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10 pages, 3467 KiB

Open AccessArticle

Comprehensive Data Augmentation Approach Using WGAN-GP and UMAP for Enhancing Alzheimer’s Disease Diagnosis

by Emi Yuda, Tomoki Ando, Itaru Kaneko, Yutaka Yoshida and Daisuke Hirahara

Electronics 2024, 13(18), 3671; https://doi.org/10.3390/electronics13183671 - 16 Sep 2024

Viewed by 561

In this study, the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was used to improve the diagnosis of Alzheimer’s disease using medical imaging and the Alzheimer’s disease image dataset across four diagnostic classes. The WGAN-GP was employed for data augmentation. The original [...] Read more.

In this study, the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was used to improve the diagnosis of Alzheimer’s disease using medical imaging and the Alzheimer’s disease image dataset across four diagnostic classes. The WGAN-GP was employed for data augmentation. The original dataset, the augmented dataset and the combined data were mapped using Uniform Manifold Approximation and Projection (UMAP) in both a 2D and 3D space. The same combined interaction network analysis was then performed on the test data. The results showed that, for the test accuracy, the score was 30.46% for the original dataset (unbalanced), whereas for the WGAN-GP augmented dataset (balanced), it improved to 56.84%, indicating that the WGAN-GP augmentation can effectively address the unbalanced problem. Full article

(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)

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15 pages, 634 KiB

Open AccessFeature PaperArticle

RGMeta: Enhancing Cold-Start Recommendations with a Residual Graph Meta-Embedding Model

by Fuzhe Zhao, Chaoge Huang, Han Xu, Wen Yang and Wenlin Han

Electronics 2024, 13(17), 3473; https://doi.org/10.3390/electronics13173473 - 1 Sep 2024

Viewed by 471

Abstract

Traditional recommendation models grapple with challenges such as the scarcity of similar user or item references and data sparsity, rendering the cold-start problem particularly formidable. Meta-learning has emerged as a promising avenue to address these issues, particularly in solving the item cold-start problem [...] Read more.

Traditional recommendation models grapple with challenges such as the scarcity of similar user or item references and data sparsity, rendering the cold-start problem particularly formidable. Meta-learning has emerged as a promising avenue to address these issues, particularly in solving the item cold-start problem by generating meta-embeddings for new items as their initial ID embeddings. This approach has shown notable success in enhancing the accuracy of click-through rate predictions. However, prevalent meta-embedding models often focus solely on the attribute features of the item, neglecting crucial user information associated with it during the generation of initial ID embeddings for new items. This oversight hinders the exploitation of valuable user-related information to enhance the quality and accuracy of the initial ID embedding. To tackle this limitation, we introduce the residual graph meta-embedding model (RGMeta). RGMeta adopts a comprehensive approach by considering both the attribute features and target users of both old and new items. Through the integration of residual connections, the model effectively combines the representation information of the old neighbor items with the intrinsic features of the new item, resulting in an improved initial ID embedding generation. Experimental results demonstrate that RGMeta significantly enhances the performance of the cold-start phase, showcasing its effectiveness in overcoming challenges associated with sparse data and limited reference points. Our proposed model presents a promising step forward in leveraging both item attributes and user-related information to address cold-start problems in recommendation systems. Full article

(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)

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24 pages, 1233 KiB

Open AccessArticle

A Novel Feature Selection Strategy Based on the Harris Hawks Optimization Algorithm for the Diagnosis of Cervical Cancer

by Minhui Dong, Yu Wang, Yuki Todo and Yuxiao Hua

Electronics 2024, 13(13), 2554; https://doi.org/10.3390/electronics13132554 - 28 Jun 2024

Viewed by 663

Abstract

Cervical cancer is the fourth most commonly diagnosed cancer and one of the leading causes of cancer-related deaths among females worldwide. Early diagnosis can greatly increase the cure rate for cervical cancer. However, due to the need for substantial medical resources, it is [...] Read more.

Cervical cancer is the fourth most commonly diagnosed cancer and one of the leading causes of cancer-related deaths among females worldwide. Early diagnosis can greatly increase the cure rate for cervical cancer. However, due to the need for substantial medical resources, it is difficult to implement in some areas. With the development of machine learning, utilizing machine learning to automatically diagnose cervical cancer has currently become one of the main research directions in the field. Such an approach typically involves a large number of features. However, a portion of these features is redundant or irrelevant. The task of eliminating redundant or irrelevant features from the entire feature set is known as feature selection (FS). Feature selection methods can roughly be divided into three types, including filter-based methods, wrapper-based methods, and embedded-based methods. Among them, wrapper-based methods are currently the most commonly used approach, and many researchers have demonstrated that these methods can reduce the number of features while improving the accuracy of diagnosis. However, this method still has some issues. Wrapper-based methods typically use heuristic algorithms for FS, which can result in significant computational time. On the other hand, heuristic algorithms are often sensitive to parameters, leading to instability in performance. To overcome this challenge, a novel wrapper-based method named the Binary Harris Hawks Optimization (BHHO) algorithm is proposed in this paper. Compared to other wrapper-based methods, the BHHO has fewer hyper-parameters, which contributes to better stability. Furthermore, we have introduced a rank-based selection mechanism into the algorithm, which endows BHHO with enhanced optimization capabilities and greater generalizability. To comprehensively evaluate the performance of the proposed BHHO, we conducted a series of experiments. The experimental results show that the proposed BHHO demonstrates better accuracy and stability compared to other common wrapper-based FS methods on the cervical cancer dataset. Additionally, even on other disease datasets, the proposed algorithm still provides competitive results, proving its generalizability. Full article

