There are very few examples of the use of various architectures for recurrent neural
networks to predict student learning outcomes. In fact, the only architecture used to
solve this problem is the LSTM architecture. In the works devoted to the use of LSTM
to predict educational outcomes, the results of a detailed theoretical substantiation of
the preference of this particular architecture of the RNN are not presented. In this
regard, it seems advisable to provide such justification in the framework of this study.
The main property of input data for prediction of educational outcomes is its
temporary nature. Some sequence of user actions unfolds in time and is evaluated
(classified) by an external observer as evidence of the presence or absence of an
educational result (objective or metaobjective). In this regard, the RNN used to classify
user actions should perform a procedure for adjusting the weights of neurons for a
certain set of states in the past. At the same time, the length of the sequence of these
states is not predetermined: it can be both short (for example, for objective results),
and quite long.
This document discusses using the Levenberg-Marquardt algorithm for forecasting stock exchange share rates on the Karachi Stock Exchange. It provides an overview of artificial neural networks and how they can be used for financial forecasting applications. The Levenberg-Marquardt algorithm is presented as an efficient method for training neural networks to minimize errors through gradient descent. The document applies this method to train a neural network to predict the direction of change in share prices on the Karachi Stock Exchange. The network is trained on historical stock price data and testing shows it can achieve the performance goal of forecasting next day price changes.
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...IAEME Publication
Artificial neural networks can achieve high computation rates by employing a massive number of simple processing elements with a high degree of connectivity between the elements. Neural networks with feedback connections provide a computing model capable of exploiting fine- grained parallelism to solve a rich class of complex problems. In this paper we discuss a complex series-parallel system subjected to finite common cause and finite human error failures and its reliability using neural network method.
Machine Learning Algorithms for Image Classification of Hand Digits and Face ...IRJET Journal
This document discusses machine learning algorithms for image classification using five different classification schemes. It summarizes the mathematical models behind each classification algorithm, including Nearest Class Centroid classifier, Nearest Sub-Class Centroid classifier, k-Nearest Neighbor classifier, Perceptron trained using Backpropagation, and Perceptron trained using Mean Squared Error. It also describes two datasets used in the experiments - the MNIST dataset of handwritten digits and the ORL face recognition dataset. The performance of the five classification schemes are compared on these datasets.
Rainfall Prediction using Data-Core Based Fuzzy Min-Max Neural Network for Cl...IJERA Editor
This paper proposes the Rainfall Prediction System by using classification technique. The advanced and modified neural network called Data Core Based Fuzzy Min Max Neural Network (DCFMNN) is used for pattern classification. This classification method is applied to predict Rainfall. The neural network called fuzzy min max neural network (FMNN) that creates hyperboxes for classification and predication, has a problem of overlapping neurons that resoled in DCFMNN to give greater accuracy. This system is composed of forming of hyperboxes, and two kinds of neurons called as Overlapping Neurons and Classifying neurons, and classification used for prediction. For each kind of hyperbox its data core and geometric center of data is calculated. The advantage of this method is it gives high accuracy and strong robustness. According to evaluation results we can say that this system gives better prediction of rainfall and classification tool in real environment.
The aim of this research is to find accurate solution for the Troesch’s problem by using high performance technique based on parallel processing implementation.
Design/methodology/approach – Feed forward neural network is designed to solve important type of differential equations that arises in many applied sciences and engineering applications. The suitable designed based on choosing suitable learning rate, transfer function, and training algorithm. The authors used back propagation with new implement of Levenberg - Marquardt training algorithm. Also, the authors depend new idea for choosing the weights. The effectiveness of the suggested design for the network is shown by using it for solving Troesch problem in many cases.
Findings – New idea for choosing the weights of the neural network, new implement of Levenberg - Marquardt training algorithm which assist to speeding the convergence and the implementation of the suggested design demonstrates the usefulness in finding exact solutions.
Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for He...chokrio
Ameliorating the lifetime in heterogeneous wireless sensor network is an important task because the sensor nodes are limited in the resource energy. The best way to improve a WSN lifetime is the clustering based algorithms in which each cluster is managed by a leader called Cluster Head. Each other node must communicate with this CH to send the data sensing. The nearest base station nodes must also send their data to their leaders, this causes a loss of energy. In this paper, we propose a new approach to ameliorate a threshold distributed energy efficient clustering protocol for heterogeneous wireless sensor networks by excluding closest nodes to the base station in the clustering process. We show by simulation in MATLAB that the proposed approach increases obviously the number of the received packet messages and prolongs the lifetime of the network compared to TDEEC protocol.
