Nature computing has evolved with exciting performance to solve complex real-world combinatorial optimization problems. These problems span across engineering, medical sciences, and sciences generally. The Ebola virus has a propagation strategy that allows individuals in a population to move among susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population groups. Motivated by the effectiveness of this strategy of propagation of the disease, a new bio-inspired and population-based optimization algorithm is proposed. This study presents a novel metaheuristic algorithm named Ebola Optimization Search Algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. First, we designed an improved SIR model of the disease, namely SEIR-HVQD: Susceptible (S), Exposed (E), Infected (I), Recovered (R), Hospitalized (H), Vaccinated (V), Quarantine (Q), and Death or Dead (D). Secondly, we represented the new model using a mathematical model based on a system of first-order differential equations. A combination of the propagation and mathematical models was adapted for developing the new metaheuristic algorithm. To evaluate the performance and capability of the proposed method in comparison with other optimization methods, two sets of benchmark functions consisting of forty-seven (47) classical and thirty (30) constrained IEEE-CEC benchmark functions were investigated. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability, convergence, and sensitivity analyses. Extensive simulation results show that the EOSA outperforms popular metaheuristic algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC). Also, the algorithm was applied to address the complex problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classification of digital mammography. Results obtained showed the optimized CNN architecture successfully detected breast cancer from digital images at an accuracy of 96.0%. The source code of EOSA is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NathanielOy/EOSA_Metaheuristic</uri> . 

/pdf/ebola-optimization-search-algorithm-a-new-nature-inspired-2emb16n1.pdf

Ebola Optimization Search Algorithm: A new nature-inspired metaheuristic algorithm with application in medical image classification problem

Nature computing has evolved with exciting performance to solve complex real-world combinatorial optimization problems. These problems span across engineering, medical sciences, and sciences generally. The Ebola virus has a propagation strategy that allows individuals in a population to move among susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population groups. Motivated by the effectiveness of this strategy of propagation of the disease, a new bio-inspired and population-based optimization algorithm is proposed. This study presents a novel metaheuristic algorithm named Ebola Optimization Search Algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. First, we designed an improved SIR model of the disease, namely SEIR-HVQD: Susceptible (S), Exposed (E), Infected (I), Recovered (R), Hospitalized (H), Vaccinated (V), Quarantine (Q), and Death or Dead (D). Secondly, we represented the new model using a mathematical model based on a system of first-order differential equations. A combination of the propagation and mathematical models was adapted for developing the new metaheuristic algorithm. To evaluate the performance and capability of the proposed method in comparison with other optimization methods, two sets of benchmark functions consisting of forty-seven (47) classical and thirty (30) constrained IEEE-CEC benchmark functions were investigated. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability, convergence, and sensitivity analyses. Extensive simulation results show that the EOSA outperforms popular metaheuristic algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC). Also, the algorithm was applied to address the complex problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classification of digital mammography. Results obtained showed the optimized CNN architecture successfully detected breast cancer from digital images at an accuracy of 96.0%. The source code of EOSA is publicly available at https://github.com/NathanielOy/EOSA_Metaheuristic.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09698015.pdf

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

This study proposes a lung cancer diagnosis system based on computed tomography (CT) scan images for the detection of the disease. The proposed method uses a sequential approach to achieve this goal. Consequently, two well‐organized classifiers, the convolutional neural network (CNN) and feature‐based methodology, have been used. In the first step, the CNN classifier is optimized using a newly designed optimization method called the improved Harris hawk optimizer. This method is applied to the dataset, and the classification is commenced. If the disease cannot be detected via this method, the results are conveyed to the second classifier, that is, the feature‐based method. This classifier, including Haralick and LBP features, is subsequently applied to the received dataset from the CNN classifier. Finally, if the feature‐based method also does not detect cancer, the case study is healthy; otherwise, the case study is cancerous.

Novel computer-aided lung cancer detection based on convolutional neural network-based and feature-based classifiers using metaheuristics

Text obtained in natural scenes contains various information; therefore, it is extensively used in various applications to understand the image scenarios and also to retrieve the visual information. The semantic information provided by this scene image is very much valuable for human beings to realize the whole environment. But the text in such natural images depicts a flexible appearance in an unconstrained environment which makes the text identification and character recognition process a more challenging one. Therefore, a weighted naive Bayes classifier (WNBC)-based deep learning process is used in this framework to effectively detect the text and to recognize the character from the scene images. Normally, the natural scene images may carry some kind of noise in it, and to remove that, the guided image filter is introduced at the pre-processing stage. The features that are useful for the classification process are extracted using the Gabor transform and stroke width transform techniques. Finally, with these extracted features, the text detection and character recognition is successfully achieved by WNBC and deep neural network-based adaptive galactic swarm optimization. Then, the performance metrics such as accuracy, F1-score, precision, mean absolute error, mean square error and recall metrics are evaluated to estimate the adeptness of the proposed method.

