Search

Missing rainfall data has been a prevalent issue and primarily interested in hydrology and meteorology. This research aimed to examine the capability of machine learning (ML) and spatial interpolation (SI) methods to estimate missing monthly rainfall data. Six ML algorithms (i.e. multiple linear regression (MLR), M5 model tree (M5), random forest (RF), support vector regression (SVR), multilayer perceptron (MLP), genetic programming (GP)) and four SI methods (i.e. arithmetic average (AA), inverse distance weighting (IDW), correlation coefficient weighted (CCW), normal ratio (NR)) were investigated and compared in their performance. The twelve rainfall stations, located in the Thale Sap Songkhla river basin and nearby basins, were considered as a study case. Tuning hyper-parameters for each ML method was conducted to get the most suitable model for the data sets considered. Three performance criteria matrices (i.e. NSE, OI, and r) were chosen, and the sum of those three performance criteria matrices was introduced for methods' performance comparison. The experimental results pointed out that selecting neighbouring stations were essential when applying SI methods, but not for the ML method. The overall performance showed ML better imputed missing monthly rainfall than SI due to overcoming spatial constraints. GP provided the highest performance by giving NSE = 0.825, OI = 0.877, and r = 0.909 for the training stage. Those values for the testing stage were 0.796, 0.852, and 0.902, respectively. It was followed by SVR-rbf, SVR-poly, and RF. NR provided the best performance among four SI methods, followed by CCW, AA, and IDW. When applying SI methods, it should contemplate a correlation between the target and neighbouring stations greater than 0.80., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Detecting cardiac abnormalities promptly is critical for preventing unexpected and premature fatalities. In this research, four types of cardiac arrhythmias such as Ventricular Tachycardia, Premature Ventricular Contraction, Normal Sinus Rhythm and Supraventricular Tachycardia are detected from the amassed Physiobank MIT-BIH cardiac arrhythmia database. Dimensionality reduction techniques like Stochastic Neighbour Embedding (SNE), Neighbourhood Preserving Embedding, Linear Local Tangent Space Alignment and Gaussian Process Latent Variable Model are used to reduce the dimension of the ECG signals. The appropriate features of dimensionally reduced ECG signals are selected by the Elephant Search Optimization (ESO) technique. Finally, classification is performed using the relevant classifiers, such as Support Vector Machine, Adaboost, Modest Adaboost based on Ridge Regression (Modest Adaboost.RR), Extreme Gradient Boost (XGboost) and Naïve Bayes (NBC) classifiers. Multiple classifiers without and with ESO feature selection for different cardiac cases have an average classification accuracy of 62.23% and 73.61%, respectively. These multiple classifiers are defined by a set of control parameters known as hyper-parameters, which must be tuned in order to achieve optimal results. Experts have developed many approaches for detecting cardiac arrhythmias, but these multiple classifiers do not always perform well when the usual parameters for machine learning classification models are employed. In this paper, various classifiers are used in conjunction with the Stochastic Gradient Descent (SGD), Particle Swarm Optimization (PSO) and Bayesian Tree-structured Parzen Estimator (BTPE) to enhance the cardiac arrhythmia classification accuracy via hyper-parameter tuning. Multiple classifiers with SGD, PSO and BTPE hyper-parameters tuning techniques for various cardiac cases have an average classification accuracy of 80.13%, 90.67% and 94.96%, respectively. The Classifier's performance is analysed based on metrics like Classification Accuracy, F1 score, Error Rate, Matthew's correlation coefficient, Jaccard Index and Cohen's Kappa Coefficient with and without ESO features selection method and hyper-parameters tuning techniques. The analysis utilizes the MATLAB R2014a software for result evaluation. The results show that the SNE-ESO approach, along with the XGboost-BTPE, achieved the highest classification accuracy of 99.89% for detecting {Ventricular Tachycardia}-{Normal Sinus Rhythm} cases. In terms of classification benchmarks, the results exhibit that the BTPE hyper-parameter tuning technique surpasses the SGD and PSO techniques. [ABSTRACT FROM AUTHOR]

