Academic Articles & Research Papers

FIR-SDE: fast image restoration via mean-reverting stochastic differential equation

Abstract In computer vision, zero-shot image restoration—a technique enabling degraded image restoration without large-scale paired training data—has emerged as a pivotal technique for scenarios where data is limited or paired training data...

YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO

This paper implements a systematic methodological approach to review the evolution of YOLO variants. Each variant is dissected by examining its internal architectural composition, providing a thorough understanding of its structural compone...

Real-Time Weapon Detection and Suspect Face Capturing System Using YOLOv8

The rise of violent crimes involving sharp weapons and firearms in public spaces, including educational campuses, demands an automated real-time surveillance system to assist security personnel. This study proposes a web-based weapon detect...

Using different computing systems to solve the automatic cloud classification problem according to MODIS satellite data by probabilistic neural network

The relevance of the research is caused by the necessity to develop algorithms and software to classify the cloud types based on single-layer cloud on the satellite images received from MODIS spectral radiometer used in Terra and Aqua remot...

Computer Vision

This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the worl...

Computer vision: algorithms and applications

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old ...

Two-Argument Activation Functions Learn Soft XOR Operations Like Cortical Neurons

Neurons in the brain are complex machines with distinct functional compartments that interact nonlinearly. In contrast, neurons in artificial neural networks abstract away this complexity, typically down to a scalar activation function of a...

Parametric Predictions for Pure Electric Vehicles

Demand for pure electric vehicles has been found to be increasing over the years. This has necessitated the development of a model that would serve as a predicting machine for manufacturing different types of pure electric vehicles. Direct ...

Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics

Artificial neural networks are powerful algorithms for constructing nonlinear empirical models from operational data. Their use is becoming increasingly popular in the complex modeling tasks required by diagnostic, safety, and control appli...

Evaluation of Indoor Thermal Environments Using a Novel Predicted Mean Vote Model Based on Artificial Neural Networks

The assessment of indoor thermal environments is crucial to achieving thermal comfort and energy efficiency. However, the inaccurate evaluation and strong nonlinear variations of thermal comfort parameters limit engineering designs. Therefo...

Artificial Neural Networks

Neural Networks for River Flow Prediction

The surface‐water hydrographs of rivers exhibit large variations due to many natural phenomena. One of the most commonly used approaches for interpolating and extending streamflow records is to fit observed data with an analytic power model...

An Overview of Neural Network

Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in ...

Building neural networks

Foundations. Motivation. Neural-Network Fundamentals. Single Neuron Computations. Network Computations. Network Simulation. Foundations Summary. Suggested Readings. Bibliography. Paradigms. The Backpropagation Network. The Counterpropagatio...

Bayesian Regularization of Neural Networks

Bayesian regularized artificial neural networks (BRANNs) are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation. Bayesian regularization is a mathematical process that converts ...

Essence of Neural Networks

* Introduction * Classifying Patterns * Clustering Patterns * Pattern Association * A Sample of Recurrent Networks * Some Other Network Models and A Few Practical Points * Links to Artificial Intelligence * Synthesising Symbols with Neural ...

Artificial neural networks: a tutorial

Artificial neural nets (ANNs) are massively parallel systems with large numbers of interconnected simple processors. The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the arti...

Estradiol-Dependent Memory and Hippocampal Spine Remodeling Require Proteasomal Protein Degradation.

Determining the Optimum Layer Combination for Cross-Laminated Timber Panels According to Timber Strength Classes Using Artificial Neural Networks

The primary aim of this work was to determine the effects of production parameters, such as wood species and timber strength classes, on some mechanical properties of cross-laminated timber (CLT) panels using artificial neural network (ANN)...

Assimilation of ASCAT Radar Backscatter Coefficients over Southwestern France

In this work, Advanced SCATterometer (ASCAT) backscatter data are directly assimilated into the interactions between soil, biosphere, and atmosphere (ISBA) land surface model using Meteo-France’s global Land Data Assimilation System (LDAS-M...