Computer Science Masters Papers

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Browsing Computer Science Masters Papers by Subject "Algorithms."

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    Brain Cancer Detection Using MRI Scans
    (North Dakota State University, 2020) Thotapally, Shanthanreddy
    An estimate of about 700,000 Americans today live with a brain tumor. Of these, 70% are benign and 30% are malicious. The average survival rate of all the malicious brain tumor patients is 35%. Diagnosing these tumors early on gives the best chance for survival. The Doctors use MRI scans to identify the presence of a tumor and it’s characteristics like the type and size. In this paper, I implemented a Deep learning convolutional neural network model that classifies the brain tumors using MRI scans. We shall use VGG-16 deep-learning approach to implement the machine learning algorithm. The proposed system can be divided into 3 parts: data input and preprocessing, building the VGG-16 model, image classification using the built model. Using this approach, I have achieved 80% accuracy. The accuracy of the model developed will depend on how correctly the affected brain tumor images can be classified from the unaffected.
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    Glowworm Swarm Optimization Algorithm for Multi-Threshold Image Segmentation
    (North Dakota State University, 2016) Kasam, Shashi Kanth
    Glowworm Smarm Optimization (GSO), one of the nature-inspired swarm intelligence algorithms is used for finding solutions to optimization problems. Glowworms emit light to attract its mates and mates choosing the brighter member is the basis for this algorithm. We apply this algorithm in conjunction with traditional multi-threshold image segmentation by Otsu method to arrive at our results. The glowworm selection of mates and the movement of these has been mapped to a scientific algorithm to solve the problem and make multi-level threshold image segmentation a relatively efficient one. This algorithm has been implemented in MATLAB and the results are thus presented.
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    Implementation of a Clonal Selection Algorithm
    (North Dakota State University, 2014) Valluru, Srikanth
    Some of the best optimization solutions were inspired by nature. Clonal selection algorithm is a technique that was inspired from genetic behavior of the immune system, and is widely implemented in the scientific and engineering fields. The clonal selection algorithm is a population-based search algorithm describing the immune response to antibodies by generating more cells that identify these antibodies, increasing its affinity when subjected to some internal process. In this paper, we have implemented the artificial immune network using the clonal selection principle within the optimal lab system. The basic working of the algorithm is to consider the individuals of the populations in a network cell and to calculate fitness of each cell, i.e., to evaluate all the cells against the optimization function then cloning cells accordingly. The algorithm is evaluated to check the efficiency and performance using few standard benchmark functions such as Alpine, Ackley, Rastrigin, Schaffer, and Sphere.
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    Mining Connected Frequent Boolean Expressions
    (North Dakota State University, 2017) Kolte, Deepak
    In this paper, we are finding Connected Frequent Boolean Expressions from cancer dataset [14] and protein protein interaction network [14] to discover group of dysregulated genes. Frequent Itemset Mining is a process of finding different sets of items that occur together frequently in a set of transactions. These itemsets are called Frequent Itemsets (FBE). Connected FBE (CFBE) are a group of items that not only classify as FBE but they are also connected in a graph/network. The nodes in this graph are the items and the edges between them indicate relationships. This can particularly be very helpful in cases where the items are not independent of each other and the presence of one item with another specific item can decide whether the group of items will be frequent or not.
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    Particle Swarm Optimization Algorithm: Variants and Comparisons
    (North Dakota State University, 2015) Mattaparthi, Sowjanya
    Since the introduction of Particle Swarm optimization by Dr. Eberhart and Dr. Kennedy, there have been many variations of the algorithm proposed by many researchers and various applications presented using the algorithm. In this paper, we applied variants of Particle swarm optimization on various benchmark functions in multiple dimensions, using the computational procedure to find the optimal solutions for those functions. We ran the variants of the algorithm 51 times on each of the 17-benchmark functions and computed the average, variance and standard deviation for 10, 30, and 50 dimensions. Using the results, we found the suitable variants of the algorithm for the benchmark functions by considering the minimum optimal solution produced by each variant.