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 reporting out of Chittagong University of Engineering and Technology by NewsRx editors, research stated, “Ai r-writing is a widely used technique for writing arbitrary characters or numbers in the air.” Our news reporters obtained a quote from the research from Chittagong University of Engineering and Technology: “In this study, a data collection technique was developed to collect hand motion data for Bengali air-writing, and a motion sens or-based data set was prepared. The feature set as then utilized to determine th e most effective machine learning (ML) model among the existing well-known super vised machine learning models to classify Bengali characters from air-written da ta. Our results showed that medium Gaussian SVM had the highest accuracy (96.5% ) in the classification of Bengali character from air writing data. In addition, the proposed system achieved over 81% accuracy in real-time class ification.”
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 Ro botics. According to news reporting out of Wuhan, People’s Republic of China, by NewsRx editors, research stated, “Industrial robots provide a new production mo de for automatic assembly. However, the motion accuracy of robots is hard to mee t high-precision assembly requirements in many current scenarios.” Funders for this research include National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC).
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 originating from Umm Al-Qura Unive rsity by NewsRx correspondents, research stated, “Ransomware attacks have escala ted recently and are affecting essential infrastructure and enterprises across t he globe.” Funders for this research include Imam Abdulrahman Bin Faisal University. The news reporters obtained a quote from the research from Umm Al-Qura Universit y: “Unfortunately, ransomware uses sophisticated encryption techniques to encryp t important files on the targeted machine and then demands payment to decrypt th e data. Artificial intelligent techniques including machine learning have been i ncreasingly applied in the field of cybersecurity and greatly contributed to det ecting and preventing different kinds of attacks However, the number of studies that applied machine learning to detect ransomware are still limited by the obfu scation of malware, the lack of setting up a proper analysis environment, the ac curacy of models, and the high false-positive rate. Thus, it is crucial to devel op effective ransomware detection based on machine learning techniques. This stu dy aims to build a robust machine-learning model that can recognize unknown samp les using memory dumps to detect ransomware with high accuracy and minimal false positives providing an extensive analysis of how memory traces can assist in th e detection of ransomware. This goal was achieved by building a new dataset comp osed of recent ransomware group attack samples like Revil, Lockbit, and BlackCat , as well as a number of benign dynamically analyzed with in an enhanced cuckoo sandbox to ensure the most reliable results. Then, a set of machine learning models were developed, and a comparative performance analysis was conducted.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on robotics are presented in a new rep ort. According to news reporting originating from Seoul, South Korea, by NewsRx correspondents, research stated, “Detecting parcels accurately and efficiently h as always been a challenging task when unloading from trucks onto conveyor belts because of the diverse and complex ways in which parcels are stacked.” Financial supporters for this research include Korean Government Msit; Korea Eva luation Institute of Industrial Technology; Ministry of Trade, Industry, And Ene rgy (Motie), Korea.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on robotics have been pr esented. According to news reporting from Shenzhen, People’s Republic of China, by NewsRx journalists, research stated, “In the transition from virtual environm ents to real-world applications, the role of physics engines is crucial for accu rately emulating and representing systems. To address the prevalent issue of ina ccurate simulations, this paper introduces a novel physics engine uniquely desig ned with a compliant contact model designed for robotic grinding.” Financial supporters for this research include National Key R&D Pro gram of China; Science, Technology And Innovation Commission of Shenzhen Municip ality.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Autoimmune Diseases a nd Conditions - Lupus have been presented. According to news reporting out of Qu ezon City, Philippines, by NewsRx editors, research stated, “The development of lupus low disease activity state (LLDAS) as a treat-to-target endpoint for SLE p atients has been validated. Its attainment has been associated with improved out comes.”
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 Kafr El-Shaikh, Egypt, by NewsRx editors, the research stated, “Being a cheap, simple, and low-energy consumer, solar stills have been introduced by water and energy scientists as an alternative desalination method to fossil fuel-based ones.” Funders for this research include Prince Sattam Bin Abdulaziz University; Sticht ing Volksbond Rotterdam; Office of Defense Nuclear Nonproliferation.
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 reporting ou t of Chongqing, People’s Republic of China, by NewsRx editors, research stated, “This study aimed to subtype multiple sclerosis (MS) patients using unsupervised machine learning on white matter (WM) fiber tracts and investigate the implicat ions for cognitive function and disability outcomes. We utilized the automated f iber quantification (AFQ) method to extract 18 WM fiber tracts from the imaging data of 103 MS patients in total.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on robotics is the subject of a new report. According to news reporting originating from Jeddah, Saudi Arabia, by Ne wsRx correspondents, research stated, “This paper addresses the topic of stabili zing a class of nonholonomic systems in chained form impacted by matched uncerta inties and time-varying perturbations. To design efficient stabilizing control l aw, the whole problem is divided into two sub-problems.” Funders for this research include Deputyship For Research & Innova tion, Ministry of Education, Saudi Arabia.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting out of Shangha i, People’s Republic of China, by NewsRx editors, research stated, “The safety o f aircraft landing on wet runways is of great importance in runway risk manageme nt.” Our news editors obtained a quote from the research from Tongji University: “In order to ensure landing safety on wet runways, real-time risk warning is require d. This paper proposes a method to assess aircraft landing risk in real-time bas ed on finite element-virtual prototype-machine learning co-simulation. Firstly, a tire-water film-runway finite element model was constructed, a virtual prototy pe model was built based on the Airbus A320 model, and the results of the tire-w ater film-runway local finite element dynamic analysis were transferred to the s ystem simulation of the virtual prototype for co-simulation. Secondly, consideri ng the influence of wet state parameters on the runway, a database of aircraft a nti-skid failure risk was constructed, and three machine learning models were tr ained to predict aircraft landing risk. The results show that the Support Vector Machine (SVM) model has better generalization capability and should be used to predict the risk level of aircraft landing. The efficacy of the comprehensive ta xiing model was validated using an empirical formula for determining the aircraf t’s landing distance on a wet runway.”
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