Glaucoma is a progressive eye disease. This study presents a custom technique to enhance retinal fundus images to detect glaucoma. Contrast enhancement is a crucial stage in medical image analysis to improve the visual impression of diseases. CLAHE is a common technique to improve images. Clip Limit (CL) and subimages may restrict the potential benefits of the typical approach and pose difficulties. This study introduces Enhanced CLAHE and Automated CLAHE to address the shortcomings of the base method. These methods demonstrate progress in improving retinal landmarks in various ways by looking directly at the in-depth description of retinal images. The proposed methods, along with the baseline CLAHE, were compared using quality assessment tools such as the Peak-Signal-to-Noise Ratio (PSNR). The results help to determine the degree of contrast enhancement and the overall richness of the image.

Normally, flight delays and cancellations have significant impact on airlines operations and passenger’s satisfaction. Flight delays reduce the performance of airline operations and make significant effect on airports on time performance. Previously statistical models have been used for flight delays analysis. This study was applied in Indian aviation industry and it has given statistical analysis of domestic airlines. In this research paper, we have applied Machine Learning models with the help of computational intelligence techniques for predicting airport transport management system. We have also applied computational intelligence techniques such as Particle Swarm Optimization (PSO) and Ant Colonization Optimization (ACO) to optimize the prediction model for delay period time and calculating the most optimal dependability. We have made comprehensive analysis of Data Efficiency Model for different airlines with various approaches as well as comparative analysis of accuracy for predicting airport model by using various machine learning models. In this study we have presented invaluable insights for the analysis of flight delay models.

Vehicular ad hoc networks (VANETs) using reliable protocols of routing have become crucial in identifying the changes to topology on a continuous basis for a large collection of vehicles. For this purpose, it becomes important to identify an optimal configuration of these protocols. There are several possible configurations that have been preventing the configuration of efficient protocols that do not make use of automatic and intelligent design tools. It can further motivate using the techniques of metaheuristics like the tools, which are well‐suited to be able to solve these problems. The glowworm swarm optimization (GSO), simulated annealing (SA), and slow heat‐based SA‐GSO algorithms have been proposed in this work. The SA is a method of optimization, which imitates the manner in which the thermal system has been frozen down to its lowest state of energy. In the GSO, there is guidance to the rules of feasibility, where the swarm converges to its feasible regions very fast. Additionally, for overcoming any premature convergence, there is a local search strategy that is based on the SA and is used for making a search that is near to its true optimum solutions. Finally, this sluggish temperature‐based SA‐GSO algorithm will be employed to solve routing problems and problems of heat transfer. There is a hybrid slow heat SA‐GSO algorithm with a faster speed of convergence and higher precision of computation that is more effective in solving problems of constrained engineering.

This chapter gives information about the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by big data applications. Big data is one area where we can store, extract, and process a large amount of data. All these data are very often unstructured. Using big data, security functions are required to work over the heterogeneous composition of diverse hardware, operating systems, and network domains. A clearly defined security boundary like firewalls and demilitarized zones (DMZs), conventional security solutions, are not effective for big data as it expands with the help of public clouds. This chapter discusses the different concepts like characteristics, risks, life cycle, and data collection of big data, map reduce components, issues and challenges in big data, cloud secure alliance, approaches to solve security issues, introduction of cybercrime, YARN, and Hadoop components.