Learning Approach Research Articles

AI-driven canine cataract detection: a machine learning approach using support vector machine

ABSTRACT The pivotal role of the Support Vector Machine becomes evident in addressing the impact of aging and genetics on canine cataract development. Timely detection, crucial for preventing illness in dogs, is facilitated by Artificial Intelligence. This proves especially beneficial for veterinarians grappling with detection challenges. Within the realm of Artificial Intelligence, the subset of Machine Learning emerges as a promising avenue for tackling intricate tasks. The current study introduces a model for grading canine cataracts through images captured on standard mobile phone cameras. Notably, the proposed system centers around harnessing the power of the Support Vector Machine, a potent Machine Learning algorithm tailored for classifying (grading) cataracts based on images. Rigorous evaluation, employing K-fold Cross-validation, validates the images’ reliability. Both binary-class and multi-class experimentation contribute to the dataset. Impressively, the accuracy rates for the trained classification model stand at 83% (without Cross-validation) and 81% (with Cross-validation for K = 10), underscoring the robustness of the approach.

A dedicated wound care module for third-year baccalaureate nurses: does it increase their knowledge and confidence?

Wound care is an important component of nursing care, consuming a significant amount of working hours. Literature reports the existence of many barriers to evidence-based wound care and that nursing students have an increasing need for education in this field. While blended learning activities have shown some benefits in learning outcomes, dedicated wound care modules within baccalaureate nursing courses occur infrequently. The aim of this study was to facilitate an increase in confidence and knowledge of evidence-based wound care in third-year baccalaureate nursing students. In this descriptive pre-post research design, a dedicated wound care module within a baccalaureate nursing course (using a constructivist learning approach) was offered to students in a university in Australia. Pre- and post-implementation surveys were completed (2018-2020). Due to a small number of matching participants, only descriptive statistics were calculated for all variables. A total of 276 students were invited to respond to the survey and 41 responded initially, 35 responded post the wound care module, and 26 responded to the survey after six months. Positive outcomes were noted in increased levels of confidence in the student nurses' ability to assess, manage and prevent wounds; as well as to apply evidence-based practice and change management following the educational wound care module within the baccalaureate nursing course. Implementation of a dedicated wound care module within a baccalaureate nursing course in this sample of third-year students fostered a positive change in the knowledge of evidence-based wound management, assessment and prevention. Education of the next generation of registered nurses in this valuable area of practice is an important part of baccalaureate nursing education.

A hybrid wavelet-deep learning approach for vibration-based damage detection in monopile offshore structures considering soil interaction

A hybrid wavelet-deep learning approach for vibration-based damage detection in monopile offshore structures considering soil interaction

DeepSurv landmarking: a deep learning approach for dynamic survival analysis with longitudinal data

DeepSurv landmarking: a deep learning approach for dynamic survival analysis with longitudinal data

Latent cognitive phenotypes in juvenile myoclonic epilepsy: Clinical, sociodemographic, and neuroimaging associations.

Application of cluster analytic procedures has advanced understanding of the cognitive heterogeneity inherent in diverse epilepsy syndromes and the associated clinical and neuroimaging features. Application of this unsupervised machine learning approach to the neuropsychological performance of persons with juvenile myoclonic epilepsy (JME) has yet to be attempted, which is the intent of this investigation. A total of 77 JME participants, 19 unaffected siblings, and 44 unrelated controls, 12 to 25 years of age, were administered a comprehensive neuropsychological battery (intelligence, language, memory, executive function, and processing speed), which was subjected to factor analysis followed by K-means clustering of the resultant factor scores. Identified cognitive phenotypes were characterized and related to clinical, family, sociodemographic, and cortical and subcortical imaging features. Factor analysis revealed three underlying cognitive dimensions (general ability, speed/response inhibition, and learning/memory), with JME participants performing worse than unrelated controls across all factor scores, and unaffected siblings performing worse than unrelated controls on the general mental ability and learning/memory factors, with no JME vs sibling differences. K-means clustering of the factor scores revealed three latent groups including above average (31.4% of participants), average (52.1%), and abnormal performance (16.4%). Participant groups differed in their distributions across the latent groups (p < 0.001), with 23% JME, 22% siblings, and 2% unrelated controls in the abnormal performance group; and 18% JME, 21% siblings, and 59% unrelated controls in the above average group. Clinical epilepsy variables were unassociated with cluster membership, whereas family factors (lower parental education) and abnormally increased thickness and/or volume in the frontal, parietal, and temporal-occipital regions were associated with the abnormal cognition group. Distinct cognitive phenotypes characterize the spectrum of neuropsychological performance of patients with JME for which there is familial (sibling) aggregation. Phenotypic membership was associated with parental (education) and imaging characteristics (increased cortical thickness and volume) but not basic clinical seizure features.

