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Cerebral palsy-affected individuals' brain-computer interface for wheelchair movement in an indoor environment using mental tasks

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EGYPTIAN INFORMATICS JOURNAL 2024年 26卷

作者： Ramakrishnan, Jayabrabu Jazan Univ Coll Engn & Comp Sci Jazan 45142 Saudi Arabia

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.

关键词： Brain-Computer Interface Electroencephalogram Fast Fourier Transform bat optimization algorithm Feed Forward Neural Network Cerebral palsy

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A novel optimization model based on fuzzy time series for short-term Air Quality Index forecasting

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KNOWLEDGE-BASED SYSTEMS 2024年 296卷

作者： Zhang, Haipeng Wang, Jianzhou Nie, Ying Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

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.

关键词： Air Quality Index Fuzzy time series bat optimization algorithm

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Nonsteroidal anti-inflammatory drug solubility optimization through green chemistry solvent: Artificial intelligence technique

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CASE STUDIES IN THERMAL ENGINEERING 2024年 53卷

作者： Majrashi, Mohammed Ali A. Alamoudi, Jawaher Abdullah Alrashidi, Amal Algarni, Majed Ahmed Alshehri, Sameer Univ Jeddah Coll Med Dept Pharmacol Jeddah 23890 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Pharm Dept Pharmaceut Sci POB 84428 Riyadh 11671 Saudi Arabia Taif Univ Coll Pharm Dept Clin Pharm POB 21944 Taif 11099 Saudi Arabia Taif Univ Coll Pharm Dept Pharmaceut & Ind Pharm POB 11099 Taif 21944 Saudi Arabia

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.

关键词： Model prediction Machine learning Solubility Supercritical fluid bat optimization algorithm

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Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第6期28卷 6796-6810页

作者： Ebrahimi-Khusfi, Zohre Taghizadeh-Mehrjardi, Ruhollah Nafarzadegan, Ali Reza Univ Jiroft Fac Nat Resources Dept Nat Sci Jiroft Iran Univ Tubingen Dept Geosci Soil Sci & Geomorphol Tubingen Germany Ardakan Univ Fac Agr & Nat Resources Ardakan Iran Univ Hormozgan Dept Nat Resources Engn Bandar Abbas Hormozgan Iran

Accurate prediction of the dust concentration (DC) is necessary to reduce its undesirable environmental effects in different geographical areas. Although the adaptive neuro-fuzzy inference system (ANFIS) is a powerful model for predicting dust events, no attempt has been made to investigate its uncertainty and interpretability. In this study, therefore, the uncertainty of the ANFIS model was quantified using uncertainty estimation based on local errors and clustering methods. Furthermore, we used a model-agnostic interpretation to make the ANFIS model interpretable. In addition, we used the bat optimization algorithm (bat) to increase the prediction accuracy of the ANFIS model. Seven explanatory variables were chosen for predicting DC in the cold and warm months across semi-arid regions of Iran. The results showed that the ANFIS+bat model increased the correlation coefficient by 10% and 16% for predicting DC in the cold and warm months, respectively, compared with the ANFIS model. Furthermore, the uncertainty analysis indicated a lower prediction interval (i.e., lower uncertainty) for the ANFIS+bat model compared with the ANFIS model for predicting DC in the cold and warm months. In addition, the model-agnostic interpretation tool findings indicated the highest contributions of air temperature and maximum wind speed for predicting DC in the cold and warm months, respectively. Prediction of DC using the proposed model will allow decision-makers to better plan for measures to mitigate the risks of wind erosion and air pollution.

关键词： Air pollution ANFIS bat optimization algorithm Interpretability Machine learning Uncertainty

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bat-inspired Metaheuristic Convolutional Neural Network algorithms for CAD-based Lung Cancer Prediction

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2021年第1期24卷 65-71页

作者： Priyadharshini, P. Zoraida, B. S. E. Bharathidasan Univ Dept Comp Sci & Engn Tiruchirappalli India

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 bat optimization-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.

