首页期刊导航|Robotics & Machine Learning Daily News
期刊信息/Journal information
Robotics & Machine Learning Daily News
NewsRx
Robotics & Machine Learning Daily News

NewsRx

Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News
正式出版
收录年代

    G.B. Pant University of Agriculture and Technology Researchers Release New Study Findings on Machine Learning (Comparison of phenological weather indices based statistical, machine learning and hybrid models for soybean yield forecasting in ...)

    48-49页
    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 artificial in telligence have been published. According to news originating from Uttarakhand, India, by NewsRx correspondents, research stated, "Early information exchange re garding predicted crop production could play a role in lowering the danger of fo od insecurity. In this study total six multivariate models were developed using past time series yield data and weather indices viz." The news reporters obtained a quote from the research from G.B. Pant University of Agriculture and Technology: "SMLR, PCA-SMLR, ANN, PCA-ANN, SMLR-ANN and PCA-S MLR-ANN for three major soybean producing districts of Uttarakhand viz. Almora, Udham Singh Nagar and Uttarkashi. Further analysis was done by fixing 80% of the data for calibration and the remaining dataset for validation to predict soybean yield. Phenology wise average values were computed using the daily weath er data. These average values are subsequently employed in the computation of bo th weighted and unweighted weather indices. The PCA-SMLR-ANN, SMLR-ANN and PCA-A NN models were found to be the best soybean yield predictor model for Almora, Ud ham Singh Nagar and Uttarkashi districts, respectively."
      原文链接:
    • NETL
    • NSTL

    University of Oklahoma Health Sciences Center Reports Findings in Radical Cystec tomy (Evaluating the Differences of Wound Related Complications in Robotically A ssisted Radical Cystectomy vs. Open Radical Cystectomy)

    49-50页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Surgery-Radical Cyst ectomy is the subject of a report. According to news reporting originating from Oklahoma City, Oklahoma, by NewsRx correspondents, research stated, "To determin e whether robotic assisted radical cystectomy (RARC) with intracorporeal urinary diversion (ICUD) compared to open radical cystectomy (ORC) or RARC with extraco rporeal urinary diversion (ECUD) would result in a decreased rate of surgical si te complications. RARC has been shown to be non-inferior to ORC." Our news editors obtained a quote from the research from the University of Oklah oma Health Sciences Center, "Both RARC and ORC are complicated by a high rate of perioperative morbidity, including wound related complications, which may be de creased by a robotic approach with intracorporeal diversion. A retrospective rev iew of our bladder cancer database for patients undergoing radical cystectomy fr om 2013 to 2021. Patients were stratified by surgical technique as RARC with ICU D vs. ORC vs. RARC with ECUD. Surgical site complications were measured at both 30 and 90-day intervals. Of the 269 patients, 127 (47.2%) had RARC with ICUD, 118 (43.7%) had ORC, and 24 (8.9%) had RARC with ECUD (mean ages 71.0, 69.5, 67.5 respectively). A comparison of the three groups demonstrated statistical significance at both the 30-day (p <0.001) and 90-day (p <0.001) timeframes for total surgical site complications, with RARC with ICUD having the fewest amount of patients exp erience a surgical site complication (0.8 %) followed by ORC (25.4% ) and RARC with ECUD (29.2%). Overall, we observed lower surgical s ite complication rates among patients undergoing RARC with ICUD compared to pati ents who underwent ORC or RARC with ECUD."
      原文链接:
    • NETL
    • NSTL

    Reports from Imperial College London Highlight Recent Research in Machine Learni ng (Machine-learning structural reconstructions for accelerated point defect cal culations)