(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)

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18 pages, 2966 KiB

Open AccessArticle

Beyond Trial and Error: Lane Keeping with Monte Carlo Tree Search-Driven Optimization of Reinforcement Learning

by Bálint Kővári, Bálint Pelenczei, István Gellért Knáb and Tamás Bécsi

Electronics 2024, 13(11), 2058; https://doi.org/10.3390/electronics13112058 - 25 May 2024

Cited by 1 | Viewed by 631

Abstract

In recent years, Reinforcement Learning (RL) has excelled in the realm of autonomous vehicle control, which is distinguished by the absence of limitations, such as specific training data or the necessity for explicit mathematical model identification. Particularly in the context of lane keeping, [...] Read more.

In recent years, Reinforcement Learning (RL) has excelled in the realm of autonomous vehicle control, which is distinguished by the absence of limitations, such as specific training data or the necessity for explicit mathematical model identification. Particularly in the context of lane keeping, a diverse set of rewarding strategies yields a spectrum of realizable policies. Nevertheless, the challenge lies in discerning the optimal behavior that maximizes performance. Traditional approaches entail exhaustive training through a trial-and-error strategy across conceivable reward functions, which is a process notorious for its time-consuming nature and substantial financial implications. Contrary to conventional methodologies, the Monte Carlo Tree Search (MCTS) enables the prediction of reward function quality through Monte Carlo simulations, thereby eliminating the need for exhaustive training on all available reward functions. The findings obtained from MCTS simulations can be effectively leveraged to selectively train only the most suitable RL models. This approach helps alleviate the resource-heavy nature of traditional RL processes through altering the training pipeline. This paper validates the theoretical framework concerning the unique property of the Monte Carlo Tree Search algorithm by emphasizing its generality through highlighting crossalgorithmic and crossenvironmental capabilities while also showcasing its potential to reduce training costs. Full article

(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)

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17 pages, 3510 KiB

Open AccessArticle

A Hybrid Model Based on CEEMDAN-GRU and Error Compensation for Predicting Sunspot Numbers

by Jianzhong Yang, Song Liu, Shili Xuan and Huirong Chen

Electronics 2024, 13(10), 1904; https://doi.org/10.3390/electronics13101904 - 13 May 2024

Cited by 1 | Viewed by 960

Abstract

To improve the predictive accuracy of sunspot numbers, a hybrid model was built to forecast future sunspot numbers. In this paper, we present a prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit (GRU), and error [...] Read more.

To improve the predictive accuracy of sunspot numbers, a hybrid model was built to forecast future sunspot numbers. In this paper, we present a prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit (GRU), and error compensation for predicting sunspot numbers. CEEMAND is applied to decompose the original sunspot number data into several components, which are then used to train and test the GRU for the optimal parameters of the corresponding sub-models. Error compensation is utilized to solve the delay phenomenon between the original sunspot number and the predictive result. We compare our method with the informer, extreme gradient boosting combined with deep learning (XGboost-DL), and empirical mode decomposition combined long short-term memory neutral network and attention mechanism (EMD-LSTM-AM) methods, and evaluation metrics, such as RMSE and MAE, are used to measure their performance. Our method decreases more than 2.2813 and 3.5827 relative to RMSE and MAE, respectively. Thus, the experiment can demonstrate that our method has an obvious advantage compared to others. Full article

(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)

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23 pages, 4558 KiB

Open AccessArticle

Diachronic Semantic Tracking for Chinese Words and Morphemes over Centuries

by Yang Chi, Fausto Giunchiglia and Hao Xu

Electronics 2024, 13(9), 1728; https://doi.org/10.3390/electronics13091728 - 30 Apr 2024

Viewed by 802

Abstract

Lexical semantic changes spanning centuries can reveal the complicated developing process of language and social culture. In recent years, natural language processing (NLP) methods have been applied in this field to provide insight into the diachronic frequency change for word senses from large-scale [...] Read more.