Hyper-parameter optimization is a crucial task for designing kernel-based machine learning models. Their values can be set by using various optimization algorithms. But a data-dependent objective function makes hyper-parameter's configuration changes over time in a dynamic environment. A dynamic environment is an environment where training data keep getting added continuously over time. To find the optimum values for the hyper-parameters in such an environment, one needs to run the optimization algorithm repeatedly over time. But due to the dependency of the objective function on data, the average time complexity of the optimization process increases. This paper work proposes a novel knowledge-based approach that uses particle swarm optimization (PSO) as the base optimization algorithm to optimize the hyper-parameters of support vector machine. We have introduced two major modules for designing this framework—a knowledge transfer module and a drift detection module. The knowledge transfer module in our proposed framework generates knowledge by running PSO and transfers this knowledge to the consequent time instances. On the other hand, the drift detection module is responsible for detecting changes in the objective function when new data get added to the existing data. This drift detection module helps to utilize the transferred knowledge at a particular time instance to reduce the execution time of the overall optimization process. The proposed framework has been evaluated using various standard datasets such as Adult, DNA, Nist-Digits, Segment, Splice, Mushroom and Usps for five consecutive time instances and found the average execution time as 30.72 s, which is better than the execution time 36.89 s recorded using general PSO. We have also found that our proposed framework performs the optimization of hyper-parameters much faster than the other existing approaches such as grid search, chained-PSO, dynamic model selection and quantized dynamic multi-PSO. [ABSTRACT FROM AUTHOR]

Dam reservoir operations are a critical issue for decision-makers in maximizing the use of water resources. Artificial Intelligence and Machine Learning models (AI & ML) approaches are increasingly popular for reservoir inflow predictions. In this study, the multilayer perceptron neural network (MLP), Support Vector Regression (SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and the Extreme Gradient Boosting (XG-Boost), were adopted to forecast reservoir inflows for the monthly and daily timeframes. Results showed that: (1) For the monthly timeframe, all the four models were proficient in obtaining efficient monthly reservoir inflows by scoring at least an R² of 0.5; with the XG-Boost ranked as the best model, followed by the MLPNN, SVR, and lastly ANFIS. (2) the XG-Boost still outperforms all other models for forecasting daily inflow; but however, with reduced performance. The models were still ranked in the same order, with the ANFIS showing very poor performance in scenario-2, scenario-3, and scenario-4. (3) For daily inflows, the best scenarios are scenario-5, scenario-6, scenario-7 as the models were trained based on the 1,3,5, days-lag forecasted inflow, and overall, the XG-Boost outperforms all the other models. [ABSTRACT FROM AUTHOR]

Healthcare systems in recent times have witnessed timely diagnoses with a high level of accuracy. Internet of Medical Things (IoMT)-enabled deep learning (DL) models have been used to support medical diagnostics in real time, thus resolving the issue of late-stage diagnosis of various diseases and increasing performance accuracy. The current approach for the diagnosis of leukemia uses traditional procedures, and in most cases, fails in the initial period. Hence, several patients suffering from cancer have died prematurely due to the late discovery of cancerous cells in blood tissue. Therefore, this study proposes an IoMT-enabled convolutional neural network (CNN) model to detect malignant and benign cancer cells in the patient's blood tissue. In particular, the hyper-parameter optimization through radial basis function and dynamic coordinate search (HORD) optimization algorithm was used to search for optimal values of CNN hyper-parameters. Utilizing the HORD algorithm significantly increased the effectiveness of finding the best solution for the CNN model by searching multidimensional hyper-parameters. This implies that the HORD method successfully found the values of hyper-parameters for precise leukemia features. Additionally, the HORD method increased the performance of the model by optimizing and searching for the best set of hyper-parameters for the CNN model. Leukemia datasets were used to evaluate the performance of the proposed model using standard performance indicators. The proposed model revealed significant classification accuracy compared to other state-of-the-art models. [ABSTRACT FROM AUTHOR]

