查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Why should you care about AI and machi ne learning? It’s changing the world aroundus at a startling pace. You need to know how it works and what it means for your industry. We’re at awatershed mome nt in history.Machine learning is changing the workplace, it’s changing the economy, and it’s changing society. Findout how and why in >Secrets of Ma chine Learning: How It Works and What It Means for You and learnwhat you need t o know for your industry.Computers can spot potential lung cancer better than doctors, sniff out fraud be tter than bankers,and make fake videos that are almost impossible to tell apart from the real thing. Machines will soonwrite up instant legal documents as goo d as lawyers and haul goods from coast to coast without drivers.“It’s [already] everywhere,” says author, Tom Kohn, “From voice assi stants like Siri and Alexa, to movierecommendations such as those on Netflix or Amazon Prime, to search engines or social media, similarmachine-learning techn ologies are powering our lives”. If you want to know how and why, read this book .
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New study results on artificial intell igence have been published. According to newsreporting from Hong Kong Polytechn ic University by NewsRx journalists, research stated, “The air conditioningsyst ems in electric city buses usually operate in rapidly changing ambient condition s and are morelikely to suffer from mechanical faults. Although many fault dete ction and diagnosis (FDD) methods havebeen developed for building air condition ing systems, they are difficult to be applied to bus air conditionerssince its operation is highly dynamic and fault-free data are usually unavailable.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Investigators publish new report on Ar tificial Intelligence. According to news reportingfrom College Park, Maryland, by NewsRx journalists, research stated, “Big data and artificial intelligence(A I) have transformed the finance industry by altering the way data and informatio n are generated,processed, and incorporated into decision-making processes. Dat a and information have emerged as a newclass of assets, facilitating efficient contracting and risk-sharing among corporate stakeholders.”The news correspondents obtained a quote from the research from the University o f Maryland, “Researchershave also increasingly embraced machine learning and AI analytics tools, which enable them toexploit empirical evidence to an extent t hat far surpasses traditional methodologies. In this review article,prepared fo r a special issue on Artificial Intelligence (AI) and Finance in the Pacific-Bas in Finance Journal,we aim to provide a summary of the evolving landscape of AI applications in finance and accountingresearch and project future avenues of ex ploration. Given the burgeoning mass of literature in this field,it would be un productive to attempt an exhaustive catalogue of these studies. Instead, our goa l is tooffer a structured framework for categorizing current research and guidi ng future studies. We stress theimportance of blending financial domain experti se with state-of-the-art data analytics skills.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Researchers detail new data in Robotic s. According to news reporting originatingfrom Waurn Ponds, Australia, by NewsR x correspondents, research stated, “Due to the disturbances andvarying latency, a teleoperated robotic manipulator might not comply with the master control com mands.Although prior studies on minimising the impact of network latency and di sturbances on teleoperatedrobots were conducted, there has been very little res earch on the prediction of minimal operation regionsof robotic arms, especially in the worst-case scenarios when the disturbances and time delays still prevaileven after impact minimisation.”Financial support for this research came from Telematics Trust.Our news editors obtained a quote from the research from Deakin University, “Thi s study investigatesthe problem and proposes a novel solution to predicting min imal operation regions of networked controlrobotic manipulators. The proposed m ethod can be used to forecast safe operation regions in which themanipulators w ill certainly enter and exclude regions that the robots will never penetrate. Le veraging ona Lyanonov Krasovskii criterion, the method performs region predicti on by establishing minimal reachablebounding sets of the nonlinear, perturbed r obotic arm’s state vectors guided via a time-varying delaydominantnetwork. Tho ugh predominantly nonlinear, the entire prediction process is formulated as a tractable Linear Matrix Inequality (LMI) optimisation problem, which can be solved efficiently and effectively.Efficacy of the proposed method is validated with simulations where a simulated robotic arm is distortedwith time-varying delays and disturbances. This study investigates the nonlinear problem of predicting minimal operation regions of robotic arms subject to time-varying delays, uncertai nties and disturbances.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Machine Learning is th e subject of a report. According to newsreporting originating in Beijing, Peopl e’s Republic of China, by NewsRx journalists, research stated,“Understanding th e mechanisms of C-H activation of alkanes is a very important research topic. Th ereactions of metal clusters with alkanes have been extensively studied to reve al the electronic featuresgoverning C-H activation, while the experimental clus ter reactivity was qualitatively interpreted case bycase in the literature.”The news reporters obtained a quote from the research from the Chinese Academy o f Sciences, “Herein,we prepared and mass-selected over 100 rhodium-based cluste rs (RhVO and RhCoO) to react with lightalkanes, enabling the determination of r eaction rate constants spanning six orders of magnitude. Asatisfactory model be ing able to quantitatively describe the rate data in terms of multiple cluster electronic features (average electron occupancy of valence s orbitals, the minimu m natural charge on themetal atom, cluster polarizability, and energy gap invol ved in the agostic interaction) has been constructedthrough a machine learning approach.