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Path planning is a crucial element of mobile robotics applications, attracting considerable interest from academics. This paper presents a path-planning approach that utilises the Enhanced Firefly Algorithm (EFA), a new meta-heuristic technique. The Enhanced Firefly Algorithm (FA) differs from the ordinary FA by incorporating a linear reduction in the α parameter. This modification successfully resolves the constraints of the normal FA. The research involves experiments on three separate maps, using the regular FA and the suggested Enhanced FA in 20 different runs for each map. The evaluation criteria encompass the algorithms' ability to move from the initial location to the final position without experiencing any collisions. The assessment of path quality relies on elements such as the distance of the path and the algorithms' ability to converge and discover optimum solutions. The results demonstrate significant improvements made by the Enhanced FA, with a 10.270% increase in the shortest collision-free path for Map 1, a 0.371% increase for Map 2, and a 0.163% increase for Map 3, compared to the regular FA. This work highlights the effectiveness of the Enhanced Firefly Algorithm in optimising path planning for mobile robotics applications, providing potential improvements in navigation efficiency and collision avoidance. [ABSTRACT FROM AUTHOR]

With the development of deep learning, numerous models have been proposed for human activity recognition to achieve state‐of‐the‐art recognition on wearable sensor data. Despite the improved accuracy achieved by previous deep learning models, activity recognition remains a challenge. This challenge is often attributed to the complexity of some specific activity patterns. Existing deep learning models proposed to address this have often recorded high overall recognition accuracy, while low recall and precision are often recorded on some individual activities due to the complexity of their patterns. Some existing models that have focused on tackling these issues are always bulky and complex. Since most embedded systems have resource constraints in terms of their processor, memory and battery capacity, it is paramount to propose efficient lightweight activity recognition models that require limited resources consumption, and still capable of achieving state‐of‐the‐art recognition of activities, with high individual recall and precision. This research proposes a high performance, low footprint deep learning model with a squeeze and excitation block to address this challenge. The squeeze and excitation block consist of a global average‐pooling layer and two fully connected layers, which were placed to extract the flattened features in the model, with best‐fit reduction ratios in the squeeze and excitation block. The squeeze and excitation block served as channel‐wise attention, which adjusted the weight of each channel to build more robust representations, which enabled our network to become more responsive to essential features while suppressing less important ones. By using the best‐fit reduction ratio in the squeeze and excitation block, the parameters of the fully connected layer were reduced, which helped the model increase responsiveness to essential features. Experiments on three publicly available datasets (PAMAP2, WISDM, and UCI‐HAR) showed that the proposed model outperformed existing state‐of‐the‐art with fewer parameters and increased the recall and precision of some individual activities compared to the baseline, and the existing models. [ABSTRACT FROM AUTHOR]

Current suicide risk assessments for predicting suicide attempts are time consuming, of low predictive value and have inadequate reliability. This paper aims to develop a predictive model for suicide attempts among patients with depression using machine learning algorithms as well as presents a comparative study on single predictive models with ensemble predictive models for differentiating depressed patients with suicide attempts from non-suicide attempters. We applied and trained eight different machine learning algorithms using a dataset that consists of 75 patients diagnosed with a depressive disorder. A recursive feature elimination was used to reduce the features via three-fold cross validation. An ensemble predictive models outperformed the single predictive models. Voting and bagging revealed the highest accuracy of 92% compared to other machine learning algorithms. Our findings indicate that history of suicide attempt, religion, race, suicide ideation and severity of clinical depression are useful factors for prediction of suicide attempts. [ABSTRACT FROM AUTHOR]