The travelling salesman problem (TSP) is probably one of the most famous problems in combinatorial optimization. There are many techniques to solve the TSP problem such as Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Simulated Annealing (SA).In this paper, we conduct a comparison study to evaluate the performance of these three algorithms in terms of execution time and shortest distance. JAVA programing is used to implement the algorithms using three benchmarks on the same platform conditions. Among the three algorithms, we found out that the Simulated Annealing has the shortest time in execution(<1s) but for the shortest distance, it comes in the second order. Furthermore, in term of shortest distance between the cities, ACO performs better than GA and SA. However, ACO comes in the last order in term of time execution.
In December 2019, the first case of the coronavirus was reported, specifically in Wuhan, in China, and the virus began to spread very quickly until it reached more than 3 million cases around the world. But with the lack of technology and medical equipment and the existence of low health awareness in many countries, there is an intense research to combat with that massive problem. In this context, objective of this paper is to discover the spread of the virus according to the countries, by following what users around the world publish on social networking sites, especially on Twitter. In detail, we proposed an explainable artificial intelligence (XAI)-based text classification model that depends on three main steps to discover approximate numbers of infects, by checking the symptoms that published within Twitter posts. A dataset publishing cases of infected and the accompanying symptoms of more than 112 k cases was considered and a model with Naïve Bayes was trained through a comparative work with eight different classifiers. Naïve Bayes is a transparent machine learning technique so it is easier to use probabilistic relations between inputs and the outputs to explain results. Eventually, that XAI-based Naïve Bayes model reached to the highest accuracy rate with 93.6%.
Electricity is the nervous system in our live on earth and it is involved in many electronic devices and systems. In this paper, the end use of electricity was forecasted in the medium-term scale. The medium-term forecasting is conducted using the following forecasting algorithms: Auto-Regressive Integrated Moving Average (ARIMA), Hybrid ARIMA, and Linear Regression with horizon value equals five (20182022). After evaluating tested algorithms using MAPE it is noticed that Hybrid ARIMA was the best forecasting algorithm to be used with MAPE 1.77%, while ARIMA MAPE equals 4.81% and linear regression MAPE equals 7.04%. The deviation of the forecasted 5 years (2018-2022) is decreasing by 1.21% with comparison to the last 5 years (2013-2017).
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