Computational Intelligence最新文献

{"title":"Homomorphisms and Embeddings of STRIPS Planning Models","authors":"Arnaud Lequen, Martin C. Cooper, Frédéric Maris","doi":"10.1111/coin.70013","DOIUrl":"https://doi.org/10.1111/coin.70013","url":null,"abstract":"<p>Determining whether two STRIPS planning instances are isomorphic is the simplest form of comparison between planning instances. It is also a particular case of the problem concerned with finding an isomorphism between a planning instance <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>P</mi>\u0000 </mrow>\u0000 <annotation>$$ P $$</annotation>\u0000 </semantics></math> and a sub-instance of another instance <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mrow>\u0000 <mi>P</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mo>′</mo>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {P}^{prime } $$</annotation>\u0000 </semantics></math>. One application of such a mapping is to efficiently produce a compiled form containing all solutions to <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>P</mi>\u0000 </mrow>\u0000 <annotation>$$ P $$</annotation>\u0000 </semantics></math> from a compiled form containing all solutions to <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mrow>\u0000 <mi>P</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mo>′</mo>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {P}^{prime } $$</annotation>\u0000 </semantics></math>. We also introduce the notion of <i>embedding</i> from an instance <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>P</mi>\u0000 </mrow>\u0000 <annotation>$$ P $$</annotation>\u0000 </semantics></math> to another instance <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mrow>\u0000 <mi>P</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mo>′</mo>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {P}^{prime } $$</annotation>\u0000 </semantics></math>, which allows us to deduce that <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mrow>\u0000 <mi>P</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mo>′</mo>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {P}^{prime } $$</annotation>\u0000 </s","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Words: ESC-Net Revolutionizes VQA by Elevating Visual Features and Defying Language Priors","authors":"Souvik Chowdhury,&nbsp;Badal Soni","doi":"10.1111/coin.70010","DOIUrl":"https://doi.org/10.1111/coin.70010","url":null,"abstract":"<div>\u0000 \u0000 <p>Language prior is a pressing problem in the VQA domain where a model provides an answer favoring the most frequent related answer. There are some methods that are adopted to mitigate language prior issue, for example, ensemble approach, the balanced data approach, the modified evaluation strategy, and the modified training framework. In this article, we propose a VQA model, “Ensemble of Spatial and Channel Attention Network (ESC-Net),” to overcome the language bias problem by improving the visual features. In this work, we have used regional and global image features along with an ensemble of combined channel and spatial attention mechanisms to improve visual features. The model is a simpler and effective solution than existing methods to solve language bias. Extensive experiment show a remarkable performance improvement of 18% on the VQACP v2 dataset with a comparison to current state-of-the-art (SOTA) models.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142762484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-View Self-Supervised Auxiliary Task for Few-Shot Remote Sensing Classification","authors":"Baodi Liu,&nbsp;Lei Xing,&nbsp;Xujian Qiao,&nbsp;Qian Liu","doi":"10.1111/coin.70009","DOIUrl":"https://doi.org/10.1111/coin.70009","url":null,"abstract":"<div>\u0000 \u0000 <p>In the past few years, the swift advancement of remote sensing technology has greatly promoted its widespread application in the agricultural field. For example, remote sensing technology is used to monitor the planting area and growth status of crops, classify crops, and detect agricultural disasters. In these applications, the accuracy of image classification is of great significance in improving the efficiency and sustainability of agricultural production. However, many of the existing studies primarily rely on contrastive self-supervised learning methods, which come with certain limitations such as complex data construction and a bias towards invariant features. To address these issues, additional techniques like knowledge distillation are often employed to optimize the learned features. In this article, we propose a novel approach to enhance feature acquisition specific to remote sensing images by introducing a classification-based self-supervised auxiliary task. This auxiliary task involves performing image transformation self-supervised learning tasks directly on the remote sensing images, thereby improving the overall capacity for feature representation. In this work, we design a texture fading reinforcement auxiliary task to reinforce texture features and color features that are useful for distinguishing similar classes of remote sensing. Different auxiliary tasks are fused to form a multi-view self-supervised auxiliary task and integrated with the main task to optimize the model training in an end-to-end manner. The experimental results on several popular few-shot remote sensing image datasets validate the effectiveness of the proposed method. The performance better than many advanced algorithms is achieved with a more concise structure.