Automatic Learning Techniques Research Articles

An approach for classification of breast cancer using lightweight deep convolution neural network

The rapid advancement of deep learning has generated considerable enthusiasm regarding its utilization in addressing medical imaging issues. Machine learning (ML) methods can help radiologists to diagnose breast cancer (BCs) barring invasive measures. Informative hand-crafted features are essential prerequisites for traditional machine learning classifiers to achieve accurate results, which are time-consuming to extract. In this paper, our deep learning algorithm is created to precisely identify breast cancers on screening mammograms, employing a training method that effectively utilizes training datasets with either full clinical annotation or solely the cancer status of the entire image. The proposed approach utilizes Lightweight Convolutional Neural Network (LWCNN) that allows automatic extraction features in an end-to-end manner. We have tested LWCNN model in two experiments. In the first experiment, the model was tested with two cases' original and enhancement datasets 1. It achieved 95 %, 93 %, 99 % and 98 % for training and testing accuracy respectively. In the second experiment, the model has been tested with two cases' original and enhancement datasets 2. It achieved 95 %, 91 %, 99 % and 92 % for training and testing accuracy respectively. Our proposed method, which uses various convolutional network to classify screening mammograms achieved exceptional performance when compared to other methods. The findings from these experiments clearly indicate that automatic deep learning techniques can be trained effectively to attain remarkable accuracy across a wide range of mammography datasets. This holds significant promise for improving clinical tools and reducing both false positive and false negative outcomes in screening mammography.

Spatio-temporal change and driving mechanisms of land use/cover in Qarhan Salt Lake area during from 2000 to 2020, based on machine learning

The significance of land use classification has garnered attention due to its implications for climate and ecosystems. This paper establishes a connection by introducing and applying automatic machine learning (Auto ML) techniques to salt lake landscape, with a specific focus on the Qarhan Salt Lake area. Utilizing Landsat-5 Thematic Mappe (TM) and Landsat-8 Operational Land Imager (OLI) imagery, six machine learning algorithms were employed to classify eight land use types from 2000 to 2020. Results show that XGBLD performed optimally with 77% accuracy. Over two decades, salt fields, construction land, and water areas increased due to transformations in saline land and salt flats. The exposed lakes area exhibited a rise followed by a decline, mainly transforming into salt flats. Agricultural land areas slightly increased, influenced by both human activities and climate. Our analysis reveals a strong correlation between salt fields and precipitation, while exposed lakes demonstrate a significant negative correlation with evaporation and temperature, highlighting their vulnerability to climate change. Additionally, human water usage was identified as a significant factor impacting land use change, emphasizing the dual influence of anthropogenic activities and natural factors. This paper addresses the void in the application of Auto ML in salt lake environments and provides valuable insights into the dynamic evolution of land use types in the Qarhan Salt Lake region.

Prediction of Total Petroleum Hydrocarbons and Heavy Metals in Acid Tars Using Machine Learning

Hazardous petroleum wastes are an inevitable source of environmental pollution. Leachates from these wastes could contaminate soil and potable water sources and affect human health. The management of acid tars, as a byproduct of refining and petrochemical processes, represented one of the major hazardous waste problems in Romania. Acid tars are hazardous and toxic waste and have the potential to cause pollution and environmental damage. The need for the identification, study, characterization, and subsequently either the treatment, valorization, or elimination of acid tars is determined by the fact that they also have high concentrations of hydrocarbons and heavy metals, toxic for the storage site and its neighboring residential area. When soil contamination with acid tars occurs, sustainable remediation techniques are needed to restore soil quality to a healthy production state. Therefore, it is necessary to ensure a rapid but robust characterization of the degree of contamination with hydrocarbons and heavy metals in acid tars so that appropriate techniques can then be used for treatment/remediation. The first stage in treating these acid tars is to determine its properties. This article presents a software program that uses machine learning to estimate selected properties of acid tars (pH, Total Petroleum Hydrocarbons—TPH, and heavy metals). The program uses the Automatic Machine Learning technique to determine the Machine Learning algorithm that has the lowest estimation error for the given dataset, with respect to the Mean Average Error and Root Mean Squared Error. The chosen algorithm is used further for properties estimation, using the R2 correlation coefficient as a performance criterion. The dataset used for training has 82 experimental points with continuous, unique values containing the coordinates and depth of acid tar samples and their properties. Based on an exhaustive search performed by the authors, a similar study that considers machine learning applications was not found in the literature. Further research is required because the method presented therein can be improved because it is dataset dependent, as is the case with every ML problem.

