# ANN Artificial Neural Network IEEE PAPERS AND PROJECTS-2020

Artificial neural networks or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems “learn” to perform tasks by considering examples, generally without being programmed with task-specific rules

The objective of this study is to evaluate the performance of the artificial neural network (ANN) approach for predicting interlayer conditions and layer modulus of a multi-layered flexible pavement structure. To achieve this goal, two ANN based back-calculation modelsIn the current work, after generating experimental data points for different volume fraction of nanoparticles ($$\phi$$) and different temperatures, an algorithm to find the best neuron number in the hidden layer of artificial neural network (ANN) method is proposed to find theNanofluids are widely applicable in thermal devices with porous structures. Silica nanoparticles have been dispersed in different heat transfer fluids in order to increase their thermal conductivity and heat transfer capability. In this study, group method of dataEffective selection of tunnel support patterns is one of the key factors affecting the safety and operation cost of tunnel engineering. This study developed an artificial neural network (ANN) model for estimating tunnel support patterns ahead of tunnel face. In this respectReduction in sea water level can make services in nearshore structures difficult, and sea water level rise increases the risk to residential areas or the surrounding fields. For strategic planning, it is vital to take into account the present and future fluctuations of Caspian SeaModeling and prediction of bed loads is an important but difficult issue in river engineering. The introduced empirical equations due to restricted applicability even in similar conditions provide different accuracies with each other and measured data. In this paper, three differentBackground Optimal detection and surveillance of bladder cancer (BCa) rely primarily on the cystoscopic visualization of bladder lesions. AI-assisted cystoscopy improve image recognition and accelerate data acquisition. Objective To provide a comprehensive review

Fuzzy Base Artificial Neural Network Model For Text Extraction From Images

Content Extraction assumes a significant job in discovering essential and important data. Content extraction includes discovery, restriction, following, binarization, extraction, improvement and acknowledgment of the content from the given picture. This paperIn this paper, artificial neural networks (ANNs) are developed to predict the thermal conductivity ($$k_ {\text {nf}}$$) of multi-walled carbon nanotubes (MWCNTs)-CuO/water nanofluid. After generating experimental data points, an algorithm is proposed to find theThe artificial neural network (ANN) is a sort of machine learning method which has been used in determination of risk of human disorders. In the current investigation, we have created an ANN and trained it based on the genetic data of 401 multiple sclerosis (MS)This paper presents the application of artificial neural network technique for solving a class of third-order linear and nonlinear boundary value problems with mixed nonlinear boundary conditions. This technique overcomes the singular behavior of problems and outlines theSeven artificial neural network (ANN) models were developed to predict daytime actual evapotranspiration (ET) for Nissouri Creek in Oxford County, Canada, from April to July 201 using the Bowen ratio energy balance method as target output for the first time. InIn order to study the design direction of watch modelling in the future, the needs of consumer groups were collected by using network social media. Enterprises and designers carried out preliminary design according to the needs description of consumers. Then, these designThis research focuses on predicting the International Roughness Index (IRI) of rigid pavements using the Artificial Neural Network (ANN) model that uses climate and traffic parameters as inputs. A Long-Term Pavement Performance (LTPP) database is used toThe main purpose of this research is to predict the ground surface settlement in tunneling of a single circular tunnel with simultaneous changes in the mechanical properties of soil and geometrical properties of the tunnel section. In this research, numerical and parametricIn viticulture, it is critical to predict productivity levels of the different vineyard zones to undertake appropriate cropping practices. To overcome this challenge, the final yield was predicted by combining vegetation indices (VIs) to sense the health status of the crop and byIt is of a high importance to introduce intelligent systems for estimation and optimization of blasting-induced ground vibration because it is one the most unwanted phenomena of blasting and it can damage surrounding structures. Hence, in this paper, estimation andAbstract Delay Tolerant Network (DTN) is a kind of network that there is no continuous network connectivity among nodes. There are no end-to-end and constant connection paths from source nodes to destination nodes due to the mobility nature of nodes. In such Abstract Cu 3 (BTC) a common type of metal organic framework (MOF), was synthesized through electrochemical route for CO 2 capture and its separation from N 2. Taguchi method was employed for optimization of key parameters affecting the synthesis of Cu 3 (BTC) 2

An artificial neural network model supported with multi criteria decision making approaches for maintenance planning in hydroelectric power plants

Power plants are the large-scale production facilities with the main purpose of realizing uninterrupted, reliable, efficient, economic and environmentally friendly energy generation. Maintenance is one of the critical factors in achieving these comprehensive goals, which areBackground To analyze the clinical results of an artificial neural network (ANN) that has been processed in order to improve the predictability of intracorneal ring segments (ICRS) implantation in keratoconus. Methods This retrospective, comparative, nonrandomized, pilot

Application of the artificial neural network to optimize the formulation of self-nanoemulsifying drug delivery system containing rosuvastatin

The objectives of this study were to optimize the formula of the self-nanoemulsifying drug delivery system (SNEDDS) containing rosuvastatin and to evaluate its physicochemical characteristics. The solubility and compatibility of rosuvastatin in surfactants, cosurfactants

Artificial Neural Network Model to Prediction of Eutrophication and Microcystis Aeruginosa Bloom

Maekuang reservoir is one of the water resources which provides water supply, livestock, and recreational in Chiangmai city, Thailand. The water quality and Microcystis aeruginosa are a severe problem in many reservoirs. M. aeruginosa is the most widespread toxicGiven the excellent thermal properties of MXene, MXene nanomaterials-based nanofluids have the potential of being used as heat transfer fluids. In this work, we have investigated the dynamic viscosity of MXene-palm oil nanofluid. To prepare the nanofluidThe other point is that the solution is continuous overall the domain of integration. 3 Formalism of artificial neural network Especially the last decade has seen a significant raise of inter- est in machine learning where the learning part is done by the artificial neural networks . ArtificialRecently, artificial neural networks (ANNs) have been applied to various robot-related research areas due to their powerful spatial feature abstraction and temporal information prediction abilities. Decision-making has also played a fundamental role in the research

