Algorithms最新文献_第6页

A Novel Deep Reinforcement Learning (DRL) Algorithm to Apply Artificial Intelligence-Based Maintenance in Electrolysers 将基于人工智能的维护应用于电解槽的新型深度强化学习（DRL）算法

IF 2.3

Algorithms Pub Date : 2023-11-27 DOI: 10.3390/a16120541

Abiodun Abiola, Francisca Segura Manzano, J. Andújar

{"title":"A Novel Deep Reinforcement Learning (DRL) Algorithm to Apply Artificial Intelligence-Based Maintenance in Electrolysers","authors":"Abiodun Abiola, Francisca Segura Manzano, J. Andújar","doi":"10.3390/a16120541","DOIUrl":"https://doi.org/10.3390/a16120541","url":null,"abstract":"Hydrogen provides a clean source of energy that can be produced with the aid of electrolysers. For electrolysers to operate cost-effectively and safely, it is necessary to define an appropriate maintenance strategy. Predictive maintenance is one of such strategies but often relies on data from sensors which can also become faulty, resulting in false information. Consequently, maintenance will not be performed at the right time and failure will occur. To address this problem, the artificial intelligence concept is applied to make predictions on sensor readings based on data obtained from another instrument within the process. In this study, a novel algorithm is developed using Deep Reinforcement Learning (DRL) to select the best feature(s) among measured data of the electrolyser, which can best predict the target sensor data for predictive maintenance. The features are used as input into a type of deep neural network called long short-term memory (LSTM) to make predictions. The DLR developed has been compared with those found in literatures within the scope of this study. The results have been excellent and, in fact, have produced the best scores. Specifically, its correlation coefficient with the target variable was practically total (0.99). Likewise, the root-mean-square error (RMSE) between the experimental sensor data and the predicted variable was only 0.1351.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"6 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139234889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multi-Class Deep Learning Approach for Early Detection of Depressive and Anxiety Disorders Using Twitter Data 利用推特数据早期检测抑郁和焦虑症的多类深度学习方法

IF 2.3

Algorithms Pub Date : 2023-11-27 DOI: 10.3390/a16120543

Lamia Bendebane, Zakaria Laboudi, Asma Saighi, Hassan Al-Tarawneh, Adel Ouannas, Giuseppe Grassi

{"title":"A Multi-Class Deep Learning Approach for Early Detection of Depressive and Anxiety Disorders Using Twitter Data","authors":"Lamia Bendebane, Zakaria Laboudi, Asma Saighi, Hassan Al-Tarawneh, Adel Ouannas, Giuseppe Grassi","doi":"10.3390/a16120543","DOIUrl":"https://doi.org/10.3390/a16120543","url":null,"abstract":"Social media occupies an important place in people’s daily lives where users share various contents and topics such as thoughts, experiences, events and feelings. The massive use of social media has led to the generation of huge volumes of data. These data constitute a treasure trove, allowing the extraction of high volumes of relevant information particularly by involving deep learning techniques. Based on this context, various research studies have been carried out with the aim of studying the detection of mental disorders, notably depression and anxiety, through the analysis of data extracted from the Twitter platform. However, although these studies were able to achieve very satisfactory results, they nevertheless relied mainly on binary classification models by treating each mental disorder separately. Indeed, it would be better if we managed to develop systems capable of dealing with several mental disorders at the same time. To address this point, we propose a well-defined methodology involving the use of deep learning to develop effective multi-class models for detecting both depression and anxiety disorders through the analysis of tweets. The idea consists in testing a large number of deep learning models ranging from simple to hybrid variants to examine their strengths and weaknesses. Moreover, we involve the grid search technique to help find suitable values for the learning rate hyper-parameter due to its importance in training models. Our work is validated through several experiments and comparisons by considering various datasets and other binary classification models. The aim is to show the effectiveness of both the assumptions used to collect the data and the use of multi-class models rather than binary class models. Overall, the results obtained are satisfactory and very competitive compared to related works.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"24 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139229886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Cryptocurrency Price Forecasting by Integrating Machine Learning with Social Media and Market Data 通过将机器学习与社交媒体和市场数据相结合，增强加密货币价格预测能力

