Board (SERB), Science and Engineering Research Board (SERB) through the "Teacher s Associate for Research Excellence (TARE)" scheme.
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 report. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, "To evaluate the con sistency between doctors and artificial intelligence (AI) software in analysing and diagnosing pulmonary nodules, and assess whether the characteristics of pulm onary nodules derived from the two methods are consistent for the interpretation of carcinomatous nodules. This retrospective study analysed participants aged 4 0-74 in the local area from 2011 to 2013." Financial support for this research came from Beijing Science and Technology Pla nning Project. The news correspondents obtained a quote from the research from Capital Medical University, "Pulmonary nodules were examined radiologically using a low-dose che st CT scan, evaluated by an expert panel of doctors in radiology, oncology, and thoracic departments, as well as a computer-aided diagnostic( CAD) system based o n the three-dimensional(3D) convolutional neural network (CNN) with DenseNet arc hitecture(InferRead CT Lung, IRCL). Consistency tests were employed to assess th e uniformity of the radiological characteristics of the pulmonary nodules. The r eceiver operating characteristic (ROC) curve was used to evaluate the diagnostic accuracy. Logistic regression analysis is utilized to determine whether the two methods yield the same predictive factors for cancerous nodules. A total of 570 subjects were included in this retrospective study. The AI software demonstrate d high consistency with the panel's evaluation in determining the position and d iameter of the pulmonary nodules (kappa = 0.883, concordance correlation coeffic ient (CCC) = 0.809, p = 0.000). The comparison of the solid nodules' attenuation characteristics also showed acceptable consistency (kappa = 0.503). In patients diagnosed with lung cancer, the area under the curve (AUC) for the panel and AI were 0.873 (95%CI: 0.829-0.909) and 0.921 (95%CI: 0.8 84-0.949), respectively. However, there was no significant difference (p = 0.095 0). The maximum diameter, solid nodules, subsolid nodules were the crucial facto rs for interpreting carcinomatous nodules in the analysis of expert panel and IR CL pulmonary nodule characteristics."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Robotics is the subjec t of a report. According to news reporting originating in Beijing, People's Repu blic of China, by NewsRx journalists, research stated, "PurposeMobile robots wit h independent wheel control face challenges in steering precision, motion stabil ity and robustness across various wheel and steering system types. This paper ai ms to propose a coordinated torque distribution control approach that compensate s for tracking deviations using the longitudinal moment generated by active upon a two-degree-of-freedom robot model, an adaptive robust controller is used to compute the total longitudinal moment, while the robot actuator is regulated based on the difference between autonomous steering and the longitudinal moment ."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Drugs and Therapies-Personalized Medicine is the subject of a report. According to news originating from Barcelona, Spain, by NewsRx correspondents, research stated, "The rapid evo lution of highly adaptable and reusable artificial intelligence models facilitat es the implementation of digital twinning and has the potential to redefine card iovascular risk prevention. Digital twinning combines vast amount of data from d iverse sources to construct virtual models of an individual." Our news journalists obtained a quote from the research from Universitat de Barc elona, "Emerging artificial intelligence models, called generalist AI, enable pr ocessing different types of data, including data from electronic health records, laboratory results, medical texts, imaging, genomics or graphs. Among their unp recedented capabilities are an easy adaptation of a model to previously unseen m edical tasks and the ability to reason and explain output using precise medical language derived from scientific literature,medical guidelines, or knowledge gr aphs. The proposed combination of a digital twinning approach with generalist AI is a path to accelerate the implementation of precision medicine and enhance ea rly recognition and prevention of cardiovascular disease."
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 Salt Lake City, Utah, by NewsRx journalists, research stated, "Seasonal snow -derived water is a crit ical component of the water supply in the mountains and downstream regions, and the accurate characterization of available water in the form of snow -water -equ ivalent (SWE), peak SWE, and snowmelt onset are essential inputs for water manag ement efforts. Arising from recent advancements in artificial intelligence (AI) and machine learning (ML), we introduce a large-scale ML SWE model leveraging pu blicly available data sources and open -source software." Funders for this research include Cooperative Institute for Research to Operatio ns in Hydrology (CIROH) through the NOAA Cooperative, University of Alabama, Uni ted States.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on artificial intelligence have been published. According to news reporting from Jiangsu, People's Republic of China, by NewsRx journalists, research stated, "Discriminant analysis of sim ilar food samples is an important aspect of achieving food quality control. The effective combination of Raman spectroscopy and machine learning algorithms has become an extremely attractive approach to develop intelligent discrimination te chniques." The news journalists obtained a quote from the research from Nanjing University of Finance and Economics: "Feature spectral analysis can help researchers gain a deeper understanding of the data patterns in food quality discrimination. Herei n, this work takes the discrimination of three brands of dairy products as an ex ample to investigate the Raman spectral feature based on the support vector mach ines (SVM), extreme learning machines (ELM) and convolutional neural network (CN N) algorithms. The results show that there are certain differences in the optima l spectral feature interval corresponding to different machine learning algorith ms. Selecting the appropriate spectral feature interval can maintain high recogn ition accuracy and improve the computational efficiency of the algorithm. For ex ample, the SVM algorithm has a recognition accuracy of 100% in the 890-980 cm-1, 1410-1500 cm-1 fusion spectral range, which takes about 200 s. Th e ELM algorithm also has a recognition accuracy of 100% in the 890 -980 cm-1, 1410-1500 cm-1 fusion spectral range, which takes less than 0.3 s. Th e CNN algorithm has a recognition accuracy of 100% in the 890-980 cm-1, 1050-1180 cm-1, 1410-1500 cm-1 fusion spectral range, which takes about 80 s."
NETL
NSTL