Neural Computing & Applications最新文献

{"title":"Efficient multi-task learning with adaptive temporal structure for progression prediction.","authors":"Menghui Zhou, Yu Zhang, Tong Liu, Yun Yang, Po Yang","doi":"10.1007/s00521-023-08461-9","DOIUrl":"10.1007/s00521-023-08461-9","url":null,"abstract":"<p><p>In this paper, we propose a novel efficient multi-task learning formulation for the class of progression problems in which its state will continuously change over time. To use the shared knowledge information between multiple tasks to improve performance, existing multi-task learning methods mainly focus on feature selection or optimizing the task relation structure. The feature selection methods usually fail to explore the complex relationship between tasks and thus have limited performance. The methods centring on optimizing the relation structure of tasks are not capable of selecting meaningful features and have a bi-convex objective function which results in high computation complexity of the associated optimization algorithm. Unlike these multi-task learning methods, motivated by a simple and direct idea that the state of a system at the current time point should be related to all previous time points, we first propose a novel relation structure, termed adaptive global temporal relation structure (AGTS). Then we integrate the widely used sparse group Lasso, fused Lasso with AGTS to propose a novel convex multi-task learning formulation that not only performs feature selection but also adaptively captures the global temporal task relatedness. Since the existence of three non-smooth penalties, the objective function is challenging to solve. We first design an optimization algorithm based on the alternating direction method of multipliers (ADMM). Considering that the worst-case convergence rate of ADMM is only sub-linear, we then devise an efficient algorithm based on the accelerated gradient method which has the optimal convergence rate among first-order methods. We show the proximal operator of several non-smooth penalties can be solved efficiently due to the special structure of our formulation. Experimental results on four real-world datasets demonstrate that our approach not only outperforms multiple baseline MTL methods in terms of effectiveness but also has high efficiency.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":" ","pages":"1-16"},"PeriodicalIF":4.5,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10171734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9771492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Multilingual text categorization and sentiment analysis: a comparative analysis of the utilization of multilingual approaches for classifying twitter data.","authors":"George Manias,&nbsp;Argyro Mavrogiorgou,&nbsp;Athanasios Kiourtis,&nbsp;Chrysostomos Symvoulidis,&nbsp;Dimosthenis Kyriazis","doi":"10.1007/s00521-023-08629-3","DOIUrl":"10.1007/s00521-023-08629-3","url":null,"abstract":"<p><p>Text categorization and sentiment analysis are two of the most typical natural language processing tasks with various emerging applications implemented and utilized in different domains, such as health care and policy making. At the same time, the tremendous growth in the popularity and usage of social media, such as Twitter, has resulted on an immense increase in user-generated data, as mainly represented by the corresponding texts in users' posts. However, the analysis of these specific data and the extraction of actionable knowledge and added value out of them is a challenging task due to the domain diversity and the high multilingualism that characterizes these data. The latter highlights the emerging need for the implementation and utilization of domain-agnostic and multilingual solutions. To investigate a portion of these challenges this research work performs a comparative analysis of multilingual approaches for classifying both the sentiment and the text of an examined multilingual corpus. In this context, four multilingual BERT-based classifiers and a zero-shot classification approach are utilized and compared in terms of their accuracy and applicability in the classification of multilingual data. Their comparison has unveiled insightful outcomes and has a twofold interpretation. Multilingual BERT-based classifiers achieve high performances and transfer inference when trained and fine-tuned on multilingual data. While also the zero-shot approach presents a novel technique for creating multilingual solutions in a faster, more efficient, and scalable way. It can easily be fitted to new languages and new tasks while achieving relatively good results across many languages. However, when efficiency and scalability are less important than accuracy, it seems that this model, and zero-shot models in general, can not be compared to fine-tuned and trained multilingual BERT-based classifiers.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":" ","pages":"1-17"},"PeriodicalIF":6.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10165589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9715044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data.","authors":"Tawsifur Rahman,&nbsp;Muhammad E H Chowdhury,&nbsp;Amith Khandakar,&nbsp;Zaid Bin Mahbub,&nbsp;Md Sakib Abrar Hossain,&nbsp;Abraham Alhatou,&nbsp;Eynas Abdalla,&nbsp;Sreekumar Muthiyal,&nbsp;Khandaker Farzana Islam,&nbsp;Saad Bin Abul Kashem,&nbsp;Muhammad Salman Khan,&nbsp;Susu M Zughaier,&nbsp;Maqsud Hossain","doi":"10.1007/s00521-023-08606-w","DOIUrl":"10.1007/s00521-023-08606-w","url":null,"abstract":"<p><p>Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and <i>F</i>1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O₂%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s00521-023-08606-w.