This document discusses using the Levenberg-Marquardt algorithm for forecasting stock exchange share rates on the Karachi Stock Exchange. It provides an overview of artificial neural networks and how they can be used for financial forecasting applications. The Levenberg-Marquardt algorithm is presented as an efficient method for training neural networks to minimize errors through gradient descent. The document applies this method to train a neural network to predict the direction of change in share prices on the Karachi Stock Exchange. The network is trained on historical stock price data and testing shows it can achieve the performance goal of forecasting next day price changes.
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...IAEME Publication
Artificial neural networks can achieve high computation rates by employing a massive number of simple processing elements with a high degree of connectivity between the elements. Neural networks with feedback connections provide a computing model capable of exploiting fine- grained parallelism to solve a rich class of complex problems. In this paper we discuss a complex series-parallel system subjected to finite common cause and finite human error failures and its reliability using neural network method.
Machine Learning Algorithms for Image Classification of Hand Digits and Face ...IRJET Journal
This document discusses machine learning algorithms for image classification using five different classification schemes. It summarizes the mathematical models behind each classification algorithm, including Nearest Class Centroid classifier, Nearest Sub-Class Centroid classifier, k-Nearest Neighbor classifier, Perceptron trained using Backpropagation, and Perceptron trained using Mean Squared Error. It also describes two datasets used in the experiments - the MNIST dataset of handwritten digits and the ORL face recognition dataset. The performance of the five classification schemes are compared on these datasets.
Rainfall Prediction using Data-Core Based Fuzzy Min-Max Neural Network for Cl...IJERA Editor
This paper proposes the Rainfall Prediction System by using classification technique. The advanced and modified neural network called Data Core Based Fuzzy Min Max Neural Network (DCFMNN) is used for pattern classification. This classification method is applied to predict Rainfall. The neural network called fuzzy min max neural network (FMNN) that creates hyperboxes for classification and predication, has a problem of overlapping neurons that resoled in DCFMNN to give greater accuracy. This system is composed of forming of hyperboxes, and two kinds of neurons called as Overlapping Neurons and Classifying neurons, and classification used for prediction. For each kind of hyperbox its data core and geometric center of data is calculated. The advantage of this method is it gives high accuracy and strong robustness. According to evaluation results we can say that this system gives better prediction of rainfall and classification tool in real environment.
The aim of this research is to find accurate solution for the Troesch’s problem by using high performance technique based on parallel processing implementation.
Design/methodology/approach – Feed forward neural network is designed to solve important type of differential equations that arises in many applied sciences and engineering applications. The suitable designed based on choosing suitable learning rate, transfer function, and training algorithm. The authors used back propagation with new implement of Levenberg - Marquardt training algorithm. Also, the authors depend new idea for choosing the weights. The effectiveness of the suggested design for the network is shown by using it for solving Troesch problem in many cases.
Findings – New idea for choosing the weights of the neural network, new implement of Levenberg - Marquardt training algorithm which assist to speeding the convergence and the implementation of the suggested design demonstrates the usefulness in finding exact solutions.
Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for He...chokrio
Ameliorating the lifetime in heterogeneous wireless sensor network is an important task because the sensor nodes are limited in the resource energy. The best way to improve a WSN lifetime is the clustering based algorithms in which each cluster is managed by a leader called Cluster Head. Each other node must communicate with this CH to send the data sensing. The nearest base station nodes must also send their data to their leaders, this causes a loss of energy. In this paper, we propose a new approach to ameliorate a threshold distributed energy efficient clustering protocol for heterogeneous wireless sensor networks by excluding closest nodes to the base station in the clustering process. We show by simulation in MATLAB that the proposed approach increases obviously the number of the received packet messages and prolongs the lifetime of the network compared to TDEEC protocol.
Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Al...IJECEIAES
This document discusses using backpropagation algorithms to extract reflectivity parameters from Doppler weather radar images. It begins with an introduction to pattern recognition using neural networks and an overview of artificial neural networks. It then describes different backpropagation algorithms that can be used for training multilayer perceptrons, including Levenberg-Marquardt, conjugate gradient, and resilient backpropagation. The document presents a method to preprocess Doppler radar images and use a neural network trained with backpropagation to identify colors in the image and estimate the corresponding reflectivity values based on a provided color scale. It analyzes using various backpropagation algorithms to identify colors in Doppler radar images and extract reflectivity information without human intervention.
