Software Quality Measurement

Software measurement helps to quantify the quality and the effectiveness of software to find areas of improvement and to provide information needed to make appropriate decisions. In the recent studies, software metrics are widely used for quality assessment. These metrics are divided into two categories: syntactic and semantic. A literature review shows that syntactic ones are widely discussed and are generally used to measure software internal attributes like complexity. It also shows a lack of studies that focus on measuring external attributes like using internal ones. This chapter presents a thorough analysis of most quality measurement concepts. Moreover, it makes a comparative study of object-oriented syntactic metrics to identify their effectiveness for quality assessment and in which phase of the development process these metrics may be used. As reliability is an external attribute, it cannot be measured directly. In this chapter, the authors discuss how reliability can be measured using its correlation with syntactic metrics.

Green Computing

With the increase in the number of computers, the amount of energy consumed by them is on a significant rise, which in turn is increasing carbon content in atmosphere. With the realization of this problem, measures are being taken to minimize the power usage of computers. The solution is green computing. It is the efficient utilization of computing resources while minimizing environmental impact and ensuring both economic and social benefits. Green computing is a balanced and sustainable approach towards achieving a healthier and safer environment without compromising the technological needs of the current and future generations. This chapter studies the architectural aspects, the scope, and the applications of green computing. The emphasis of this study is on current trends in green computing, challenges in the field of green computing, and the future trends of green computing.

Human Health Risk Assessment via Amalgamation of Probability and Fuzzy Numbers

This chapter presents an approach to combine probability distributions with imprecise (fuzzy numbers) parameters (mean and standard deviation) as well as fuzzy numbers (FNs) of various types and shapes within the same framework. The amalgamation of probability distribution and fuzzy numbers are done by generating three algorithms. Human health risk assessment is performed through the proposed algorithms. It is found that the chapter provides an exertion to perform human health risk assessment in a specific manner that has more efficacies because of its capacity to exemplify uncertainties of risk assessment model in its own fashion. It affords assistance to scientists, environmentalists, and experts to perform human health risk assessment providing better efficiency to the output.

Assessing Animal Emotion and Behavior Using Mobile Sensors and Affective Computing

Affective computing is a very active and young field. It is driven by several promising areas that could benefit from affective intelligence such as virtual reality, smart surveillance, perceptual interfaces, and health. This chapter suggests new design for the detection of animal affect and emotion under an affective computing framework via mobile sensors and machine learning. The authors review existing literature and suggest new use cases by conceptual reevaluation of existing work done in affective computing and animal sensors.

A Cognitive Information Retrieval Using POP Inference Engine Approaches

The focus of this chapter is to design a cognitive information retrieval (CIR) framework using inference engine (IE). IE permits one to analyze the central concepts of information retrieval: information, information needs, and relevance. The aim is to propose an inference engine in which adequate user preferences are considered. As the cognitive inference engine (CIE) approach is involved, the complex inquiries are required to return more important outcomes as opposed to customary database questions which get irrelevant and unsolicited responses or results. The chapter highlights the framework of a cognitive rule-based engine in which preference queries are dealt with while keeping in mind the intention of the user, their performance, and optimization.

A Study on Risk Management in Financial Market

The basic aim of risk management is to recognize, assess, and prioritize risk in order to assure that the uncertainty should not deviate from the intended purpose of the business goals. Risk can take place from various sources, which includes uncertainty in financial markets, recessions, inflation, interest rates, currency fluctuations, etc. Various methods used for this management of risk are faced with various decisions such as the market price, historical data, statistical methodologies, etc. For stock prices, the information derives from the historical data where the next price depends only upon the current price and some of the outside factors. Financial market is very risky to invest money, but the proper prediction with handling the risk will benefit a lot. Various types of risk in the financial market and the appropriate solutions to overcome the risk are analyzed in this study.

Addressing Security Issues and Standards in Internet of Things

In the IoTs era, the short-range mobile transceivers will be implanted in a variety of daily requirements. In this chapter, a detail survey in several security and privacy concerns related to internet of things (IoTs) by defining some open challenges are discussed. The privacy and security implications of such an evolution should be carefully considered to the promising technology. The protection of data and privacy of users has been identified as one of the key challenges in the IoT. In this chapter, the authors present internet of things with architecture and design goals. They survey security and privacy concerns at different layers in IoTs. In addition, they identify several open issues related to the security and privacy that need to be addressed by research community to make a secure and trusted platform for the delivery of future internet of things. The authors also discuss applications of IoTs in real life. A novel approach based on cognitive IoT is presented, and a detailed study is undertaken. In the future, research on the IoTs will remain a hot issue.

Clustering Techniques

Clustering is a process of grouping a set of data points in such a way that data points in the same group (called cluster) are more similar to each other than to data points lying in other groups (clusters). Clustering is a main task of exploratory data mining, and it has been widely used in many areas such as pattern recognition, image analysis, machine learning, bioinformatics, information retrieval, and so on. Clusters are always identified by similarity measures. These similarity measures include intensity, distance, and connectivity. Based on the applications of the data, different similarity measures may be chosen. The purpose of this chapter is to produce an overview of much (certainly not all) of clustering algorithms. The chapter covers valuable surveys, the types of clusters, and methods used for constructing the clusters.

Neuro-Fuzzy Models and Applications

The modern techniques of artificial intelligence have found application in almost all the fields of human knowledge. Among them, two important techniques of artificial intelligence, fuzzy systems (FS) and artificial neural networks (ANNs), have found many applications in various fields such as production, control systems, diagnostic, supervision, etc. They evolved and improved throughout the years to adapt arising needs and technological advancements. However, a great emphasis is given in the engineering field. The techniques of artificial intelligence based on fuzzy logic and neural networks are frequently applied together for solving engineering problems where the classic techniques do not supply an easy and accurate solution. Separately, each one of these techniques possesses advantages and disadvantages that, when mixed together, provide better results than the ones achieved with the use of each isolated technique. As ANNs and fuzzy systems have often been applied together, the concept of a fusion between them started to take shape. Neuro-fuzzy systems were born which utilize the advantages of both techniques. Such systems show two distinct ways of behavior. In a first phase, called learning phase, it behaves like neural networks that learn internal parameters off-line. Later, in the execution phase, it behaves like a fuzzy logic system. A neuro-fuzzy system is a fuzzy system that uses a learning algorithm derived from or inspired by neural network theory to determine its parameters (fuzzy sets and fuzzy rules) by processing data samples. Neural networks and fuzzy systems can be combined to join its advantages and to cure its individual illness. Neural networks introduce its computational characteristics of learning in the fuzzy systems and receive from them the interpretation and clarity of systems representation. Thus, the disadvantages of the fuzzy systems are compensated by the capacities of the neural networks. These techniques are complementary, which justifies its use together. This chapter deals with an analysis of neuro-fuzzy systems. Benefits of these systems are studied with its limitations too. Comparative analyses of various categories of neuro-fuzzy systems are discussed in detail. Apart from these, real-time applications of such systems are also presented.

Design of Cognitive Healthcare System for Coronary Cardiac Disease Detection

This chapter focuses on clinical decision system (CDS) uses in healthcare units. In this chapter, cognitive approaches are taken using soft computing techniques to design clinical decision systems (CDS) for modern healthcare units. Cognitive computing-based approach is considered. It focuses on cardiac disease detection exclusively by considering its surrounding factors. Fuzzy logic is utilized as one part. The other part includes diabetic detection using deep neural network (DNN) for the automatic identification of the disease. The experiment was done with the Pima Indian dataset. The classification result has been presented in the result section. The decision system in the healthcare unit is a suitable example of a multi-agent system.