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Real-world robotic operations often face uncertainties that can impede accurate control of manipulators. This study proposes a recurrent neural network (RNN) combining kinematic and dynamic models to address this issue. Assuming an unknown mass matrix, the proposed method enables effective trajectory tracking for manipulators. In detail, a kinematic controller is designed to determine the desired joint acceleration for a given task with error feedback. Subsequently, integrated with the kinematics controller, the RNN is proposed to combine the robot's dynamic model and a mass matrix estimator. This integration allows the manipulator system to handle uncertainties and synchronously achieve trajectory tracking effectively. Theoretical analysis demonstrates the learning and control capabilities of the RNN. Simulative experiments conducted on a Franka Emika Panda manipulator, and comparisons validate the effectiveness and superiority of the proposed method.

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The remarkable human ability to predict others' intent during physical interactions develops at a very early age and is crucial for development. Intent prediction, defined as the simultaneous recognition and generation of human-human interactions, has many applications such as in assistive robotics, human-robot interaction, video and robotic surveillance, and autonomous driving. However, models for solving the problem are scarce. This paper proposes two attention-based agent models to predict the intent of interacting 3D skeletons by sampling them via a sequence of glimpses. The novelty of these agent models is that they are inherently multimodal, consisting of perceptual and proprioceptive pathways. The action (attention) is driven by the agent's generation error, and not by reinforcement. At each sampling instant, the agent completes the partially observed skeletal motion and infers the interaction class. It learns where and what to sample by minimizing the generation and classification errors. Extensive evaluation of our models is carried out on benchmark datasets and in comparison to a state-of-the-art model for intent prediction, which reveals that classification and generation accuracies of one of the proposed models are comparable to those of the state of the art even though our model contains fewer trainable parameters. The insights gained from our model designs can inform the development of efficient agents, the future of artificial intelligence (AI).

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High speed side-view videos of sliding drops enable researchers to investigate drop dynamics and surface properties. However, understanding the physics of sliding requires knowledge of the drop width. A front-view perspective of the drop is necessary. In particular, the drop's width is a crucial parameter owing to its association with the friction force. Incorporating extra cameras or mirrors to monitor changes in the width of drops from a front-view perspective is cumbersome and limits the viewing area. This limitation impedes a comprehensive analysis of sliding drops, especially when they interact with surface defects. Our study explores the use of various regression and multivariate sequence analysis (MSA) models to estimate the drop width at a solid surface solely from side-view videos. This approach eliminates the need to incorporate additional equipment into the experimental setup. In addition, it ensures an unlimited viewing area of sliding drops. The Long Short Term Memory (LSTM) model with a 20 sliding window size has the best performance with the lowest root mean square error (RMSE) of 67 µm. Within the spectrum of drop widths in our dataset, ranging from 1.6 to 4.4 mm, this RMSE indicates that we can predict the width of sliding drops with an error of 2.4%. Furthermore, the applied LSTM model provides a drop width across the whole sliding length of 5 cm, previously unattainable.

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BACKGROUND: Visualization of sports has a lot of potential for future development in data sports because of how quickly things are changing and how much sports depend on data. Presently, conventional systems fail to accurately address sports persons' dynamic health data change with less error rate. Further, those systems are unable to distinguish players' health data and their visualization in a precise manner. An excellent starting point for building fitness solutions based on computer vision technology is the data visualization technology that arose in the age of big data analytics. OBJECTIVE: This research presents a Big Data Analytic assisted Computer Vision Model (BD-CVM) for effective sports persons healthcare data management with improved accuracy and precision. METHODS: The fitness and health of professional athletes are analyzed using information from a publicly available sports visualization dataset. Machine learning-assisted computer vision dynamic algorithm has been used for an effective image featuring and classification by categorizing sports videos through temporal and geographical data. RESULTS: The significance of big data's great potential in screening data during a sporting event can be reasonably analyzed and processed effectively with less error rate. The proposed BD-CVM utilized an error analysis module which can be embedded in the design further to ensure the accuracy requirements in the data processing from sports videos. CONCLUSION: The research findings of this paper demonstrate that the strategy presented here can potentially improve accuracy and precision and optimize mean square error in sports data classification and visualization.