Efficiency of Neural Networks Study in the Design of TrussesIRJET Journal
The document examines the efficiency of different types of artificial neural networks (ANNs) in the design of trusses. It analyzes generalized regression, radial basis function, and linear layer neural networks using the MATLAB neural network tool. Various truss models are analyzed using the ANNs and STAAD Pro software. The ANNs are trained and tested for interpolation and extrapolation to calculate percentage errors. Parameters like spread constants, number of trainings, number of input/output variables are varied to study their effect on the ANN performance and efficiency. The study aims to determine the most suitable ANN type for truss design based on the percentage error results.
This document summarizes research on neural networks. It discusses the basic structure and components of neural networks, including network topology (feed forward and recurrent), transfer functions, and learning algorithms (supervised, unsupervised, reinforcement). It also overview popular neural network models like multilayer perceptrons, radial basis function networks, Kohonen's self-organizing maps, and Hopfield networks. Finally, it outlines some applications of neural networks such as process control, pattern recognition, and more.
Kernal based speaker specific feature extraction and its applications in iTau...TELKOMNIKA JOURNAL
This document summarizes kernel-based speaker recognition techniques for an automatic speaker recognition system (ASR) in iTaukei cross-language speech recognition. It discusses kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and kernel linear discriminant analysis (KLDA) for nonlinear speaker-specific feature extraction to improve ASR classification rates. Evaluation of the ASR system using these techniques on a Japanese language corpus and self-recorded iTaukei corpus showed that KLDA achieved the best performance, with an equal error rate improvement of up to 8.51% compared to KPCA and KICA.
This document presents research using artificial neural networks to identify toxic gases in real time. A multi-layer perceptron neural network was trained using data from a multi-sensor system that detected hydrogen sulfide, nitrogen dioxide, and their mixture. Features extracted from the sensor responses were used as inputs to the neural network. The network was trained online using backpropagation and achieved 100% accuracy classifying gases during training and 96.6% accuracy during testing, with low error rates. This model achieved better performance than previous methods and can identify low concentrations of toxic gases in real time, which has applications for air quality monitoring and safety.
On the High Dimentional Information Processing in Quaternionic Domain and its...IJAAS Team
There are various high dimensional engineering and scientific applications in communication, control, robotics, computer vision, biometrics, etc.; where researchers are facing problem to design an intelligent and robust neural system which can process higher dimensional information efficiently. The conventional real-valued neural networks are tried to solve the problem associated with high dimensional parameters, but the required network structure possesses high complexity and are very time consuming and weak to noise. These networks are also not able to learn magnitude and phase values simultaneously in space. The quaternion is the number, which possesses the magnitude in all four directions and phase information is embedded within it. This paper presents a well generalized learning machine with a quaternionic domain neural network that can finely process magnitude and phase information of high dimension data without any hassle. The learning and generalization capability of the proposed learning machine is presented through a wide spectrum of simulations which demonstrate the significance of the work.
Application of support vector machines for prediction of anti hiv activity of...Alexander Decker
This document describes a study that used support vector machines (SVM) to develop a quantitative structure-activity relationship (QSAR) model to predict the anti-HIV activity of TIBO derivatives. The SVM model achieved high correlation (q2=0.96) and low error (RMSE=0.212), outperforming artificial neural networks and multiple linear regression models developed on the same data set. The results indicate that SVM is a valuable tool for QSAR modeling and predicting anti-HIV activity of chemical compounds.
Expert system design for elastic scattering neutrons optical model using bpnnijcsa
In present paper, a proposed expert system is designed to obtain a trained formulae for the optical model
parameters used in elastic scattering neutrons of light nuclei for (7Li), at energy range between [(1) to
(20)] MeV. A simple algorithm has used to design this expert system, while a multi-layer backwardpropagation
neural network (BPNN) is applied for training and testing the data used in this model. This
group of formulae may get a simple expert system occurring from governing formulae model, and predicts
the critical parameters usually resulted from the complicated computer coding methods. This expert system
may use in nuclear reactions yields in both fission and fusion nature who gives more closely results to the
real model.
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
The document describes an algorithm called X-TREPAN that extracts decision trees from trained neural networks. X-TREPAN is an enhancement of the TREPAN algorithm that allows it to handle both multi-class classification and multi-class regression problems. It can also analyze generalized feed forward networks. The algorithm was tested on several real-world datasets and was found to generate decision trees with good classification accuracy while also maintaining comprehensibility.
