Displaying publications 61 - 80 of 1459 in total

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  1. Fulltext An improved multi-strategy beluga whale optimization for global optimization problems
    Chen H, Wang Z, Wu D, Jia H, Wen C, Rao H, et al.
    Math Biosci Eng, 2023 Jun 09;20(7):13267-13317.
    PMID: 37501488 DOI: 10.3934/mbe.2023592
    This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergence accuracy and speed of beluga whale optimization (BWO). In IBWO, we use a new group action strategy (GAS), which replaces the exploration phase in BWO. It was inspired by the group hunting behavior of beluga whales in nature. The GAS keeps individual belugas whales together, allowing them to hide together from the threat posed by their natural enemy, the tiger shark. It also enables the exchange of location information between individual belugas whales to enhance the balance between local and global lookups. On this basis, the dynamic pinhole imaging strategy (DPIS) and quadratic interpolation strategy (QIS) are added to improve the global optimization ability and search rate of IBWO and maintain diversity. In a comparison experiment, the performance of the optimization algorithm (IBWO) was tested by using CEC2017 and CEC2020 benchmark functions of different dimensions. Performance was analyzed by observing experimental data, convergence curves, and box graphs, and the results were tested using the Wilcoxon rank sum test. The results show that IBWO has good optimization performance and robustness. Finally, the applicability of IBWO to practical engineering problems is verified by five engineering problems.
    Matched MeSH terms: Algorithms
  2. Fulltext Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey
    Zehra S, Faseeha U, Syed HJ, Samad F, Ibrahim AO, Abulfaraj AW, et al.
    Sensors (Basel), 2023 Jun 05;23(11).
    PMID: 37300067 DOI: 10.3390/s23115340
    Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.
    Matched MeSH terms: Algorithms*
  3. Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques
    Amin HU, Ullah R, Reza MF, Malik AS
    J Neuroeng Rehabil, 2023 Jun 02;20(1):70.
    PMID: 37269019 DOI: 10.1186/s12984-023-01179-8
    BACKGROUND: Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task.

    METHODS: EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects.

    RESULTS: The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers.

    CONCLUSION: The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.

    Matched MeSH terms: Algorithms
  4. New modified algorithm: θ-turbulent flow of water-based optimization
    Naderipour A, Davoudkhani IF, Abdul-Malek Z
    Environ Sci Pollut Res Int, 2023 Jun;30(28):71726-71740.
    PMID: 34472027 DOI: 10.1007/s11356-021-16072-x
    The reactive power control of a power system is discussed under two types of variables: continuous variables (e.g., generator bus voltages) and discrete variables (e.g., transformer taps and the size of switched shunt capacitors). This paper proposes a novel and powerful algorithm, named turbulent flow of water-based optimization (TFWO) as well as a new improved version of this algorithm, called θ-TFWO, for optimal reactive power distribution (ORPD) to reduce losses. The proposed method is applied to two large-scale IEEE 57-bus systems. Furthermore, to demonstrate the competitive performance of the suggested algorithm, its performance was compared to that of several other algorithms, including biogeography-based optimization (BBO), social spider algorithm (SSA), and optics inspired optimization (OIO), in terms of solving the ORPD problem. The results confirmed the robustness and effectiveness of the proposed method as a powerful optimizer applicable to optimal reactive power distribution in power systems.
    Matched MeSH terms: Algorithms*
  5. Load-frequency control in an islanded microgrid PV/WT/FC/ESS using an optimal self-tuning fractional-order fuzzy controller
    Naderipour A, Abdul-Malek Z, Davoodkhani IF, Kamyab H, Ali RR
    Environ Sci Pollut Res Int, 2023 Jun;30(28):71677-71688.