Classification of dementia from spoken speech using feature selection and the bag of acoustic words model

<p>Memory disorders and dementia are a central factor in the decline of functioning and daily activities in older individuals. The workload related to standardized speech tests in clinical settings has led to a growing emphasis on developing automatic machine learning techniques for analyzing naturally spoken speech. This study presented a bag of acoustic words approach for distinguishing dementia patients from control individuals based on audio speech recordings. In this approach, each individual's speech was segmented into voiced periods, and these segments were characterized by acoustic features using the open-source openSMILE library. Word histogram representations were formed from the characterized speech segments of each speaker, which were used for classifying subjects. The formation of word histograms involved a clustering phase where feature vectors were quantized. It is well-known that partitional clustering involves instability in clustering results due to the selection of starting points, which can cause variability in classification outcomes. This study aimed to address instability by utilizing robust K-spatial-medians clustering, efficient K-means$ ++ $ clustering initialization, and selecting the smallest clustering error from repeated clusterings. Additionally, the study employed feature selection based on the Wilcoxon signed-rank test to achieve computational efficiency in the methods. The results showed that it is possible to achieve a consistent 75% classification accuracy using only twenty-five features, both with the external ADReSS 2020 test data and through leave-one-subject-out cross-validation of the entire dataset. The results rank at the top compared to international research, where the same dataset and only acoustic features have been used to diagnose patients.</p>

Evaluating data-driven and hybrid modeling of terrestrial actual evapotranspiration based on an automatic machine learning approach

The performances of physics-based, data-driven, and hybrid models for estimating terrestrial actual evapotranspiration (ETa) is currently under debate, which requires thorough evaluations of those models particularly with recent developments in automatic machine learning (AML) techniques. In this study, six AML-based models were first constructed using the H2O-AML platform, from which an optimal (AML-OP) model was selected for estimating daily ETa at ecosystem scales. In addition, hybrid models were developed by combining the six AML models with surface conductance (Gs) inverted from the Penman-Monteith equation and an optimal (PM-OP) model was also selected. With 15 predictor variables as model inputs that were compiled from various data sources, the performances of those models for estimating daily ETa were evaluated using observed data from the FLUXNET2015 dataset. The results revealed that no models showed consistently low noise levels across different ecosystem types, making it necessary to use AML techniques for selecting ecosystem-specific models. Interestingly, the AML-OP models (root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE) were 0.16–0.31 mm d-1 and 9 %–36 % respectively) showed slightly better predictive results than the PM-OP models (RMSE and SMAPE were 0.23–0.36 mm d-1 and 15 %–68 % respectively), likely owing to model parameter uncertainties and tight constraints of physical models on application condition. Secondly, as ETa nonlinearly responds to environmental variables, model predictability under extreme weather (drought and heatwave) conditions was examined. The results showed that the prediction of the AML-OP and PM-OP models expectedly worsened (RMSE and SMAPE increased by 0.06–0.77 mm d-1 and −19 % to 79 %, respectively); however, the AML-OP model still outperformed the PM-OP model in most ecosystems, further underscoring the need to understand ETa regulation mechanisms under varying climatic conditions. Finally, the PM-OP models developed here provided better daily ETa estimates compared to other recently proposed hybrid models (RMSE reduced by 0.98–1.80 mm d-1). Both models can be better applied to wetlands that have been less frequently evaluated previously (RMSE reduction of 0.22 mm d-1 and 0.18 mm d-1 for the AML-OP and PM-OP models).

Sprinkler Irrigation Automation System to Reduce the Frost Impact Using Machine Learning

Frosts reduce the ambient temperature to the freezing point of water, affecting the agricultural sector and the integrity of plant tissues, severely damaged by freezing, destroying plant cells. In addition, losses are generated in the economy due to the death of cattle due to cold, hunger, diseases, etc. Latin America is a region that depends, to a considerable extent, on its crops for its consumption and export, so frost represents an urgent problem to solve, considering that in Perú the area of agriculture is not technical. Among the methods most used by farmers is anticipated irrigation, through automatic learning techniques, which allows predicting the behavior of a variable based on previous historical data. In this paper, sprinkler irrigation is implemented in crops exposed to frost, using an automated system with machine learning techniques and prediction models. Therefore, three types of models are evaluated (linear regression, random forests, and decision trees) to predict the occurrence of frosts, reducing damage to plants. The results show that the protection activation indicator from 1.1°C to 1.7°C was updated to decrease the number of false positives. On the three models evaluated, it is determined that the most accurate method is the Random Forest Regression method, which has 80.91% reliability, absolute mean error, and mean square error close to zero.

