The technique of measuring brain signals or activities by placing electrodes on the scalp of human beings is called Electroencephalogram (EEG). Brain-computer interface (BCI) is a technique to capture brain signals an...
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The technique of measuring brain signals or activities by placing electrodes on the scalp of human beings is called Electroencephalogram (EEG). Brain-computer interface (BCI) is a technique to capture brain signals and translate them into control signals to run external devices. With the combination of these two techniques, we can create BCI using brain signals. Methods: In this study, the author considered conducting two types of methods offline and online both in the indoor environment using the Fast Fourier Transform (FFT) technique with Feed Forward Neural Network trained with bat optimization algorithm (FFNNBOA). The study was carried out on two different age groups between 30 to 45 years and 46 to 60 years with four different tasks. Based on the execution of the four different tasks concerning two different age groups, the accuracy obtained during classification is 94.35 % and 93.76 % for offline and online modes. Results: The results it is observed that the classification accuracy for the age group belonging 46 to 60 is comparably higher than that of the conventional classification model. The offline and online tests were conducted for both age groups persons and obtained the recognizing accuracy of 95 %, 93.25 %, and 93.75 %, 91.75 % for the two modes. This study confirms that the performances of the subjects belonging to age groups 30 to 45 are higher than the age groups belonging to 46 to 60 in terms of classification, offline, and online mode. Finally, this study also identified that subject S4 from the 30 to 45 age group showed 100 % accuracy in both offline and online signal acquisition.
The problem of air quality forecasting has caused a heated discussion among scholars at home and abroad. Short term air quality forecasting substantially impacts regulatory effectiveness to improve environmental and h...
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The problem of air quality forecasting has caused a heated discussion among scholars at home and abroad. Short term air quality forecasting substantially impacts regulatory effectiveness to improve environmental and human health. Therefore, many scholars have proposed a lot of Air Quality Index (AQI) forecasting models to improve air quality forecasting performance. However, the fuzziness of air quality data is often ignored, which may reduce the effectiveness of the forecasting. In this study, a new fuzzy forecasting system that includes a fuzzy time series, data preprocessing technique, multi-objective bat optimization algorithm, and forecasting algorithms is proposed to increase air quality forecasting performance and accuracy. To evaluate the forecasting system effectiveness, one-hour AQI data collected from four cities in China are applied in three experiments. The experiment results find that the proposed forecasting system can effectively utilize the fuzziness of air quality data, and it not only improves the forecasting accuracy but also achieves a higher degree of certainty, which can effectively assist air quality supervision.
Solar energy has been widely adopted in power systems, particularly using the photovoltaic (PV) generation technology. In this respect, the power generation of such a technology is highly impacted by several factors, ...
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Solar energy has been widely adopted in power systems, particularly using the photovoltaic (PV) generation technology. In this respect, the power generation of such a technology is highly impacted by several factors, such as temperature and solar irradiance. As an effective solution, maximum power point tracking (MPPT) approaches have been developed and used in PV systems to increase the efficiency subject to the changing climate conditions. In this respect, a combinatorial MPPT technique is presented in this paper based on the fuzzy controller and bat optimization algorithm to desirably tune the control parameters. To this end, the membership functions of the fuzzy logic controller (FLC) are appropriately specified to cope with uncertainties caused by changing climatic conditions. The studied PV system, equipped with the MPPT technique, operates jointly with an electrical energy storage system which is based on a lead-acid battery. By employing this hybrid generation system, the solar power generation intermittency can be well compensated and the stabilized power output can be achieved. The proposed model is then simulated on a typical hybrid energy system, including a PV system and a battery energy storage (BES) system. In this respect, the superior performance of the suggested control scheme is verified through making a comprehensive comparison with other well-known techniques. Besides, the behavior of the system under varying climate conditions is studied and the desired performance of the suggested combinatorial controller is validated. For example, the presented bat-FLC scheme can help the hybrid system reach 99% efficiency for partial shading conditions (PSCs) which is 18% more compared to the prevalent perturb and observe (P&O) technique. (C) 2020 Elsevier Ltd. All rights reserved.
This research paper presents a comprehensive thermodynamic and heat transfer study on predicting the ternary solubility of Nystatin in SC-CO2-Ethanol (supercritical CO2 and ethanol). The employed process is a thermal-...
