Advances in computational intelligence最新文献_第2页

Automatic image captioning in Thai for house defect using a deep learning-based approach 使用基于深度学习的方法为房屋缺陷自动添加泰语图像标题

Advances in computational intelligence Pub Date : 2023-12-29 DOI: 10.1007/s43674-023-00068-w

Manadda Jaruschaimongkol, Krittin Satirapiwong, Kittipan Pipatsattayanuwong, Suwant Temviriyakul, Ratchanat Sangprasert, Thitirat Siriborvornratanakul

{"title":"Automatic image captioning in Thai for house defect using a deep learning-based approach","authors":"Manadda Jaruschaimongkol, Krittin Satirapiwong, Kittipan Pipatsattayanuwong, Suwant Temviriyakul, Ratchanat Sangprasert, Thitirat Siriborvornratanakul","doi":"10.1007/s43674-023-00068-w","DOIUrl":"10.1007/s43674-023-00068-w","url":null,"abstract":"<div><p>This study aims to automate the reporting process of house inspections, which enables prospective buyers to make informed decisions. Currently, the inspection report generated by an inspector involves inserting all defect images into a spreadsheet software and manually captioning each image with identified defects. To the best of our knowledge, there are no previous works or datasets that have automated this process. Therefore, this paper proposes a new image captioning dataset for house defect inspection, which is benchmarked with three deep learning-based models. Our models are based on the encoder–decoder architecture where three image encoders (i.e., VGG16, MobileNet, and InceptionV3) and one GRU-based decoder with an additive attention mechanism of Bahdanau are experimented. The experimental results indicate that, despite similar training losses in all models, VGG16 takes the least time to train a model, while MobileNet achieves the highest BLEU-1 to BLEU-4 scores of 0.866, 0.850, 0.823, and 0.728, respectively. However, InceptionV3 is suggested as the optimal model, since it outperforms the others in terms of accurate attention plots and its BLEU scores are comparable to the best scores obtained by MobileNet.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139090588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An empirical study of deep learning-based feature extractor models for imbalanced image classification 基于深度学习的不平衡图像分类特征提取模型的实证研究

Advances in computational intelligence Pub Date : 2023-11-23 DOI: 10.1007/s43674-023-00067-x

Ammara Khan, Muhammad Tahir Rasheed, Hufsa Khan

{"title":"An empirical study of deep learning-based feature extractor models for imbalanced image classification","authors":"Ammara Khan, Muhammad Tahir Rasheed, Hufsa Khan","doi":"10.1007/s43674-023-00067-x","DOIUrl":"10.1007/s43674-023-00067-x","url":null,"abstract":"<div><p>Deep learning has played an important role in many real-life applications, especially in image classification. It is often found that some domain data are highly skewed, i.e., most of the data belongs to a handful of majority classes, and the minority classes only contain small amounts of information. It is important to acknowledge that skewed class distribution poses a significant challenge to machine learning algorithms. Due to which in case of imbalanced data distribution, the majority of machine and deep learning algorithms are not effective or may fail when it is highly imbalanced. In this study, a comprehensive analysis in case of imbalanced dataset is performed by considering deep learning based well known models. In particular, the best feature extractor model is identified and the current trend of latest feature extraction model is investigated. Moreover, to determine the global scientific research on the image classification of imbalanced mushroom dataset, a bibliometric analysis is conducted from 1991 to 2022. In summary, our findings may offer researchers a quick benchmarking reference and alternative approach to assessing trends in imbalanced data distributions in image classification research.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"3 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138449164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chart-to-text generation using a hybrid deep network 使用混合深度网络生成图表到文本

Advances in computational intelligence Pub Date : 2023-11-02 DOI: 10.1007/s43674-023-00066-y

Nontaporn Wonglek, Siriwalai Maneesinthu, Sivakorn Srichaiyaperk, Teerapon Saengmuang, Thitirat Siriborvornratanakul

引用次数: 0

Self-supervised learning advanced plant disease image classification with SimCLR 基于SimCLR的自监督学习高级植物病害图像分类

Advances in computational intelligence Pub Date : 2023-10-31 DOI: 10.1007/s43674-023-00065-z

Songpol Bunyang, Natdanai Thedwichienchai, Krisna Pintong, Nuj Lael, Wuthipoom Kunaborimas, Phawit Boonrat, Thitirat Siriborvornratanakul

引用次数: 0

Modeling of the chaotic situation in the recruitment processes 招聘过程中混乱局面的建模

Advances in computational intelligence Pub Date : 2023-08-04 DOI: 10.1007/s43674-023-00064-0

