Computing and Artificial Intelligence

Predict and estimate the current stock prices by using Adaptive Neuro-Fuzzy Inference System

Ying Bai — 2025-04-03

To correctly and accurately predict and estimate the stock prices to get the maximum profit is a challenging task, and it is critically important to all financial institutions under the current fluctuation situation. In this study, we try to use a popular AI method, Adaptive Neuro Fuzzy Inference System (ANFIS), to easily and correctly predict and estimate the current and future possible stock prices. Combining with some appropriate pre-data-processing techniques, the current stock prices could be accurately and quickly estimated via those models. A normalization preprocess for training and testing data was used to improve the prediction accuracy, which is our contribution and a plus to this method. In this research, an ANFIS algorithm is designed and built to help decision-makers working in the financial institutions to easily and conveniently predict the current stock prices. The minimum training and checking RMSE values for the ANFIS model can be 0.103842 and 0.0651076. The calculation of accuracy was carried out using the RMSE calculation. The experiments conducted found that the smallest RMSE calculation result was 0.103842 with training data. Other issuers can use this method because it can predict stock prices quite well.

An efficient ray tracing algorithm and its implementation based on adaptive octree decomposition

Chunlong Dong — 2025-04-08

This paper proposes a ray tracing algorithm based on adaptive octree decomposition to solve the problem of low efficiency of calculating the intersection point of light rays and complex surfaces in the optical simulation of vehicle lights. The algorithm significantly improves the efficiency of solving the intersection problem by discretizing the complex surface into a series of polygonal facets and using a bilinear interpolation algorithm to optimize the light refraction calculation. Experiments show that the algorithm can greatly reduce the computation time on vehicle light models of different complexity, especially when dealing with complex surfaces, the algorithm improves performance by nearly 50%. The algorithm has been successfully applied to simulating optical performance in the design of vehicle lights and has achieved good application results, providing an efficient solution for the optical simulation in the design of vehicle lights.

Offensive and defensive cybersecurity solutions in healthcare

Cheryl Ann Alexander — 2025-04-09

Healthcare services usually implement defensive data strategies; however, offensive data strategies offer new opportunities because they focus on improving profitability or revenues. Offensive data also helps develop new medicine, diagnosis, and treatment due to the ease of data-sharing rather than data control or other restrictions. Balancing defensive data and offensive data is balancing data control and flexibility. It is a challenge to keep a balance between the two. Sometimes, it is necessary to favor one over the other, depending on the situation. A robust cybersecurity program is contingent on the availability of resources in healthcare organizations and the cybersecurity management staff. In this paper, a cybersecurity system with the functions of both defensive cybersecurity and offensive cybersecurity in a medical center is proposed based on big data, artificial intelligence (AI)/machine learning (ML)/deep learning (DL).