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 . According to news reporting out of Mizoram, India, by NewsRx editors, research stated, "In this paper, chemometric methods were used for exploratory analysis, categorization, and quantification of gasoline fuel using Fourier Transform Inf rared Spectroscopy (FTIR) data." Funders for this research include Science And Engineering Research Board; Depart ment of Science And Technology, Ministry of Science And Technology, India. Our news editors obtained a quote from the research from Mizoram University: "Du ring exploratory analysis, Principal Component Analysis (PCA) was employed, and to categorise the gasoline samples, Support Vector Machine Classification (SVMC) , Linear Discrimination Analysis (LDA), and Partial Least Squares Discriminant A nalysis (PLS-DA) were used. The concentration of Benzene, Methyl Tert-butyl Ethe r (MTBE), and Public Distribution System (PDS) Kerosene were determined using th e Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and Support Vector Machine Regression (SVMR). All of the chemometric models had 100% accuracy and high R-square and RMSEC significance values. Th e SVM classification techniques were identified as a suitable choice for both cl assifying oxygenated and adulterated samples among all approaches. Both PLSR and PCR can also be suitable choices for quantification when dealing with high dime nsional data."
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 Shandong, People's Rep ublic of China, by NewsRx journalists, research stated, "This article investigat es the singularity-free finite-time adaptive optimal control problem for coordin ated robots, where the position and velocity are constrained within the asymmetr ic yet time-varying ranges. Different from the existing results concerning const rained control, the imposed feasibility conditions are relaxed by skillfully int egrating a nonlinear state-dependent function into the backstepping design proce dure." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). The news reporters obtained a quote from the research from the Shandong Universi ty of Science and Technology, "Therein, the typical feature of the designed fini te-time controller lies in the application of the modified smooth switching func tion, rendering the designed controller powerful enough to eliminate singularity problem. Notably, with the aid of the constructed optimal cost function and neu ral networkbased critic architecture, the optimal control law is established un der the backstepping design framework. It is theoretically verified that the des igned controller is of satisfied optimization and finite-time tracking ability, and desired constrained objective in the meanwhile."
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 Leipzig, Germany, by N ewsRx editors, research stated, "The growing number of cars and limited street s pace present significant challenges for cities, applying not only to moving but extending to stationary traffic. The quest for parking spaces exacerbates traffi c congestion, noise, and air pollution, particularly in residential areas." Our news correspondents obtained a quote from the research from University of Le ipzig: "To develop effective parking solutions for these challenges, a trustful data foundation on available parking space capacities, its usage and parking typ e is crucial. Gathering this data is currently time-consuming, requiring manual labeling and street inspections. Moreover, it must be repeated to keep the data current. Research on parking space management has heavily focused on monitoring designated parking lots with fixed cameras to identify free or occupied parking spaces. However, due to privacy concerns fixed cameras are not applicable for th e larger part of the street space in European cities. This paper introduces a no vel computer visionbased method for automatically collecting parking space capa cities and parking type information. Our approach combines both street view and aerial imagery, which are recorded by a moving camera source. We tackle challeng es in geo-referencing images, identifying parking types, classifying moving and stationary cars and dealing with partial occlusions in images. By not permanentl y recording the same environment, our approach lowers the surveillance risk, mak ing parking capacity estimation scalable."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Liver Cance r is the subject of a report. According to news reporting originating from Guang dong, People's Republic of China, by NewsRx correspondents, research stated, "Im mune infiltration within the tumor microenvironment (TME) plays a significant ro le in the onset and progression of hepatocellular carcinoma (HCC). Machine learn ing applied to pathological images offers a practical means to explore the TME a t the cellular level." Our news editors obtained a quote from the research from the Chinese Academy of Sciences, "Our former research employed a transfer learning procedure to adapt a convolutional neural network (CNN) model for cell recognition, which could reco gnize tumor cells, lymphocytes, and stromal cells autonomously and accurately wi thin the images. This study introduces