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The research on acceleration-level visual servoing of manipulators is crucial yet insufficient, which restricts the potential application range of visual servoing. To address this issue, this paper proposes a quadratic programming-based acceleration-level image-based visual servoing (AIVS) scheme, which considers joint constraints. Besides, aiming to address the unknown problems in visual servoing systems, a data-driven learning algorithm is proposed to facilitate estimating structural information. Building upon this foundation, a data-driven acceleration-level image-based visual servoing (DAIVS) scheme is proposed, integrating learning and control capabilities. Subsequently, a recurrent neural network (RNN) is developed to tackle the DAIVS scheme, followed by theoretical analyses substantiating its stability. Afterwards, simulations and experiments on a Franka Emika Panda manipulator with eye-in-hand structure and comparisons among the existing methods are provided. The obtained results demonstrate the feasibility and practicality of the proposed schemes and highlight the superior learning and control ability of the proposed RNN. This method is particularly well-suited for visual servoing applications of manipulators with unknown structure.

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Soil health is essential for whirling stale soil into rich coffee-growing land. By keeping healthy soil, coffee producers may improve plant growth, leaf health, buds, cherry and bean quality, and yield. Traditional soil monitoring is tedious, time-consuming, and error-prone. Enhancing the monitoring system using AI-based IoT technologies for quick and precise changes. Integrated soil fertility control system to optimize soil health, maximize efficiency, promote sustainability, and prevent crop threads using real-time data analysis to turn infertile land into fertile land. The RNN-IoT approach uses IoT sensors in the coffee plantation to collect real-time data on soil temperature, moisture, pH, nutrient levels, weather, CO2 levels, EC, TDS, and historical data. Data transmission using a wireless cloud platform. Testing and training using recurrent neural networks (RNNs) and gated recurrent units gathered data for predicting soil conditions and crop hazards. Researchers are carrying out detailed qualitative testing to evaluate the proposed RNN-IoT approach. Utilize counterfactual recommendations for developing alternative strategies for irrigation, fertilization, fertilizer regulation, and crop management, taking into account the existing soil conditions, forecasts, and historical data. The accuracy is evaluated by comparing it to other deep learning algorithms. The utilization of the RNN-IoT methodology for soil health monitoring enhances both efficiency and accuracy in comparison to conventional soil monitoring methods. Minimized the ecological impact by minimizing water and fertilizer utilization. Enhanced farmer decision-making and data accessibility with a mobile application that provides real-time data, AI-generated suggestions, and the ability to detect possible crop hazards for swift action.

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The confining pressure has a great effect on the internal force of the tunnel. During construction, the confining pressure which has a crucial impact on tunnel construction changes due to the variation of groundwater level and applied load. Therefore, the safety of tunnels must have the magnitude of confining pressure accurately estimated. In this study, a complete tunnel confining pressure time axis was obtained through high-frequency field monitoring, the data are segmented into a training set and a testing set. Using GRU and RNN models, a confining pressure prediction model was established, and the prediction results were analyzed. The results indicate that the GRU model has a fast-training speed and higher accuracy. On the other hand, the training speed of the RNN model is slow, with lower accuracy. The dynamic characteristics of soil pressure during tunnel construction require accurate prediction models to maintain the safety of the tunnel. The comparison between GRU and RNN models not only highlights the advantages of the GRU model but also emphasizes the necessity of balancing speed accuracy in tunnel construction confining pressure prediction modeling. This study is helpful in improving the understanding of soil pressure dynamics and developing effective prediction tools to promote safer and more reliable tunnel construction practices.