IF 2.3

Algorithms Pub Date : 2023-11-27 DOI: 10.3390/a16120542

Loris Belcastro, Domenico Carbone, Cristian Cosentino, F. Marozzo, Paolo Trunfio

{"title":"Enhancing Cryptocurrency Price Forecasting by Integrating Machine Learning with Social Media and Market Data","authors":"Loris Belcastro, Domenico Carbone, Cristian Cosentino, F. Marozzo, Paolo Trunfio","doi":"10.3390/a16120542","DOIUrl":"https://doi.org/10.3390/a16120542","url":null,"abstract":"Since the advent of Bitcoin, the cryptocurrency landscape has seen the emergence of several virtual currencies that have quickly established their presence in the global market. The dynamics of this market, influenced by a multitude of factors that are difficult to predict, pose a challenge to fully comprehend its underlying insights. This paper proposes a methodology for suggesting when it is appropriate to buy or sell cryptocurrencies, in order to maximize profits. Starting from large sets of market and social media data, our methodology combines different statistical, text analytics, and deep learning techniques to support a recommendation trading algorithm. In particular, we exploit additional information such as correlation between social media posts and price fluctuations, causal connection among prices, and the sentiment of social media users regarding cryptocurrencies. Several experiments were carried out on historical data to assess the effectiveness of the trading algorithm, achieving an overall average gain of 194% without transaction fees and 117% when considering fees. In particular, among the different types of cryptocurrencies considered (i.e., high capitalization, solid projects, and meme coins), the trading algorithm has proven to be very effective in predicting the price trends of influential meme coins, yielding considerably higher profits compared to other cryptocurrency types.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"61 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139230632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating the Frequencies of Maximal Theta-Gamma Coupling in EEG during the N-Back Task: Sensitivity to Methodology and Temporal Instability 估计 N-Back 任务期间脑电图中最大 Theta-Gamma 耦合的频率：对方法和时间不稳定性的敏感性

IF 2.3

Algorithms Pub Date : 2023-11-27 DOI: 10.3390/a16120540

D. Sinitsyn, A. Poydasheva, I. Bakulin, A. Zabirova, D. Lagoda, Natalia Suponeva, M. Piradov

{"title":"Estimating the Frequencies of Maximal Theta-Gamma Coupling in EEG during the N-Back Task: Sensitivity to Methodology and Temporal Instability","authors":"D. Sinitsyn, A. Poydasheva, I. Bakulin, A. Zabirova, D. Lagoda, Natalia Suponeva, M. Piradov","doi":"10.3390/a16120540","DOIUrl":"https://doi.org/10.3390/a16120540","url":null,"abstract":"Phase-amplitude coupling (PAC) of theta and gamma rhythms of the brain has been observed in animals and humans, with evidence of its involvement in cognitive functions and brain disorders. This motivates finding individual frequencies of maximal theta-gamma coupling (TGC) and using them to adjust brain stimulation. This use implies the stability of the frequencies at least during the investigation, which has not been sufficiently studied. Meanwhile, there is a range of available algorithms for PAC estimation in the literature. We explored several options at different steps of the calculation, applying the resulting algorithms to the EEG data of 16 healthy subjects performing the n-back working memory task, as well as a benchmark recording with previously reported strong PAC. By comparing the results for the two halves of each session, we estimated reproducibility at a time scale of a few minutes. For the benchmark data, the results were largely similar between the algorithms and stable over time. However, for the EEG, the results depended substantially on the algorithm, while also showing poor reproducibility, challenging the validity of using them for personalizing brain stimulation. Further research is needed on the PAC estimation algorithms, cognitive tasks, and other aspects to reliably determine and effectively use TGC parameters in neuromodulation.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"26 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139232675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparing Activation Functions in Machine Learning for Finite Element Simulations in Thermomechanical Forming 比较热机械成型有限元模拟机器学习中的激活函数

