Performance Evaluation of Multiple Classifiers for Predicting Fake News

Abstract

The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To assess their performance, we used 14 different classifiers in this study. Secondly, we looked at how soft voting and hard voting classifiers performed in a mixture of distinct individual classifiers. Finally, heuristics are used to create 9 models of stacking classifiers. The F1 score, prediction, recall, and accuracy have all been used to assess performance. Models 6 and 7 achieved the best accuracy of 96.13 while having a larger computational complexity. For benchmarking purposes, other individual classifiers are also tested.

Realistic pattern formations on rough surfaces

“…Figure 14 demonstrates again that adding surface roughness into the process of pattern generation can indeed provide more varied simulations for animal coats. Moreover, the patterns on surface S provide valid simulations just like M. [26] Figure 14 (a) 5 diag(1, 1) 15 0.5 400 0.05 Genet [1] Figure 14 (b) 8 diag(1, 0.01) 10 0.5 800 0.1 Plecostomus [12] Figure 14 (c) 8 diag(1, 0.01) 10 0.5 3000 0.1 Cheetah [46] Figure 14 (d) 0.2 diag(20, 20) 2 0.5 400 0.05…”

“…In Figure 10, we show the simulations of the animal coats in Figure 1 parameter values for generating each animal coat pattern are listed in Tables 1 and 2. Figure 10 (b) 15 15 0.5 800 0.1 Plecostomus [12] Figure 10 (c) 15 5 0.5 4000 0.1 Cheetah [46] Figure 10 (d) 15 5 0.5 800 0.1…”

“…In 1952, Turing proposed the idea of diffusion-driven instability [47], in which simple mechanisms evolve from a homogeneous state into spatial heterogeneous patterns. In recent years, a variety of application areas have Figure 1: Skin patterns in real-life: (a) emperor angelfish [26], (b) genet [1], (c) pecostomus [12], (d) cheetah [46].…”

“…In particular, reaction-diffusion systems are a well-accepted class of models for multiple pigmentation processes. Examples include marine [27] and emperor [26] angelfish, genets [1], plecostomi [12], cheetah [46], zebra fish [2], and many other mammal skin patterns [34]; see Figure 1. Although animal skin pattern simulation through reaction-diffusion systems has been explored in many researches, see the second row of Figure 5 for typical examples, very few of them have taken the skin texture into account.…”

“…Figure 10: On random rough surface M with parameters in Table1 and Table 2, actual animal coat patterns simulation results: (a) emperor angelfish in[26]; (b) genet in [1]; (c) plecostomus in[12]; (d) cheetah in[46].…”