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The fundamental focus of national spiritual civilization creation has been the effects of college physical instruction. The contemporary teaching methods exhibit greater flexibility, leading to a lack of reasonable assessment of physical education quality.To tackle this issue, we suggest implementing a technique that utilizes a recurrent neural network (RNN) to assess the standard of IPE. Additionally, we want to develop an automated assessment system specifically designed for this purpose. We gathered a dataset (Student satisfaction, course size, classroom feedback, teacher efficacy and course research). The study employs Min-Max normalization to eliminate redundant elements and ensure uniformity and principal component analysis (PCA) is used to discover relevant properties using already processed data. We simulate trials with Python 3.11 software to assess the efficiency of the suggested algorithm. A simulation environment was constructed to test the proposed approach, yielding notable performance metrics,Accuracy (95.68%), Precision (94.52%), Recall (86.59%) and F1-Score (88.56%). Comparative analysis demonstrates the efficacy of the suggested strategy, addressing limitations related to data availability and network complexities. Future efforts seek to improve RNN structures for various instructional materials, increase the clarity of assessments for better understanding and utilize large statistics to strengthen the model's resilience, resulting in a comprehensive manner supported by evidence based acceptance of the impact of IPE (AU)

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Nanotechnology has emerged as a promising frontier in revolutionizing the early diagnosis and surgical management of gastric cancers. The primary factors influencing curative efficacy in GIC patients are drug inefficacy and high surgical and pharmacological therapy recurrence rates. Due to its unique optical features, good biocompatibility, surface effects, and small size effects, nanotechnology is a developing and advanced area of study for detecting and treating cancer. Considering the limitations of GIC MRI and endoscopy and the complexity of gastric surgery, the early diagnosis and prompt treatment of gastric illnesses by nanotechnology has been a promising development. Nanoparticles directly target tumor cells, allowing their detection and removal. It also can be engineered to carry specific payloads, such as drugs or contrast agents, and enhance the efficacy and precision of cancer treatment. In this research, the boosting technique of machine learning was utilized to capture nonlinear interactions between a large number of input variables and outputs by using XGBoost and RNN-CNN as a classification method. The research sample included 350 patients, comprising 200 males and 150 females. The patients' mean ± SD was 50.34 ± 13.04 with a mean age of 50.34 ± 13.04. High-risk behaviors (P = 0.070), age at diagnosis (P = 0.034), distant metastasis (P = 0.004), and tumor stage (P = 0.014) were shown to have a statistically significant link with GC patient survival. AUC was 93.54%, Accuracy 93.54%, F1-score 93.57%, Precision 93.65%, and Recall 93.87% when analyzing stomach pictures. Integrating nanotechnology with advanced machine learning techniques holds promise for improving the diagnosis and treatment of gastric cancer, providing new avenues for precision medicine and better patient outcomes.

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Clinical event sequences consist of hundreds of clinical events that represent records of patient care in time. Developing accurate predictive models of such sequences is of a great importance for supporting a variety of models for interpreting/classifying the current patient condition, or predicting adverse clinical events and outcomes, all aimed to improve patient care. One important challenge of learning predictive models of clinical sequences is their patient-specific variability. Based on underlying clinical conditions, each patient's sequence may consist of different sets of clinical events (observations, lab results, medications, procedures). Hence, simple population-wide models learned from event sequences for many different patients may not accurately predict patient-specific dynamics of event sequences and their differences. To address the problem, we propose and investigate multiple new event sequence prediction models and methods that let us better adjust the prediction for individual patients and their specific conditions. The methods developed in this work pursue refinement of population-wide models to subpopulations, self-adaptation, and a meta-level model switching that is able to adaptively select the model with the best chance to support the immediate prediction. We analyze and test the performance of these models on clinical event sequences of patients in MIMIC-III database.

