Nowadays iris recognition has become a promising biometric for human identification and authentication. In this case, feature extraction from near-infrared (NIR) iris images under less-constraint environments is rather challenging to identify an individual accurately. This paper extends a texture descriptor to represent the local spatial patterns. The iris texture is first divided into several blocks from which the shape and appearance of intrinsic iris patterns are extracted with the help of block-based Local Binary Patterns (LBPb). The concepts of uniform, rotation, and invariant patterns are employed to reduce the length of feature space. Additionally, the simplicity of the image descriptor allows for very fast feature extraction. The recognition is performed using a supervised machine learning classifier with various distance metrics in the extracted feature space as a dissimilarity measure. The proposed approach effectively deals with lighting variations, blur focuses on misaligned images and elastic deformation of iris textures. Extensive experiments are conducted on the largest and most publicly accessible CASIA-v4 distance image database. Some statistical measures are computed as performance indicators for the validation of classification outcomes. The area under the Receiver Operating Characteristic (ROC) curves is illustrated to compare the diagnostic ability of the classifier for the LBP and its extensions. The experimental results suggest that the LBPb is more effective than other rotation invariants and uniform rotation invariants in local binary patterns for distant iris recognition. The Braycurtis distance metric provides the highest possible accuracy compared to other distance metrics and competitive methods.

Monitoring systems for electrical appliances have gained massive popularity nowadays. These frameworks can provide consumers with helpful information for energy consumption. Non-intrusive load monitoring (NILM) is the most common method for monitoring a household’s energy profile. This research presents an optimized approach for identifying load needs and improving the identification of NILM occupancy surveillance. Our study suggested implementing a dimensionality reduction algorithm, popularly known as genetic algorithm (GA) along with XGBoost, for optimized occupancy monitoring. This exclusive model can masterly anticipate the usage of appliances with a significantly reduced number of voltage-current characteristics. The proposed NILM approach pre-processed the collected data and validated the anticipation performance by comparing the outcomes with the raw dataset’s performance metrics. While reducing dimensionality from 480 to 238 features, our GA-based NILM approach accomplished the same performance score in terms of accuracy (73%), recall (81%), ROC-AUC Score (0.81), and PR-AUC Score (0.81) like the original dataset. This study demonstrates that introducing GA in NILM techniques can contribute remarkably to reduce computational complexity without compromising performance.