A Review of the Image Classification Models Used for the Prediction of Bed Defects in the Selective Laser Sintering Process

Defects formed during the spreading of powder, known as powder bed defects, are a major issue in additive manufacturing processes. Deep learning (DL)-based image classification models can be utilised to detect defects caused by the powder spreading process. The aim of this research was to review and compare the performance of the EfficientNet_v2 deep learning image classification model against the commonly used VGG-16 model on a selective laser sintering powder bed defects (SLS PBDs) dataset. It was observed that the EfficientNet_v2 model achieved higher performance than the commonly used VGG-16 model, with a defect prediction accuracy of 97.54% and model sensitivity of 96.3%

Research Study on basic Understanding of Artificial Neural Networks

Artificial neural networks are a computing system inspired by human neuron, designed to simulate the way human brain analyzes and processes information. They are the foundation of artificial intelligence and machine learning technology. This research paper focuses on the basic understanding of Artificial neural networks. ANN create a lots of excitement in Machine learning research and that results a huge development on many AI and machine learning systems like text processing, speech recognition, image processing. Neural networks consist of input and output layers, in many cases hidden layer consisting of units that transform the input into something that the output layer can use. They are essential tools for finding patterns which are far too complex or numerous for a human programmer to extract and teach the machine to recognize

Research Study on basic Understanding of Artificial Neural Networks

Artificial neural networks are a computing system inspired by human neuron, designed to simulate the way human brain analyzes and processes information. They are the foundation of artificial intelligence and machine learning technology. This research paper focuses on the basic understanding of Artificial neural networks. ANN create a lots of excitement in Machine learning research and that results a huge development on many AI and machine learning systems like text processing, speech recognition, image processing. Neural networks consist of input and output layers, in many cases hidden layer consisting of units that transform the input into something that the output layer can use. They are essential tools for finding patterns which are far too complex or numerous for a human programmer to extract and teach the machine to recognize

Lightweight optical constellation modeling by concatenating artificial neural networks

A lightweight optical constellations modeling method based on concatenating ANNs is proposed. Statistical validation of the reproduced constellations is shown. The method accelerates data generation and facilitates detecting (un)intentioned misconfigurations, among others.This work has been partially supported by the EC through the MSC REAL-NET project (G.A. 813144), by the AEI/FEDER through the TWINS project (TEC2017-90097-R), and by the ICREA institution.Peer ReviewedPostprint (author's final draft

Descriptive statistics of Neural Network and Regression Based Results for Short Term Electricity Demand Prediction

In the realm of data analysis and predictive modeling, both neural networks and regression techniques play pivotal roles. This study aims to provide a comparative analysis of the descriptive statistics derived from neural network and regression-based results. Utilizing a dataset representative of real-world scenarios, we explore how these two approaches perform in terms of descriptive measures such as mean squared error, coefficient of determination (R-squared), standard error, and others. The research involves implementing both neural network and regression models on the dataset and evaluating their performance using various statistical metrics. Through a systematic examination of the descriptive statistics derived from these models, we aim to elucidate the strengths and weaknesses of each approach in capturing the underlying patterns and making accurate predictions. Additionally, we delve into the interpretability aspect, assessing the ease of understanding the results provided by neural networks compared to regression models. Furthermore, the study investigates the impact of factors such as dataset size, complexity, and feature selection on the performance and descriptive statistics of neural networks and regression techniques. By conducting experiments across different scenarios and datasets, we aim to provide insights into the conditions under which each method excels and where potential limitations lie. The findings of this research contribute to a deeper understanding of the characteristics and capabilities of neural network and regression models in data analysis and prediction tasks. This comparative analysis serves as a valuable resource for researchers, practitioners, and stakeholders seeking to leverage these methodologies effectively in various domains, ranging from finance and economics to healthcare and beyond

Analysis of the Convolutional Neural Network Model in Detecting Brain Tumor

Detecting brain tumors is an active area of research in brain image processing. This paper proposes a methodology to segment and classify brain tumors using magnetic resonance images (MRI). Convolutional Neural Networks (CNN) are one of the effective detection methods and have been employed for tumor segmentation. We optimized the total number of layers and epochs in the model.  First, we run the CNN with 1000 epochs to see its best-optimized number.  Then we consider six models, increasing the number of layers from one to six.  It allows seeing the overfitting according to the number of layers

Метод распознавания объектов в системах технического зрения роботов

Обоснование метода распознавания объектов по контурам их изображений, обеспечивающего простую реализацию в системах технического зрения робото

Predicting Impact of COVID-19 on Crude Oil Price Image with Directed Acyclic Graph Deep Convolution Neural Network

Deep learning methods have achieved amazing results in sequential input, prediction and image classification. In this study, we propose image transformation of time series crude oil price by incorporating 2-D Directed Acyclic Graph to Convolutional Neural Network (DAG) based on image processing properties. Crude oil price time series is converted into 2-D images, utilizing 10 distinctive technical indicators. Geometric Brownian Motion was utilized to produces data for a 10-day time span. Thus, 10x10 sized 2-D images are constructed. Each image is then labelled as Buy or Sell depending on the returns of the time series. The results show that integrating DAG with CNN improves the prediction accuracy by 14.18%.  DAG perform best with an accuracy of 99.16%, sensitivity of 100% and specificity of 99.19%. COVID-19 has negatively affected Nigeria crude oil price which indicates a downward trend of crude oil price. The study recommends poly-cultural economy of Nigeria economy for national development of the nation