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22 pages, 5652 KiB

Open AccessArticle

Personalized Federated Transfer Learning for Building Energy Forecasting via Model Ensemble with Multi-Level Masking in Heterogeneous Sensing Environment

by Hakjae Kim, Sarangerel Dorjgochoo, Hansaem Park and Sungju Lee

Electronics 2025, 14(9), 1790; https://doi.org/10.3390/electronics14091790 - 28 Apr 2025

Viewed by 197

Abstract

Effective building energy prediction is essential for optimizing energy management, but existing models struggle with data scarcity and sensor heterogeneity across different buildings. Conventional approaches, including centralized and transfer learning methods, fail to generalize well due to varying sensor configurations and inconsistent data [...] Read more.

Effective building energy prediction is essential for optimizing energy management, but existing models struggle with data scarcity and sensor heterogeneity across different buildings. Conventional approaches, including centralized and transfer learning methods, fail to generalize well due to varying sensor configurations and inconsistent data availability. To overcome these challenges, this study proposes a Personalized Federated Learning (pFL) framework that integrates multi-level feature masking, model ensemble techniques, and knowledge transfer to enhance predictive performance across diverse buildings. The proposed feature masking strategy extracts the most relevant time-series features, while model ensemble learning improves generalization, and knowledge transfer enables adaptive fine-tuning for each building. These techniques allow pFL to retain global knowledge while personalizing to local energy consumption patterns, making it more effective than traditional FL methods. Experiments conducted on a campus energy dataset demonstrate that pFL consistently outperforms FedAvg, FedProx, and standalone models in energy prediction accuracy. The most significant improvements are observed in buildings with highly fluctuating consumption patterns, validating the effectiveness of the proposed approach in handling heterogeneous sensing environments. This study highlights the potential of Federated Learning for scalable and adaptive energy prediction. Future work will focus on refining multi-horizon forecasting and developing strategies to enhance knowledge sharing among buildings for improved long-term performance. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

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25 pages, 14455 KiB

Open AccessArticle

Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction

by Yan Liu, Yongtuo Cui and Xiaoyu Yu

Electronics 2025, 14(7), 1426; https://doi.org/10.3390/electronics14071426 - 1 Apr 2025

Viewed by 358

Abstract

In semiconductor manufacturing, the final testing phase is critical for ensuring chip quality and operational efficiency. Accurate yield prediction at this stage optimizes testing workflows, boosts production efficiency, and enhances quality control. However, existing research primarily focuses on wafer-level yield prediction, leaving the [...] Read more.

In semiconductor manufacturing, the final testing phase is critical for ensuring chip quality and operational efficiency. Accurate yield prediction at this stage optimizes testing workflows, boosts production efficiency, and enhances quality control. However, existing research primarily focuses on wafer-level yield prediction, leaving the unique challenges of final testing—such as test condition variability and complex failure patterns—insufficiently addressed. This is especially critical for Radio Frequency Front-End (RFFE) chips, where high precision is essential, highlighting the need for a specialized prediction approach. In our study, a rigorous RF correlation parameter selection process was applied, leveraging metrics such as Spearman’s correlation coefficient and variance inflation factors to identify key RF-related features, such as multiple frequency-point PAE measurements and other critical electrical parameters, that directly influence final test yield. To overcome the limitations of traditional methods, this study proposes a multistrategy dynamic weighted fusion model for yield prediction. The proposed approach combines convolutional neural networks (CNNs) and long short-term memory (LSTM) networks with sliding window averaging to capture both local features and long-term dependencies in RFFE test data, while employing a learnable weighting mechanism to dynamically fuse outputs from multiple submodels for enhanced prediction accuracy. It further incorporates incremental training to adapt to shifting production conditions and utilizes principal component analysis (PCA) in data preprocessing to reduce dimensionality and address multicollinearity. Evaluated on a dataset of over 24 million RFFE chips, the proposed model achieved a Mean Absolute Error (MAE) below 0.84% and a Root Mean Square Error (RMSE) of 1.24%, outperforming single models by reducing MAE and RMSE by 7.69% and 13.29%, respectively. These results demonstrate the high accuracy and adaptability of the fusion model in predicting semiconductor final test yield. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

