Automated Screening for Three Inborn Metabolic Disorders: A Pilot Study

Background: Inborn metabolic disorders (IMDs) form a large group of rare, but often serious, metabolic disorders. Aims: Our objective was to construct a decision tree, based on classification algorithm for the data on three metabolic disorders, enabling us to take decisions on the screening and clinical diagnosis of a patient. Settings and Design: A non-incremental concept learning classification algorithm was applied to a set of patient data and the procedure followed to obtain a decision on a patient’s disorder. Materials and Methods: Initially a training set containing 13 cases was investigated for three inborn errors of metabolism. Results: A total of thirty test cases were investigated for the three inborn errors of metabolism. The program identified 10 cases with galactosemia, another 10 cases with fructosemia and the remaining 10 with propionic acidemia. The program successfully identified all the 30 cases. Conclusions: This kind of decision support systems can help the healthcare delivery personnel immensely for early screening of IMDs

Early Detection and Continuous Monitoring of Atrial Fibrillation from ECG Signals with a Novel Beat-Wise Severity Ranking Approach

Irregularities in heartbeats and cardiac functioning outside of clinical settings are often not available to the clinicians, and thus ignored. But monitoring these with high-risk population might assist in early detection and continuous monitoring of Atrial Fibrillation(AF). Wearable devices like smart watches and wristbands, which can collect Electrocardigraph(ECG) signals, can monitor and warn users of unusual signs in a timely manner. Thus, there is a need to develop a real-time monitoring system for AF from ECG. We propose an algorithm for a simple beat-by-beat ECG signal multilevel classifier for AF detection and a quantitative severity scale (between 0 to 1) for user feedback. For this study, we used ECG recordings from MIT BIH Atrial Fibrillation, MIT BIH Long-term Atrial Fibrillation Database. All ECG signals are preprocessed for reducing noise using filter. Preprocessed signal is analyzed for extracting 39 features including 20 of amplitude type and 19 of interval type. The feature space for all ECG recordings is considered for Classification. Training and testing data include all classes of data i.e., beats to identify various episodes for severity. Feature space from the test data is fed to the classifier which determines the class label based on trained model. A class label is determined based on number of occurences of AF and other arrhythmia episodes such as AB(Atrial Bigeminy), SBR(Sinus Bradycardia), SVTA(Supra Ventricular Tacchyarrhythmia). Accuracy of 96.7764% is attained with Random Forest algorithm, Furthermore, precision and recall are determined based on correct and incorrect classifications for each class. Precision and recall on average of Random Forest Classifier are obtained as 0.968 and 0.968 respectievely. This work provides a novel approach to enhance existing method of AF detection by identifying heartbeat class and calculates a quantitative severity metric that might help in early detection and continuous monitoring of AF

The Application of Physics Informed Neural Networks to Compositional Modeling

Compositional modeling is essential when simulating processes involving significant changes in reservoir fluid composition. It is computationally expensive because we typically need to predict the states and properties of multicomponent fluid mixtures at several different points in space and time. To speed up this process, several researchers have used machine learning algorithms to train deep learning (DL) models on data from the rigorous phase-equilibrium (flash) calculations. However, one shortcoming of the DL models is that there is no explicit consideration for the governing physics. So, there is no guarantee that the model predictions will honor the thermodynamical constraints of phase equilibrium (Ihunde & Olorode, 2022). This work is the first attempt to incorporate thermodynamics constraints into the training of DL models to ensure that they yield two-phase flash predictions that honor the physical laws that govern phase equilibrium. A space-filling mixture design is used to generate one million different compositions at different pressures (Ihunde & Olorode, 2022). Stability analysis and flash calculations are performed on these compositions to obtain the corresponding phase compositions and vapor fraction (Ihunde & Olorode, 2022). Physics-informed neural network (PINN) and standard deep neural network (DNN) models were trained to predict two-phase flash results using the data from the actual phase-equilibrium calculations (Ihunde & Olorode, 2022). Considering the stochasticity of the deep learning optimization process, we used the seven-fold cross-validation to obtain reliable estimates of average model accuracy and variance (Ihunde & Olorode, 2022). Comparing the PINN and standard DNN models reveals that PINNs can incorporate physical constraints into DNNs without significantly lowering the model accuracy (Ihunde & Olorode, 2022). The evaluation of the model results with the test data shows that both PINN and standard DNN models yield coefficients of determination of ~97% (Ihunde & Olorode, 2022). However, the root-mean-square error of the physics-constraint errors in the PINN model is over 55% lower than that of the standard DNN model (Ihunde & Olorode, 2022). This indicates that PINNs significantly outperform DNNs in honoring the governing physics. Finally, we demonstrate the significance of honoring the governing physics by comparing the resulting phase envelopes obtained from overall compositions computed from the PINN, DNN, and linear regression model predictions (Ihunde & Olorode, 2022)

Deeply Smile Detection Based on Discriminative Features with Modified LeNet-5 Network

