Computational Intelligence最新文献

{"title":"Question-driven text summarization using an extractive-abstractive framework","authors":"Mahsa Abazari Kia,&nbsp;Aygul Garifullina,&nbsp;Mathias Kern,&nbsp;Jon Chamberlain,&nbsp;Shoaib Jameel","doi":"10.1111/coin.12689","DOIUrl":"https://doi.org/10.1111/coin.12689","url":null,"abstract":"<p>Question-driven automatic text summarization is a popular technique to produce concise and informative answers to specific questions using a document collection. Both query-based and question-driven summarization may not produce reliable summaries nor contain relevant information if they do not take advantage of extractive and abstractive summarization mechanisms to improve performance. In this article, we propose a novel extractive and abstractive hybrid framework designed for question-driven automatic text summarization. The framework consists of complimentary modules that work together to generate an effective summary: (1) discovering appropriate non-redundant sentences as plausible answers using an open-domain multi-hop question answering system based on a convolutional neural network, multi-head attention mechanism and reasoning process; and (2) a novel paraphrasing generative adversarial network model based on transformers rewrites the extracted sentences in an abstractive setup. Experiments show this framework results in more reliable abstractive summary than competing methods. We have performed extensive experiments on public datasets, and the results show our model can outperform many question-driven and query-based baseline methods (an R1, R2, RL increase of 6%–7% for over the next highest baseline).</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141488141","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":"Explainable artificial intelligence for medical imaging: Review and experiments with infrared breast images","authors":"Kaushik Raghavan,&nbsp;Sivaselvan Balasubramanian,&nbsp;Kamakoti Veezhinathan","doi":"10.1111/coin.12660","DOIUrl":"https://doi.org/10.1111/coin.12660","url":null,"abstract":"<p>There is a growing trend of using artificial intelligence, particularly deep learning algorithms, in medical diagnostics, revolutionizing healthcare by improving efficiency, accuracy, and patient outcomes. However, the use of artificial intelligence in medical diagnostics comes with the critical need to explain the reasoning behind artificial intelligence-based predictions and ensure transparency in decision-making. Explainable artificial intelligence has emerged as a crucial research area to address the need for transparency and interpretability in medical diagnostics. Explainable artificial intelligence techniques aim to provide insights into the decision-making process of artificial intelligence systems, enabling clinicians to understand the factors the algorithms consider in reaching their predictions. This paper presents a detailed review of saliency-based (visual) methods, such as class activation methods, which have gained popularity in medical imaging as they provide visual explanations by highlighting the regions of an image most influential in the artificial intelligence's decision. We also present the literature on non-visual methods, but the focus will be on visual methods. We also use the existing literature to experiment with infrared breast images for detecting breast cancer. Towards the end of this paper, we also propose an “attention guided Grad-CAM” that enhances the visualizations for explainable artificial intelligence. The existing literature shows that explainable artificial intelligence techniques are not explored in the context of infrared medical images and opens up a wide range of opportunities for further research to make clinical thermography into assistive technology for the medical community.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141488438","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":"Cooperative networking and information processing system of wireless communication UAV under the background of intelligent service","authors":"Zhiyong Chen","doi":"10.1111/coin.12688","DOIUrl":"https://doi.org/10.1111/coin.12688","url":null,"abstract":"<p>In order to solve the huge impact of the digital information age on many technical and industrial fields, a periodic fast search genetic algorithm is proposed. Based on the reconnaissance mission, this paper introduces the common allocation strategy into mission planning, and constructs the mathematical model of multi unmanned aerial vehicle (UAV) cooperative reconnaissance mission planning decision-making. The proposed periodic fast search genetic algorithm is used to solve the problem of multi UAV cooperative reconnaissance mission planning. In 2020, the industry growth rate of global UAV technology expenditure was as high as 30.6%, and the compound growth rate of UAV in China reached 63.5%, which is enough to see the great prospect of the integrated development of UAV technology and different industries. The experiment evaluates the log verification module of UAV by comparing the two data structures of Merkle tree and linear, and the time and memory overhead of storing and verifying logs, which shows the effectiveness of the log verification scheme in this paper.