    50-51页
    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 artificial intelligence. According to news originating from Imperial College Lon don by NewsRx correspondents, research stated, "Defects dictate the properties o f many functional materials." Funders for this research include Rcuk | Engineering And Physical Sciences Resea rch Council. Our news correspondents obtained a quote from the research from Imperial College London: "To understand the behaviour of defects and their impact on physical pr operties, it is necessary to identify the most stable defect geometries. However , global structure searching is computationally challenging for high-throughput defect studies or materials with complex defect landscapes, like alloys or disor dered solids. Here, we tackle this limitation by harnessing a machine-learning s urrogate model to qualitatively explore the structural landscape of neutral poin t defects. By learning defect motifs in a family of related metal chalcogenide a nd mixed anion crystals, the model successfully predicts favourable reconstructi ons for unseen defects in unseen compositions for 90% of cases, th ereby reducing the number of first-principles calculations by 73%."
      原文链接:
    • NETL
    • NSTL

    Data from School of Engineering and Sciences Update Knowledge in Robotics and Ar tificial Intelligence (Learning manufacturing computer vision systems using tiny YOLOv4)

    51-52页
    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 and artifici al intelligence have been presented. According to news originating from the Scho ol of Engineering and Sciences by NewsRx editors, the research stated, "Implemen ting and deploying advanced technologies are principal in improving manufacturin g processes, signifying a transformative stride in the industrial sector. Comput er vision plays a crucial innovation role during this technological advancement, demonstrating broad applicability and profound impact across various industrial operations. This pivotal technology is not merely an additive enhancement but a revolutionary approach that redefines quality control, automation, and operatio nal efficiency parameters in manufacturing landscapes." Our news reporters obtained a quote from the research from School of Engineering and Sciences: "By integrating computer vision, industries are positioned to opt imize their current processes significantly and spearhead innovations that could set new standards for future industrial endeavors. However, the integration of computer vision in these contexts necessitates comprehensive training programs f or operators, given this advanced system's complexity and abstract nature. Histo rically, training modalities have grappled with the complexities of understandin g concepts as advanced as computer vision. Despite these challenges, computer vi sion has recently surged to the forefront across various disciplines, attributed to its versatility and superior performance, often matching or exceeding the ca pabilities of other established technologies. Nonetheless, there is a noticeable knowledge gap among students, particularly in comprehending the application of Artificial Intelligence (AI) within Computer Vision. This disconnect underscores the need for an educational paradigm transcending traditional theoretical instr uction. Cultivating a more practical understanding of the symbiotic relationship between AI and computer vision is essential. To address this, the current work proposes a project-based instructional approach to bridge the educational divide . This methodology will enable students to engage directly with the practical as pects of computer vision applications within AI. By guiding students through a h ands-on project, they will learn how to effectively utilize a dataset, train an object detection model, and implement it within a microcomputer infrastructure. This immersive experience is intended to bolster theoretical knowledge and provi de a practical understanding of deploying AI techniques within computer vision. The main goal is to equip students with a robust skill set that translates into practical acumen, preparing a competent workforce to navigate and innovate in th e complex landscape of Industry 4.0."
      原文链接:
    • NETL
    • NSTL

    University of Hamburg Researcher Advances Knowledge in Robotics (Field Performan ce of a Dual Arm Robotic System for Efficient Tomato Harvesting)

    52-53页
    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 originating from Hamburg, Germany, by NewsRx correspondents, research stated, "The robot device that is being addressed in t his research has two arms: one for picking the fruit and the other for chopping it." The news journalists obtained a quote from the research from University of Hambu rg: "The arms find and locate pods with the help of a complex vision system that employs cameras. In this human-robot workflow, the operator chooses the tomatoe s they want picked, and then the robot does the actual picking. The robot manage ment and communication system use the EtherCAT bus to create a link with the gra phical user interface (GUI), enabling human administration and control. The obje ctive of this project is to create and assess a robotic system for harvesting to matoes, equipped with dual arms. This system incorporates a mobile model equippe d with two robotic arms and an end effector to enhance the efficiency of tomato harvesting. The system uses a GUI to enhance interaction between the robot and t he human operator."
      原文链接:
    • NETL
    • NSTL

