The aim of the work is to identify the duplication in a video database with the aid of feature extraction techniques. The process includes extraction of image features (shape, color, and texture) for duplicate identification. The color contains 256 features, shape contains 200 features, the texture contains two different features namely gray-level co-occurrence matrix (GLCM) (22 features in 4 degrees) and grey-level run length matrix (GLRLM) (11 features) are extracted. In this paper, the preliminary work is to convert video into frames and then each frame into blocks subsequently including feature extraction. A query video is then considered for the same process of feature extraction and compared with the normal video. The distance between query video and normal video if found to be similar then the video identified as duplicate video. The results are performed for various evaluation matrix and plotted graphs are shown. The sensitivity value for whole feature extractions is 0.88, the specificity value for whole feature extractions is 0.83 and the accuracy value for whole extractions is 0.86. The entire process implemented in the working platform of MATLAB.

Near-duplicate video (NDV) detection is an important issue of copyright protection. However, the traditional detection methods are very imprecise and complex. To solve the problem, this paper introduces the opposition-based solution generation strategy into the conventional whale optimization algorithm (OWOA), creating a novel NDV detection method called the OWOA. The author detailed how to use the hybrid method to extract different types of features, ranging from color, shape to texture, and compared the OWOA with traditional feature extraction methods through experiments. The comparison shows that the OWOA achieved the optimal performance in DNV detection. The research findings can greatly assist regulatory authorities in monitoring and detecting edited contents.

Data security in cloud services is achieved by imposing a broad range of privacy settings and restrictions. However, the different security techniques used fail to eliminate the hazard of serious data leakage, information loss and other vulnerabilities. Therefore, better security policy requirements are necessary to ensure acceptable data protection levels in the cloud. The two procedures presented in this paper are intended to build a new cloud data security method. Here, sensitive data stored in big datasets is protected from abuse via the data sanitization procedure relying on an improved apriori approach to clean the data. The main objective in this case is to generate a key using an optimization technique known as Corona-integrated Archimedes Optimization with Tent Map Estimation (CIAO-TME). Such a technique deals with both restoration and sanitization of data. The problem of optimizing the data preservation ratio (IPR), the hiding ratio (HR), and the degree of modification (DOM) is formulated and researched as well.

Speech is an important mode of communication for people. For a long time, researchers have been working hard to develop conversational machines which will communicate with speech technology. Voice recognition is a part of a science called signal processing. Speech recognition is becoming more successful for providing user authentication. The process of user recognition is becoming more popular now a days for providing security by authenticating the users. With the rising importance of automated information processing and telecommunications, the usefulness of recognizing an individual from the features of user voice is increasing. In this paper, the three stages of speech recognition processing are defined as pre-processing, feature extraction and decoding. Speech comprehension has been significantly enhanced by using foreign languages. Automatic Speech Recognition (ASR) aims to translate text to speech. Speaker recognition is the method of recognizing an individual through his/her voice signals. The new speaker initially privileges identity for speaker authentication, and then the stated model is used for identification. The identity argument is approved when the match is above a predefined threshold. The speech used for these tasks may be either text-dependent or text-independent. The article uses Bacterial Foraging Optimization Algorithm (BFO) for accurate speech recognition through Mel Frequency Cepstral Coefficients (MFCC) model using DNN. Speech recognition efficiency is compared to that of the conventional system.