Today Big Data, is any set of data that is larger than the capacity to be processed using traditional database tools to capture, share, transfer, store, manage and analyze within an acceptable time frame; from the point of view of service providers, Organizations need to deal with a large amount of data for the purpose of analysis. And IT department are facing tremendous challenge in protecting and analyzing these increased volumes of information. The reason organizations are collecting and storing more data than ever before is because their business depends on it. The type of information being created is no more traditional database-driven data referred to as structured data rather it is data that include documents, images, audio, video, and social media contents known as unstructured data or Big Data. Big Data Analytics is a way of extracting value from these huge volumes of information, and it drives new market opportunities and maximizes customer retention. Moreover, this paper focuses on discussing and understanding Big Data technologies and Analytics system with Hadoop distributed filesystem (HDFS). This can help predict future, obtain information, take proactive actions and make way for better strategic decision making.

Metaheuristic algorithms are optimization algorithms that are used to address complicated issues that cannot be solved using standard approaches. These algorithms are inspired by natural processes such as genetics, swarm behavior, and evolution, and they are used to explore a broad search space to identify the global optimum of a problem. Genetic algorithms, particle swarm optimization, ant colony optimization, simulated annealing, and tabu search are examples of popular metaheuristic algorithms. These algorithms have been widely utilized to address complicated issues in domains like as engineering, finance, and computer science. In general, the history of metaheuristic algorithms spans several decades and involves the development of various optimization algorithms that are inspired by natural systems. Metaheuristic algorithms have become a valuable tool in solving complex optimization problems in various fields, and they are likely to continue to play an important role in the development of new technologies and applications.

Today, computer vision is considered one of the most important sub-fields of artificial intelligence, due to the variety of its applications and capabilities to transfer the human ability to understand and describe a scene or image to the computer, so that it becomes able to recognize objects, shapes, colors, behavior and other capabilities of understanding the image. Image processing is one of the branches of computer science, and it is a way to perform some operations on an image in order to obtain an improved model for this image or extract some useful information from it. Often the data that is collected is primary data, meaning that it is not suitable for direct use in applications, so its need to analyze or pre-process it and then use it. For example: to build a data set that has been used in a model that classifies images as to whether they contain a house or not, depending on an image as input for this program. Our first step will be to collect hundreds of house images, but the problem is that these images will not be of the same dimensions, for example, so it’s to Change its dimensions, i.e., processing it in advance before submitting it to the model. The above is just one of the many reasons why image processing is important for any computer vision application

Recent progress in deep learning methods has shown that key steps in object detection and recognition, including feature extraction, region proposals, and classification, can be done using ImageAi libraries. Object detection is a computer vision technique that works to identify and locate objects within an image or video. Specifically, object detection draws bounding boxes around these detected objects, which allow us to locate where said objects are in a given scene. Object detection is commonly confused with image recognition, so before we proceed, it’s important that we clarify the distinctions between them. In that it aids in our comprehension and analysis of scenes in images or videos, object detection is intrinsically tied to other related computer vision techniques like image recognition and image segmentation. Significant variations. Image segmentation develops a pixel-level comprehension of a scene's elements while image recognition just produces a class label for an identified object. Object detection differs from these other jobs in that it has the capacity to specifically find objects inside an image or video. This enables us to count such things and later track them.