Open Access

Article

Article ID: 2220

Offensive and defensive cybersecurity solutions in healthcare

by Cheryl Ann Alexander, Lidong Wang

Computing and Artificial Intelligence, Vol.3, No.2, 2025;

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).

Open Access

Article

Article ID: 2514

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

by Chunlong Dong, Shengjun Xue, Limin Zhao

Computing and Artificial Intelligence, Vol.3, No.2, 2025;

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.

Open Access

Article

Article ID: 2485

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

by Ying Bai, Dali Wang

Computing and Artificial Intelligence, Vol.3, No.2, 2025;

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.

Open Access

Article

Article ID: 2331

Design and implementation of an intelligent waste classification device

by Yung-Hsiang Chen, Chan-Hong Chao

Computing and Artificial Intelligence, Vol.3, No.1, 2025;

This study presents a guideline for an intelligent waste classification device developed using a Raspberry Pi, a camera, and Google’s Teachable Machine (TM) for image recognition. The device is designed to identify waste and classify it into recyclable and non-recyclable categories to improve recycling efficiency. The system is primarily controlled by the Raspberry Pi, with the camera capturing images, which are then processed by TM for image model training to facilitate waste classification. This paper describes the hardware and software components as well as their applications and verifies the effectiveness of the device in practical use. The device is cost-effective, offers good scalability, and is practical for waste classification in households, offices, and public spaces. This study provides valuable insights for the design and future applications of intelligent waste classification systems.

Open Access

Article

Article ID: 1819

Multilevel rules mining association for processing big data using genetic algorithm

by Gebeyehu Belay Gebremeskel, Teshale Wubie Yilma

Computing and Artificial Intelligence, Vol.3, No.1, 2025;

Data mining is a machine learning method and a subset of artificial intelligence that focuses on developing algorithms to enable a computer to learn from data and past experiences within its context. Multilevel association rules mining is a crucial area for discovering interesting relationships between data elements at various levels of abstraction. Many existing algorithms addressing this issue rely on exhaustive search methods such as Apriori and FP-growth. However, these methods incur significant computational costs when applied to big data applications searching for association rules. Therefore, we propose a novel genetic-based method with three key innovations to speed up the search for multilevel association rules and reduce excessive computation. Firstly, we utilize the category tree to describe multilevel application data sets as domain knowledge. Next, we introduce a unique tree-encoding schema based on the category tree to develop the heuristic multilevel association-mining algorithm. Lastly, we present a genetic algorithm based on the tree-encoding schema that greatly decreases the association rule search space. This method is valuable for mining multilevel association rules in big data applications.

Open Access

Article

Article ID: 1884

The potential role of domain vectors in optimizing digital data structure

by Wolfgang Orthuber

Computing and Artificial Intelligence, Vol.3, No.1, 2025;

Each piece of information represents a selection from a set of possibilities. This set is the “domain of information”, which must be uniformly known before any information transport. The components of digital information are also sequences of numbers that represent a selection from a domain of information. So far, however, there is no guarantee that this domain is uniformly known. There is still no infrastructure that makes it possible to publish the domain of digital information in a uniform manner. Therefore, a standardized machine-readable online definition of the binary format and the domain of digital number sequences is proposed. These are uniquely identified worldwide as domain vectors (DVs) by an efficient Internet address of the online definition. As a result, optimized, language-independent digital information can be uniformly defined, identified, efficiently exchanged and compared worldwide for more and more applications.