Computational Intelligence最新文献

AgriFusion: A Low-Carbon Sustainable Computing Approach for Precision Agriculture Through Probabilistic Ensemble Crop Recommendation 农业融合：通过概率集合作物推荐实现精准农业的低碳可持续计算方法

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2024-11-19 DOI: 10.1111/coin.70006

T. R. Mahesh, Arastu Thakur, A. K. Velmurugan, Surbhi Bhatia Khan, Thippa Reddy Gadekallu, Saeed Alzahrani, Mohammed Alojail

{"title":"AgriFusion: A Low-Carbon Sustainable Computing Approach for Precision Agriculture Through Probabilistic Ensemble Crop Recommendation","authors":"T. R. Mahesh, Arastu Thakur, A. K. Velmurugan, Surbhi Bhatia Khan, Thippa Reddy Gadekallu, Saeed Alzahrani, 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}

引用次数: 0

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2024-11-19 DOI: 10.1111/coin.70008

Jiaye Wen, Wenan Zhou

{"title":"SDKT: Similar Domain Knowledge Transfer for Multivariate Time Series Classification Tasks","authors":"Jiaye Wen, 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}

引用次数: 0

An Efficient and Robust 3D Medical Image Classification Approach Based on 3D CNN, Time-Distributed 2D CNN-BLSTM Models, and mRMR Feature Selection 基于三维 CNN、时间分布式二维 CNN-BLSTM 模型和 mRMR 特征选择的高效稳健的三维医学图像分类方法

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2024-10-23 DOI: 10.1111/coin.70000

Enver Akbacak, Nedim Muzoğlu

{"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, 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}

引用次数: 0

Comprehensive analysis of feature-algorithm interactions for fall detection across age groups via machine learning 通过机器学习全面分析各年龄组跌倒检测中特征与算法之间的相互作用

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2024-10-23 DOI: 10.1111/coin.12697

Erhan Kavuncuoğlu

{"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}

引用次数: 0

A Benchmark Proposal for Non-Generative Fair Adversarial Learning Strategies Using a Fairness-Utility Trade-off Metric 使用公平-效用权衡指标的非生成公平对抗学习策略基准提案

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2024-10-21 DOI: 10.1111/coin.70003

Luiz Fernando F. P. de Lima, Danielle Rousy D. Ricarte, Clauirton A. Siebra

{"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, Danielle Rousy D. Ricarte, 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}

引用次数: 0

Synthetic Image Generation Using Deep Learning: A Systematic Literature Review 使用深度学习生成合成图像：系统性文献综述

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2024-10-21 DOI: 10.1111/coin.70002

Aisha Zulfiqar, Sher Muhammad Daudpota, Ali Shariq Imran, Zenun Kastrati, Mohib Ullah, Suraksha Sadhwani

{"title":"Synthetic Image Generation Using Deep Learning: A Systematic Literature Review","authors":"Aisha Zulfiqar, Sher Muhammad Daudpota, Ali Shariq Imran, Zenun Kastrati, Mohib Ullah, 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}

引用次数: 0

Modified local Granger causality analysis based on Peter-Clark algorithm for multivariate time series prediction on IoT data 基于彼得-克拉克算法的修正局部格兰杰因果关系分析，用于物联网数据的多变量时间序列预测

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2024-10-21 DOI: 10.1111/coin.12694

Fei Lv, Shuaizong Si, Xing Xiao, Weijie Ren

{"title":"Modified local Granger causality analysis based on Peter-Clark algorithm for multivariate time series prediction on IoT data","authors":"Fei Lv, Shuaizong Si, Xing Xiao, 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}

引用次数: 0

Adaptive Synaptic Adjustment Mechanism to Improve Learning Performances of Spiking Neural Networks 改善尖峰神经网络学习性能的自适应突触调整机制

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2024-10-17 DOI: 10.1111/coin.70001

Hyun-Jong Lee, Jae-Han Lim

{"title":"Adaptive Synaptic Adjustment Mechanism to Improve Learning Performances of Spiking Neural Networks","authors":"Hyun-Jong Lee, Jae-Han Lim","doi":"10.1111/coin.70001","DOIUrl":"https://doi.org/10.1111/coin.70001","url":null,"abstract":"<div>\u0000 \u0000 <p>Spiking Neural Networks (SNNs) are currently attracting researchers' attention due to their efficiencies in various tasks. Spike-timing-dependent plasticity (STDP) is an unsupervised learning process that utilizes bio-plausibility based on the relative timing of pre/post-synaptic spikes of neurons. Integrated with STDP, SNNs perform well consuming less energy. However, it is hard to ensure that synaptic weights always converge to values guaranteeing accurate prediction because STDP does not change synaptic weights with supervision. To address this limitation, researchers have proposed mechanisms for inducing STDP to converge synaptic weights on the proper values referring to current synaptic weights. Thus, if the current weights fail to describe proper synaptic connections, they cannot induce STDP to update synaptic weights properly. To solve this problem, we propose an adaptive mechanism that helps STDP to converge synaptic weights directly based on input data features: Adaptive synaptic template (AST). AST leads synaptic weights to describe synaptic connections according to the data features. It prevents STDP from changing synaptic weights based on abnormal weights that fail to describe the proper synaptic connections. This is because it does not use the current synaptic weights that can disturb proper weight convergence. We integrate AST with an SNN and conduct experiments to compare it with a baseline (the SNN without AST) and benchmarks (previous works to improve STDP). Our experimental results show that the SNN using AST classifies various data sets with 6%–39% higher accuracy than the baseline and benchmarks.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449154","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}

引用次数: 0

Hybrid HAN-CNN with aspect term extraction for sentiment analysis using product review 利用产品评论进行情感分析的混合 HAN-CNN 与方面词提取技术

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2024-10-07 DOI: 10.1111/coin.12698

P. C. D. Kalaivaani, K. Sathyarajasekaran, N. Krishnamoorthy, T. Kumaravel

引用次数: 0

TCSR: Self-attention with time and category for session-based recommendation TCSR：基于会话推荐的时间和类别自我关注

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2024-09-25 DOI: 10.1111/coin.12695

Xiaoyan Zhu, Yu Zhang, Jiaxuan Li, Jiayin Wang, Xin Lai

{"title":"TCSR: Self-attention with time and category for session-based recommendation","authors":"Xiaoyan Zhu, Yu Zhang, Jiaxuan Li, Jiayin Wang, Xin Lai","doi":"10.1111/coin.12695","DOIUrl":"https://doi.org/10.1111/coin.12695","url":null,"abstract":"<p>Session-based recommendation that uses sequence of items clicked by anonymous users to make recommendations has drawn the attention of many researchers, and a lot of approaches have been proposed. However, there are still problems that have not been well addressed: (1) Time information is either ignored or exploited with a fixed time span and granularity, which fails to understand the personalized interest transfer pattern of users with different clicking speeds; (2) Category information is either omitted or considered independent of the items, which defies the fact that the relationships between categories and items are helpful for the recommendation. To solve these problems, we propose a new session-based recommendation method, TCSR (self-attention with time and category for session-based recommendation). TCSR uses a non-linear normalized time embedding to perceive user interest transfer patterns on variable granularity and employs a heterogeneous SAN to make full use of both items and categories. Moreover, a cross-recommendation unit is adapted to adjust recommendations on the item and category sides. Extensive experiments on four real datasets show that TCSR significantly outperforms state-of-the-art approaches.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320613","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}

引用次数: 0