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - Fresh data on Agriculture - Agricultur al Robots are presented in a new report. Accordingto news reporting originating from Foshan, People’s Republic of China, by NewsRx correspondents, researchsta ted, “Fruit-picking robots are crucial for achieving efficient orchard harvestin g. To genuinely meetthe commercial production needs of farmers, the new generat ion of fruit-picking robots must be capableof demonstrating complete and contin uous observation, movement, and picking behaviors throughoutcomplex orchards, a kin to real human employees.”Funders for this research include National Natural Science Foundation of China ( NSFC), National NaturalScience Foundation of Guangdong Province, Bingtuan Scien ce and Technology Program, GuangdongFoshan Special Project.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New study results on artificial intell igence have been published. According to newsoriginating from Gyeonggi Do, Sout h Korea, by NewsRx correspondents, research stated, “This paperminimizes fake n ews, which has been a hot topic recently, using blockchain and artificial intell igencetechnology, and verifies it with blockchain. Also, using Artificial Intel ligence technology, we want to createan algorithm that predicts how fake news w ill spread in the future.”The news journalists obtained a quote from the research from Joongbu University: “You can see variousattempts at a news media platform based on Blockchain tech nology. However, the Blockchain news mediaplatform is still not getting the mar ket response we expected. It is questionable whether the reason issimply becaus e it is a new technology, so it takes a long time to gain trust from consumers, whetherconsumers are not yet expecting an innovative news media platform, or wh ether the explosive growthof the Blockchain news media platform is difficult fo r other reasons. Research to answer this or directresearch between Blockchain a nd media platforms is still lacking. In addition, the method of verifying faken ews using artificial intelligence was verified, ANN, CBR, and MDA were changed, and the experiment wasverified for progress. In addition, the use of 5-fold cro ss-validation as a comparative method was added asdescribed above to more close ly examine the possibility of its usefulness even in general situations. Also, through various fields of artificial intelligence and blockchain, verification wo rk was done with blockchain,and fake news prediction was made using artificial intelligence.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Artificial Intelligenc e is the subject of a report. According tonews reporting originating from Barce lona, Spain, by NewsRx correspondents, research stated, “To assessthe ability o f an artificial intelligence software to detect pneumothorax in chest radiograph s done afterpercutaneous transthoracic biopsy. We included retrospectively in o ur study adult patients who underwentCT-guided percutaneous transthoracic biops ies from lung, pleural or mediastinal lesions from June 2019to June 2020, and w ho had a follow-up chest radiograph after the procedure.”Our news editors obtained a quote from the research from University Hospital Val l d’Hebron, “Thesechest radiographs were read to search the presence of pneumot horax independently by an expert thoracicradiologist and a radiodiagnosis resid ent, whose unified lecture was defined as the gold standard, and theresult of e ach radiograph after interpretation by the artificial intelligence software was documented forposterior comparison with the gold standard. A total of 284 chest radiographs were included in the studyand the incidence of pneumothorax was 14 .4%. There were no discrepancies between the two readers’interpret ation of any of the postbiopsy chest radiographs. The artificial intelligence so ftware was ableto detect 41/41 of the present pneumothorax, implying a sensitiv ity of 100% and a negative predictivevalue of 100%, with a specificity of 79.4% and a positive predictive value of 45% . The accuracy was82.4%, indicating that there is a high probabili ty that an individual will be adequately classified by thesoftware. It has also been documented that the presence of Port-a-cath is the cause of 8 of the 50 of falsepositives by the software. The software has detected 100% o f cases of pneumothorax in the postbiopsychest radiographs.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Machine Learning is th e subject of a report. According to newsreporting from Amsterdam, Netherlands, by NewsRx journalists, research stated, “The Response EvaluationCriteria in Sol id Tumors (RECIST) aims to provide a standardized approach to assess treatment r esponsein solid tumors. However, discrepancies in the selection of measurable a nd target lesions among radiologistsusing these criteria pose a significant lim itation to their reproducibility and accuracy.”The news correspondents obtained a quote from the research from Netherlands Canc er Institute,“This study aimed to understand the factors contributing to this v ariability. Machine learning models wereused to replicate, in parallel, the sel ection process of measurable and target lesions by two radiologistsin a cohort of 40 patients from an internal pan-cancer dataset. The models were trained on l esioncharacteristics such as size, shape, texture, rank, and proximity to other lesions. Ablation experimentswere conducted to evaluate the impact of lesion d iameter, volume, and rank on the selection process.The models successfully repr oduced the selection of measurable lesions, relying primarily on size-relatedfe atures. Similarly, the models reproduced target lesion selection, relying mostly on lesion rank. Beyondthese features, the importance placed by different radio logists on different visual characteristics can vary,specifically when choosing target lesions. Worth noting that substantial variability was still observed between radiologists in both measurable and target lesion selection. Despite the s uccessful replication oflesion selection, our results still revealed significan t inter-radiologist disagreement.”
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