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This paper shows a charging scheme for electric vehicles that would be faster, effective, and reliable than the existing system. This work proposed the method of changing the current parameters at discrete intervals dependent on the battery’s using the five-level battery charging method. To validate the results and design technique, the proposed work is executed by Matrix Laboratory, the obtained output results were extremely gratifying. Further motivated to build a practical working model and verify it with the obtained software values. Both the results were convincing and confirmed to be much faster than the existing schemes.

In today’s digital world of information, a fact verification system to disprove assertions made in speech, print media or online content is the need of the hour. We propose a system which would verify a claim against a source and classify the claim to be true, false, out-of-context or an inappropriate claim with respect to the textual source provided to the system. A true label is used if the claim is true, false if it is false, if the claim has no relation with the source then it is classified as out-of-context and if the claim cannot be verified at all then it is classified as inappropriate. This would help us to verify a claim or a fact as well as know about the source or our knowledge base against which we are trying to verify our facts. We used a two-step approach to achieve our goal. At first, we retrieved evidence related to the claims from the textual source using the Term Frequency-Inverse Document Frequency(TF-IDF) vectors. Later we classified the claim-evidence pairs as true, false, inappropriate and out of context using a modified version of textual entailment module. Textual entailment module calculates the probability of each sentence supporting the claim, contradicting the claim or not providing any relevant information using Bi-LSTM network to assess the veracity of the claim. The accuracy of the best performing system is 64.49%

Voice-based smart home applications have taken a rapid growth in the past few years. Voice recognition is the ability of the system to receive, interpret, analyze and carry out the spoken commands. This paper proposes a ROS based voice - controlled robot, which runs using a differential drive mechanism and takes the voice commands from the user and moves a wheeled robot accordingly. The system is based on a statistical model such as the Hidden Markov Model (HMM). The HMM approach to speech enhancement relies on the Viterbi Algorithm.