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The transport of peptides from the cytoplasm to the endoplasmic reticulum (ER) by transporters associated with antigen processing (TAP) is a critical step in the intracellular presentation of cytotoxic T lymphocyte (CTL) epitopes. The development and application of computational methods, especially deep learning methods and new neural network strategies that can automatically learn feature representations with limited knowledge, provide an opportunity to develop fast and efficient methods to identify TAP-binding peptides. Herein, this study presents a comprehensive analysis of TAP-binding peptide sequences to derive TAP-binding motifs and preferences for N-terminal and C-terminal amino acids. A novel recurrent neural network (RNN)-based method called DeepTAP, using bidirectional gated recurrent unit (BiGRU), was developed for the accurate prediction of TAP-binding peptides. Our results demonstrated that DeepTAP achieves an optimal balance between prediction precision and false positives, outperforming other baseline models. Furthermore, DeepTAP significantly improves the prediction accuracy of high-confidence neoantigens, especially the top-ranked ones, making it a valuable tool for researchers studying antigen presentation processes and T-cell epitope screening. DeepTAP is freely available at https://github.com/zjupgx/deeptap and https://pgx.zju.edu.cn/deeptap.

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This paper proposes a learning control framework for the robotic manipulator's dynamic tracking task demanding fixed-time convergence and constrained output. In contrast with model-dependent methods, the proposed solution deals with unknown manipulator dynamics and external disturbances by virtue of a recurrent neural network (RNN)-based online approximator. First, a time-varying tangent-type barrier Lyapunov function (BLF) is introduced to construct a fixed-time virtual controller. Then, the RNN approximator is embedded in the closed-loop system to compensate for the lumped unknown term in the feedforward loop. Finally, we devise a novel fixed-time, output-constrained neural learning controller by integrating the BLF and RNN approximator into the main framework of the dynamic surface control (DSC). The proposed scheme not only guarantees the tracking errors converge to the small neighborhoods about the origin in a fixed time, but also preserves the actual trajectories always within the prescribed ranges and thus improves the tracking accuracy. Experiment results illustrate the excellent tracking performance and verify the effectiveness of the online RNN estimate for unknown dynamics and external disturbances.

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【Objective】 To explore the prediction of clinical red blood cells (RBCs) consumption under major public health emergencies, so as to provide scientific basis for blood collection and blood inventory management. 【Methods】 The clinical consumption of different types of RBCs in Yichang from 2001 to 2017 was analyzed and modeled using the recurrent neural network (RNN) model, and the clinical RBCs consumption between January 2019 and December 2021(36 months) were scientifically predicted. 【Results】 The RNN model showed good prediction performance. The root mean square errors (RMSE) of RNN prediction values of A, B, O, AB type of RBCs were 156.7, 133.4, 204.2 and 51.3, respectively, with the average relative errors (MRE) at 6.4%, 6.6%, 8.5% and 7.1%, respectively. The model predicted the changing trend of RBCs consumption during the first round of COVID-19 outbreak (January to June, 2020) and forecasted the lowest level of consumption in February 2020 and a subsequent recovery in growth. The prediction of RBCs consumption during the second round of COVID-19 pandemic (January to June, 2021) was of high accuracy. For example, the relative errors of RNN models for A type RBCs consumption were 5.2% in Feb 2021 (the lowest level, 1 621.5 U) and 2.5% in May 2021 (the highest level, 2 397.0 U). 【Conclusion】 The artificial intelligence RNN model can predict clinical RBCs consumption well under major public health emergencies.

