This paper presents a novel approach to hand gesture recognition based on machine learning techniques applied to data collected from wearable sensors. The proposed system aims to accurately identify intuitive hand gestures to facilitate the development of contactless user interfaces. To achieve robust and reliable recognition performance, various feature extraction methods were systematically employed, and an ensemble learning strategy was developed using Support Vector Machines (SVM), Random Forests (RF), Gradient Boosting (GB), and a Voting Classifier (VC). The gesture dataset utilized in this study consists of over 4,000 samples collected from multiple individuals, capturing diverse gesture classes and conditions. Rigorous experimental evaluation demonstrates that the proposed ensemble approach attains superior accuracy and robustness compared to traditional single-model methods. This research contributes valuable methodological insights and provides an accessible, scalable framework that enhances the practical applicability of sensor-based gesture recognition systems in diverse real-world applications.
Jia-Di Zhang, Zexiao Liang, Shiting Wu et al.· 2026 3rd World Conference on...· 0 citations
The Flexible Job Shop Scheduling Problem (FJSP) is a critical combinatorial optimization problem in modern manufacturing systems, characterized by multiple constraints, dynamic requirements, and NP-hard complexity. This paper presents an improved Ant Colony Optimization (ACO) algorithm specifically designed to address the FJSP by minimizing makespan. The proposed algorithm incorporates enhanced pheromone update mechanisms, dual-level solution encoding for operation sequencing and machine assignment, and adaptive search strategies. Experimental results on a benchmark problem with 4 jobs, 3 processes per job, and 6 machines demonstrate that the improved ACO achieves superior performance compared to traditional metaheuristics, reducing makespan by approximately 8.7% compared to genetic algorithms. The convergence analysis shows stable optimization behavior within 40 iterations, validating the effectiveness and practical applicability of the proposed approach.
Jia-Di Zhang, Zexiao Liang, Shiting Wu et al.· 2026 3rd World Conference on...· 0 citations
The Traveling Salesman Problem (TSP) is a classic NP-hard combinatorial optimization problem where traditional intelligent algorithms often suffer from slow convergence and premature convergence to local optima. This paper proposes a novel hybrid optimization framework (HGA-PSO-ACO) that integrates the global exploration capability of the Genetic Algorithm (GA), the rapid convergence characteristics of Particle Swarm Optimization (PSO), and the positive feedback mechanism of Ant Colony Optimization (ACO) through a three-stage adaptive coordination model. The key design challenge is to determine when each component should dominate the search and how information should be transferred across stages without causing premature homogenization of the population. To address this issue, the algorithm employs fitness variance (FVD) for real-time population state monitoring and implements dynamic algorithm switching strategies. Experimental validation on a 40-city TSP benchmark demonstrates that the hybrid algorithm achieves a 40% faster convergence speed, a 2.1% lower relative error, and a 58.3% reduction in run-to-run standard deviation compared to individual algorithms, indicating improved solution stability together with favorable parameter sensitivity control.
Xuhang Chen, Zheng-Rui Zhang, Ze-Hong Chen et al.· 2026 3rd World Conference on...· 0 citations