Characteristic Analysis and Route Optimization of Heterogeneous Neural Network in Logistics Allocation System.

This paper presents a hybrid algorithm for the creation of heterogeneous single layer neural networks (SLNNs). The proposed self-adaptive heterogeneous hybrid extreme learning machine (SA-He-HyELM) trains a series of SLNNs with different neuron types in the hidden layer utilizing the extreme learning machine (ELM) algorithm. These networks are evolved into heterogeneous networks (networks having different combinations of hidden neurons) with the help of a modified genetic algorithm (GA). The algorithm is able to handle two architecturally different neuron types: traditional low order (linear) units and higher order units with different transfer functions. The GA is fully self-adaptive and uses one novel hybrid crossover operator along with a self-adaptive mutation operator in order to retain ELM’s simplicity and minimize the number of parameters need tuning. The experimental part of the current paper involves testing SA-He-HyELM with traditional ELM and other three ELM-based methods. The experimental part utilized a series of regression and classification experiments on relatively large datasets. In all cases the proposed method managed to get lower MSE or higher classification accuracy when compared to the aforementioned methods.

A logistics distribution vehicle scheduling model under cloud computing environment is established based on the analysis of factors affecting resource scheduling. The order information and logistics distribution vehicle information processing are completed under the framework of cloud computing, so as to obtain the most reasonable logistics distribution plan. To solve the problem of vehicle allocation in logistics distribution, a distribution path algorithm model and a minimum delivery cost algorithm model are established to provide the best strategy for logistics distribution scheme.

Aiming at the problems of low efficiency and slow iteration convergence of existing distribution vehicle routing optimisation algorithms, a logistics distribution vehicle routing optimisation algorithm based on cloud model is proposed. Data are trained and learned using the deep belief network (DBN) model. According to label data and model training results set manually, the road condition of logistics distribution route is predicted. Combined with genetic algorithm and cloud model theory, the real-time solution and update of time-sharing weights of time-sharing weighted network paths are realised, and the global optimal solution is obtained. The experimental results show that compared with the existing algorithms, the logistics distribution time of the research algorithm varies in the range of 70 s-37 s, and the efficiency of logistics distribution is high. When the number of iterations is 1,000, the path length of this algorithm is 2,400 km, and the iteration convergence is fast.

ABSTRACTIn recent years, object-based segmentation methods and shallow-model classification algorithms have been widely integrated for remote sensing image supervised classification. However, as the image resolution increases, remote sensing images contain increasingly complex characteristics, leading to higher intraclass heterogeneity and interclass homogeneity and thus posing substantial challenges for the application of segmentation methods and shallow-model classification algorithms. As important methods of deep learning technology, convolutional neural networks (CNNs) can hierarchically extract higher-level spatial features from images, providing CNNs with a more powerful recognition ability for target detection and scene classification in high-resolution remote sensing images. However, the input of the traditional CNN is an image patch, the shape of which is scarcely consistent with a given segment. This inconsistency may lead to errors when directly using CNNs in object-based remote sensing classification: jagged errors may appear along the land cover boundaries, and some land cover areas may overexpand or shrink, leading to many obvious classification errors in the resulting image. To address the above problem, this paper proposes an object-based and heterogeneous segment filter convolutional neural network (OHSF-CNN) for high-resolution remote sensing image classification. Before the CNN processes an image patch, the OHSF-CNN includes a heterogeneous segment filter (HSF) to process the input image. For the segments in the image patch that are obviously different from the segment to be classified, the HSF can differentiate them and reduce their negative influence on the CNN training and decision-making processes. Experimental results show that the OHSF-CNN not only can take full advantage of the recognition capabilities of deep learning methods but also can effectively avoid the jagged errors along land cover boundaries and the expansion/shrinkage of land cover areas originating from traditional CNN structures. Moreover, compared with the traditional methods, the proposed OHSF-CNN can achieve higher classification accuracy. Furthermore, the OHSF-CNN algorithm can serve as a bridge between deep learning technology and object-based segmentation algorithms thereby enabling the application of object-based segmentation methods to more complex high-resolution remote sensing images.

We consider different parallel architectures and methods for self-organization and minimization of complexity for heterogeneous polynomial neural networks (PNN) in problems of pattern recognition and in diagnostics of states. Constructive estimates for the heterogeneity index and parallelism in the process of autonomous classifying decision making with the use of PNNs of different kinds are obtained. It is shown that the parallelism, self-organization, and robustness of heterogeneous PNNs can significantly increase in group (multiagent) solutions of difficult problems in pattern recognition, image analysis, large-scale (vector) diagnostics of states, and adaptive routing of data flows.

We consider different mathematical models, architectures and methods for learning, selforganization and minimization of complexity for heterogeneous polynomial neural networks (PNN) in problems of vector (widened) pattern recognition, data classification and diagnostics of states. Constructive estimates for the heterogeneity index and parallelism in the process of autonomous classifying decision making with the use of PNNs of different kinds are obtained. It is shown that the parallelism, self-organization, and robustness of heterogeneous PNNs can significantly increase in group (multiagent) solutions of difficult problems in pattern recognition, image analysis, large-scale (vector) diagnostics of states, and adaptive routing of data flows.