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":1.8,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141488641","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":"Masked self-supervised pre-training model for EEG-based emotion recognition","authors":"Xinrong Hu,&nbsp;Yu Chen,&nbsp;Jinlin Yan,&nbsp;Yuan Wu,&nbsp;Lei Ding,&nbsp;Jin Xu,&nbsp;Jun Cheng","doi":"10.1111/coin.12659","DOIUrl":"https://doi.org/10.1111/coin.12659","url":null,"abstract":"<p>Electroencephalogram (EEG), as a tool capable of objectively recording brain electrical signals during emotional expression, has been extensively utilized. Current technology heavily relies on datasets, with its performance being limited by the size of the dataset and the accuracy of its annotations. At the same time, unsupervised learning and contrastive learning methods largely depend on the feature distribution within datasets, thus requiring training tailored to specific datasets for optimal results. However, the collection of EEG signals is influenced by factors such as equipment, settings, individuals, and experimental procedures, resulting in significant variability. Consequently, the effectiveness of models is heavily dependent on dataset collection efforts conducted under stringent objective conditions. To address these challenges, we introduce a novel approach: employing a self-supervised pre-training model, to process data across different datasets. This model is capable of operating effectively across multiple datasets. The model conducts self-supervised pre-training without the need for direct access to specific emotion category labels, enabling it to pre-train and extract universally useful features without predefined downstream tasks. To tackle the issue of semantic expression confusion, we employed a masked prediction model that guides the model to generate richer semantic information through learning bidirectional feature combinations in sequence. Addressing challenges such as significant differences in data distribution, we introduced adaptive clustering techniques that manage by generating pseudo-labels across multiple categories. The model is capable of enhancing the expression of hidden features in intermediate layers during the self-supervised training process, enabling it to learn common hidden features across different datasets. This study, by constructing a hybrid dataset and conducting extensive experiments, demonstrated two key findings: (1) our model performs best on multiple evaluation metrics; (2) the model can effectively integrate critical features from different datasets, significantly enhancing the accuracy of emotion recognition.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425081","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 SDN improvement scheme for multi-path QUIC transmission in satellite networks","authors":"Hongxin Ma,&nbsp;Meng Wang,&nbsp;Hao Lv,&nbsp;Jinyao Liu,&nbsp;Xiaoqiang Di,&nbsp;Hui Qi","doi":"10.1111/coin.12650","DOIUrl":"https://doi.org/10.1111/coin.12650","url":null,"abstract":"<p>In recent years, with the development of low-earth orbit broadband satellites, the combination of multi-path transmission and software-defined networking (SDN) for satellite networks has seen rapid advancement. The integration of SDN and multi-path transmission contributes to improving the efficiency of transmission and reducing network congestion. However, the current SDN controllers do not support the multi-path QUIC protocol (MPQUIC), and the routing algorithm used in current satellite networks based on minimum hop count struggles to meet the real-time requirements for some applications. Therefore, this paper designs and implements an SDN controller that supports the MPQUIC protocol and proposes a multi-objective optimization-based routing algorithm. This algorithm selects paths with lower propagation delays and higher available bandwidth for subflow transmission to improve transmission throughput. Considering the high-speed mobility of satellite nodes and frequent link switching, this paper also designs a flow table update algorithm based on the predictability of satellite network topology. It enables proactive rerouting upon link switching, ensuring stable transmission. The performance of the proposed solution is evaluated through satellite network simulation environments. The experimental results highlight that SDN-MPQUIC significantly improves performance metrics: it reduces average completion time by 37.3% to 59.3% compared to QSMPS and by 52.8% to 72.4% compared to Disjoint for files with different sizes. Additionally, SDN-MPQUIC achieves an average throughput improvement of 81.4% compared to QSMPS and 147.8% compared to Disjoint, while demonstrating a 26.3% lower retransmission rate than QSMPS.