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Digital image security plays an essential role in the shared communication model. Encryption and decryption process is commonly applied to securely transmit the images in various real-time applications. In addition, the generation of encryption/decryption keys is also essential to achieve enhanced image security. This study presents a multiple share creation scheme with an optimal signcryption (MSS-OSC) technique for digital image security. The MSS-OSC technique primarily generates a set of various shares for every digital image that needs to be transmitted. In addition, the encryption of generated shares takes place via the optimal signcryption (OSC) technique. Moreover, genetic programming (GP) is employed to optimally choose the keys involved in the encryption and decryption process. The detailed experimental validation of the MSS-OSC technique is investigated using a set of benchmark test images. The results analysis demonstrated that the MSS-OSC technique had a superior performance by accomplishing maximum digital image security.

The stagnation of the domestic market has brought the majority of the small and medium-sized enterprises (SME) to their knees, leading them to reinvent their way of doing business and find new strategies in order to survive and grow when the environmental conditions are deeply changing. On the one hand, new trends create a strong disruption on a structural level among the productive fabrics, but on the other hand, they represent also an opportunity, which opens new scenarios and new possibilities for the relaunch of SMEs. Among the most important challenges for Italian SMEs is internationalization, which is the possibility for enterprises to trade their goods not only on the domestic market but also on the foreign markets trying to find new opportunities to obtain some advantages. This is a very complicated process, traumatic and challenging in term of resources, but the possibility to have a genetic patrimony and a productive value, as the ones of the “Made in Italy, gives to the products of Italian enterprises a high level of competition and strong differentiation, making this process more accessible. The growth and competitiveness of enterprises, in particular SMEs, increasingly depend on the ability to apply new knowledge, working methods, and technologies as well as on the opportunity to participate in the commercialization of research developments in order to create new products, services or processes. Therefore, companies should strive to benefit from the opportunities and competitive advantages that innovation brings. SMEs play an important role in economic growth and provide most of the new jobs in Italy. Within the framework of this paper, the insight into the SMEs internationalization process is presented. The article provides an analysis of SMEs in the process of internationalization. Besides it concentrates on the new threats and opportunities represented by the new industrial revolution - Industry 4.0. Analyzing the impact of Industry 4.0 on the internationalization of Italian SMEs, the authors explain the solutions that are being used and the ones that should be taken.

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The relevance of the study is that globalization and the integration of the international economy provide excellent opportunities for economic development through international trade and investment. It is necessary to make the product and services more competitive. One of the factors determining the competitiveness of trade is how quickly and how economically the goods can be delivered. The competition in the logistics industry is continually growing, which forces participants to improve their efficiency, improve quality and reduce costs. In these conditions effective IT solutions are vital tools for logistics and the ability to manage the logistics processes in the business environment in the context of globalization is a factor of competitiveness. Within the framework of this paper, the existing business processes of cargo delivery were described. The study of the process of interaction of international supply chain participants made it possible to identify the ineffectiveness of existing processes to understand what can be replaced and corrected with the introduction of blockchain technology. The main reason of inefficiency is the low level of business processes digitalization. The cases of existing blockchain projects for the logistics industry were investigated. The article provides possible cases of using blockchain for the logistics industry.

The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease (i.e., . As a countermeasure, contact tracing and social distancing are essential to prevent the transmission of the virus, which can be achieved using indoor location analytics. Based on the indoor location analytics, the human mobility on a site can be monitored and planned to minimize human's contact and enforce social distancing to contain the transmission of COVID-19. Given the indoor location data, the clustering can be applied to cluster spatial data, spatio-temporal data and movement behavior features for proximity detection or contact tracing applications. More specifically, we propose the Coherent Moving Cluster (CMC) algorithm for contact tracing, the density-based clustering (DBScan) algorithm for identification of hotspots and the trajectory clustering (TRACLUS) algorithm for clustering indoor trajectories. The feature extraction mechanism is then developed to extract useful and valuable features that can assist the proposed system to construct the network of users based on the similarity of the movement behaviors of the users. The network of users is used to model an optimization problem to manage the human mobility on a site. The objective function is formulated to minimize the probability of contact between the users and the optimization problem is solved using the proposed effective scheduling solution based on OR-Tools. The simulation results show that the proposed indoor location analytics system outperforms the existing clustering methods by about 30% in terms of accuracy of clustering trajectories. By adopting this system for human mobility management, the count of close contacts among the users within a confined area can be reduced by 80% in the scenario where all users are allowed to access the site.

Technology advancements have enabled the capture of Renewable Energy Sources (RES) on a massive scale. Smart Grids (SGs) that combine conventional and RES are predicted as a sustainable method of power generation. Moreover, environmental conditions impact all RES, causing changes in the amount of electricity produced by these sources. Furthermore, availability is dependent on daily or annual cycles. Although smart meters allow real-time demand prediction, precise models that predict the electricity produced by RES are also required. Prediction Models (PMs) accurately guarantee grid stability, efficient scheduling, and energy management. For example, the SG must be smoothly transformed into the conventional energy source for that time and guarantee that the electricity generated meets the predicted demand if the model predicts a period of Renewable Energy (RE) loss. The literature also suggests scheduling methods for demand-supply matching and different learning-based PMs for sources of RE using open data sources. This paper developed a model that accurately replicates a microgrid, predicts demand and supply, seamlessly schedules power delivery to meet demand, and gives actionable insights into the SG system’s operation. Furthermore, this work develops the Demand Response Program (DRP) using improved incentive-based payment as cost suggestion packages. The test results are valued in different cases for optimizing operating costs through the multi-objective ant colony optimization algorithm (MOACO) with and without the input of the DRP.

A wireless sensor network (WSN) encompasses a massive set of sensors with limited abilities for gathering sensitive data. Since security is a significant issue in WSN, there is a possibility of different types of attacks. In Distributed Denial of Service (DDOS) attack, the malicious node can adapt to several attacks, namely flooding, black hole, warm hole, etc., to interrupt the working of the WSN. The recently developed deep learning (DL) models can effectively detect DDoS attacks in the network. Therefore, this article proposes a heuristic feature selection with a Deep Learning-based DDoS (HFSDL-DDoS) attack detection model in WSN. The proposed HFSDL-DDoS technique intends to identify and categorize the occurrence of DDoS attacks in WSN. In addition, the HFSDL-DDoS technique involves the immune clonal genetic algorithm (ICGA) based feature selection (FS) approach to improve the detection performance. Moreover, a fruit fly algorithm (FFA) with bidirectional long, short-term memory (BiLSTM) based classification model is employed. The experimental analysis of the HFSDL-DDoS technique is performed, and the results are examined interms of several performance measures. The resultant experimental results pointed out the betterment of the HFSDL-DDoS technique over the other techniques.