This paper proposes heterogeneous modular deep neural network (DNN) to address a complex problem of detection of diabetic retinopathy and simultaneously the five types of abnormalities. The modular approach gives the advantage to extract class specific features for the classifier, which helps to outperform the classical convolutional neural networks. Moreover, the heterogeneous nature of modular DNN provides the economy in the overall architecture and also enables to extract region specific features which further contribute to higher accuracy of detection. Extensive simulation studies were performed using benchmark dataset DIARETDB0 and results show that the proposed approach performs better or equivalently good than the other standard approaches.

By the development of social media, sentiment analysis has changed to one of the most remarkable research topics in the field of natural language processing which tries to dig information from textual data containing users’ opinions or attitudes toward a particular topic. In this regard, deep neural networks have emerged as promising techniques that have been extensively used for this aim in recent years and obtained significant results. Considering the fact that deep neural networks can automatically extract features from data, it can be claimed that intermediate representations extracted from these networks can be also used as appropriate features. While different deep neural networks are able to extract various types of features due to their distinct structures, we decided to combine features extracted from heterogeneous neural networks using multi-view classifiers to enhance the overall performance of document-level sentiment analysis by considering the correlation between them. The proposed multi-view deep network makes use of intermediate features extracted from convolutional and recursive neural networks to perform classification. Based on the results of the experiments, the proposed multi-view deep network not only outperforms single-view deep neural networks but also has superior efficiency and generalization performance.

This paper describes a hierarchical evolutionary technique developed to design and train feedforward neural networks with different activation functions on their hidden-layer neurons (heterogeneous neural networks). At the upper level, a genetic algorithm is used to determine the number of neurons in the hidden layer and the type of the activation function of those neurons. At the second level, neural nets compete against each other across generations so that the nets with the lowest test errors survive. Finally, on the third level, a co-evolutionary approach is used to train each of the created networks by adjusting both the weights of the hidden-layer neurons and the parameters for their activation functions.

A cascade and heterogeneous neural network for CT pulmonary nodule detection and its evaluation on both phantom and patient data.

This study aims to improve the efficiency of logistics distribution scheduling in the supply chain and reduce logistics costs. In the current research, the time and cost problems of logistics distribution always have some limitations to meet the needs of practical applications; therefore, this paper designed genetic algorithm-based method to solve the vehicle scheduling problem. The analysis of arithmetic examples was carried out. The analysis found that the method effectively reduced the cost of distribution and had higher computing speed, showing a high performance in solving the problem of logistics distribution scheduling. The method can further optimize the logistics distribution of the supply chain. This work provides a new approach for the study of logistics distribution scheduling model and is conducive to the better application of genetic algorithm in this field.

Contributors

This paper investigates the application of a new class of neighborhood search algorithms—cyclic transfers—to multivehicle routing and scheduling problems. These algorithms exploit the two-faceted decision structure inherent to this problem class: First, assigning demands to vehicles and, second, routing each vehicle through its assigned demand stops. We describe the application of cyclic transfers to vehicle routing and scheduling problems. Then we determine the worst-case performance of these algorithms for several classes of vehicle routing and scheduling problems. Next, we develop computationally efficient methods for finding negative cost cyclic transfers. Finally, we present computational results for three diverse vehicle routing and scheduling problems, which collectively incorporate a variety of constraint and objective function structures. Our results show that cyclic transfer methods are either comparable to or better than the best published heuristic algorithms for several complex and important vehicle routing and scheduling problems. Most importantly, they represent a novel approach to solution improvement which holds promise in many vehicle routing and scheduling problem domains.

As the number and diversity of distributed information sources on the Internet exponentially increase, various search services are developed to help the users to locate relevant information. But they still exist some drawbacks such as the difficulty of mathematically modeling retrieval process, the lack of adaptivity and the indiscrimination of search. This paper shows how heterogeneous neural networks can be used in the design of an intelligent distributed information retrieval (DIR) system. In particular, three typical neural network models - Kohoren's SOFM Network, Hopfield Network, and Feed Forward Network with Back Propagation algorithm are introduced to overcome the above drawbacks in current research of DIR by using their unique properties. This preliminary investigation suggests that Neural Networks are useful tools for intelligent search for distributed information sources.

This paper presents HEDNet, an emotionally supportive dialogue system based on a heterogeneous neural network. HEDNet integrates CNN, Bi-LSTM, and an attention mechanism to enhance emotion recognition, intent recognition, and response generation, improving emotional and contextual understanding of user input. Evaluated on multiple sentiment dialogue datasets, HEDNet outperformed baseline models in emotion classification, response quality, and user satisfaction. Ablation studies verified the contribution of each module, emphasizing the importance of CNN, Bi-LSTM, and attention mechanisms. This work aims to enhance user experience through personalized, empathetic interactions.