Inquiry (E-Journal - Faculty of Business and Administration, International University of Sarajevo)
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Some Extensions to Classic Lotka-Volterra Modeling For Predator Prey Applications
Get PDFIn this paper we present some specific cases of the classic Nonlinear Lotka-Volterra (NLV) approach to modeling predator-prey dynamic systems [1,5], and propose to implement them using "mathematical" (Matlab) approach as well as "ad-hoc" approach using Agent Based Modeling (implemented using NetLogo modeling environment), [6]. Examples of various scenarios are introduced in a gradual way, from simpler to more complex ones. The emphasis is given to gaining insight into predator-prey relationship, as well as some structural results [2,3] as applied to classic complex systems modeling and control, as well as understanding stability in multispecies communities. The paper sets the scene for further research using NLV (mathematical) and ABM (ad-hoc) models. With this "parallel" approach we hope to address some classic problems such as Gause's Law and Paradox of the Plankton, Paradox of Enrichment (system level instability), Oksanen's description and trophic level numbers, and other current Complex Systems paradigms such as adaptivity, emergence, etc.
Predicting the Secondary Structure of Proteins by the Use of Hamming Distances and Alignment Scores
Get PDFResearchers are confident about the validity of the basic hypothesis that the secondary and tertiary structures of a protein are uniquely determined by its sequence of amino acids, that is its primary structure. In this article we use a database of 200 proteins. To find the secondary structure of a new protein, the first thirteen residues of this protein are taken as a substring. Then the conformations of the central amino acids of thirteen residue substrings of the proteins in the database, whose hamming distances are less than a given threshold or alignment scores exceed a given limit are collected in a basin. The commonest conformation in this basin is attached as the conformation of the central amino acid of the substring of the unknown protein. Using this technique, for MHsim threshold 3.0, a correct estimation rate of 53.4% is obtained with 4.74% indecisives and for MHsim threshold 5.0, the success was 56.93% with76.59% indecisives. When the half of the proteins, whose secondary structure estimations are higher, subjected to same calculation the following results are obtained; for MHsim threshold 3.0, correct estimation rate is 79.52% with 58.87% indecisives and for MHsim threshold 5.0, correct estimation rate is 65.52% with 5.02% indecisives. Average correct estimation rate for the alignment scores was %54
Regression Analysis to Predict the Secondary Structure of Proteins
Get PDFA method is presented for protein secondary structure prediction based on the use of multidimensional regression. 200 proteins are chosen from RCSB Protein Database. Their secondary structures obtained through x-ray crystallography analyses are downloaded from the same source. Primary and secondary structure of proteins are concatenated separately to create a sequence of 169 026 residues. First 150 000 of the amino acid residues and corresponding secondary structures are chosen to create a regression model. The remaining 19 026 residues are used for testing. Since we expect three outputs a-helices "S", b-sheets "H", and coiled coils "C", our regression modes consists of parameters. These parameters are tuned and a correct classification rate of 62.50% is achieved on the test data. Furthermore, the performance of the regression model compared with online secondary structure estimation algorithms on 14 unused proteins, and the performance of the regression model is found comparable with the online estimation tools
Diagnosis of Parkinson’s Disease using Fuzzy C-Means Clustering and Pattern Recognition
Get PDFParkinson’s disease (PD) is a global public health problem of enormous dimension. In this study, we aimed to discriminate between healthy people and people with Parkinson’s disease (PD). Various studies revealed, that voice is one of the earliest indicator of PD, and for that reason, Parkinson dataset that contains biomedical voice of human is used. The main goal of this paper is to automatically detect whether the speech/voice of a person is affected by PD. We examined the performance of fuzzy c-means (FCM) clustering and pattern recognition methods on Parkinson’s disease dataset. The first method has the main aim to distinguish performance between two classes, when trying to differentiate between normal speaking persons and speakers with PD. This method could greatly be improved by classifying data first and then testing new data using these two patterns. Thus, second method used here is pattern recognition. The experimental results have demonstrated that the combination of the fuzzy c-means method and pattern recognition obtained promising results for the classification of PD
Denver Groups Classification of Human Chromosomes Using Fuzzy C-Means Clustering
Get PDFUnbanded human chromosome can be classified into seven Denver Groups (A-G) based their lengths and the ratio of the length of the shorter arm to the whole length of the chromosome, which is called the centromere index (CI). In this article, the fuzzy c-means method will be used to perform the Denver Group classification of a given set of human chromosomes. The objective in clustering is to partition a given human chromosome set into homogeneous clusters; by homogeneous we mean that all points in the same cluster share similar attributes and they do not share similar attributes with points in other clusters. However, the separation of clusters and the meaning of similarity are fuzzy notions and can be described as such. It is found that the clusters iterations converge, highly depend on the initial partition matrix
Gene Expression Data Clustering
Get PDFGene expression analysis is becoming very important in order to understand complex living organisms. Rather than analyzing genes individually, there is more powerful approach, microarray technology to analyze the genes expression in high throughput. This new approach brings new analyses problems that make the interpretation difficult. To understand the correlated gene expression analysis easier some clustering methods are applied to the gene expression analysis. In this paper, different approach is represented to start to cluster with using some computational strategies
Intrusion Detection System using Fuzzy Logic
Get PDFIntrusion detection plays an important role in today’s computer and communication technology. As such it is very important to design time efficient Intrusion Detection System (IDS) low in both, False Positive Rate (FPR) and False Negative Rate (FNR), but high in attack detection precision. To achieve that, this paper proposes IDS model based on Fuzzy Logic. Proposed model consists of three parts, Input Reduction System (IRS), which uses Principal Component Analysis to reduce the dimensions of the system from 41 to 10, Classification System, which uses Fuzzy C Means to create data clusters based on training data and Pattern Recognition System based on Nearest Neighborhood method, which classifies new-coming data records to their respective clusters. Based on different attack types, the system performance in classification process is different and the best performance is achieved for PROBE attack, with 99.3% success rate, and the best performance in pattern recognition is achieved for U2R with 58.8% of success rate
Artificial Neural Network Techniques in Authorship Attribution
No full textThis paper covers a text classification problem: the identification of the author of a text. It is necessary to find author of a text with given information from a set of candidates whose sample texts were provided. Attempting to solve authorship problems by choosing only features that exist in the anonymous texts did not yield good results. In contrast to other classification tasks, it is not clear which features of a text should be used to classify an author. Also methods that are chosen for classification are important .In this paper, methods for the machine learning of an authorship attribution classifier are investigated using 2 books from each 3 writers as the data set
Efficient Algorithm for Primer Design
Get PDFPCR is one of the most popular technique that enable amplify specific region of the genome. It allows performing variety of analyses and application including DNA cloning for sequencing; functional analysis of genes; the diagnosis of hereditary diseases. Primer design is one of the most fundamental in PCR-based methods. Many different parameters need to be taken into account. There is many commercial program, some of them are online available, for the primer design. In this paper we present an algorithm which is not located in analyzing large sequence but for infrequent users
Randomized Algorithms in Bioinformatics: RANDOMIZEDCAMSORT
No full textRandomized algorithms make random decisions throughout their operation. At first glance, making random decisions does not seem particularly helpful. Basing an algorithm on random decisions sounds like a recipe for disaster, but the fact that a randomized algorithm undertakes a nondeterministic sequence of operations often means that, unlike deterministic algorithms; no input can reliably produce worst-case results. (Karp 1991). Randomized algorithms are often used in hard problems where an exact, polynomial-time algorithm is not known. In this paper we will see how randomized algorithms solve the Sorting problems