Multimodal data fusion based on IGERNNC algorithm for detecting pathogenic brain regions and genes in Alzheimer's disease.

A western North Pacific tropical cyclone (TC) intensity prediction scheme has been developed based on climatology and persistence (CLIPER) factors as potential predictors and using genetic neural network (GNN) model. TC samples during June–October spanning 2001–2010 are used for model development. The GNN model input is constructed from potential predictors by employing both a stepwise regression method and an Isometric Mapping (Isomap) algorithm. The Isomap algorithm is capable of finding meaningful low-dimensional architectures hidden in their nonlinear high-dimensional data space and separating the underlying factors. In this scheme, the new developed model, which is termed the GNN-Isomap model, is used for monthly TC intensity prediction at 24- and 48-h lead times. Using identical modeling samples and independent samples, predictions of the GNN-Isomap model are compared with the widely used CLIPER method. By adopting different numbers of nearest neighbors, results of sensitivity experiments show that the mean absolute prediction errors of the independent samples using GNN-Isomap model at 24- and 48-h forecasts are smaller than those using CLIPER method. Positive skills are obtained as compared to the CLIPER method with being above 12 % at 24 h and above 14 % at 48 h. Analyses of the new scheme suggest that the useful linear and nonlinear prediction information of the full pool of potential predictors is excavated in terms of the stepwise regression method and the Isomap algorithm. Moreover, the GNN is built by integrating multiple individual neural networks with the same expected output and network architecture is optimized by an evolutionary genetic algorithm, so the generalization capacity of the GNN-Isomap model is significantly enhanced, indicating a potentially better operational weather prediction.

This chapter reviews and expands our work on the relationship between linkage structure, that is how decision variables of a problem are linked with (dependent on) one another, and the performance of three basic types of genetic evolutionary algorithms (GEAs): hill climbing, genetic algorithm and bottom-up self-assembly (compositional). It explores how concepts and quantitative methods from the field of social/complex networks can be used to characterize or explain problem difficulty for GEAs. It also re-introduces two novel concepts – inter-level conflict and specificity – which view linkage structure from a level perspective. In general, the basic GEAs performed well on our test problems with linkage structures resembling those empirically observed in many real-world networks. This is a positive indication that the structure of real-world networks which evolved without any central organization such as biological networks is not only influenced by evolution and therefore exhibit non-random properties, but also influences its own evolution in the sense that certain structures are easier for evolutionary forces to adapt for survival. However, this necessarily implies the difficulty of certain other structures. Hence, the need to go beyond basic GEAs to what we call GEAs with “brains”, of which linkage-learning GEAs is one species.

Due to the conflict between reducing cost and improving water supply performance, how to select the appropriate pipe diameter is a current challenge. In this paper, the problem is transformed into a multi-objective optimization problem, and the evolutionary genetic optimization algorithm is used to solve the problem to determine the optimal selection of pipe diameter in the pipe network. To solve this problem, the evolutionary genetic algorithm was coupled with EPANET hydraulic simulation software in Python environment. The results show that NSGA-II and NSGA-III perform better in two typical case tests. Moreover, the increase of the objective functions will lead to an increase in the amount of data in the optimal solution set, and will affect the optimal value of each objective function. That shows that the balance between the economy and reliability of water supply can be successfully found by coupling the hydraulic model and the multi-objective optimization algorithm, which can provide an auxiliary decision for enterprises.

March 1-3, 1995, San Diego, California Evolutionary programming is one of the predominate algorithms withing the rapidly expanding field of evolutionary computation. These edited contributions to the Fourth Annual Conference on Evolutionary Programming are by leading scientists from academia, industry, and defense. The papers describe both the theory and practical application of evolutionary programming, as well as other methods of evolutionary computation including evolution strategies, genetic algorithms, genetic programming, and cultural algorithms.Topics include :- Novel Areas of Evolutionary Programming and Evolution Strategies.- Evolutionary Computation with Medical Applications.- Issues in Evolutionary Optimization Pattern Discovery, Pattern Recognition, and System Identification.- Hierarchical Levels of Learning.- Self-Adaptation in Evolutionary Computation.- Morphogenic Evolutionary Computation.- Issues in Evolutionary Optimization.- Evolutionary Applications to VLSI and Part Placement.- Applications of Evolutionary Computation to Biology and Biochemistry Control.- Applications of Evolutionary Computation.- Genetic and Inductive Logic Programming.- Genetic Neural Networks.- The Future of Evolutionary Computation.A Bradford Book. Complex Adaptive Systems series

