Historical maps classification has become an important application in today’s scenario of everchanging land boundaries. Historical map changes include the change in boundaries of cities/states, vegetation regions, water bodies and so forth. Change detection in these regions are mainly carried out via satellite images. Hence, an extensive knowledge on satellite image processing is necessary for historical map classification applications. An exhaustive analysis on the merits and demerits of many satellite image processing methods are discussed in this paper. Though several computational methods are available, different methods perform differently for the various satellite image processing applications. Wrong selection of methods will lead to inferior results for a specific application. This work highlights the methods and the suitable satellite imaging methods associated with these applications. Several comparative analyses are also performed in this work to show the suitability of several methods. This work will help support the selection of innovative solutions for the different problems associated with satellite image processing applications.

Analyzing the sentiments of people from social media content through text, speech, and images is becoming vital in a variety of applications. Many existing research studies on sentiment analysis rely on textual data, and similar to the sharing of text, users of social media share more photographs and videos. Compared to text, images are said to exhibit the sentiments in a much better way. So, there is an urge to build a sentiment analysis model based on images from social media. In our work, we employed different transfer learning models, including the VGG-19, ResNet50V2, and DenseNet-121 models, to perform sentiment analysis based on images. They were fine-tuned by freezing and unfreezing some of the layers, and their performance was boosted by applying regularization techniques. We used the Twitter-based images available in the Crowdflower dataset, which contains URLs of images with their sentiment polarities. Our work also presents a comparative analysis of these pre-trained models in the prediction of image sentiments on our dataset. The accuracies of our fine-tuned transfer learning models involving VGG-19, ResNet50V2, and DenseNet-121 are 0.73, 0.75, and 0.89, respectively. When compared to previous attempts at visual sentiment analysis, which used a variety of machine and deep learning techniques, our model had an improved accuracy by about 5% to 10%. According to the findings, the fine-tuned DenseNet-121 model outperformed the VGG-19 and ResNet50V2 models in image sentiment prediction.

MATEC Web of Conferences, open access proceedings in Materials science, Engineering and Chemistry

The SMEs in Romania, which remained reliable after the economic crisis, had a better dynamic of business development in relation to large and very large companies. The increase of the fiscal value of these companies has improved every year, leading to spectacular results in certain fields of activity, particularly services, trade and IT. After the economic crisis, the SMEs from the Western region had a dynamic business market, outstripping all other regions of economic development. The result was the increase in the number of SMEs and the number of jobs in this region. In this article, we propose a mathematical model based on multivariate analysis, specifically on cluster analysis, in order to analyse the economic activities of SMEs in the Western development region. Cluster analysis is a multivariate analysis method that includes algorithms able to identify and group efficiently and descriptively a similar group in terms of certain common properties, in a lot of time objects. The purpose of this paper is the analysis of the economic efficiency of SMEs’ activities in the Western area of Romania and the extraction of activities through modeling in order to maximise the efficiency of the companies in the future.