A deep learning approach for ovarian cancer detection and classification based on fuzzy deep learning

Different oncologists make their own decisions about the detection and classification of the type of ovarian cancer from histopathological whole slide images. However, it is necessary to have an automated system that is more accurate and standardized for decision-making, which is essential for early detection of ovarian cancer. To help doctors, an automated detection and classification of ovarian cancer system is proposed. This model starts by extracting the main features from the histopathology images based on the ResNet-50 model to detect and classify the cancer. Then, recursive feature elimination based on a decision tree is introduced to remove unnecessary features extracted during the feature extraction process. Adam optimizers were implemented to optimize the network’s weights during training data. Finally, the advantages of combining deep learning and fuzzy logic are combined to classify the images of ovarian cancer. The dataset consists of 288 hematoxylin and eosin (H&E) stained whole slides with clinical information from 78 patients. H&E-stained Whole Slide Images (WSIs), including 162 effective and 126 invalid WSIs were obtained from different tissue blocks of post-treatment specimens. Experimental results can diagnose ovarian cancer with a potential accuracy of 98.99%, sensitivity of 99%, specificity of 98.96%, and F1-score of 98.99%. The results show promising results indicating the potential of using fuzzy deep-learning classifiers for predicting ovarian cancer.

Critical thinking disposition and learning approach as predictors of mathematics performance

In the Philippines, improving pre-service math teachers’ critical thinking is receiving increasing attention, emphasizing the importance of tailoring instructional methods to students’ learning approaches for a more equitable environment and enhanced mathematics performance. Thus, this study aimed to determine if the critical thinking disposition subscales (reflective, attentiveness, open-mindedness, organization, perseverance, and intrinsic motivation) and learning approach (deep approach and surface approach) predict the mathematics performance of pre-service math teachers. This study employed a descriptive-correlational research design to randomly selected 125 pre-service math teachers from Central Luzon, Philippines. The survey instruments are administered through the student-educator negotiated CTDs scale, the revised two-factor study process questionnaire, and the 40- item validated test. Using descriptive analysis, findings revealed that preservice mathematics teachers have moderate levels of CTD, most of which use a deep approach and have average mathematics performance. Regression analysis showed that CTD and the deep approach were predictors. Therefore, pre-service mathematics teachers with a higher CTD and a deep approach are likelier to perform better in mathematics. These findings provide valuable insights into enhancing mathematics teacher education.

Improving imbalanced class intrusion detection in IoT with ensemble learning and ADASYN-MLP approach

Improving imbalanced class intrusion detection in IoT with ensemble learning and ADASYN-MLP approach

Optimizing olefin purification: An artificial intelligence-based process-conscious PI controller tuning for double dividing wall column distillation

This study investigates the light olefin separation using dual dividing wall columns (DWCs) and compares various proportional-integral (PI) controller tuning methods. A novel process-conscious reinforcement learning (RL) approach, utilizing Deep Deterministic Policy Gradient (DDPG), is developed by incorporating domain-specific knowledge into the reward function to ensure compliance with critical process variables and product purity. The DDPG-based PI tuning is evaluated against traditional methods such as Aspen’s recommended initial tuning, Ziegler-Nichols (ZN), and Cohen-Coon (CC). The result shows that the DDPG method significantly enhances control stability and accuracy, reducing error by factors of 11.9, 2.3, and 1.6 compared to Aspen, ZN, and CC, respectively. Additionally, DDPG demonstrates superior energy efficiency, consuming 13% less energy than the next best method. It also reduces purification costs by up to 13.5% and CO2 emissions by 20% compared to other methods. This study highlights the potential of integrating advanced RL techniques into industrial process control, delivering substantial improvements in stability, energy efficiency, economic, and environmental performance.

A hybrid unsupervised learning approach for noise removal in particle image velocimetry

A hybrid unsupervised learning approach for noise removal in particle image velocimetry

Multilevel hybrid handcrafted feature extraction based depression recognition method using speech

Background and purposeDiagnosis of depression is based on tests performed by psychiatrists and information provided by patients or their relatives. In the field of machine learning (ML), numerous models have been devised to detect depression automatically through the analysis of speech audio signals. While deep learning approaches often achieve superior classification accuracy, they are notably resource-intensive. This research introduces an innovative, multilevel hybrid feature extraction-based classification model, specifically designed for depression detection, which exhibits reduced time complexity. Materials and methodsMODMA dataset consisting of 29 healthy and 23 Major depressive disorder audio signals was used. The constructed model architecture integrates multilevel hybrid feature extraction, iterative feature selection, and classification processes. During the Hybrid Handcrafted Feature (HHF) generation stage, a combination of textural and statistical methods was employed to extract low-level features from speech audio signals. To enhance this process for high-level feature creation, a Multilevel Discrete Wavelet Transform (MDWT) was applied. This technique produced wavelet subbands, which were then input into the hybrid feature extractor, enabling the extraction of both high and low-level features. For the selection of the most pertinent features from these extracted vectors, Iterative Neighborhood Component Analysis (INCA) was utilized. Finally, in the classification phase, a one-dimensional nearest neighbor classifier, augmented with ten-fold cross-validation, was implemented to achieve detailed, results. ResultsThe HHF-based speech audio signal classification model attained excellent performance, with the 94.63 % classification accuracy. ConclusionsThe findings validate the remarkable proficiency of the introduced HHF-based model in depression classification, underscoring its computational efficiency.

