Student Profile Modeling Using Boosting Algorithms

The student profile has become an important component of education systems. Many systems objectives, as e-recommendation, e-orientation, e-recruitment and dropout prediction are essentially based on the profile for decision support. Machine learning plays an important role in this context and several studies have been carried out either for classification, prediction or clustering purpose. In this paper, the authors present a comparative study between different boosting algorithms which have been used successfully in many fields and for many purposes. In addition, the authors applied feature selection methods Fisher Score, Information Gain combined with Recursive Feature Elimination to enhance the preprocessing task and models’ performances. Using multi-label dataset predict the class of the student performance in mathematics, this article results show that the Light Gradient Boosting Machine (LightGBM) algorithm achieved the best performance when using Information gain with Recursive Feature Elimination method compared to the other boosting algorithms.

Automated hierarchical classification of scanned documents using convolutional neural network and regular expression

<p>This research proposed automated hierarchical classification of scanned documents with characteristics content that have unstructured text and special patterns (specific and short strings) using convolutional neural network (CNN) and regular expression method (REM). The research data using digital correspondence documents with format PDF images from pusat data teknologi dan informasi (technology and information data center). The document hierarchy covers type of letter, type of manuscript letter, origin of letter and subject of letter. The research method consists of preprocessing, classification, and storage to database. Preprocessing covers extraction using Tesseract optical character recognition (OCR) and formation of word document vector with Word2Vec. Hierarchical classification uses CNN to classify 5 types of letters and regular expression to classify 4 types of manuscript letter, 15 origins of letter and 25 subjects of letter. The classified documents are stored in the Hive database in Hadoop big data architecture. The amount of data used is 5200 documents, consisting of 4000 for training, 1000 for testing and 200 for classification prediction documents. The trial result of 200 new documents is 188 documents correctly classified and 12 documents incorrectly classified. The accuracy of automated hierarchical classification is 94%. Next, the search of classified scanned documents based on content can be developed.</p>

Fundamental Categories of Artificial Neural Networks

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

ConvChrome: Predicting Gene Expression Based on Histone Modifications using Deep Learning Techniques.

Background: Gene regulation is a complex and a dynamic process that not only depends on the DNA sequence of genes, but also is influenced by a key factor called Epigenetic Mechanisms. This factor along with other factors contributes to change the behavior of DNA. While these factors cannot affect the structure of DNA, they can control the behavior of DNA by turning genes "on" or "off" that leads to determine which proteins are transcribed. Objective: This paper will focus on histone modifications mechanism, histones are the group of proteins that bundle the DNA into a structural form called nucleosomes (coils); how DNA wraps with these histone proteins describes how gene can be accessed to express or not. When histones bound tightly to DNA, that make the gene cannot be expressed and vise verse. It is important to know Histone Modifications’ combinatorial patterns, and how these combinatorial patterns can affect and work together to control the process of gene expression. Methods: In this paper, ConvChrome deep learning methodologies are proposed for predicting the gene expression behavior from Histone modifications data as an input to use more than one Convolutional Network model, this happens in order to recognize patterns of histones signals and to interpret their spatial relationship arranged on chromatin structure to give insights into regulatory signatures of histone modifications. Results and Conclusion: The experiments results show that ConvChrome achieved 88.741 % in terms of Area under the Curve (AUC) score, which is an outstanding improvement over the baseline for gene expression classification prediction task from combinatorial interactions among five histone modifications on 56 different cell-types.

Classification of imbalanced cloud image data using deep neural networks: performance improvement through a data science competition

Image data classification using machine learning is an effective method for detecting atmospheric phenomena. However, extreme weather events with a small number of cases cause a decrease in classification prediction accuracy owing to the imbalance in data between the target class and the other classes. To build a highly accurate classification model, I held a data analysis competition to determine the best classification performance for two classes of cloud image data, specifically tropical cyclones including precursors and other classes. For the top models in the competition, minority data oversampling, majority data undersampling, ensemble learning, deep layer neural networks, and cost-effective loss functions were used to improve the classification performance of the imbalanced data. In particular, the best model of 209 submissions succeeded in improving the classification capability by 65.4% over similar conventional methods in a measure of the low false alarm ratio.

On Multiquadric Shape Determining Strategies in Image Reconstruction Applications: A Comparative Study

After being introduced to approximate two-dimensional geographical surfaces in 1971, the multivariate radial basis functions (RBFs) have been receiving a great amount of attention from scientists and engineers. Over decades, RBFs have been applied to a wide variety of problems. Approximation, interpolation, classification, prediction, and neural networks are inevitable in nowadays science, engineering, and medicine. Moreover, numerically solving partial differential equations (PDEs) is also a powerful branch of RBFs under the name of the ‘Meshfree/Meshless’ method. Amongst many, the so-called ‘Generalized Multiquadric (GMQ)’ is known as one of the most used forms of RBFs. It is of (ɛ 2 + r 2) β form, where r = ║x-x Θ║2 for x, x Θ ∈ ℝ n represents the distance function. The key factor playing a very crucial role for MQ, or other forms of RBFs, is the so-called ‘shape parameter ɛ’ where selecting a good one remains an open problem until now. This paper focuses on measuring the numerical effectiveness of various choices of ɛ proposed in literature when used in image reconstruction problems. Condition number of the interpolation matrix, CPU-time and storage, and accuracy are common criteria being utilized. The results of the work shall provide useful information on selecting a ‘suitable and reliable choice of MQ-shape’ for further applications in general.

Simple, Flexible Method to Extract Digital Image Features

The process of digital image features extraction is very important and it is required in many applications such as classification, prediction and regression. The extracted features for each image must be unique and capable to be used as an image identifier. In this paper we will introduce a method of image features extraction; it will be shown that this method will enhance the efficiency of the features extraction process. The proposed method will be experimentally tested using various images; the obtained experimental results will be compared with other existing methods of feature extraction to show the advantages of the proposed method and to show how to increase the speed up of the method.

A Biomedical Voice Measurement Diagnosis of Parkinson’s Disease through the utilization of Artificial Neural Network

In 2016 alone, there were a total of 120,000 cases of PD diagnosed and documented, however, experts believe that there are still loss cases which remain to be undiagnosed because of external factors such as medical cost and accuracy of diagnosis. The detection and diagnosis of PD on its early onset has become a problem in the medical field because of the slow progression of its symptoms. With the advent of technology, different diagnosing methods are being introduced and explored - one of which is through the concept of Neural Network. This paper highlights the human voice of patients using Multilayer Perceptron Neural Network (MLP) to accurately diagnose individuals who are diagnosed with PD. It was seen that the MLP classification prediction has achieved an average of 91.5% accuracy.