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This research paper presents a comprehensive thermodynamic and heat transfer study on predicting the ternary solubility of Nystatin in SC-CO2-Ethanol (supercritical CO2 and ethanol). The employed process is a thermal-based green processing for preparation of solid nanoparticles. The data collection, consisting of temperature and pressure as input features and ternary solubility as the target variable, was used to train and evaluate four different machine learning algorithms: Random Forest (RF), Extra Trees (ET), NU-SVR, and EPSILON-SVR. The hyper-parameter tuning process employed the bat optimization algorithm (BA), a nature-inspired optimization technique to fine-tune the models and enhance their predictive capabilities. The ET model had a notable R2 score of 0.98526, RMSE of 2.48774E-02, and MAE of 2.13417E-02. The RF model also yielded strong performance, achieving an R2 score of 0.98436, RMSE of 2.55130E-02, and MAE of 2.06314E-02. However, the NU-SVR model exhibited superior performance compared to other models, as evidenced by its remarkable R2 score of 0.99943, thereby showcasing its exceptional precision. The RMSE and MAE for NU-SVR were 4.92372E-03 and 3.94943E-03, respectively, underscoring its precision in predicting ternary solubility. The EPSILON-SVR model, while still respectable, obtained a score of 0.93574 in terms of R2, RMSE of 4.37434E-02, and MAE of 3.79800E-02.
The increasing number of mixed matrix membranes (MMMs) based on metal organic frameworks (MOFs) for carbon capture has created a demand for accurate and swift evaluation of their separation performance. Machine learni...
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The increasing number of mixed matrix membranes (MMMs) based on metal organic frameworks (MOFs) for carbon capture has created a demand for accurate and swift evaluation of their separation performance. Machine learning (ML) has emerged as a valuable tool for this purpose, providing an efficient approach for screening these materials and accelerating their practical application. In this study, we developed and optimized a novel and reliable hybrid machine learning paradigm based on the extreme learning machines (ELM) method using the batalgorithmoptimization. In order to predict the performance of MMMs, including gas permeability and selectivity parameters, nine machine learning models were developed by incorporating descriptors and fingerprints for polymer featurization, physical and structural features of MOFs as well as operating conditions. The impact of input features on MMMs performance was also explored using RReliefF analysis. The study found that the performance of the hybrid ELM-based algorithms was significantly improved by using the batalgorithm. Furthermore, the RReliefF analysis revealed that the cage size of the MOF and the type of polymer matrix used are the most significant parameters in forecasting the permeability of MOF-based MMMs, while the loading amount and pressure were identified as critical determinants of selectivity. Overall, these findings contribute to the development of more efficient and accurate methods for evaluating MMMs, which are crucial for carbon capture applications.
In this real-world, lung cancer (LC) is the foremost reason for mortality in both mankind in the present time, with an inspiring figure of around five million deaths every year. Computer tomography (CT) can deliver va...
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In this real-world, lung cancer (LC) is the foremost reason for mortality in both mankind in the present time, with an inspiring figure of around five million deaths every year. Computer tomography (CT) can deliver valuable information when diagnosing lung illnesses. The chief goal of this work is to identify cancer nodules in the lungs from a given input image of the lungs and to organize LC and its harshness. To locate cancer nodules in the lungs, Fuzzy c-means (FCM) based segmentation is used. In this paper, a batoptimization-based learning rate modified Convolutional Neural Network algorithm is introduced to effectively predict lung cancer. Additionally, to improve the proposed classification performance, input image is decomposed with support of the Discrete Wavelet Transform (DWT). With is used to decompose the image into four sub-bands, in such case we considered the Low (LL) band image. And then segmented images are split into two groups of images, which are used for the training and testing process. the proposed scheme has validated with the help of the LIDC-IDRI publically available dataset. They are studied by applying a convolutional neural network, and instantly trained neural network for predicting LC. In the end, the system efficiency is checked by using MATLAB tool to obtain the results of this model. In this experimentation, we achieved the accuracy of 97.43 % with a minimum classification error of 2.57 % in lung cancer prediction. This method is used to diagnose lung cancer correctly, and also this method may also overcome the previous drawbacks in the lung cancer diagnosis method.
Geophysical methods, especially the gravity method, are very helpful in ore and mineral explorations. Here, gravity modeling and interpretation for the subsurface geologic structures generally assumes either homogenou...
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Geophysical methods, especially the gravity method, are very helpful in ore and mineral explorations. Here, gravity modeling and interpretation for the subsurface geologic structures generally assumes either homogenous or spatially varying densities within target source rocks and surrounding structures. Therefore, the use of simplegeometric bodies helps in the validation of the subsurface ore and mineral targets. A bat optimization algorithm is a recently developed metaheuristic algorithm that is used in various geophysical applications to explore and explain the parameters of buried ore and mineral targets. Using the bat optimization algorithm, we were elucidating gravity anomaly profiles for ore and mineral cases. To perform global optimization, the bat optimization algorithm is based on the echolocation behavior of bats. The global optimum solution in the bat optimization algorithm reached the suggested minimum value of the objective function. The bat optimization algorithm is applied to gravity data to estimate the target parameters (e.g., amplitude coefficient, depth, origin location, and geometric shape). The stability and efficiency of the introduced optimizing algorithm have been checked on two synthetic models represented in a spherical model and an infinitely horizontal cylinder model using two different kinds of noise. Furthermore, successful applications of the proposed algorithm for discovering the ore and minerals in Canada, Cuba, and India were presented. The results match well with the available geological and borehole information and other results from the published literature.
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