Shail Kumar Dinkar

TL;DR: A novel algorithm called opposition based Laplacian antlion optimizer (OB-L-ALO) to accelerate the performance of the original ALO is presented, showing diversity in solving the real world optimization problems.

TL;DR: A new efficient version of recently proposed Antlion Optimizer (ALO) namely Opposition based Levy Flight Antlions optimizer (OB-LF-ALO), conceptualized on the theory of Opposition based learning integrated with Levy Flight for random walk in place of uniform distributed randomWalk in original ALO is proposed.

TL;DR: A modified version of freshly developed Equilibrium Optimizer for segmentation of gray-scale images using multi-level thresholding and utilizing Otsu’s interclass variance function to obtain optimum threshold values for image segmentation is proposed.

TL;DR: This paper proposes a novel variant of antlion optimizer (ALO), namely accelerated opposition-based antlions Optimizer (OB-ac-ALO) which is conceptualized withOpposition-based learning (OBL) model and integrated with acceleration coefficient (ac).

TL;DR: This work presents an efficient hybridized approach for the classification of electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat abnormalities by removing the inherent noise of ECG signals in preprocessing phase using discrete wavelet transformation (DWT).