The article analyzes the need to create innovative manufacturing enterprises in modern conditions, and justifies their role in the development of society. The principles, priorities and strategic requirements to the innovative manufacturing enterprises in the process of economic development are analyzed. International, regional and local recommendations for the formation of innovative enterprises structures are generalized. Perspective applications of components of the fourth industrial revolution are considered. Ways of improving the innovation infrastructure and environment are examined from the scientific and theoretical and methodological point of view. Priorities, mechanisms, elements of communication, the parameters and criteria for the management of innovative manufacturing enterprises structures are identified.

Remote sensing image scene classification has gained remarkable attention because of its versatile use in different applications like geospatial object detection, natural hazards detection, geographic image retrieval, environment monitoring and etc. We have used the strength of convolutional neural network in scene image classification and proposed a new CNN to classify the images. Pre-trained VGG16 and ResNet50 are used to reduce overfitting and the training time in this paper. We have experimented on a recently proposed NWPU-RESISC45 dataset which is the largest dataset of remote sensing scene images. This paper found a significant improvement of accuracy by applying the proposed CNN and also the approaches have applied.

This paper presents a novel approach for training and output prediction of data of a Linear variable differential transformer (LVDT). LVDT is a commonly used device used in laboratories for measuring linear displacements in specific situations. This article considers application of Artificial Neural Networks (ANNs) for learning and output estimation of LVDT. Real-time experiments were conducted and results were collected for training of ANNs. The Regression results and outputs verified the learning and prediction capability of ANNs.

This paper provides insight to the dynamics that come with the emergence of IoT in the furniture and kitchen manufacturing industry. By implementing the concept of IoT companies are currently evaluating how internal knowledge and skillsets correspond to the new technical requirements that the emerging digital setting outlines and by directing internal research they are learning more about IoT and connected products as they proceed. One current major problem is that there are no open protocols that can connect all products regardless of supplier. Nevertheless, implementation of IoT does not solely involve technical aspects and companies are also faced with the dilemma on how to design and develop corresponding commercial processes. To this point early product implementations have arrived on the consumer markets and the future vision is to achieve full integration that imbeds connectivity and interaction among all products in the home.

Nowadays the rise of the artificial intelligence is with high speed. Even we are far away from the moment when machines are going to make decisions instead of human beings, the development in some fields of artificial intelligence is astonishing. Deep neural networks are such a filed. They are in a big expansion in a new millennium. Their application is wide: they are used in processing images, video, speech, audio, and text. In the last decade, researches put special attention and resources in the development of special kind of neural networks, convolutional neural networks. These networks have been widely applied to a variety of pattern recognition problems. Convolutional neural networks were trained on millions of images and it is difficult to outperform the accuracies that have been achieved. On the other hand, when we have a small dataset to train the network, there is no success to do it from a scratch. This article exploits the technique of transfer learning for classifying the images of small datasets. It consists fine-tuning of the pre-trained neural network. Here in details is presented the selection of hyper parameters in such networks, in order to maximize the classification accuracy. In the end, the directions have been proposed for the selection of the hyper parameters and of the pre-trained network which can be suitable for transfer learning.