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142724263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AgriFusion: A Low-Carbon Sustainable Computing Approach for Precision Agriculture Through Probabilistic Ensemble Crop Recommendation","authors":"T. R. Mahesh,&nbsp;Arastu Thakur,&nbsp;A. K. Velmurugan,&nbsp;Surbhi Bhatia Khan,&nbsp;Thippa Reddy Gadekallu,&nbsp;Saeed Alzahrani,&nbsp;Mohammed Alojail","doi":"10.1111/coin.70006","DOIUrl":"https://doi.org/10.1111/coin.70006","url":null,"abstract":"<div>\u0000 \u0000 <p>Optimizing crop production is essential for sustainable agriculture and food security. This study presents the AgriFusion Model, an advanced ensemble-based machine learning framework designed to enhance precision agriculture by offering highly accurate and low-carbon crop recommendations. By integrating Random Forest, Gradient Boosting, and LightGBM, the model combines their strengths to boost predictive accuracy, robustness, and energy efficiency. Trained on a comprehensive dataset of 2200 instances covering key parameters like nitrogen, phosphorus, potassium, temperature, humidity, pH, rainfall, and crop type, the model underwent rigorous preprocessing for data integrity. The RandomizedSearchCV method was employed to do hyperparameter tuning, namely improving the number of trees in the Random Forest algorithm and the learning rates in the Gradient Boosting algorithm. This ensemble approach achieves a remarkable accuracy rate of 99.48%, optimizes computer resources, lowers carbon footprint, and responds efficiently to a variety of agricultural situations. The model's performance is confirmed using metrics including cross-validation, accuracy, precision, recall, and F1 score. This demonstrates how the model might improve agricultural decision-making, make the most use of available resources, and promote ecologically responsible farming practices.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SDKT: Similar Domain Knowledge Transfer for Multivariate Time Series Classification Tasks","authors":"Jiaye Wen,&nbsp;Wenan Zhou","doi":"10.1111/coin.70008","DOIUrl":"https://doi.org/10.1111/coin.70008","url":null,"abstract":"<div>\u0000 \u0000 <p>Multivariate time series data classification has a wide range of applications in reality. With rapid development of deep learning, convolutional networks are widely used in this task and have achieved the current best performance. However, due to high difficulty and cost of collecting this type of data, labeled data is still scarce. In some tasks, the model shows overfitting, resulting in relatively poor classification performance. In order to improve the classification performance under such situation, we proposed a novel classification method based on transfer learning—similar domain knowledge transfer (call SDKT for short). Firstly, we designed a multivariate time series domain distance calculation method (call MTSDDC for short), which helped selecting the source domain that is most similar to target domain; Secondly, we used ResNet as a pre-trained classifier, transferred the parameters of the similar domain network to the target domain network and continue to fine-tune the parameters. To verify our method, we conducted experiments on several public datasets. Our study has also shown that the transfer effect from the source domain to the target domain is highly negatively correlated with the distance between them, with an average Pearson coefficient of −0.78. For the transfer of most similar source domain, compared to the ResNet model without transfer and the current best model, the average accuracy improvements on the datasets we used are 4.01% and 1.46% respectively.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Efficient and Robust 3D Medical Image Classification Approach Based on 3D CNN, Time-Distributed 2D CNN-BLSTM Models, and mRMR Feature Selection","authors":"Enver Akbacak,&nbsp;Nedim Muzoğlu","doi":"10.1111/coin.70000","DOIUrl":"https://doi.org/10.1111/coin.70000","url":null,"abstract":"<div>\u0000 \u0000 <p>The advent of 3D medical imaging has been a turning point in the diagnosis of various diseases, as voxel information from adjacent slices helps radiologists better understand complex anatomical relationships. However, the interpretation of medical images by radiologists with different levels of expertise can vary and is also time-consuming. In the last decades, artificial intelligence-based computer-aided systems have provided fast and more reliable diagnostic insights with great potential for various clinical purposes. This paper proposes a significant deep learning based 3D medical image diagnosis method. The method classifies MedMNIST3D, which consists of six 3D biomedical datasets obtained from CT, MRA, and electron microscopy modalities. The proposed method concatenates 3D image features extracted from three independent networks, a 3D CNN, and two time-distributed ResNet BLSTM structures. The ultimate discriminative features are selected via the minimum redundancy maximum relevance (mRMR) feature selection method. Those features are then classified by a neural network model. Experiments adhere to the rules of the official splits and evaluation metrics of the MedMNIST3D datasets. The results reveal that the proposed approach outperforms similar studies in terms of accuracy and AUC.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive analysis of feature-algorithm interactions for fall detection across age groups via machine learning","authors":"Erhan Kavuncuoğlu","doi":"10.1111/coin.12697","DOIUrl":"https://doi.org/10.1111/coin.12697","url":null,"abstract":"<p>Fall detection in daily activities hinges on both feature selection and algorithm choice. This study delves into their intricate interplay using the Sisfall dataset, testing 10 machine learning algorithms on 26 features encompassing diverse falls and age groups. Individual feature analysis yields key insights. RFC with the autocorrelation feature outperformed the other classifiers, achieving 97.94% accuracy and 97.51% sensitivity (surpassing F3-SVM at 96.18% and F17-LightGBM at 95.79%). The F3-SVM exhibited exceptional specificity (98.72%) for distinguishing daily activities. Time-series features employed by SVM achieved a peak accuracy of 98.60% on unseen data, exceeding motion, basic statistical, and frequency domain features. Feature combinations further excel: the Quintuple approach, fusing top-performing features, reaches 98.69% accuracy, 98.28% sensitivity, and 99.08% specificity with the ETC, demonstrating notable sensitivity owing to its adaptability. This study underscores the crucial interplay of features and algorithms, with the Quintuple-ETC approach emerging as the most effective. Rigorous hyperparameter tuning strengthens its performance in real-world fall-detection applications. Furthermore, the study investigates algorithm transferability, training models on young participants' data and applying them to the elderly—a significant challenge in machine learning. This highlights the importance of understanding the data transfer between age groups in healthcare, aging management, and medical diagnostics.