Data Science Commons^™

Adam Optimization Algorithmfor Wide And Deep Neural Network, Imran Khan Mohd Jais, Amelia Ritahani Ismail

Knowledge Engineering and Data Science

The objective of this research is to evaluate the effects of Adam when used together with a wide and deep neural network. The dataset used was a diagnostic breast cancer dataset taken from UCI Machine Learning. Then, the dataset was fed into a conventional neural network for a benchmark test. Afterwards, the dataset was fed into the wide and deep neural network with and without Adam. It was found that there were improvements in the result of the wide and deep network with Adam. In conclusion, Adam is able to improve the performance of a wide and deep neural network.

Selection Of Marine Security Policyusing Fuzzy-Ahp Topsis Hybrid Approach, Hozairi Hozairi, Buhari Buhari, Heru Lumaksono, Marcus Tukan

Knowledge Engineering and Data Science

The research was focused on the integration of Fuzzy set theory with Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to choose the optimum maritime security policy to achieve Indonesia recognition as the world's maritime axis. The method used is AHP with fuzzy based enhancement. Here, the weight of each criterion is calculated to overcome the criticism of the scale of unbalanced rating, uncertainty, and inaccuracy in the pairwise of comparison process. The best recommendation for Indonesian maritime policies is multi task single agency which is greatly infuenced by several factors such as …

High Dimensional Data Clustering Using Self-Organized Map, Ruth Ema Febrita, Wayan Firdaus Mahmudy, Aji Prasetya Wibawa

Knowledge Engineering and Data Science

As the population grows and e economic development, houses could be one of basic needs of every family. Therefore, housing investment has promising value in the future. This research implements the Self-Organized Map (SOM) algorithm to cluster house data for providing several house groups based on the various features. K-means is used as the baseline of the proposed approach. SOM has higher silhouette coefficient (0.4367) compared to its comparison (0.236). Thus, this method outperforms k-means in terms of visualizing high-dimensional data cluster. It is also better in the cluster formation and regulating the data distribution.

Detecting Special-Cause Variation 'Events' From Process Data Signatures, Timothy M. Young, Olga Khaliukova, Nicolas André, Alexander Petutschnigg, Timothy G. Rials, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The ability to detect the special-cause variation of incoming feedstocks from advanced sensor technology is invaluable to manufacturers. Many on-line sensors produce data signatures that require further off-line statistical processing for interpretation by operational personnel. However, early detection of changes in variation in incoming feedstocks may be imperative to promote early-stage preventive measures. A method is proposed in this applied study for developing control bands to quantify the variation of data signatures in the context of statistical process control (SPC). Control bands based on pointwise prediction intervals constructed from the Bonferroni Inequality and Bayesian smoothing splines are developed. Applications using …

Profiling And Identifying Individual Usersby Their Command Line Usage And Writing Style, Darusalam Darusalam, Helen Ashman

Knowledge Engineering and Data Science

Profiling and identifying individual users is an approach for intrusion detection in a computer system. User profiles are important in many applications since they record highly user-specific information - profiles are basically built to record information about users or for users to share experiences with each other. This research extends previous research on re-authenticating users with their user profiles. This research focuses on the potential to add psychometric user characteristics into the user model so as to be able to detect unauthorized users who may be masquerading as a genuine user. There are five participants involved in the investigation for …

Energy Efficiency Metrics Of University Data Centers, Leonel Hernandez, Genett Jimenez, Piedad Marchena

Knowledge Engineering and Data Science

The data centers are fundamental pieces in the network and computing infrastructure,and evidently today more than ever they are relevant. Since they support the processing, analysis, assurance of the data generated in the network and by the applications in the cloud, which every day increases its volume thanks to technologies such as Internet of Things, Virtualization, and cloud computing, among others. Precisely the management of this large volume of information makes the data centers consume a lot of energy, generating great concern to owners and administrators. Green Data Centers offer a solution to this problem, reducing the impact produced by …