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A rapid and cost-effective method to detect the infection of SARS-CoV-2 is fundamental to mitigating the current COVID-19 pandemic. Herein, a surface-enhanced Raman spectroscopy (SERS) sensor with a deep learning algorithm has been developed for the rapid detection of SARS-CoV-2 RNA in human nasopharyngeal swab (HNS) specimens. The SERS sensor was prepared using a silver nanorod array (AgNR) substrate by assembling DNA probes to capture SARS-CoV-2 RNA. The SERS spectra of HNS specimens were collected after RNA hybridization, and the corresponding SERS peaks were identified. The RNA detection range was determined to be 103-109 copies/mL in saline sodium citrate buffer. A recurrent neural network (RNN)-based deep learning model was developed to classify 40 positive and 120 negative specimens with an overall accuracy of 98.9%. For the blind test of 72 specimens, the RNN model gave a 97.2% accuracy prediction for positive specimens and a 100% accuracy for negative specimens. All the detections were performed in 25 min. These results suggest that the DNA-functionalized AgNR array SERS sensor combined with a deep learning algorithm could serve as a potential rapid point-of-care COVID-19 diagnostic platform.

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In this paper, we propose a novel technique for the inspection of high-density polyethylene (HDPE) pipes using ultrasonic sensors, signal processing, and deep neural networks (DNNs). Specifically, we propose a technique that detects whether there is a diversion on a pipe or not. The proposed model transmits ultrasound signals through a pipe using a custom-designed array of piezoelectric transmitters and receivers. We propose to use the Zadoff-Chu sequence to modulate the input signals, then utilize its correlation properties to estimate the pipe channel response. The processed signal is then fed to a DNN that extracts the features and decides whether there is a diversion or not. The proposed technique demonstrates an average classification accuracy of 90.3% (when one sensor is used) and 99.6% (when two sensors are used) on 34 inch pipes. The technique can be readily generalized for pipes of different diameters and materials.

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Low-cost 3D video sensors equipped with routines for extracting skeleton data facilitate the widespread use of virtual reality (VR) for rehabilitation. However, the accuracy of the extracted skeleton data is often limited. Accuracy can be improved using a motion tracker, e.g., using a recurrent neural network (RNN). Yet, training an RNN requires a considerable amount of relevant and accurate training data. Training databases can be obtained using gold-standard motion tracking sensors. This limits the use of the RNN trackers in environments and tasks that lack accessibility to gold-standard sensors. Digital goniometers are typically cheaper, more portable, and simpler to use than gold-standard motion tracking sensors. The current work suggests a method for generating accurate skeleton data suitable for training an RNN motion tracker based on the offline fusion of a Kinect 3D video sensor and an electronic goniometer. The fusion applies nonlinear constraint optimization, where the constraints are based on an advanced shoulder-centered kinematic model of the arm. The model builds on the representation of the arm as a triangle (the arm triangle). The shoulder-centered representation of the arm triangle motion simplifies constraint representation and consequently the optimization problem. To test the performance of the offline fusion and the RNN trained using the optimized data, arm motion of eight participants was recorded using a Kinect sensor, an electronic goniometer, and, for comparison, a passive-marker-based motion tracker. The data generated by fusing the Kinect and goniometer recordings were used for training two long short-term memory (LSTM) RNNs. The input to one RNN included both the Kinect and the goniometer data, and the input to the second RNN included only Kinect data. The performance of the networks was compared to the performance of a tracker based on a Kalman filter and to the raw Kinect measurements. The accuracy of the fused data was high, and it considerably improved data accuracy. The accuracy for both trackers was high, and both were more accurate than the Kalman filter tracker and the raw Kinect measurements. The developed methods are suitable for integration with immersive VR rehabilitation systems in the clinic and the home environments.