Survey on Artificial Neural Network Learning Technique AlgorithmsIRJET Journal
This document discusses different types of learning algorithms used in artificial neural networks. It begins with an introduction to neural networks and their ability to learn from their environment through adjustments to synaptic weights. Four main learning algorithms are then described: error correction learning, which uses algorithms like backpropagation to minimize error; memory based learning, which stores all training examples and analyzes nearby examples to classify new inputs; Hebbian learning, where connection weights are adjusted based on the activity of neurons; and competitive learning, where neurons compete to respond to inputs to become specialized feature detectors through a winner-take-all mechanism. The document provides details on how each type of learning algorithm works.
Vertex covering has important applications for wireless sensor networks such as monitoring link failures,
facility location, clustering, and data aggregation. In this study, we designed three algorithms for
constructing vertex cover in wireless sensor networks. The first algorithm, which is an adaption of the
Parnas & Ron’s algorithm, is a greedy approach that finds a vertex cover by using the degrees of the
nodes. The second algorithm finds a vertex cover from graph matching where Hoepman’s weighted
matching algorithm is used. The third algorithm firstly forms a breadth-first search tree and then
constructs a vertex cover by selecting nodes with predefined levels from breadth-first tree. We show the
operation of the designed algorithms, analyze them, and provide the simulation results in the TOSSIM
environment. Finally we have implemented, compared and assessed all these approaches. The transmitted
message count of the first algorithm is smallest among other algorithms where the third algorithm has
turned out to be presenting the best results in vertex cover approximation ratio.
This document summarizes a research paper that proposes using a genetic algorithm to efficiently cluster wireless sensor nodes. The genetic algorithm aims to minimize the total communication distance between sensors and the base station in order to prolong the network lifetime. Simulation results showed that the genetic algorithm can quickly find good clustering solutions that reduce energy consumption compared to previous clustering methods. The full paper provides details on wireless sensor networks, related clustering algorithms, genetic algorithms, and the proposed genetic algorithm-based clustering method.
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for
comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
Cao nicolau-mc dermott-learning-neural-cybernetics-2018-preprintNam Le
This paper proposes using latent representation models, specifically autoencoders (AEs) and variational autoencoders (VAEs), to improve network anomaly detection. The models are trained on only normal data and introduce regularizers that compress normal data into a tight region around the origin in the latent space, while anomalies will have representations further away. This new latent feature space is then used as input to one-class classifiers to detect anomalies. The goal is for the models to perform well even with limited training data and be insensitive to hyperparameter settings, in order to address challenges of network anomaly detection like lack of labeled anomaly data and high dimensionality.
This document provides an overview of self-organizing maps (SOM) as an unsupervised learning technique. It discusses the principles of self-organization including self-amplification, competition, and cooperation. The Willshaw-von der Malsburg model and Kohonen feature maps are presented as two approaches to building topographic maps through self-organization. The Kohonen SOM learning algorithm is described as involving competition between neurons to determine a winning neuron, cooperation between neighboring neurons, and adaptive changes to synaptic weights based on Hebbian learning principles.
As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP) neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.
The document discusses image recognition using convolutional neural networks (CNNs). It explains that CNNs consist of multiple layers of small neuron collections that look at small portions of an input image called receptive fields. The results are tiled to overlap and represent the original image better. CNNs learn filters through training rather than relying on hand-engineered features. Convolution involves calculating the overlap between functions as one is translated, and is used in CNNs to identify patterns across translated versions of inputs like images. Pointwise nonlinearities are applied between CNN layers to introduce nonlinearity.
Abstract— This presents a comprehensible neural network tree (CNNTREE). CNNTREE is a proposed general modular neural network structure, where each node in this tree is a comprehensible expert neural network (CENN). One advantage of using CNNTREE is that it is a “gray box”; because it can be interpreted easily for symbolic systems; where each node in the CNNTREE is equivalent for symbolic operator in the symbolic system. Another advantage of CNNTREE is that it could be trained as any normal multi layer feed forward neural network. An evolutionary algorithm is given for designing the CNNTREE. Back propagation is also checked as local learning algorithm that fits for real time learning constraints. The tree generalization and training performance are examined using experiments with a digit recognition problem.
