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Numerous research based on offline Tamil recognition deals only with few Tamil characters since it becomes extremely complicated in distinguishing small variations in large handwritten document. The writer's complexity affects the overall formation of the characters. Such types of complexities are due to discontinuation of structures, unnecessary over loops, variation in shapes as well as irregular curves. This complex issue results in enhanced error value rate. Therefore, to conquer such issues, this paper proposes a novel approach to enhance the offline Tamil handwritten character recognition by utilizing four principal steps: pre-processing, segmentation, feature extraction and classification. For optimal segmentation of Tamil characters, this paper utilizes the Tsallis entropy approach-based atom search (TEAS) optimization algorithm. Then a Newton algorithm based deep convolution extreme learning (DELM) approach is utilized for the extraction and classification of input images. Finally, experiments are carried out for numerous Tamil handwritten recognition-based approaches. The proposed Tamil character recognition utilizes the datasets of isolated Tamil handwritten characters established by HP lab India to evaluate the efficiency of the system. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the parameter-wise optimization training process is implemented to achieve an optimal configuration of focused time lagged recurrent neural network (FTLRNN) models by embedding the gamma, laguarre, and multi-channel tapped delay line memory structure. The aim is to examine the prediction ability of the proposed models in order to predict one-day-ahead electric power load simultaneously as usual to oppose 1–24h forecast in sequel with a special emphasis on seasonal changes over a year. An improved delta-bar-delta algorithm is used to accelerate the training of neural networks and to improve the stability of the convergence. Experimental results indicate that the FTLRNN with time delay neural network (TDNN) clearly outperformed the gamma and laguarre based short-term memory structure in various performance metrics such as mean square error (MSE), normalized MSE, correlation coefficient (r) and mean absolute percentage error (MAPE) during evaluation process. Empirical results show that the proposed dynamic NN model consistently performs well on daily, weekly, and monthly average basis in terms of prediction accuracy. It is noticed from the literature review that an optimally configured FTLRNN with multi-channel tapped delay line memory structure is not currently available to solve short-term electrical power load prediction. The proposed method gives acceptable errors in all seasons, months and on daily basis. The average prediction error on three weeks is obtained as low as 1.67%. [ABSTRACT FROM AUTHOR]

In this article, a new optimization technique known as Kho-Kho optimization (KKO) algorithm is presented. This proposed technique is a population based meta-heuristic method which is inspired from the strategies used by players in a well known tag-team game played in India, i.e. Kho-Kho. The performance and superiority of the proposed method with respect to other existing methods is evaluated using twenty nine benchmark functions and real-time optimization problems related to power system i.e. combined emission economic dispatch and combined heat and power economic dispatch problem. • This paper presents a new Kho-Kho optimization algorithm (KKO). • KKO is inspired by a well-known tag-team game played in India known as Kho-Kho. • KKO mimics the strategies used by a chasing team to catch the runners of a running team. • Twenty-nine benchmark functions are used to test the performance of the proposed algorithm. • Further, KKO is used to solve real-time optimization problems of power system. [ABSTRACT FROM AUTHOR]