Recent Advances in Computer Science and Communications - Volume 18, Issue 1, 2025

Scientific, technical, and educational research domains all heavily rely on handwritten mathematical expressions. The extensive use of online handwritten mathematical expression recognition is a consequence of the availability of strong computational touch-screen appliances, such as the recent development of deep neural networks as superior sequence recognition models.

Further investigation and enhancement of these technologies are vital to tackle the contemporary obstacles presented by the widespread adoption of remote learning and work arrangements as a result of the global health crisis.

Handwritten document processing has gained more attention in the last ten years due to notable developments in deep neural network-based computer vision models and sequence recognition, as well as the widespread proliferation of touch and pen-enabled smartphones and tablets. It comes naturally to people to write by hand in daily interactions.

In this article, authors implemented Hand written expressions using RNN-based encoder for the CROHME dataset. Later, the proposed model was validated using CNN-based encoder and end-to-end encoder decoder techniques. The proposed model is also validated on other datasets.

The RNN-based encoder model yields 82.78%, while the CNN-based encoder model and end-to-end encoder-decoder technique results in 81.38% and 80.73%, respectively.

1.6% accuracy improvement was attained over CNN-based encoder while 2.4% accuracy improvement over end-to-end encoder-decoder. CROHME dataset 2019 version results in better accuracy than other datasets.

With the development of intelligent technology, Unmanned aerial vehicles (UAVs) are widely used in military and civilian fields. Path planning is the most important part of UAV navigation system. Its purpose is to find a smooth and feasible path from the start to the end.

In order to obtain a better flight path, this paper presents an improved Aquila optimizer combing the opposition-based learning and the local escaping operator, named LEOAO, to deal with the UAV path planning problem in three-dimensional environments.

UAV path planning is modelled as a constrained optimization problem in which the cost function consists of one objective: path length and four constraints: safe distance, flight height, turning angle and climbing/diving angle. In this paper, the LEOAO is introduced to find the optimal path by minimizing the cost function, and B-Spline is invited to represent a smooth path. The local escaping operator is used to enhance the search ability of the algorithm.

To test the performance of LEOAO, two scenarios are applied based on basic terrain function. Experiments show that the proposed LEOAO outperforms other algorithms such as the grey wolf optimizer, whale optimization algorithm, including the original Aquila optimizer.

The proposed algorithm combines the opposition-based learning and local escaping operator. The opposition-based learning algorithm has the ability to accelerate convergence. And the introduction of LEO effectively balances the exploration and exploitation abilities of the algorithm and improves the quality of the population. Finally, the improved Aquila optimizer obtains a better path.

Cloud computing is usually introduced to execute computing intensive tasks for data processing and data mining. As a supplement to cloud computing, edge computing is provided as a new paradigm to effectively reduce processing latency, energy consumption cost and bandwidth consumption for time-sensitive tasks or resource-sensitive tasks. To better meet such requirements during task assignment in edge computing systems, an intelligent task migration assignment mechanism based on blockchain is proposed, which jointly considers the factors of resource allocation, resource control and credit degree.

In this paper, an optimization problem is firstly constructed to minimize the total cost of completing all tasks under constraints of delay, energy consumption, communication, and credit degree. Here, the terminal node mines computing resources from edge nodes to complete task migration. An incentive method based on blockchain is provided to mobilize the activity of terminal nodes and edge nodes, and to ensure the security of the transaction during migration. The designed allocation rules ensure the fairness of rewards for successfully mining resource. To solve the optimization problem, an intelligent migration algorithm that utilizes a dual “actor-reviewer” neural network on inverse gradient update is proposed which makes the training process more stable and easier to converge.

Compared to the existing two benchmark mechanisms, the extensive simulation results indicate that the proposed mechanism based on neural network can converge at a faster speed and achieve the minimal total cost.

To satisfy the requirements of delay and energy consumption for computing intensive tasks in edge computing scenarios, an intelligent, blockchain based task migration assignment mechanism with joint resource allocation and control is proposed. To realize this mechanism effectively, a dual “actor-reviewer” neural network algorithm is designed and executed.