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Browsing by Subject "Neural networks (Computer science)"

Browsing by Subject "Neural networks (Computer science)"

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  • Applying data mining for predicting computer science students academic performance 
    Al Youssef, Jad (Notre Dame University-Louaize, 2019-05)
    The aim of this thesis is to study the impact of a three-year-study of computer science at Notre Dame University on the students’ academic performances. For this reason, the GPAs of the students after their first year of study were compared with their GPAs upon graduation. To perform this, a Decision Tree, as well as Neural Networks were used. These algorithms helped us to predict the final GPA based on the first two semesters’ GPA. In addition, the most decisive courses on the student GPA were identified. For this reason, Relief algorithm was used to identify the three most important courses ...
  • Bitcoin estimation and prediction through neural network and machine learning 
    Hanna, Marianne G. (Notre Dame University-Louaize, 2021)
    Bitcoin is the most popular purely digital cryptocurrency nowadays. It started in 2009 as a peer to peer payment system, decentralized, pseudo-anonymous and secure system of money. The main objective of this study is to estimate and predict the weekly close bitcoin price by including other variables like commodities, indexes and demand / supply variables through different kind of machine learning and deep learning such as multiple regression, time series ARIMA model, artificial neural network, combination of multiple regression and ARIMA model, and finally combination of multiple regression, ...
  • Integrating Neural networks and GIS for solving the travelling salesman problem 
    Ibrahim, Rita Jack (Notre Dame University-Louaize, 2000-06)
    In this thesis, we investigate the use of neural networks for solving the Traveling Salesman Problem (TSP). First, we review the main elements of the theory of NP-completeness. Then, we explain what makes some problems computationally intractable. We review some heuristic approaches used to provide near-optimal solutions to NP-complete problems. Then, we introduce the topic of neural networks and describe some of the most popular neural network models. We pay a special attention to a recent model, named the Hybrid Neural Network model (HNN), used for solving optimization problems and the Hybrid ...
  • Machine learning based renewable energy solutions for smart grid technologies 
    Khoury, Danny (Notre Dame University-Louaize, 2019-05)
    Smart grid engineering is the key for an optimized use of extensive energy resources which allows the hybrid renewable energy sources microgrid to be integrated and therefore dispatch their power generation to the grid over long distance DC transmission lines using the HDVC transmission technologies. However, the required number of generating units of wind-turbine generators and photovoltaic arrays, and the associated storage capacity for standalone and/or grid connected hybrid microgrid is determined using a sizing algorithm based on the observation that the state of charge of battery should ...
  • Plant leaf classification using dual path convolutional neural networks 
    Rizk, Sara (Notre Dame University-Louaize, 2019-05)
    Taxon identification is highly needed for a wide variety of research including ecology, agronomy and medicine. As of 1970, classification of plants was introduced into computer vision techniques. Most research conducted in this area focuses on leaves due to their availability as well as their ability to discretize. The most common features researchers base their work on are shape, texture and venation. This research study proposes a dual path, dual feature model for plant leaf identification. We weigh our research on shape and venation features. Sobel operators are used for primary and secondary ...
  • Temporal difference method with hebbian plasticity rule as a learning algorithm in networks of chaotic spiking neurons 
    Aoun, Mario A. (Notre Dame University-Louaize., 2007)
    The aim of this thesis is to deluge the latest studies in chaotic dynamics and their relevance in neural computing, as also to inspect a new learning algorithm for a network of chaotic spiking neurons as it is recently proposed by Nigel Crook et al. [1]. The thesis will tackle the latest research in the field of information processing and chaotic neural networks, and will contribute to the recent work of Nigel Crook et al. [1] by finding a suitable learning algorithm for chaotic neurons. The learning algorithm based on biological realism will be implemented in a network of chaotic spiking ...