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Wei Y, Rao X, Fu Y, Song L, Chen H, Li J. Machine learning prediction model based on enhanced bat algorithm and support vector machine for slow employment prediction. PLoS One 2023; 18:e0294114. [PMID: 37943766 PMCID: PMC10635481 DOI: 10.1371/journal.pone.0294114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 10/23/2023] [Indexed: 11/12/2023] Open
Abstract
The employment of college students is an important issue that affects national development and social stability. In recent years, the increase in the number of graduates, the pressure of employment, and the epidemic have made the phenomenon of 'slow employment' increasingly prominent, becoming an urgent problem to be solved. Data mining and machine learning methods are used to analyze and predict the employment prospects for graduates and provide effective employment guidance and services for universities, governments, and graduates. It is a feasible solution to alleviate the problem of 'slow employment' of graduates. Therefore, this study proposed a feature selection prediction model (bGEBA-SVM) based on an improved bat algorithm and support vector machine by extracting 1694 college graduates from 2022 classes in Zhejiang Province. To improve the search efficiency and accuracy of the optimal feature subset, this paper proposed an enhanced bat algorithm based on the Gaussian distribution-based and elimination strategies for optimizing the feature set. The training data were input to the support vector machine for prediction. The proposed method is experimented by comparing it with peers, well-known machine learning models on the IEEE CEC2017 benchmark functions, public datasets, and graduate employment prediction dataset. The experimental results show that bGEBA-SVM can obtain higher prediction Accuracy, which can reach 93.86%. In addition, further education, student leader experience, family situation, career planning, and employment structure are more relevant characteristics that affect employment outcomes. In summary, bGEBA-SVM can be regarded as an employment prediction model with strong performance and high interpretability.
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Affiliation(s)
- Yan Wei
- Department of Information Technology, Wenzhou Vocational College of Science and Technology, Wenzhou, 325006, China
| | - Xili Rao
- Department of Information Technology, Wenzhou Vocational College of Science and Technology, Wenzhou, 325006, China
| | - Yinjun Fu
- The Section of Employment, Wenzhou Vocational College of Science and Technology, Wenzhou, 325006, China
| | - Li Song
- Department of Information Technology, Wenzhou Vocational College of Science and Technology, Wenzhou, 325006, China
| | - Huiling Chen
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
| | - Junhong Li
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, 325035, China
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Peng Y, Chen Y. Integrative soft computing approaches for optimizing thermal energy performance in residential buildings. PLoS One 2023; 18:e0290719. [PMID: 37683030 PMCID: PMC10491398 DOI: 10.1371/journal.pone.0290719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 08/11/2023] [Indexed: 09/10/2023] Open
Abstract
As is known, early prediction of thermal load in buildings can give valuable insight to engineers and energy experts in order to optimize the building design. Although different machine learning models have been promisingly employed for this problem, newer sophisticated techniques still require proper attention. This study aims at introducing novel hybrid algorithms for estimating building thermal load. The predictive models are artificial neural networks exposed to five optimizer algorithms, namely Archimedes optimization algorithm (AOA), Beluga whale optimization (BWO), forensic-based investigation (FBI), snake optimizer (SO), and transient search algorithm (TSO), for attaining optimal trainings. These five integrations aim at predicting the annual thermal energy demand. The accuracy of the models is broadly assessed using mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) indicators and a ranking system is accordingly developed. As the MAPE and R2 reported, all obtained relative errors were below 5% and correlations were above 92% which confirm the general acceptability of the results and all used models. While the models exhibited different performances in training and testing stages, referring to the overall results, the BWO emerged as the most accurate algorithm, followed by the AOA and SO simultaneously in the second position, the FBI as the third, and TSO as the fourth accurate model. Mean absolute error (MAPE) and Considering the wide variety of artificial intelligence techniques that are used nowadays, the findings of this research may shed light on the selection of proper techniques for reliable energy performance analysis in complex buildings.
