Semi-Supervised Anomaly Detection of Dissolved Oxygen Sensor in Wastewater Treatment Plants.

As the world progresses toward a digitally connected and sustainable future, the integration of semi-supervised anomaly detection in wastewater treatment processes (WWTPs) promises to become an essential tool in preserving water resources and assuring the continuous effectiveness of plants. When these complex and dynamic systems are coupled with limited historical anomaly data or complex anomalies, it is crucial to have powerful tools capable of detecting subtle deviations from normal behavior to enable the early detection of equipment malfunctions. To address this challenge, in this study, we analyzed five semi-supervised machine learning techniques (SSLs) such as Isolation Forest (IF), Local Outlier Factor (LOF), One-Class Support Vector Machine (OCSVM), Multilayer Perceptron Autoencoder (MLP-AE), and Convolutional Autoencoder (Conv-AE) for detecting different anomalies (complete, concurrent, and complex) of the Dissolved Oxygen (DO) sensor and aeration valve in the WWTP. The best results are obtained in the case of Conv-AE algorithm, with an accuracy of 98.36 for complete faults, 97.81% for concurrent faults, and 98.64% for complex faults (a combination of incipient and concurrent faults). Additionally, we developed an anomaly detection system for the most effective semi-supervised technique, which can provide the detection of delay time and generate a fault alarm for each considered anomaly.

Convolutional Neural Network-Machine Learning Model: Hybrid Model for Meningioma Tumour and Healthy Brain Classification.

This paper presents a hybrid study of convolutional neural networks (CNNs), machine learning (ML), and transfer learning (TL) in the context of brain magnetic resonance imaging (MRI). The anatomy of the brain is very complex; inside the skull, a brain tumour can form in any part. With MRI technology, cross-sectional images are generated, and radiologists can detect the abnormalities. When the size of the tumour is very small, it is undetectable to the human visual system, necessitating alternative analysis using AI tools. As is widely known, CNNs explore the structure of an image and provide features on the SoftMax fully connected (SFC) layer, and the classification of the items that belong to the input classes is established. Two comparison studies for the classification of meningioma tumours and healthy brains are presented in this paper: (i) classifying MRI images using an original CNN and two pre-trained CNNs, DenseNet169 and EfficientNetV2B0; (ii) determining which CNN and ML combination yields the most accurate classification when SoftMax is replaced with three ML models; in this context, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) were proposed. In a binary classification of tumours and healthy brains, the EfficientNetB0-SVM combination shows an accuracy of 99.5% on the test dataset. A generalisation of the results was performed, and overfitting was prevented by using the bagging ensemble method.

A Robust Machine Learning Model for Diabetic Retinopathy Classification.

Ensemble learning is a process that belongs to the artificial intelligence (AI) field. It helps to choose a robust machine learning (ML) model, usually used for data classification. AI has a large connection with image processing and feature classification, and it can also be successfully applied to analyzing fundus eye images. Diabetic retinopathy (DR) is a disease that can cause vision loss and blindness, which, from an imaging point of view, can be shown when screening the eyes. Image processing tools can analyze and extract the features from fundus eye images, and these corroborate with ML classifiers that can perform their classification among different disease classes. The outcomes integrated into automated diagnostic systems can be a real success for physicians and patients. In this study, in the form image processing area, the manipulation of the contrast with the gamma correction parameter was applied because DR affects the blood vessels, and the structure of the eyes becomes disorderly. Therefore, the analysis of the texture with two types of entropies was necessary. Shannon and fuzzy entropies and contrast manipulation led to ten original features used in the classification process. The machine learning library PyCaret performs complex tasks, and the empirical process shows that of the fifteen classifiers, the gradient boosting classifier (GBC) provides the best results. Indeed, the proposed model can classify the DR degrees as normal or severe, achieving an accuracy of 0.929, an F1 score of 0.902, and an area under the curve (AUC) of 0.941. The validation of the selected model with a bootstrap statistical technique was performed. The novelty of the study consists of the extraction of features from preprocessed fundus eye images, their classification, and the manipulation of the contrast in a controlled way.