Academic Articles & Research Papers

Prediction Intervals for Artificial Neural Networks

The artificial neural network (ANN) is becoming a very popular model for engineering and scientific applications. Inspired by brain architecture, artificial neural networks represent a class of nonlinear models capable of learning from data...

Learning and Evolution in Neural Networks

This article describes simulations on populations of neural networks that both evolve at the population level and learn at the individual level. Unlike other simulations, the evolutionary task (finding food in the environment) and the learn...

Neural networks in the capital markets

PART ONE: NEURAL NETWORKS: Introduction Design Considerations Methods for Optimal Network Design Data Modelling Considerations Testing Strategies and Metrics PART TWO: EQUITY APPLICATIONS: Modelling Stock Returns in the Framework of APT: A ...

Introduction to artificial neural networks

A neural network is a data processing system consisting of a large number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks ...

Deep learning and metaheuristic optimization for predicting drug release from nanocarrier systems: a data-driven framework for formulation development.

Statistics with Confidence

Uncertainty and Psychological Research Variables and Measurement Exploring, Describing, Displaying and Summarizing Research Design and Probability Sampling Distributions and Confidence Intervals Statistical Models and Significance Tests Pre...

Wind–Wave Coupling Effect on the Dynamic Response of a Combined Wind–Wave Energy Converter

There is a huge energy demand from offshore renewable energy resources. To maximize the use of various renewable energy sources, a combined floating energy system consisting of different types of energy devices is an ideal option to reduce ...

Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches

Abstract This paper introduces the design and exploration of a compact, high-performance multiple-input multiple-output (MIMO) antenna for 6G applications operating in the terahertz (THz) frequency range. Leveraging a meta learner-based sta...

Statistics for people who (think they) hate statistics

Part I. Yippee! I'm in Statistics Chapter 1. Statistics or Sadistics?: It's Up to You Part II. Sigma Freud and Descriptive Statistics Chapter 2. Computing and Understanding Averages: Means to an End Chapter 3. Vive la Difference: Understand...

The Significance of Machine Learning and Deep Learning Techniques in Cybersecurity: A Comprehensive Review

People in the modern era spend most of their lives in virtual environments that offer a range of public and private services and social platforms. Therefore, these environments need to be protected from cyber attackers that can steal data o...

Lack of Usefulness of Literature-Based Helicobacter pylori Resistance Data for Predicting First-Line Empirical Treatment Effectiveness in the European Registry on H. pylori Management (Hp-EuReg).

Predicting Microbial Protein Synthesis in Cattle: Evaluation of Extant Equations and Steps Needed to Improve Accuracy and Precision of Future Equations

Predictions of microbial crude protein (MCP) synthesis for beef cattle generally rely on empirical regression equations, with intakes of energy and protein as key variables. Using a database from published literature, we developed new equat...

Factors predicting active treatment free-survival and initial experience with mpMRI as a selection tool in patient undergoing active surveillance: 10-year follow-up of SIUrO PRIAS-ITA working group

Predicting PANSS symptoms in schizophrenia spectrum disorders using speech only: an international, multi-centre, computational secondary analysis across multiple languages.

Survey of Natural Language Processing Techniques in Bioinformatics

Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives....

Large Language Models and the Elliott Wave Principle: A Multi-Agent Deep Learning Approach to Big Data Analysis in Financial Markets

Traditional technical analysis methods face limitations in accurately predicting trends in today’s complex financial markets. Meanwhile, existing AI-driven approaches, while powerful in processing large datasets, often lack interpretability...

Natural Language Processing-Based Financial Time Series Forecasting: Utilizing Sentiment Analysis for Improved Stock Price Prediction

This study explores the application of natural language processing (NLP) techniques in financial time series forecasting, specifically in predicting stock prices. Historical stock price data and textual data from financial news articles and...

Ensembles machine learning for short duration rainfall intensity forecasting

Abstract This study introduces an ensemble machine learning (EML) framework to enhance short-duration rainfall-intensity prediction under limited-data conditions. By integrating multiple base learners within a stacking architecture, the pro...

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Abstract Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an ...

Artificial Intelligence in Routine IVF Practice

Background: Artificial Intelligence (AI) has emerged as a transformative tool in in vitro fertilization (IVF) as it has done in other sectors. In IVF, AI offers advancements in embryo selection, treatment personalization, and outcome predic...