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31 pages, 610 KB

Open AccessArticle

Framework to Diagnose the Metabolic Syndrome Types without Using a Blood Test Based on Machine Learning

by Mauricio Barrios, Miguel Jimeno, Pedro Villalba and Edgar Navarro

Appl. Sci. 2020, 10(23), 8404; https://doi.org/10.3390/app10238404 - 26 Nov 2020

Cited by 4 | Viewed by 3832

Metabolic Syndrome (MetS) is a set of risk factors that increase the probability of heart disease or even diabetes mellitus. The diagnosis of the pathology implies compliance with at least three of five risk factors. Doctors obtain two of those factors in a medical consultation: waist circumference and blood pressure. The other three factors are biochemical variables that require a blood test to determine triglyceride, high-density lipoprotein cholesterol, and fasting plasma glucose. Consequently, scientists are developing technology for non-invasive diagnostics, but medical personnel also need the risk factors involved in MetS to start a treatment. This paper describes the segmentation of MetS into ten types based on harmonized Metabolic Syndrome criteria. It proposes a framework to diagnose the types of MetS based on Artificial Neural Networks and Random undersampling Boosted tree using non-biochemical variables such as anthropometric and clinical information. The framework works over imbalanced and balanced datasets using the Synthetic Minority Oversampling Technique and for validation uses random subsampling to get performance evaluation indicators between the classifiers. The results showed an excellent framework for diagnosing the 10 MetS types that have Area under Receiver Operating Characteristic (AROC) curves with a range of 71% to 93% compared with AROC 82.86% from traditional MetS. Full article

(This article belongs to the Special Issue Applied Artificial Intelligence (AI))

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14 pages, 3388 KB

Open AccessArticle

Discrete Optimization of a Gear Pump after Tooth Root Undercutting by Means of Multi-Dimensional Logic Functions

by Marian A. Partyka and Maria Natorska

Appl. Sci. 2020, 10(13), 4682; https://doi.org/10.3390/app10134682 - 7 Jul 2020

Cited by 2 | Viewed by 2763

Abstract

In this paper, the optimization of a gear pump after tooth root undercutting has been investigated; this requires the volumetric, mechanical and total efficiencies of the pump to be calculated. Due to conflict in the existing model, the total efficiency is often calculated with the assumption that the other efficiencies have acceptable values. Multiple-dimensional logical functions are an additional independent method that can be used for the optimization of a pump. Full article

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3 pages, 179 KB

Open AccessProceeding Paper

Minish HAT: A Tool for the Minimization of Here-and-There Logic Programs and Theories in Answer Set Programming

by Rodrigo Martin and Pedro Cabalar

Proceedings 2019, 21(1), 22; https://doi.org/10.3390/proceedings2019021022 - 31 Jul 2019

Viewed by 1515

Abstract

When it comes to the writing of a new logic program or theory, it is of great importance to obtain a concise and minimal representation, for simplicity and ease of interpretation reasons. There are already a few methods and many tools, such as Karnaugh Maps or the Quine-McCluskey method, as well as their numerous software implementations, that solve this minimization problem in Boolean logic. This is not the case for Here-and-There logic, also called three-valued logic. Even though there are theoretical minimization methods for logic theories and programs, there aren’t any published tools that are able to obtain a minimal equivalent logic program. In this paper we present the first version of a tool called that is able to efficiently obtain minimal and equivalent representations for any logic program in Here-and-There. The described tool uses an hybrid method both leveraging a modified version of the Quine-McCluskey algorithm and Answer Set Programming techniques to minimize fairly complex logic programs in a reduced time. Full article

(This article belongs to the Proceedings of The 2nd XoveTIC Conference (XoveTIC 2019))

20 pages, 3697 KB

Open AccessArticle

Entry Aggregation and Early Match Using Hidden Markov Model of Flow Table in SDN

by Cheng Wang and Hee Yong Youn

Sensors 2019, 19(10), 2341; https://doi.org/10.3390/s19102341 - 21 May 2019

Cited by 25 | Viewed by 5025

Abstract

The usage of multiple flow tables (MFT) has significantly extended the flexibility and applicability of software-defined networking (SDN). However, the size of MFT is usually limited due to the use of expensive ternary content addressable memory (TCAM). Moreover, the pipeline mechanism of MFT causes long flow processing time. In this paper a novel approach called Agg-ExTable is proposed to efficiently manage the MFT. Here the flow entries in MFT are periodically aggregated by applying pruning and the Quine–Mccluskey algorithm. Utilizing the memory space saved by the aggregation, a front-end ExTable is constructed, keeping popular flow entries for early match. Popular entries are decided by the Hidden Markov model based on the match frequency and match probability. Computer simulation reveals that the proposed scheme is able to save about 45% of space of MFT, and efficiently decrease the flow processing time compared to the existing schemes. Full article

(This article belongs to the Special Issue Software-Defined Networking for Sensor Networks and Internet of Things)

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