Lexical semantic changes spanning centuries can reveal the complicated developing process of language and social culture. In recent years, natural language processing (NLP) methods have been applied in this field to provide insight into the diachronic frequency change for word senses from large-scale historical corpus, for instance, analyzing which senses appear, increase, or decrease at which times. However, there is still a lack of Chinese diachronic corpus and dataset in this field to support supervised learning and text mining, and at the method level, few existing works analyze the Chinese semantic changes at the level of morpheme. This paper constructs a diachronic Chinese dataset for semantic tracking applications spanning 3000 years and extends the existing framework to the level of Chinese characters and morphemes, which contains four main steps of contextual sense representation, sense identification, morpheme sense mining, and diachronic semantic change representation. The experiment shows the effectiveness of our method in each step. Finally, in an interesting statistic, we discover the strong positive correlation of frequency and changing trend between monosyllabic word sense and the corresponding morpheme. Full article

(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)

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26 pages, 1194 KiB

Open AccessArticle

Multicriteria Machine Learning Model Assessment—Residuum Analysis Review

by Jan Kaniuka, Jakub Ostrysz, Maciej Groszyk, Krzysztof Bieniek, Szymon Cyperski and Paweł D. Domański

Electronics 2024, 13(5), 810; https://doi.org/10.3390/electronics13050810 - 20 Feb 2024

Viewed by 988

Abstract

The use of machine learning (ML) and its applications is one of the leading research areas nowadays. Neural networks have recently gained enormous popularity and many works in various fields use them in the hope of improving previous results. The application of the [...] Read more.

The use of machine learning (ML) and its applications is one of the leading research areas nowadays. Neural networks have recently gained enormous popularity and many works in various fields use them in the hope of improving previous results. The application of the artificial intelligence (AI) methods and the rationale for this decision is one issue, but the assessment of such a model is a completely different matter. People mostly use mean square error or less often mean absolute error in the absolute or percentage versions. One should remember that an error does not equal an error and a single value does not provide enough knowledge about the causes of some behavior. Proper interpretation of the results is crucial. It leads to further model improvement. It might be challenging, but allows us to obtain better and more robust solutions, which ultimately solve real-life problems. The ML model assessment is the multicriteria task. A single measure delivers only a fraction of the picture. This paper aims at filling that research gap. Commonly used integral measures are compared with alternative measures like factors of the Gaussian and non-Gaussian statistics, robust statistical estimators, tail index and the fractional order. The proposed methodology delivers new single-criteria indexes or the multicriteria approach, which extend the statistical concept of the moment ratio diagram (MRD) into the index ratio diagram (IRD). The proposed approach is validated using real data from the Full Truck Load cost estimation example. It compares 35 different ML regression algorithms applied to that task. The analysis gives an insight into the properties of the selected methods, enables their comparison and homogeneity analysis and ultimately leads towards constructive suggestions for their eventual proper use. The paper proposes new indexes and concludes that correct selection of the residuum analysis methodology makes the assessment and the ML regression credible. Full article

(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)

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15 pages, 771 KiB

Open AccessArticle

PhishTransformer: A Novel Approach to Detect Phishing Attacks Using URL Collection and Transformer

by Sultan Asiri, Yang Xiao and Tieshan Li

Electronics 2024, 13(1), 30; https://doi.org/10.3390/electronics13010030 - 20 Dec 2023

Cited by 1 | Viewed by 1481

Abstract

Phishing attacks are a major threat to online security, resulting in millions of dollars in losses. These attacks constantly evolve, forcing the cyber security community to improve detection systems. One major problem with current detection systems is that they cannot detect new phishing [...] Read more.

Phishing attacks are a major threat to online security, resulting in millions of dollars in losses. These attacks constantly evolve, forcing the cyber security community to improve detection systems. One major problem with current detection systems is that they cannot detect new phishing attacks, such as Browser in the Browser (BiTB) and malvertising attacks. These attacks hide behind legitimate Uniform Resource Locators (URLs) and can evade detection systems that only analyze a web page URL without exploring the page content. To address this problem, we propose PhishTransformer, a deep-learning model that can detect phishing attacks by analyzing URLs and page content. We propose only using URLs embedded within a webpage, such as hyperlinks and JFrames, to train PhishTransformer. This helps reduce the number of features that need to be extracted from the page content, which makes training the model more efficient. PhishTransformer combines convolutional neural networks and transformer encoders to extract features from website URLs and page content. These features are then used to train a classifier that can distinguish between phishing attacks and legitimate websites. We tested PhishTransformer on a dataset of 10,000 URLs. Our results show that PhishTransformer can achieve an F1-score of 99%, precision of 99%, and recall of 99%. This result suggests that PhishTransformer is a promising new approach to phishing detection. Full article

(This article belongs to the Special Issue Advancements in Cross-Disciplinary AI: Theory and Application—2nd Edition)

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