Academic Articles & Research Papers

Epidemiology of Drowning Incidents among Children at Sultan Qaboos University Hospital Oman

Objectives: We sought to study the epidemiology of drowning among children reported at Sultan Qaboos University Hospital in Oman. Methods: We conducted a retrospective study of the patients who presented to the emergency department with a h...

Quantum Circuit for Imputation of Missing Data

The imputation of missing data is a common procedure in data analysis that consists in predicting missing values of incomplete data points. In this work, we analyze a variational quantum circuit for the imputation of missing data. We constr...

Introduction to Quantum Computing

Over the past few decades, tremendous growth has been witnessed in cryptography in which different security techniques and concerns were projected and put into practice. The classical methods of cryptography are depended on binary bits, whi...

The state of quantum computing applications in health and medicine

Abstract Quantum computing hardware and software have made enormous strides over the last years1. Questions around quantum computing’s impact on research and society have changed from “if” to “when/how”. The 2020s have been described as the...

Quantum Computing 40 Years Later

This chapter sketches the foundations of the subject, discussing salient features of quantum information, formulates a mathematical model of quantum computation and highlights some implications of the model. It reviews two particularly prom...

Effective Concurrency Testing for Distributed Systems

Despite their wide deployment, distributed systems remain notoriously hard to reason about. Unexpected interleavings of concurrent operations and failures may lead to undefined behaviors and cause serious consequences. We present Morpheus, ...

Exploiting availability prediction in distributed systems

Loosely-coupled distributed systems have significant scale and cost advantages over more traditional architectures, but the availability of the nodes in these systems varies widely. Availability modeling is crucial for predicting per-machin...

CrystalBall: predicting and preventing inconsistencies in deployed distributed systems

We propose a new approach for developing and deploying distributed systems, in which nodes predict distributed consequences of their actions, and use this information to detect and avoid errors. Each node continuously runs a state explorati...

Forecasting student achievement in MOOCs with natural language processing

Student intention and motivation are among the strongest predictors of persistence and completion in Massive Open Online Courses (MOOCs), but these factors are typically measured through fixed-response items that constrain student expressio...

A renaissance of neural networks in drug discovery

INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to cho...

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...