CBO-IE: A Data Mining Approach for Healthcare IoT Dataset Using Chaotic Biogeography-Based Optimization and Information Entropy

Self Cite

The use of digital medical images is increasing with advanced computational power that has immensely contributed to developing more sophisticated machine learning techniques. Determination of age and gender of individuals was manually performed by forensic experts by their professional skills, which may take a few days to generate results. A fully automated system was developed that identifies the gender of humans and age based on digital images of teeth. Since teeth are a strong and unique part of the human body that exhibits least subject to risk in natural structure and remains unchanged for a longer duration, the process of identification of gender- and age-related information from human beings is systematically carried out by analyzing OPG (orthopantomogram) images. A total of 1142 digital X-ray images of teeth were obtained from dental colleges from the population of the middle-east part of Karnataka state in India. 80% of the digital images were considered for training purposes, and the remaining 20% of teeth images were for the testing cases. The proposed gender and age determination system finds its application widely in the forensic field to predict results quickly and accurately. The prediction system was carried out using Multiclass SVM (MSVM) classifier algorithm for age estimation and LIBSVM classifier for gender prediction, and 96% of accuracy was achieved from the system.

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images

Santosh¹,

Pradeep²,

Goel³

et al. 2022

Self Cite

“…When the classification findings were analyzed, they were used to provide input to the feature convergence optimization process, which in turn was used to optimize the classification results. In Akadi et al [ 19 ], the authors used an mRMR filter approach to increase the overall performance of the GA by boosting the gene selection process of the GA using an SVM classifier to boost the gene selection process of the GA. Several authors, including Gunavathi and Hemalatha [ 20 ], have proposed a statistical strategy for gene selection, which is detailed below. These approaches, when combined with GA-SVM/kNN, were utilized to find biomarker genes.…”

Section: Related Workmentioning

confidence: 99%

Cancer Categorization Using Genetic Algorithm to Identify Biomarker Genes

Sathya¹,

Jeyaselvi

Joshi

et al. 2022

In the microarray gene expression data, there are a large number of genes that are expressed at varying levels of expression. Given that there are only a few critically significant genes, it is challenging to analyze and categorize datasets that span the whole gene space. In order to aid in the diagnosis of cancer disease and, as a consequence, the suggestion of individualized treatment, the discovery of biomarker genes is essential. Starting with a large pool of candidates, the parallelized minimal redundancy and maximum relevance ensemble (mRMRe) is used to choose the top m informative genes from a huge pool of candidates. A Genetic Algorithm (GA) is used to heuristically compute the ideal set of genes by applying the Mahalanobis Distance (MD) as a distance metric. Once the genes have been identified, they are input into the GA. It is used as a classifier to four microarray datasets using the approved approach (mRMRe-GA), with the Support Vector Machine (SVM) serving as the classification basis. Leave-One-Out-Cross-Validation (LOOCV) is a cross-validation technique for assessing the performance of a classifier. It is now being investigated if the proposed mRMRe-GA strategy can be compared to other approaches. It has been shown that the proposed mRMRe-GA approach enhances classification accuracy while employing less genetic material than previous methods. Microarray, Gene Expression Data, GA, Feature Selection, SVM, and Cancer Classification are some of the terms used in this paper.

“…Edge detection is utilised here to detect and identify the sharp discontinuities in the MRI image. Here, the Canny edge detection approach is applied for suitable localization of edge points [ 28 ]. And, the discovered edge sites utilizing the edge detection model are displayed in Figure 4 .…”

Section: Proposed Modelmentioning

confidence: 99%

Improved Machine Learning Method for Intracranial Tumor Detection with Accelerated Particle Swarm Optimization

Pradeep¹,

Gangadharan

Hatamleh

et al. 2022

Self Cite

The field of image processing is distinguished by the variety of functions it offers and the wide range of applications it has in biomedical imaging. It becomes a difficult and time-consuming process for radiologists to do the manual identification and categorization of the tumour. It is a complex and time-consuming procedure conducted by radiologists or clinical professionals to remove the contaminated tumour region from magnetic resonance (MR) pictures. It is the goal of this study to improve the performance and reduce the complexity of the image segmentation process by investigating FCM predicted image segmentation procedures in order to reduce the intricacy of the process. Furthermore, relevant characteristics are collected from each segmented tissue and aligned as input to the classifiers for autonomous identification and relegation of encephalon cancers in order to increase the accuracy and quality rate of the neural network classifier. An evaluation, validation, and presentation of the experimental performance of the suggested approach have been completed. A unique APSO (accelerated particle swarm optimization) based artificial neural network model (ANNM) for the relegation of benign and malignant tumours is presented in this study effort, which allows for the automated identification and categorization of brain tumours. Using APSO training to improve the suggested ANNM model parameters would give a unique method to alleviate the stressful work of radiologists performing manual identification of encephalon cancers from MR images. The use of an APSO-based ANNM (artificial neural network model) model for automated brain tumour classification has been presented in order to demonstrate the resilience of the classification model. It has been suggested to utilise the improved enhanced fuzzy c means (IEnFCM) method for image segmentation, while the GLCM (gray level co-occurrence matrix) feature extraction approach has been employed for feature extraction from magnetic resonance imaging (MR pictures).