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To address the problem of low prediction accuracy of precipitation time series data, an improved overall mean empirical modal decomposition-prediction-reconstruction model (MDPRM) is constructed in this paper. First, the non-stationary precipitation time series are decomposed into multiple decomposition terms by the improved overall mean empirical modal decomposition (MEEMD). Then, a particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN) and recurrent neural network (RNN) models are used to make predictions according to the characteristics of different decomposition terms. Finally, the prediction results of each decomposition term are superimposed and reconstructed to form the final prediction results. In addition, the application is carried out with the summer precipitation in the Wujiang River basin of Guizhou Province from 1961 to 2018, using the first 38 years of data to train MDPRM and the last 20 years of data to test MDPRM, and comparing with a feedback neural network (BP), a support vector machine (SVM), a particle swarm optimization support vector machine (PSO-SVM), a convolutional neural network (CNN), and a recurrent neural network (RNN), etc. The results show that the mean relative error (MAPE) of the proposed MDPRM is reduced from 0.31 to 0.09, the root mean square error (RMSE) is reduced from 0.56 to 0.30, and the consistency index (α) is significantly improved from 0.33 to 0.86, which has a higher prediction accuracy. Finally, the trained MDPRM predicts the average summer precipitation in the Wujiang River basin from 2019 to 2028 to be 466.42 mm, the minimum precipitation in 2020 to be 440.94 mm, and the maximum precipitation in 2024 to be 497.94 mm. Based on the prediction results, the agricultural drought level is evaluated using the Z index, which indicates that the summer is normal in the 10-year period. The study provides technical support for the effective guidance of regional water resources' allocation and scheduling and drought mitigation.

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The use of electroencephalography (EEG) has recently grown as a means to diagnose neurodegenerative pathologies such as Alzheimer's disease (AD). AD recognition can benefit from machine learning methods that, compared with traditional manual diagnosis methods, have higher reliability and improved recognition accuracy, being able to manage large amounts of data. Nevertheless, machine learning methods may exhibit lower accuracies when faced with incomplete, corrupted, or otherwise missing data, so it is important do develop robust pre-processing techniques do deal with incomplete data. The aim of this paper is to develop an automatic classification method that can still work well with EEG data affected by artifacts, as can arise during the collection with, e.g., a wireless system that can lose packets. We show that a recurrent neural network (RNN) can operate successfully even in the case of significantly corrupted data, when it is pre-filtered by the robust principal component analysis (RPCA) algorithm. RPCA was selected because of its stated ability to remove outliers from the signal. To demonstrate this idea, we first develop an RNN which operates on EEG data, properly processed through traditional PCA; then, we use corrupted data as input and process them with RPCA to filter outlier components, showing that even with data corruption causing up to 20% erasures, the RPCA was able to increase the detection accuracy by about 5% with respect to the baseline PCA.

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The development of digital cancer twins relies on the capture of high-resolution representations of individual cancer patients throughout the course of their treatment. Our research aims to improve the detection of metastatic disease over time from structured radiology reports by exposing prediction models to historical information. We demonstrate that Natural language processing (NLP) can generate better weak labels for semi-supervised classification of computed tomography (CT) reports when it is exposed to consecutive reports through a patient's treatment history. Around 714,454 structured radiology reports from Memorial Sloan Kettering Cancer Center adhering to a standardized departmental structured template were used for model development with a subset of the reports included for validation. To develop the models, a subset of the reports was curated for ground-truth: 7,732 total reports in the lung metastases dataset from 867 individual patients; 2,777 reports in the liver metastases dataset from 315 patients; and 4,107 reports in the adrenal metastases dataset from 404 patients. We use NLP to extract and encode important features from the structured text reports, which are then used to develop, train, and validate models. Three models-a simple convolutional neural network (CNN), a CNN augmented with an attention layer, and a recurrent neural network (RNN)-were developed to classify the type of metastatic disease and validated against the ground truth labels. The models use features from consecutive structured text radiology reports of a patient to predict the presence of metastatic disease in the reports. A single-report model, previously developed to analyze one report instead of multiple past reports, is included and the results from all four models are compared based on accuracy, precision, recall, and F1-score. The best model is used to label all 714,454 reports to generate metastases maps. Our results suggest that NLP models can extract cancer progression patterns from multiple consecutive reports and predict the presence of metastatic disease in multiple organs with higher performance when compared with a single-report-based prediction. It demonstrates a promising automated approach to label large numbers of radiology reports without involving human experts in a time- and cost-effective manner and enables tracking of cancer progression over time.