This study conducted in Lima, Peru, a combination of spatial decision making system and machine learning was utilized to identify potential solar power plant construction sites within the city. Sundial measurements of solar radiation, precipitation, temperature, and altitude were collected for the study. Gene Expression Programming (GEP), which is based on the evolution of intelligent models, and Artificial Neural Networks (ANN) were both utilized in this investigation, and the results obtained from each were compared. Eighty percent of the data was utilized during the training phase, while the remaining twenty percent was utilized during the testing phase. On the basis of the findings, it was determined that the GEP is the most suitable network for predicting the location. The test state’s Nash-Sutcliffe efficiency (NSE) was 0.90, and its root-mean-square error (RMSE) was 0.04. Following the generation of the final map based on the results of the GEP model, it was determined that 9.2% of the province’s study area is suitable for the construction of photovoltaic solar power plants, while 53.5% is acceptable and 37.3% is unsuitable. The ANN model reveals that only 1.7% of the study area is suitable for the construction of photovoltaic solar power plants, while 66.8% is acceptable and 31.5% is unsuitable.
Because solar energy is among the renewable energies, it has traditionally been used to provide lighting in buildings. When solar energy is effectively utilized during the day, the environment is not only more comfortable for users, but it also utilizes energy more efficiently for both heating and cooling purposes. Because of this, increasing the building’s energy efficiency requires first controlling the amount of light that enters the space. Considering that the only parts of the building that come into direct contact with the sun are the windows, it is essential to make use of louvers in order to regulate the amount of sunlight that enters the building. Through the use of Ant Colony Optimization (ACO), the purpose of this study is to estimate the proportions and technical specifications of external louvers, as well as to propose a model for designing the southern openings of educational space in order to maximize energy efficiency and intelligent consumption, as well as to ensure that the appropriate amount of light is provided. According to the findings of this research, the design of external louvers is heavily influenced by a total of five distinct aspects: the number of louvers, the depth of the louvers, the angle of rotation of the louvers, the distance between the louvers and the window, and the reflection coefficient of the louvers. The results of the 2067 simulated case study show that the best reflection rates of the louvers are between 0 and 15 percent, and the most optimal distance between the louvers and the window is in the range of 0 to 18 centimeters. Additionally, the results show that the best distance between the louvers and the window is in the range of 0 to 18 centimeters.
Converting solar energy to chemical energy is a capable technique of generating renewable energy. Production of hydrogen through water splitting recognized by the photocatalyst is cost-effective for large-scale hydrogen production. In this chapter, we describe the prevailing water splitting scheme based on hydrogen production as well as on the photocatalysts. Important design philosophies of the elements involved in inclusive water-splitting schemes based on one-step and two-step photoexcitation are discussed. We summarize the challenges and potential developments associated with solar water splitting through metal-based photocatalysts for approaching in demand necessities.
The advent of architectures such as the Internet of Things (IoT) has led to the dramatic growth of data and the production of big data. Managing this often-unlabeled data is a big challenge for the real world. Hierarchical Clustering (HC) is recognized as an efficient unsupervised approach to unlabeled data analysis. In data mining, HC is a mechanism for grouping data at different scales by creating a dendrogram. One of the most common HC methods is Agglomerative Hierarchical Clustering (AHC) in which clusters are created bottom-up. In addition, ensemble clustering approaches are used today in complex problems due to the weakness of individual clustering methods. Accordingly, we propose a clustering framework using AHC methods based on ensemble approaches, which includes the clusters clustering technique and a novel similarity measurement. The proposed algorithm is a Meta-Clustering Ensemble scheme based on Model Selection (MCEMS). MCEMS uses the bi-weighting policy to solve the model selection associated problem to improve ensemble clustering. Specifically, multiple AHC individual methods cluster the data from different aspects to form the primary clusters. According to the results of different methods, the similarity between the instances is calculated using a novel similarity measurement. The MCEMS scheme involves the creation of meta-clusters by re-clustering of primary clusters. After clusters clustering, the number of optimal clusters is determined by merging similar clusters and considering a threshold. Finally, the similarity of the instances to the meta-clusters is calculated and each instance is assigned to the meta-cluster with the highest similarity to form the final clusters. Simulations have been performed on some datasets from the UCI repository to evaluate MCEMS scheme compared to state-of-the-art algorithms. Extensive experiments clearly prove the superiority of MCEMS over HMM, DSPA and WHAC algorithms based on Wilcoxon test and Cophenetic correlation coefficient.
Liquid structures such as droplets and slugs exist inside gas channels of polymer electrolyte fuel cells in low-temperature applications. The efficiency of these electrochemical devices depends on the effective removal of the produced water. The gas channels' specifications like section geometry, corner angles, and surface wettability properties substantially control the liquid removal process. Here, five channels with various section geometries are modeled and the liquid-slug discharge process is investigated using a transient volume of fluid method. The numerical model consists of a segment of the cathode-side gas channel with the working conditions of an operational fuel cell. The dynamic two-phase flow simulations show that channels with smaller width and height eventuate in proper flow distribution at the gas feed. A channel with the sectional dimensions of 0.5 mm × 0.5 mm results in. 35.18% faster GDL (Gas-Diffusion Layer) clearance, 29.32% faster liquid expulsion compared to other channels having 2–3 times higher dimensions. Therefore, this channel is recommended as the best design for improved fuel cell performance.
Wireless Sensor Networks (WSN) has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of applications. In spite of this, it is challenging to design energy-efficient WSN. The routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of network. In order to solve the restricted energy problem, it is essential to reduce the energy utilization of data, transmitted from the routing protocol and improve network development. In this background, the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-hop Routing Protocol (DEAOA-MHRP) for WSN. The aim of the proposed DEAOA-MHRP model is select the optimal routes to reach the destination in WSN. To accomplish this, DEAOA-MHRP model initially integrates the concepts of Different Evolution (DE) and Arithmetic Optimization Algorithms (AOA) to improve convergence rate and solution quality. Besides, the inclusion of DE in traditional AOA helps in overcoming local optima problems. In addition, the proposed DEAOA-MRP technique derives a fitness function comprising two input variables such as residual energy and distance. In order to ensure the energy efficient performance of DEAOA-MHRP model, a detailed comparative study was conducted and the results established its superior performance over recent approaches.
At present, the prediction of brain tumors is performed using Machine Learning (ML) and Deep Learning (DL) algorithms.Although various ML and DL algorithms are adapted to predict brain tumors to some range, some concerns still need enhancement, particularly accuracy, sensitivity, false positive and false negative, to improve the brain tumor prediction system symmetrically.Therefore, this work proposed an Extended Deep Learning Algorithm (EDLA) to measure performance parameters such as accuracy, sensitivity, and false positive and false negative rates.In addition, these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network (CNN) way further using the SPSS tool, and respective graphical illustrations were shown.The results were that the mean performance measures for the proposed EDLA algorithm were calculated, and those measured were accuracy (97.665%), sensitivity (97.939%), false positive (3.012%), and false negative (3.182%) for ten iterations.Whereas in the case of the CNN, the algorithm means accuracy gained was 94.287%, mean sensitivity 95.612%, mean false positive 5.328%, and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms, including CNN, and ensures symmetrically improved parameters.Thus EDLA algorithm introduces novelty concerning its performance and particular activation function.This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner.This algorithm would apply to brain tumor diagnosis and be involved in various
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