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Affiliation(s)
- Yao Peng
- Hunan Urban Construction Vocational and Technical College, Hunan, China
| | - Yang Chen
- Xiangtan Housing and Urban-Rural Development Bureau, Xiangtan, China
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Bobobee ED, Wang S, Zou C, Takyi-aninakwa P, Zhou H, Appiah E. State of charge estimation of ternary lithium-ion batteries at variable ambient temperatures. INT J ELECTROCHEM SC 2023; 18:100062. [DOI: 10.1016/j.ijoes.2023.100062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
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Gupta D, Gujre N, Singha S, Mitra S. Role of existing and emerging technologies in advancing climate-smart agriculture through modeling: A review. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Wang J, Raza A, Hu Y, Buttar NA, Shoaib M, Saber K, Li P, Elbeltagi A, Ray RL. Development of Monthly Reference Evapotranspiration Machine Learning Models and Mapping of Pakistan—A Comparative Study. Water 2022; 14:1666. [DOI: 10.3390/w14101666] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Accurate estimation of reference evapotranspiration (ETo) plays a vital role in irrigation and water resource planning. The Penman–Monteith method recommended by the Food and Agriculture Organization (FAO PM56) is widely used and considered a standard to calculate ETo. However, FAO PM56 cannot be used with limited meteorological variables, so it is compulsory to choose an alternative model for ETo estimation, which requires fewer variables. This study built ten machine learning (ML) models based on multi-function, neural network, and tree-based structure against the FAO PM56 method. For this purpose, time series temperature data on a monthly scale are only used to train ML models. The developed ML models were applied to estimate ETo at different test stations and the obtained results were compared with the FAO PM56 method to verify and validate their performance in ETo estimation for the selected stations. In addition, multiple statistical indicators, including root-mean-square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (r) were calculated to compare the performance of each ML model on ETo estimation. Among the applied ML models, the ETo tree boost (TB) ML model outperformed the other ML models in estimating ETo in diverse climatic conditions based on statistical indicators (R2, NSE, r, RMSE, and MAE). Moreover, the observed R2, NSE, and r were the highest for the TB ML model, while RMSE and MAE were found to be the lowest at the study sites compared to other applied ML models. Lastly, ETo point data yielded from the TB ML model was used in an interpolation process to create monthly and annual ETo maps. Based on the ETo maps, this study suggests mainly a focus on areas with high ETo values and proper irrigation scheduling of crops to ensure water sustainability.
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Tejada AT, Ella VB, Lampayan RM, Reaño CE. Modeling Reference Crop Evapotranspiration Using Support Vector Machine (SVM) and Extreme Learning Machine (ELM) in Region IV-A, Philippines. Water 2022; 14:754. [DOI: 10.3390/w14050754] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The need for accurate estimates of reference crop evapotranspiration (ETo) is important in irrigation planning and design, irrigation scheduling, reservoir management among other applications. ETo can be accurately determined using the internationally accepted FAO Penman–Monteith (FAO-56 PM) equation. However, this requires numerous observed data, including solar radiation, air temperature, relative humidity, and wind speed, which in most cases are unavailable, particularly in developing countries such as the Philippines. This study developed models based on Support Vector Machines (SVMs) and Extreme Learning Machines (ELMs) for the estimation of daily ETo using different input combinations of meteorological data in Region IV-A, Philippines. The performance of machine learning models was compared with the different established alternative empirical models for ETo. The results show that the SVM and ELM models, with at least Tmax, Tmin, and Rs as inputs, provide the best daily ETo estimates. The accuracy of machine learning models was also found to be superior compared to the empirical models given with same input requirements. In general, SVM and ELM models showed similar modeling performance, although the former showed lower run time than the latter
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Hebbalaguppae Krishnashetty P, Balasangameshwara J, Sreeman S, Desai S, Bengaluru Kantharaju A. Cognitive computing models for estimation of reference evapotranspiration: A review. COGN SYST RES 2021. [DOI: 10.1016/j.cogsys.2021.07.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Dullah H, Malek MA, Omar H, Mangi SA, Hanafiah MM. Assessing changes of carbon stock in dipterocarp forest due to hydro-electric dam construction in Malaysia. Environ Sci Pollut Res Int 2021; 28:44264-44276. [PMID: 33847888 DOI: 10.1007/s11356-021-13833-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 04/05/2021] [Indexed: 06/12/2023]
