Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach

Neural Computing and Applications - Selecting a subset of candidate features is one of the important steps in the data mining process. The ultimate goal of feature selection is to select an optimal...     

Modeling heterogeneous photocatalytic inactivation of E. coli using suspended and immobilized TiO2 reactors

A study was carried out to develop a kinetic model of the photocatalytic inactivation of Escherichia coli using different TiO₂ catalysts. The model developed is based on a reaction scheme that involves effectively coupling mass‐transfer fluxes between bacteria and catalyst surface on one hand and bacterial degradation reaction on the other. The photocatalytic results were derived from experiments led in a batch reactor under both dark and Ultra Violet (UV) irradiation conditions. Using a reference catalyst, the robustness of the developed model was tested under solar conditions. The experimental data validated the model as successfully able to reproduce evolutions in the viable bacteria concentration in the range of parameters studied without any further adjustment of the kinetic parameters. The model was used to simulate the bacterial degradation kinetics under different working conditions to describe the partitioning of both bacterial adhesion and photocatalytic reaction in the solution to be treated © 2015 American Institute of Chemical Engineers AIChE J, 61: 2532–2542, 2015     

Investigation of Heat‐Induced Degradation of Virgin Olive Oil Using Front Face Fluorescence Spectroscopy and Chemometric Analysis

A simple setup using a 365‐nm light‐emitting diode coupled to a USB spectrometer through an optical fiber, in a front‐face fluorescence configuration, was used to investigate the heat‐induced deterioration of virgin olive oil at different heating temperatures and times. The samples were heated for 30, 60, 120 and 180 min for every temperature setting of 140, 160 and 180 °C, respectively. Two important results are reported in this article. First, a neo‐formed compound around 665 nm due to the degradation of chlorophyll was observed. This new peak was attributed to pyropheophytins. The second result showed an important rise of the peak around 489 nm, which corresponded to the oxidation products. The correlation obtained between the peroxide value and the 489 nm peak using principal component analysis revealed the mechanism of the oxidation process. It further showed that the peak around 489 nm is a direct consequence of the degradation of hydroperoxide.     

Folksonomy link prediction based on a tripartite graph for tag recommendation

Nowadays social tagging has become a popular way to annotate, search, navigate and discover online resources, in turn leading to the sheer amount of user-generated metadata. This paper addresses the problem of recommending suitable tags during folksonomy development from a graph-based perspective. The proposed approach adapts the Katz measure, a path-ensemble based proximity measure, for the use in social tagging systems. We model a folksonomy as a weighted, undirected tripartite graph. We then apply the Katz measure to this graph, and exploit it to provide tag recommendations for individual users. We evaluate our method on two real-world folksonomies collected from CiteULike and Last.fm. The experimental results demonstrate that the proposed method improves the recommendation performance and is effective for both active taggers and cold-start taggers compared to existing algorithms.     

A fuzzy record-to-record travel algorithm for solving rough set attribute reduction

Attribute reduction can be defined as the process of determining a minimal subset of attributes from an original set of attributes. This paper proposes a new attribute reduction method that is based on a record-to-record travel algorithm for solving rough set attribute reduction problems. This algorithm has a solitary parameter called the DEVIATION, which plays a pivotal role in controlling the acceptance of the worse solutions, after it becomes pre-tuned. In this paper, we focus on a fuzzy-based record-to-record travel algorithm for attribute reduction (FuzzyRRTAR). This algorithm employs an intelligent fuzzy logic controller mechanism to control the value of DEVIATION, which is dynamically changed throughout the search process. The proposed method was tested on standard benchmark data sets. The results show that FuzzyRRTAR is efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.