Incorporating Kalman Filter in the Optimization of Quantum Neural Network Par...Waqas Tariq
Kalman filter have been used for the estimation of instantaneous states of linear dynamic systems. It is a good tool for inferring of missing information from noisy measurement. The quantum neural network is another approach to the merging of fuzzy logic with the neural network and that by the investment of quantum mechanics theory in building the structure of neural network. The gradient descent algorithm has been used, widely, in training the neural network, but the problem of local minima is one of the disadvantages of this algorithm. This paper presents an algorithm to train the quantum neural network by using the extended kalman filter.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
Noise-robust classification with hypergraph neural networknooriasukmaningtyas
This paper presents a novel version of hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional reduction technique to the feature matrices of the image datasets in order to reduce the “noise” and the redundant features in the feature matrices of the image datasets and to reduce the runtime constructing the hypergraph of the hypergraph neural network method. Then, the classic graph based semisupervised learning method, the classic hypergraph based semi-supervised learning method, the graph neural network, the hypergraph neural network, and our proposed hypergraph neural network are employed to solve the noisy label learning problem. The accuracies of these five methods are evaluated and compared. Experimental results show that the hypergraph neural network methods achieve the best performance when the noise level increases. Moreover, the hypergraph neural network methods are at least as good as the graph neural network.
Short Term Electrical Load Forecasting by Artificial Neural NetworkIJERA Editor
This paper presents an application of artificial neural networks for short-term times series electrical load
forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of
training process. Historical data of hourly power load as well as hourly wind power generation are sourced from
European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training
with the adaptive learning factor starting at different initial value and errors behave volatile with constant
learning factors with different values
Reflectivity Parameter Extraction from RADAR Images Using Back Propagation Al...IJECEIAES
This document discusses using backpropagation algorithms to extract reflectivity parameters from Doppler weather radar images. It begins with an introduction to pattern recognition using neural networks and an overview of artificial neural networks. It then describes different backpropagation algorithms that can be used for training multilayer perceptrons, including Levenberg-Marquardt, conjugate gradient, and resilient backpropagation. The document presents a method to preprocess Doppler radar images and use a neural network trained with backpropagation to identify colors in the image and estimate the corresponding reflectivity values based on a provided color scale. It analyzes using various backpropagation algorithms to identify colors in Doppler radar images and extract reflectivity information without human intervention.
Efficiency of Neural Networks Study in the Design of TrussesIRJET Journal
The document examines the efficiency of different types of artificial neural networks (ANNs) in the design of trusses. It analyzes generalized regression, radial basis function, and linear layer neural networks using the MATLAB neural network tool. Various truss models are analyzed using the ANNs and STAAD Pro software. The ANNs are trained and tested for interpolation and extrapolation to calculate percentage errors. Parameters like spread constants, number of trainings, number of input/output variables are varied to study their effect on the ANN performance and efficiency. The study aims to determine the most suitable ANN type for truss design based on the percentage error results.
This document summarizes research on neural networks. It discusses the basic structure and components of neural networks, including network topology (feed forward and recurrent), transfer functions, and learning algorithms (supervised, unsupervised, reinforcement). It also overview popular neural network models like multilayer perceptrons, radial basis function networks, Kohonen's self-organizing maps, and Hopfield networks. Finally, it outlines some applications of neural networks such as process control, pattern recognition, and more.
Kernal based speaker specific feature extraction and its applications in iTau...TELKOMNIKA JOURNAL
This document summarizes kernel-based speaker recognition techniques for an automatic speaker recognition system (ASR) in iTaukei cross-language speech recognition. It discusses kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and kernel linear discriminant analysis (KLDA) for nonlinear speaker-specific feature extraction to improve ASR classification rates. Evaluation of the ASR system using these techniques on a Japanese language corpus and self-recorded iTaukei corpus showed that KLDA achieved the best performance, with an equal error rate improvement of up to 8.51% compared to KPCA and KICA.
This document presents research using artificial neural networks to identify toxic gases in real time. A multi-layer perceptron neural network was trained using data from a multi-sensor system that detected hydrogen sulfide, nitrogen dioxide, and their mixture. Features extracted from the sensor responses were used as inputs to the neural network. The network was trained online using backpropagation and achieved 100% accuracy classifying gases during training and 96.6% accuracy during testing, with low error rates. This model achieved better performance than previous methods and can identify low concentrations of toxic gases in real time, which has applications for air quality monitoring and safety.
