The definition of �expert-level analysis� is wherein the subtasks have been made available for automatic processing. Hence, expert-level analysis becomes more and more demanding until it covers all science and philosophy.

The role of Machine Learning (ML) and Artificial Intelligence (AI) in NLP (Natural Language Processing) and text analytics is to improve, accelerate and automate the NLP process, so that the human beings understand the meaning of the text documents. The underlying Natural Language features and text analytics give semi-structured and unstructured text the decision data and insights.

In real-time scenario, most of the NLP features and processes are cascaded with ML algorithms in the AI solution space and most of the APIs are deployed in the public cloud computing platform, whose function cost is low and which leads to some security issues in the cloud computing era with respect to AI/NLP/ML.

In the first paper, �Processing the Textual Information Using Open Natural Language Processing�, the author, A Muthusamy, has discussed how NLP, the application of computational technique and components of AI, helps to analyze and synthesize standard English language; it also draws from many disciplines like computer science and computational linguistics to process and analyze a large amount of natural language data. The main aim of this paper is to overcome the limitations in N-gram approach and to use open NLP tool kit for processing the text in an efficient manner. The second paper, �Meta-Analysis Review of Efficient Mechanisms for Resource, Energy, and Data Management in Fog Computing�, by Sorush Niknamian, classifies the management strategies into three main categories: resource, energy and data management. In addition, it defines the new challenges in each of these categories. Finally, the differences between the reviewed strategies are investigated in terms of scalability, reliability, time and query attributes.

The third paper, �Portfolio Asset Identification Using Graph Algorithms on a Quantum Annealer�, by Angad Kalra, Faisal Qureshi and Michael Tisi, has dual objectives: to explore how commercially available quantum hardware and algorithms can solve real-world problems in finance; and to compare quantum solutions to their classical counterparts. Specifically, the D-Wave quantum annealing computer (D-Wave 2000Q) is used to address the problem of asset correlation identification for financial portfolio management. The paper explores how graph algorithms can be implemented on the D-Wave 2000Q machine to cluster asset correlations in order to identify various financial portfolios. Numerical experiments are conducted using four quantum/classical algorithm pairs on four real-world financial time series datasets spanning 10 years. The quantum solution is as good as (and sometimes better than) the classical one. However, the quantum solution fails to scale beyond certain levels of data dimensionality. The last paper, �Cloud-Based Security Solutions� by Sourav Mukherjee, shows how cloud computing unlocks the doors to multiple and infinite venues which include upscaling and downscaling the resources in no time and pay as you go model (i.e., pay based on the usage). Even with the potential advantages attained from cloud computing, the security of the booming technology is under question, which may impact cloud adoption. Based on the several attacks and vulnerabilities in recent times, a more intense debate about cloud security research has started to find out the probable ways to avoid such attacks.