Identifying momentary suicidal ideation using machine learning in patients at high-risk for suicide.

Strategies to detect the presence of suicidal ideation (SI) or characteristics of ideation that indicate marked suicide risk are critically needed to guide interventions and improve care during care transition periods. Some studies indicate that machine learning can be applied to momentary data to improve classification of SI. This study examined whether the classification accuracy of these models varies as a function of type of training data or characteristics of ideation. A total of 257 psychiatric inpatients completed a 3-week battery of ecological momentary assessment and measures of suicide risk factors. The accuracy of machine learning models in classifying the presence, duration, or intensity of ideation was compared across models trained on baseline and/or momentary suicide risk data. Relative feature importance metrics were examined to identify the risk factors that were most important for outcome classification. Models including both baseline and momentary features outperformed models with only one feature type, providing important information in both correctly classifying and differentiating individual characteristics of SI. Models classifying SI presence, duration, and intensity performed similarly. Results of this study may not generalize beyond a high-risk, psychiatric inpatient sample, and additional work is needed to examine temporal ordering of the relationships identified. Our results support using machine learning approaches for accurate identification of SI characteristics and underscore the importance of understanding the factors that differentiate and drive different characteristics of SI. Expansion of this work can support use of these models to guide intervention strategies.

BOATMAP: Bayesian Optimization Active Targeting for Monomorphic Arrhythmia Pace-mapping

Recent advances in machine learning and deep learning have presented new opportunities for learning to localize the origin of ventricular activation from 12-lead electrocardiograms (ECGs), an important step in guiding ablation therapies for ventricular tachycardia. Passively learning from population data is faced with challenges due to significant variations among subjects, and building a patient-specific model raises the open question of where to select pace-mapping data for training. This work introduces BOATMAP, a novel active learning approach designed to provide clinicians with interpretable guidance that progressively assists in locating the origin of ventricular activation from 12-lead ECGs. BOATMAP inverts the input–output relationship in traditional machine learning solutions to this problem and learns the similarity between a target ECG and a paced ECG as a function of the pacing site coordinates. Using Gaussian processes (GP) as a surrogate model, BOATMAP iteratively refines the estimated similarity landscape while providing suggestions to clinicians regarding the next optimal pacing site. Furthermore, it can incorporate constraints to avoid suggesting pacing in non-viable regions such as the core of the myocardial scar. Tested in a realistic simulation environment in various heart geometries and tissue properties, BOATMAP demonstrated the ability to accurately localize the origin of activation, achieving an average localization accuracy of 3.9±3.6mm with only 8.0±4.0 pacing sites. BOATMAP offers real-time interpretable guidance for accurate localization and enhancing clinical decision-making.

A Data and Model-Driven Deep Learning Approach to Robust Downlink Beamforming Optimization

A Data and Model-Driven Deep Learning Approach to Robust Downlink Beamforming Optimization

Enhancing network security using unsupervised learning approach to combat zero-day attack

Enhancing network security using unsupervised learning approach to combat zero-day attack

HF Trimer: A New Full-Dimensional Potential Energy Surface and Rigorous 12D Quantum Calculations of Vibrational States.

HF trimer, as the smallest and the lightest cyclic hydrogen-bonded (HB) cluster, has long been a favorite prototype system for spectroscopic and theoretical investigations of the structure, energetics, spectroscopy, and dynamics of hydrogen-bond networks. Recently, rigorous quantum 12D calculations of the coupled intra- and intermolecular vibrations of this fundamental HB trimer (J. Chem. Phys. 2023, 158, 234109) were performed, employing an older ab initio-based many-body potential energy surface (PES). While the theoretical results were found to be in reasonably good agreement with the available spectroscopic data, it was also evident that it is highly desirable to develop a more accurate 12D PES of HF trimer. Motivated by this, here we report a new, and the first fully ab initio 12D PES of this paradigmatic system. Approximately 42,540 geometries were sampled and calculated at the level of CCSD(T)-F12a/AVTZ. The permutationally invariant polynomial-neural network based Δ-machine learning approach (J. Phys. Chem. Lett. 2022, 13, 4729) was employed to perform cost-efficient calculations of the basis-set-superposition error (BSSE) correction. By strategically selecting data points, this approach facilitated the construction of a high-precision PES with BSSE correction, while requiring only a minimal number of BSSE value computations. The fitting error of the final PES is only 0.035 kcal/mol. To assess its performance, the 12D fully coupled quantum calculations of excited intra- and intermolecular vibrational states of HF trimer are carried out using the rigorous methodology developed by us earlier. The results are found to be in a significantly better agreement with the available spectroscopic data than those obtained with the previously existing semiempirical 12D PES.

