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In this study, we attempted to find the optimal hyper-parameters of the convolutional recurrent neural network (CRNN) by investigating its performance on acoustic event detection. Important hyper-parameters such as the input segment length, learning rate, and criterion for the convergence test, were determined experimentally. Additionally, the effects of batch normalization and dropout on the performance were measured experimentally to obtain their optimal combination. Further, we studied the effects of varying the batch data on every iteration during the training. From the experimental results using the TUT sound events synthetic 2016 database, we obtained optimal performance with a learning rate of 1/10000. We found that a longer input segment length aided performance improvement, and batch normalization was far more effective than dropout. Finally, performance improvement was clearly observed by varying the starting points of the batch data for each iteration during the training.

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.

Linear profiles monitoring have been successfully implemented in many industrial applications. The design structures of control charts for profiles monitoring are mostly based on two major classifications namely Classical and Bayesian. This study investigates the novel Bayesian exponentially weighted moving average and multivariate exponentially weighted moving average control charts for the monitoring of linear profiles, when explanatory variable(s) are random. The informative priors of normal and inverse gamma; and Bramwell, Holdsworth, Pinton (BHP) and Levy distributions are considered as conjugate and non-conjugate priors respectively. The proposed Bayesian schemes are evaluated using different run length characteristics. The schemes are also validated with simulation study and real-world data sets. The outcomes demonstrate that the Bayesian methods perform effectively better than the competing methods. The specified values of hyper-parameters are selected carefully after elicitation and sensitivity analysis of hyper-parameters. It has been observed that careful consideration is required while selecting the priors and possible values of hyper-parameters. The selection of appropriate priors and corresponding hyper-parameters comes up with efficient control structures which provide tangible benefits. 2018 Elsevier Ltd The authors would like to acknowledge the Editor and Referees for their constructive comments that lead to significant improvements in the manuscript. The authors would like to acknowledge their parent Universities for providing excellent research facilities. Moreover, Dr. Muhammad Riaz would like to acknowledge Deanship of Scientific Research for supporting the study under Project Number IN171016. Scopus

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is widely accepted as a robust derivative-free continuous optimization algorithm for non-linear and non-convex optimization problems. CMA-ES is well known to be almost parameterless, meaning that only one hyper-parameter, the population size, is proposed to be tuned by the user. In this paper, we propose a principled approach called self-CMA-ES to achieve the online adaptation of CMA-ES hyper-parameters in order to improve its overall performance. Experimental results show that for larger-than-default population size, the default settings of hyper-parameters of CMA-ES are far from being optimal, and that self-CMA-ES allows for dynamically approaching optimal settings., Comment: 13th International Conference on Parallel Problem Solving from Nature (PPSN 2014) (2014)