AI-BASED DECISION-MAKING TOOLS FOR PORT MANAGEMENT: SHIP-INFRASTRUCTURE OPERABILITY AND OVERTOPPING

In recent years, port operators have shown an increasing interest in developing innovative automatic learning techniques, to provide complete predictive packages for safety and efficiency systems, trained and calibrated with field database. Another relevant aspect in these kinds of projects is to determine the long wave influence. The port-ship resonance, due to the coincidence between the long wave periods and the ship horizontal motions, causes downtimes with consequent economic losses. The Port Authority of A Coruña (Spain) started years ago to work towards the application of Artificial Intelligence, providing Machine Learning-based models in their facilities of its Outer Port of Punta Langosteira, together with the University of A Coruña. The main objective was to provide a system to predict operability and overtopping with the forecast of maritime climate information.

Optical coherence tomography imaging can identify Merino lambs’ wool using automatic machine learning vision

Merino lambs’ wool fiber has a unique chemical structure that gives the wool many unique properties and technical benefits. For example, the small fiber diameters mean that Merino wool is soft, countering the scratchiness commonly associated with wool. It is also biodegradable, organic, and environmentally friendly, making it a popular choice of sustainable fabric material. Unfortunately, some fiber sellers unduly sell coarse wool tops to wool yarn factories as Merino wool tops. It is, therefore, an important task to identify the actual wool type for quality assurance in the textile manufacturing process. This paper describes applying the spectral-domain optical coherence tomography (OCT) imaging and automatic machine learning (AutoML) techniques for distinguishing Merino wool from coarse wool. We present the results of wool measurements that were performed by the OCT scans and AutoML algorithms. We conclude that OCT imaging and AutoML algorithms can be applied to distinguish Merino wool from coarse wool in a simple, non-destructive, and contactless manner.

Evaluation of Matrix Metalloproteases by Artificial Intelligence Techniques in Negative Biopsies as New Diagnostic Strategy in Prostate Cancer.

Usually, after an abnormal level of serum prostate-specific antigen (PSA) or digital rectal exam, men undergo a prostate needle biopsy. However, the traditional sextant technique misses 15-46% of cancers. At present, there are problems regarding disease diagnosis/prognosis, especially in patients' classification, because the information to be handled is complex and challenging to process. Matrix metalloproteases (MMPs) have high expression by prostate cancer (PCa) compared with benign prostate tissues. To assess the possible contribution to the diagnosis of PCa, we evaluated the expression of several MMPs in prostate tissues before and after PCa diagnosis using machine learning, classifiers, and supervised algorithms. A retrospective study was conducted on 29 patients diagnosed with PCa with previous benign needle biopsies, 45 patients with benign prostatic hyperplasia (BHP), and 18 patients with high-grade prostatic intraepithelial neoplasia (HGPIN). An immunohistochemical study was performed on tissue samples from tumor and non-tumor areas using specific antibodies against MMP -2, 9, 11, and 13, and the tissue inhibitor of MMPs -3 (TIMP-3), and the protein expression by different cell types was analyzed to which several automatic learning techniques have been applied. Compared with BHP or HGPIN specimens, epithelial cells (ECs) and fibroblasts from benign prostate biopsies before the diagnosis of PCa showed a significantly higher expression of MMPs and TIMP-3. Machine learning techniques provide a differentiable classification between these patients, with greater than 95% accuracy, considering ECs, being slightly lower when considering fibroblasts. In addition, evolutionary changes were found in paired tissues from benign biopsy to prostatectomy specimens in the same patient. Thus, ECs from the tumor zone from prostatectomy showed higher expressions of MMPs and TIMP-3 compared to ECs of the corresponding zone from the benign biopsy. Similar differences were found for expressions of MMP-9 and TIMP-3, between fibroblasts from these zones. The classifiers have determined that patients with benign prostate biopsies before the diagnosis of PCa showed a high MMPs/TIMP-3 expression by ECs, so in the zone without future cancer development as in the zone with future tumor, compared with biopsy samples from patients with BPH or HGPIN. Expression of MMP -2, 9, 11, and 13, and TIMP-3 phenotypically define ECs associated with future tumor development. Also, the results suggest that MMPs/TIMPs expression in biopsy tissues may reflect evolutionary changes from prostate benign tissues to PCa. Thus, these findings in combination with other parameters might contribute to improving the suspicion of PCa diagnosis.

