A Meta-Heuristic Model for Data Classification Using Target Optimization

Abstract

The paper contains an extensive experimental study which focuses on a major idea on Target Optimization (TO) prior to the training process of artificial machines. Generally, during training process of an artificial machine, output is computed from two important parameters i.e. input and target. In general practice input is taken from the training data and target is randomly chosen, which may not be relevant to the corresponding training data. Hence, the overall training of the neural network becomes inefficient. The present study tries to put forward TO as an efficient methodology which may be helpful in addressing the said problem. The proposed work tries to implement the concept of TO and compares the outcomes with the conventional classifiers. In this regard, different benchmark data sets are used to compare the effect of TO on data classification by using Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) optimization techniques.

DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks

“…Therefore, there is a requirement for detecting it before any decision. For predicting diabetes, the data used are (1) the plasma glucose concentration;(2) diastolic blood pressure;(3) 2-h serum insulin (mu U/mL);(4) body mass index;(5) the diabetes pedigree function;(6) age in years [29][30][31]. These data can be collected using health sensors embedded in smart phones.…”

“…The health parameters chosen to predict the risk of hypertension are (1) systolic blood pressure (SBP); (2) diastolic blood pressure (DBP); (3) total cholesterol (TC); (4) high-density lipoprotein (HDL); (5) low-density lipoprotein (LDL); (6) plasma glucose concentration (PGC) and (7) HR [28]. To detect diabetes mellitus, the data considered are (1) PGC; (2) DBP; (3) 2-h serum insulin (mu U/mL); (4) body mass index (BMI); (5) the diabetes pedigree function; (6) age in years [29][30][31]. These data are collected from the users and stored in the fog nodes for synthesis and analysis.…”