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15 pages, 3702 KiB

Open AccessArticle

Multiple Differential Convolution and Local-Variation Attention UNet: Nucleus Semantic Segmentation Based on Multiple Differential Convolution and Local-Variation Attention

by Xiaoming Sun, Shilin Li, Yongji Chen, Junxia Chen, Hao Geng, Kun Sun, Yuemin Zhu, Bochao Su and Hu Zhang

Electronics 2025, 14(6), 1058; https://doi.org/10.3390/electronics14061058 - 7 Mar 2025

Viewed by 488

Abstract

Nucleus accurate segmentation is a crucial task in biomedical image analysis. While convolutional neural networks (CNNs) have achieved notable progress in this field, challenges remain due to the complexity and heterogeneity of cell images, especially in overlapping regions of nuclei. To address the [...] Read more.

Nucleus accurate segmentation is a crucial task in biomedical image analysis. While convolutional neural networks (CNNs) have achieved notable progress in this field, challenges remain due to the complexity and heterogeneity of cell images, especially in overlapping regions of nuclei. To address the limitations of current methods, we propose a mechanism of multiple differential convolution and local-variation attention in CNNs, leading to the so-called multiple differential convolution and local-variation attention U-Net (MDLA-UNet). The multiple differential convolution employs multiple differential operators to capture gradient and direction information, improving the network’s capability to detect edges. The local-variation attention utilizes Haar discrete wavelet transforms for level-1 decomposition to obtain approximate features, and then derives high-frequency features to enhance the global context and local detail variation of the feature maps. The results on the MoNuSeg, TNBC, and CryoNuSeg datasets demonstrated superior segmentation performance of the proposed method for cells having complex boundaries and details with respect to existing methods. The proposed MDLA-UNet presents the ability of capturing fine edges and details in feature maps and thus improves the segmentation of nuclei with blurred boundaries and overlapping regions. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

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21 pages, 1595 KiB

Open AccessArticle

Aspect-Based Sentiment Analysis with Enhanced Opinion Tree Parsing and Parameter-Efficient Fine-Tuning for Edge AI

by Shih-wei Liao, Ching-Shun Wang, Chun-Chao Yeh and Jeng-Wei Lin

Electronics 2025, 14(4), 690; https://doi.org/10.3390/electronics14040690 - 10 Feb 2025

Viewed by 626

Abstract

Understanding user opinions from user comments or reviews in social media text mining is essential for marketing campaigns and many other applications. However, analyzing social media user comments presents significant challenges due to the complexity of discerning relationships between opinions and aspects, particularly [...] Read more.

Understanding user opinions from user comments or reviews in social media text mining is essential for marketing campaigns and many other applications. However, analyzing social media user comments presents significant challenges due to the complexity of discerning relationships between opinions and aspects, particularly when comments vary greatly in length. To effectively explore aspects and opinions in the sentences, techniques based on mining opinion sentiment of the referred aspects (implicitly or explicitly) in the user comments with ACOS (aspect-category-opinion-sentiment) quadruple extraction have been proposed. Among many others, the opinion tree parsing (OTP) scheme has been shown to be effective and efficient for the ACOS quadruple extraction task in aspect-based sentiment analysis (ABAS). In this study, we continue the efforts to design an efficient ABSA scheme. We extend the original OTP scheme further with richer context parsing rules, utilizing conjunctions and semantic modifiers to provide more context information in the sentence and thus effectively improving the accuracy of the analysis. Meanwhile, regarding the limitations of computation resources for edge devices in edge computing scenario, we also investigate the trade-off between computation saving (in terms of the percentage of model parameters to be updated) and the model’s performance (in terms of inference accuracy) on the proposed scheme under PEFT (parameter-efficient fine-tuning). We evaluate the proposed scheme on publicly available ACOS datasets. Experiment results show that the proposed enhanced OTP (eOTP) model improves the OTP scheme both in precision and recall measurements on the public ACOS datasets. Meanwhile, in the design trade-off evaluation for resource-constrained devices, the experiment results show that, in model training, eOTP requires very limited parameters (less than 1%) to be retrained by keeping most of the parameters frozen (not modified) in the fine-tuning process, at the cost of a slight performance drop (around 4%) in F1-score compared with the case of full fine-tuning. These demonstrate that the proposed scheme is efficient and feasible for resource-constrained scenarios such as for mobile edge/fog computing services. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

