This study introduces the Enhanced NIRMAL (Novel Integrated Robust Multi-Adaptation Learning with Damped Nesterov Acceleration) optimizer, an improved version of the original NIRMAL optimizer. By incorporating an $(α, r)$-damped Nesterov acceleration mechanism, Enhanced NIRMAL improves convergence stability while retaining chess-inspired strategies of gradient descent, momentum, stochastic perturbations, adaptive learning rates, and non-linear transformations. We evaluate Enhanced NIRMAL against Adam, SGD with Momentum, Nesterov, and the original NIRMAL on four benchmark image classification datasets: MNIST, FashionMNIST, CIFAR-10, and CIFAR-100, using tailored convolutional neural network (CNN) architectures. Enhanced NIRMAL achieves a test accuracy of 46.06\% and the lowest test loss (1.960435) on CIFAR-100, surpassing the original NIRMAL (44.34\% accuracy) and closely rivaling SGD with Momentum (46.43\% accuracy). These results underscore Enhanced NIRMAL's superior generalization and stability, particularly on complex datasets.

This study proposes NIRMAL (Novel Integrated Robust Multi-Adaptation Learning), a novel optimization algorithm that combines multiple strategies inspired by the movements of the chess piece. These strategies include gradient descent, momentum, stochastic perturbations, adaptive learning rates, and non-linear transformations. We carefully evaluated NIRMAL against two widely used and successful optimizers, Adam and SGD with Momentum, on four benchmark image classification datasets: MNIST, FashionMNIST, CIFAR-10, and CIFAR-100. The custom convolutional neural network (CNN) architecture is applied on each dataset. The experimental results show that NIRMAL achieves competitive performance, particularly on the more challenging CIFAR-100 dataset, where it achieved a test accuracy of 45.32\%and a weighted F1-score of 0.4328. This performance surpasses Adam (41.79\% accuracy, 0.3964 F1-score) and closely matches SGD with Momentum (46.97\% accuracy, 0.4531 F1-score). Also, NIRMAL exhibits robust convergence and strong generalization capabilities, especially on complex datasets, as evidenced by stable training results in loss and accuracy curves. These findings underscore NIRMAL's significant ability as a versatile and effective optimizer for various deep learning tasks.

During the coronavirus pandemic, digital transformation and automation efforts have accelerated as organizations look to streamline workflows and reduce operational costs. On Thursday, IBM announced a definitive agreement to acquire Italy-based process mining software company, myInvenio. "Digital transformation is accelerating across industries as companies face increasing challenges with managing critical IT systems and complex business applications that span the hybrid cloud landscape," said Dinesh Nirmal, general manager, IBM Automation. The move highlights IBM's investments to provide an AI-enabled automation suite "one-stop shop" for organizations, the company said, with capabilities such as robotic process automation, document processing and process mining among others. IBM said the acquisition will provide companies with "data-driven software" in areas such as sales, production and accounting and could help organizations identify processes for potential AI-enabled automation.

The big data and analytics market continues to morph as Artificial Intelligence (AI) fields, such as machine learning and deep learning, provide new ways to generate business insights. Here are four themes from Strata Data that universally apply across various industries and company sizes. Both humans and machines are needed to deliver the best result. Pinterest's SVP of Engineering, Li Wan, shared the challenges and opportunities of creating a visual discovery engine. Wan described the challenges with naming an image while also understanding what's in it and the style behind the image.