and also supports the management of disaster using natural language processing (NLP). Both Recursive and Recurrent Neural Networks can be used for applying along with NLP. At the same time, Convolutional Neural Networks can be used in image classification, text processing, and computer vision [19].
2.1.1.4 Deep Reinforcement Learning
Problems that are goal-oriented can be solved by taking decisions in the order of a sequence is performed by algorithms in reinforcement learning [11]. Here, learning can be carried out based on the reinforcement series, and modeling is based on decision processes such as Markov. These algorithms play a major role in finding solutions for the problems where the decisions to be made need to be in sequence and their applications extend to management of resources and controlling traffic light. Major problem in such algorithms is the preparation of the environment for training the data and performing tasks based on them. When a deep neural network (NN) is combined with reinforcement learning, it is referred to as deep reinforcement learning. The major goal of such algorithms is creation of software agents who learn themselves for establishing the policies in a successful manner. Additionally, they help in attaining the rewards pending in long term. In terms of performance, deep reinforcement learning solves the problems including sequential tasks that are complex such as robotics and computer vision. The need of more training data along with time for training for attaining better performance is a major drawback of deep reinforcement learning as they are computationally expensive. Data can be also be secured with usage of grids [6, 21, 24].
2.1.1.5 Optimization
Optimization helps in identifying the suitable model using objective function and is considered to be one of the important methods in case of disaster management. Several optimization methods are available, and based on their performance, they can be recommended for further applications [24].
2.1.2 Disaster Management
Disaster management starts with the phase of mitigation where activities are related to prevention of emergencies in the future including the consequences. It is the initial phase in managing the disasters. The activities related to mitigation includes enforcement of standards, providing hospital care along with shelters, and providing education for the public. This awareness can help people and also the stakeholders to deal with hazards and strategies for mitigation. The next phase is the preparedness, where the disaster is going to happen. These include the activities that can help in saving the lives of people along with helping the rescue operations like food stocking, providing emergency data, and evacuations. Following this phase comes the response stage. This stage takes place mainly at the time of disaster: evacuation of areas that are being threatened, fire fighting works, efforts such as search along with rescue, and management of shelter including assistance using humanitarians. When the disaster has occurred, the recovery stage deals with repair and the efforts related to reconstruction for returning the life to a normal level. Actions in case of recovery are cleaning the debris, assessment of damage, and reconstruction of infrastructure. It also includes assistance related to finance from the agencies of government or companies that provide insurance.
The objective of proposed work is as follows:
1 i) To develop the enhanced framework, which can perform the cognitive tasks to improve the performance of the weather forecasting;
2 ii) To reduce the fluctuations in data update from the entity column that can avoid the localization issues to update the directions;
3 iii) Using Improved Bayesian Hidden Markov Frameworks (IBHMFs), the performance can be predicted from the independent variable as sequence time series analysis data.
The chapter is structure as follows: Section 2.2 presents the big data in knowledge engineering. Section 2.3 presents the proposed system. Section 2.4 discusses the results obtained. Section 2.5 concludes the chapter.
2.2 Big Data in Knowledge Engineering
Information plays a major part in big data rules; the technologies for intervention can accumulate the information according to the needs. There are many computations mathematically performed for streaming data that are used as per requirements. The Internet provides many resources to learn and a platform for online learners. In such cases, big data that has multiple source providing ways can develop applications according to customers’ expectations. There are many functions used for online learning techniques such as fragmented knowledge transfer that relies on big data models. Also, mining can extract the information based on demand that can create a framework that can be used according to the knowledge of engineering.
The software from IBM that was built with NN [13] optimized resources are also having certain rules to be achieved. They are applied to domains such as machine learning, computer vision, and NLP. Online learning not only used a fragmented knowledge method but also used the translation for various language learners. Those accessible translations are associated with libraries that support the online providers to reach the reader or learner in a fast or rapid manner. Mobile applications are also interconnected for knowledge services using big data learning procedures that can interpret the various users mode. Intelligent systems in AI such as robotics, automatic sensors, and latest technologies are all designed with the help of big data framework. There are four generations in knowledge engineering, which are the driving force of consuming data and massive volume of data access through the internet. To understand this generation, cognitive tasks are introduced, which have the sequential flow toward the customers’ expectations.
2.2.1 Cognitive Tasks for Time Series Sequential Data
Time series [14] analysis such as frequent changes in the market, sensor updates in space, and instant observation maintenance are some of the examples for sequential data. The research work focuses on time series [22, 23] sequential data such as weather forecasting to predict the accuracy of natural disaster through the changes in sequential information. Since the longitudinal and latitude values from GPS are not always the same and the feed forward network is used to analyze the information which can have multiple hidden states. We have the various features from the Kaggle dataset for identifying the current state without a hidden model using a Bayesian Markov chain model, which can access the columns such as entity and economic changes due to various disasters [2].
Information processing are of two types: i) behavioral processes and ii) cognitive processes that are mostly used in knowledge engineering [15, 16]. Big data streaming data are through online processing via social users from Instagram, Twitter, etc., as video files, audio files, text data, and so on. They are organized according to the need of sequential changes. Transition based on current state and its path as directed graph are the most important analysis for maintaining the network flow. Applications such as weather forecast for detecting the severity through the temperature changes, climate changes, and complex tasks to predict the climate that brings a disaster such as earthquake and volcanic difference [12].
2.2.2 Neural Network for Analyzing the Weather Forecasting
NN is the most important concept in the AI system that can control the weather forecast system such that the natural disaster can be reduced. This work proposed a novel approach to predict the hidden state that updates the new changes in current state that can be directed graph and true to its transition matrix that can differentiate the modeling level that have performance analysis based on the range of economic difference that can step into the neural approach that can be in cognitive task [15] when there is a combination of multilevel longitudinal data that are from various directions from north, south, east, and west that are frequently changed. IBHMF’s main task is to reduce