Genetic Algorithm-based Convolutional Neural Network Feature Engineering for Optimizing Coronary Heart Disease Prediction Performance.

Objectives: This study aimed to optimize early coronary heart disease (CHD) prediction using a genetic algorithm (GA)-based convolutional neural network (CNN) feature engineering approach. We sought to overcome the limitations of traditional hyperparameter optimization techniques by leveraging a GA for superior predictive performance in CHD detection.

Methods: Utilizing a GA for hyperparameter optimization, we navigated a complex combinatorial space to identify optimal configurations for a CNN model.

Thematic evolution of smart learning environments, insights and directions from a 20-year research milestones: A bibliometric analysis.

Smart learning environments (SLEs) have been developed to create an effective learning environment gradually and sustainably by applying technology. Given the growing dependence on technology daily, SLE will inevitably be incorporated into the teaching and learning process. Without transforming technology-enhanced learning environments into SLE, they are restricted to adding sophistication and lack pedagogical benefits, leading to wasteful educational investments.

Development of a Conceptual Framework for Tuberculosis Management and Control; an Evidence Synthesis Using Text Mining Software: A Review.

Background: The use of electronic systems supported by text-mining software applications that support the End TB strategy' needs to be explored. This study aimed to address this knowledge gap, and synthesis of evidence.

Methods: The PubMed database was searched for structured review articles published in English since 2012 on interventions to control and manage TB.

Development of Tuberculosis Vulnerability Assessment Conceptual Framework Using Automatic Content Analysis.

The tuberculosis prevention and control model needs to be explored. This study aimed to create a conceptual framework for measuring TB vulnerability to guide the prevention program's effectiveness. SLR method was employed, resulting in 1.

'Everything is data': towards one big data ecosystem using multiple sources of data on higher education in Indonesia.

Big data is increasingly being promoted as a game changer for the future of science, as the volume of data has exploded in recent years. Big data characterized, among others, the data comes from multiple sources, multi-format, comply to 5-V's in nature (value, volume, velocity, variety, and veracity). Big data also constitutes structured data, semi-structured data, and unstructured-data.

Vegetation indices' spatial prediction based novel algorithm for determining tsunami risk areas and risk values.

This paper aims to propose a new algorithm to detect tsunami risk areas based on spatial modeling of vegetation indices and a prediction model to calculate the tsunami risk value. It employs atmospheric correction using DOS1 algorithm combined with -NN algorithm to classify and predict tsunami-affected areas from vegetation indices data that have spatial and temporal resolutions. Meanwhile, the model uses the vegetation indices (.

The Effectiveness and Efficiency of Nursing Care Documentation Using the SIMPRO Model.

Purpose: The study aims to develop and test the effectiveness and efficiency of the SIMPRO. SIMPRO was developed with NANDA-I, Nursing Intervention Classification, and Nursing Outcome Classification nursing language.

Method: The research was divided into two parts, in which we used two different designs-incremental and quasi-experimental design.