    PMID: 34241794 DOI: 10.1007/s11356-021-14799-1
    Due to the increased complexity and nonlinear nature of microgrid systems such as photovoltaic, wind-turbine fuel cell, and energy storage systems (PV/WT/FC/ESSs), load-frequency control has been a challenge. This paper employs a self-tuning controller based on the fuzzy logic to overcome parameter uncertainties of classic controllers, such as operation conditions, the change in the operating point of the microgrid, and the uncertainty of microgrid modeling. Furthermore, a combined fuzzy logic and fractional-order controller is used for load-frequency control of the off-grid microgrid with the influence of renewable resources because the latter controller benefits robust performance and enjoys a flexible structure. To reach a better operation for the proposed controller, a novel meta-heuristic whale algorithm has been used to optimally determine the input and output scale coefficients of the fuzzy controller and fractional orders of the fractional-order controller. The suggested approach is applied to a microgrid with a diesel generator, wind turbine, photovoltaic systems, and energy storage devices. The comparison made between the results of the proposed controller and those of the classic PID controller proves the superiority of the optimized fractional-order self-tuning fuzzy controller in terms of operation characteristics, response speed, and the reduction in frequency deviations against load variations.
    Matched MeSH terms: Algorithms*
  6. Fulltext Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway
    Ismail AM, Remli MA, Choon YW, Nasarudin NA, Ismail NN, Ismail MA, et al.
    J Integr Bioinform, 2023 Jun 01;20(2).
    PMID: 37341516 DOI: 10.1515/jib-2022-0051
    Analyzing metabolic pathways in systems biology requires accurate kinetic parameters that represent the simulated in vivo processes. Simulation of the fermentation pathway in the Saccharomyces cerevisiae kinetic model help saves much time in the optimization process. Fitting the simulated model into the experimental data is categorized under the parameter estimation problem. Parameter estimation is conducted to obtain the optimal values for parameters related to the fermentation process. This step is essential because insufficient identification of model parameters can cause erroneous conclusions. The kinetic parameters cannot be measured directly. Therefore, they must be estimated from the experimental data either in vitro or in vivo. Parameter estimation is a challenging task in the biological process due to the complexity and nonlinearity of the model. Therefore, we propose the Artificial Bee Colony algorithm (ABC) to estimate the parameters in the fermentation pathway of S. cerevisiae to obtain more accurate values. A metabolite with a total of six parameters is involved in this article. The experimental results show that ABC outperforms other estimation algorithms and gives more accurate kinetic parameter values for the simulated model. Most of the estimated kinetic parameter values obtained from the proposed algorithm are the closest to the experimental data.
    Matched MeSH terms: Algorithms*
  7. Automatic slice thickness measurement on three types of Catphan CT phantoms
    Anam C, Naufal A, Sutanto H, Arifin Z, Hidayanto E, Tan LK, et al.
    Biomed Phys Eng Express, 2023 May 30;9(4).
    PMID: 37216929 DOI: 10.1088/2057-1976/acd785
    Objective. To develop an algorithm to measure slice thickness running on three types of Catphan phantoms with the ability to adapt to any misalignment and rotation of the phantoms.Method. Images of Catphan 500, 504, and 604 phantoms were examined. In addition, images with various slice thicknesses ranging from 1.5 to 10.0 mm, distance to the iso-center and phantom rotations were also examined. The automatic slice thickness algorithm was carried out by processing only objects within a circle having a diameter of half the diameter of the phantom. A segmentation was performed within an inner circle with dynamic thresholds to produce binary images with wire and bead objects within it. Region properties were used to distinguish wire ramps and bead objects. At each identified wire ramp, the angle was detected using the Hough transform. Profile lines were then placed on each ramp based on the centroid coordinates and detected angles, and the full-width at half maximum (FWHM) was determined for the average profile. The slice thickness was obtained by multiplying the FWHM by the tangent of the ramp angle (23°).Results. Automatic measurements work well and have only a small difference (<0.5 mm) from manual measurements. For slice thickness variation, automatic measurement successfully performs segmentation and correctly locates the profile line on all wire ramps. The results show measured slice thicknesses that are close (<3 mm) to the nominal thickness at thin slices, but slightly deviated for thicker slices. There is a strong correlation (R2= 0.873) between automatic and manual measurements. Testing the algorithm at various distances from the iso-center and phantom rotation angle also produced accurate results.Conclusion. An automated algorithm for measuring slice thickness on three types of Catphan CT phantom images has been developed. The algorithm works well on various thicknesses, distances from the iso-center, and phantom rotations.
