Augmenting intelligence and neural networks – part one

The synergy between augmenting intelligence and neural networks provides a great impact on our daily lives. Augmenting intelligence and neural networks act as a powerful weapon in artificial intelligence and it helps to mimic the human’s behavior to resolve the critical issue like decision making and problem-solving techniques. The artificial intelligence has undergone many recent developments and transformations in machine learning, deep learning, artificial networks, automation, robotics, expert systems and natural language processing. This special issue addressed the issues in the augmenting networks and implemented a new technique based on the recent advances in artificial neural networks.

The first paper titled “Cosmetic applied based face recognition for biometric passport” focuses on face recognition to improve the biometric authentication for an e-passport, and it also introduces facial permanent mark detection from the makeup or cosmetic-applied faces, twins and similar faces. To develop an efficient and accurate routing protocol for MANET that consumes less energy, with an increased network lifetime, authors of the second paper introduced “A generic algorithmic protocol approaches to improve network life time and energy efficient using combined genetic algorithm with simulated annealing in MANET” Next paper titled “A wireless IoT system towards gait detection technique sing FSR sensor and wearable IoT devices” and this paper used the Gait analysis for identifying a physically challenged person even in crowded areas. The fourth paper “Implementing and analyzing FAR and FRR for face and voice recognition (Multimodal) using KNN classifier” produces better results in a noisy environment with better accuracy and this proposed model incorporates a multimodal biometric system, which plays a major role in improving the accuracy and reducing FAR and FRR performance metrics. Next paper titled “Experimental analysis of Medicare data using hierarchical grouping mechanism” proposes a Hierarchical Grouping with an experimental model to handle the complex data and analysis of the categorical data which consist of heterogeneously typed columns.