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17 pages, 1154 KiB

Open AccessArticle

Optimization of Artificial Intelligence Algorithm Selection: PIPRECIA-S Model and Multi-Criteria Analysis

by Stefan Popović, Dejan Viduka, Ana Bašić, Violeta Dimić, Dejan Djukic, Vojkan Nikolić and Aleksandar Stokić

Electronics 2025, 14(3), 562; https://doi.org/10.3390/electronics14030562 - 30 Jan 2025

Viewed by 1089

Abstract

In the age of digitization and the ever-present use of artificial intelligence (AI), it is essential to develop methodologies that enable the systematic evaluation and ranking of different AI algorithms. This paper investigated the application of the PIPRECIA-S model as a methodological framework [...] Read more.

In the age of digitization and the ever-present use of artificial intelligence (AI), it is essential to develop methodologies that enable the systematic evaluation and ranking of different AI algorithms. This paper investigated the application of the PIPRECIA-S model as a methodological framework for the multi-criteria ranking of AI algorithms. Analyzing relevant criteria such as efficiency, flexibility, ease of implementation, stability and scalability, the paper provided a comprehensive overview of existing algorithms and identified their strengths and weaknesses. The research results showed that the PIPRECIA-S model enabled a structured and objective assessment, which facilitated decision-making in selecting the most suitable algorithms for specific applications. This approach not only advances the understanding of AI algorithms but also contributes to the development of strategies for their implementation in various industries. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

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35 pages, 663 KiB

Open AccessArticle

Technological Convergence of Blockchain and Artificial Intelligence: A Review and Challenges

by Nakhoon Choi and Heeyoul Kim

Electronics 2025, 14(1), 84; https://doi.org/10.3390/electronics14010084 - 27 Dec 2024

Cited by 2 | Viewed by 2525

Abstract

Blockchain and artificial intelligence are two of the most prominent technologies in computer science today and have attracted considerable attention from various research communities. Recently, several initiatives have been launched to explore the combination of these two pioneering technologies. The main goal is [...] Read more.

Blockchain and artificial intelligence are two of the most prominent technologies in computer science today and have attracted considerable attention from various research communities. Recently, several initiatives have been launched to explore the combination of these two pioneering technologies. The main goal is to combine the data integrity, privacy, and decentralization properties of blockchain with the ability of artificial intelligence to process, analyze, predict, and refine massive data sets. The combination of blockchain and AI technologies is expected to address key challenges in the digital realm, such as data security, transparency, and streamlined decision-making. However, there is a problem that many studies have focused on the advancement of a single technology as the main perspective. To overcome these recent research limitations, we provide a broad view of the combination of blockchain and artificial intelligence and analyze the limitations of existing research and their causes. Furthermore, we identify challenges and attempts to be addressed through this analysis. The analysis in this paper is organized into a comprehensive section dedicated to the application of artificial intelligence in blockchain and vice versa. Based on our analysis, we identify existing challenges and propose a novel framework for researchers to overcome these limitations, thus expanding new research opportunities. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

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28 pages, 7268 KiB

Open AccessArticle

Cross-Project Software Defect Prediction Using Differential Perception Combined with Inheritance Federated Learning

by Aili Wang, Yanxiang Feng, Mingji Yang, Haibin Wu, Yuji Iwahori and Haisong Chen

Electronics 2024, 13(24), 4893; https://doi.org/10.3390/electronics13244893 - 11 Dec 2024

Cited by 1 | Viewed by 990

Abstract

Cross-project software defect prediction (CPDP) refers to the construction of defect prediction models by collecting multi-source project data, but the heterogeneity of data among projects and the modern problem of “data islands” hinder its development. In response to these challenges, we propose a [...] Read more.