Signature Pattern Recognition Using Kohonen Network, Nadia Roosmalita Sari, Mohammad Zoqi Sarwani, Yudha Alif Aulia, Wayan Firdaus Mahmudy

Knowledge Engineering and Data Science

A signature is a special form of handwriting that used for human identification process. The current identification process is extremely ineffective. People have to manually compare signatures with the previously stored data. This study proposed SOM Kohonen algorithm as the method of signature pattern recognition. This method has able to visualize high-dimensional data. The image processing method is used in this study in pre-processing data phase. The accuracy of SOM Kohonen was 70 %, indicated the method used was good enough for pattern recognition.

Digit Classification Of Majapahit Relic Inscriptionusing Glcm-Svm, Tri Septianto, Endang Setyati, Joan Santoso

Knowledge Engineering and Data Science

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition.Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture …

Change Vulnerability Forecasting For Southeast Asiausing Deep Learning Algorithm, Amelia Ritahani Ismail, Nur 'Atikah Binti Mohd Ali, Junaida Sulaiman

Knowledge Engineering and Data Science

Climate change is expected to change people’s livelihood in significant ways. Several vulnerability factors and readiness factors used for measuring the prediction index of that particular country on how vulnerable of a country towards global change. Primary data was collected from University of Notre Dame Global Adaptation Index (NDGAIN). The data has been trained for the forecasting purpose with support from the validated statistical analysis. The summary of the predicted index is visualized using machine learning tools. The results developed the correlation between vulnerability and readiness factors and shows the stability of the country towards climate change. The framework is …

Network Traffic Time Series Performance Analysisusing Statistical Methods, Purnawansyah Purnawansyah, Haviluddin Haviluddin, Rayner Alfred, Achmad Fanany Onnlita Gaffar

Knowledge Engineering and Data Science

This paper presents an approach for a network traffic characterization by using statistical techniques. These techniques are obtained using the decomposition, winter’s exponential smoothing and autoregressive integrated moving average (ARIMA). In this paper, decomposition and winter’s exponential smoothing techniques were used additive and multiplicative model. Then, ARIMA based-on Box-Jenkins methodology. The results of ARIMA (1,0,2) was shown the best model that can be used to the internet network traffic forecasting

Decision Support System Determination Of Main Work Unitin Wpp-711 Using Fuzzy Topsis, Hozairi Hozairi, Yaser Krisnafi

Knowledge Engineering and Data Science

Decision-making to determine the working units for being prioritized to be developed in order to improve fishery monitoring in WPP-711 is imperative. The Ministry of Maritime Affairs and Fisheries should make no mismatch decision-making through long-term calculation and analysis. The problem of determining the priority of working units is a complex problem, thus it is required to find an appropriate method to avoid a mismatch decision. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a decision-making method capable of solving multi-criteria problems. TOPSIS working principle determines the alternative by considering the shortest distance from the positive …

Market Basket Analysis To Identify Customer Behaviorsby Way Of Transaction Data, Fachrul Kurniawan, Binti Umayah, Jihad Hammad, Supeno Mardi Susiki Nugroho, Mochammad Hariadi

Knowledge Engineering and Data Science

Transaction data is a set of recording data result in connections with sales-purchase activities at a particular company. In these recent years, transaction data have been prevalently used as research objects in means of discovering new information. One of the possible attempts is to design an application that can be used to analyze the existing transaction data. That application has the quality of market basket analysis. In addition, the application is designed to be desktop-based whose components are able to process as well as re-log the existing transaction data. The used method in designing this application is by way of …

Capital Letter Pattern Recognition In Text To Speechby Way Of Perceptron Algorithm, Novan Wijaya