In December of 2019, a novel coronavirus (COVID-19) appeared in Wuhan city, China and has been reported in many countries with millions of people infected within only four months. Chest computed Tomography (CT) has proven to be a useful supplement to reverse transcription polymerase chain reaction (RT-PCR) and has been shown to have high sensitivity to diagnose this condition. Therefore, radiological examinations are becoming crucial in early examination of COVID-19 infection. Currently, CT findings have already been suggested as an important evidence for scientific examination of COVID-19 in Hubei, China. However, classification of patient from chest CT images is not an easy task. Therefore, in this paper, a deep bidirectional long short-term memory network with mixture density network (DBM) model is proposed. To tune the hyperparameters of the DBM model, a Memetic Adaptive Differential Evolution (MADE) algorithm is used. Extensive experiments are drawn by considering the benchmark chest-Computed Tomography (chest-CT) images datasets. Comparative analysis reveals that the proposed MADE-DBM model outperforms the competitive COVID-19 classification approaches in terms of various performance metrics. Therefore, the proposed MADE-DBM model can be used in real-time COVID-19 classification systems. [ABSTRACT FROM AUTHOR]

The selection of the hyper-parameters plays a critical role in the task of prediction based on the recurrent neural networks (RNN). Traditionally, the hyper-parameters of the machine learning models are selected by simulations as well as human experiences. In recent years, multiple algorithms based on Bayesian optimization (BO) are developed to determine the optimal values of the hyper-parameters. In most of these methods, gradients are required to be calculated. In this work, the particle swarm optimization (PSO) is used under the BO framework to develop a new method for hyper-parameter optimization. The proposed algorithm (BO-PSO) is free of gradient calculation and the particles can be optimized in parallel naturally. So the computational complexity can be effectively reduced which means better hyper-parameters can be obtained under the same amount of calculation. Experiments are done on real world power load data, where the proposed method outperforms the existing state-of-the-art algorithms, BO with limit-BFGS-bound (BO-L-BFGS-B) and BO with truncated-newton (BO-TNC), in terms of the prediction accuracy. The errors of the prediction result in different models show that BO-PSO is an effective hyper-parameter optimization method. [ABSTRACT FROM AUTHOR]

• Considered dynamic nature of data for real world problems. • Proposed shift detection for shifting optimum. • Proposed knowledge based search with MFO. • Fast convergence rate to minimize execution time. • Proposed framework is highly inclined towards accuracy of SVM. In the real world, most of the collections of data are dynamic in nature, i.e. their size may grow with time. This dynamic nature of the data not only reduces the performance of the classifiers but also demands more optimized models for retaining the performance. Due to this, machine learning models developed in a static environment cannot be deployed efficiently to solve the real-world problems. Nowadays, maximum existing works consider only the static behaviour of the data for the training of machine learning models where the size of the collection of training data does not change over time. This paperwork imposes Support Vector Machine (SVM) in a dynamic environment. It has been identified that shifting of the optimum values of two hyper-parameters C (Penalty Parameter) and γ (Kernel Parameter) in the search space is one of the primary reasons for the performance degradation of SVM in dynamic environment. This paper proposes a novel framework that uses a new optimization module Knowledge-Based-Search (KBS) along with Moth –Flame Optimization (MFO) to optimize C and γ in a dynamic environment to train SVM efficiently. KBS uses knowledge gathered at various instances of time, which are the bi-products of MFO. MFO in our framework is the base optimization algorithm which works underneath KBS. The experiments have shown that KBS helps in controlling the exponential growth of the time complexity of the optimization process where only MFO is used to optimize C and γ. Integration of KBS with MFO brings down the time complexity to a large extent. To validate the proposed framework we have used a simulated dynamic environment for profit/loss classification problem for organizations. The experiments have also shown that KBS's integration with MFO outperforms integration of KBS with other modern optimization techniques such as Particle Swarm Optimization (PSO), Multi-Verse Optimization (MVO), Grey-Wolf Optimization (GWO), Cuckoo Search (CS), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Fire-Fly Algorithm (FFA) and Salp Swarm Algorithm (SSA). [ABSTRACT FROM AUTHOR]