March 1-3, 1995, San Diego, California Evolutionary programming is one of the predominate algorithms withing the rapidly expanding field of evolutionary computation. These edited contributions to the Fourth Annual Conference on Evolutionary Programming are by leading scientists from academia, industry, and defense. The papers describe both the theory and practical application of evolutionary programming, as well as other methods of evolutionary computation including evolution strategies, genetic algorithms, genetic programming, and cultural algorithms.Topics include :- Novel Areas of Evolutionary Programming and Evolution Strategies.- Evolutionary Computation with Medical Applications.- Issues in Evolutionary Optimization Pattern Discovery, Pattern Recognition, and System Identification.- Hierarchical Levels of Learning.- Self-Adaptation in Evolutionary Computation.- Morphogenic Evolutionary Computation.- Issues in Evolutionary Optimization.- Evolutionary Applications to VLSI and Part Placement.- Applications of Evolutionary Computation to Biology and Biochemistry Control.- Applications of Evolutionary Computation.- Genetic and Inductive Logic Programming.- Genetic Neural Networks.- The Future of Evolutionary Computation.A Bradford Book. Complex Adaptive Systems series

In order to improve the efficiency and accuracy of the prediction of expressway traffic flow, this paper, based on the characteristics of the data of the expressway traffic flow, focuses on an optimized method of prediction with the application of the neural network with genetic algorithm. Applying genetic algorithm, optimizing BP neural network structure and establishing a new mixed model, this algorithm speed up the slow convergence velocity of traditional BP neural network prediction and increases the possibility to escape local minima. This algorithm based on the optimized genetic neural network predicts the actual data of the expressway traffic flow, the result of which shows that the application of the optimized method of prediction with the genetic neural network algorithm is effective and that it improves the rate and the accuracy of the prediction of the expressway traffic flow.

Alzheimer’s disease (AD) is a common neurodegenerative disorder causing dementia in the elderly population. Functional disconnection of brain is considered to be the main cause of AD. In this study, we applied a newly developed association (Asso) mapping approach to directly quantify the functional disconnections and to explore the diagnostic effects for AD with resting-state functional magnetic resonance imaging data from 36 AD patients and 42 age-, gender-, and education-matched healthy controls (HC). We found that AD patients showed decreased Asso in left dorsoanterior insula (INS) while increased functional connections of INS with right medial prefrontal cortex (MPFC) and left posterior cingulate cortex (PCC). The changed Asso and functional connections were closely associated with cognitive performances. In addition, the reduced Asso and increased functional connections could serve as effective neuromarkers to distinguish AD patients from HC. Our research provided new evidence for functional disconnections in AD and demonstrated that functional disconnections between cognition-memory networks may be potential early biomarkers for AD.

Through the application of genetic algorithms (genetic algorithm, simplified as GA) and BP(Back Propation) neural network, I built a prediction model of roses diseases, in which I choose six indicators as the input of network, they are the minimum temperature, maximum temperature, average temperature, minimum humidity, maximum humidity, average humidity in the greenhouse, then I choose three diseases index as the output of the network, they are rose powdery mildew, downy mildew, gray mold. Using Matlab platform to complete the model, the results show that: based on genetic algorithm and BP neural network model the diseases of roses can be predicted. The average relative error is about 6.37%, so it has good effect, and compared with the traditional BP network model, the results show that the model based on genetic algorithms and BP neural network is superior to the traditional BP network model, which have higher precision and stability.

Based on safety assessment factors determined by operation characteristics of a certain tailing ,genetic BP neural network evaluation model is established. To overcome such problems of BP neural network as slow convergence ,poor generalization ability and easy to fall into local minimum value,this paper proposes to use genetic algorithm to optimize threshold value,weights and structure of neural network. Thus,by taking advantage of extensive mapping ability of neural network and global search ability of genetic algorithm,neural network and genetic algorithm will have complementary advantages and the learning speed of network will be accelerated. The application of the described method shows optimized fitting precision,improved accuracy and efficiency ,and enhanced generalization ability of BP neural network. In conclusion,this model can effectively reflect and accurately evaluate non-linear relations between security levels and evaluation factors in tailing.

Using multimodal neuroimaging data to characterize brain network is currently an advanced technique for Alzheimer’s disease(AD) Analysis. Over recent years the neuroimaging community has made tremendous progress in the study of resting-state functional magnetic resonance imaging (rs-fMRI) derived from blood-oxygen-level-dependent (BOLD) signals and Diffusion Tensor Imaging (DTI) derived from white matter fiber tractography. However, Due to the heterogeneity and complexity between BOLD signals and fiber tractography, Most existing multimodal data fusion algorithms can not sufficiently take advantage of the complementary information between rs-fMRI and DTI. To overcome this problem, a novel Hypergraph Generative Adversarial Networks (HGGAN) is proposed in this paper, which utilizes Interactive Hyperedge Neurons module (IHEN) and Optimal Hypergraph Homomorphism algorithm (OHGH) to generate multimodal connectivity of Brain Network from rs-fMRI combination with DTI. To evaluate the performance of this model, We use publicly available data from the ADNI database to demonstrate that the proposed model not only can identify discriminative brain regions of AD but also can effectively improve classification performance.

