CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection

In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9-100%), sensitivity (77.6-100%), precision (91.8-96.08%), specificity (91.6-100%) and F-Score (90-100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC'2022 benchmarks functions.

Applying Deep Learning-Based Human Motion Recognition System in Sports Competition

The exploration here intends to compensate for the traditional human motion recognition (HMR) systems' poor performance on large-scale datasets and micromotions. To this end, improvement is designed for the HMR in sports competition based on the deep learning (DL) algorithm. First, the background and research status of HMR are introduced. Then, a new HMR algorithm is proposed based on kernel extreme learning machine (KELM) multidimensional feature fusion (MFF). Afterward, a simulation experiment is designed to evaluate the performance of the proposed KELM-MFF-based HMR algorithm. The results showed that the recognition rate of the proposed KELM-MFF-based HMR is higher than other algorithms. The recognition rate at 10 video frame sampling points is ranked from high to low: the proposed KELM-MFF-based HMR, support vector machine (SVM)-MFF-based HMR, convolutional neural network (CNN) + optical flow (CNN-T)-based HMR, improved dense trajectory (IDT)-based HMR, converse3D (C3D)-based HMR, and CNN-based HMR. Meanwhile, the feature recognition rate of the proposed KELM-MFF-based HMR for the color dimension is higher than the time dimension, by up to 24%. Besides, the proposed KELM-MFF-based HMR algorithm's recognition rate is 92.4% under early feature fusion and 92.1% under late feature fusion, higher than 91.8 and 90.5% of the SVM-MFF-based HMR. Finally, the proposed KELM-MFF-based HMR algorithm takes 30 and 15 s for training and testing. Therefore, the algorithm designed here can be used to deal with large-scale datasets and capture and recognize micromotions. The research content provides a reference for applying extreme learning machine algorithms in sports competitions.