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":" ","pages":"1-23"},"PeriodicalIF":6.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10157130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9717265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"A comparative study of anti-swing radial basis neural-fuzzy LQR controller for multi-degree-of-freedom rotary pendulum systems","authors":"Zied Ben Hazem, Z. Bingul","doi":"10.1007/s00521-023-08599-6","DOIUrl":"https://doi.org/10.1007/s00521-023-08599-6","url":null,"abstract":"","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"27 3 1","pages":"17397-17413"},"PeriodicalIF":6.0,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73803663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 new Covid-19 diagnosis strategy using a modified KNN classifier.","authors":"Asmaa H Rabie,&nbsp;Alaa M Mohamed,&nbsp;M A Abo-Elsoud,&nbsp;Ahmed I Saleh","doi":"10.1007/s00521-023-08588-9","DOIUrl":"10.1007/s00521-023-08588-9","url":null,"abstract":"<p><p>Covid-19 is a very dangerous disease as a result of the rapid and unprecedented spread of any previous disease. It is truly a crisis that threatens the world since its first appearance in December 2019 until our time. Due to the lack of a vaccine that has proved sufficiently effective so far, the rapid and more accurate diagnosis of this disease is extremely necessary to enable the medical staff to identify infected cases and isolate them from the rest to prevent further loss of life. In this paper, Covid-19 diagnostic strategy (CDS) as a new classification strategy that consists of two basic phases: Feature selection phase (FSP) and diagnosis phase (DP) has been introduced. During the first phase called FSP, the best set of features in laboratory test findings for Covid-19 patients will be selected using enhanced gray wolf optimization (EGWO). EGWO combines both types of selection techniques called wrapper and filter. Accordingly, EGWO includes two stages called filter stage (FS) and wrapper stage (WS). While FS uses many different filter methods, WS uses a wrapper method called binary gray wolf optimization (BGWO). The second phase called DP aims to give fast and more accurate diagnosis using a hybrid diagnosis methodology (HDM) based on the selected features from FSP. In fact, the HDM consists of two phases called weighting patient phase (WP²) and diagnostic patient phase (DP²). WP² aims to calculate the belonging degree of each patient in the testing dataset to class category using naïve Bayes (NB) as a weight method. On the other hand, K-nearest neighbor (KNN) will be used in DP² based on the weights of patients in the testing dataset as a new training dataset to give rapid and more accurate detection. The suggested CDS outperforms other strategies according to accuracy, precision, recall (or sensitivity) and F-measure calculations that are equal to 99%, 88%, 90% and 91%, respectively, as showed in experimental results.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":" ","pages":"1-25"},"PeriodicalIF":6.0,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9717272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"MOCOVIDOA: a novel multi-objective coronavirus disease optimization algorithm for solving multi-objective optimization problems.","authors":"Asmaa M Khalid,&nbsp;Hanaa M Hamza,&nbsp;Seyedali Mirjalili,&nbsp;Khaid M Hosny","doi":"10.1007/s00521-023-08587-w","DOIUrl":"10.1007/s00521-023-08587-w","url":null,"abstract":"<p><p>A novel multi-objective Coronavirus disease optimization algorithm (MOCOVIDOA) is presented to solve global optimization problems with up to three objective functions. This algorithm used an archive to store non-dominated POSs during the optimization process. Then, a roulette wheel selection mechanism selects the effective archived solutions by simulating the frameshifting technique Coronavirus particles use for replication. We evaluated the efficiency by solving twenty-seven multi-objective (21 benchmarks & 6 real-world engineering design) problems, where the results are compared against five common multi-objective metaheuristics. The comparison uses six evaluation metrics, including IGD, GD, MS, SP, HV, and delta <i>p</i> (<math><mrow><mi>Δ</mi><mi>P</mi></mrow></math>). The obtained results and the Wilcoxon rank-sum test show the superiority of this novel algorithm over the existing algorithms and reveal its applicability in solving multi-objective problems.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":" ","pages":"1-29"},"PeriodicalIF":6.0,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153059/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9708143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"A new classification method for diagnosing COVID-19 pneumonia based on joint CNN features of chest X-ray images and parallel pyramid MLP-mixer module.","authors":"Yiwen Liu, Wenyu Xing, Mingbo Zhao, Mingquan Lin","doi":"10.1007/s00521-023-08604-y","DOIUrl":"10.1007/s00521-023-08604-y","url":null,"abstract":"<p><p>During the past three years, the coronavirus disease 2019 (COVID-19) has swept the world. The rapid and accurate recognition of covid-19 pneumonia are ,therefore, of great importance. To handle this problem, we propose a new pipeline of deep learning framework for diagnosing COVID-19 pneumonia via chest X-ray images from normal, COVID-19, and other pneumonia patients. In detail, the self-trained YOLO-v4 network was first used to locate and segment the thoracic region, and the output images were scaled to the same size. Subsequently, the pre-trained convolutional neural network was adopted to extract the features of X-ray images from 13 convolutional layers, which were fused with the original image to form a 14-dimensional image matrix. It was then put into three parallel pyramid multi-layer perceptron (MLP)-Mixer modules for comprehensive feature extraction through spatial fusion and channel fusion based on different scales so as to grasp more extensive feature correlation. Finally, by combining all image features from the 14-channel output, the classification task was achieved using two fully connected layers as well as Softmax classifier for classification. Extensive simulations based on a total of 4099 chest X-ray images were conducted to verify the effectiveness of the proposed method. Experimental results indicated that our proposed method can achieve the best performance in almost all cases, which is good for auxiliary diagnosis of COVID-19 and has great clinical application potential.