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425109","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":"Application of concept drift detection and adaptive framework for non linear time series data from cardiac surgery","authors":"Rajarajan Ganesan,&nbsp;Tarunpreet Kaur,&nbsp;Alisha Mittal,&nbsp;Mansi Sahi,&nbsp;Sushant Konar,&nbsp;Tanvir Samra,&nbsp;Goverdhan Dutt Puri,&nbsp;Shayam Kumar Singh Thingnum,&nbsp;Nitin Auluck","doi":"10.1111/coin.12658","DOIUrl":"https://doi.org/10.1111/coin.12658","url":null,"abstract":"<p>The quality of machine learning (ML) models deployed in dynamic environments tends to decline over time due to disparities between the data used for training and the upcoming data available for prediction, which is commonly known as drift. Therefore, it is important for ML models to be capable of detecting any changes or drift in the data distribution and updating the ML model accordingly. This study presents various drift detection techniques to identify drift in the survival outcomes of patients who underwent cardiac surgery. Additionally, this study proposes several drift adaptation strategies, such as adaptive learning, incremental learning, and ensemble learning. Through a detailed analysis of the results, the study confirms the superior performance of ensemble model, achieving a minimum mean absolute error (MAE) of 10.684 and 2.827 for predicting hospital stay and ICU stay, respectively. Furthermore, the models that incorporate a drift adaptive framework exhibit superior performance compared to the models that do not include such a framework.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425080","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 novel feature integration method for named entity recognition model in product titles","authors":"Shiqi Sun,&nbsp;Kun Zhang,&nbsp;Jingyuan Li,&nbsp;Xinghang Sun,&nbsp;Jianhe Cen,&nbsp;Yuanzhuo Wang","doi":"10.1111/coin.12654","DOIUrl":"https://doi.org/10.1111/coin.12654","url":null,"abstract":"<p>Entity recognition of product titles is essential for retrieving and recommending product information. Due to the irregularity of product title text, such as informal sentence structure, a large number of professional attribute words, a large number of unrelated independent entities of various combinations, the existing general named entity recognition model is limited in the e-commerce field of product title entity recognition. Most of the current studies focus on only one of the two challenges instead of considering the two challenges together. Our approach proposes NEZHA-CNN-GlobalPointer architecture with the addition of label semantic network, and uses multigranularity contextual and label semantic information to fully capture the internal structure and category information of words and texts to improve the entity recognition accuracy. Through a series of experiments, we proved the efficiency of our approach over a dataset of Chinese product titles from JD.com, improving the F1-value by 5.98%, when compared to the BERT-LSTM-CRF model on the product title corpus.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425111","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":"Retraction: Ala Saleh Alluhaidan. Artificial intelligence for public perception of drones as a tool for telecommunication technologies. Comput Intell 40: e12507, 2024 (10.1111/coin.12507)","authors":"","doi":"10.1111/coin.12675","DOIUrl":"https://doi.org/10.1111/coin.12675","url":null,"abstract":"<p>The above article, published online on 17 February 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Diana Inkpen, and Wiley Periodicals LLC. The article was published as part of a guest-edited special issue. Following publication, it came to our attention that two of those named as Guest Editors of this issue were being impersonated and/or misrepresented by a fraudulent entity. An investigation by the publisher found that all of the articles, including this one, experienced compromised editorial handling and peer review which was not in line with the journal's ethical standards. Therefore, a decision has been made to retract this article. We did not find any evidence of misconduct by the authors. The authors have been informed of the decision to retract but do not agree with this decision.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.12675","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430269","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":"Deep learning for personalized health monitoring and prediction: A review","authors":"Robertas Damaševičius,&nbsp;Senthil Kumar Jagatheesaperumal,&nbsp;Rajesh N. V. P. S. Kandala,&nbsp;Sadiq Hussain,&nbsp;Roohallah Alizadehsani,&nbsp;Juan M. Gorriz","doi":"10.1111/coin.12682","DOIUrl":"https://doi.org/10.1111/coin.12682","url":null,"abstract":"<p>Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL-based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425112","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":"Retraction: K Logeswaran, P Suresh. High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation. Comput Intell 38: 1596–1615, 2022 (10.1111/coin.12490)","authors":"","doi":"10.1111/coin.12677","DOIUrl":"https://doi.org/10.1111/coin.12677","url":null,"abstract":"<p>The above article, published online on 14 November 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Diana Inkpen, and Wiley Periodicals LLC. The article was published as part of a guest-edited special issue. Following publication, it came to our attention that two of those named as Guest Editors of this issue were being impersonated and/or misrepresented by a fraudulent entity. An investigation by the publisher found that all of the articles, including this one, experienced compromised editorial handling and peer review which was not in line with the journal's ethical standards. Therefore, a decision has been made to retract this article. We did not find any evidence of misconduct by the authors. The authors have been informed of the decision to retract.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.12677","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430268","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}