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Metaheuristic-Based Kernel Extreme Learning Machine Model for Disease Diagnosis in Industrial Internet of Things Sensor Networks
Published in Mohamed Elhoseny, K. Shankar, Mohamed Abdel-Basset, Artificial Intelligence Techniques in IoT Sensor Networks, 2020
S. Dhanasekaran, I. S. Hephzi Punithavathi, P. Duraipandy, A. Sivanesh Kumar, P. Vijayakarthik, S. Rajasekaran, B. S. Murugan
The LZ77 technique is a dictionary-centric compression technique. It has developed a dictionary with adjacent strings. For input string, dictionary is identified. If there is an equivalent initiate, the projected procedure string is returned with a distance as well as length of string recorded in a dictionary. Since the string is not mapped, the primary incidence of string is remained the same in the dictionary application. It returns a group of data which contains comparative position (corresponding distance), length of the corresponding string (corresponding length), and a flag denoting a chunk of information is encoded (marker). Assume that the corresponding length and distance is indicated as length as well as distance.
L
Published in Philip A. Laplante, Comprehensive Dictionary of Electrical Engineering, 2018
Lyapunov's second method the method of stability assessment that relies on the use of energylike functions without resorting to direct solution of the associated evolution equations. This is also called Lyapunov's direct method. LZ77 refers to string-based compression schemes based on Lempel and Ziv's 1977 method. An input string of symbols that matches an
Multimedia Data Compression
Published in Sreeparna Banerjee, Elements of Multimedia, 2019
LZ77 is a sliding window, dictionary-based algorithm that addresses byte sequences from former contents instead of the original data. All data are coded in the same form because there is generally only one coding scheme. An example is provided below.
Feature Selection for Supervised Learning and Compression
Published in Applied Artificial Intelligence, 2022
Phillip Taylor, Nathan Griffiths, Vince Hall, Zhou Xu, Alex Mouzakitis
Lossless compression aims to compress the data in such a way that the uncompressed version is indistinguishable from the original (Salomon and Motta 2010). Typically, lossless compression inspects the frequencies of symbols and looks for repeating symbols or sequences of symbols in the data stream. Perhaps the most simple method of compression is run-length encoding, in which symbols are encoded along with their number of consecutive repetitions. For example, the string ‘AAAABBA’ can be encoded as ‘A4B2A1.’ Two other notable lossless compression algorithms are LZ77 dictionary encoding (Ziv and Lempel 1978) and Huffman coding (Huffman 1952). LZ77 uses a sliding window and searches for repeating sequences, which are encoded as the length and location of its first occurrence in the window. Huffman coding produces a variable length prefix-code defining the path to the encoded symbol in a Huffman tree. Symbols that occur with higher frequencies are located closer to the root node in the tree and thus have shorter Huffman codes. Taken together, LZ77 and Huffman encoding make up the DEFLATE compression algorithm (Deutsch 1996), which is the basis of the ZIP file format.