Application of machine learning for antibiotic resistance in water and wastewater: A systematic review.

Antibiotic resistance (AR) is considered one of the greatest global threats in the current century, which can only be overcome if all interconnected areas of humans, animals and the environment are taken into account as part of the One Health concept proposed by the World Health Organization (WHO). Water and wastewater are among the most important environmental media of AR sources, where the phenomena are generally non-linear. Therefore, the aim of this study was to investigate the application of machine learning-based methods (MLMs) to solve AR-induced problems in water and wastewater. For this purpose, most relevant databases were searched in the period between 1987 and 2023 to systematically analyze and categorize the applications. Accordingly, the results showed that out of 12 applications, 11 (91.6%) were for shallow learning and 1 (8.3%) for deep learning. In shallow learning category, n = 6, 50% of the applications were regression and n = 4, 33.3% were classification, mainly using artificial neural networks, decision trees and Bayesian methods for the following objectives: Predicting the survival of antibiotic-resistant bacteria (ARB), determining the order of influencing parameters on AR-based scores, and identifying the major sources of antibiotic resistance genes (ARGs). In addition, only one study (8.3%) was found for clustering and no study for association. Surprisingly, deep learning had been used in only one study (8.3%) to predict ARGs sequences. Therefore, working on the knowledge gaps of AR, especially using clustering, association and deep learning methods, would be a promising option to analyze more aspects of the related problems. However, there is still a long way to go to consider and apply MLMs as unique approaches to study different aspects of AR in water and wastewater.

Predicting the Cognitive Ability of Young Women Using a New Feature Selection Algorithm.

Cognitive abilities are the capabilities to perform mental processes that include executive function, comprehension, decision-making, work performance, and educational attainment. This study aimed to investigate the relationship between several biomarkers and individuals' cognitive ability using various machine learning methods. A total of 144 young women aged between 18 and 24 years old were recruited into the study. Cognitive performance was assessed using a standard questionnaire. A panel of biochemical, hematological, inflammatory, and oxidative stress biomarkers in serum and urine was measured for all participants. A novel combination of feature selection and feature scoring techniques within a hierarchical ensemble structure has been proposed to identify the most effective features in recognizing the importance of various biomarker signatures in cognitive abilities classification. Multiple feature selection methods were employed in conjunction with different classifiers to construct this model. In this manner, using three filter methods, the scores of each feature were considered. The combination of high-scoring features for each filter method was stored as the primary feature subset. A high-accuracy feature subset was selected by using a wrapper method. The collection of highly scored features from each filter method formed the primary feature subset. A wrapper method was also employed to select a feature subset with high accuracy. To ensure robustness and minimize random variations in the feature subset search process, a repeative tenfold cross-validation was conducted. The most frequently recurring features were determined. This iterative step facilitated the identification of an optimal feature subset, effectively reducing the dimensionality of features while maintaining accuracy. Among the 47 extracted factors, serum level of NOx (nitrite ± nitrate), alkaline phosphatase (ALP), and phosphate as well as blood platelet count (PLT) was entered into the model of cognitive abilities with the highest accuracy of approximately 70.9% using a decision tree classifier. Therefore, the serum levels of NOx, ALP, phosphate, and blood PLT count may be important markers of the cognitive abilities in apparently healthy young women. These factors my provide a simple procedure to identify mental abilities and earlier cognitive decline in healthy adults.

Telemedicine Based on Human Activity Recognition in Elderly Healthcare.

Nowadays, telemedicine can provide remote clinical services for the elderly, using smart devices like embedded sensors, via real-time communication with the healthcare provider. In particular, inertial measurement sensors such as accelerometers embedded in smartphones can provide sensory data fusion for human activities. Thus, the technology of Human Activity Recognition can be applied to handle such data. In recent studies, the three-dimensional axis has been used to detect human activities. Since most changes in individual activities occur in the x- and y-axis, the label of each activity is determined using a new two-dimensional Hidden Markov Mode based on these two axes. To evaluate the proposed method, we use the WISDM dataset which is based on an accelerometer. The proposed strategy is compared to General Model and User-Adaptive Model. The results indicate that the proposed model is more accurate than the others.

Non-Alcoholic Fatty Liver Disease Diagnosis with Multi-Group Factors.

Although various clinical factors affect the diagnosis of Non-alcoholic Fatty Liver Disease (NAFLD), most studies only use single-source data such as images or laboratory data. Nevertheless, using different categories of features can help to get better results. Hence, one of the most important purposes of this paper is to employ a multi-group of effective factors such as velocimetry, psychological, demographic and anthropometric, and lab test data. Then, some Machine Learning (ML) methods are applied to classify the samples into two healthy and patient with NAFLD groups. The data used here belongs to the PERSIAN Organizational Cohort study at Mashhad University of Medical Sciences. To quantify the scalability of the models, different validity metrics are used. The obtained results illustrate that the proposed method can lead to an increase in the efficiency of the classifiers.

Assessment and Classification of Mass Lesions Based on Expert Knowledge Using Mammographic Analysis.

BACKGROUND: Masses are one of the most important indicators of breast cancer in mammograms, and their classification into two groups as benign and malignant is highly necessary. Computer Aided Diagnosis (CADx) helps radiologists enhance the accuracy of their decision. Hence, the system is required to support and assess with radiologist's interaction as an expert. METHODS: In this research, classification of breast masses using mammography in the two main views which include MLO and CC, is evaluated with respect to the shape, texture and asymmetry aspect. Additionally, a method was developed and proposed using the classification of breast tissue density based on the decision tree. DISCUSSION: This study therefore, aims to provide a method based on the human decision-making model that will help in designing the perfect tool for radiologists, regardless of the complexity of computing, costly procedures and also reducing the diagnosis error. CONCLUSION: Results show that the proposed system for entirely fat, scattered fibroglandular densities, heterogeneously dense, and extremely dense breast achieved 100, 99, 99 and 98% true malignant rate, respectively with cross-validation procedure.