On the High Dimentional Information Processing in Quaternionic Domain and its...IJAAS Team
There are various high dimensional engineering and scientific applications in communication, control, robotics, computer vision, biometrics, etc.; where researchers are facing problem to design an intelligent and robust neural system which can process higher dimensional information efficiently. The conventional real-valued neural networks are tried to solve the problem associated with high dimensional parameters, but the required network structure possesses high complexity and are very time consuming and weak to noise. These networks are also not able to learn magnitude and phase values simultaneously in space. The quaternion is the number, which possesses the magnitude in all four directions and phase information is embedded within it. This paper presents a well generalized learning machine with a quaternionic domain neural network that can finely process magnitude and phase information of high dimension data without any hassle. The learning and generalization capability of the proposed learning machine is presented through a wide spectrum of simulations which demonstrate the significance of the work.
Application of support vector machines for prediction of anti hiv activity of...Alexander Decker
This document describes a study that used support vector machines (SVM) to develop a quantitative structure-activity relationship (QSAR) model to predict the anti-HIV activity of TIBO derivatives. The SVM model achieved high correlation (q2=0.96) and low error (RMSE=0.212), outperforming artificial neural networks and multiple linear regression models developed on the same data set. The results indicate that SVM is a valuable tool for QSAR modeling and predicting anti-HIV activity of chemical compounds.
Expert system design for elastic scattering neutrons optical model using bpnnijcsa
In present paper, a proposed expert system is designed to obtain a trained formulae for the optical model
parameters used in elastic scattering neutrons of light nuclei for (7Li), at energy range between [(1) to
(20)] MeV. A simple algorithm has used to design this expert system, while a multi-layer backwardpropagation
neural network (BPNN) is applied for training and testing the data used in this model. This
group of formulae may get a simple expert system occurring from governing formulae model, and predicts
the critical parameters usually resulted from the complicated computer coding methods. This expert system
may use in nuclear reactions yields in both fission and fusion nature who gives more closely results to the
real model.
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
The document describes an algorithm called X-TREPAN that extracts decision trees from trained neural networks. X-TREPAN is an enhancement of the TREPAN algorithm that allows it to handle both multi-class classification and multi-class regression problems. It can also analyze generalized feed forward networks. The algorithm was tested on several real-world datasets and was found to generate decision trees with good classification accuracy while also maintaining comprehensibility.
Survey on Artificial Neural Network Learning Technique AlgorithmsIRJET Journal
This document discusses different types of learning algorithms used in artificial neural networks. It begins with an introduction to neural networks and their ability to learn from their environment through adjustments to synaptic weights. Four main learning algorithms are then described: error correction learning, which uses algorithms like backpropagation to minimize error; memory based learning, which stores all training examples and analyzes nearby examples to classify new inputs; Hebbian learning, where connection weights are adjusted based on the activity of neurons; and competitive learning, where neurons compete to respond to inputs to become specialized feature detectors through a winner-take-all mechanism. The document provides details on how each type of learning algorithm works.
Vertex covering has important applications for wireless sensor networks such as monitoring link failures,
facility location, clustering, and data aggregation. In this study, we designed three algorithms for
constructing vertex cover in wireless sensor networks. The first algorithm, which is an adaption of the
Parnas & Ron’s algorithm, is a greedy approach that finds a vertex cover by using the degrees of the
nodes. The second algorithm finds a vertex cover from graph matching where Hoepman’s weighted
matching algorithm is used. The third algorithm firstly forms a breadth-first search tree and then
constructs a vertex cover by selecting nodes with predefined levels from breadth-first tree. We show the
operation of the designed algorithms, analyze them, and provide the simulation results in the TOSSIM
environment. Finally we have implemented, compared and assessed all these approaches. The transmitted
message count of the first algorithm is smallest among other algorithms where the third algorithm has
turned out to be presenting the best results in vertex cover approximation ratio.
This document summarizes a research paper that proposes using a genetic algorithm to efficiently cluster wireless sensor nodes. The genetic algorithm aims to minimize the total communication distance between sensors and the base station in order to prolong the network lifetime. Simulation results showed that the genetic algorithm can quickly find good clustering solutions that reduce energy consumption compared to previous clustering methods. The full paper provides details on wireless sensor networks, related clustering algorithms, genetic algorithms, and the proposed genetic algorithm-based clustering method.
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for
comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
Cao nicolau-mc dermott-learning-neural-cybernetics-2018-preprintNam Le
This paper proposes using latent representation models, specifically autoencoders (AEs) and variational autoencoders (VAEs), to improve network anomaly detection. The models are trained on only normal data and introduce regularizers that compress normal data into a tight region around the origin in the latent space, while anomalies will have representations further away. This new latent feature space is then used as input to one-class classifiers to detect anomalies. The goal is for the models to perform well even with limited training data and be insensitive to hyperparameter settings, in order to address challenges of network anomaly detection like lack of labeled anomaly data and high dimensionality.