    Study Data from SRM Institute of Science and Technology Provide New Insights int o Intelligent Systems (Fuzzy rule based classifier model for evidence based clin ical decision support systems)

    53-53页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on intelligent s ystems. According to news originating from Tamil Nadu, India, by NewsRx correspo ndents, research stated, "Clinicians benefit from the use of artificial intellig ence and machine learning techniques applied to health data within health record s, which identify commonalities between them. It enables them to get evidence-ba sed support in recommending shared treatment paths for undiagnosed health record s." Financial supporters for this research include Bill And Melinda Gates Foundation . Our news reporters obtained a quote from the research from SRM Institute of Scie nce and Technology: "The collective inference from these patterns, drawn from an array of health records, further enhances the capacity to mine essential featur es, supporting public health experts in their management of population health co nditions. This paper presents a novel mapping tool model designed to analyze ele ctronic health records and provide healthcare providers with evidence-based deci sion support. The work focuses on the analysis of health records from hospital d atabases, encompassing parameters extracted from routine health checkups. By scr utinizing patterns within examined health records, healthcare providers can seam lessly align with newer health records for diagnosis and treatment recommendatio ns. Core to this approach is the integration of a fuzzy rule-based classifier sy stem within the proposed system. This incorporation facilitates the processing o f health records, extracting pertinent features to augment decision-making with the support of knowledge bases. The model architecture provides flexibility and customizability, enabling easy configuration of the system to accurately map new health records to the examined dataset. Additionally, the model utilizes a spec ially developed distance-measure technique tailored for the proposed fuzzy-based system."
      原文链接:
    • NETL
    • NSTL

    Investigators at Yanshan University Detail Findings in Robotics (Fixed-time Comp osite Learning Control of Robots With Prescribed Time Error Constraints)

    54-54页
    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 Robotics is now availab le. According to news reporting from Qinhuangdao, People's Republic of China, by NewsRx journalists, research stated, "This article investigates the adaptive co mposite learning control problem of robots subject to uncertain dynamics and pre scribed time error constraints. Existing prescribed time error constraint method s only achieve semiglobal results or guarantee system order-dependent convergenc e rate." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news correspondents obtained a quote from the research from Yanshan Universi ty, "In this article, by integrating a new prescribed time performance function into a tracking error-based barrier function, a novel prescribed time error cons traint method is proposed with the following appealing features: 1) the constrai nt method is global; 2) the tracking error converges to a compact set with a pro ximate exponential rate, which can be preassigned by the user regardless of syst em order; 3) both settling time and compact set can be preassigned by the user. To handle the uncertain dynamics caused by inaccurate measurement of parameters, a novel fixed-time composite learning robot control (FTCLRC) method is develope d by combining a newly designed nonsingular fixed-time integral terminal sliding mode and the Moore-Penrose pseudoinverse-based composite learning technique. In comparison with existing composite learning robot control methods that can only ensure exponential convergence, or finite-time convergence, which is dependent on the unpredictable excitation strengths and initial system states, the propose d FTCLRC can guarantee that both the tracking error and parameters estimation er ror converge to zero in fixed-time, under a weak IE without singularity issue. I n particular, the convergence time only depends on the user-designed parameters, independent of the system's initial states, and the unpredictable excitation st rengths."
      原文链接:
    • NETL
    • NSTL

    Findings from Sage Bionetworks Update Understanding of Machine Learning (Causali ty-aware Predictions In Static Anticausal Machine Learning Tasks)

