查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on computational intelli gence have been presented. According to news reporting out of Toyama, Japan, by NewsRx editors, research stated, “In recent years, several attempts have been ma de to quantitatively evaluate covert attention using microsaccades. However, sev eral unclear aspects exist regarding the measurement method of microsaccades, an d a unified analysis method does not exist.” Our news journalists obtained a quote from the research from Toyama Prefectural University: “Therefore, the current status is such that the interpretation of th e results is divided among the research groups. To address this problem, empiric al studies on microsaccades must be accumulated and measured and evaluated using a unified method. Therefore, in this study, to accumulate empirical studies on microsaccades, an experiment was conducted to investigate the effect of the pres ence or absence of gazing at a fixation point on the interval of occurrence of m icrosaccades in a measurement task. The participants were 15 healthy young peopl e, and we compared the following two types of measurement tasks. Task-I: The par ticipants freely visually searched a white wall 1 m away for 120 s. Task-II: The participants gazed at a fixation point located 1 m ahead at eye level for 120 s .”
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 reporting out of Chennai, India, by New sRx editors, research stated, “Tomato is one of the most popular and most import ant food crops consumed globally. The quality and quantity of yield by tomato pl ants are affected by the impact made by various kinds of diseases.” Our news correspondents obtained a quote from the research from School of Comput er Science and Engineering: “Therefore, it is essential to identify these diseas es early so that it is possible to reduce the occurrences and effect of the dise ases on tomato plants to improve the overall crop yield and to support the farme rs. In the past, many research works have been carried out by applying the machi ne learning techniques to segment and classify the tomato leaf images. However, the existing machine learning-based classifiers are not able to detect the new t ypes of diseases more accurately. On the other hand, deep learning-based classif iers with the support of swarm intelligence-based optimization techniques are ab le to enhance the classification accuracy, leading to the more effective and acc urate detection of leaf diseases. This research paper proposes a new method for the accurate classification of tomato leaf diseases by harnessing the power of a n ensemble model in a sample dataset of tomato plants, containing images pertain ing to nine different types of leaf diseases. This research introduces an ensemb le model with an exponential moving average function with temporal constraints a nd an enhanced weighted gradient optimizer that is integrated into fine-tuned Vi sual Geometry Group-16 (VGG-16) and Neural Architecture Search Network (NASNet) mobile training methods for providing improved learning and classification accur acy.”
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 Zibo, People’s Repub lic of China, by NewsRx correspondents, research stated, “This research paper pr esents a comprehensive study on optimizing the critical artificial intelligence (AI) factors influencing cost management in civil engineering projects using a m ulti-criteria decision-making (MCDM) approach.” Funders for this research include University-enterprise-partnership Program of S olearth Architecture. Our news correspondents obtained a quote from the research from Shandong Univers ity of Technology: “The problem addressed revolves around the need to effectivel y manage costs in civil engineering endeavors amidst the growing complexity of p rojects and the increasing integration of AI technologies. The methodology emplo yed involves the utilization of three MCDM tools, specifically Delphi, interpret ive structural modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). A total of 17 AI factors, categorized into eight broad groups, were identified and analyzed. Through the application of different MCDM techniques, the relative importance and interrelationships among these factors were determined. The key findings reveal the critical role of certain AI factors , such as risk mitigation and cost components, in optimizing the cost management processes. Moreover, the hierarchical structure generated through ISM and the i nfluential factors identified via MICMAC provide insights for prioritizing strat egic interventions.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on Machine Learn ing. According to news reporting originating in Beijing, People’s Republic of Ch ina, by NewsRx journalists, research stated, “Scaling-up, and optimization of in dustrial reactors mainly depend on step-by-step experiments and engineering expe rience, which is usually time-consuming, high cost, and high risk. Although nume rical simulation can reproduce high resolution details of hydrodynamics, thermal transfer, and reaction process in reactors, it is still challenging for industr ial reactors due to huge computational cost.” Funders for this research include National Natural Science Foundation of China ( NSFC), Youth Innovation Promotion Association, Chinese Academy of Sciences.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Gram-Negative Bacteria - Klebsiella is the subject of a report. According to news originating from Ghe nt, Belgium, by NewsRx correspondents, research stated, “Phages are increasingly considered promising alternatives to target drug-resistant bacterial pathogens. However, their often-narrow host range can make it challenging to find matching phages against bacteria of interest.” Our news journalists obtained a quote from the research from Ghent University, “ Current computational tools do not accurately predict interactions at the strain level in a way that is relevant and properly evaluated for practical use. We pr esent PhageHostLearn, a machine learning system that predicts strain-level inter actions between receptor-binding proteins and bacterial receptors for Klebsiella phage-bacteria pairs. We evaluate this system both in silico and in the laborat ory, in the clinically relevant setting of finding matching phages against bacte rial strains. PhageHostLearn reaches a cross-validated ROC AUC of up to 81.8% in silico and maintains this performance in laboratory validation.”
