The fusion of IoT and Computational Intelligence makes it possible to increase energetic efficiency of our homes. Connected devices can be optimally adjusted to the needs of a family. In this article we present our developed IoT convection installation for a small house with the developed remote platform control system. The computer control model is gartering sensors readings and information from users about conditions in the house and by the use of Computational Intelligence optimizes parameters to adjust the developed IoT convection system for better comfort of a family. We have done a full convection installation, both in practical and theoretical models, together with remote control system and proposed security model. Optimization results show increased comfort of use with lower changes in the temperature inside. The system after optimization shows significant improvement in lower changes of the temperature and lower consumption.

To avoid the over-fitting phenomenon that appeared in performing Graph Neural Networks (GNNs) on test examples, the feature encoding scheme of GNNs usually introduces the dropout procedure. However, after learning latent node representations under this scheme, Gaussian noise produced by the dropout operation is inevitably transmitted into the next neighborhood aggregation step, which necessarily hampers the unbiased aggregation ability of GNN models. To address this issue, we present a novel aggregator, De-Noising Aggregation (DNAG), which utilizes Principal Component Analysis (PCA) to preserve the aggregated real signals from neighboring features and simultaneously filter out the Gaussian noise. The idea is different from using PCA on traditional applications to reduce the feature dimension. We regard PCA as an aggregator to compress the neighboring node features to have better expressive de-noising power. We propose new training architectures to simplify the intensive computation of PCA in DNAG. Numerical experiments show the apparent superiority of the proposed DNAG models in gaining more de-noising capability and achieving the state-of-the-art for a set of predictive tasks on several graph-structured datasets.

In this article, we propose malfunction classifications for trucks, a novel idea for smart fleet management systems. In the proposed cooperative cooperative intelligent transportation (C-ITS), the developed neural network work with information from truck fleets to select the trucks that need a service. From the results returned from the deep neural network classifier, the applied heuristic algorithm uses the classification outputs to select the most important results. The proposed process is multithreaded; thus, the composed system gains additional efficiency. The implemented deep learning model achieved an accuracy above 98%, and an above 95% recall. The developed solution was tested on the Scania Truck data collection. The research results show the importance of the advances and validate our concept for potential further development.

3D Convolutional Neural Networks (3D CNNs) have been explored to learn spatio-temporal information for video-based human action recognition. Expensive computational cost and memory demand resulted from standard 3D CNNs, however, hinder their application in practical scenarios. In this work, we address the aforementioned limitations by proposing a novel Dual 3D Convolutional Network (D3DNet) with two complementary lightweight branches. A coarse branch maintains large temporal receptive field by a fast temporal downsampling strategy and simulates the expensive 3D convolutions using a combination of more efficient spatial convolutions and temporal convolutions. Meanwhile, a fine branch progressively downsamples the video in the temporal domain and adopts 3D convolutional units with reduced channel capacities to capture multi-resolution spatio-temporal information. Instead of learning these two branches independently, a shallow spatio-temporal downsampling module is shared for these two branches for efficient low-level feature learning. Besides, lateral connections are learned to effectively fuse the information from the two branches at multiple stages. The proposed network makes good balance between inference speed and action recognition performance. Based on RGB information only, it achieves competing performance on five popular video-based action recognition datasets, with inference speed of 3200 FPS on a single NVIDIA GTX 2080Ti card.

Hyperspectral image (HSI) classification has been a hot topic for decides, as HSIs have rich spatial and spectral information, and provide a strong basis for distinguishing different land-cover objects. Benefiting from the development of deep learning technologies, deep learning-based HSI classification methods have achieved promising performance. Recently, several neural architecture search (NAS) algorithms have been proposed for HSI classification, which further improves the accuracy of HSI classification to a new level. In this article, NAS and transformer are combined for handling the HSI classification task for the first time. Compared with the previous work, the proposed method has two main differences. First, we revisit the search spaces designed in previous HSI classification NAS methods and propose a novel hybrid search space, consisting of the space-dominated cell and the spectrum-dominated cell. Compared with search spaces proposed in previous works, the proposed hybrid search space is more aligned with the characteristic of HSI data, that is, HSIs have a relatively low spatial resolution and an extremely high spectral resolution. Second, to further improve the classification accuracy, we attempt to graft the emerging transformer module on the automatically designed convolutional neural network (CNN) to add global information to local region focused features learned by CNN. Experimental results on three public HSI datasets show that the proposed method achieves much better performance than comparison approaches, including manually designed networks and NAS-based HSI classification methods. Especially on the most recently captured dataset Houston University, overall accuracy is improved by nearly 6 percentage points. Code is available at https://github.com/Cecilia-xue/HyT-NAS .