Knowledge Engineering and Data Science

Computer vision is a data transformation retrieved or generated from webcam into another form in means of determining decision. All kinds of transformations are carried through to attain specific aims. One of the supporting techniques in implementing computer vision on a system is digital image processing as the objective of digital image processing is to transform digital-formatted picture so that it can be processed in computer. Computer vision and digital image processing can be implemented in a system of capital letter introduction and real-time handwriting reading on a whiteboard supported by artificial neural network mode “perceptron algorithm” used as a …

Sql Logic Error Detection Using Start End Mid Algorithm, Jevri Tri Ardiansyah, Aji Prasetya Wibawa, Triyanna Widiyaningtyas, Okazaki Yasuhisa

Knowledge Engineering and Data Science

Database is an important part of a system and it stores data to be manipulated. SQL (Structured Query Language) is used for manipulating those data to extract information and make decision. There are two types of error which make SQL is challenging to learn, namely syntax error and logic error. Compiler can detect syntax error, but it does not show error warning while logical error occurred. It makes logic error more difficult to understand than syntax error. A web based SQL compiler with errors detection ability by using Start End Mid algorithm is then developed, To help database's user to …

Photovoltaic System Optimization For An Austere Location Using Time Series Data, Torrey J. Wagner, Eric Lang, Warren Assink, Douglas S. Dudis

Faculty Publications

In this work we test experimental photovoltaic, storage and generator technologies and investigate their potential to meet austere location energy needs. After defining the energy requirements and insolation of a 1,100-person base, we develop a microgrid model and simulation. Cost optimizations were then performed using hourly time-series data to explore the cost and performance trade-space of a PV-battery-generator system. The work highlights the cost of resiliency and the dependencies of optimum system component sizes on duration and the fully burdened cost of fuel.

An Improved Smote Algorithm Based On Genetic Algorithm For Imbalanced Data Collection, Qiong Gu, Xian-Ming Wang, Zhao Wu, Bing Ning, Chun-Sheng Xin

Electrical & Computer Engineering Faculty Publications

Classification of imbalanced data has been recognized as a crucial problem in machine learning and data mining. In an imbalanced dataset, minority class instances are likely to be misclassified. When the synthetic minority over-sampling technique (SMOTE) is applied in imbalanced dataset classification, the same sampling rate is set for all samples of the minority class in the process of synthesizing new samples, this scenario involves blindness. To overcome this problem, an improved SMOTE algorithm based on genetic algorithm (GA), namely, GASMOTE was proposed. First, GASMOTE set different sampling rates for different minority class samples. A combination of the sampling rates …

Adaptive Graph Construction For Isomap Manifold Learning, Loc Tran, Zezhong Zheng, Guoquing Zhou, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.)

Electrical & Computer Engineering Faculty Publications

Isomap is a classical manifold learning approach that preserves geodesic distance of nonlinear data sets. One of the main drawbacks of this method is that it is susceptible to leaking, where a shortcut appears between normally separated portions of a manifold. We propose an adaptive graph construction approach that is based upon the sparsity property of the ℓ₁ norm. The ℓ₁ enhanced graph construction method replaces k-nearest neighbors in the classical approach. The proposed algorithm is first tested on the data sets from the UCI data base repository which showed that the proposed approach performs better than …

Data Completion Methods For Improved Developmental Stage Annotation Of Drosophila Embryos In Images, Chitsanu Janyalikit

Electrical & Computer Engineering Theses & Dissertations

Drosophila melanogaster is a dominant model organism for studying the function of animal genes in initial stages of embryogenesis. Usually, images containing Drosophila gene expression patterns are captured at different developmental stages to study the interconnection of animal genes. To achieve most biologically meaningful results, gene expression images from a similar stage should be compared. Currently, biologists manually classify embryos in images into different stages, which is time intensive and infeasible for current massively produced gene expression images. Therefore, there is a need to develop an automatic system for the annotation.