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 out of Erlangen, Germany, by NewsRx e ditors, research stated, “Policy learning in robot-assisted surgery (RAS) lacks data efficient and versatile methods that exhibit the desired motion quality for delicate surgical interventions. To this end, we introduce Movement Primitive D iffusion (MPD), a novel method for imitation learning (IL) in RAS that focuses o n gentle manipulation of deformable objects.” Financial support for this research came from Erlangen National High Performance Computing Center.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on robotics are disc ussed in a new report. According to news originating from Semarang, Indonesia, b y NewsRx correspondents, research stated, “Hand exoskeleton robots have been dev eloped as rehabilitation robots and assistive devices. Based on the material use d, they can be soft or hard exoskeletons.” The news reporters obtained a quote from the research from Diponegoro University : “Soft materials such as fabric can be used as a component of the wearable robo t to increase comfortability. In this paper, we proposed an affordable soft hand exoskeleton based on fabric and motor-tendon actuation for hand flexion/extensi on motion assistance in daily activities. On-off control and PI compensator were implemented to regulate finger flexion and extension of the soft exoskeleton. T he controllers were embedded into a microcontroller using Simulink software. The input signal command comes from the potentiometer and electromyography (EMG) se nsor to drive the flexion/extension movement. Based on the experiments, the prop osed controller successfully controlled the exoskeleton hand to facilitate a use r in grasping various objects.”
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 out of Tianjin, People’s Republic of China, by NewsRx editors, research stated, “Flexible surface textures are often utiliz ed in the design of robots that need to manipulate objects requiring a strong fr ictional force. In this study, we designed and prepared flexible silicone rubber films with surface textures inspired by groove patterns found at the tips of hu man fingers.” Financial support for this research came from Beijing Natural Science Foundation .
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 - Robotics and Automation is now available. According to news reporting out of Ann Arbor, Mich igan, by NewsRx editors, research stated, “Developing autonomous vehicles (AVs) that operate in diverse and demanding environments is a difficult challenge. Two fundamental tools that can accelerate this process are testing an AV in diverse simulated environments and identifying core system weaknesses.” Financial support for this research came from U.S. Army DEVCOM Ground Vehicle Sy stems Center. Our news journalists obtained a quote from the research from the University of M ichigan, “While most efforts focus on improving these tools for on-road AVs, thi s letter focuses on an analogous set of tools for off-road AVs. A method called Black-Box Adversarially Compounding Regret Through Evolution (BACRE) is proposed for identifying adversarial scenarios using an evolutionary algorithm guided by a novel regret-based metric for general navigation tasks. A black-box approach is often preferable when system complexity can be diverse, like with off-road AV s, and when whole-system testing is required. A custom simulation platform is al so provided to assist with the automated testing of AVs in diverse, unstructured environments. Numerical experiments demonstrate that BACRE’s evolutionary proce ss gradually increases scenario complexity to degrade vehicle performance (an ef fective and explainable process that comparable methods cannot achieve).”
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Machine Learning are discuss ed in a new report. According to news reporting from Wuhan, People’s Republic of China, by NewsRx journalists, research stated, “The rapid increase in populatio n accelerates the rate of change of Land use/Land cover (LULC) in various parts of the world. This phenomenon caused a huge strain for natural resources.” 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 Wuhan University , “Hence, continues monitoring of LULC changes gained a significant importance f or management of natural resources and assessing the climate change impacts. Rec ently, application of machine learning algorithms on RS (remote sensing) data fo r rapid and accurate mapping of LULC gained significant importance due to growin g need of LULC estimation for ecosystem services, natural resource management an d environmental management. Hence, it is crucial to access and compare the perfo rmance of different machine learning classifiers for accurate mapping of LULC. T he primary objective of this study was to compare the performance of CART (Class ification and Regression Tree), RF (Random Forest) and SVM (Support Vector Machi ne) for LULC estimation by processing RS data on Google Earth Engine (GEE). In t otal four classes of LULC (Water Bodies, Vegetation Cover, Urban Land and Barren Land) for city of Lahore were extracted using satellite images from Landsat-7, Landsat-8 and Landsat-9 for years 2008, 2015 and 2022, respectively. According t o results, RF is the best performing classifier with maximum overall accuracy of 95.2% and highest Kappa coefficient value of 0.87, SVM achieved m aximum accuracy of 89.8% with highest Kappa of 0.84 and CART showe d maximum overall accuracy of 89.7% with Kappa value of 0.79.”
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