Image Retrieval Performance Tuning Using Optimization Algorithms

Abstract

The relentlessness of modern life's pace has pushed many people to look for ways to save time so they may continue living the way they choose. The management of traffic presents a considerable obstacle due to the fact that a large proportion of persons have difficulties associated with transportation. As a result, fixing traffic problems becomes an absolute need. Machine learning stands out as a useful resource in this setting, providing deeper understanding and better analytical tools for sifting through complicated statistical data. The feasibility of travel for both large and light vehicles may be quickly assessed by experts, allowing for more timely and well-informed decisions. These evaluations serve as the basis for developing separate roadways, laws and sets of rules for various classes of vehicles. This study examines six characteristics of vehicular traffic and travel situations over 38,114 occurrences. Our goal is to improve traffic management through prediction and optimization using six different optimizers from the deep learning area. Based on observed patterns of truck traffic over a certain time period, these methods help determine optimal distribution paths for sent commodities. Six different deep learning optimizer models are compared and contrasted in this study. The objective is to use these examples to determine which optimizer is best. It's not easy to pick the best optimizer for use in deep learning. To this goal, we conducted an in-depth analysis of six industry-leading optimizers to identify the best tool for predicting traffic accidents. We ran extensive tests using a dataset that had 30,492 training examples (80%) and 7,622 testing instances (20%). Different seed values, ranging from 20 to 100, were used in each iteration of the experiment. We tested and compared the following optimizers: the Adaptive Gradient (AG) Algorithm, the Adaptive Learning Rate (ALR) Method, the Root Mean Squared (RMS) Propagation, the Adaptive Moment (AM) Estimation, the Nesterov-accelerated Adaptive Moment (NAM) Estimation, and the Stochastic Gradient (SG) Descent, taking into account processing times, prediction accuracy, and error analysis. The results of the experiment showed that the NAM Estimation Optimizer was much superior. Time spent processing data was cut down, and errors were kept to a minimum (0.03%). Prediction accuracy was also exceptionally high at 99.85%. This result reaffirms NAM Estimation's promise as a leading method for improving traffic management and making accurate trip predictions.