A comparative evaluation of nature-inspired algorithms for feature selection problems

Feature selection is a critical component of machine learning and data mining which addresses challenges like irrelevance, noise, redundancy in large-scale data etc., which often result in the curse of dimensionality. This study employs a K-nearest neighbour wrapper to implement feature selection using six nature-inspired algorithms, derived from human behaviour and mammal-inspired techniques. Evaluated on six real-world datasets, the study aims to compare the performance of these algorithms in terms of accuracy, feature count, fitness, convergence and computational cost. The findings underscore the efficacy of the Human Learning Optimization, Poor and Rich Optimization and Grey Wolf Optimizer algorithms across multiple performance metrics. For instance, for mean fitness, Human Learning Optimization outperforms the others, followed by Poor and Rich Optimization and Harmony Search. The study suggests the potential of human-inspired algorithms, particularly Poor and Rich Optimization, in robust feature selection without compromising classification accuracy.

Machine learning algorithms for the diagnosis of Alzheimer and Parkinson disease

Dementia is a general term used to indicate any disorder related to human memory. The various memory-related problems severely affect the human brain and so the individual feels difficulty in doing their normal physical as well as mental activities. There are different types of dementia that exist, but the commonly seen and fatal types of dementia are Alzheimer's disease (AD) and Parkinson's disease (PD). In this paper different efficient Machine Learning Techniques are selected analysed their behaviours in the diagnosis of AD and PD using Positron Emission Tomography (PET). The PET image dataset used in this work consists of 1050 images with AD, PD and Healthy Brain images. The total number of images is split into two different categories in the ratio of 7:3 for training and testing respectively. The different machine learning classifiers used are Bagged Ensemble, ID3, Naive Bayes and Multiclass Support Vector Machine. The classification of the AD and PD with the reference of a healthy brain is done by comparing the input image with the trained samples in the PET image database. In the comparison of trained samples with the input image for the PET images, the bagged ensemble learning classifier worked better than the other classification algorithms and yielded an accuracy of 90.3%.

Stress and displacement patterns in the craniofacial skeleton with rapid maxillary expansion-a finite element method study

Background

Rapid maxillary expansion (RME), indicated in the treatment of maxillary deficiency directs high forces to maxillary basal bone and to other adjacent skeletal bones. The aim of this study is to (i) evaluate stress distribution along craniofacial sutures and (ii) study the displacement of various craniofacial structures with rapid maxillary expansion therapy by using a Finite Element model.

Methods

An analytical model was developed from a dried human skull of a 12 year old male. CT scan images of the skull were taken in axial direction parallel to the F-H plane at 1 mm interval, processed using Mimics software, required portion of the skull was exported into stereo-lithography model. ANSYS software was used to solve the mathematical equation. Contour plots of the displacement and stresses were obtained from the results of the analysis performed.

Results

At Node 47005, maximum X-displacement was 5.073 mm corresponding to the incisal edge of the upper central incisor. At Node 3971, maximum negative Y-displacement was -0.86 mm which corresponds to the anterior zygomatic arch, indicating posterior movement of craniofacial complex. At Node 32324, maximum negative Z-displacement was -0.92 mm representing the anterior and deepest convex portion of the nasal septum; indicating downward displacement of structures medial to the area of force application.

Conclusions

Pyramidal displacement of maxilla was evident. Apex of pyramid faced the nasal bone and base was located on the oral side. Posterosuperior part of nasal cavity moved minimally in lateral direction and width of nasal cavity at the floor of the nose increased, there was downward and forward movement of maxilla with a tendency toward posterior rotation. Maximum von Mises stresses were found along midpalatal, pterygomaxillary, nasomaxillary and frontomaxillary sutures.