Computer Engineering Commons^™

Hybrid Deep Neural Networks For Mining Heterogeneous Data, Xiurui Hou

Dissertations

In the era of big data, the rapidly growing flood of data represents an immense opportunity. New computational methods are desired to fully leverage the potential that exists within massive structured and unstructured data. However, decision-makers are often confronted with multiple diverse heterogeneous data sources. The heterogeneity includes different data types, different granularities, and different dimensions, posing a fundamental challenge in many applications. This dissertation focuses on designing hybrid deep neural networks for modeling various kinds of data heterogeneity.

The first part of this dissertation concerns modeling diverse data types, the first kind of data heterogeneity. Specifically, image data and …

A Review Paper: Analysis Of Weka Data Mining Techniques For Heart Disease Prediction System, Basma Jumaa Saleh, Ahmed Yousif Falih Saedi, Ali Talib Qasim Al-Aqbi, Lamees Abdalhasan Salman

Library Philosophy and Practice (e-journal)

Data mining is characterized as searching for useful information through very large data sets. Some of the key and most common techniques for data mining are association rules, classification, clustering, prediction, and sequential models. For a wide range of applications, data mining techniques are used. Data mining plays a significant role in disease detection in the health care industry. The patient should be needed to detect a number of tests for the disease. However, the number of tests should be reduced by using data mining techniques. In time and performance, this reduced test plays an important role. Heart disease is …

Indonesian Library User Behaviour During Covid 19 Pandemic On Digital Library Platform, Irhamni

English Language Institute

COVID-19 pandemic has significantly changed library user behavior, workplaces, and some public areas including in the library. During the COVID 19 pandemics, the digital library with a mobile app like ipusnas has facilitated to accessing library resources. The ipusnas has increased people's accessibility to library materials. This research focuses on the use of the digital library has a significant impact on library users behavior; it can influence how they read, access the library, and their interaction with the library resources.

Analysis And Optimization Of Combustion Characteristics Of Cement Kiln Cooperatively Disposing Domestic Refuse, Jingbing Wu, Hanqing Tang, Xu Jun

Journal of System Simulation

Abstract: Because the traditional methods can hardly analyze the complex combustion characteristics of cement kiln mixed with domestic refuse, a data mining technology is introduced. A domestic cement plant is selected as the object, and its operating data and relevant parameters are collected. The influence coefficient of each parameter on coal consumption and NO_x emission is analyzed by using Stability Selection algorithm. The mathematical model of coal consumption and NO_x emission is established with Random Forest algorithm, and the key optimization parameters and their optimal values are obtained by K-means clustering algorithm. The result shows that this method …

Multitask-Based Association Rule Mining, Peli̇n Yildirim Taşer, Kökten Ulaş Bi̇rant, Derya Bi̇rant

Turkish Journal of Electrical Engineering and Computer Sciences

Recently, there has been a growing interest in association rule mining (ARM) in various fields. However, standard ARM algorithms fail to discover rules for multitask problems as they do not consider task-oriented investigation and, therefore, they ignore the correlation among the tasks. Considering this situation, this paper proposes a novel algorithm, named multitask association rule miner (MTARM), that tends to jointly discover rules by considering multiple tasks. This paper also introduces two novel concepts: single-task rule and multiple-task rule. In the first phase of the proposed approach, highly frequent local rules (single-task rules) are explored for each task separately and …

A Direct Data-Cluster Analysis Method Based On Neutrosophic Set Implication, Florentin Smarandache, Sudan Jha, Gyanendra Prasad Joshi, Lewis Nkenyereya, Dae Wan Kim

Branch Mathematics and Statistics Faculty and Staff Publications

Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters. A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets. This paper focuses on cluster analysis based on neutrosophic set implication, i.e., a k-means algorithm with a threshold-based clustering technique. This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm. To evaluate the validity of the proposed method, several validity measures and validity indices are applied to the Iris dataset (from the University of California, Irvine, Machine …