The recent technological growth at a rapid pace has paved way for the big data that denotes to the exponential growth of the information’s. The big data analytics are the trending concepts that have emerged as the promising technology that offers more enhanced perceptions from the huge set of the data that have been produced from the diverse areas. The review in the paper proceeds with the methods of the big-data-analytics and the machine-learning in handling, the huge set of data flow. The overview of the utilization of the machine-learning algorithms in the analytics of high voluminous data would provide with the deeper and the richer analysis of the huge set of information gathered to extract the valuable and turn it into actionable information’s. The paper is to review the part of machine-learning algorithms in the analytics of high voluminous data

Cancer is a deadly disease that is costing the lives of many people. Over 9.6 million death is reported in 2018 due to cancer. We propose an ideal methodology to identify and classify cancer cells using pathological images with the help of capsule network. Capsule network’s capability to learn patterns based on previous iterations can be exploited for this purpose. This can help in identification of cancer at early stages and work at the root cause of the disease and walk towards completely shutting down the disease. Image processing is done along with fuzzification and further, it is handled with capsule network classifier and analysed.

Soft computing provides the better solution for some computational issues like NP-hard problem and their applications applied to sustainable computing systems, which helps to resolve the problems of energy consumption, fuel reduction, delay tolerance, loss rate and global warming. The most integration models of soft computing techniques are fuzzy logic, deep learning, evolutionary computation, optimization algorithm, metaheuristics, Bayesian networks, expert systems, perceptron, differential algorithms, pattern recognition and reasoning models. Sustainable systems addressed the solution for power consumption in soft computing approaches and help to provide efficient solutions for the problem-solving methods. Soft computing gives beneficial use in the future applications of sustainable systems.Notably, researcher uses the soft computing approaches in data mining, machine learning, sustainable computing, capsule networks, neural networks and fuzzy linear systems to provide the intelligent solutions for the real-life problems and that are related to the sustainable systems. The goal of this special issue is to bring the theoretical preparations and practical applications of research contribution in all aspects of soft computing applications applied to sustainable computing. Further, it validates through the soft computing models in terms of precision, uncertainty, partial truth and approximation analysis.This special issue features some of the articles related to pattern recognition applications and includes saliency detection, gender classification, biotic cross-pollination and multimodal biometric authentication for optimized results. Next, some of the article discussed the optimization algorithms in neural networks, vehicular networks and multimedia applications. Soft computing models enhanced the evolution of the Internet of things (IoT) in smart systems for secure and efficient communication. Remaining articles explained that the process of fuzzy-neuro model in soft computing and it states that the process of fuzzy entropy and hybrid data fusion techniques.