Efficient and cost effective ways of irrigation have emerged as the need of the hour due to limited sweet water resources, especially the countries that are seriously hit by a lack of sweet water reservoirs. The majority of the water is wasted due to inefficient ways of watering plants. In this paper, we propose an intelligent approach for efficient plant irrigation that has a database of daily water needs of a type of plant and decides the amount of water for a plant type on the basis of the current moisture in soil, humidity, and time of the day. This approach not only saves sweet water by efficient utilization, but also supports smart consumption of energy. Our approach employs IoT and a set of sensors to efficiently record plant data and their watering needs and the approach is implemented with a mobile phone application interface that is used to continuously monitor and control the efficient watering system. The results of this study are easy to reproduce as the sensors used are cheap and easy to access. The study discusses in this paper is experimented on small area (such as tunnel farm) but the results of the experiments show that the used approach can be generalized and can be used for large size fields for efficient irrigation. The results of the experiments also outperform the manual approach and the similar approaches for sensor based irrigation systems.

Typical fire monitoring and warning systems use a single smoke detector that is connected to a fire management system to give early warnings before the fire spreads out up to a damaging level. However, it is found that only smoke detector-based fire monitoring systems are not efficient and intelligent since they generate false warnings in case of a person is smoking, etc. There is need of a multi-sensor based intelligent and smart fire monitoring system that employs various parameters, such as presence of flame, temperature of the room, smoke, etc. To achieve such a smart solution, a multi-sensor solution is required that can intelligently use the data of sensors and generate true warnings for further fire control and management. This paper presents an intelligent Fire Monitoring and Warning System (FMWS) that is based on Fuzzy Logic to identify the true existence of dangerous fire and send alert to Fire Management System (FMS). This paper discusses design and application of a Fuzzy Logic Fire Monitoring and Warning System that also sends an alert message using Global System for Mobile Communication (GSM) technology. The system is based on tiny, low cost, and very small in size sensors to ensure that the solution is reproduceable. Simulation work is done in MATLAB ver. 7.1 (The MathWorks, Natick, MA, USA) and the results of the experiments are satisfactory.

This paper presents an intelligent approach to handle heterogeneous and large-sized data using machine learning to generate true recommendations for the future customers. The Collaborative Filtering (CF) approach is one of the most popular techniques of the RS to generate recommendations. We have proposed a novel CF recommendation approach in which opinion based sentiment analysis is used to achieve hotel feature matrix by polarity identification. Our approach combines lexical analysis, syntax analysis and semantic analysis to understand sentiment towards hotel features and the profiling of guest type (solo, family, couple etc). The proposed system recommends hotels based on the hotel features and guest type as additional information for personalized recommendation. The developed system not only has the ability to handle heterogeneous data using big data Hadoop platform but it also recommend hotel class based on guest type using fuzzy rules. Different experiments are performed over the real world dataset obtained from two hotel websites. Moreover, the values of precision and recall and F-measure have been calculated and results are discussed in terms of improved accuracy and response time, significantly better than the traditional approaches.

In an era of super computing, data is increasing exponentially requiring more proficiency from the available technologies of data storage, data processing, and analysis. Such continuous massive growth of structured and unstructured data is referred to as a ''Big data''. The processing and storage of big data through a conventional technique is not possible. Due to improved proficiency of Big Data solution in handling data, such as NoSQL caused the developers in the previous decade to start preferring big data databases, such as Apache Cassandra, Oracle, and NoSQL. NoSQL is a modern database technology that is designed to provide scalability to support voluminous data, leading to the rise of NoSQL as the most viable database solution. These modern databases aim to overcome the limitations of relational databases such as unlimited scalability, high performance, data modeling, data distribution, and continuous availability. These days, the larger enterprises need to shift NoSQL databases due to their more flexible models. It is a great challenge for business organizations and enterprises to transform their existing databases to NoSQL databases considering heterogeneity and complexity in relational data. In addition, with the emergence of big data, data cleansing has become a great challenge. In this paper, we proposed an approach that has two modules: data transformation and data cleansing module. The first phase is the transformation of a relational database to Oracle NoSQL database through model transformation. The second phase provides data cleansing ability to improve data quality and prepare it for big data analytics. The experiments show the proposed approach successfully transforms the relational database to a big data database and improve data quality. INDEX TERMS Relational databases, NoSQL, big data, data cleansing.

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