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Benchmark Proposal for Non-Generative Fair Adversarial Learning Strategies Using a Fairness-Utility Trade-off Metric","authors":"Luiz Fernando F. P. de Lima,&nbsp;Danielle Rousy D. Ricarte,&nbsp;Clauirton A. Siebra","doi":"10.1111/coin.70003","DOIUrl":"https://doi.org/10.1111/coin.70003","url":null,"abstract":"<div>\u0000 \u0000 <p>AI systems for decision-making have become increasingly popular in several areas. However, it is possible to identify biased decisions in many applications, which have become a concern for the computer science, artificial intelligence, and law communities. Therefore, researchers are proposing solutions to mitigate bias and discrimination among decision-makers. Some explored strategies are based on GANs to generate fair data. Others are based on adversarial learning to achieve fairness by encoding fairness constraints through an adversarial model. Moreover, it is usual for each proposal to assess its model with a specific metric, making comparing current approaches a complex task. Therefore, this work proposes a systematical benchmark procedure to assess the fair machine learning models. The proposed procedure comprises a fairness-utility trade-off metric (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>FU-score</mi>\u0000 </mrow>\u0000 <annotation>$$ FUhbox{-} score $$</annotation>\u0000 </semantics></math>), the utility and fairness metrics to compose this assessment, the used datasets and preparation, and the statistical test. A previous work presents some of these definitions. The present work enriches the procedure by increasing the applied datasets and statistical guarantees when comparing the models' results. We performed this benchmark evaluation for the non-generative adversarial models, analyzing the literature models from the same metric perspective. This assessment could not indicate a single model which better performs for all datasets. However, we built an understanding of how each model performs on each dataset with statistical confidence.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthetic Image Generation Using Deep Learning: A Systematic Literature Review","authors":"Aisha Zulfiqar,&nbsp;Sher Muhammad Daudpota,&nbsp;Ali Shariq Imran,&nbsp;Zenun Kastrati,&nbsp;Mohib Ullah,&nbsp;Suraksha Sadhwani","doi":"10.1111/coin.70002","DOIUrl":"https://doi.org/10.1111/coin.70002","url":null,"abstract":"<p>The advent of deep neural networks and improved computational power have brought a revolutionary transformation in the fields of computer vision and image processing. Within the realm of computer vision, there has been a significant interest in the area of synthetic image generation, which is a creative side of AI. Many researchers have introduced innovative methods to identify deep neural network-based architectures involved in image generation via different modes of input, like text, scene graph layouts and so forth to generate synthetic images. Computer-generated images have been found to contribute a lot to the training of different machine and deep-learning models. Nonetheless, we have observed an immediate need for a comprehensive and systematic literature review that encompasses a summary and critical evaluation of current primary studies' approaches toward image generation. To address this, we carried out a systematic literature review on synthetic image generation approaches published from 2018 to February 2023. Moreover, we have conducted a systematic review of various datasets, approaches to image generation, performance metrics for existing methods, and a brief experimental comparison of DCGAN (deep convolutional generative adversarial network) and cGAN (conditional generative adversarial network) in the context of image generation. Additionally, we have identified applications related to image generation models with critical evaluation of the primary studies on the subject matter. Finally, we present some future research directions to further contribute to the field of image generation using deep neural networks.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modified local Granger causality analysis based on Peter-Clark algorithm for multivariate time series prediction on IoT data","authors":"Fei Lv,&nbsp;Shuaizong Si,&nbsp;Xing Xiao,&nbsp;Weijie Ren","doi":"10.1111/coin.12694","DOIUrl":"https://doi.org/10.1111/coin.12694","url":null,"abstract":"<p>Climate data collected through Internet of Things (IoT) devices often contain high-dimensional, nonlinear, and auto-correlated characteristics, and general causality analysis methods obtain quantitative causality analysis results between variables based on conditional independence tests or Granger causality, and so forth. However, it is difficult to capture dynamic properties between variables of temporal distribution, which can obtain information that cannot be obtained by the mean detection method. Therefore, this paper proposed a new causality analysis method based on Peter-Clark (PC) algorithm and modified local Granger causality (MLGC) analysis method, called PC-MLGC, to reveal the causal relationships between variables and explore the dynamic properties on temporal distribution. First, the PC algorithm is applied to compute the relevant variables of each variable. Then, the results obtained in the previous stage are fed into the modified local Granger causality analysis model to explore causalities between variables. Finally, combined with the quantitative causality analysis results, the dynamic characteristic curves between variables can be obtained, and the accuracy of the causal relationship between variables can be further verified. The effectiveness of the proposed method is further demonstrated by comparing it with standard Granger causality analysis and a two-stage causal network learning method on one benchmark dataset and two real-world datasets.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}