A Hybrid ANN-GWO Algorithm for Prediction of Heart Disease

Abstract The paper investigates the powerful of hybridizing two computational intelligence methods viz., Gray Wolf Optimization (GWO) and Artificial Neural Networks (ANN) for prediction of heart disease. Gray wolf optimization is a global search method while gradient-based back propagation method is a local search one. The proposed algorithm implies the ability of ANN to find a relationship between the input and the output variables while the stochastic search ability of GWO is used for finding the initial optimal weights and biases of the ANN to reduce the probability of ANN getting stuck at local minima and slowly converging to global optimum. For evaluation purpose, the performance of hybrid model (ANN-GWO) was compared with standard back-propagation neural network (BPNN) using Root Mean Square Error (RMSE). The results demonstrate that the proposed model increases the convergence speed and the accuracy of prediction

Optimal Type-3 Fuzzy System for Solving Singular Multi-Pantograph Equations

In this study a new machine learning technique is presented to solve singular multi-pantograph differential equations (SMDEs). A new optimized type-3 fuzzy logic system (T3-FLS) by unscented Kalman filter (UKF) is proposed for solution estimation. The convergence and stability of presented algorithm are ensured by the suggested Lyapunov analysis. By two SMDEs the effectiveness and applicability of the suggested method is demonstrated. The statistical analysis show that the suggested method results in accurate and robust performance and the estimated solution is well converged to the exact solution. The proposed algorithm is simple and can be applied on various SMDEs with variable coefficients.publishedVersio

A Layered Recurrent Neural Network for Imputing Air Pollutants Missing Data and Prediction of <em>NO</em>₂, <em>O</em>₃, <em>PM</em>₁₀, and <em>PM</em>_2.5

To fulfill the national air quality standards, many countries have created emissions monitoring strategies on air quality. Nowadays, policymakers and air quality executives depend on scientific computation and prediction models to monitor that cause air pollution, especially in industrial cities. Air pollution is considered one of the primary problems that could cause many human health problems such as asthma, damage to lungs, and even death. In this study, we present investigated development forecasting models for air pollutant attributes including Particulate Matters (PM2.5, PM10), ground-level Ozone (O3), and Nitrogen Oxides (NO2). The dataset used was collected from Dubrovnik city, which is located in the east of Croatia. The collected data has missing values. Therefore, we suggested the use of a Layered Recurrent Neural Network (L-RNN) to impute the missing value(s) of air pollutant attributes then build forecasting models. We adopted four regression models to forecast air pollutant attributes, which are: Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Artificial Neural Network (ANN) and L-RNN. The obtained results show that the proposed method enhances the overall performance of other forecasting models

Optimal type-3 fuzzy system for solving singular multi-pantograph equations

In this study a new machine learning technique is presented to solve singular multi-pantograph differential equations (SMDEs). A new optimized type-3 fuzzy logic system (T3-FLS) by unscented Kalman filter (UKF) is proposed for solution estimation. The convergence and stability of presented algorithm are ensured by the suggested Lyapunov analysis. By two SMDEs the effectiveness and applicability of the suggested method is demonstrated. The statistical analysis show that the suggested method results in accurate and robust performance and the estimated solution is well converged to the exact solution. The proposed algorithm is simple and can be applied on various SMDEs with variable coefficients

An integrated hybrid approach to the examination timetabling problem

This paper is derived from an interest in the development of automated approaches to tackle examination timetabling problems effectively. We propose a hybrid approach that incorporates effective heuristic operators within the great deluge algorithm. The latter was chosen because of consistently good performances being reported within the examination timetabling research domain. The additional heuristic procedures further enhance the overall effectiveness of this integrated hybrid approach. These procedures are drawn from methodologies that have appeared in the literature under term the "electromagnetic-like mechanism". The aim is to move sample points towards a high quality solution while avoiding local optima by utilising a calculated force value. This value, which is calculated dynamically, is treated as a decay rate in determining the level within the great deluge algorithm. To evaluate the proposed algorithm, we carry out experimental work on two types of examination timetabling datasets. All the related results and analysis obtained illustrate that this hybrid approach is effective when compared with existing approaches in the literature.Great deluge approach Examination timetabling Heuristics Metaheuristics

Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset