Data Science Commons^™

Cooperative Co-Evolution For Feature Selection In Big Data With Random Feature Grouping, A.N.M. Bazlur Rashid, Mohiuddin Ahmed, Leslie F. Sikos, Paul Haskell-Dowland

Research outputs 2014 to 2021

© 2020, The Author(s). A massive amount of data is generated with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). However, irrelevant features may degrade the classification performance of machine learning (ML) algorithms. Feature selection (FS) is a technique used to select a subset of relevant features that represent the dataset. Evolutionary algorithms (EAs) are widely used search strategies in this domain. A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. The existing solutions have poor performance because …

Correction To: Cooperative Co‑Evolution For Feature Selection In Big Data With Random Feature Grouping (Journal Of Big Data, (2020), 7, 1, (107), 10.1186/S40537-020-00381-Y), A. N.M.Bazlur Rashid, Mohiuddin Ahmed, Leslie F. Sikos, Paul Haskell‑Dowland

Research outputs 2014 to 2021

© 2020, The Author(s). Following publication of the original article [1], the author reported that the 2nd author affiliation was incorrect. It should only be “School of Science, Edith Cowan University, Joondalup, WA, Australia”. The affiliation is presented correctly in this correction article. The original article [1] has been corrected.

Invariance And Invertibility In Deep Neural Networks, Han Zhang

Theses and Dissertations

Machine learning is concerned with computer systems that learn from data instead of being explicitly programmed to solve a particular task. One of the main approaches behind recent advances in machine learning involves neural networks with a large number of layers, often referred to as deep learning. In this dissertation, we study how to equip deep neural networks with two useful properties: invariance and invertibility. The first part of our work is focused on constructing neural networks that are invariant to certain transformations in the input, that is, some outputs of the network stay the same even if the input …

The Trust Principles For Digital Repositories, Dawei Lin, Jonathan Crabtree, Ingrid Dillo, Robert R. Downs, Rorie Edmunds, David Giaretta, Marisa De Giusti, Hervé L'Hours, Wim Hugo, Reyna Jenkyns, Varsha Khodiyar, Maryann E. Martone, Mustapha Mokrane, Vivek Navale, Jonathan Petters, Barbara Sierman, Dina V. Sokolova, Martina Stockhause, John Westbrook

Copyright, Fair Use, Scholarly Communication, etc.

As information and communication technology has become pervasive in our society, we are increasingly dependent on both digital data and repositories that provide access to and enable the use of such resources. Repositories must earn the trust of the communities they intend to serve and demonstrate that they are reliable and capable of appropriately managing the data they hold.

Following a year-long public discussion and building on existing community consensus , several stakeholders, representing various segments of the digital repository community, have collaboratively developed and endorsed a set of guiding principles to demonstrate digital repository trustworthiness. Transparency, Responsibility, User focus, …

Synthesizing Aspect-Driven Recommendation Explanations From Reviews, Trung-Hoang Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Explanations help to make sense of recommendations, increasing the likelihood of adoption. However, existing approaches to explainable recommendations tend to rely on rigid, standardized templates, customized only via fill-in-the-blank aspect sentiments. For more flexible, literate, and varied explanations covering various aspects of interest, we synthesize an explanation by selecting snippets from reviews, while optimizing for representativeness and coherence. To fit target users' aspect preferences, we contextualize the opinions based on a compatible explainable recommendation model. Experiments on datasets of several product categories showcase the efficacies of our method as compared to baselines based on templates, review summarization, selection, and text …

Health-Aware Food Planner: A Personalized Recipe Generation Approach Based On Gpt-2, Bushra Aljbawi

Theses and Dissertations (Comprehensive)

"What to eat today?" With the flourish of Internet, more and more people nowadays are inclined to find an answer to this most problematic question online. The recent explosion of food networks; however, produces large volumes of recipes, making it even harder to make an informed decision. This yields the need for advanced decision-making algorithms and efficient recommendation systems. Conventional recommender systems are not feasible anymore as food is a complicated feature that presents unique challenges and is less studied. For example, it can be one of the main reasons for obesity and many other chronic diseases. Food recommender system …

Causal Discovery In Radiographic Markers Of Knee Osteoarthritis And Prediction For Knee Osteoarthritis Severity With Attention-Long Short-Term Memory, Yanfei Wang, Lei You, Jacqueline Chyr, Lan Lan, Weiling Zhao, Yujia Zhou, Hua Xu, Philip Noble, Xiaobo Zhou

Faculty, Staff and Student Publications

The goal of this study is to build a prognostic model to predict the severity of radiographic knee osteoarthritis (KOA) and to identify long-term disease progression risk factors for early intervention and treatment. We designed a long short-term memory (LSTM) model with an attention mechanism to predict Kellgren/Lawrence (KL) grade for knee osteoarthritis patients. The attention scores reveal a time-associated impact of different variables on KL grades. We also employed a fast causal inference (FCI) algorithm to estimate the causal relation of key variables, which will aid in clinical interpretability. Based on the clinical information of current visits, we accurately …

