Journal of Information and Computing Science
Journal of Information and Computing Science (JICS) serves as a platform for the publication of original research contributions especially in the field of information and computing science. It mainly publishes original research papers including computational methods, mathematical and computer modelling, computational fluid, computational biology and bioinformatics, information and control, optimization, pattern recognition and image processing, machine learning and data mining, quantum computing, etc.
The Computation of Common Infinity-norm Lyapunov Functions for Linear Switched Systems
This paper studies the problem of the computation of common infinity-norm Lyapunov functions. For a set of continuous-time LTI systems or discrete-time LTI systems whose system matrices are upper triangular form or lower triangular form, it is proved that there exist common infinity-norm Lyapunov functions for them. Then four algorithms of computing common infinity-norm Lyapunov functions are presented. Finally, several examples are listed.
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Speeding Up Fractal Image Compression Using DCT Descriptors
The main problem in the Fractal image compression (FIC) method is the long encoding time. In order to speed up the encoding stage, an improved PIFS scheme is introduced in this paper; In the introduced PIFS scheme the input color image is, converted from RGB color space to YUV color space, and then the chromatic bands are down sampled by 2. The zero-mean range and domain blocks are classified using a block descriptor which is determined using a pair of low frequency DCT coefficients due to the energy packing properties of DCT. The DCT descriptors are also used to address the isometric state of each range and domain blocks. The coefficients of the optimal affine approximation are quantized, and then coded using differential pulse code modulation (DPCM) and shift coding. The conducted tests results on Lena's standard image indicated that the encoding time is reduced to (1 sec) while keeping the image quality above the acceptable level and no significant reduction in the compression ratio was occurred.
Synchronization Criterions between Two Identical or Different Fractional Order Chaotic Systems
This paper discusses chaos synchronization between two identical or different fractional order chaotic systems. Base on linear matrix inequality, two new synchronization criterions are constructed by which it is proved that two identical (L\u00fc system) or different (L\u00fc and Chen systems) fractional order chaotic systems are synchronized using the simple linear feedback control laws. Finally, simulations results show the method is effective.
Yuhua Xu,Wuneng Zhou, Jianan Fang, Ling Pan and Wen Sun
A Novel Sparse Learning Method: Compressible Bayesian Elastic Net Model
In this paper, we study the combination of compression and Bayesian elastic net. By including a compression operation into the \u21131 and \u21132 regularization, the assumption on model sparsity is relaxed to compressibility: model coefficients are compressed before being penalized, and sparsity is achieved in a compressed domain rather than the original space. We focus on the design of compression operations, by which we can encode various compressibility assumptions and inductive biases. We show that use of a compression operation provides an opportunity to leverage auxiliary information from various sources. The compressible Bayesian elastic net has another two major advantages. Firstly, as a Bayesian method, the distributional results on the estimates are straightforward, making the statistical inference easier. Secondly, it chooses the two penalty parameters simultaneously, avoiding the \u201cdouble shrinkage problem\u201d in the elastic net method. We conduct extensive experiments on braincomputer interfacing, handwritten character recognition and text classification. Empirical results show clear improvements in prediction performance by including compression in Bayesian elastic net. We also analyze the learned model coefficients under appropriate compressibility assumptions, which further demonstrate the advantages of learning compressible models instead of sparse models.
Keyang Cheng, Qirong Mao, Xiaoyang Tan and Yongzhao Zhan
Fast Iterative Method-FIM : Application to the Convection-Diffusion Equations
In this paper we present a new algorithm for solving linear systems by iterative method. In this method the rate of convergence has been improved well. The results show that this new algorithm converges faster than AOR, SOR, SSOR, etc .and works with high accuracy. The Main difference between these methods with the others is that uses affine combinations and also less parameter. Besides, we have shown that a divergent process can be converges by using FIM method. Finally the method is tested by some numerical experiments.
