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Unmanned aerial vehicles (UAVs)/drones have become very popular in recent years as they are widely used in several domains. They are widely used in both military and civilian applications such as aerial photography, entertainment, search and rescue missions, reconnaissance, traffic monitoring, and logistics. Typically, UAVs are operated from a controller or a ground control station (GCS) with the help of different communication protocols such as MAVLink, UranusLink, UAVCAN. These communication protocols are used to exchange messages. The messages contain considerable information about the UAV and certain control commands sent from GCS to UAV or UAV to GCS. Though these protocols provide better communication along with secure aspects, however, mostly there is no subtle mechanism for securing these messages and are prone to many security attacks such as man-in-the-middle (MITM) attack, denial-of-services (DoS) attack, packet data injection attack, and eavesdropping. This can result in serious consequences, for instance, crash land of a military or civilian UAV, steal important data of a military operation, false injection of reports in a reconnaissance or search and rescue operation, and many more. So, there is a need for a secure communication protocol which can ensure the required security standard sets for communication of UAVs. This survey presents the applications, general architecture, attacks on UAVs, and an insight into the security issues of UAV’s communication protocols and proposes a new secure communication protocol for the stated issues of UAVs.

Artificial intelligence has been applied in various fields including medicine. Cancer is one of the most common and dangerous diseases that need to be diagnosed for the best treatment. Herein, we introduce a new model using complex fuzzy inference system (CFIS) for colorectal cancer diagnosis. The time series characteristic of colorectal disease is signification in examination, diagnosis, and treatment. The model is implemented on a colonography dataset to evaluate the performance. The obtained results show that it gets high quality in comparison with some other compared methods.

A diagnosing heart disease is significant for researchers investigated to develop applications for improvement of an expert system in health care. The aim of this paper is to design an expert system in diagnoses using deep learning neural networks and fuzzy rules for physicians to make the right decisions in diagnosing heart disease. Based on the historical data of patients, our proposed approach is to quantify uncertain information represented in fuzzy rules in the Knowledge Base for diagnosing heart disease domain. The proposed approach helps doctors to make the right decision in the diagnosing heart disease. All doctors’ preferences can be updated to the Knowledge Base which can significantly improve the quality of heart disease diagnoses. The experimental results show that the proposed system is capable of diagnosing heart disease with a high confidence of accuracy in decision-making as compared to conventional neural networks.

Machine Learning (ML) is a potential tool that can be used to make predictions on the future based on the past history data. It constructs a model from input examples to make data-driven predictions or decisions. The growing concept “Big Data” need to be brought a great deal accomplishment in the field from claiming data science. It gives data quantifiability in a variety of ways that endow into data science. ML techniques have made huge societal effects in extensive varieties of applications. Effective and interactive ML relies on the design of novel interactive and collaborative techniques based on an understanding of end-user capabilities, behaviors, and necessities. ML could additionally make utilized within conjunction for enormous information to build effective predictive frameworks or to solve complex data analytic societal problems. In this chapter, we concentrate on the most recent progress over researches with respect to machine learning for big data analytic and different techniques in the context of modern computing environments for various societal applications. Specifically, our aim is to investigate opportunities and challenges of ML on big data and how it affects the society. The chapter covers discussion on ML in Big Data in specific societal areas.

Fuzzy clustering on the picture fuzzy set is widely applied to practical problems. However, the assessment on its convergence has not been considered yet. In this paper, we investigate the theoretical analysis of FC-PFS especially the convergence of the algorithm. Future directions are also discussed.

Link prediction in an online social network aims to determine new interactions among its members which are probably to arise in the near future. The previous researches dealt with the prediction task after calculating similarity scores between nodes in the link graph. New links are then predicted by implementing a supervised method from the scores. However, real-world applications often contain sparse and imbalanced data from the network, which may lead to difficulty in predicting new links. The selection of an appropriate classification method is indeed an important matter. Firstly, this paper proposes several extended metrics to calculate the similarity scores between nodes. Then, we design a new sampling method to make the training and testing data based on the data created by the extended metrics. Lastly, we assess some well-known classification methods namely J48, Weighted SVM, Gboost, Naive Bayes, Random Forest, Logistics Regressive, and Xgboost in order to choose the best method and equivalent environments for the link prediction problem. A number of open directions to the problem are suggested further.

In this paper, we propose the concept of fuzzy equivalence on standard neutrosophic sets and rough standard neutrosophic sets. We also provide some formulas for fuzzy equivalence on standard neutrosophic sets and rough standard neutrosophic sets. We also apply these formulas for cluster analysis. Numerical examples are illustrated.