Cross-project software defect prediction (CPDP) refers to the construction of defect prediction models by collecting multi-source project data, but the heterogeneity of data among projects and the modern problem of “data islands” hinder its development. In response to these challenges, we propose a CPDP algorithm based on differential perception combined with inheritance federated learning (FedDPI). Firstly, we design an efficient data preprocessing scheme, which lays a reliable data foundation for federated learning by integrating oversampling and optimal feature selection methods. Secondly, a two-stage collaborative optimization mechanism is proposed in the federated learning framework: the inheritance private model (IPM) is introduced in the local training stage, and the differential perception algorithm is used in the global aggregation stage to dynamically allocate aggregation weights, forming positive feedback for training to overcome the negative impact of data heterogeneity. In addition, we utilize the Ranger optimization algorithm to improve the convergence speed and privacy security of the model through its irreversible mixed optimization operation. The experimental results show that FedDPI significantly improves predictive performance in various defect item data combination experiments. Compared with different deep learning and federated learning algorithms, the average improvement in AUC and G-mean indicators is 0.2783 and 0.2673, respectively, verifying the practicality and effectiveness of federated learning and two-stage collaborative optimization mechanisms in the field of CPDP. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

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12 pages, 107820 KiB

Open AccessArticle

A Doodle-Based Control for Characters in Story Visualization

by Hyemin Yang, Heekyung Yang and Kyungha Min

Electronics 2024, 13(23), 4628; https://doi.org/10.3390/electronics13234628 - 23 Nov 2024

Viewed by 877

Abstract

We propose a story visualization technique that allows users to control the arrangement, poses, and styles of characters in a scene based on user-input doodle sketches. Our method utilizes a text encoder to process scene prompts and an image encoder to handle doodle [...] Read more.

We propose a story visualization technique that allows users to control the arrangement, poses, and styles of characters in a scene based on user-input doodle sketches. Our method utilizes a text encoder to process scene prompts and an image encoder to handle doodle sketches, generating inputs for a predefined scene generation model. Furthermore, we achieve efficient model training by fine-tuning the backbone network by applying a small dataset and employing a LoRA-based fine-tuning technique. We demonstrate that our method can generate characters with various poses and styles from doodle sketches, and it can validate the advantages of our approach by comparing it with the results from other story visualization studies. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

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14 pages, 1015 KiB

Open AccessArticle

Evaluating Causal Reasoning Capabilities of Large Language Models: A Systematic Analysis Across Three Scenarios

by Lei Wang and Yiqing Shen

Electronics 2024, 13(23), 4584; https://doi.org/10.3390/electronics13234584 - 21 Nov 2024

Viewed by 2980

Abstract

Large language models (LLMs) have shown their capabilities in numerical and logical reasoning, yet their capabilities in higher-order cognitive tasks, particularly causal reasoning, remain less explored. Current research on LLMs in causal reasoning has focused primarily on tasks such as identifying simple cause-effect [...] Read more.

Large language models (LLMs) have shown their capabilities in numerical and logical reasoning, yet their capabilities in higher-order cognitive tasks, particularly causal reasoning, remain less explored. Current research on LLMs in causal reasoning has focused primarily on tasks such as identifying simple cause-effect relationships, answering basic “what-if” questions, and generating plausible causal explanations. However, these models often struggle with complex causal structures, confounding variables, and distinguishing correlation from causation. This work addresses these limitations by systematically evaluating LLMs’ causal reasoning abilities across three representative scenarios, namely analyzing causation from effects, tracing effects back to causes, and assessing the impact of interventions on causal relationships. These scenarios are designed to challenge LLMs beyond simple associative reasoning and test their ability to handle more nuanced causal problems. For each scenario, we construct four paradigms and employ three types of prompt scheme, namely zero-shot prompting, few-shot prompting, and Chain-of-Thought (CoT) prompting in a set of 36 test cases. Our findings reveal that most LLMs encounter challenges in causal cognition across all prompt schemes, which underscore the need to enhance the cognitive reasoning capabilities of LLMs to better support complex causal reasoning tasks. By identifying these limitations, our study contributes to guiding future research and development efforts in improving LLMs’ higher-order reasoning abilities. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