Conceptual Category of Knowledage and Its Correlationbased Metrizable Space
Abstract.The phenomenon was understood as the basic element to constitute knowledge in this study, where the granular structure was taken as the basic structure of phenomenon formalized representation. Based on the isomorphism between formal concept analysis and rough set theory, the granular structure model was established in conceptual category of phenomenon, and the correlation among concept categories was given as well as correlation distance, proving that the concept element space induced by correlation distance \u0398 was a metrizable space. Based on the conclusions in this study, the knowledge representation model can be established more completely than conceptual model in semantic description, providing a basis for phenomenon formalization research by using the topologic method.
A Framework for Reducing Multidimensional Database to Two Dimensions
This work used a method of Matrix Decomposition Algorithm to obtain a new dataset of genetic epistasis as a surrogate for a multidimensional dataset which transformed multidimensional database to a 2- dimensional database. It employed decomposition algorithms based on Boyce Codd Normal Form for minimizing anomalies. The decomposition and reversible algorithms were used on relationship among object attributes and were implemented. The implemented program ran on sample genetic epistasis datasets of up to 10 dimensions and it was shown that multidimensional datasets can be reduced to two dimensions. It was established that the time taken to generate a sequence of tuples from multidimensional database to a 2- dimensional dataset was directly proportional to the number of genes considered. The result showed that the reduced 2-dimensional database did not require any in-built functions which take long processing time for generating query result as against querying of multidimensional dataset. The reduced 2-dimensional dataset was reversible to the original multidimensional dataset for lossless join operation which indicated that there was no loss of data values or tuple. The method was compared with existing reduction techniques and it was found that data access was very fast with decomposition algorithm than relational model.
Adio Akinwale, Kolawole Adesina and Olusegun Folorunso
A Genetic Algorithm Approach for Reliability of Bridge Network in Fuzzy System
This paper presents a Genetic Algorithm (GA) approach for solving constrained reliability optimization problem of the five unit bridge network. Considering uncertainty for cost, the reliability optimization problem has been solved by GA technique to maximize system reliability. GA is an efficient method for solving this type of optimization problems. This paper successfully applies the GA technique to obtain the optimal solution of the complex system reliability model under cost constraint in fuzzy environment in which the system cost and component costs might be imprecise. Triangular fuzzy number is used to represent the fuzzy cost coefficients. Static penalty method has been used to handle the cost constraint of the problem. To solve the reliability optimization model, we have developed the GA using MATLAB with tournament selection process, elitism mechanism, arithmetic crossover and uniform mutation operations. Finally, computational results are presented for the reliability of bridge network in crisp and fuzzy environment.
Development of Media Search Engine &its Optimization
The project entitled \u201cMEDIA SEARCH ENGINE\u201d is regarding to the development of a web search engine which would include information\u2019s of different videos, audios, images etc related to corporate, educational, political, social & entertainment sectors. It would enable the viewers to access huge collection of resources in a very fast and easy way. They will be also able to submit their own articles, videos, audios, pictures. Users can access information\u2019s stored in its database by just giving a keyword as input & within few moments all information\u2019s related to that key word would appear on the screen indexed according to their popularity. Through this project huge amount of useful information\u2019s can be store & retrieve easily.