IF 2.3

Algorithms Pub Date : 2023-11-25 DOI: 10.3390/a16120537

Olivier Pantalé

引用次数: 0

Improved Load Frequency Control in Power Systems Hosting Wind Turbines by an Augmented Fractional Order PID Controller Optimized by the Powerful Owl Search Algorithm 利用强大的猫头鹰搜索算法优化的增量分数阶 PID 控制器改进风力涡轮机所在电力系统的负载频率控制

IF 2.3

Algorithms Pub Date : 2023-11-25 DOI: 10.3390/a16120539

F. Amiri, Mohsen Eskandari, Mohammad Hassan Moradi

{"title":"Improved Load Frequency Control in Power Systems Hosting Wind Turbines by an Augmented Fractional Order PID Controller Optimized by the Powerful Owl Search Algorithm","authors":"F. Amiri, Mohsen Eskandari, Mohammad Hassan Moradi","doi":"10.3390/a16120539","DOIUrl":"https://doi.org/10.3390/a16120539","url":null,"abstract":"The penetration of intermittent wind turbines in power systems imposes challenges to frequency stability. In this light, a new control method is presented in this paper by proposing a modified fractional order proportional integral derivative (FOPID) controller. This method focuses on the coordinated control of the load-frequency control (LFC) and superconducting magnetic energy storage (SMES) using a cascaded FOPD–FOPID controller. To improve the performance of the FOPD–FOPID controller, the developed owl search algorithm (DOSA) is used to optimize its parameters. The proposed control method is compared with several other methods, including LFC and SMES based on the robust controller, LFC and SMES based on the Moth swarm algorithm (MSA)–PID controller, LFC based on the MSA–PID controller with SMES, and LFC based on the MSA–PID controller without SMES in four scenarios. The results demonstrate the superior performance of the proposed method compared to the other mentioned methods. The proposed method is robust against load disturbances, disturbances caused by wind turbines, and system parameter uncertainties. The method suggested is characterized by its resilience in addressing the challenges posed by load disturbances, disruptions arising from wind turbines, and uncertainties surrounding system parameters.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"15 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139236305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Heart Disease Prediction Using Concatenated Hybrid Ensemble Classifiers 使用并列混合集合分类器预测心脏病

IF 2.3

Algorithms Pub Date : 2023-11-25 DOI: 10.3390/a16120538

A. B. Majumder, Somsubhra Gupta, Dharmpal Singh, Biwaranjan Acharya, V. Gerogiannis, Andreas Kanavos, P. Pintelas

{"title":"Heart Disease Prediction Using Concatenated Hybrid Ensemble Classifiers","authors":"A. B. Majumder, Somsubhra Gupta, Dharmpal Singh, Biwaranjan Acharya, V. Gerogiannis, Andreas Kanavos, P. Pintelas","doi":"10.3390/a16120538","DOIUrl":"https://doi.org/10.3390/a16120538","url":null,"abstract":"Heart disease is a leading global cause of mortality, demanding early detection for effective and timely medical intervention. In this study, we propose a machine learning-based model for early heart disease prediction. This model is trained on a dataset from the UC Irvine Machine Learning Repository (UCI) and employs the Extra Trees Classifier for performing feature selection. To ensure robust model training, we standardize this dataset using the StandardScaler method for data standardization, thus preserving the distribution shape and mitigating the impact of outliers. For the classification task, we introduce a novel approach, which is the concatenated hybrid ensemble voting classification. This method combines two hybrid ensemble classifiers, each one utilizing a distinct subset of base classifiers from a set that includes Support Vector Machine, Decision Tree, K-Nearest Neighbor, Logistic Regression, Adaboost and Naive Bayes. By leveraging the concatenated ensemble classifiers, the proposed model shows some promising performance results; in particular, it achieves an accuracy of 86.89%. The obtained results highlight the efficacy of combining the strengths of multiple base classifiers in the problem of early heart disease prediction, thus aiding and enabling timely medical intervention.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"70 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139237654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NDARTS: A Differentiable Architecture Search Based on the Neumann Series NDARTS：基于诺依曼数列的可微分架构搜索