Gene expression information in embryo images usually appears as …

Tropical Cyclone Intensity Estimation Using Temporal And Spatial Features From Satellite Data, Gholamreza Fetanat Haghighi

Dissertations

Accurate intensity estimation of tropical cyclones (TC) is an important topic of research due to its economic impact and public safety concerns. An accurate measure of the current wind strength is necessary to accurately predict TC intensity. We have developed and tested automated method to estimate TC intensity based on the existing historical satellite images alone. The Hurricane Satellite data (HURSAT-B1) is used to develop the algorithm, which focuses on the North Atlantic from 1978-2009. The algorithm is trained and validated using aircraft reconnaissance-based data. Here, the data is restricted to include only fixes that are over water and are …

Hyperspectral Image Classification Using A Spectral-Spatial Sparse Coding Model, Ender Oguslu, Guoqing Zhou, Jiang Li, Lorenzo Bruzzone (Ed.)

Electrical & Computer Engineering Faculty Publications

We present a sparse coding based spectral-spatial classification model for hyperspectral image (HSI) datasets. The proposed method consists of an efficient sparse coding method in which the l₁/l_q regularized multi-class logistic regression technique was utilized to achieve a compact representation of hyperspectral image pixels for land cover classification. We applied the proposed algorithm to a HSI dataset collected at the Kennedy Space Center and compared our algorithm to a recently proposed method, Gaussian process maximum likelihood (GP-ML) classifier. Experimental results show that the proposed method can achieve significantly better performances than the GP-ML classifier when training data …

Model Individualization For Real-Time Operator Functional State Assessment, Guangfan Zhang, Roger Xu, Wei Wang, Aaron A. Pepe, Feng Li, Jiang Li, Frederick Mckenzie, Tom Schnell, Nick Anderson, Dean Heitkamp

Electrical & Computer Engineering Faculty Publications

Proper assessment of Operator Functional State (OFS) and appropriate workload modulation offer the potential to improve mission effectiveness and aviation safety in both overload and under-load conditions. Although a wide range of research has been devoted to building OFS assessment models, most of the models are based on group statistics and little or no research has been directed towards model individualization, i.e., tuning the group statistics based model for individual pilots. Moreover, little emphasis has been placed on monitoring whether the pilot is disengaged during low workload conditions. The primary focus of this research is to provide a real-time engagement …

Real-Time Anomaly Detection In Full Motion Video, Glenn Konowicz,, Jiang Li, Donnie Self (Ed.)

Electrical & Computer Engineering Faculty Publications

Improvement in sensor technology such as charge-coupled devices (CCD) as well as constant incremental improvements in storage space has enabled the recording and storage of video more prevalent and lower cost than ever before. However, the improvements in the ability to capture and store a wide array of video have required additional manpower to translate these raw data sources into useful information. We propose an algorithm for automatically detecting anomalous movement patterns within full motion video thus reducing the amount of human intervention required to make use of these new data sources. The proposed algorithm tracks all of the objects …

Distributed Cluster-Based Outlier Detection In Wireless Sensor Networks, Swetha Gali

Electrical & Computer Engineering Theses & Dissertations

Wireless sensor networks find several potential applications in a variety of fields, such as environmental monitoring and control, battlefields, surveillance, smart buildings, human health monitoring, etc. These sensor networks consist of a large number of very tiny, inexpensive, and low power sensor nodes, which are deployed in a variety of harsh environments that may result in the sensor data getting corrupted. It is thus critical to detect and report these abnormal values in the sensor data, in order to have a better understanding of the monitored environment. Detection of the abnormal values is of special interest for the sensor network …

Autoassociative-Heteroassociative Neural Network, Claudia V. Kropas-Hughes, Steven K. Rogers, Mark E. Oxley, Matthew Kabrisky

AFIT Patents

An efficient neural network computing technique capable of synthesizing two sets of output signal data from a single input signal data set. The method and device of the invention involves a unique integration of autoassociative and heteroassociative neural network mappings, the autoassociative neural network mapping enabling a quality metric for assessing the generalization or prediction accuracy of the heteroassociative neural network mapping.