Enhancing early detection of autistic spectrum disorder in children using machine learning approaches

Diagnosing Autism Spectrum Disorder (ASD) presents a multifaceted challenge, demanding accurate and efficient screening methods. Applying machine learning techniques offers a promising avenue for enhancing diagnostic accuracy and efficiency. This research investigates the efficiency of machine learning in distinguishing individuals with ASD from those without, utilizing a comprehensive dataset comprising screening questions, demographic factors, and ASD related diagnostic classifications. We applied chi-square feature selection technique and also tested Random Forest, Logistic Regression, Gradient Boosting Classifier, and Extra Trees Classifier. Each model showed optimal performance and exhibit high precision, recall, and F1-score for both ASD-positive and ASD-negative instances. Additionally, AUROC curves further validated the models’ exceptional discriminatory abilities, with exceptional results. Our findings highlight the potential of machine learning algorithms for enhancing ASD diagnosis accuracy and efficiency in clinical settings. Further research and validation on larger datasets are required to understand the importance of machine learning methods in ASD diagnosis.

Implementation of Blended Learning Approach for Tertiary Level Training on Vehicle Electrification

Implementation of Blended Learning Approach for Tertiary Level Training on Vehicle Electrification

Acoustic fingerprints in nature: A self-supervised learning approach for ecosystem activity monitoring

According to the World Health Organization, healthy communities rely on well-functioning ecosystems. Clean air, fresh water, and nutritious food are inextricably linked to ecosystem health. Changes in biological activity convey important information about ecosystem dynamics, and understanding such changes is crucial for the survival of our species. Scientific edge cyberinfrastructures collect distributed data and process it in situ, often using machine learning algorithms. Most current machine learning algorithms deployed on edge cyberinfrastructures, however, are trained on data that does not accurately represent the real stream of data collected at the edge. In this work we explore the applicability of two new self-supervised learning algorithms for characterizing an insufficiently curated, imbalanced, and unlabeled dataset collected by using a set of nine microphones at different locations at the Morton Arboretum, an internationally recognized tree-focused botanical garden and research center in Lisle, IL. Our implementations showed completely autonomous characterization capabilities, such as the separation of spectrograms by recording location, month, week, and hour of the day. The models also showed the ability to discriminate spectrograms by biological and atmospheric activity, including rain, insects, and bird activity, in a completely unsupervised fashion. We validated our findings using a supervised deep learning approach and with a dataset labeled by experts, confirming competitive performance in several features. Toward explainability of our self-supervised learning approach, we used acoustic indices and false color spectrograms, showing that the topology and orientation of the clouds of points in the output space over a 24-h period are strongly linked to the unfolding of biological activity. Our findings show that self-supervised learning has the potential to learn from and process data collected at the edge, characterizing it with minimal human intervention. We believe that further research is crucial to extending this approach for complete autonomous characterization of raw data collected on distributed sensors at the edge.

GWO-ANFIS-RD3PG: A reinforcement learning approach with dynamic adjustment and dual replay mechanism for building energy forecasting

Reliable prediction of building energy consumption is essential for effective and sustainable energy management. Traditional forecasting methods, however, face challenges when dealing with sudden changes in energy consumption. To address the above-mentioned issue, we introduce the GWO-ANFIS-RD3PG prediction model in this paper. This model aims to ensure global forecasting accuracy while minimizing prediction errors at sudden change points. A Grey Wolf Optimization (GWO) algorithm with the Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to predict the energy consumption type and its fluctuation magnitude for the next time step. Within the Recurrent Dual Experience Replay Buffer DDPG (RD3PG) prediction module, we employ Long Short-Term Memory (LSTM), as the actor network in the Deep Deterministic Policy Gradient (DDPG), to account for long-term dependencies in time series data. Notably, we introduce an adaptive action modification mechanism that allows the agent to optimize actions based on the output of the aforementioned ANFIS, enabling real-time adjustments during sudden change. To further enhance the model’s performance, we adopt a dual experience replay mechanism to store data samples from sudden change points, capturing insight information during these anomalies. Experimental results on two real-world datasets demonstrate that this method reduces MAE by 5.81% and 13.47%, and RMSE by 5.69% and 15.21% compared to the state-of-art models, showing the significance of the proposed method for energy efficiency improvement in real-world building operations.