Previous studies on supporting free- form keyword queries over RDBMSs provide users with linked-structures (e.g., a set of joined tuples) that are relevant to a given keyword query. Most of them focus on ranking individual tuples from one table or joins of multiple tables containing a set of keywords. The problem of keyword search in a data cube with text-rich dimension(s) (so-called text cube) is studied. The text cube is built on a multidimensional text database, where each row is associated with some text data (a document) and other structural dimensions (attributes). A cell in the text cube aggregates a set of documents with matching attribute values in a subset of dimensions. Given a keyword query, the goal is to find the top-k most relevant cells. This project studies the problem of keyword-based top k search in text cube, i.e., given a keyword query, find the top-k most relevant cells in a text cube. When users want to retrieve information from a text cube using keyword queries, relevant cells, rather than relevant documents, are preferred as the answers, because:(i) relevant cells are easy for users to browse; and (ii)relevant cells provide users insights about the relationship between the values of relational attributes and the text data. The proposed algorithm uses relevance scoring formula for finding the top-k relevant cells by exploring only a small portion of the whole text cube (when k is small) and enables early terminatio.
Traffic management system is a key parameterfor highly efficient transportation system rather than depending on the speed of the vehicles and how smart it works. Police may not have all the dynamic data, which changes in a fraction of seconds, such as traffic due to accidents. The idea is to propose a system that can identify a truck in a road network and make decisions on clearing the traffic signals by getting results from the individual junction in a network. The system uses Narrow Band Internet of Things (NB-IoT) which is faster and energy-saving, and it sends signals to the particular traffic system based on the previous or the next traffic node. The Artificial Intelligence (AI) model will be biased independently. The system sends a message using radio waves from unused bandwidth. The main aim of the system is to make the passengers spend less time on traffic signals and save time as well as energy of the transportation system. It works based on getting traffic density on an individual traffic signal and the system decides which message should be open and which to close.
Remote monitoring system has been applied in different applications such as agriculture, industrial automation, defence, telecommunication and health care. In health care applications, wireless networks get the impact with Wireless Body Area Network (WBAN). WBAN is helpful in monitoring patients’ health and it also possesses secure transmission and access control mechanism with different sensors. WBAN monitors the patient’s health and transfers the information to data pool without influencing patient’s daily routine activities. Further, the health report data are sent to the doctor over the network from the place of the patient without any data loss and delay. Due to increasing usage of wireless services, the available networks have been congested with heavy traffic which leads to miscommunication and delay. To overcome this scenario, solution has been proposed with help of Cognitive Radio Networks (CRN). Collected information are transferred to cognitive controller which acts as central node. Cognitive controller selects the channel to transfer the information with QoS as well as without any delay. Based on the input parameters, the channel selection process is optimized and it will also improve the system performance with secure transmissions. Using Fuzzy Inference System (FIS) optimizing, the channel selection process has been carried out and it also provides more accurate solution to choose the channel. For the optimization of the proposed approach Mamdani and Sugeno methods have been used. These methods yield the best results with minimum error probability of 0.9 compared to the existing methods and these methods have achieved efficiencies of 98% and 99%, respectively.
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