This document provides an overview of self-organizing maps (SOM) as an unsupervised learning technique. It discusses the principles of self-organization including self-amplification, competition, and cooperation. The Willshaw-von der Malsburg model and Kohonen feature maps are presented as two approaches to building topographic maps through self-organization. The Kohonen SOM learning algorithm is described as involving competition between neurons to determine a winning neuron, cooperation between neighboring neurons, and adaptive changes to synaptic weights based on Hebbian learning principles.
As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their configurations has an impact on the accuracy of the localization system, which is also addressed in this paper. Five different training algorithms are evaluated to find the training algorithm that gives the best result. The multi-layer Perceptron (MLP) neural network model was trained using Matlab. In order to evaluate the performance of the proposed method in real time, the model obtained was then implemented on the Arduino microcontroller. With four anchor nodes, an average 2D localization error of 0.2953 m has been achieved with a 12-12-2 neural network structure. The proposed method can also be implemented on any other embedded microcontroller system.
The document discusses image recognition using convolutional neural networks (CNNs). It explains that CNNs consist of multiple layers of small neuron collections that look at small portions of an input image called receptive fields. The results are tiled to overlap and represent the original image better. CNNs learn filters through training rather than relying on hand-engineered features. Convolution involves calculating the overlap between functions as one is translated, and is used in CNNs to identify patterns across translated versions of inputs like images. Pointwise nonlinearities are applied between CNN layers to introduce nonlinearity.
Abstract— This presents a comprehensible neural network tree (CNNTREE). CNNTREE is a proposed general modular neural network structure, where each node in this tree is a comprehensible expert neural network (CENN). One advantage of using CNNTREE is that it is a “gray box”; because it can be interpreted easily for symbolic systems; where each node in the CNNTREE is equivalent for symbolic operator in the symbolic system. Another advantage of CNNTREE is that it could be trained as any normal multi layer feed forward neural network. An evolutionary algorithm is given for designing the CNNTREE. Back propagation is also checked as local learning algorithm that fits for real time learning constraints. The tree generalization and training performance are examined using experiments with a digit recognition problem.
Incorporating Kalman Filter in the Optimization of Quantum Neural Network Par...Waqas Tariq
Kalman filter have been used for the estimation of instantaneous states of linear dynamic systems. It is a good tool for inferring of missing information from noisy measurement. The quantum neural network is another approach to the merging of fuzzy logic with the neural network and that by the investment of quantum mechanics theory in building the structure of neural network. The gradient descent algorithm has been used, widely, in training the neural network, but the problem of local minima is one of the disadvantages of this algorithm. This paper presents an algorithm to train the quantum neural network by using the extended kalman filter.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
Noise-robust classification with hypergraph neural networknooriasukmaningtyas
This paper presents a novel version of hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional reduction technique to the feature matrices of the image datasets in order to reduce the “noise” and the redundant features in the feature matrices of the image datasets and to reduce the runtime constructing the hypergraph of the hypergraph neural network method. Then, the classic graph based semisupervised learning method, the classic hypergraph based semi-supervised learning method, the graph neural network, the hypergraph neural network, and our proposed hypergraph neural network are employed to solve the noisy label learning problem. The accuracies of these five methods are evaluated and compared. Experimental results show that the hypergraph neural network methods achieve the best performance when the noise level increases. Moreover, the hypergraph neural network methods are at least as good as the graph neural network.
Short Term Electrical Load Forecasting by Artificial Neural NetworkIJERA Editor
This paper presents an application of artificial neural networks for short-term times series electrical load
forecasting. An adaptive learning algorithm is derived from system stability to ensure the convergence of
training process. Historical data of hourly power load as well as hourly wind power generation are sourced from
European Open Power System Platform. The simulation demonstrates that errors steadily decrease in training
with the adaptive learning factor starting at different initial value and errors behave volatile with constant
learning factors with different values
Pattern recognition system based on support vector machinesAlexander Decker
This document describes a study that uses support vector machines (SVM) to develop quantitative structure-activity relationship (QSAR) models for predicting the anti-HIV activity of 1,3,4-oxadiazole substituted naphthyridine derivatives based on their molecular descriptors. The SVM model achieved a cross-validation R2 value of 0.90 and RMSE of 0.145, outperforming artificial neural network and multiple linear regression models. An external validation on an independent test set found the SVM model had an R value of 0.96 and RMSE of 0.166, demonstrating good predictive ability.