Hybrid deep neural network with adaptive galactic swarm optimization for text extraction from scene images

An efficient copy move forgery detection using adaptive watershed segmentation with AGSO and hybrid feature extraction

Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour

In the present decade of accelerated advances in Medical Sciences, most studies fail to lay focus on ageing diseases. These are diseases that display their symptoms at a much advanced stage and makes a complete recovery almost improbable. Parkinson's disease (PD) is the second most commonly diagnosed neurodegenerative disorder of the brain. One could argue, that it is almost incurable and inflicts a lot of pain on the patients. All these make it quite clear that there is an oncoming need for efficient, dependable and expandable diagnosis of Parkinson's disease. A dilemma of this intensity requires the automating of the diagnosis to lead accurate and reliable results. It has been observed that most PD Patients demonstrate some sort of impairment in speech or speech dysphonia, which makes speech measurements and indicators one of the most important aspects in prediction of PD. The aim of this work is to compare various machine learning models in the successful prediction of the severity of Parkinson's disease and develop an effective and accurate model in order to help diagnose the disease accurately at an earlier stage which could in turn help the doctors to assist in the cure and recovery of PD Patients. For the aforementioned purpose we plan on using the Parkinson's Tele monitoring dataset which was acquired from the UCIML repository.

Prediction of Parkinson’s Disease using Machine Learning Techniques on Speech dataset

Facial images convey important demographic information such as ethnicity and gender. In this paper, machine learning approach is taken to solve the ethnicity classification problem. Artificial neural networks trained by state of the art optimization algorithms are used to classify faces as Caucasian or non-Caucasian based on the color of the skin. A feedforward neural network is trained using Galactic Swarm Optimization (GSO) algorithm which gives superior performance to other training algorithms such as backpropagation and Particle Swarm Optimization (PSO) which have been used earlier. In this paper, the RGB values of the skin are taken as inputs to the neural network. Each pixel of the image will be classified according to their RGB values and the class having the maximum number of pixels will be the output. Simulation results indicate that the neural network trained with GSO gives a more accurate classification and converges faster than the other state of the art optimization algorithms.

Accurate Facial Ethnicity Classification Using Artificial Neural Networks Trained with Galactic Swarm Optimization Algorithm

In this we are developing a graphical user interface using MATLAB for the users to check the information related to books in real time. We are taking the photos of the book cover using GUI, then by using MSER algorithm it will automatically detect all the features from the input image, after this it will filter bifurcate non-text features which will be based on morphological difference between text and non-text regions. We implemented a text character alignment algorithm which will improve the accuracy of the original text detection. We will also have a look upon the built in MATLAB OCR recognition algorithm and an open source OCR which is commonly used to perform better detection results, post detection algorithm is implemented and natural language processing to perform word correction and false detection inhibition. Finally, the detection result will be linked to internet to perform online matching. More than 86% accuracy can be obtained by this algorithm.

Image based book cover recognition and retrieval

Wearable Detection is a societally and economically critical yet a very challenging issue because of the number of layers and clothing someone could be wearing. Also layering, pose, body style, and shape become an issue. In this paper, we handle the wearable detection issue using recovery approaches. For model picture, we use the comparable styles from substantial database—labeled pictures and utilize cases to perceive dress things in the inquiry. Our tests come about moreover show that the general posture estimation issue can profit by apparel detection. In addition, for the correct detection and classification of what a person is wearing, we use the process of image segmentation and pose estimation to segment the image into superpixels and then analyze accordingly. In addition, we use a large novel dataset and tools for labeling garment items, to retrieve similar style to help with clothing classification.

D. Geraldine Bessie Amali

Papers

Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour

Prediction of Parkinson’s Disease using Machine Learning Techniques on Speech dataset

Accurate Facial Ethnicity Classification Using Artificial Neural Networks Trained with Galactic Swarm Optimization Algorithm

Image based book cover recognition and retrieval

A New Technique for Accurate Segmentation, and Detection of Outfit Using Convolution Neural Networks