This paper outlines a state-of-the-art method for smoke and fire detection utilizing Convolutional Neural Networks (CNNs). The current smoke detectors installed in buildings pose a challenge for effective fire detection. The inefficiency of traditional methods in terms of speed and cost led to the exploration of using Artificial Intelligence (AI) to identify and alert from Closed Circuit Television (CCTV) footage. In this paper, an analytical overview of AI is conducted by using a selfcreated dataset of video frames containing flames and smoke. The data undergoes pre-processing before being used to train a CNN-based machine learning model. The goal of this review study is to understand the available literature in the field and propose a highly accurate, cost-effective, and simple system for fire detection in various scenarios.
The most convenient speech processing tool is Artificial Neural Networks (ANNs). The effectiveness has been tested with various real-time applications. The classifier using artificial neural networks identifies utterances based on features extracted from the speech signal. The proposed approach to multilingual speaker identification consists of two parts, such as a training part and a testing part. In the training part, the classifier is trained using speech feature vectors. The spoken language contains complete information, such as details about the content of the message and details about the speaker of that message. In the present work, the speech signal databases of different speakers in a multilingual environment were recorded in three Indian languages, i.e., Hindi, Marathi, and Rajasthani. The cepstral characteristics of the speech signal were extracted: Mel-Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC). The system is designed for speaker recognition through multilingual speech signals using MFCC, GFCC, and combined functions as acoustic characteristics. Training and testing were performed using the Neural Network (NN) function, robust Backpropagation Algorithm (BPA), and Radial Basis Functions (RBF), and the results were compared. The accuracy of the speaker identification system is 94.89% using BPA and 96.62% using the RBF neural network.
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