    Matched MeSH terms: Algorithms*
  8. Fulltext Optimized Visual Internet of Things for Video Streaming Enhancement in 5G Sensor Network Devices
    Budati AK, Islam S, Hasan MK, Safie N, Bahar N, Ghazal TM
    Sensors (Basel), 2023 May 25;23(11).
    PMID: 37299798 DOI: 10.3390/s23115072
    The global expansion of the Visual Internet of Things (VIoT)'s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput.
    Matched MeSH terms: Algorithms*
  9. HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
    Islam MS, Hasan KF, Sultana S, Uddin S, Lio' P, Quinn JMW, et al.
    Neural Netw, 2023 May;162:271-287.
    PMID: 36921434 DOI: 10.1016/j.neunet.2023.03.004
    Deep learning-based models have achieved significant success in detecting cardiac arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the performance of such models, we have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditional dilated convolutional neural network (CNN) models disregard the correlation between contexts and gradient dispersion. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. As a result of incorporating both local and global feature information and an attention mechanism, the model's performance for prediction is improved. By combining the fusion features with a dilated CNN and a hierarchical attention mechanism, the trained HARDC model showed significantly improved classification results and interpretability of feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score normalization, filtering, denoising, and segmentation are used to prepare the raw data for analysis. CGAN (Conditional Generative Adversarial Network) is then used to generate synthetic signals from the processed data. The experimental results demonstrate that the proposed HARDC model significantly outperforms other existing models, achieving an accuracy of 99.60%, F1 score of 98.21%, a precision of 97.66%, and recall of 99.60% using MIT-BIH generated ECG. In addition, this approach significantly reduces run time when using dilated CNN compared to normal convolution. Overall, this hybrid model demonstrates an innovative and cost-effective strategy for ECG signal compression and high-performance ECG recognition. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise.
    Matched MeSH terms: Algorithms*
  10. Fulltext Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters
    Tao H, Jawad AH, Shather AH, Al-Khafaji Z, Rashid TA, Ali M, et al.
    Environ Int, 2023 May;175:107931.
    PMID: 37119651 DOI: 10.1016/j.envint.2023.107931
    This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.
    Matched MeSH terms: Algorithms
  11. A hybrid ensemble approach to accelerate the classification accuracy for predicting malnutrition among under-five children in sub-Saharan African countries
    Khan MNA, Yunus RM
    Nutrition, 2023 Apr;108:111947.
    PMID: 36641887 DOI: 10.1016/j.nut.2022.111947
    BACKGROUND: The proper intake of nutrients is essential to the growth and maturation of youngsters. In sub-Saharan Africa, 1 in 7 children dies before age 5 y, and more than a third of these deaths are attributed to malnutrition. The main purpose of this study was to develop a majority voting-based hybrid ensemble (MVBHE) learning model to accelerate the prediction accuracy of malnutrition data of under-five children in sub-Saharan Africa.

    METHODS: This study used available under-five nutritional secondary data from the Demographic and Health Surveys performed in sub-Saharan African countries. The research used bagging, boosting, and voting algorithms, such as random forest, decision tree, eXtreme Gradient Boosting, and k-nearest neighbors machine learning methods, to generate the MVBHE model.

    RESULTS: We evaluated the model performances in contrast to each other using different measures, including accuracy, precision, recall, and the F1 score. The results of the experiment showed that the MVBHE model (96%) was better at predicting malnutrition than the random forest (81%), decision tree (60%), eXtreme Gradient Boosting (79%), and k-nearest neighbors (74%).