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17 pages, 19605 KiB

Open AccessArticle

TOLGAN: An End-To-End Framework for Producing Traditional Orient Landscape

by Booyong Kim, Heekyung Yang and Kyungha Min

Electronics 2024, 13(22), 4468; https://doi.org/10.3390/electronics13224468 - 14 Nov 2024

Viewed by 638

Abstract

We present TOLGAN that generates traditional oriental landscape (TOL) image from a map that specifies the locations and shapes of the elements composing TOL. Users can create a TOL map by using a user interface or a segmentation scheme from a photograph. We [...] Read more.

We present TOLGAN that generates traditional oriental landscape (TOL) image from a map that specifies the locations and shapes of the elements composing TOL. Users can create a TOL map by using a user interface or a segmentation scheme from a photograph. We design the generator of TOLGAN as a series of decoding layers where the map is applied between the layers. The generated TOL image is further enhanced through an AdaIN architecture. The discriminator of TOLGAN processes a generated image and its groundtruth TOL artwork image. TOLGAN is trained through a dataset composed of paired TOL artwork images and their TOL maps. We present a tool through which users can produce a TOL map by specifying and organizing the elements of TOL artworks. TOLGAN successfully generates a series of TOL images from the TOL map. We evaluate our approach using a quantitative way by estimating FID and ArtFID scores and a qualitative way by executing two user studies. Through these studies, we prove the excellence of our approach by comparing our results with those from several important existing works. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

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11 pages, 2979 KiB

Open AccessArticle

Rare Data Image Classification System Using Few-Shot Learning

by Juhyeok Lee and Mihui Kim

Electronics 2024, 13(19), 3923; https://doi.org/10.3390/electronics13193923 - 4 Oct 2024

Viewed by 1367

Abstract

Advances in deep learning can address a variety of computer vision problems. In particular, deep learning has shown high performance in image processing. However, large datasets are required to train deep learning models. Previous studies have addressed the problem of data scarcity via [...] Read more.

Advances in deep learning can address a variety of computer vision problems. In particular, deep learning has shown high performance in image processing. However, large datasets are required to train deep learning models. Previous studies have addressed the problem of data scarcity via the few-shot learning technique. However, a drawback of these studies is that large datasets are required when new tasks are performed. Hence, this study uses data augmentation techniques to address this shortcoming. Furthermore, we propose an image classification system with a few-shot learning technique that achieves high accuracy, even on rare datasets. Compared with traditional image classification models, the proposed system improves classification accuracy by approximately 18% using 100 data points. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

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Review

Jump to: Research

40 pages, 37522 KiB

Open AccessReview

A Review of State-of-the-Art Methodologies and Applications in Action Recognition

by Lanfei Zhao, Zixiang Lin, Ruiyang Sun and Aili Wang

Electronics 2024, 13(23), 4733; https://doi.org/10.3390/electronics13234733 - 29 Nov 2024

Cited by 1 | Viewed by 1990

Abstract

Action recognition, a vital subfield of computer vision, profoundly enhances security, health, and human–computer interaction through its sophisticated analytical capabilities. The review presents a comprehensive analysis of action recognition methodologies, systematically classified by model architecture and input modalities, encompassing traditional techniques, RGB-based neural [...] Read more.

Action recognition, a vital subfield of computer vision, profoundly enhances security, health, and human–computer interaction through its sophisticated analytical capabilities. The review presents a comprehensive analysis of action recognition methodologies, systematically classified by model architecture and input modalities, encompassing traditional techniques, RGB-based neural networks, skeleton-based networks, and advanced pose estimation methods for extracting skeletal data. A rigorous comparative evaluation of the architectures and outcomes of advanced methods within each category are also conducted. Notably, prior reviews have yet to examine action recognition from the perspective of practical applications. This review, for the first time, presents the practical applications of multiple action recognition techniques and forecasts the future trajectory of the field by integrating recent research trends. It aids in understanding the evolution of behavior recognition systems and exploring advanced strategies, facilitating the integration of intelligent systems into human society. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

