A K -means Interval Type-2 Fuzzy Neural Network for Medical Diagnosis

Abstract(#br)This paper proposes a new medical diagnosis algorithm that uses a K -means interval type-2 fuzzy neural network (KIT2FNN). This KIT2FNN classifier uses a K -means clustering algorithm as the pre-classifier and an interval type-2 fuzzy neural network as the main classifier. Initially, the training data are classified into k groups using the K -means clustering algorithm and these data groups are then used sequentially to train the structure of the k classifiers for the interval type-2 fuzzy neural network (IT2FNN). The test data are also initially used to determine to which classifier they are best suited and then they are inputted into the corresponding main classifier for classification. The parameters for the proposed IT2FNN are updated using the steepest descent gradient..

Identification of clathrin proteins by incorporating hyperparameter optimization in deep learning and PSSM profiles

Background and Objectives: Clathrin is an adaptor protein that serves as the principal element of the vesicle-coating complex and is important for the membrane cleavage to dispense the invaginated vesicle from the plasma membrane. The functional loss of clathrins has been tied to a lot of human diseases, i.e., neurodegenerative disorders, cancer, Alzheimer's diseases, and so on. Therefore, creating a precise model to identify its functions is a crucial step towards understanding human diseases and designing drug targets. Methods:We present a deep learning model using a two-dimensional convolutional neural network (CNN) and position-specific scoring matrix (PSSM) profiles to identify clathrin proteins from high throughput sequences. Traditionally, the 2D CNNs take images as an input so we treated the PSSM profile with a 20 × 20 matrix as an image of 20 × 20 pixels. The input PSSM profile was then connected to our 2D CNN in which we set a variety of parameters to improve the performance of the model. Based on the 10-fold cross-validation results, hyper-parameter optimization process was employed to find the best model for our dataset. Finally, an independent dataset was used to assess the predictive ability of the current model.Results:Our model could identify clathrin proteins with sensitivity of 92.2%, specificity of 91.2%, accuracy of 91.8%, and MCC of 0.83 in the independent dataset. Compared to state-of-the-art traditional neural networks, our method achieved a significant improvement in all typical measurement metrics. Conclusions:Throughout the proposed study, we provide an effective tool for investigating clathrin proteins and our achievement could promote the use of deep learning in biomedical research. We also provide source codes and dataset freely at https://www.github.com/khanhlee/deep-clathrin/.Accepted versio

Chaotic Synchronization Using a Self-Evolving Recurrent Interval Type-2 Petri Cerebellar Model Articulation Controller

In this manuscript, the synchronization of four-dimensional (4D) chaotic systems with uncertain parameters using a self-evolving recurrent interval type-2 Petri cerebellar model articulation controller is studied. The design of the synchronization control system is comprised of a recurrent interval type-2 Petri cerebellar model articulation controller and a fuzzy compensation controller. The proposed network structure can automatically generate new rules or delete unnecessary rules based on the self-evolving algorithm. Furthermore, the gradient-descent method is applied to adjust the proposed network parameters. Through Lyapunov stability analysis, bounded system stability is guaranteed. Finally, the effectiveness of the proposed controller is illustrated using numerical simulations of 4D chaotic systems

Robust Fault Estimation Using the Intermediate Observer: Application to the Quadcopter

In this paper, an actuator fault estimation technique is proposed for quadcopters under uncertainties. In previous studies, matching conditions were required for the observer design, but they were found to be complex for solving linear matrix inequalities (LMIs). To overcome these limitations, in this study, an improved intermediate estimator algorithm was applied to the quadcopter model, which can be used to estimate actuator faults and system states. The system stability was validated using Lyapunov theory. It was shown that system errors are uniformly ultimately bounded. To increase the accuracy of the proposed fault estimation algorithm, a magnitude order balance method was applied. Experiments were verified with four scenarios to show the effectiveness of the proposed algorithm. Two first scenarios were compared to show the effectiveness of the magnitude order balance method. The remaining scenarios were described to test the reliability of the presented method in the presence of multiple actuator faults. Different from previous studies on observer-based fault estimation, this proposal not only can estimate the fault magnitude of the roll, pitch, yaw, and thrust channel, but also can estimate the loss of control effectiveness of each actuator under uncertainties