MFIPC: Point Cloud Registration Algorithm via Multi-Feature Fusion and Interval Pairing Consistency
Inspired by the Fast point Feature Histogram (FPFH) feature extraction algorithm, this paper proposes a new 3D point cloud registration method, MFIPC (Multi-feature Fusion and Interval Pairing Consistency). The method uses feature fusion and interval pairwise consistency to improve the registration accuracy. In the MFIPC framework, the point cloud is first downsampled to optimize computational efficiency and expand the analysis domain. Then, clustering algorithm using local directional centrality (CDC) classification algorithm is used to calculate the DCM (directional centrality measure) value of each point. The Gaussian curvature values of the points are calculated at the same time, and these eigenvalues are fused. To further refine the registration process, the range between the minimum and maximum eigenvalues is divided into several equal intervals and sorted in ascending order. A sorting algorithm is used to assign each eigenvalue to a corresponding interval. For the global point cloud computing step, after the operation is completed, the number of points in each interval and its proportion are calculated. The program processes both point clouds in order to analyze their interval percentage. This algorithm significantly improves the robustness of MFIPC in establishing point correspondence. To verify the effectiveness of MFIPC for 3D point cloud registration, we conducted extensive testing on various datasets, including 3DMatch, RESSO, ModelNet40, Stanford Rabbit, and Dragon. The experimental results show that the algorithm has high efficiency, good consistency of point cloud, significantly reduced registration errors, low error and high registration accuracy
A Dual-Task Semi-Supervised Neural Network Based on Skew Normal Mixture Model for Brain MR Image Segmentation
Accurate segmentation of brain magnetic resonance (MR) images is critical in brain disease research and treatment. While deep learning methods have advanced image segmentation by extracting hierarchical features, they typically require large labeled datasets for precise results. Acquiring annotated medical data remains challenging due to the need for specialized expertise and privacy restrictions. To address this, we propose a semi-supervised model combining dual tasks: segmentation and boundary feature regression. For class imbalance in segmentation, the network employs focal loss to extract common features from annotated data. To handle asymmetric data distributions, a skew Normal Mixture-based Level set loss guides the network to learn individual image characteristics, enhancing class distribution fitting. This dual-feature integration enables strong performance on limited datasets. In regression, Level set signed distance functions focus the model on boundary information, mitigating partial volume effects on focal loss. Experiments on IBSR and MRBrainS18 datasets demonstrate our method\u2019s advantages over current state-of-the-art approaches.
Prescribed-Time Adaptive Fuzzy Tracking Control of UAV Swarms Under Deception Attack
In addressing the tracking control problem for unmanned aerial vehicle (UAV) swarms, we consider several challenges: the unmeasurable state of the swarm system, potential deception attacks on actuators, external random disturbances, and the nonlinear dynamics of each UAV. To tackle these issues, we first introduce a time-varying function and utilize a coordinate transformation method to convert the time tracking problem into an error variable constraint problem. Next, we propose an adaptive time tracking control method employing one-to-one mapping and inversion techniques, aimed at achieving system convergence to a specified accuracy within a designated time frame. To mitigate the impact of possible deception attacks on actuators, we design an attack compensator that removes disturbances caused by time-varying attack gains. Additionally, we implement an observer to estimate the unmeasurable state of the system and utilize a fuzzy logic system to manage unknown functions. Finally, we validate the effectiveness of our control method through simulations.
Lie Symmetries, Conservation Laws and Solutions for (4+1)-Dimensional Time Fractional KP Equation with Variable Coefficients in Fluid Mechanics
In recent years, high-dimensional fractional equations have gained prominence as a pivotal focus of interdisciplinary research spanning mathematical physics, fluid mechanics, and related fields. In this paper, we investigate a (4+1)-dimensional time-fractional Kadomtsev-Petviashvili (KP) equation with variable coefficients. We first derive the (4+1)-dimensional time-fractional KP equation with variable coefficients in the sense of the Riemann-Liouville fractional derivative using the semi-inverse and variational methods. The symmetries and conservation laws of this equation are analyzed through Lie symmetry analysis and a new conservation theorem, respectively. Finally, both exact and numerical solutions of the fractional-order equation are obtained using the Hirota bilinear method and the pseudo-spectral method. The effectiveness and reliability of the proposed approach are demonstrated by comparing the numerical solutions of the derived models with exact solutions in cases where such solutions are known.
