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



Bilinear graph neural network with neighbor interactions
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Abstract Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the

Deep neural network structures solving variational inequalities
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Motivated by structures that appear in deep neural networks, we investigate nonlinear composite models alternating proximity and affine operators defined on different spaces. We first show that a wide range of activation operators used in neural networks are actually

A convolutional neural networkbased linguistic steganalysis for synonym substitution steganography
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In this paper, a linguistic steganalysis method based on two-level cascaded convolutional neural networks (CNNs) is proposed to improve the systems ability to detect stego texts, which are generated via synonym substitutions. The first-level network sentence-level CNN

A Comprehensive Analysis of Convolutional Neural Network Models
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Deep learning is an emerging field of machine learning that has been grown rapidly and applies to many domains with high success frequency including image processing, speech recognition and text processing. Experiments shows its high applicability and significant Nanofluids are attractive alternatives for the current heat transfer fluids due to their remarkably higher thermal conductivity which leads to the improved thermal performance. Nanofluids are applicable in porous media for improving their heat transfer. Proposing

A novel countermeasure technique to protect WSN against denial-of-sleep attacks using firefly and Hopfield neural network (HNN) algorithms
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Wireless sensor networks (WSNs) contain numerous nodes that their main goals are to monitor and control environments. Also, sensor nodes distribute based on network usage. One of the most significant issues in this type of network is the energy consumption of sensorBackground Precise prediction of cancer types is vital for cancer diagnosis and therapy. Through a predictive model, important cancer marker genes can be inferred. Several studies have attempted to build machine learning models for this task however none has taken into The availability of reliable interatomic potentials is necessary for carrying out computer simulations of complex materials. While electronic structure methods like density functional theory have been applied with great success to many systems, the high computational costs 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 models Ground vibration (PPV) is one of the hazard effects induced by blasting operations in open- pit mines, which can affect the surrounding structures, particularly the stability of benches and slopes in open-pit mines, and impact underground water, railway, highway, and

Branch and bound for piecewise linear neural network verification
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Abstract The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models. In this context, verification involves proving or disproving that an NN model satisfies certain

A surrogate model for computational homogenization of elastostatics at finite strain using HDMR-based neural network
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We propose a surrogate model for two-scale computational homogenization of elastostatics at finite strains. The macroscopic constitutive law is made numerically available via an explicit formulation of the associated macro-energy density. This energy density is

A Comprehensive Study of Applying Convolutional Neural Network for Computer Vision
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Computer vision makes computers enable to recognize and understand information from digital images. It is a multidisciplinary field and aims to develop automated systems that able to extract information from the digital images. Advancements in algorithms and processing

Detection of exomoons in simulated light curves with a regularized convolutional neural network
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Context. Many moons have been detected around planets in our Solar System, but none has been detected unambiguously around any of the confirmed extrasolar planets. Aims. We test the feasibility of a supervised convolutional neural network to classify photometric transit Handwritten signatures are an undeniable and unique way to prove the identity of persons. Owing to the simplicity and uniqueness, it finds an essential place in the area of behavioral biometric. Signatures are the most widely accepted biometric trait by law enforcement Nowadays, a lot of people have the oral health problems due to continuous changes in the lifestyle such as the persons speech which can be affected by the malocclusion in teeth and the crooked teeth. The dental problems can cause cavity and bacterial infection. The dental People start posting tweets containing texts, images, and videos as soon as a disaster hits an area. The analysis of these disaster-related tweet texts, images, and videos can help humanitarian response organizations in better decision-making and prioritizing their tasks Blasting operation is considered as one of the cheapest methods to break the rock into small pieces in surface and underground mines. Ground vibration is a side effect of blasting and can result in damage to, or failure of, nearby structures. Therefore, it is imperative to predict

Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality
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In this study, we prove that an intrinsic low dimensionality of covariates is the main factor that determines the performance of deep neural networks (DNNs). DNNs generally provide outstanding empirical performance. Hence, numerous studies have actively investigated the Blood flow analysis in the artery is a paramount study in the field of arterial stenosis evaluation. Studies conducted so far have reported the analysis of blood flow parameters using different techniques, but the regression analysis is not adequately used. Artificial Shrimp is a worlds important trade goods with high economic value and also one of the most important sources of animal protein. Considering the costs of calculation and hardware, this paper presents a convolutional neural network (CNN) architecture (named as

A comparative analysis of optimization and generalization properties of two-layer neural network and random feature models under gradient descent dynamics
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A fairly comprehensive analysis is presented for the gradient descent dynamics for training two-layer neural network models in the situation when the parameters in both layers are updated. General initialization schemes as well as general regimes for the network widthBackground Predicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery. The recent rapid growth of the amount of

SwaNN: Switching among cryptographic tools for privacypreserving neural network predictions
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The rise of cloud computing technology led to a paradigm shift in technological services that enabled enterprises to delegate their data analytics tasks to cloud servers which have domain-specific expertise and computational resources for the required analytics. Machine

Shallow convolutional neural network for COVID-19 outbreak screening using chest X-rays
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Among radiological imaging data, chest X-rays are of great use in observing COVID-19 mani- festations. For mass screening, using chest X-rays, a computationally efficient AI-driven tool is the must to detect COVID-19 positive cases from non-COVID ones. For this purpose, we Effective 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 respect In 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 the

