Ais-Psmaca: Towards Proposing an Artificial Immune System for Strengthening Psmaca: An Automated Protein Structure Prediction using Multiple Attractor Cellular Automata

Predicting the structure of proteins from their amino acid sequences has gained a remarkable attention in recent years. Even though there are some prediction techniques addressing this problem, the approximate accuracy in predicting the protein structure is closely 75%. An automated procedure was evolved with MACA (Multiple Attractor Cellular Automata) for predicting the structure of the protein. Artificial Immune System (AIS-PSMACA) a novel computational intelligence technique is used for strengthening the system (PSMACA) with more adaptability and incorporating more parallelism to the system. Most of the existing approaches are sequential which will classify the input into four major classes and these are designed for similar sequences. AIS-PSMACA is designed to identify ten classes from the sequences that share twilight zone similarity and identity with the training sequences with mixed and hybrid variations. This method also predicts three states (helix, strand, and coil) for the secondary structure. Our comprehensive design considers 10 feature selection methods and 4 classifiers to develop MACA (Multiple Attractor Cellular Automata) based classifiers that are build for each of the ten classes. We have tested the proposed classifier with twilight-zone and 1-high-similarity benchmark datasets with over three dozens of modern competing predictors shows that AIS-PSMACA provides the best overall accuracy that ranges between 80% and 89.8% depending on the dataset

Identification of protein coding regions in genomic DNA using fuzzy Cellular Automata

Genes carry the instructions for making proteins that are found in a cell as a specific sequence of nucleotides that are found in DNA molecules.But, the regions of these genes that code for proteins may occupy only a small region of the sequence. Identifying the coding regions play a vital role in understanding these genes.In this paper we propose a Cellular Automata (CA)based pattern classifier to identify the coding region of a DNA sequence.CA is simple, efficient and produces more accurate classifier than that have previously been obtained for a range of different sequence lengths.Experimental results confirm the scalability of the proposed FCA based classifier to handle large volume of datasets irrespective of the number of classes, tuples and attributes.Good classification accuracy has been established

Monitoring and Track Multiple and Vehicles at the Right Time in Addition to Dealing with Disabilities through the Operator for Fuzzy Cells

The detection and tracking of mobile vehicles at current traffic sites is the emerging research area for intelligent transport systems. In this search, we offer a mysterious cellular automated engine model to address the problem of environmental changes associated with the methods and methods of the rear subtraction of dynamic vehicle tracking. The proposed model deals with the fuzzy cellular operator, which has been set up with less vague "exclusive" support rules and "operates as the following mode logic to deal with degrees of uncertainty in the rule of similar operations. In fact/ at each step, the background update is determined according to the number of active cells and the blur mapping function; therefore, mobile compounds that resemble their grey level are simply detected with a grey background. Furthermore, the treatment technique is used on a visual measurement basis to detect the blockage of the vehicle and the breakdown of the vehicle by category of blockage and the results show the experimental method proposed is more robust and accurate than the traditional methods of detecting and tracking vehicles in a timely manner.The detection and tracking of mobile vehicles at current traffic sites is the emerging research area for intelligent transport systems. In this search, we offer a mysterious cellular automated engine model to address the problem of environmental changes associated with the methods and methods of the rear subtraction of dynamic vehicle tracking. The proposed model deals with the fuzzy cellular operator, which has been set up with less vague "exclusive" support rules and "operates as the following mode logic to deal with degrees of uncertainty in the rule of similar operations. In fact/ at each step, the background update is determined according to the number of active cells and the blur mapping function; therefore, mobile compounds that resemble their grey level are simply detected with a grey background. Furthermore, the treatment technique is used on a visual measurement basis to detect the blockage of the vehicle and the breakdown of the vehicle by category of blockage and the results show the experimental method proposed is more robust and accurate than the traditional methods of detecting and tracking vehicles in a timely manner

A Survey of Cellular Automata: Types, Dynamics, Non-uniformity and Applications

Cellular automata (CAs) are dynamical systems which exhibit complex global behavior from simple local interaction and computation. Since the inception of cellular automaton (CA) by von Neumann in 1950s, it has attracted the attention of several researchers over various backgrounds and fields for modelling different physical, natural as well as real-life phenomena. Classically, CAs are uniform. However, non-uniformity has also been introduced in update pattern, lattice structure, neighborhood dependency and local rule. In this survey, we tour to the various types of CAs introduced till date, the different characterization tools, the global behaviors of CAs, like universality, reversibility, dynamics etc. Special attention is given to non-uniformity in CAs and especially to non-uniform elementary CAs, which have been very useful in solving several real-life problems.Comment: 43 pages; Under review in Natural Computin

Methodology for predicting and/or compensating the behavior of optical frequency comb

RESUMEN: Optical frequency comb spectrum can change its behavior due to temperature fluctuations, normal dispersion, and mechanical vibrations. Such limitations can affect the peak power and wavelength separation of comb lines. In the propagation through single−mode fiber, the linear and non−linear phenomena can modify spectral shape, phase shifts and flatness of spectrum. To find a strategy of compensation, the PhD thesis is focused on a prediction methodology based on fuzzy cellular automata, intuitionistic fuzzy sets and fuzzy entropy measures. The research work proposes a predictor called intuitionistic fuzzy cellular automata based on mean vector and a validation measure called general intuitionistic fuzzy entropy based on adequacy and non−adequacy. In the accomplished experiments, the method was used in three experiments: mode−locked lasers, cascaded intensity modulators−Mach Zehnder modulators, and microresonator ring. The obtained results showed that the power and phase distortions were reduced by using a pulse shaper, where the method was programmed. In addition, the stability and/or instability of spectrum were found for the microresonator ring