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":" ","pages":"1-13"},"PeriodicalIF":4.5,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10147369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9720341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Time-series benchmarks based on frequency features for fair comparative evaluation.","authors":"Zhou Wu,&nbsp;Ruiqi Jiang","doi":"10.1007/s00521-023-08562-5","DOIUrl":"10.1007/s00521-023-08562-5","url":null,"abstract":"<p><p>Time-series prediction and imputation receive lots of attention in academic and industrial areas. Machine learning methods have been developed for specific time-series scenarios; however, it is difficult to evaluate the effectiveness of a certain method on other new cases. In the perspective of frequency features, a comprehensive benchmark for time-series prediction is designed for fair evaluation. A prediction problem generation process, composed of the finite impulse response filter-based approach and problem setting module, is adopted to generate the NCAA2022 dataset, which includes 16 prediction problems. To reduce the computational burden, the filter parameters matrix is divided into sub-matrices. The discrete Fourier transform is introduced to analyze the frequency distribution of transformed results. In addition, a baseline experiment further reflects the benchmarking capability of NCAA2022 dataset.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":" ","pages":"1-13"},"PeriodicalIF":6.0,"publicationDate":"2023-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10122570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9717266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"An intelligent identification and classification system for malicious uniform resource locators (URLs).","authors":"Qasem Abu Al-Haija,&nbsp;Mustafa Al-Fayoumi","doi":"10.1007/s00521-023-08592-z","DOIUrl":"10.1007/s00521-023-08592-z","url":null,"abstract":"<p><p>Uniform Resource Locator (URL) is a unique identifier composed of protocol and domain name used to locate and retrieve a resource on the Internet. Like any Internet service, URLs (also called websites) are vulnerable to compromise by attackers to develop Malicious URLs that can exploit/devastate the user's information and resources. Malicious URLs are usually designed with the intention of promoting cyber-attacks such as spam, phishing, malware, and defacement. These websites usually require action on the user's side and can reach users across emails, text messages, pop-ups, or devious advertisements. They have a potential impact that can reach, in some cases, to compromise the machine or network of the user, especially those arriving by email. Therefore, developing systems to detect malicious URLs is of great interest nowadays. This paper proposes a high-performance machine learning-based detection system to identify Malicious URLs. The proposed system provides two layers of detection. Firstly, we identify the URLs as either benign or malware using a binary classifier. Secondly, we classify the URL classes based on their feature into five classes: benign, spam, phishing, malware, and defacement. Specifically, we report on four ensemble learning approaches, viz. the ensemble of bagging trees (En_Bag) approach, the ensemble of k-nearest neighbor (En_kNN) approach, and the ensemble of boosted decision trees (En_Bos) approach, and the ensemble of subspace discriminator (En_Dsc) approach. The developed approaches have been evaluated on an inclusive and contemporary dataset for uniform resource locators (ISCX-URL2016). ISCX-URL2016 provides a lightweight dataset for detecting and categorizing malicious URLs according to their attack type and lexical analysis. Conventional machine learning evaluation measurements are used to evaluate the detection accuracy, precision, recall, F Score, and detection time. Our experiential assessment indicates that the ensemble of bagging trees (En_Bag) approach provides better performance rates than other ensemble methods. Alternatively, the ensemble of the k-nearest neighbor (En_kNN) approach provides the highest inference speed. We also contrast our En_Bag model with state-of-the-art solutions and show its superiority in binary classification and multi-classification with accuracy rates of 99.3% and 97.92%, respectively.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":" ","pages":"1-17"},"PeriodicalIF":6.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10117275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9771495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Quantized ℋ∞ stabilization for delayed memristive neural networks","authors":"Zhilian Yan, Dandan Zuo, Tong Guo, Jianping Zhou","doi":"10.1007/s00521-023-08510-3","DOIUrl":"https://doi.org/10.1007/s00521-023-08510-3","url":null,"abstract":"","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"138 1","pages":"16473-16486"},"PeriodicalIF":6.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73419898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}