Abstract
Deforestation and forest degradation are among the leading global concerns, as they could reduce the carbon sink and sequestration potential of the forest. The impoundment of Kenyir River, Hulu Terengganu, Malaysia, in 1985 due to the development of hydropower station has created a large area of water bodies following clearance of forested land. This study assessed the loss of forest carbon due to these activities within the period of 37 years, between 1972 and 2019. The study area consisted of Kenyir Lake catchment area, which consisted mainly of forests and the great Kenyir Lake. Remote sensing datasets have been used in this analysis. Satellite images from Landsat 1-5 MSS and Landsat 8 OLI/TRIS that were acquired between the years 1972 and 2019 were used to classify land uses in the entire landscape of Kenyir Lake catchment. Support vector machine (SVM) was adapted to generate the land-use classification map in the study area. The results show that the total study area includes 278,179 ha and forest covers dominated the area for before and after the impoundment of Kenyir Lake. The assessed loss of carbon between the years 1972 and 2019 was around 8.6 million Mg C with an annual rate of 0.36%. The main single cause attributing to the forest loss was due to clearing of forest for hydro-electric dam construction. However, the remaining forests surrounding the study area are still able to sequester carbon at a considerable rate and thus balance the carbon dynamics within the landscapes. The results highlight that carbon sequestration scenario in Kenyir Lake catchment area shows the potential of the carbon sink in the study area are acceptable with only 17% reduction of sequestration ability. The landscape of the study area is considered as highly vegetated area despite changes due to dam construction.
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Affiliation(s)
- Hayana Dullah
- Civil Engineering Department, College of Engineering, Universiti Tenaga Nasional, 43000, Kajang, Selangor, Malaysia.
| | - Marlinda Abdul Malek
- Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional, 43000, Kajang, Selangor, Malaysia
| | - Hamdan Omar
- Forest Research Institute Malaysia (FRIM), Kepong, Selangor, Malaysia
| | - Sajjad Ali Mangi
- Department of Civil Engineering, Mehran University of Engineering and Technology, SZAB Campus Khairpur Mirs, Sindh, Pakistan
| | - Marlia Mohd Hanafiah
- Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia
- Centre for Tropical Climate Change System, Institute of Climate Change, Universiti Kebangsaaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia
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Ahmed AN, Van Lam T, Hung ND, Van Thieu N, Kisi O, El-shafie A. A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem. Appl Soft Comput 2021; 105:107282. [DOI: 10.1016/j.asoc.2021.107282] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Latif SD. Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment. Environ Sci Pollut Res Int 2021; 28:30294-30302. [PMID: 33590396 DOI: 10.1007/s11356-021-12877-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 02/05/2021] [Indexed: 06/12/2023]
Abstract
One of the most critical parameters in concrete design is compressive strength. As the compressive strength of concrete is correctly measured, time and cost can be decreased. Concrete strength is relatively resilient to impacts on the environment. The production of concrete compressive strength is greatly influenced by severe weather conditions and increases in humidity rates. In this research, a model has been developed to predict concrete compressive strength utilizing a detailed dataset obtained from previously published studies based on a deep learning method, namely, long short-term memory (LSTM), and a conventional machine learning (ML) algorithm, namely, support vector machine (SVM). The input variables of the model include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age of specimens. To demonstrate the efficiency of the proposed models, three statistical indices, namely, the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), were used. Findings shows that LSTM outperformed SVM with R2=0.98, R2= 0.78, MAE=1.861, MAE=6.152, and RMSE=2.36, RMSE=7.93, respectively. The results of this study suggest that high-performance concrete (HPC) compressive strength can be reliably measured using the proposed LSTM model.
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Affiliation(s)
- Sarmad Dashti Latif
- Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000, Selangor, Malaysia.
