IMORL: incremental multiple-object recognition and localization

A machine-learning-derived online prediction model for depression risk in COPD patients: A retrospective cohort study from CHARLS

BACKGROUND

Depression associated with Chronic Obstructive Pulmonary Disease (COPD) is a detrimental complication that significantly impairs patients' quality of life. This study aims to develop an online predictive model to estimate the risk of depression in COPD patients.

METHODS

This study included 2921 COPD patients from the 2018 China Health and Retirement Longitudinal Study (CHARLS), analyzing 36 behavioral, health, psychological, and socio-demographic indicators. LASSO regression filtered predictive factors, and six machine learning models-Logistic Regression, Support Vector Machine, Multilayer Perceptron, LightGBM, XGBoost, and Random Forest-were applied to identify the best model for predicting depression risk in COPD patients. Temporal validation used 2013 CHARLS data. We developed a personalized, interpretable risk prediction platform using SHAP.

RESULTS

A total of 2921 patients with COPD were included in the analysis, of whom 1451 (49.7 %) presented with depressive symptoms. 11 variables were selected to develop 6 machine learning models. Among these, the XGBoost model exhibited exceptional predictive performance in terms of discrimination, calibration, and clinical applicability, with an AUROC range of 0.747-0.811. In validation sets encompassing diverse population characteristics, XGBoost achieved the highest accuracy (70.63 %), sensitivity (59.05 %), and F1 score (63.17 %).

LIMITATIONS

The target population for the model is COPD patients. And the clinical benefits of interventions based on the prediction results remain uncertain.

CONCLUSION

We developed an online prediction platform for clinical application, allowing healthcare professionals to swiftly and efficiently evaluate the risk of depression in COPD patients, facilitating timely interventions and treatments.

Maximum Information Exploitation Using Broad Learning System for Large-Scale Chaotic Time-Series Prediction

How to make full use of the evolution information of chaotic systems for time-series prediction is a difficult issue in dynamical system modeling. In this article, we propose a maximum information exploitation broad learning system (MIE-BLS) for extreme information utilization of large-scale chaotic time-series modeling. An improved leaky integrator dynamical reservoir is introduced in order to capture the linear information of chaotic systems effectively. It can not only capture the information of the current state but also achieve the compromise with historical states in the dynamical system. Furthermore, the feature is mapped to the enhancement layer by nonlinear random mapping to exploit nonlinear information. The cascading mechanism promotes the information propagation and achieves feature reactivation in dynamical modeling. Discussions about maximum information exploration and the comparisons with ResNet, DenseNet, and HighwayNet are presented in this article. Simulation results on four large-scale data sets illustrate that MIE-BLS could achieve better performance of information exploration in large-scale dynamical system modeling.

Adaptive Neural Output-Feedback Decentralized Control for Large-Scale Nonlinear Systems With Stochastic Disturbances

This paper addresses the problem of adaptive neural output-feedback decentralized control for a class of strongly interconnected nonlinear systems suffering stochastic disturbances. An state observer is designed to approximate the unmeasurable state signals. Using the approximation capability of radial basis function neural networks (NNs) and employing classic adaptive control strategy, an observer-based adaptive backstepping decentralized controller is developed. In the control design process, NNs are applied to model the uncertain nonlinear functions, and adaptive control and backstepping are combined to construct the controller. The developed control scheme can guarantee that all signals in the closed-loop systems are semiglobally uniformly ultimately bounded in fourth-moment. The simulation results demonstrate the effectiveness of the presented control scheme.

Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics

This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.

Biologically Motivated Novel Localization Paradigm by High-Level Multiple Object Recognition in Panoramic Images

This paper presents the novel paradigm of a global localization method motivated by human visual systems (HVSs). HVSs actively use the information of the object recognition results for self-position localization and for viewing direction. The proposed localization paradigm consisted of three parts: panoramic image acquisition, multiple object recognition, and grid-based localization. Multiple object recognition information from panoramic images is utilized in the localization part. High-level object information was useful not only for global localization, but also for robot-object interactions. The metric global localization (position, viewing direction) was conducted based on the bearing information of recognized objects from just one panoramic image. The feasibility of the novel localization paradigm was validated experimentally.

Dynamic class imbalance learning for incremental LPSVM

Linear Proximal Support Vector Machines (LPSVMs), like decision trees, classic SVM, etc. are originally not equipped to handle drifting data streams that exhibit high and varying degrees of class imbalance. For online classification of data streams with imbalanced class distribution, we propose a dynamic class imbalance learning (DCIL) approach to incremental LPSVM (IncLPSVM) modeling. In doing so, we simplify a computationally non-renewable weighted LPSVM to several core matrices multiplying two simple weight coefficients. When data addition and/or retirement occurs, the proposed DCIL-IncLPSVM(1) accommodates newly presented class imbalance by a simple matrix and coefficient updating, meanwhile ensures no discriminative information lost throughout the learning process. Experiments on benchmark datasets indicate that the proposed DCIL-IncLPSVM outperforms classic IncSVM and IncLPSVM in terms of F-measure and G-mean metrics. Moreover, our application to online face membership authentication shows that the proposed DCIL-IncLPSVM remains effective in the presence of highly dynamic class imbalance, which usually poses serious problems to previous approaches.