Quality Evaluation and Spatiotemporal Characteristics Analysis of Agricultural Development in Zhejiang Province
This paper establishes a comprehensive and scientifically rigorous evaluation index system specifically designed for high-quality agricultural development. Utilizing methods such as the entropy weight method, comprehensive evaluation index method, and Dagum Gini coefficient, it provides an in-depth analysis of the level and spatiotemporal dynamics of agricultural development in Zhejiang Province. The results indicate that Zhejiang Province has consistently demonstrated an improvement in the overall quality of its agricultural development, evident from a substantial increase in the agricultural development index, particularly after 2018. This upward trend is primarily driven by significant advancements in agricultural production efficiency, coupled with the rapid integration and expansion of mechanization and technology. Additionally, the study highlights notable regional disparities and burgeoning trends in agricultural development within Zhejiang. Specifically, the northern region maintains its leading position, while the eastern region is progressively gaining prominence. To facilitate comprehensive and high-quality agricultural development across Zhejiang Province, the paper suggests strategies including fostering coordinated regional development, optimizing resource allocation, and refining the structure of the agricultural industry.
New Perturbation Analysis of Generalized Singular Values of Grassmann Matrix Pairs with Arbitrary Permutation
In this paper we give new perturbation analysis of generalized singular values of Grassmann matrix pairs with arbitrary permutation from a new perspective. The new explicit expressions of the chordal metric between generalized singular values of Grassmann matrix pairs with arbitrary permutation are presented, which result in only two small-size singular value decompositions to evaluate. The proposed results are generalizations of several results on bounds on perturbation of generalized singular values.
Application of Improved LSTM-WBLS Model in Daily Precipitation Forecast
The popular Long Short-Term Memory (LSTM) based precipitation prediction models suffer from overfitting and time lag. Broad Learning System (BLS), which does not require multiple iterations, helps to solve the above disadvantages of LSTM. Weighted Broad Learning System (WBLS) reduces the impact of noise and outliers on precipitation prediction accuracy by introducing a weighted penalty factor constraint to assign sample weights in the BLS. Thus, a LSTM-WBLS daily precipitation prediction model is proposed in this paper. The daily precipitation at Badong station in Hubei province is selected for empirical study. And the influence of air pressure, temperature, humidity, wind speed and sunshine on precipitation is considered. The experimental results demonstrate that the LSTM-BLS model has significantly improved the prediction accuracy in the evaluation indexes of RMSE, MAE and R2 compared with existing prediction models. The prediction accuracy of the new model outperforms existing models at different time steps, proving its stability. In particular, the direct calculation of weights by WBLS does not make any reduction in operational efficiency of LSTM-WBLS.
Remote Sensing Image Scene Classification Based on Deep Learning Feature Fusion
In view that traditional manual feature extraction method cannot effectively extract the overall deep image information, a new method of scene classification based on deep learning feature fusion is proposed for remote sensing images. First, the Grey Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP) are used to extract the shallow information of texture features with relevant spatial characteristics and local texture features as well; second, the deep information of images is extracted by the AlexNet migration learning network, and a 256-dimensional fully connected layer is added as feature output while the last fully connected layer is removed; and the two features are adaptively integrated, then the remote sensing images are classified and identified by the Grid Search optimized Support Vector Machine (GS-SVM). The experimental results on 21 types of target data of the public dataset UC Merced and 7 types of target data of RSSCN7 produced average accuracy rates of 94.77% and 93.79%, respectively, showing that the proposed method can effectively improve the classification accuracy of remote sensing image scenes.
Frequency Domain Acoustic Echo Cancellation Using Auxiliary Function Based Independent Component Analysis
The performance of traditional Acoustic Echo Cancellation (AEC) is restricted due to the double-talk detector it used to determine the double-talk and single-talk scenarios. While Blind Source Separation (BSS) signal model is a full duplex model with both far-end and near-end signals, thus the BSS-based AEC does not need the double-talk detector. This paper adopts Auxiliary function based Independent Component Analysis (Aux-ICA) algorithm to realize acoustic echo cancellation in frequency domain, in which the object function is minimizing the mutual information, and the auxiliary function technique is used for optimization. Simulation results show that this method has lower computational complexity and better performance in acoustic echo cancellation under continuous double-talk scenarios.