A new recognition method of vehicle license plates based on neural network is presented in this paper. For the Back Propagation (BP) neural network often trap into the local minimum in the training process, a Genetic Neural Network (GNN), GABP was constructed by combining the Genetic Algorithm(GA)with BP neural network. The training of the GABP neural network was finished in two steps. The GA was firstly used to make a thorough searching in the global space for the weights and thresholds of the neural network, which can ensure they fall into the neighborhood of global optimal solution. Then, in order to improve the convergence precision, the gradient method was used to finely train the network and find the global optimum or second-best solution with good performance. On the other side, feature extraction is also important for improving the recognition rate of the network. So both the structure features and the statistic features are used in this paper, which include mesh feature, direction line element feature and Zernike moments feature. Experimental results show that the proposed method can save the time of training network and achieve a highly recognition rate.

The clinical progression of Alzheimer's disease( AD ) can't be accurately evaluated by single modality data alone. Multi-modal data have a good effect on the diagnosis of AD. Clarifying the complementarity between modalities is crucial for the assessment of each stage of AD. Few studies have specifically explored the complementarity between different modalities due to the lack of completely aligned and paired multi-modal data and the limitation of sample size. However, collecting the full set of aligned and paired data is expensive or even impractical. In addition, the limited number of samples poses a great challenge to the robustness of the model. In this paper, different machine learning( ML ) methods were used to explore data complementarity between T1-weighted magnetic resonance imaging ( MRI ), cerebrospinal fluid ( CSF ), and fluorodeoxyglucose-positron emission tomography ( FDG-PET ) modalities. The different modal data of Alzheimer's Neuroimaging Initiative ( ADNI ) and the self-extracted neuroimaging data were experimentally explored. Experiments show that there is obvious complementarity between MRI and CSF. By fusing MRI and CSF data, three binary classification tasks using multi-modal fusion data have achieved varying degrees of improvement. At the same time, we also explored the important features of multi-modal fusion data through SHapley Additive exPlanations ( SHAP ), and found that most important features are supported by relevant literature.

This paper aimed to establish a nonlinear relationship between laser cladding process parameters and the crack density of a high-hardness, nickel-based laser cladding layer, and to control the cracking of the cladding layer via an intelligent algorithm. By using three main process parameters (overlap rate, powder feed rate, and scanning speed), an orthogonal experiment was designed, and the experimental results were used as training and testing datasets for a neural network. A neural network prediction model between the laser cladding process parameters and coating crack density was established, and a genetic algorithm was used to optimize the prediction results. To improve their prediction accuracy, genetic algorithms were used to optimize the weights and thresholds of the neural networks. In addition, the performance of the neural network was tested. The results show that the order of influence on the coating crack sensitivity was as follows: overlap rate > powder feed rate > scanning speed. The relative error between the predicted value and the experimental value of the three-group test genetic algorithm-optimized neural network model was less than 9.8%. The genetic algorithm optimized the predicted results, and the technological parameters that resulted in the smallest crack density were as follows: powder feed rate of 15.0726 g/min, overlap rate of 49.797%, scanning speed of 5.9275 mm/s, crack density of 0.001272 mm/mm2. Therefore, the amount of crack generation was controlled by the optimization of the neural network and genetic algorithm process.

In this paper, an improved neural network enterprise credit rating model, which is grounded on a genetic algorithm, is suggested. With the characteristics of self-adaptiveness and self-learning, the genetic algorithm is utilized to adjust and enhance the thresholds and weights of the neural network connections. The potential problems of the backpropagation (BP) neural network with slothful speed of convergence and the possibility of falling into the local minimum point are solved to a convinced degree using the genetic algorithm in combination. The hybrid technique of the genetic BP neural network is applied to a credit rating system. Using commercial banks’ datasets, our experimental evaluations suggest that, using a combination of the BP neural network and the genetic algorithm, the proposed model has high accuracy in enterprise credit rating and has good application value. Moreover, the proposed model is approximately 15.9% more accurate than the classical BP neural network approach.

Fusion of multimodal medical data provides multifaceted, disease-relevant information for diagnosis or prognosis prediction modeling. Traditional fusion strategies such as feature concatenation often fail to learn hidden complementary and discriminative manifestations from high-dimensional multimodal data. To this end, we proposed a methodology for the integration of multimodality medical data by matching their moments in a latent space, where the hidden, shared information of multimodal data is gradually learned by optimization with multiple feature collinearity and correlation constrains. We first obtained the multimodal hidden representations by learning mappings between the original domain and shared latent space. Within this shared space, we utilized several relational regularizations, including data attribute preservation, feature collinearity and feature-task correlation, to encourage learning of the underlying associations inherent in multimodal data. The fused multimodal latent features were finally fed to a logistic regression classifier for diagnostic prediction. Extensive evaluations on three independent clinical datasets have demonstrated the effectiveness of the proposed method in fusing multimodal data for medical prediction modeling.