1,720,977 research outputs found
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Scaling up data mining techniques to large datasets using parallel and distributed processing
No full textAdvances in hardware and software technology enable us to collect, store and distribute large quantities of data on a very large scale. Automatically discovering and extracting hidden knowledge in the form of patterns from these large data volumes is known as data mining. Data mining technology is not only a part of business intelligence, but is also used in many other application areas such as research, marketing and financial analytics. For example medical scientists can use patterns extracted from historic patient data in order to determine if a new patient is likely to respond positively to a particular treatment or not; marketing analysts can use extracted patterns from customer data for future advertisement campaigns; finance experts have an interest in patterns that forecast the development of certain stock market shares for investment recommendations. However, extracting knowledge in the form of patterns from massive data volumes imposes a number of computational challenges in terms of processing time, memory, bandwidth and power consumption. These challenges have led to the development of parallel and distributed data analysis approaches and the utilisation of Grid and Cloud computing. This chapter gives an overview of parallel and distributed computing approaches and how they can be used to scale up data mining to large datasets
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Quadcopter pid controller design and path planning using bio-inspired meta-heuristic algorithms
Get PDFThe usage of Quadcopter in commercial fields has evolved significantly due to its phenomenal development. However, controlling the movements of a Quadcopter is a demanding
task due to its complex dynamics. The usage of the Proportional-Integral-Derivative(PID)
controller for stability control is quite challenging in regards to the complexity of Quadcopter’s nonlinear structure. Conventional methods like Ziegler-Nichols(ZN) for tuning the
PID controller for a Quadcopter do not provide efficient performance and might also cause
the system to be severely damaged. In this thesis, we are addressing the problem of the
controlling a Quadcopter using Metaheuristic-based PID controller. Multi-Objective Fitness
Function is proposed to reduce the overall time of the step response effectively. Path planning is one of the important concepts for a Quadcopter to move from one point to another
point effectively. A novel Neighborhood Search Genetic Algorithm (NSGA) is presented for
path planning by balancing the diversity inside the Genetic Algorithm using a Neighborhood
Search to produce an efficient path. The performance of the NSGA has been compared to
traditional A∗
, standard GA, and PSO. NSGA produced superior results in terms of cost.Computing SciencesCollege of Science and Engineerin
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A personal facial expression monitoring system using deep learning
No full textA thesis Submitted in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE in COMPUTER SCIENCE from Texas A&M University-Corpus Christi in Corpus Christi, Texas.Facial expression recognition has been a challenge for many years. With the recent growth in machine learning, a real-time facial expression recognition system using deep learning technology can be useful for an emotion monitoring system for Human-computer interaction(HCI). We proposed a Personal Facial Expression Monitoring System (PFEMS). We designed a custom Convolutional Neural Network model and used it to train and test different facial expression images with the TensorFlow machine learning library. PFEMS has two parts, a recognizer for validation and a data training model for data training. The recognizer contains a facial detector and a facial expression recognizer. The facial detector extracts facial images from video frames and the facial expression recognizer distinguishes the extracted images. The data training model uses the Convolutional Neural Network to train data and the recognizer also uses Convolutional Neural Network to monitor the emotional state of a user through their facial expressions. The system recognizes the six universal emotions, angry, disgust, happy, surprise, sad and fear, along with neutral.Computing SciencesCollege of Science and Engineerin
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An infrastructure for interactive environments
Get PDFA thesis Submitted in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE in COMPUTER SCIENCE from Texas A&M University-Corpus Christi in Corpus Christi, Texas.We present an infrastructure for interactive environments. The result of our implementation is a fast and reliable system used to recognize and track people in indoor surroundings. The system is aware of the location and identity of people, then can interact with the human or monitor their activity. Our system is combined with three main components, which are body tracking, face recognition, and the controller. The controller is responsible for collecting and matching the information from body tracking and face recognition, and give command signals to interaction and monitoring modules. To ensure the system works effectively, it is required to meet two conditions. First, the tracking and the recognition modules need to be fast and accurate. Second, there is a method to match faces with bodies efficiently. Our work provides solutions for those demands. The system deploys RGB cameras and Kinect depth sensors to obtain human information as the input. Overall, the people recognition-tracking of the system works at around 10 fps, and the system can make some basic interactions.Computing SciencesCollege of Science and Engineerin