    CONCLUSIONS: The random forest algorithm demonstrated the highest prediction accuracy (81%) compared with the decision tree, eXtreme Gradient Boosting, and k-nearest neighbors algorithms. The accuracy was then enhanced to 96% using the MVBHE model. The MVBHE model is recommended by the present study as the best way to predict malnutrition in under-five children.

    Matched MeSH terms: Algorithms
  12. QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm
    Al-Fakih AM, Qasim MK, Algamal ZY, Alharthi AM, Zainal-Abidin MH
    SAR QSAR Environ Res, 2023 Apr;34(4):285-298.
    PMID: 37157994 DOI: 10.1080/1062936X.2023.2208374
    One of the recently developed metaheuristic algorithms, the coyote optimization algorithm (COA), has shown to perform better in a number of difficult optimization tasks. The binary form, BCOA, is used in this study as a solution to the descriptor selection issue in classifying diverse antifungal series. Z-shape transfer functions (ZTF) are evaluated to verify their efficiency in improving BCOA performance in QSAR classification based on classification accuracy (CA), the geometric mean of sensitivity and specificity (G-mean), and the area under the curve (AUC). The Kruskal-Wallis test is also applied to show the statistical differences between the functions. The efficacy of the best suggested transfer function, ZTF4, is further assessed by comparing it to the most recent binary algorithms. The results prove that ZTF, especially ZTF4, significantly improves the performance of the original BCOA. The ZTF4 function yields the best CA and G-mean of 99.03% and 0.992%, respectively. It shows the fastest convergence behaviour compared to other binary algorithms. It takes the fewest iterations to reach high classification performance and selects the fewest descriptors. In conclusion, the obtained results indicate the ability of the ZTF4-based BCOA to find the smallest subset of descriptors while maintaining the best classification accuracy performance.
    Matched MeSH terms: Algorithms
  13. Fulltext Analysis of Facial Occlusion Challenge in Thermal Images for Human Affective State Recognition
    Al Qudah M, Mohamed A, Lutfi S
    Sensors (Basel), 2023 Mar 27;23(7).
    PMID: 37050571 DOI: 10.3390/s23073513
    Several studies have been conducted using both visual and thermal facial images to identify human affective states. Despite the advantages of thermal facial images in recognizing spontaneous human affects, few studies have focused on facial occlusion challenges in thermal images, particularly eyeglasses and facial hair occlusion. As a result, three classification models are proposed in this paper to address the problem of thermal occlusion in facial images, with six basic spontaneous emotions being classified. The first proposed model in this paper is based on six main facial regions, including the forehead, tip of the nose, cheeks, mouth, and chin. The second model deconstructs the six main facial regions into multiple subregions to investigate the efficacy of subregions in recognizing the human affective state. The third proposed model in this paper uses selected facial subregions, free of eyeglasses and facial hair (beard, mustaches). Nine statistical features on apex and onset thermal images are implemented. Furthermore, four feature selection techniques with two classification algorithms are proposed for a further investigation. According to the comparative analysis presented in this paper, the results obtained from the three proposed modalities were promising and comparable to those of other studies.
    Matched MeSH terms: Algorithms
  14. Fulltext Multi-View Human Action Recognition Using Skeleton Based-FineKNN with Extraneous Frame Scrapping Technique
    Malik NUR, Sheikh UU, Abu-Bakar SAR, Channa A
    Sensors (Basel), 2023 Mar 02;23(5).
    PMID: 36904953 DOI: 10.3390/s23052745
    Human action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long Short-Term Memory) suffer from highly complex models. These algorithms involve a huge number of weights adjustments during the training phase, and as a consequence, require high-end configuration machines for real-time HAR applications. Therefore, this paper presents an extraneous frame scrapping technique that employs 2D skeleton features with a Fine-KNN classifier-based HAR system to overcome the dimensionality problems.To illustrate the efficacy of our proposed method, two contemporary datasets i.e., Multi-Camera Action Dataset (MCAD) and INRIA Xmas Motion Acquisition Sequences (IXMAS) dataset was used in experiment. We used the OpenPose technique to extract the 2D information, The proposed method was compared with CNN-LSTM, and other State of the art methods. Results obtained confirm the potential of our technique. The proposed OpenPose-FineKNN with Extraneous Frame Scrapping Technique achieved an accuracy of 89.75% on MCAD dataset and 90.97% on IXMAS dataset better than existing technique.
