Analytics

15 pages, 4396 KiB

Open AccessArticle

Breast Cancer Classification Using Fine-Tuned SWIN Transformer Model on Mammographic Images

by Oluwatosin Tanimola, Olamilekan Shobayo, Olusogo Popoola and Obinna Okoyeigbo

Analytics 2024, 3(4), 461-475; https://doi.org/10.3390/analytics3040026 - 11 Nov 2024

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Breast cancer is the most prevalent type of disease among women. It has become one of the foremost causes of death among women globally. Early detection plays a significant role in administering personalized treatment and improving patient outcomes. Mammography procedures are often used to detect early-stage cancer cells. This traditional method of mammography while valuable has limitations in its potential for false positives and negatives, patient discomfort, and radiation exposure. Therefore, there is a probe for more accurate techniques required in detecting breast cancer, leading to exploring the potential of machine learning in the classification of diagnostic images due to its efficiency and accuracy. This study conducted a comparative analysis of pre-trained CNNs (ResNet50 and VGG16) and vision transformers (ViT-base and SWIN transformer) with the inclusion of ViT-base trained from scratch model architectures to effectively classify mammographic breast cancer images into benign and malignant cases. The SWIN transformer exhibits superior performance with 99.9% accuracy and a precision of 99.8%. These findings demonstrate the efficiency of deep learning to accurately classify mammographic breast cancer images for the diagnosis of breast cancer, leading to improvements in patient outcomes. Full article

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12 pages, 300 KiB

Open AccessArticle

Modified Bayesian Information Criterion for Item Response Models in Planned Missingness Test Designs

by Alexander Robitzsch

Analytics 2024, 3(4), 449-460; https://doi.org/10.3390/analytics3040025 - 8 Nov 2024

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Abstract

The Bayesian information criterion (BIC) is a widely used statistical tool originally derived for fully observed data. The BIC formula includes the sample size and the number of estimated parameters in the penalty term. However, not all variables are available for every subject in planned missingness designs. This article demonstrates that a modified BIC, tailored for planned missingness designs, outperforms the original BIC. The modification adjusts the penalty term by using the average number of estimable parameters per subject rather than the total number of model parameters. This new criterion was successfully applied to item response theory models in two simulation studies. We recommend that future studies utilizing planned missingness designs adopt the modified BIC formula proposed here. Full article

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10 pages, 4948 KiB

Open AccessArticle

Adaptive Weighted Multiview Kernel Matrix Factorization and Its Application in Alzheimer’s Disease Analysis

by Yarui Cao and Kai Liu

Analytics 2024, 3(4), 439-448; https://doi.org/10.3390/analytics3040024 - 4 Nov 2024

Viewed by 377

Abstract

Recent technology and equipment advancements have provided us with opportunities to better analyze Alzheimer’s disease (AD), where we could collect and employ the data from different image and genetic modalities that may potentially enhance the predictive performance. To perform better clustering in AD analysis, in this paper, we propose a novel model to leverage data from all different modalities/views, which can learn the weights of each view adaptively. Different from previous vanilla Non-negative matrix factorization which assumes data is linearly separable, we propose a simple yet efficient method based on kernel matrix factorization, which is not only able to deal with non-linear data structure but also can achieve better prediction accuracy. Experimental results on the ADNI dataset demonstrate the effectiveness of our proposed method, which indicates promising prospects for kernel application in AD analysis. Full article

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14 pages, 4102 KiB

Open AccessArticle

Electric Vehicle Sentiment Analysis Using Large Language Models

by Hemlata Sharma, Faiz Ud Din and Bayode Ogunleye

Analytics 2024, 3(4), 425-438; https://doi.org/10.3390/analytics3040023 - 1 Nov 2024

Viewed by 766

Abstract

Sentiment analysis is a technique used to understand the public’s opinion towards an event, product, or organization. For example, sentiment analysis can be used to understand positive or negative opinions or attitudes towards electric vehicle (EV) brands. This provides companies with valuable insight into the public’s opinion of their products and brands. In the field of natural language processing (NLP), transformer models have shown great performance compared to traditional machine learning algorithms. However, these models have not been explored extensively in the EV domain. EV companies are becoming significant competitors in the automotive industry and are projected to cover up to 30% of the United States light vehicle market by 2030 In this study, we present a comparative study of large language models (LLMs) including bidirectional encoder representations from transformers (BERT), robustly optimised BERT (RoBERTa), and a generalised autoregressive pre-training method (XLNet) using Lucid Motors and Tesla Motors YouTube datasets. Results evidenced that LLMs like BERT and her variants are off-the-shelf algorithms for sentiment analysis, specifically when fine-tuned. Furthermore, our findings present the need for domain adaptation whilst utilizing LLMs. Finally, the experimental results showed that RoBERTa achieved consistent performance across the EV datasets with an F1 score of at least 92%. Full article

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19 pages, 4076 KiB

Open AccessArticle

The Analyst’s Hierarchy of Needs: Grounded Design Principles for Tailored Intelligence Analysis Tools

by Antonio E. Girona, James C. Peters, Wenyuan Wang and R. Jordan Crouser

Analytics 2024, 3(4), 406-424; https://doi.org/10.3390/analytics3040022 - 29 Oct 2024

Viewed by 399

Abstract

Intelligence analysis involves gathering, analyzing, and interpreting vast amounts of information from diverse sources to generate accurate and timely insights. Tailored tools hold great promise in providing individualized support, enhancing efficiency, and facilitating the identification of crucial intelligence gaps and trends where traditional tools fail. The effectiveness of tailored tools depends on an analyst’s unique needs and motivations, as well as the broader context in which they operate. This paper describes a series of focus discovery exercises that revealed a distinct hierarchy of needs for intelligence analysts. This reflection on the balance between competing needs is of particular value in the context of intelligence analysis, where the compartmentalization required for security can make it difficult to group design patterns in stakeholder values. We hope that this study will enable the development of more effective tools, supporting the well-being and performance of intelligence analysts as well as the organizations they serve. Full article

(This article belongs to the Special Issue Advances in Applied Data Science: Bridging Theory and Practice)

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Analytics, Volume 3, Issue 4 (December 2024) – 5 articles

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