Named Entity Recognition for English Language Using Deep Learning Based Bi Directional LSTM-RNN

The NER has been important in different applications like data Retrieval and Extraction, Text Summarization, Machine Translation, Question Answering (Q-A), etc. While several investigations have been carried out for NER in English, a high-accuracy tool still must be designed per the Literature Survey. This paper suggests an English Named Entities Recognition methodology using NLP algorithms called Bi-Directional Long short-term memory-based recurrent neural network (LSTM-RNN). Most English Language NER systems use detailed features and handcrafted algorithms with gazetteers. The proposed model is language-independent and has no domain-specific features or handcrafted algorithms. Also, it depends on semantic knowledge from word vectors realized by an unsupervised learning algorithm on an unannotated corpus. It achieved state-of-the-art performance in English without the use of any morphological research or without using gazetteers of any sort. A little database group of 200 sentences includes 3080 words. The features selection and generations are presented to catch the Name Entity. The proposed work is desired to forecast the Name Entity of the focus words in a sentence with high accuracy with the benefit of practical knowledge acquisition techniques

Machine learning for IoT-based smart farming

Agriculture balances food requirements for mankind, and the supply of essential raw materials for many industries is the fundamental occupation in India. Smart farming allows analyzing the growth of crops and the parameters which influence crop growth and supports farmers in their activities, it is more profitable and reduces irrigation wastages. The proposed model is a smart farming system that analyzes the influence of parameters on crop growth and predicts the soil condition using a machine learning algorithm. Temperature, Ph, humidity, gas, and water level are the few most essential parameters to determine the quantity of water required and to find hazardous gas in any agriculture field. This system comprises temperature, pH, humidity, smoke detector, and water level sensor, deployed in an agricultural field, sends data through a microprocessor, developing an IoT device with cloud. In this study, we present a model that predicts soil series with regard to land type and, in accordance with the prediction, suggests appropriate crops. For soil land classification and crop prediction application is developed using KNN algorithms. Three steps are necessary for its implementation: the first is data collecting using sensors placed in an agricultural field, the second is data cleaning and storage, and the third is predictive processing utilizing the ML technique. The results obtained through the algorithms are sent to the cloud, which helps in decision-making in advance

Simulation of Single Phase Switching Capacitor 49 Level Inverter with Reduced THD

The study of single phase Switched Capacitors Multi Level Inverter (MLI) is used with Switched Capacitor Converter (SCC) units. The SCC is used to increase the input DC voltage by connecting capacitor in string and shunt. This increassed DC link voltage is converted in to multilevel i.e. 49 level AC output. This SCMLI topology is used to reduce the number of switches, diodes, isolated dc power supply and Total Harmonic Distortion (THD). The SCMLI provides 49 level output voltage using 14 power switches and 3 isolated power supply. The performance of the SCMLI topology is confirmed by using MATLAB simulation result.</jats:p

Reciprocal Repository for Decisive Data Access in Disruption Tolerant Networks

Disruption tolerant systems (DTNs) comprise of cell phones that get in touch with one another deftly. Because of the low hub thickness and erratic hub versatility, just discontinuous system availability exists in DTNs, and the consequent trouble of keeping up start to finish correspondence connections makes it important to utilize "convey and-forward" strategies for information transmission. Instances of such systems incorporate gatherings of people moving in a debacle recuperation zones, military war zones, or urban detecting applications. In this paper we propose a a decisive strategy for stock the information at Network Central Locations (NCLs), with different hubs.</jats:p