Numerical Solution of Micropolar Fluid Flow with Zero Mass Flux of Nanoparticles at Stretching Surface with a Porous Medium
This paper investigates the comprehensive investigation of mixed convection flow and heat transfers analysis of Micropolar by considering porous medium and heat absorption. Subsequently, mathematical formulation was modeled using boundary conditions. Two major aspects, Brownian motion and thermophoresis are addressed in the energy and concentration equation which are coupled with momentum equation. By using similarity variables, the entire system of partial differential equations govern through momentum, energy, angular momentum and concentration are converted into system of nonlinear ordinary differential equations. Nonlinear systems are solved by using numerical approach with bvp4c function in MATLAB that is based on the collocation method, specifically the three-point Lobatto IIIa formula is directed to type of finite difference method. Results are obtained for various emerging parameters. It has been observed that skin friction decreases for increasing values of Hartmann number and Eckert number. Decreasing trend of bar graphs is observed against Nusselt number and Sherwood number against Brownian motion and thermophoresis parameters.
School Bus Routing Considering Operation and Travel Costs
In order to solve the problems of high operating cost and poor service quality of school bus due to the scattered distribution of bus stops in rural areas, multi-objective SBRP (School Bus Routing Problem) models were developed for the mixed-load and non-mixed-load scenarios. In the non-mixed-load scenario, a model of the SBRP was developed to optimize the students' travel cost and school operating cost, while in the mixed-load scenario, another model of the SBRP was developed to consider the input cost and operation cost of the school bus. Several heuristic algorithms were compared, based on which the simulated annealing algorithm was selected to solve the models, and the horizontal comparison of the solution results based on genetic algorithm were determined. Tests were conducted on an international bench mark case and the constructed models were solved by introducing different search operators into the simulated annealing algorithm, then the proposed approach was applied to the optimal design of school bus routes in Wulian county, Rizhao, Shandong province. The results showed that in the non-mixed-load scenario, compared with the original school bus operation mode, the school bus input, mileage and travel cost were reduced by 28.6%, 37.8% and 35.6%, respectively, and students' travel cost was reduced by 4.3% considering the students' perception of school bus service. While in the mixed-load scenario, the proposed approach reduced the school bus input, mileage and travel cost by 37.5%, 42.0% and 35.8%, respectively; due to the complexity of the mixed-load scenario, it is difficult to take the travel cost into account, thus the students' travel cost was increased by 0.5%. The proposed SBRP models were verified to be effective and the simulated annealing approach can optimize service quality and reduce operation cost of rural school bus to a greater extent than the genetic algorithm.
Prediction of ${\rm PM}_{2.5}$ Concentration in Beijing Based on Bayesian Hierarchical Autoregressive Spatio-Temporal Model
Here, a hierarchical autoregressive spatio-temporal model under the Bayesian framework is proposed to address the simultaneous multi-site ${\rm PM}_{2.5}$ prediction. The true daily average concentration of ${\rm PM}_{2.5}$ is regarded as a potential spatio-temporal process, then the temporal correlation is described by the first-order autoregressive process and the spatial correlation is captured based on the Mat\u00e9rn process, which greatly improves the efficiency in dimension reduction and synchronous prediction. In addition, meteorological factors such as daily maximum temperature, relative humidity and wind speed are used as explanatory variables to improve the prediction accuracy. The combination of Bayesian method and MCMC can realize parameter estimation and prediction process due to the model's hierarchical structure. The empirical analysis of daily ${\rm PM}_{2.5}$ concentration in Beijing shows that the proposed model has good interpolation or prediction performance in both spatial and temporal dimensions.