Surface Damage Identification for Heritage Site Protection: A Mobile Crowd-sensing Solution Based on Deep Learning

This article addresses the general problem of built heritage protection against both deterioration and loss. To continuously monitor and update the structural health status, a crowd-sensing solution based on powerful and automatic deep learning technique is proposed. The aim of this solution is to get rid of the limitations of manual and visual damage detection methods that are costly and time-consuming. Instead, automatic visual inspection for damage detection on walls is efficiently and effectively performed using an embedded Convolutional Neural Network (CNN). This CNN detects the most frequent types of surface damage on wall photos. The study has been conducted in the Kasbah of Algiers where the four following types of damages have been considered: Efflorescence, spall, crack, and mold. The CNN is designed and trained to be integrated into a mobile application for a participatory crowd-sensing solution. The application should be widely and freely deployed, so any user can take a picture of a suspected damaged wall and get an instant and automatic diagnosis through the embedded CNN. In this context, we have chosen MobileNetV2 with a transfer learning approach. A set of real images have been collected and manually annotated and have been used for training, validation, and test. Extensive experiments have been conducted to assess the efficiency and the effectiveness of the proposed solution, using a 5-fold cross-validation procedure. Obtained results show in particular a mean weighted average precision of 0.868 ± 0.00862 (with a 99% of confidence level) and a mean weighted average recall of 0.84 ± 0.00729 (with a 99% of confidence level). To evaluate the performance of MobileNetV2 as a feature extractor, we conducted a comparative study with other small backbones. Further analysis of CNN activation using Grad-Cam has also been done. Obtained results show that our method remains effective even when using a small network and medium- to low-resolution images. MobileNetV2-based CNN size is smaller, and computational cost better, compared to the other CNNs, with similar performance results. Finally, detected surface damages have also been plotted on a geographic map, giving a global view of their distribution.

A Deep Learning Approach to Estimate the Incidence of Infectious Disease Cases for Routinely Collected Ambulatory Records: The Example of Varicella-Zoster.

The burden of infectious diseases is crucial for both epidemiological surveillance and prompt public health response. A variety of data, including textual sources, can be fruitfully exploited. Dealing with unstructured data necessitates the use of methods for automatic data-driven variable construction and machine learning techniques (MLT) show promising results. In this framework, varicella-zoster virus (VZV) infection was chosen to perform an automatic case identification with MLT. Pedianet, an Italian pediatric primary care database, was used to train a series of models to identify whether a child was diagnosed with VZV infection between 2004 and 2014 in the Veneto region, starting from free text fields. Given the nature of the task, a recurrent neural network (RNN) with bidirectional gated recurrent units (GRUs) was chosen; the same models were then used to predict the children’s status for the following years. A gold standard produced by manual extraction for the same interval was available for comparison. RNN-GRU improved its performance over time, reaching the maximum value of area under the ROC curve (AUC-ROC) of 95.30% at the end of the period. The absolute bias in estimates of VZV infection was below 1.5% in the last five years analyzed. The findings in this study could assist the large-scale use of EHRs for clinical outcome predictive modeling and help establish high-performance systems in other medical domains.

Feature importance: Opening a soil-transmitted helminth machine learning model via SHAP.

In the field of landscape epidemiology, the contribution of machine learning (ML) to modeling of epidemiological risk scenarios presents itself as a good alternative. This study aims to break with the ”black box” paradigm that underlies the application of automatic learning techniques by using SHAP to determine the contribution of each variable in ML models applied to geospatial health, using the prevalence of hookworms, intestinal parasites, in Ethiopia, where they are widely distributed; the country bears the third-highest burden of hookworm in Sub-Saharan Africa. XGBoost software was used, a very popular ML model, to fit and analyze the data. The Python SHAP library was used to understand the importance in the trained model, of the variables for predictions. The description of the contribution of these variables on a particular prediction was obtained, using different types of plot methods. The results show that the ML models are superior to the classical statistical models; not only demonstrating similar results but also explaining, by using the SHAP package, the influence and interactions between the variables in the generated models. This analysis provides information to help understand the epidemiological problem presented and provides a tool for similar studies.

Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method

In the world of optimization, especially concerning metaheuristics, solving complex problems represented by applying big data and constraint instances can be difficult. This is mainly due to the difficulty of implementing efficient solutions that can solve complex optimization problems in adequate time, which do exist in different industries. Big data has demonstrated its efficiency in solving different concerns in information management. In this paper, an approach based on multiprocessing is proposed wherein clusterization and parallelism are used together to improve the search process of metaheuristics when solving large instances of complex optimization problems, incorporating collaborative elements that enhance the quality of the solution. The proposal deals with machine learning algorithms to improve the segmentation of the search space. Particularly, two different clustering methods belonging to automatic learning techniques, are implemented on bio-inspired algorithms to smartly initialize their solution population, and then organize the resolution from the beginning of the search. The results show that this approach is competitive with other techniques in solving a large set of cases of a well-known NP-hard problem without incorporating too much additional complexity into the metaheuristic algorithms.

Neural architecture search via standard machine learning methodologies

<abstract><p>In the context of deep learning, the more expensive computational phase is the full training of the learning methodology. Indeed, its effectiveness depends on the choice of proper values for the so-called hyperparameters, namely the parameters that are not trained during the learning process, and such a selection typically requires an extensive numerical investigation with the execution of a significant number of experimental trials. The aim of the paper is to investigate how to choose the hyperparameters related to both the architecture of a Convolutional Neural Network (CNN), such as the number of filters and the kernel size at each convolutional layer, and the optimisation algorithm employed to train the CNN itself, such as the steplength, the mini-batch size and the potential adoption of variance reduction techniques. The main contribution of the paper consists in introducing an automatic Machine Learning technique to set these hyperparameters in such a way that a measure of the CNN performance can be optimised. In particular, given a set of values for the hyperparameters, we propose a low-cost strategy to predict the performance of the corresponding CNN, based on its behavior after only few steps of the training process. To achieve this goal, we generate a dataset whose input samples are provided by a limited number of hyperparameter configurations together with the corresponding CNN measures of performance obtained with only few steps of the CNN training process, while the label of each input sample is the performance corresponding to a complete training of the CNN. Such dataset is used as training set for a Support Vector Machines for Regression and/or Random Forest techniques to predict the performance of the considered learning methodology, given its performance at the initial iterations of its learning process. Furthermore, by a probabilistic exploration of the hyperparameter space, we are able to find, at a quite low cost, the setting of a CNN hyperparameters which provides the optimal performance. The results of an extensive numerical experimentation, carried out on CNNs, together with the use of our performance predictor with NAS-Bench-101, highlight how the proposed methodology for the hyperparameter setting appears very promising.</p></abstract>

IoT based monitoring of air quality and traffic using regression analysis

Dynamic traffic management (DTM) systems are used to reduce the negative externalities of traffic congestion, such as air pollution in urban areas. They require traffic and environmental monitoring infrastructures. In this paper we present a prototype of a low-cost Internet of Things (IoT) system for monitoring traffic flow and the Air Quality Index (AQI). The computation of the traffic flows is based on processing video in the compressed domain. Only using motion vectors as input, traffic flow is computed in real-time over an embedded architecture. An estimation of the AQI is supported by machine learning regression techniques, using different feature data obtained from the IoT device. These automatic learning techniques overcome the need for complex calibration and other limitations of embedded devices in making the needed measurements of the pollutant gases for the computation of the actual AQI. The experimentation with the data obtained from different cities representing different scenarios with a variety of climate and traffic conditions, allows validating the proposed architecture. As regressors, Linear Regression (LR), Gaussian Process Regression (GPR) and Random Forest (RF) are compared using the performance metrics R2, MSE, MAE and MRE resulting in a relevant improvement of the AQI estimations of our proposal.

Machine learning based analysis of speech dimensions in functional oropharyngeal dysphagia

Background and objective: The normal swallowing process requires a complex coordination of anatomical structures driven by sensory and cranial nerves. Alterations in such coordination cause swallowing malfunctions, namely dysphagia. The dysphagia screening methods are quite subjective and experience dependent. Bearing in mind that the swallowing process and speech production share some anatomical structures and mechanisms of neurological control, this work aims to evaluate the suitability of automatic speech processing and machine learning techniques for screening of functional dysphagia.Methods: Speech recordings were collected from 46 patients with functional oropharyngeal dysphagia produced by neurological causes, and 46 healthy controls. The dimensions of speech including phonation, articulation, and prosody were considered through different speech tasks. Specific features per dimension were extracted and analyzed using statistical tests. Machine learning models were applied per dimension via nested cross-validation. Hyperparameters were selected using the AUC - ROC as optimization criterion.Results: The Random Forest in the articulation related speech tasks retrieved the highest performance measures (AUC=0.86±0.10, sensitivity=0.91±0.12) for individual analysis of dimensions. In addition, the combination of speech dimensions with a voting ensemble improved the results, which suggests a contribution of information from different feature sets extracted from speech signals in dysphagia conditions.Conclusions: The proposed approach based on speech related models is suitable for the automatic discrimination between dysphagic and healthy individuals. These findings seem to have potential use in the screening of functional oropharyngeal dysphagia in a non-invasive and inexpensive way.

LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities

Predictive maintenance is a field of research that has emerged from the need to improve the systems in place. This research focuses on controlling the degradation of photovoltaic (PV) modules in outdoor solar panels, which are exposed to a variety of climatic loads. Improved reliability, operation, and performance can be achieved through monitoring. In this study, a system capable of predicting the output power of a solar module was implemented. It monitors different parameters and uses automatic learning techniques for prediction. Its use improved reliability, operation, and performance. On the other hand, automatic learning algorithms were evaluated with different metrics in order to optimize and find the best configuration that provides an optimal solution to the problem. With the aim of increasing the share of renewable energy penetration, an architectural proposal based on Edge Computing was included to implement the proposed model into a system. The proposed model is designated for outdoor predictions and offers many advantages, such as monitoring of individual panels, optimization of system response, and speed of communication with the Cloud. The final objective of the work was to contribute to the smart Energy system concept, providing solutions for planning the entire energy system together with the identification of suitable energy infrastructure designs and operational strategies.

Bayesian Differentiable Architecture Search for Efficient Domain Matching Fault Diagnosis

Intelligent fault diagnosis is essential for downtime reduction in modern industries. However, domain (i.e., working condition) variants are unavoidable for fault diagnosis under changing environment. Though one can obtain a customized deep learning model for a given domain, it is not practical to design deep neural networks manually for multiple domains. Differentiable architecture search (DARTS), as an automatic machine learning technique, can automate the network design process for a specific domain efficiently by using hypernetwork and differentiable search strategy. Nevertheless, the representation of hypernetwork is restricted by several unfair factors, and the searched architecture of DARTS is overconfident. To address these issues, a one-shot neural architecture search approach, which involves two-stage learning, is proposed for efficient domain matching fault diagnosis. In the first training stage, the warmup and path-dropout strategies are taken to enhance the competitiveness of the parametric operators and alleviate the coadaptation problem to obtain an intuitively fair hypernetwork. In the second matching stage, variational inference is introduced into a differentiable search strategy to estimate the uncertainty of model matching, and a scale mixture prior is used to softly constrain the matching stage. Then, a candidate architectures set can be sampled and ordered from the posterior. Multidomain experiment is implemented by adding noise to the raw signal, and the proposed method outperforms four commonly used deep neural networks for aeroengine bevel gear fault diagnosis.

AI techniques applied to diagnosis of vibrations failures in wind turbines

Supervision and fault diagnosis in wind turbines using automatic learning techniques allow early detection of the degeneration of the components, as well as the detection and diagnosis of sudden failures. This contribution evaluates different machine learning methodologies to predict, detect and diagnose electrical and mechanical failures of wind turbines. An integrated monitoring and diagnostic system is proposed using automatic learning algorithms adapted to the different components and faults of the wind turbine

Artificial neural network analysis of the academic performance of students in virtual learning environments

Educational institutions continually strive to improve the services they offer, their aim is to have the best possible teaching staff, increase the quality of teaching and the academic performance of their students. A knowledge of the factors that affect student learning could help universities and study centres adjust their curricula and teaching methods to the needs of their students. One of the first measures employed by teaching institutions was to create Virtual Learning Environments (VLEs). This type of environment makes it possible to attract a larger number of students because it enables them to study from wherever they are in the world, meaning that the student’s location is no longer a constraint. Moreover, VLEs facilitate access to teaching resources, they make it easier to monitor the activity of the teaching staff and of the interactions between students and teachers. Thus, online environments make it possible to assess the factors that cause the students’ academic performance to increase or decrease.To understand the factors that influence the university learning process, this paper applies a series of automatic learning techniques to a public dataset, including tree-based models and different types of Artificial Neural Networks (ANNs). Having applied these techniques to the dataset, the number of times students have accessed the resources made available on VLE platforms has been identified as a key factor affecting student performance. This factor has been analysed by conducting a real case study which has involved 120 students doing a masters degree over a VLE platform. Concretely, the case study participants were masters degree students in areas related to computer engineering at the University of Salamanca.