UH Biocomputation Group - Mohammad Tayarani-Najaran

A Genetic Programming-Based Convolutional Deep Learning Algorithm for Identifying COVID-19 Cases via X-ray Images

2023-05-10T22:14:35+01:00

This week on Journal Club session Mohammad Tayarani-Najaran will talk about his (very) recently published paper "A Genetic Programming-Based Convolutional Deep Learning Algorithm for Identifying COVID-19 Cases via X-ray Images".

Evolutionary algorithms have been successfully employed to find the best structure for many learning algorithms including neural networks. Due to their flexibility and promising results, Convolutional Neural Networks (CNNs) have found their application in many image processing applications. The structure of CNNs greatly affects the performance of these algorithms both in terms of accuracy and computational cost, thus, finding the best architecture for these networks is a crucial task before they are employed. In this paper, we develop a genetic programming approach for the optimization of CNN structure in diagnosing COVID-19 cases via X-ray images. A graph representation for CNN architecture is proposed and evolutionary operators including crossover and mutation are specifically designed for the proposed representation. The proposed architecture of CNNs is defined by two sets of parameters, one is the skeleton which determines the arrangement of the convolutional and pooling operators and their connections and one is the numerical parameters of the operators which determine the properties of these operators like filter size and kernel size. The proposed algorithm in this paper optimizes the skeleton and the numerical parameters of the CNN architectures in a co-evolutionary scheme. The proposed algorithm is used to identify covid-19 cases via X-ray images.

Papers:

M. Najaran, "A Genetic Programming-Based Convolutional Deep Learning Algorithm for Identifying COVID-19 Cases via X-ray Images", 2023, Artificial Intelligence in Medicine, 102571

Date: 2023/05/12
Time: 14:00
Location: online

An Evolutionary Ensemble Learning for Diagnosing COVID-19 via Cough Signals

2023-03-01T15:44:29+00:00

This week on Journal Club session Mohammad Tayarani-Najaran will talk about his paper "An Evolutionary Ensemble Learning for Diagnosing COVID-19 via Cough Signals".

Objective The spread of the COVID-19 disease has caused great concern around the world and detecting the positive cases is crucial in curbing the pandemic. One of the symptoms of the disease is the dry cough it causes. It has previously been shown that cough signals can be used to identify a variety of diseases including tuberculosis, asthma, etc. In this paper, we proposed an algorithm to diagnose via cough signals the COVID-19 disease. Methods The proposed algorithm is an ensemble scheme that consists of a number of base learners, where each base learner uses a different feature extractor method, including statistical approaches and convolutional neural networks (CNN) for automatic feature extraction. Features are extracted from the raw signal and some transforms performed it, including Fourier, wavelet, Hilbert-Huang, and short-term Fourier transforms. The outputs of these base-learners are aggregated via a weighted voting scheme, with the weights optimised via an evolutionary paradigm. This paper also proposes a memetic algorithm for training the CNNs in the base-learners, which combines the speed of gradient descent (GD) algorithms and global search space coverage of the evolutionary algorithms. Results Experiments were performed on the proposed algorithm and different rival algorithms which included a number of CNN architectures in the literature and generic machine learning algorithms. The results suggested that the proposed algorithm achieves better performance compared to the existing algorithms in diagnosing COVID-19 via cough signals. Conclusion This research showed that COVID-19 could be diagnosed via cough signals and CNNs could be employed to process these signals and it may be further improved by the optimization of CNN architecture.

Papers:

M. Najaran, "An Evolutionary Ensemble Learning for Diagnosing COVID-19 via Cough Signals", 2023, Intelligent Medicine.

Date: 2023/03/03
Time: 14:00
Location: online

Applications of Artificial Intelligence in Battling against Covid-19: A Literature Review

2022-12-15T07:28:49+00:00

This week on Journal Club session Mohammad Tayarani-Najaran will talk about a paper "Applications of Artificial Intelligence in Battling against Covid-19: A Literature Review".

Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.

Papers:

M. Tayarani, N., "Applications of Artificial Intelligence in Battling against Covid-19: A Literature Review", 2021, Chaos, Solitons & Fractals, 142, 110338

Date: 2022/12/16
Time: 14:00
Location: online

A Novel Ensemble Machine Learning and an Evolutionary Algorithm in Modeling the COVID-19 Epidemic and Optimizing Government Policies

2022-03-23T15:45:26+00:00

This week on Journal Club session Mohammad Tayarani-Najaran will talk about his paper "A Novel Ensemble Machine Learning and an Evolutionary Algorithm in Modeling the COVID-19 Epidemic and Optimizing Government Policies".

The spread of the COVID-19 disease has prompted a need for immediate reaction by governments to curb the pandemic. Many countries have adopted different policies and studies are performed to understand the effect of each of the policies on the growth rate of the infected cases. In this article, the data about the policies taken by all countries at each date, and the effect of the policies on the growth rate of the pandemic are used to build a model of the pandemic's behavior. The model takes as input a set of policies and predicts the growth rate of the pandemic. Then, a population-based multiobjective optimization algorithm is developed, which uses the model to search through the policy space and finds a set of policies that minimize the cost induced to the society due to the policies and the growth rate of the pandemic. Because of the complexity of the modeling problem and the uncertainty in measuring the growth rate of the pandemic via the models, an ensemble learning algorithm is proposed in this article to improve the performance of individual learning algorithms. The ensemble consists of ten learning algorithms and a metamodel algorithm that is built to predict the accuracy of each learning algorithm for a given data record. The metamodel is a set of support vector machine (SVM) algorithms that is used in the aggregation phase of the ensemble algorithm. Because there is uncertainty in measuring the growth rate via the models, a landscape smoothing operator is proposed in the optimization process, which aims at reducing uncertainty. The algorithm is tested on open access data online and experiments on the ensemble learning and the policy optimization algorithms are performed.

Papers:

M. Tayarani-Najaran, "A Novel Ensemble Machine Learning and an Evolutionary Algorithm in Modeling the COVID-19 Epidemic and Optimizing Government Policies", 2022, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1--11

Date: 2022/03/25
Time: 14:00
Location: online

Maximum Satisfiability: Anatomy of the Fitness Landscape for a Hard Combinatorial Optimization Problem

2018-10-19T12:25:06+01:00

Mohammad Tayarani-Najaran's journal club session on his article "Maximum Satisfiability Anatomy of the Fitness Landscape for a Hard Combinatorial Optimization Problem (Adam Prugel-Bennett; Mohammad Tayarani-Najaran, 2011)".

The fitness landscape of MAX-3-SAT is investigated for random instances above the satisfiability phase transition. This paper includes a scaling analysis of the time to reach a local optimum, the number of local optima, the expected probability of reaching a local optimum as a function of its fitness, the expected fitness found by local search and the best fitness, the probability of reaching a global optimum, the size and relative positions of the global optima, the mean distance between the local and global optima, the expected fitness as a function of the Hamming distance from an optimum and their basins of attraction. These analyses show why the problem becomes hard for local search algorithms as the system size increases. The paper also shows how a recently proposed algorithm can exploit long-range correlations in the fitness landscape to improve on the state-of-the-art heuristic algorithms.

Date: 19/10/2018
Time: 16:00
Location: D120