Facial expressions are caused by specific movements of the face muscles; they are regarded as a visible manifestation of a person\u27s inner thought process, internal emotional states, and intentions. A smile is a facial expression that often indicates happiness, satisfaction, or agreement. Many applications use smile detection such as automatic image capture, distance learning systems, interactive systems, video conferencing, patient monitoring, and product rating. The smile detection system is divided into two stages: feature extraction and classification. As a result, the accuracy of smile detection is dependent on both phases. In recent years, numerous researchers and scholars have identified various approaches to smile detection, however, their accuracy is still under the desired level. To this end, we propose an effective Convolutional Neural Network (CNN) architecture based on modified LeNet-5 Network (MLeNet-5) for detecting smiles in images. The proposed system generates low-level face identifiers and detect smiles using a strong binary classifier. In our experiments, the proposed MLenet-5 system used the SMILEsmilesD and (GENKI-4 K) databases in which the smile detection rate of the proposed method improves the accuracy by 2% on SMILEsmilesD database and 5% on GENKI-4 K database relative to LeNet-5-based CNN network. In addition, the proposed system decreases the number of parameters compared to LeNet-5-based CNN network and most of the existing models while maintaining the robustness and effectiveness of the results

Deeply Smile Detection Based on Discriminative Features with Modified LeNet-5 Network

Facial expressions are caused by specific movements of the face muscles; they are regarded as a visible manifestation of a person\u27s inner thought process, internal emotional states, and intentions. A smile is a facial expression that often indicates happiness, satisfaction, or agreement. Many applications use smile detection such as automatic image capture, distance learning systems, interactive systems, video conferencing, patient monitoring, and product rating. The smile detection system is divided into two stages: feature extraction and classification. As a result, the accuracy of smile detection is dependent on both phases. In recent years, numerous researchers and scholars have identified various approaches to smile detection, however, their accuracy is still under the desired level. To this end, we propose an effective Convolutional Neural Network (CNN) architecture based on modified LeNet-5 Network (MLeNet-5) for detecting smiles in images. The proposed system generates low-level face identifiers and detect smiles using a strong binary classifier. In our experiments, the proposed MLenet-5 system used the SMILEsmilesD and (GENKI-4 K) databases in which the smile detection rate of the proposed method improves the accuracy by 2% on SMILEsmilesD database and 5% on GENKI-4 K database relative to LeNet-5-based CNN network. In addition, the proposed system decreases the number of parameters compared to LeNet-5-based CNN network and most of the existing models while maintaining the robustness and effectiveness of the results

A study of instance-based algorithms for supervised learning tasks : mathematical, empirical, and psychological evaluations

This dissertation introduces a framework for specifying instance-based algorithms that can solve supervised learning tasks. These algorithms input a sequence of instances and yield a partial concept description, which is represented by a set of stored instances and associated information. This description can be used to predict values for subsequently presented instances. The thesis of this framework is that extensional concept descriptions and lazy generalization strategies can support efficient supervised learning behavior.The instance-based learning framework consists of three components. The pre-processor component transforms an instance into a more palatable form for the performance component, which computes the instance's similarity with a set of stored instances and yields a prediction for its target value(s). Therefore, the similarity and prediction functions impose generalizations on the stored instances to inductively derive predictions. The learning component assesses the accuracy of these prediction(s) and updates partial concept descriptions to improve their predictive accuracy.This framework is evaluated in four ways. First, its generality is evaluated by mathematically determining the classes of symbolic concepts and numeric functions that can be closely approximated by IB_1, a simple algorithm specified by this framework. Second, this framework is empirically evaluated for its ability to specify algorithms that improve IB_1's learning efficiency. Significant efficiency improvements are obtained by instance-based algorithms that reduce storage requirements, tolerate noisy data, and learn domain-specific similarity functions respectively. Alternative component definitions for these algorithms are empirically analyzed in a set of five high-level parameter studies. Third, this framework is evaluated for its ability to specify psychologically plausible process models for categorization tasks. Results from subject experiments indicate a positive correlation between a models' ability to utilize attribute correlation information and its ability to explain psychological phenomena. Finally, this framework is evaluated for its ability to explain and relate a dozen prominent instance-based learning systems. The survey shows that this framework requires only slight modifications to fit these highly diverse systems. Relationships with edited nearest neighbor algorithms, case-based reasoners, and artificial neural networks are also described

Design and Investigation of a Multi Agent Based XCS Learning Classifier System with Distributed Rules

This thesis has introduced and investigated a new kind of rule-based evolutionary online learning system. It addressed the problem of distributing the knowledge of a Learning Classifier System, that is represented by a population of classifiers. The result is a XCS-derived Learning Classifier System 'XCS with Distributed Rules' (XCS-DR) that introduces independent, interacting agents to distribute the system's acquired knowledge evenly. The agents act collaboratively to solve problem instances at hand. XCS-DR's design and architecture have been explained and its classification performance has been evaluated and scrutinized in detail in this thesis. While not reaching optimal performance, compared to the original XCS, it could be shown that XCS-DR still yields satisfactory classification results. It could be shown that in the simple case of applying only one agent, the introduced system performs as accurately as XCS

Architecting system of systems: artificial life analysis of financial market behavior

This research study focuses on developing a framework that can be utilized by system architects to understand the emergent behavior of system architectures. The objective is to design a framework that is modular and flexible in providing different ways of modeling sub-systems of System of Systems. At the same time, the framework should capture the adaptive behavior of the system since evolution is one of the key characteristics of System of Systems. Another objective is to design the framework so that humans can be incorporated into the analysis. The framework should help system architects understand the behavior as well as promoters or inhibitors of change in human systems. Computational intelligence tools have been successfully used in analysis of Complex Adaptive Systems. Since a System of Systems is a collection of Complex Adaptive Systems, a framework utilizing combination of these tools can be developed. Financial markets are selected to demonstrate the various architectures developed from the analysis framework --Introduction, page 3