Topik Modeling Penelitian Dosen Jptei Uny Pada Google Scholar Menggunakan Latent Dirichlet Allocation, Akhsin Nurlayli, Moch. Ari Nasichuddin

Elinvo (Electronics, Informatics, and Vocational Education)

The mapping of research topics for lecturers is necessary to determine the research tendencies in a department or study program. This study aims to implement topic modeling in the publication titles of the Department of Electronics and Informatics Education Engineering of Universitas Negeri Yogyakarta (JPTEI UNY) lecturers taken from Google Scholar. The method used for topic modeling is the Latent Dirichlet Allocation (LDA). LDA is a generative probabilistic model for finding the semantic structure of a corpus collection based on the hierarchical bayesian analysis. After the topic modeling process, the results showed that JPTEI UNY lecturers tend to have …

Neural Network Classification Of Brainwave Alpha Signalsin Cognitive Activities, Ahmad Azhari, Adhi Susanto, Andri Pranolo, Yingchi Mao

Knowledge Engineering and Data Science

The signal produced by human brain waves is one unique feature. Signals carry information and are represented in electrical signals generated from the brain in a typical waveform. Human brain wave activity will always be active even when sleeping. Brain waves will produce different characteristics in different individuals. Physical and behavioral characteristics can be identified from patterns of brain wave activity. This study aims to distinguish signals from each individual based on the characteristics of alpha signals from brain waves produced. Brain wave signals are generated by giving several mental perception tasks measured using an Electroencephalogram (EEG). To get different …

Optimisation Of Rice Fertiliser Composition Using Genetic Algorithms, Retno Dewi Anissa, Wayan Firdaus Mahmudy, Agus Wahyu Widodo

Knowledge Engineering and Data Science

There are so many problems with food scarcity. One of them is not too good rice quality. So, an enhancement in rice production through an optimal fertiliser composition. Genetic algorithm is used to optimise the composition for a more affordable price. The process of genetic algorithm is done by using a representation of a real code chromosome. The reproduction process using a one-cut point crossover and random mutation, while for the selection using binary tournament selection process for each chromosome. The test results showed the optimum results are obtained on the size of the population of 10, the crossover rate …

Handwriting Character Recognition Usingvector Quantization Technique, Haviluddin Haviluddin, Rayner Alfred, Ni’Mah Moham, Herman Santoso Pakpahan, Islamiyah Islamiyah, Hario Jati Setyadi

Knowledge Engineering and Data Science

This paper seeks to explore Learning Vector Quantization (LVQ) processing stage to recognize The Buginese Lontara script from Makassar as well as explaining its accuracy. The testing results of LVQ obtained an accuracy degree of 66.66 %. The most optimal variant of network architecture in the recognition process is a variation of learning rate of 0.02, a maximum epoch of 5000 and a hidden layer of 90 neurons which was the result of recognition based on feature 8. Based on these variations, the obtained performance with a mean square error (MSE) of 0.0306 and the time required during the learning …

Comparison Of Indonesian Imports Forecastingby Limited Period Using Sarima Method, Harits Ar Rosyid, Mutyara Whening Aniendya, Heru Wahyu Herwanto

Knowledge Engineering and Data Science

The development of Indonesia's imports fluctuate over years. Inability to anticipate such rapid changes can cause economic slump due to inappropriate policy. For instance, recent years imports in rice led to the extermination of rice reserves. The reason is to maintain the market price of rice in Indonesia. To overcome these changes, forecasting the amount of imports should assist the Government in determining the optimum policy. This can be done by utilizing an algorithm to forecast time series data, in this case the amount of imports in the next few months with a high degree of accuracy. This study uses …

Comparison Of Naïve Bayes Algorithm And Decision Tree C4.5for Hospital Readmission Diabetes Patientsusing Hba1c Measurement, Utomo Pujianto, Asa Luki Setiawan, Harits Ar Rosyid, Ali M. Mohammad Salah

Knowledge Engineering and Data Science

Diabetes is a metabolic disorder disease in which the pancreas does not produce enough insulin or the body cannot use insulin produced effectively. The HbA1c examination, which measures the average glucose level of patients during the last 2-3 months, has become an important step to determine the condition of diabetic patients. Knowledge of the patient's condition can help medical staff to predict the possibility of patient readmissions, namely the occurrence of a patient requiring hospitalization services back at the hospital. The ability to predict patient readmissions will ultimately help the hospital to calculate and manage the quality of patient care. …

The Application Of Gray-Scale Level-Set Method In Segmentation Of Concrete Deck Delamination Using Infrared Images, Chongsheng Cheng, Zhigang Shen