    Matched MeSH terms: Algorithms
  15. A performance comparison study on PM2.5 prediction at industrial areas using different training algorithms of feedforward-backpropagation neural network (FBNN)
    Chinatamby P, Jewaratnam J
    Chemosphere, 2023 Mar;317:137788.
    PMID: 36642141 DOI: 10.1016/j.chemosphere.2023.137788
    Presence of particulate matters with aerodynamic diameter of less than 2.5 μm (PM2.5) in the atmosphere is fast increasing in Malaysia due to industrialization and urbanization. Prolonged exposure of PM2.5 can cause serious health effects to human. This research is aimed to identify the most reliable model to predict the PM2.5 pollution using multi-layered feedforward-backpropagation neural network (FBNN). Air quality and meteorological data were collected from Department of Environment (DOE) Malaysia. Six different training algorithms consisting of thirteen various training functions were trained and compared. FBNN model with the highest coefficient correlation (R2) and lowest root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were selected as the best performing model. Levenberg Marquardt (trainlm) is the best performing algorithms compared to other algorithms with R2 value of 0.9834 and the lowest error values for RMSE (2.3981), MAE (1.7843) and MAPE (0.1063).
    Matched MeSH terms: Algorithms
  16. Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models
    Elbeltagi A, Pande CB, Kumar M, Tolche AD, Singh SK, Kumar A, et al.
    Environ Sci Pollut Res Int, 2023 Mar;30(15):43183-43202.
    PMID: 36648725 DOI: 10.1007/s11356-023-25221-3
    Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12 months. Models were developed using monthly rainfall data for the period of 2000-2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjusted R2, Mallows' (Cp), Akaike's (AIC), Schwarz's (SBC), and Amemiya's PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient ([Formula: see text]), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12 months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of [Formula: see text], MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models.
    Matched MeSH terms: Algorithms
  17. Fulltext Diabetes disease prediction system using HNB classifier based on discretization method
    Al-Hameli BA, Alsewari AA, Basurra SS, Bhogal J, Ali MAH
    J Integr Bioinform, 2023 Mar 01;20(1).
    PMID: 36810102 DOI: 10.1515/jib-2021-0037
    Diagnosing diabetes early is critical as it helps patients live with the disease in a healthy way - through healthy eating, taking appropriate medical doses, and making patients more vigilant in their movements/activities to avoid wounds that are difficult to heal for diabetic patients. Data mining techniques are typically used to detect diabetes with high confidence to avoid misdiagnoses with other chronic diseases whose symptoms are similar to diabetes. Hidden Naïve Bayes is one of the algorithms for classification, which works under a data-mining model based on the assumption of conditional independence of the traditional Naïve Bayes. The results from this research study, which was conducted on the Pima Indian Diabetes (PID) dataset collection, show that the prediction accuracy of the HNB classifier achieved 82%. As a result, the discretization method increases the performance and accuracy of the HNB classifier.
    Matched MeSH terms: Algorithms*
  18. Microalgae identification: Future of image processing and digital algorithm
    Chong JWR, Khoo KS, Chew KW, Vo DN, Balakrishnan D, Banat F, et al.
    Bioresour Technol, 2023 Feb;369:128418.