    55-55页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting out of Seattle, Washington, by News Rx editors, the research stated, "We propose a counterfactual approach to train ‘causality-aware' predictive models that are able to leverage causal information in static anticausal machine learning tasks (i.e., prediction tasks where the o utcome influences the inputs). In applications plagued by confounding, the appro ach can be used to generate predictions that are free from the influence of obse rved confounders." Our news journalists obtained a quote from the research from Sage Bionetworks, " In applications involving observed mediators, the approach can be used to genera te predictions that only capture the direct or the indirect causal influences. M echanistically, we train supervised learners on (counterfactually) simulated inp uts that retain only the associations generated by the causal relations of inter est. We focus on linear models, where analytical results connecting covariances, causal effects, and prediction mean square errors are readily available. Quite importantly, we show that our approach does not require knowledge of the full ca usal graph. It suffices to know which variables represent potential confounders and/or mediators. We investigate the stability of the method with respect to dat aset shifts generated by selection biases and also relax the linearity assumptio n by extending the approach to additive models better able to account for nonlin earities in the data."
      原文链接:
    • NETL
    • NSTL

    Data on Machine Learning Described by a Researcher at Indian Institute of Techno logy Roorkee (Local interface remapping based curvature computation on unstructu red grids in volume of fluid methods using machine learning)

    56-56页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on artificial intelligence have been published. According to news reporting from Roorkee, India, by NewsRx jour nalists, research stated, "The volume of fluid method is widely used for interfa ce capturing in two-phase flows including surface tension." Financial supporters for this research include Science And Engineering Research Board. Our news reporters obtained a quote from the research from Indian Institute of T echnology Roorkee: "Calculation of surface forces requires accurate local interf acial curvature, which, despite receiving considerable attention, remains a chal lenge due to the abrupt variation of volume fraction near the interface. Based o n recent studies showing the potential of data-driven techniques, a machine lear ning (ML) model using a multi-layered artificial neural network is initially dev eloped to predict curvature on structured grids. Known shapes in the form of cir cular interface segments are used to generate a synthetic training dataset consi sting of interfacial curvature and volume fractions. An optimum model configurat ion is carefully obtained, with a larger 5 x 5 input stencil showing increased a ccuracy for test data along with analytical test cases. However, an extension of the model to unstructured grids, required in simulations involving complex geom etries, is non-trivial. To overcome the limitations, a local interface remapping algorithm is proposed where the stencil around a target cell is transformed int o a structured stencil for the generation of the input dataset."
      原文链接:
    • NETL
    • NSTL

    Third Military Medical University Reports Findings in Artificial Intelligence (A new artificial intelligence system for both stomach and small bowel capsule end oscopy)

    57-57页
    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 originating in Chongqi ng, People's Republic of China, by NewsRx journalists, research stated, "Despite the benefits of artificial intelligence (AI) in small bowel (SB) capsule endosc opy (CE) image reading, information on its application in the stomach and SB CE is lacking. In this multicenter, retrospective diagnostic study, gastric imaging data were added to the deep learning (DL)-based SmartScan (SS), which has been described previously." The news reporters obtained a quote from the research from Third Military Medica l University, "A total of 1,069 magnetically controlled gastrointestinal (GI) CE examinations (comprising 2,672,542 gastric images) were used in the training ph ase for recognizing gastric pathologies, producing a new AI algorithm named SS P lus. 342 fully automated, magnetically controlled CE (FAMCE) examinations were i ncluded in the validation phase. The performance of both senior and junior endos copists with both the SS Plus- Assisted Reading (SSP-AR) and conventional reading (CR) modes was assessed. SS Plus was designed to recognize 5 types of gastric l esions and 17 types of SB lesions. SS Plus reduced the number of CE images requi red for review to 873.90 (1000) (median, IQR 814.50-1,000) versus 44,322.73 (42, 393) (median, IQR 31,722.75-54,971.25) for CR. Furthermore, with SSP-AR, endosco pists took 9.54 min (8.51) (median, IQR 6.05-13.13) to complete the CE video rea ding. In the 342 CE videos, SS Plus identified 411 gastric and 422 SB lesions, w hereas 400 gastric and 368 intestinal lesions were detected with CR. Moreover, j unior endoscopists remarkably improved their CE image reading ability with SSP-A R."
      原文链接:
    • NETL
    • NSTL