Department of Construction Engineering and Management: Faculty Publications

Conventional nondestructive delamination detection of concrete pavements through thermography is often based on temperature contrasts between delaminated and sound areas. Non-uniform backgrounds caused by the environmental conditions are often challenging for contrast-based methods to robustly differentiate the delaminated areas from the sound areas. Instead of focusing on the temperature contrast, this study proposes a temperature gradient-based level set method (LSM) to detect boundaries for delamination segmentations. A modified edge indicator function is developed to represent the normalized temperature gradient of a thermal image. The experimental study was conducted to evaluate its applicability and stability for boundary detection in terms of …

Information Extraction From Primary Care Visits To Support Patient-Provider Interactions, Daniel Baruch Gutstein

College of Computing and Digital Media Dissertations

The extent of electronic health record systems usage in clinical settings has affected the dynamic between clinicians and patients and has thus been connected to physician morale and the quality of care patients receive. Recent research has also uncovered a correlation between physician burnout and negative physician attitudes electronic health record systems. In order to begin exploring the nature of the relationship between electronic health record usage, physician burnout, and patient care, it is necessary to first analyze patient-provider interactions within the context of verbal features such as turn-taking and non-verbal features such as eye-contact. While previous works have sought …

Numerical, Secondary Big Data Quality Issues, Quality Threshold Establishment, & Guidelines For Journal Policy Development, Anita Lee-Post, Ram Pakath

Marketing & Supply Chain Faculty Publications

An IS researcher may obtain Big Data from primary or secondary data sources. Sometimes, acquiring primary Big Data is infeasible due to availability, accessibility, cost, time, and/or complexity considerations. In this paper, we focus on Big Data-based IS research and discuss ways in which one may, post hoc, establish quality thresholds for numerical Big Data obtained from secondary sources. We also present guidelines for developing journal policies aimed at ensuring the veracity and verifiability of such data when used for research purposes.

Enhancing Clinical Concept Extraction With Contextual Embeddings, Yuqi Si, Jingqi Wang, Hua Xu, Kirk Roberts

Faculty, Staff and Student Publications

OBJECTIVE: Neural network-based representations ("embeddings") have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Recently, however, more advanced embedding methods and representations (eg, ELMo, BERT) have further pushed the state of the art in NLP, yet there are no common best practices for how to integrate these representations into clinical tasks. The purpose of this study, then, is to explore the space of possible options in utilizing these new models for clinical concept extraction, including comparing these to traditional word embedding methods (word2vec, GloVe, fastText).

MATERIALS AND METHODS: Both off-the-shelf, open-domain embeddings and …

Automated Morgan Keenan Classification Of Observed Stellar Spectra Collected By The Sloan Digital Sky Survey Using A Single Classifier, Michael J. Brice, Răzvan Andonie

All Faculty Scholarship for the College of the Sciences

The classification of stellar spectra is a fundamental task in stellar astrophysics. Stellar spectra from the Sloan Digital Sky Survey are applied to standard classification methods, k-nearest neighbors and random forest, to automatically classify the spectra. Stellar spectra are high dimensional data and the dimensionality is reduced using astronomical knowledge because classifiers work in low dimensional space. These methods are utilized to classify the stellar spectra into a complete Morgan Keenan classification (spectral and luminosity) using a single classifier. The motion of stars (radial velocity) causes machine-learning complications through the feature matrix when classifying stellar spectra. Due to the nature …

What About The Environment?: Exploring The Neglected Third Dimension Of Antimicrobial Resistance, Paige E. Montfort

Independent Study Project (ISP) Collection

Antimicrobial resistance (AMR) is one of the most urgent and complex health risks of our time, with links to human health, animal health, and the environment. The majority of research and policy related to AMR, however, has been dedicated to human and animal health. The third dimension — the environment — has been relatively neglected. Conversations about this problem have begun, but gaps in understanding remain. This study explores the key barriers that have hindered developments related to the environmental aspect of AMR and some of the solutions that have begun to or could be utilized to overcome these barriers. …

Eavesdropping Hackers: Detecting Software Vulnerability Communication On Social Media Using Text Mining, Susan Mckeever, Brian Keegan, Andrei Quieroz

Conference papers

Abstract—Cyber security is striving to find new forms of protection against hacker attacks. An emerging approach nowadays is the investigation of security-related messages exchanged on Deep/Dark Web and even Surface Web channels. This approach can be supported by the use of supervised machine learning models and text mining techniques. In our work, we compare a variety of machine learning algorithms, text representations and dimension reduction approaches for the detection accuracies of software-vulnerability-related communications. Given the imbalanced nature of the three public datasets used, we investigate appropriate sampling approaches to boost detection accuracies of our models. In addition, we examine how …