Controlling The Problem Of Bloating Using Stepwise Crossover And Double Mutation Technique

Computational Intelligence and Neuroscience

Sakalle

et al. 2022

Self Cite

Breast cancer (BC) is the second leading cause of death in developed and developing nations, accounting for 8% of deaths after lung cancer. Gene mutation, constant pain, size fluctuations, colour (roughness), and breast skin texture are all characteristics of BC. The University of Wisconsin Hospital donated the WDBC dataset, which was created via fine-needle aspiration (biopsies) of the breast. We have implemented multilayer perceptron (MLP), K-nearest neighbor (KNN), genetic programming (GP), and random forest (RF) on the WBCD dataset to classify the benign and malignant patients. The results show that RF has a classification accuracy of 96.24%, which outperforms all the other classifiers.

Section: Methodsmentioning

confidence: 99%

Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification

Computational Intelligence and Neuroscience

Sakalle

et al. 2022

Self Cite

“…The GP uses its three operators to generate solutions [25,26]. In reproduction, the best solutions are send to the next generation, which is the top 10% of GP trees.…”

Section: Gp Operatorsmentioning

confidence: 99%

EEG based personality prediction using genetic programming

Asian Journal of Control

Tomar

Sakalle

et al. 2023

The reliable correlation between personality and brain signal ensures that inferences from cognitive processes can be achieved. This research aims primarily to predict one's personality using brain signals. On grounds of Psychology, the inference of personality in this work is performed on the basis of the Myers–Briggs Type Indicator (MBTI) personality inventory. Personality consists of different types of thinking, feeling and behavior patterns. EEG signals are produced when a person is exposed to situations or scenarios via visual information and experiences various emotions or sentiments. In this study, by evaluating brain waves while a person watches personality traits elicitation materials, the identification of the personality traits of an individual is done. The elicitation materials used for the collection of the dataset comprise approximately 50 videos with the pre‐defined personality of the dramatic personae and therefore, it is considered to be the ground truth for the experimental procedure of this work. For creating a dataset, sixty participants contributed and gave brain signals. The GP model with the proposed BSH crossover, known as the BSHGP model, is implemented. The maximum performance of the BSHGP model for a 10‐fold partition scheme is 84.34%.

“…e KNN algorithm is a nonparametric set of rules; this means that it does now not reflect on consideration of the information. It is also referred to as a lazy scholar algorithm as it does now not learn from the training right away set; as an alternative, it saves the database and takes motion on it all through the break up [24].…”

Section: K-nearestmentioning

confidence: 99%

“…e adaptive nature of EAs can yield solutions that are comparable to, and often better than, human efforts since they are free of human prejudices or biases. GP software systems use a random mutation, crossover, a fitness function, and numerous generations of evolution to perform a user-defined job, which is inspired by biological evolution and its core mechanics [23][24][25].…”

Section: Genetic Programming Gp Is a Form Of Evolutionarymentioning

confidence: 99%

A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits

Sharma

Computational Intelligence and Neuroscience

et al. 2022

Self Cite

Optical character recognition (OCR) can be a subcategory of graphic design that involves extracting text from images or scanned documents. We have chosen to make unique handwritten digits available on the Modified National Institute of Standards and Technology website for this project. The Machine Learning and Depp Learning algorithms are used in this project to measure the accuracy of handwritten displays of letters and numbers. Also, we show the classification accuracy comparison between them. The results showed that the CNN classifier achieved the highest classification accuracy of 98.83%.