Comparison of artificial neural network fuzzy logic and genetic algorithm for cutting temperature and surface roughness prediction during the face milling

ABSTRACT ARTICLEINFO This paper shows the possibility of applying artificial intelligence methods in milling, as one of the most common machining operations. The main goal of the research is to obtain reliable intelligent models for selected output characteristics of the

A Comprehensive Analysis of Convolutional Neural Network Models

traditional machine learning approaches. Deep learning algorithms are mainly an inheritor of artificial neural network architecture with higher number of hidden layers, therefore known as deep neural networks . A type of feedOptimization of casting parameters is essential in terms of quality factors in foundries. Nowadays, to optimize process parameters, new approaches such as artificial neural networks method are being used. In this study, a neural network model has been developedAs one of the most important components of the lake ecosystem, microorganisms from the freshwater and sediment play an important role in many ecological processes. However, the difference and correlation of bacterial community between these two niches were not clearResearch has shown that technology, when used prudently, has the potential to improve instruction and learning both in and out of the classroom. Only a handful of African tertiary institutions have fully deployed learning management systems (LMS) and the literature isPurpose Neuromodulation, such as vagal nerve stimulation and intestinal electrical stimulation, has been introduced for the treatment of obesity and diabetes. Ideally, neuromodulation should be applied automatically after food intake. The purpose of thisSteels are the most used structural material in the world, and hydrogen content and localization within the microstructure play an important role in its properties, namely inducing some level of embrittlement. The characterization of the steels susceptibility to hydrogen

Trophic state assessment using hybrid classification tree- artificial neural network

The Aquaponics system is composed of aquaculture and hydroponics subsystems. It is a soilless form of agriculture which recirculates water from fishpond to crop growth chambers and drains back to the fishpond. Each subsystem cultivates certain ideal dry mass, fishes forThe prediction of chaotic time series is an important issue in nonlinear information procession. Due to the multi-modal, high-dimensional and non-differentiable or discontinuous characteristics of chaotic systems, global optimization techniques areBackground In this study, response surface methodology (RSM) and artificial neural network (ANN) was used to construct the predicted models of linear, quadratic and interactive effects of two independent variables viz. salicylic acid (SA) and chitosan (CS) for the production of

Artificial Neural Network Base Short-Term Electricity Load Forecasting: A Case Study of a 132/33kv Transmission Sub-Station

Forecasting of electrical load is extremely important for the effective and efficient operation of any power system. Good forecasts results help in minimizing the risk in decision making and reduces the costs of operating the power plant. This work focuses on the short-term loadBiomass ashes are used for adsorption of herbicides from aqueous solution. A relationship between physicochemical properties of biomass ashes such as carbon hydrogen nitrogen content (CHN analysis), silica content and BET surface area with their adsorption capacity In the present study, a three-layer artificial neural network (ANN) and nonlinear regression models were developed to predict the performance of biogas production from the anaerobic hybrid reactor (AHR). Firstly, the performance of an AHR which is filled with perlite (2.38

Criminal Cases Forecasting Model using A New Intelligent Hybrid Artificial Neural Network with Cuckoo Search Algorithm.

Criminal cases are social problems that concern the public order and good morals of the citizens as a whole. Research in criminology has recently focused on finding the root causes of criminal cases in order to identify the factors accelerating crimes and to find preventiveRecently, healthcare data consist of an enormous amount of information, which is challenging to maintain by manual methods. Due to the development of big data in the communities of biomedical and health care, accurate study of the medical data helps theIn this paper, an artificial neural network (ANN) trained through a deep reinforcement learning (DRL) agent is used to perform flow control. The target is to look for the wake stabilization mechanism in an active way. The flow past a 2-D cylinder with a ReynoldsLiquid cooling systems in data centers have been attracting more attentions due to its better cooling capability and less energy consumption. In order to propose an effective optimization method for the operation of indirect liquid cooling systems, this paper firstIn a conventional artificial neural model, the nonlinear activation function (AF) follows the weight sum operation. In this paper, the AF is placed before the connecting weights of each artificial neuron and hence modified artificial neural network (MANN) is proposed and theEntity and relationship extraction is an important step in building a knowledge base, which is the basis for many artificial intelligence products to be used in life, such as Amazon Echo and Intelligent Search. We propose a new artificial neural network model to identify entitiesThe risk of accidents at workplaces, particularly in the sensitive locations with unsafe behaviors, have increased substantially, needing to be managed accurately. To ameliorate the safety in such systems, enhancing the integrated resilience engineering and macro

Categorizing Functional Yoghurt Using Artificial Neural Network

Yoghurt was supplemented with low molecular weight carbohydrates (LMWC) extracted from Syzygium cumini seeds. Total soluble solids, pH, color, titratable acidity, texture, sensory and shelf life studies were quantified in control and functional-F1 (1% LMWC) andGroundwater flow in the Grasberg open-pit mine is governed by fractured flow media. Groundwater modeling in fractured media requires detailed hydraulic conductivity (K) value distribution to illustrate hydrogeological conditions of the Grasberg open-pit mine properlySoil and plants are interconnected; so destruction in the soil causes degradation in plants. In this study, predictive model of soil and plants degradation was developed using artificial neural network . For sampling of soil, parallel transects by systematic random method wereThis paper investigates the capability of adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) modeling approach to predict the unconfined compressive strength (UCS) of stabilized pond ashes with lime alone and in combination with lime

Semantic Artificial Neural Networks