A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and truncation. According to the simulation results, the truncation selection operator is the most efficient operator in terms of extending the network stability period and improving reliability. The truncation operator outperforms other selection operators, most notably the well-known roulette wheel operator, by increasing the stability period by $25.8\%$ and data throughput by $26.86\%$. Furthermore, the truncation selection operator outperforms other selection operators in terms of the network residual energy after each protocol round.

The cooling of PV models is an important process that enhances the generated electricity from these models, especially in hot areas. In this work, a new, active cooling algorithm is proposed based on active fan cooling and an artificial neural network, which is named the artificial dynamic neural network Fan cooling algorithm (DNNFC). The proposed system attaches five fans to the back of a PV model. Subsequently, only two fans work at any given time to circulate the air under the PV model in order to cool it down. Five different patterns of working fans have been experimented with in this work. To select the optimal pattern for any given time, a back propagation neural network model was trained. The algorithm is a dynamic algorithm since it re-trains the model with new recorded surface temperatures over time. In this way, the model automatically adapts to any weather and environmental conditions. The model was trained with an indoor dataset and tested with an outdoor dataset. An accuracy of more than 97% has been recorded, with a mean square error of approximately 0.02.

This paper introduces singular value decomposition (SVD), a major matrix decomposition technique. SVD serves as the underlining computational engine of many other techniques such as principal component analysis (PCA), eigen decomposition, matrix decomposition, Cholesky decomposition and others. SVD is utilized in many applications such as data analysis and dimensionality reduction, image compression, Google's PageRank algorithm, Netflix's recommender system and many more. This paper overviews the mathematics behind SVD in a simple way. It also applies SVD technique in image compression and in dimensionality reduction as the underlining technique of the PCA and data analysis.

In this paper an analytical model of the optimal number of clusters and the corresponding optimal probability are derived for different types of three-dimensional(3D) WSN settings utilizing single-hop homogeneous LEACH protocol. Three types of 3D network configurations are analyzed. These include cube, cylinder and sphere network configurations. Energy analysis of these network configurations is done in noise-prone communication environment. The noise is modelled utilizing the probability of reception pr. A small value of pr indicates more packets are dropped, which is a sign of large noise. It was shown through analysis and simulation that optimal number of clusters, optimal probability and energy consumption are affected by the geometry of the 3D WSN, noise, and transmission amplification model used.

This review paper offers a thorough analysis of different data imputation methods that can be applied to time series data. Time series data is an essential element in various analytical and predictive models used in different domains. Time series data frequently experiences missing values as a result of diverse factors, such as system errors, human influences, or inherent gaps in data collection. The presence of these missing values significantly undermines the precision and dependability of models constructed using this data. This paper classifies imputation methods into basic and advanced techniques, providing a comprehensive examination of each. The simplicity and effectiveness of basic techniques, such as mean or median imputation and linear interpolation, are discussed in specific scenarios. The study investigates the efficacy of advanced techniques, such as ARIMA statistical models, K-Nearest Neighbors machine learning approaches, and Long Short-Term Memory networks deep learning techniques, in managing intricate and extensive time series datasets. The paper emphasizes a comparative approach, assessing each method's complexity, accuracy, and computational demands. The paper concludes by emphasizing the need for continuous innovation in imputation techniques to meet the growing complexity of time series data across various domains. It advocates for a collaborative approach that combines domain expertise with advanced data science methods to develop tailored, efficient, and accurate imputation strategies.