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Oil spill detection is SAR images using meta-heuristic search algorithms
No full textIn recent years, oil spill accidents have become increasingly frequent due to the development of marine transportation and massive oil exploitation. At present, satellite remote sensing is the principal method used to monitor oil spills. Extracting the locations and extent of oil spill spots accurately in remote sensing images reaps significant benefits in terms of risk assessment and clean-up work. Many oil spill detection methods are implemented using traditional K-means and OTSU methods. In this research, traditional segmentation methods K-means and Otsu are improved using Meta- heuristic search algorithms to increase the efficiency of oil spill detection. The Meta-heuristic algorithms that are used in this research are Genetic Algorithm, Simulated Annealing, and Particle swarm optimization. In this research, Two frameworks are implemented which have image enhancement stage, segmentation stage, and Oil Extraction stage. The two frameworks differ in the segmentation stage wherein one framework, segmentation is done based on clustering using Meta-heuristic search algorithms and in other, Segmentation is done based on thresholding using Meta-heuristic search algorithm. Two fitness functions are proposed in this research. Segmentation based clustering using Meta-heuristic Search algorithm with the proposed fitness functions is compared to the K-means clustering and Fuzzy c-means algorithm. Segmentation based thresholding using Meta-heuristic Search algorithm with the proposed fitness functions is compared to the Otsu segmentation method.Computing SciencesCollege of Science and Engineerin
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Electromagnetism based K-means clustering for big data
No full textOver the past few years, Nature has been the source of inspiration for many proposed successful algorithms. This paper proposes a new nature-inspired K-means clustering algorithm which is based on the concept of Electromagnetism. The proposed algorithm starts by initializing a set of particles and later in the second step, the best particle among them is chosen based on the fitness function. After choosing the best particle, an objective function value is calculated for each particle which is initialized. Then the force and movement are calculated for each particle except for the current best particle. This way, the algorithm at each iteration searches for a local best particle and then calculates objective function values. Due to this reason, the position of the initialized particles also changes. Algorithm terminates when it reaches the maximum iterations or when the change in Within Set Sum of Squared Error (WSSSE) is less than 0.0001. The detailed explanation of this algorithm is presented. From the results, Electromagnetism based K-means provides better accuracy when compared to K-means clustering. This can be seen from the results section.Computing SciencesCollege of Science and Engineerin
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Medical Data Classification Using Binary Brain Storm Optimization
Get PDFThe volume of data in the medical domain has been on the rise with improved and accessible technologies; this big size of data increased the complexity of the process of analysis and knowl- edge discovery and thus making it more difficult to match patterns. Hence, it is necessary to use feature selection and classification models on medical diagnosis because it reduces the complexity of the data volume by using the non-trivial features in leading to more guided medical research. Binary Brain Storming Optimization (BBSO) is one of the many heuristic algorithms that have been applied on non-medical data with good reported performance. This work is a study to de- termine the performance of the BBSO on medical data through comparative analysis with other feature selecting algorithms using different classifiers. In this work, BBSO, along with three other feature selecting algorithms, namely: Binary Particle Swarm Optimization, Binary Grey Wolf Optimization, and the Genetic Algorithms, which had previously been applied in medical classifi- cation problems, were applied on five different medical data. Using Feature Selection algorithms, redundant, noisy, and irrelevant attributes are removed reducing the complexity of: the data, the classifiers model and the computational time. The resulting subset usually leads to a better clas- sifier performance. Their resulting features were utilized to develop classifier models within six different classifiers: K-Nearest Neighbors, Decision Tree, Na ̈ıve-Bayes, Random Forest, Linear Discriminant analysis and four different hyper-parameter variants of the Support Vector Machine. The results from the research demonstrated good performance on medical data, making BBSO a good Feature Selection algorithm for medical diagnosis.Computing SciencesCollege of Science and Engineerin
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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