a novel immune classification system base d on the modified CNN model. Patients with HCC from both Beijing Hospital and Th e Cancer Genome Atlas (TCGA) database were included in this study. Additionally, least absolute shrinkage and selection operator (LASSO) analyses, along with lo gistic regression, were utilized to develop a prognostic model. We proposed an i mmune classification based on the percentage of lymphocytes, with a threshold se t at the median lymphocyte percentage. Patients were categorized into high or lo w infiltration subtypes based on whether their lymphocyte percentages were above or below the median, respectively. Patients with different immune infiltration subtypes exhibited varying clinical features and distinct TME characteristics. T he low-infiltration subtype showed a higher incidence of hypertension and fatty liver, more advanced tumor stages, downregulated immune-related genes, and highe r infiltration of immunosuppressive cells. A reliable prognostic model for predi cting early recurrence of HCC based on clinical features and immune classificati on was established. The area under the curve (AUC) of the receiver operating cha racteristic (ROC) curves was 0.918 and 0.814 for the training and test sets, res pectively."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of Davis, California, by NewsRx editors, research stated, "Almonds are a major crop in California which p roduces 80% of all the world's almonds. Widespread drought and str ict groundwater regulations pose significant challenges to growers." Financial supporters for this research include National Institute of Food and Ag riculture, Almond Board of California Grant Project, USDA NIFA Award, AI Institu te for Next Generation Food Systems at UC Davis. Our news journalists obtained a quote from the research from the University of C alifornia Davis, "Irrigation regimes based on observed crop water status can hel p to optimize water use efficiency, but consistent and accurate measurement of w ater status can prove challenging. In almonds, crop water status is best represe nted by midday stem water potential measured using a pressure chamber, which des pite its accuracy is impractical for growers to measure on a regular basis. This study aimed to use machine learning (ML) models to predict stem water potential in an almond orchard based on canopy spectral reflectance, soil moisture, and d aily evapotranspiration. Both artificial neural network and random forest models were trained and used to produce high-resolution spatial maps of stem water pot ential covering the entire orchard. Also, for each ML model type, one model was trained to predict raw stem water potential values, while another was trained to predict baseline-adjusted values. Together, all models resulted in an average c oefficient of correlation of R2 = 0.73 and an average root mean squared error (R MSE) of 2.5 bars. Prediction accuracy decreased significantly when models were e xpanded to spatial maps (R2 = 0.33, RMSE = 3.31 [avg] ). These results indicate that both artificial neural networks and random forest frameworks can be used to predict stem water potential, but both approaches wer e unable to fully account for the spatial variability observed throughout the or chard. Overall, the most accurate maps were produced by the random forest model (raw stem water potential R2 = 0.47, RMSE = 2.71). The ability to predict stem w ater potential spatially can aid in the implementation of variable rate irrigati on."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Coronavirus - COVID-19 is the sub ject of a report. According to news reporting originating in Saskatoon, Canada, by NewsRx journalists, research stated, "During the pandemic, patients with deme ntia were identified as a vulnerable population. X (formerly Twitter) became an important source of information for people seeking updates on COVID-19, and, the refore, identifying posts (formerly tweets) relevant to dementia can be an impor tant support for patients with dementia and their caregivers." The news reporters obtained a quote from the research from the University of Sas katchewan, "However, mining and coding relevant posts can be daunting due to the sheer volume and high percentage of irrelevant posts. The objective of this stu dy was to automate the identification of posts relevant to dementia and COVID-19 using natural language processing and machine learning (ML) algorithms. We used a combination of natural language processing and ML algorithms with manually an notated posts to identify posts relevant to dementia and COVID-19. We used 3 dat a sets containing more than 100,000 posts and assessed the capability of various algorithms in correctly identifying relevant posts. Our results showed that (pr etrained) transfer learning algorithms outperformed traditional ML algorithms in identifying posts relevant to dementia and COVID-19. Among the algorithms teste d, the transfer learning algorithm A Lite Bidirectional Encoder Representations from Transformers (ALBERT) achieved an accuracy of 82.92% and an a rea under the curve of 83.53%. ALBERT substantially outperformed th e other algorithms tested, further emphasizing the superior performance of trans fer learning algorithms in the classification of posts. Transfer learning algori thms such as ALBERT are highly effective in identifying topic-specific posts, ev en when trained with limited or adjacent data, highlighting their superiority ov er other ML algorithms and applicability to other studies involving analysis of social media posts."