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13 pages, 1682 KiB

Open AccessReview

Mapping Computer Vision Syndrome: An Engineering Problem in Human–Computer Interaction

by Dejan Viduka, Vanja Dimitrijević, Dragan Rastovac, Milan Gligorijević, Ana Bašić, Srđan Maričić and Stevan Jokić

Electronics 2024, 13(22), 4460; https://doi.org/10.3390/electronics13224460 - 14 Nov 2024

Viewed by 1083

Abstract

Computer Vision Syndrome (CVS) is a highly prevalent syndrome today, yet it remains relatively understudied, leading to limited awareness among users about this syndrome and its preventive measures. This study aims to draw attention to this syndrome among authors and researchers and encourage [...] Read more.

Computer Vision Syndrome (CVS) is a highly prevalent syndrome today, yet it remains relatively understudied, leading to limited awareness among users about this syndrome and its preventive measures. This study aims to draw attention to this syndrome among authors and researchers and encourage further research in this area. Data were retrieved from the databases PubMed, Lens, Scopus, and Google Scholar, compiling existing articles and publications from the CVS domain. Analyses cover the period from 1 January to 31 December 2023. Zotero 6.0.27, VOSviewer 1.6.20, and Microsoft Excel software were used for data analysis. A total of 893 papers were reviewed, with 578 papers included in our analysis. The study presents five different analyses showing top authors and publishers, publication trends over the years, as well as papers by source, and, finally, the most frequently used keywords. The results highlight trends in various aspects related to this issue, through the analysis of published articles over the years, along with prominent authors and their respective countries. The focus of this research is on computer vision syndrome and its representation in scientific databases. What is clearly evident from this study is the increasing trend in research over the years, as well as the leading countries in these studies. However, it is also apparent that further research in this area is needed to bring new insights to researchers and raise awareness among users who encounter computers in their daily work. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

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20 pages, 2132 KiB

Open AccessReview

How Artificial Intelligence (AI) Is Powering New Tourism Marketing and the Future Agenda for Smart Tourist Destinations

by Lázaro Florido-Benítez and Benjamín del Alcázar Martínez

Electronics 2024, 13(21), 4151; https://doi.org/10.3390/electronics13214151 - 23 Oct 2024

Cited by 2 | Viewed by 9817

Abstract

Artificial intelligence (AI) is a disruptive technology that is being used by smart tourist destinations (STDs) to develop new business models and marketing services to increase tourists’ experiences and sales, revenue, productivity, and efficiency and STDs. However, the adoption of AI applications and [...] Read more.

Artificial intelligence (AI) is a disruptive technology that is being used by smart tourist destinations (STDs) to develop new business models and marketing services to increase tourists’ experiences and sales, revenue, productivity, and efficiency and STDs. However, the adoption of AI applications and platforms requires a high economic budget for STDs that want to integrate this digital tool into their future agenda and tourism development plans, especially when they set them up for marketing plans and operational processes. This iterative technology needs regular maintenance as well, leading to recurring costs and specialised crews in advanced technologies and marketing activities. This study aims to show the impact of AI advancements on STDs’ tourism marketing to enhance the quality of services and illustrate their future agenda to improve tourists’ experiences. A comprehensive literature review on AI technology and STDs has been conducted to illustrate new tourism marketing in their future agenda. Moreover, this study presents real examples of AI technology in a tourism context to better understand the potential of this digital tool. The findings of the current study support the idea that AI is a multipurpose tool that helps manage, monitor, and analyse sales information; revenue management; minimise prediction errors; streamline operations; and develop better marketing strategies, optimising economic resources, reducing marketing costs, and responding dynamically to changing needs for tourists and residents in STDs. Furthermore, the investment in AI technologies by STDs helps enhance the quality of products and services, and attract new investments, which benefit the regional economies and population’s quality of life. This study is the first to address the use of AI to improve tourist marketing in STDs, which is its primary uniqueness. Also, this study identifies new opportunities and initiatives through AI that can be developed to help tourism marketing in STDs. Full article

(This article belongs to the Special Issue Feature Papers in "Computer Science & Engineering", 2nd Edition)

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