关键词： Fuzzy c-means (FCM) Computer Tomography (CT) Convolutional Neural Network (CNN) bat optimization algorithm Discrete Wavelet Transform LC Prediction

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Accurately predicting the performance of MOF-based mixed matrix membranes for CO2 removal using a novel optimized extreme learning machine by bat algorithm

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SEPARATION AND PURIFICATION TECHNOLOGY 2023年第1期325卷

作者： Alizamir, Meysam Keshavarz, Ahmad Abdollahi, Farideh Khosravi, Arash Karagoz, Seckin Persian Gulf Univ ICT Res Inst Fac Intelligent Syst Engn & Data Sci IoT & Signal Proc Res Grp Bushehr *** Iran Persian Gulf Univ Fac Petr Gas & Petrochem Engn Sustainable Membrane Technol Res Grp SMTRG Bushehr *** Iran Qatar Univ Coll Engn Dept Chem Engn Doha Qatar

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 bat algorithm optimization. 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 bat algorithm. 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.

关键词： Metal organic framework Mixed matrix membranes Selectivity Permeability Extreme learning machine bat optimization algorithm

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Gravity data inversion applying a metaheuristic bat algorithm for various ore and mineral models

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JOURNAL OF GEODYNAMICS 2023年 155卷

作者： Essa, Khalid S. Diab, Zein E. Cairo Univ Fac Sci Geophys Dept PO 12613 Giza Egypt

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.

关键词： Gravity profile bat optimization algorithm Depth Ore and mineral explorations

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Profit maximization in reverse logistics based on disassembly scheduling using hybrid bee colony and bat optimization

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TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING 2019年第4期43卷 551-559页

作者： Sathish, T. SMR East Coast Coll Engn & Technol Dept Mech Engn Pattukkottai 614612 Tamil Nadu India

This work was undertaken to develop a strategy to enhance the profit in the reverse logistics of end-of-life products. In this paper, a novel strategy based on hybrid bee colony and bat optimization technique is presented to perform reverse logistics. The aim of the present paper is to maximize the profit in reverse logistic based manufacturing. The proposed optimization technique was used to schedule the disassembly of end-of-life products so that the time spent in reverse logistics was reduced. Moreover, the proposed technique increases the amount of product required during disassembly, so that the loss of component is reduced. Thus, the proposed technique can enhance the manufacturer's profit by reducing the time and cost required for disassembly. Ultimately, the proposed technique can provide a suitable technique for multi-period disassembly in manufacturing industries.

关键词： end-of-life artificial bee colony bat optimization algorithm adaptive genetic algorithm hybrid bee colony

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Chaos-enhanced synchronized bat optimizer

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APPLIED MATHEMATICAL MODELLING 2020年第Part2期77卷 1201-1215页

作者： Yu, Helong Zhao, Nannan Wang, Pengjun Chen, Huiling Li, Chengye Jilin Agr Univ Coll Informat Technol Changchun 130118 Jilin Peoples R China Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Dept Pulm & Crit Care Med Wenzhou 325000 Peoples R China

In this paper, a chaos-enhanced bat algorithm is proposed to tackle the global optimization problems. bat algorithm is a relatively new stochastic optimizer inspired by the echolocation behavior of bats in nature. Due to its effectiveness, it has been applied to many fields such as engineering design, feature selection, and machine learning. However, the classical approach is often prone to falling into local optima. This paper proposes an enhanced bat algorithm to alleviate this problem observed in the original algorithm. The proposed method controls the steps of chaotic mapping by a threshold and synchronizes the velocity of agents using a velocity inertia weight. These mechanisms are designed to boost the stability and convergence speed of the bat algorithm, instantly. Eighteen well-established and the state-of-the-art meta-heuristic approaches are considered to validate the effectiveness of the developed algorithm. Experimental results reveal that the proposed chaos-enhanced bat algorithm is not only superior to the well-established algorithms such as the original method but also the latest improved approaches. Also, the proposed method is successfully applied to I-beam design problems, welded beam design, and pressure vessel design. The results show that chaos-enhanced bat algorithm can deal with unconstrained and constrained feature spaces, effectively. (C) 2019 Elsevier Inc. All rights reserved.

关键词： bat optimization algorithm Function optimization Chaotic mapping I-beam design problems Welded beam design Pressure vessel design

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bat-inspired Metaheuristic Convolutional Neural Network algorithms for CAD-based Lung Cancer Prediction

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淡江理工学刊 2021年第1期24卷 65-71页

作者： P. Priyadharshini B. S. E. Zoraida

关键词： Fuzzy c-means (FCM) Computer Tomography (CT) Convolutional Neural Network (CNN) bat optimization algorithm Discrete Wavelet Transform LC Prediction

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