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in robotics. A ccording to news reporting from Timisoara, Romania, by NewsRx journalists, resea rch stated, "In this paper, different test cases where robotic arms are tested w ill be presented." Funders for this research include Multidisciplinary Digital Publishing Institute . The news editors obtained a quote from the research from Politehnica University Timisoara: "A robotic arm is tested for the gravity effects that can be observed on it. The other robotic arm is tested for how much precision it has by using i t to learn to write. The other robotic arm is tested on how well it can function as a solar tracker and how precisely it can function as an energy harvester. On the basis of these tests, the robotic arm's mechanical structure, electronics, and software are put to the test." According to the news editors, the research concluded: "The software is based on evolutionary software that implements genetic algorithms. The entire command sy stem is also ported to FPGAs (to hardware) to increase speed and response time."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Nanjing, People's Republ ic of China, by NewsRx journalists, research stated, "Mid-infrared (MIR) spectro scopy can characterize the content and structural changes of macromolecular comp onents in different breast tissues, which can be used for feature extraction and model training by machine learning to achieve accurate classification and recog nition of different breast tissues. In parallel, the one-dimensional convolution al neural network (1D-CNN) stands out in the field of deep learning for its abil ity to efficiently process sequential data, such as spectroscopic signals." The news correspondents obtained a quote from the research from the Nanjing Univ ersity of Aeronautics and Astronautics, "In this study, MIR spectra of breast ti ssue were collected in situ by coupling the self-developed MIR hollow optical fi ber attenuated total reflection (HOF-ATR) probe with a Fourier transform infrare d spectroscopy (FTIR) spectrometer. Staging analysis was conducted on the change s in macromolecular content and structure in breast cancer tissues. For the firs t time, a trinary classification model was established based on 1D-CNN for recog nizing normal, paracancerous and cancerous tissues. The final predication result s reveal that the 1D-CNN model based on baseline correction (BC) and data augmen tation yields more precise classification results, with a total accuracy of 95.0 9%, exhibiting superior discrimination ability than machine learnin g models of SVM-DA (90.00%), SVR (88.89%), PCA-FDA (67 .78%) and PCA-KNN (70.00%)."
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 Li quid State Machines. According to news originating from Dayton, Ohio, by NewsRx correspondents, research stated, "Spiking neural network (SNN) hardware has gain ed significant interest due to its ability to process complex data in size, weig ht, and power (SWaP) constrained environments. Memristors, in particular, offer the potential to enhance SNN algorithms by providing analog domain acceleration with exceptional energy and throughput efficiency." Our news journalists obtained a quote from the research from the University of D ayton, "Among the current SNN architectures, the Liquid State Machine (LSM), a f orm of Reservoir Computing (RC), stands out due to its low resource utilization and straightforward training process. In this paper, we present a custom memrist or-based LSM circuit design with an online learning methodology. The proposed ci rcuit implementing the LSM is designed using SPICE to ensure precise device leve l accuracy. Furthermore, we explore liquid connectivity tuning to facilitate a r eal-time and efficient design process. To assess the performance of our system, we evaluate it on multiple datasets, including MNIST, TI-46 spoken digits, acous tic drone recordings, and musical MIDI files. Our results demonstrate comparable accuracy while achieving significant power and energy savings when compared to existing LSM accelerators. Moreover, our design exhibits resilience in the prese nce of noise and neuron misfires."
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 Me ntal Health Diseases and Conditions - Anxiety Disorders. According to news repor ting from Terre Haute, Indiana, by NewsRx journalists, research stated, "Mobile robotic telepresence provides an equitable opportunity for distance learners to collaborate with face-to-face counterparts through live simulation at a campus - based point of learning. The Technology Acceptance Model serves as the theoretic al framework regarding ease of use and perceived usefulness of telepresence robo ts as the stress and anxiety of simulation and novel technologies can impact lea rning."
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