Mathematical Formulation and Investigation of Contamination in the System of Lakes: A Comparative Study
The contamination in water bodies is a big threat to the environment to control the water pollution, a model study is conducted. The pollution model for a system of three lakes that are interconnected by channels is taken into account. Three input models (periodic, exponentially decaying, and linear) were solved by employing a variational iteration technique coupled with various types of multipliers. Identification of exact multipliers for $n{\rm th}$-order differential equations is also presented. All said models were examined mathematically. We noticed that exact Lagrange multipliers in the correction functional bring more accurate and efficient results that have been deliberated graphically. The results obtained via variational iteration method (VIM)-$\lambda_E,$ already published work and the Runge-Kutta method of order four provides an excellent agreement. Additionally, the use of exact Lagrange multipliers with other techniques may be extended to other dynamical problems.
Solving Surface Potential of DC Grounding Electrode by Chebyshev Polynomial
The operation experiences have shown that the large-scale DC magnetic bias caused by DC grounding electrode can be attributed to the uneven surface potential distribution. Here, the Chebyshev polynomial is used to fit the Hankel transform kernel function in order to solve the surface potential distribution for the complex earth model of wide area depth stratification. The adaptive order fitting method of Chebyshev polynomial for kernel function is obtained via shift operation, coefficient expansion and truncation error determination, which greatly reduces the calculation difficulty of surface potential distribution in a large area caused by DC grounding electrode. Compared with the standard grounding calculation software CDEGS, the proposed Chebyshev polynomial approach achieves less than 1V of earth surface potential deviation in range of 1-100 km when the DC grounding current is 5 000 A. Moreover, the order of the Chebyshev polynomial has influence on the solution results, and it is confirmed that the 20th-order Chebyshev polynomial can meet the accuracy requirements for general DC bias risk assessment. The proposed surface potential assessment method based on shifted Chebyshev polynomial provides a basic technical means for the risk assessment of DC bias, which is helpful to reduce the difficulty of DC bias risk assessment for power grid.
Applications of Multifractal Detrended Fluctuation Analysis
In recent years, multifractal detrended fluctuation analysis (MF-DFA) has become an important tool for detecting the scale and long correlation of non-stationary time series. With the continuous development of multifractal theory, researchers have widely applied it in physics, chemistry, biology, economy, etc. In this paper, we briefly review various applications of MF-DFA, and present some empirical research using one- and two-dimensional (2D) MF-DFA. 1D MF-DFA is always applied in financial markets, energy markets, heartbeat, and atmospheric science. Furthermore, 2D MF-DFA has been studied in surface science such as image segmentation, medical image classification. In this paper, we use 1D MF-DFA to explore the market efficiency of Korean stock market, and adopt 2D MF-DFA to segment images such as the license plate and hepatic cell image. In addition, we apply the proposed algorithm to segment transmission lines under icing condition, and the proposed method achieves satisfactory segmentation results.
An Algorithm for 3D Modeling of Doppler Weather Radar Base Data
This paper proposed a Marching Trapezoidal Polyhedrons 3D modeling algorithm (MTPD) based on the cone-shaped spatial distribution of Doppler weather radar base data. In this algorithm, a trapezoidal polyhedron was introduced to replace the cube in the conventional modeling algorithm as a basic volume element for modelling. On the other hand, the hexahedral index or tetrahedral index was selected as the construction model for the 3D iso-surface based on the difference in spatial range for 3D radar modeling, to balance the efficiency of the algorithm and the precision of the modeling results. Based on this algorithm, a Doppler weather radar 3D visualization platform was developed using WebGL technology. The results revealed that the algorithm significantly improved efficiency without compromising the precision of 3D modeling when compared with the conventional modeling algorithm based on radar grid data. The durations for the algorithms were reduced by 1.9 seconds and 0.7 seconds, respectively under the hexahedral index mode and the tetrahedral index mode, while under the tetrahedral index mode, the 3D echo structure was more continuous with a higher level of precision. The Doppler weather 3D radar visualization platform based on the B/S architecture could provide a cross-platform 3D radar display, thus visualize the 3D structure of convective cloud effectively.