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Abdelsalam MM, Zahran MA. A Novel Approach of Diabetic Retinopathy Early Detection Based on Multifractal Geometry Analysis for OCTA Macular Images Using Support Vector Machine. IEEE Access 2021; 9:22844-22858. [DOI: 10.1109/access.2021.3054743] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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Ehteram M, Ahmed AN, Ling L, Fai CM, Latif SD, Afan HA, Banadkooki FB, El-shafie A. Pipeline Scour Rates Prediction-Based Model Utilizing a Multilayer Perceptron-Colliding Body Algorithm. Water 2020; 12:902. [DOI: 10.3390/w12030902] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this research, the advanced multilayer perceptron (MLP) models are utilized to predict the free rate of expansion that usually occurs around the pipeline (PL) because of waves. The MLP model was structured by integrating it with three optimization algorithms: particle swarm optimization (PSO), whale algorithm (WA), and colliding bodies’ optimization (CBO). The sediment size, wave characteristics, and PL geometry were used as the inputs for the applied models. Moreover, the scour rate, vertical scour rate along the pipeline, and scour rate at both right and left sides of the pipeline were predicted as the model outputs. Results of the three suggested models, MLP-CBO, MLP-WA, and MLP-PSO, for both testing and training sessions were assessed based on different statistical indices. The results indicated that the MLP-CBO model performed better in comparison to the MLP-PSO, MLP-WA, regression, and empirical models. The MLP-CBO can be used as a powerful soft-computing model for predictions.
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Banadkooki FB, Ehteram M, Ahmed AN, Fai CM, Afan HA, Ridwam WM, Sefelnasr A, El-shafie A. Precipitation Forecasting Using Multilayer Neural Network and Support Vector Machine Optimization Based on Flow Regime Algorithm Taking into Account Uncertainties of Soft Computing Models. Sustainability 2019; 11:6681. [DOI: 10.3390/su11236681] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Drought, climate change, and demand make precipitation forecast a very important issue in water resource management. The present study aims to develop a forecasting model for monthly precipitation in the basin of the province of East Azarbaijan in Iran over a ten-year period using the multilayer perceptron neural network (MLP) and support vector regression (SVR) models. In this study, the flow regime optimization algorithm (FRA) was applied to optimize the multilayer neural network and support vector machine. The flow regime optimization algorithm not only identifies the parameters of the SVR and MLP models but also replaces the training algorithms. The decision tree model (M5T) was also used to forecast precipitation and compare it with the results of hybrid models. Principal component analysis (PCA) was used to identify effective indicators for precipitation forecast. In the first scenario, the input data include temperature data with a delay of one to twelve months, the second scenario includes precipitation data with a delay of one to twelve months, and the third scenario includes precipitation and temperature data with a delay of one to three months. The mean absolute error (MAE) and Nash–Sutcliffe error (NSE) indices were used to evaluate the performance of the models. The results showed that the proposed MLP–FRA outperformed all the other examined models. Regarding the uncertainties of the models, it was also shown that the MLP–FRA model had a lower uncertainty band width than other models, and a higher percentage of the data will fall within the range of the confidence band. As the selected scenario, Scenario 3 had a better performance. Finally, monthly precipitation maps were generated based on the MLP–FRA model and Scenario 3 using the weighted interpolation method, which showed significant precipitation in spring and winter and a low level of precipitation in summer. The results of the present study showed that MLP–FRA has high capability to predict hydrological variables and can be used in future research.
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Shukla S, Hassan MF, Khan MK, Jung LT, Awang A. An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment. PLoS One 2019; 14:e0224934. [PMID: 31721807 PMCID: PMC6853307 DOI: 10.1371/journal.pone.0224934] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 10/24/2019] [Indexed: 02/07/2023] Open
Abstract
Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT–FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.
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Affiliation(s)
- Saurabh Shukla
- Centre for Research in Data Science (CeRDaS), Computer and Information Science Department, Universiti Teknologi PETRONAS(UTP), Seri Iskandar, Perak Darul Ridzuan, Malaysia
| | - Mohd Fadzil Hassan
- Centre for Research in Data Science (CeRDaS), Computer and Information Science Department, Universiti Teknologi PETRONAS(UTP), Seri Iskandar, Perak Darul Ridzuan, Malaysia
| | | | - Low Tang Jung
- Centre for Research in Data Science (CeRDaS), Computer and Information Science Department, Universiti Teknologi PETRONAS(UTP), Seri Iskandar, Perak Darul Ridzuan, Malaysia
| | - Azlan Awang
- Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS(UTP), Seri Iskandar, Perak Darul Ridzuan, Malaysia
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