In conventional networks, there was a tight bond between the control plane and the data plane. The introduction of Software-Defined Networking (SDN) separated these planes, and provided additional features and tools to solve some of the problems of traditional network (i.e., latency, consistency, efficiency). SDN is a flexible networking paradigm that boosts network control, programmability and automation. It proffers many benefits in many areas, including routing. More specifically, for efficiently organizing, managing and optimizing routing in networks, some intelligence is required, and SDN offers the possibility to easily integrate it. To this purpose, many researchers implemented different machine learning (ML) techniques to enhance SDN routing applications. This article surveys the use of ML techniques for routing optimization in SDN based on three core categories (i.e. supervised learning, unsupervised learning, and reinforcement learning). The main contributions of this survey are threefold. Firstly, it presents detailed summary tables related to these studies and their comparison is also discussed, including a summary of the best works according to our analysis. Secondly, it summarizes the main findings, best works and missing aspects, and it includes a quick guideline to choose the best ML technique in this field (based on available resources and objectives). Finally, it provides specific future research directions divided into six sections to conclude the survey. Our conclusion is that there is a huge trend to use intelligence-based routing in programmable networks, particularly during the last three years, but a lot of effort is still required to achieve comprehensive comparisons and synergies of approaches, meaningful evaluations based on open datasets and topologies, and detailed practical implementations (following recent standards) that could be adopted by industry. In summary, future efforts should be focused on reproducible research rather than on new isolated ideas. Otherwise, most of these applications will be barely implemented in practice.

Blockchain technology is one of the most important inventions and creative advancements that play a crucial role in today's business world. Blockchain technology is heading in the direction of systematic innovation and revolution. It is a digital ledger of transactions, and every block contains information of transactions linked by cryptographic references. Every block covers information and maintains trust among people based on how far they are. Blockchain is a system used for storing data, and it ensures the security of the system. The resurgence in blockchain technology has encouraged scholars and specialists over the past couple of years to carefully examine new ways of implementing blockchain technology with a wide range in the domain of healthcare. This rapid increase in blockchain technology has generated many endless possibilities. In this article, we provide a review of blockchain technology in healthcare. We present a detailed introduction, history, technical information, and types of blockchain technology. Motivations behind this technology and top healthcare projects completed using this technology are also discussed. This article is classified into three groups based on blockchain applications with their use cases. The evaluation of medical care technologies and relevant applications based on blockchain technology, such as sharing electronic medical records, remote patient monitoring, and supply chain management, are also discussed. In revolutionizing the healthcare industry, we illustrate the potential of blockchain technology. We have also focused on identifying the limitations of previous approaches. Finally, this article is concluded with some open research issues and future research direction.

Text-to-image generation is a method used for generating images related to given textual descriptions. It has a significant influence on many research areas as well as a diverse set of applications (e.g., photo-searching, photo-editing, art generation, computer-aided design, image reconstruction, captioning, and portrait drawing). The most challenging task is to consistently produce realistic images according to given conditions. Existing algorithms for text-to-image generation create pictures that do not properly match the text. We considered this issue in our study and built a deep learning-based architecture for semantically consistent image generation: recurrent convolutional generative adversarial network (RC-GAN). RC-GAN successfully bridges the advancements in text and picture modelling, converting visual notions from words to pixels. The proposed model was trained on the Oxford-102 flowers dataset, and its performance was evaluated using an inception score and PSNR. The experimental results demonstrate that our model is capable of generating more realistic photos of flowers from given captions, with an inception score of 4.15 and a PSNR value of 30.12 dB, respectively. In the future, we aim to train the proposed model on multiple datasets.