Harendra Verma, Vishnu Narayan Mishra, Pankaj Mathur

引用次数: 0

Framework to measure and reduce the threat surface area for smart home devices 衡量和减少智能家居设备威胁表面积的框架

Advances in computational intelligence Pub Date : 2023-08-02 DOI: 10.1007/s43674-023-00062-2

Akashdeep Bhardwaj, Keshav Kaushik, Vishal Dagar, Manoj Kumar

引用次数: 0

Multi-information fusion based on dual attention and text embedding network for local citation recommendation 基于双注意力和文本嵌入网络的多信息融合用于地方引文推荐

Advances in computational intelligence Pub Date : 2023-07-25 DOI: 10.1007/s43674-023-00063-1

Shanshan Wang, Xiaohong Li, Jin Yao, Ben You

{"title":"Multi-information fusion based on dual attention and text embedding network for local citation recommendation","authors":"Shanshan Wang, Xiaohong Li, Jin Yao, Ben You","doi":"10.1007/s43674-023-00063-1","DOIUrl":"10.1007/s43674-023-00063-1","url":null,"abstract":"<div><p>Local citation recommendation is a list of references that researchers need to cite based on a given context, so it could help researchers produce high-quality academic writing quickly and efficiently. However, existing citation recommendation methods only consider contextual content or author information, ignore the critical influence of historical citation information on citations, and learn the paper embedding at a coarse-grained level, resulting in lower-quality recommendations. To solve the above problems, we propose a novel two-stage citation recommendation model with multiple information fusion (MICR). The first stage is to enhance the target paper’s representation learning of the MICR model. To achieve the above goal, three encoders, which contain context information encoder, historical citation encoder, and author information encoder, are designed to learn rich representations of the target paper. The second stage is to select appropriate recommendation strategies for the target paper and candidate papers to achieve the goal of efficient citation recommendation. Experiments on two public citation datasets show that our model outperforms several competitive baseline methods on citation recommendation.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50512939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ranking method of the generalized intuitionistic fuzzy numbers founded on possibility measures and its application to MADM problem 基于可能性测度的广义直觉模糊数排序方法及其在MADM问题中的应用

Advances in computational intelligence Pub Date : 2023-07-18 DOI: 10.1007/s43674-023-00061-3

Totan Garai

引用次数: 0

Development of a decision support system to use in the strategic purchasing of dental implants 用于种植牙战略采购的决策支持系统的开发

Advances in computational intelligence Pub Date : 2023-07-03 DOI: 10.1007/s43674-023-00060-4

Funda Özdiler Çopur, Dilek Çökeliler Serdaroğlu, Yusuf Tansel İç, Fikret Arı

引用次数: 0

Speech emotion classification using semi-supervised LSTM 基于半监督LSTM的语音情感分类

Advances in computational intelligence Pub Date : 2023-06-22 DOI: 10.1007/s43674-023-00059-x

Nattipon Itponjaroen, Kumpee Apsornpasakorn, Eakarat Pimthai, Khwanchai Kaewkaisorn, Shularp Panitchart, Thitirat Siriborvornratanakul

{"title":"Speech emotion classification using semi-supervised LSTM","authors":"Nattipon Itponjaroen, Kumpee Apsornpasakorn, Eakarat Pimthai, Khwanchai Kaewkaisorn, Shularp Panitchart, Thitirat Siriborvornratanakul","doi":"10.1007/s43674-023-00059-x","DOIUrl":"10.1007/s43674-023-00059-x","url":null,"abstract":"<div><p>Speech mood analysis is a challenging task with unclear optimal feature selection. The nature of the dataset, whether it is from an infant or adult, is crucial to consider. In this study, the characteristics of speech were investigated using Mel-frequency cepstral coefficients (MFCC) to analyze audio files. The CREMA-D dataset, which includes six different mood states (normal, angry, happy, sad, scared, and irritated), was employed to identify mood states from speech files. A mood classification system was proposed that integrates Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) models to increase the number of labeled data in small datasets and improve classification accuracy.</p><p>A semi-supervised model was proposed in this study to improve the accuracy of speech mood classification systems. The approach was tested on a classification model that used SVM and LSTM, and it was found that the semi-supervised model outperforms both SVM and LSTM models, achieving a validation accuracy of 89.72%. This result surpasses the accuracy achieved by SVM and LSTM models alone. Moreover, the semi-supervised method was observed to accelerate the training process of the model. These outcomes illustrate the efficacy of the proposed model and its potential to enhance speech mood analysis techniques.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50504732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0