Binary chemical reaction optimization based feature selection techniques for machine learning classification problems

Feature selection is an important pre-processing technique for dimensionality reduction of high-dimensional data in machine learning (ML) field. In this paper, we propose a binary chemical reaction optimization (BCRO) and a hybrid binary chemical reaction optimization-binary particle swarm optimization (HBCRO-BPSO) based feature selection techniques to optimize the number of selected features and improve the classification accuracy. Three objective functions have been used for the proposed feature selection techniques to compare their performances with a BPSO and advanced binary ant colony optimization (ABACO) along with an implemented GA based feature selection approach called as binary genetic algorithm (BGA). Five ML algorithms including K-nearest neighbor (KNN), logistic regression, Naïve Bayes, decision tree, and random forest are considered for classification tasks. Experimental results tested on eleven benchmark datasets from UCI ML repository show that the proposed HBCRO-BPSO algorithm improves the average percentage of reduction in features (APRF) and average percentage of improvement in accuracy (APIA) by 5.01% and 3.83%, respectively over the existing BPSO based feature selection method; 4.58% and 3.12% over BGA; and 4.15% and 2.27% over ABACO when used with a KNN classifier

Smart grid infrastructure and renewable energy deployment: A conceptual review of Saudi Arabia

The transition towards smart grid introduces the potential for revolutionary changes in the present energy management systems. It provides the grid with the necessary functionalities to transform into a decentralized energy system, and integrate large-scale variable renewable energy sources with enhanced demand-side management. Saudi Arabia is among the countries with significant potential to generate electricity from renewable energy sources, especially solar. The development of smart energy, smart communication, and smart information systems to establish a state-of-the art smart grid that enables not only security and automation but also has the flexibility of integrating technological innovation in the upcoming years. The model developed is characterized as government-led, commercially-driven, based on technological readiness, and community-oriented with numerous projects systematically initiated targeting innovation, human resources development, and industrial viability. Therefore, this paper aims to highlight the trends associated while considering the case scenario of the country and outlining the challenges and potential solutions with the establishment of smart grids

The link between climate change, food security and fertility:The case of Bangladesh

Climate change is likely to worsen the food security situation through its impact on food production, which may indirectly affect fertility behaviour. This study examines the direct and indirect effects of climate change (e.g., temperature and precipitation) via the production of major crops, as well as their short- and long-term effects on the total fertility rate (TFR) in Bangladesh. We used structural equation modelling (SEM) to perform path analysis and distinguish the direct influence of climate change on fertility and its indirect influence on fertility through food security. We also applied the error correction model (ECM) to analyze the time-series data on temperature and precipitation, crop production and fertility rate of Bangladesh from 1966 to 2015. The results show that maximum temperature has a direct effect and indirect negative effect–via crop production–on TFR, while crop production has a direct positive effect and indirect negative effect–via infant mortality–on TFR. In the short term, TFR responds negatively to the maximum temperature but positively in the long term. The effect of rainfall on TFR is found to be direct, positive, but mainly short-term. Although indicators of economic development play an important part in the fertility decline in Bangladesh, some climate change parameters and crop production are non-negligible factors