Scalable recurrent neural network for hyperspectral image classification
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Hyperspectral imaging (HSI) collects hundreds of images over large spatial observation areas on the Earths surface, recording scenes at different wavelength channels and providing a vast amount of information. Recurrent neural networks (RNNs) have been widely

A physics-informed neural network for wind turbine main bearing fatigue
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Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss). Unfortunately, historical data indicates that failure can happen far earlier than the Nanofluids 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 data

A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry
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Microfluidic applications such as active particle sorting or selective enrichment require particle classification techniques that are capable of working in real time. In this paper, we explore the use of neural networks for fast label-free particle characterization during In the present paper, asymptotic expansion and Voronovskaja type theorem for the neural network operators have been proved. The above results are based on the computation of the algebraic truncated moments of the density functions generated by suitable sigmoidal Monitoring hourly river flows is indispensable for flood forecasting and disaster risk management. The objective of the present study is to develop a suite of hourly river flow forecasting models for the Albert river, located in Queensland, Australia using various Defect clusters on the wafer map can provide important clue to identify the process failures so that it is important to accurately classify the defect patterns into corresponding pattern types. In this research, we present an image-based wafer map defect pattern classification Advances in the artificial intelligence-based models can act as robust tools for modeling hydrological processes. Neural network architectures coupled with learning algorithms are considered as useful modeling tools for groundwater-level fluctuations. Emotional artificial The passivity, low power consumption, memory characteristics and nanometer size of memristors make them the best choice to simulate synapses in artificial neural networks. In this paper, based on the proposed associative memory rules, we design a memristor neural

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network .
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Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful forThis paper presents a novel approach for synthesizing facial affect; either in terms of the six basic expressions (ie, anger, disgust, fear, joy, sadness and surprise), or in terms of valence (ie, how positive or negative is an emotion) and arousal (ie, power of the emotion activation)

Houghencoder: neural network architecture for document image semantic segmentation
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In this paper, we propose a HoughEncoder neural network architecture for the semantic image segmentation task. The main feature of the proposed architecture is that it contains layers calculating direct and transposed integral operators, namely Fast Hough TransformReduction 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 Sea

Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme
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Traditional robotic control suits require profound task-specific knowledge for designing, building and testing control software. The rise of Deep Learning has enabled end-to-end solutions to be learned entirely from data, requiring minimal knowledge about theRadial Basis Function Neural Network (RBFNN) is very prominent in data processing. However, improving this technique is vital for the NN training process. This paper presents an integrated 2 Satisfiability in radial basis function neural network (RBFNN-2SAT). There

Hand Classification from Fingerprint image using Deep Neural Network
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Fingerprint security technology has attracted a great deal of attention in recent years because of its unique biometric information that does not change over an individuals lifetime and is a highly reliable and secure way to identify a certain individuals. AFIS (Automated

A rub-impact recognition method based on improved convolutional neural network
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Based on the theory of modal acoustic emission (AE), when the convolutional neural network (CNN) is used to identify rotor rub-impact faults, the training data has a small sample size, and the AE sound segment belongs to a single channel signal with less pixel Up to date technological implementations of deep convolutional neural networks are at the forefront of many issues, such as autonomous device control, effective image and pattern recognition solutions. Deep neural networks generally utilize a hybrid topology of a feature The increment of new words and text categories requires more accurate and robust classification methods. In this paper, we propose a novel multi-label text classification method that combines dynamic semantic representation model and deep neural network

Fuzzy Base Artificial Neural Network Model For Text Extraction From Images
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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 paper Breast cancer (BrC) is the leading cause of abnormal death in women. Mammograms and histopathology (Hp) biopsy images are generally recommended for early diagnosis of BrC because Hp image-based diagnosis enables doctors to make cancer diagnostic decisions Rapid growth of communication technologies and expert systems produces enormous volume of medical data. Deep learning technique is an advancement of machine learning technique for analysing the huge amount of various diseases related medical dataset. Even Interstitial lung disease (ILD) refers to a group of various abnormal inflammations of lung tissues and early diagnosis of these disease patterns is crucial for the treatment. Yet it is difficult to make an accurate diagnosis due to the similarity among the clinical manifestations Accurately predicting river flows over daily timescales is considered as an important task for sustainable management of freshwater ecosystems, agricultural applications, and water resources management. In this research paper, artificial intelligence (AI) techniques, namely The robot is a very complex multi-input multi-output nonlinear system. Due to the inaccuracy of measurement and modeling, coupled with changes in load and the effects of external disturbances, it is virtually impossible to obtain a complete kinetic model. The strong As a global and grievous mental disease, major depressive disorder (MDD) has received much attention. Accurate detection of MDD via physiological signals represents an urgent research topic. Here, a frequency-dependent multilayer brain network combined with deep Computational Psychiatry is an emerging field of science. It focuses on identifying the complex relationship between the brains neurobiology. Mental illness has recently become an important problem to be addressed as the number of people affected is increasing over 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 the A new stable adaptive neural network (ANN) control scheme for the Furuta pendulum, as a two-degree-of-freedom underactuated nonlinear system, is proposed in this paper. This approach aims to address the control problem of the Furuta pendulum in the steady state

Multifocus image fusion using convolutional neural network
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Acquiring all-in-focus images is significant in the multi-media era. Limited by the depthof- field of the optical lens, it is hard to acquire an image with all targets are clear. One possible solution is to merge the information of a few complementary images in the same scene. In The 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)