    PMID: 36470491 DOI: 10.1016/j.biortech.2022.128418
    The identification of microalgae species is an important tool in scientific research and commercial application to prevent harmful algae blooms (HABs) and recognizing potential microalgae strains for the bioaccumulation of valuable bioactive ingredients. The aim of this study is to incorporate rapid, high-accuracy, reliable, low-cost, simple, and state-of-the-art identification methods. Thus, increasing the possibility for the development of potential recognition applications, that could identify toxic-producing and valuable microalgae strains. Recently, deep learning (DL) has brought the study of microalgae species identification to a much higher depth of efficiency and accuracy. In doing so, this review paper emphasizes the significance of microalgae identification, and various forms of machine learning algorithms for image classification, followed by image pre-processing techniques, feature extraction, and selection for further classification accuracy. Future prospects over the challenges and improvements of potential DL classification model development, application in microalgae recognition, and image capturing technologies are discussed accordingly.
    Matched MeSH terms: Algorithms
  19. Nitrate Classification Based on Optical Absorbance Data Using Machine Learning Algorithms for a Hydroponics System
    Sulaiman R, Azeman NH, Abu Bakar MH, Ahmad Nazri NA, Masran AS, Ashrif A Bakar A
    Appl Spectrosc, 2023 Feb;77(2):210-219.
    PMID: 36348500 DOI: 10.1177/00037028221140924
    Nutrient solution plays an essential role in providing macronutrients to hydroponic plants. Determining nitrogen in the form of nitrate is crucial, as either a deficient or excessive supply of nitrate ions may reduce the plant yield or lead to environmental pollution. This work aims to evaluate the performance of feature reduction techniques and conventional machine learning (ML) algorithms in determining nitrate concentration levels. Two features reduction techniques, linear discriminant analysis (LDA) and principal component analysis (PCA), and seven ML algorithms, for example, k-nearest neighbors (KNN), support vector machine, decision trees, naïve bayes, random forest (RF), gradient boosting, and extreme gradient boosting, were evaluated using a high-dimensional spectroscopic dataset containing measured nitrate-nitrite mixed solution absorbance data. Despite the limited and uneven number of samples per class, this study demonstrated that PCA outperformed LDA on the high-dimensional spectroscopic dataset. The classification accuracy of ML algorithms combined with PCA ranged from 92.7% to 99.8%, whereas the classification accuracy of ML algorithms combined with LDA ranged from 80.7% to 87.6%. The PCA with the RF algorithm exhibited the best performance with 99.8% accuracy.
    Matched MeSH terms: Algorithms*
  20. A novel nonlinear time-varying sigmoid transfer function in binary whale optimization algorithm for descriptors selection in drug classification
    Mohd Yusof N, Muda AK, Pratama SF, Abraham A
    Mol Divers, 2023 Feb;27(1):71-80.
    PMID: 35254585 DOI: 10.1007/s11030-022-10410-y
    In computational chemistry, the high-dimensional molecular descriptors contribute to the curse of dimensionality issue. Binary whale optimization algorithm (BWOA) is a recently proposed metaheuristic optimization algorithm that has been efficiently applied in feature selection. The main contribution of this paper is a new version of the nonlinear time-varying Sigmoid transfer function to improve the exploitation and exploration activities in the standard whale optimization algorithm (WOA). A new BWOA algorithm, namely BWOA-3, is introduced to solve the descriptors selection problem, which becomes the second contribution. To validate BWOA-3 performance, a high-dimensional drug dataset is employed. The proficiency of the proposed BWOA-3 and the comparative optimization algorithms are measured based on convergence speed, the length of the selected feature subset, and classification performance (accuracy, specificity, sensitivity, and f-measure). In addition, statistical significance tests are also conducted using the Friedman test and Wilcoxon signed-rank test. The comparative optimization algorithms include two BWOA variants, binary bat algorithm (BBA), binary gray wolf algorithm (BGWOA), and binary manta-ray foraging algorithm (BMRFO). As the final contribution, from all experiments, this study has successfully revealed the superiority of BWOA-3 in solving the descriptors selection problem and improving the Amphetamine-type Stimulants (ATS) drug classification performance.
    Matched MeSH terms: Algorithms*
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