Analytics

25 pages, 512 KiB

Open AccessSystematic Review

Artificial Intelligence Applied to the Analysis of Biblical Scriptures: A Systematic Review

by Bruno Cesar Lima, Nizam Omar, Israel Avansi and Leandro Nunes de Castro

Analytics 2025, 4(2), 13; https://doi.org/10.3390/analytics4020013 - 11 Apr 2025

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The Holy Bible is the most read book in the world, originally written in Aramaic, Hebrew, and Greek over a time span in the order of centuries by many people, and formed by a combination of various literary styles, such as stories, prophecies, poetry, instructions, and others. As such, the Bible is a complex text to be analyzed by humans and machines. This paper provides a systematic survey of the application of Artificial Intelligence (AI) and some of its subareas to the analysis of the Biblical scriptures. Emphasis is given to what types of tasks are being solved, what are the main AI algorithms used, and their limitations. The findings deliver a general perspective on how this field is being developed, along with its limitations and gaps. This research follows a procedure based on three steps: planning (defining the review protocol), conducting (performing the survey), and reporting (formatting the report). The results obtained show there are seven main tasks solved by AI in the Bible analysis: machine translation, authorship identification, part of speech tagging (PoS tagging), semantic annotation, clustering, categorization, and Biblical interpretation. Also, the classes of AI techniques with better performance when applied to Biblical text research are machine learning, neural networks, and deep learning. The main challenges in the field involve the nature and style of the language used in the Bible, among others. Full article

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24 pages, 1216 KiB

Open AccessArticle

Traffic Prediction with Data Fusion and Machine Learning

by Juntao Qiu and Yaping Zhao

Analytics 2025, 4(2), 12; https://doi.org/10.3390/analytics4020012 - 9 Apr 2025

Viewed by 433

Abstract

Traffic prediction, as a core task to alleviate urban congestion and optimize the transport system, has limitations in the integration of multimodal data, making it difficult to comprehensively capture the complex spatio-temporal characteristics of the transport system. Although some studies have attempted to introduce multimodal data, they mostly rely on resource-intensive deep neural network architectures, which have difficultly meeting the demands of practical applications. To this end, we propose a traffic prediction framework based on simple machine learning techniques that effectively integrates property features, amenity features, and emotion features (PAE features). Validated with large-scale real datasets, the method demonstrates excellent prediction performance while significantly reducing computational complexity and deployment costs. This study demonstrates the great potential of simple machine learning techniques in multimodal data fusion, provides an efficient and practical solution for traffic prediction, and offers an effective alternative to resource-intensive deep learning methods, opening up new paths for building scalable traffic prediction systems. Full article

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21 pages, 3030 KiB

Open AccessArticle

Copula-Based Bayesian Model for Detecting Differential Gene Expression

by Prasansha Liyanaarachchi and N. Rao Chaganty

Analytics 2025, 4(2), 11; https://doi.org/10.3390/analytics4020011 - 3 Apr 2025

Viewed by 223

Abstract

Deoxyribonucleic acid, more commonly known as DNA, is a fundamental genetic material in all living organisms, containing thousands of genes, but only a subset exhibit differential expression and play a crucial role in diseases. Microarray technology has revolutionized the study of gene expression, with two primary types available for expression analysis: spotted cDNA arrays and oligonucleotide arrays. This research focuses on the statistical analysis of data from spotted cDNA microarrays. Numerous models have been developed to identify differentially expressed genes based on the red and green fluorescence intensities measured using these arrays. We propose a novel approach using a Gaussian copula model to characterize the joint distribution of red and green intensities, effectively capturing their dependence structure. Given the right-skewed nature of the intensity distributions, we model the marginal distributions using gamma distributions. Differentially expressed genes are identified using the Bayes estimate under our proposed copula framework. To evaluate the performance of our model, we conduct simulation studies to assess parameter estimation accuracy. Our results demonstrate that the proposed approach outperforms existing methods reported in the literature. Finally, we apply our model to Escherichia coli microarray data, illustrating its practical utility in gene expression analysis. Full article

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Analytics, Volume 4, Issue 2 (June 2025) – 3 articles

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