Analysis of Blood Through Trapping Vertical Porous Annulus with Cilia in the Presence of Thermal Radiation
This study describes the analysis of nanofluid flow due to trapping along porous annulus with cilia in the presence of thermal radiation and heat absorption effects. In this examination, a partial differential equation is formulated to represent the two-dimensional movement of nanofluid. This system is developed in the cylindrical coordinates system and thereafter, a long wavelength approximation is introduced to attain the simplified form of the system. Emerging parameters are attained with nanoparticle\u2019s effects in the system. Effective thermal conductivity models for heat transfer analysis are utilized. Numerical solution has been calculated for pressure rise and pressure gradient. Velocity and temperature distribution is also plotted for physical parameters. It is important to find that the trend of velocity is increasing by improving the quantity of nanoparticle concentration, radiation and mixed convection parameters. By increasing the size of the radius, velocity profile and pressure gradient have decreasing trend.
Cable Laying Path Planning Based on Optimized Ant Colony Algorithm
To address the large error and low efficiency of traditional manual design in cable laying task, the computer-aided design optimized by Ant Colony Algorithm (ACA) is applied to cable laying path planning. The shortest path for cable laying is solved via the ACA's multi terminal path calculation for complex path planning. Furthermore, the planarized cable laying path is optimized via Gompertz function in aspects of pheromone restriction and self-adaptive adjustment of volatilization factor, thus improves the ACA in both convergence speed and global performance. The simulation results show that the optimized ant colony algorithm can quickly obtain the shortest cable laying path in the task of substation digital 3D cable laying, which saves the cost of manpower and materials, and improves the design accuracy.
Prediction of Properties of Anti-Breast Cancer Drugs Based on PSO-BP Neural Network and PSO-SVM
The process of screening and developing new drugs through experiments is very slow and requires a lot of manpower and material resources, and the use of computer-aided prediction of the molecular properties of drugs can greatly save time and cost of drug development. Therefore, in order to enable anti-breast cancer candidate drugs to have good biological activity and ADMET properties for inhibiting ER\u03b1, the random forest classifier was first used for the collected 1974 compounds to screen the top 20 molecular descriptors with the most significant effects on biological activity. Then a QSAR model was established using this and ${\rm pIC}_{50}$ value as characteristic data. The biological activity values of 50 new compounds were predicted via the PSO optimized BP neural network, with the model fit of 0.8337 and the root mean square error of 0.7315, which were more consistent with the actual values than the predicted results of the BP neural network. Subsequently, in order to improve the success rate of drug development, the ADMET classification prediction model was constructed using PSO to optimize the SVM based on the existing ADMET property data. The algorithm cross-validation CV accuracy rate reached 94.0767%, and the prediction accuracy rates of the five index models were all above 79%. The results show that the proposed model has better prediction performance than the benchmark model, and the adopted prediction strategy is effective, which can provide reference for the discovery and development of anti-breast cancer drugs.
Weighted Supervised Functional Principal Components Analysis
In functional linear regression, a supervised version of functional principal components analysis (FPCA) can automatically estimate the leading functional principal components (FPCs), which not only represent the major source of variation of the functional predictor but also are simultaneously correlated with the response. However, the existing supervised FPCA (sFPCA) is only applicable to single modal functional data. In this paper, we propose a weighted version of supervised FPCA (w-sFPCA) by considering the adaptive weighting of multi-modal functional predictors. The new w-sFPCA not only assigns corresponding weights to each modal of functional predictors, but also automatically estimates the leading FPCs associated with response variables, representing the main sources of variation in functional predictors. The method is demonstrated to have a better prediction accuracy than the conventional sFPCA method by using one real application on meteorological data and two carefully designed simulation studies.
New Kantorovich’s Theorems for Newton’s Method on Lie Groups for Mappings and Matrix Optimization Problems
We propose a new Kantorovich theorem for Newton's method on Lie groups for mappings and matrix low-rank optimization problems, which arises from many applications. Under the classical hypothesis of f, we establish the convergence criteria of Newton's method from Lie group to its Lie algebra with weakened conditions, which improves the corresponding results in [20].