IF 2.3

Algorithms Pub Date : 2023-11-25 DOI: 10.3390/a16120536

Xiaoyu Han, Chenyu Li, Zifan Wang, Guohua Liu

{"title":"NDARTS: A Differentiable Architecture Search Based on the Neumann Series","authors":"Xiaoyu Han, Chenyu Li, Zifan Wang, Guohua Liu","doi":"10.3390/a16120536","DOIUrl":"https://doi.org/10.3390/a16120536","url":null,"abstract":"Neural architecture search (NAS) has shown great potential in discovering powerful and flexible network models, becoming an important branch of automatic machine learning (AutoML). Although search methods based on reinforcement learning and evolutionary algorithms can find high-performance architectures, these search methods typically require hundreds of GPU days. Unlike searching in a discrete search space based on reinforcement learning and evolutionary algorithms, the differentiable neural architecture search (DARTS) continuously relaxes the search space, allowing for optimization using gradient-based methods. Based on DARTS, we propose NDARTS in this article. The new algorithm uses the Implicit Function Theorem and the Neumann series to approximate the hyper-gradient, which obtains better results than DARTS. In the simulation experiment, an ablation experiment was carried out to study the influence of the different parameters on the NDARTS algorithm and to determine the optimal weight, then the best performance of the NDARTS algorithm was searched for in the DARTS search space and the NAS-BENCH-201 search space. Compared with other NAS algorithms, the results showed that NDARTS achieved excellent results on the CIFAR-10, CIFAR-100, and ImageNet datasets, and was an effective neural architecture search algorithm.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"34 50","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139237093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing Algorithms Used for Constructing Confidence Ellipses in Multidimensional Scaling Solutions 评估用于构建多维标度解决方案置信椭圆的算法

IF 2.3

Algorithms Pub Date : 2023-11-24 DOI: 10.3390/a16120535

P. Nikitas, E. Nikita

{"title":"Assessing Algorithms Used for Constructing Confidence Ellipses in Multidimensional Scaling Solutions","authors":"P. Nikitas, E. Nikita","doi":"10.3390/a16120535","DOIUrl":"https://doi.org/10.3390/a16120535","url":null,"abstract":"This paper assesses algorithms proposed for constructing confidence ellipses in multidimensional scaling (MDS) solutions and proposes a new approach to interpreting these confidence ellipses via hierarchical cluster analysis (HCA). It is shown that the most effective algorithm for constructing confidence ellipses involves the generation of simulated distances based on the original multivariate dataset and then the creation of MDS maps that are scaled, reflected, rotated, translated, and finally superimposed. For this algorithm, the stability measure of the average areas tends to zero with increasing sample size n following the power model, An−B, with positive B values ranging from 0.7 to 2 and high R-squared fitting values around 0.99. This algorithm was applied to create confidence ellipses in the MDS plots of squared Euclidean and Mahalanobis distances for continuous and binary data. It was found that plotting confidence ellipses in MDS plots offers a better visualization of the distance map of the populations under study compared to plotting single points. However, the confidence ellipses cannot eliminate the subjective selection of clusters in the MDS plot based simply on the proximity of the MDS points. To overcome this subjective selection, we should quantify the formation of clusters of proximal samples. Thus, in addition to the algorithm assessment, we propose a new approach that estimates all possible cluster probabilities associated with the confidence ellipses by applying HCA using distance matrices derived from these ellipses.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"2004 13","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139239171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ship Detection Algorithm Based on YOLOv5 Network Improved with Lightweight Convolution and Attention Mechanism 利用轻量级卷积和注意力机制改进的基于 YOLOv5 网络的船舶探测算法

IF 2.3

Algorithms Pub Date : 2023-11-22 DOI: 10.3390/a16120534

Langyu Wang, Yan Zhang, Yahong Lin, Shuai Yan, Yuanyuan Xu, Bo Sun

引用次数: 0