Application of Machine Learning Techniques for Software Reliability Prediction (SRP)

Software reliability is a statistical measure of how well software operates with respect to its requirements. There are two related software engineering research issues about reliability requirements. The first issue is achieving the necessary reliability, i.e., choosing and employing appropriate software engineering techniques in system design and implementation. The second issue is the assessment of reliability as a method of assurance that precedes system deployment. In past few years, various software reliability models have been introduced. These models have been developed in response to the need of software engineers, system engineers and managers to quantify the concept of software reliability. This chapter on software reliability prediction using ANNs addresses three main issues: (1) analyze, manage, and improve the reliability of software products; (2) satisfy the customer needs for competitive price, on time delivery, and reliable software product; (3) determine the software release instance that is, when the software is good enough to release to the customer.

Conclusion

This chapter summarize and concludes the issues and challenges elaborated in different chapters using machine learning approaches presented by various authors. It identifies the importance of supervised and unsupervised learning algorithms establishing classification, prediction, clustering, security policies along with object recognition and pattern matching structures. A systematic position for future research and practice is also described in detail. This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems related to health, social and engineering applications.

Application of Optimization Techniques for Gene Expression Data Analysis

The feature selection from gene expression data is the NP hard problem, few of evolutionary techniques give optimal solutions to find feature subsets. In this chapter, authors introduce some evolutionary optimization techniques and proposed a Binary Particle Swarm Optimization (BPSO) based algorithm for feature subset selection. The Feature selection is one of the important and challenging tasks for gene expression data where many traditional methods failed and evolutionary based methods were succeeded. In this study, the initial datasets are preprocessed using a quartile based fast heuristic technique to reduce the crude domain features which are less relevant in categorizing the samples of either group. The experimental results on three bench-mark datasets vis-a-vis colon cancer, defused B-cell lymphoma and leukemia data are evaluated by means of classification accuracies. Detailed comparative studies with some of popular existing algorithms like Genetic Algorithm (GA), Multi Objective GA are also made to show the superiority and effectiveness of the proposed method.

Significance of Affective Sciences and Machine Intelligence to Decipher Complexity Rooting in Urban Sciences

An urban system is a complex system. There are many factors which significantly influences the different aspects of it. The influencing factors possess different characteristics as they may be environmental, economical, socio-political or cognitive factors. It is not feasible to characterize an urban system with deterministic approach. Therefore there is a need of study on computational frameworks that can investigate cities from a system's perspective. This kind of study may help in devising different ways that can handle uncertainty and randomness of an urban system efficiently and effectively. Therefore the primary objective of this work is to highlight the significance of affective sciences in urban studies. In addition, how machine intelligence techniques can enable a system to control and monitor the randomness of a city is explained. Finally the utility of machine intelligence technique in deciphering the complexity of way finding is conceptually demonstrated.

Business Applications of Deep Learning

Deep Learning (DL) took Artificial Intelligence (AI) by storm and has infiltrated into business at an unprecedented rate. Access to vast amounts of data extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges. DL is the cornerstone technology behind products for image recognition and video annotation, voice recognition, personal assistants, automated translation and autonomous vehicles. DL works similarly to the brain by extracting high-level, complex abstractions from data in a hierarchical and discriminative or generative way. The implications of DL supported AI in business is tremendous, shaking to the foundations many industries. In this chapter, I present the most significant algorithms and applications, including Natural Language Processing (NLP), image and video processing and finance.

Coronary Heart Disease Prognosis Using Machine-Learning Techniques on Patients With Type 2 Diabetes Mellitus

Heart diseases and stroke are the number one cause of death and disability among people with type 2 diabetes (T2D). Clinicians and health authorities for many years have expressed interest in identifying individuals at increased risk of coronary heart disease (CHD). Our main objective is to develop a prognostic workflow of CHD in T2D patients using a Holter dataset.. This workflow development will be based on machine learning techniques by testing a variety of classifiers and subsequent selection of the best performing system. It will also assess the impact of feature selection and bootstrapping techniques over these systems. Among a variety of classifiers such as Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Alternating Decision Tree (ADT), Random Tree (RT) and K-Nearest Neighbour (KNN), the best performing classifier is NB. We achieved an area under receiver operating characteristics curve (AUC) of 68,06% and 74,33% for a prognosis of 3 and 4 years, respectively.

Mobile Vision for Plant Biometric System

In human's life plant plays an important part to balance the nature and supply food-&-medicine. The traditional manual plant species identification method is tedious and time-consuming process and requires expert knowledge. The rapid developments of mobile and ubiquitous computing make automated plant biometric system really feasible and accessible for anyone-anywhere-anytime. More and more research are ongoing to make it a more realistic tool for common man to access the agro-information by just a click. Based on this, the chapter highlights the significant growth of plant identification and leaf disease recognition over past few years. A wide range of research analysis is shown in this chapter in this context. Finally, the chapter showed the future scope and applications of AaaS and similar systems in agro-field.

Introduction to Machine Learning

Machine learning refers to the changes in systems that perform tasks associated with artificial intelligence. This chapter presents introduction types and application of machine learning. This chapter also presents the basic concepts related to feature selection techniques such as filter, wrapper and hybrid methods and various machine learning techniques such as artificial neural network, Naive Bayes classifier, support vector machine, k-nearest-neighbor, decision trees, bagging, boosting, random subspace method, random forests, k-means clustering and deep learning. In the last the performance measure of the classifier is presented.

How Games Improve Language in People With Language Dysfunctions

Many people as they age face a greater challenge of muscular dexterity around their facial muscles. This results in difficulty producing certain sounds, and sometimes the problem is so severe that they are unintelligible. People who could benefit from the methods in this chapter are those who are hard of hearing and do not have feedback readily accessible and people with ALS. This chapter describes a method that uses a computer learning algorithm that predicts what people are about to say based on earlier content and learns what the natural sound of their voice sounds like. This chapter illustrates speech trajectory and voice shaping. Clear Audio is a biologically inspired framework for studying natural language. Like the story behind Jurassic Park, Clear Audio attempts to make predictions about data from existing data, inspired by biological processes. Its main goal is to give feedback for speech pathology purposes.

Machine-Learning-Based Approach for Face Recognition

Face recognition has been one of the most interesting and important research areas for real time applications. There is a need and necessity to design efficient machine leaning based approach for automatic recognitions and surveillance systems. Face recognition also used the knowledge from other disciplines such as neuroscience, psychology, computer vision, pattern recognition, image processing, and machine learning, etc. This chapter provides a review of machine learning based techniques for the face recognition. First, it presents an overview of face recognition and its challenges then, a literature review of machine learning based approaches for face detection and recognition is presented.