# Innovative Topologies and Algorithms for Neural Networks
## 2 Contributions  
The papers included in this Special Issue of _Future Internet_ provide interesting examinations of deep neural networks, considering both theoretical contributions and relevant applications. Case studies are reported for object recognition, text recognition and sentiment analysis, medical applications, and other emerging fields. The first paper [1] investigates pedestrian attribute recognition within surveillance scenarios. This challenging task is approached as a form of multi-label classification. The authors propose a novel model based on a graph convolutional network (GCN), which uses a CNN to extract pedestrian features and a correlation matrix between labels to propagate information between nodes. Reported results show that the approach proposed by the authors outperforms other existing state-of-the-art methods. The second paper [2] focuses on the exploding field of IoT systems. Fog computing is used to process the huge amount of data produced by IoT applications. In the paper presented here, Deep Neural Networks Partitioning for Constrained IoT Devices is proposed as a new algorithm to partition neural networks for efficient distributed execution. The authors show that the partitioning offered by popular machine learning frameworks, such as TensorFlow, or by the general-purpose framework METIS, may produce invalid partitioning for highly constrained systems, while the proposed algorithm can be more efficient. In the third paper [3], text sentiment analysis is addressed as an important and challenging application. A sentiment-feature-enhanced deep neural network (SDNN) is proposed to integrate sentiment linguistic knowledge into a deep neural network via a sentiment attention mechanism. This helps to select the crucial sentiment-relevant context words by leveraging the sentiment lexicon in an attention mechanism, bridging the gap between traditional sentiment linguistic knowledge and deep learning methods. Experimental results are reported showing that the proposed structure achieved better performance than competitors for text sentiment classification tasks. The fourth paper [4] deals with another relevant application field, i.e., gesture recognition in video. A neural network comprising an alternate fusion of a 3D CNN and ConvLSTM, called the Multiple extraction and Multiple prediction (MEMP) network, is proposed. The main feature of the MEMP network is the repeated extraction and prediction of the temporal and spatial feature information of gesture video, which enables a high accuracy rate to be obtained. The performance of the proposed method is tested on benchmark datasets, showing high accuracy. A recognition problem is also the topic of [5]. Specifically, ship detection and recognition are addressed to better manage port resources. The authors propose an on-site processing approach, called Embedded Ship Detection and Recognition using Deep Learning (ESDR-DL). Processing of a video stream using embedded devices, and a two-stage neural network composed of a DNet for ship detection and a CNet for ship recognition, also running on embedded devices, is proposed. The ESDR-DL is deployed at the Dongying port of China, where it has been running for over a year. A medical application is the subject of [6], in which the tooth-marked tongue, an important indicator in traditional Chinese medicinal diagnosis, is considered. This paper is an example of a typical application in which a correct diagnosis relies on the experience and knowledge of the practitioner. In the study, a visual explanation method uses a CNN to extract features and a Gradient-weighted Class Activation Mapping is used to produce a coarse localization map. Experimental results demonstrate the effectiveness of the proposed method. Paper [7] concerns human activity recognition. The paper introduces a new framework combining 3D-CNN and LSTM networks. The framework integrates a motion map with the next video frame to obtain a new motion map by increasing the training video length iteratively.