ROBUST MPPT OBSERVER-BASED CONTROL SYSTEM FOR WIND ENERGY CONVERSION SYSTEM WITH UNCERTAINTIES AND DISTURBANCE

The problem of tracking the maximum power point for the wind energy conversion system (WECS) is taken into consideration in this paper. The WECS in this article is simultaneously affected by the uncertainties and the arbitrary disturbance that cause the WECSs to be much more challenging to control. A new method to synthesize a polynomial disturbance observer for estimating the aerodynamic torque, wind speed, and electromagnetic torque without using sensors is proposed in this paper. Unlike the previous methods, in this work, both the uncertainties and the disturbance are estimated, then estimations of the uncertainties and disturbance are transmitted to the Linear Quadratic Regulator (LQR) controller for eliminating the influences of the uncertainties and disturbance; and tracking the optimal power point of WECS. It should be noted that the uncertainties in this work are time-varying and both uncertainties and disturbance do not need to satisfy the bounded constraints. The wind speed and aerodynamic torque are arbitrary and unnecessary to fulfill the low-varying constraint or r th time derivative bound. On the basis of Lyapunov methodology and the sum-of-square technique (SOS), the main theorems are derived to design the polynomial disturbance observer. Finally, the simulation results are provided to demonstrate the effectiveness and merit of the proposed method

Firm Risk and Tax Avoidance in Vietnam: Do Good Board Characteristics Interfere Effectively?

This paper investigates the role of board characteristics in the relationship between tax avoidance behavior and corporate risk tolerance to elucidate the importance of corporate governance mechanisms. The applied methodology is System-GMM for 334 listed corporations in Vietnam from 2008 to 2020 to avoid endogenous problems in our models. The main findings are that higher (lower) corporate risk-taking is related to higher (lower) corporate tax avoidance if the size of the board of directors and the supervisory board are larger (lower) than six and three members, respectively. Furthermore, if the board independence ratio is lower than 48.63%, an increase in corporate risk-taking leads to increased tax avoidance. Our results support the argument that the influence of corporate risk-taking on tax avoidance behavior is governed by governance structure. Therefore, the practical implications will be towards building the optimal governance mechanism for enterprises in Vietnam

Quadrotor Formation Control via Terminal Sliding Mode Approach: Theory and Experiment Results

This article presents a formation tracking control method for the operation of multi-agent systems under disturbances. This study aims to ensure that the followers of a quadcopter converge into the desired formation while the center formation of the follower quadcopters tracks the leader’s trajectory within a finite time. The distributed finite-time formation control problem is first investigated using the fast terminal sliding mode control (FTSMC) theory. A disturbance observer is then integrated into the FTSMC to overcome the model uncertainties and bounded disturbances. Subsequently, the Lyapunov function is proposed to ensure the stability of the system. It is shown that formation tracking control can be achieved even in the presence of disturbances. Simulation and experimental results verify the effectiveness of the proposed formation tracking control method compared to existing ones

A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification

Essential genes contain key information of genomes that could be the key to a comprehensive understanding of life and evolution. Because of their importance, studies of essential genes have been considered a crucial problem in computational biology. Computational methods for identifying essential genes have become increasingly popular to reduce the cost and time-consumption of traditional experiments. A few models have addressed this problem, but performance is still not satisfactory because of high dimensional features and the use of traditional machine learning algorithms. Thus, there is a need to create a novel model to improve the predictive performance of this problem from DNA sequence features. This study took advantage of a natural language processing (NLP) model in learning biological sequences by treating them as natural language words. To learn the NLP features, a supervised learning model was consequentially employed by an ensemble deep neural network. Our proposed method could identify essential genes with sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC) values of 60.2%, 84.6%, 76.3%, 0.449, and 0.814, respectively. The overall performance outperformed the single models without ensemble, as well as the state-of-the-art predictors on the same benchmark dataset. This indicated the effectiveness of the proposed method in determining essential genes, in particular, and other sequencing problems, in general

A Novel Self-Organizing Emotional CMAC Network for Robotic Control

This paper proposes a self-organizing control system for uncertain nonlinear systems. The proposed neural network is composed of a conventional brain emotional learning network (BEL) and a cerebellar model articulation controller network (CMAC). The input value of the network is feed to a BEL channel and a CMAC channel. The output of the network is generated by the comprehensive action of the two channels. The structure of the network is dynamic, using a self-organizing algorithm allows increasing or decreasing weight layers. The parameters of the proposed network are on-line tuned by the brain emotional learning rules; the updating rules of CMAC and the robust controller are derived from the Lyapunov function; in addition, stability analysis theory is used to guaranty the proposed controller's convergence. A simulated mobile robot is applied to prove the effectiveness of the proposed control system. By comparing with the performance of other neural-network-based control systems, the proposed network produces better performance