Energy aware clustering protocol (eacp)IJCNCJournal
The document summarizes an Energy Aware Clustering Protocol (EACP) proposed for heterogeneous wireless sensor networks. EACP introduces heterogeneity by using two types of nodes: normal and advanced. Normal nodes elect cluster heads using a probability scheme based on residual and average energy. Advanced nodes use a separate probability scheme and act as gateways for normal cluster heads, transmitting their data to the base station. The performance of EACP is compared to SEP through simulations, showing better results for stability period, network life and energy savings.
Optimal neural network models for wind speed predictionIAEME Publication
The document describes using artificial neural networks for wind speed prediction. Specifically, it analyzes the performance of multilayer perceptron networks and radial basis function networks for wind speed forecasting using real-time data collected from wind farms in Coimbatore, India over one year. The models are trained on 3000 samples and tested on 1000 samples. Performance is evaluated using statistical metrics like coefficient of determination, mean absolute error, root mean square error, and mean bias error. Results show that the neural network models improve prediction accuracy compared to other approaches and the optimal model depends on factors like the number of hidden neurons and spread value.
Optimal neural network models for wind speed predictionIAEME Publication
The document presents two neural network models - multilayer perceptron (MLP) and radial basis function (RBF) networks - for wind speed prediction. It evaluates these models on real wind speed data collected over one year from wind farms in Coimbatore, India. The experimental results show that the RBF and MLP models can improve wind speed prediction accuracy compared to other approaches, according to statistical metrics like coefficient of determination, mean absolute error, root mean square error, and mean bias error.
Optimal neural network models for wind speed predictionIAEME Publication
The document presents two neural network models - multilayer perceptron (MLP) and radial basis function (RBF) networks - for wind speed prediction. It evaluates these models on real-time wind speed data collected from wind farms in Coimbatore, India over one year. The experimental results show that RBF and MLP networks can improve wind speed prediction accuracy compared to other approaches, according to statistical metrics like coefficient of determination, mean absolute error, root mean square error and mean bias error. The RBF and MLP models are able to handle the non-linear patterns in wind speed data, which conventional models struggle with, increasing prediction precision.
A PROPOSAL TO IMPROVE SEP ROUTING PROTOCOL USING INSENSITIVE FUZZY C-MEANS IN...IJCNCJournal
This document proposes improving the SEP routing protocol in wireless sensor networks by combining it with the Insensitive Fuzzy C-Means clustering algorithm. The SEP protocol is an existing heterogeneous routing protocol that increases network stability but has limitations. The proposed SEP-εFCM protocol selects cluster heads using εFCM clustering, which can create more balanced clusters and reduce energy consumption. Simulation results showed the SEP-εFCM protocol performed better than the original SEP protocol, with more remaining live nodes and energy over time.
Black-box modeling of nonlinear system using evolutionary neural NARX modelIJECEIAES
Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system.
QUAD TREE BASED STATIC MULTI HOP LEACH ENERGY EFFICIENT ROUTING PROTOCOL: A N...IJCNCJournal
This research work propounds a simple graph theory semblance Divide and Conquer Quad tree based Multi-hop Static Leach (DCQMS-Leach) energy efficient routing protocol for wireless sensor networks. The pivotal theme of this research work is to demonstrate how divide and conquer plays a pivotal role in a multi-hop static leach energy efficient routing protocol. This research work motivates, enforces, reckons the DCQMS-Leach energy efficient routing protocol in wireless sensor networks using Mat lab simulator.This research work also computes the performance concepts of DCQMS-Leach routing protocol using various performance metrics such as Packet Drop Rate (PDR), Throughput, and End to End Delay (EED) by comparing and contrasting alive nodes with number of nodes, number of each packets sent to the cluster heads with rounds, number of cluster heads with rounds, number of packets forwarded to the base station with rounds and finally dead nodes with number of rounds. In order to curtail energy consumption this research work proffers a routing methodology such as DCQMS-Leach in energy efficient wireless,sensor routing protocol. The recommended DCQMS-Leach overcomes the in adequacies of all other different leach protocols suggested by the previous researchers.
This document describes a brain-computer interface system that uses steady-state visually evoked potentials detected by electroencephalography to control a drone. The system uses five flashing lights at different frequencies to elicit neural responses, which are classified using recursive least squares adaptive filtering and canonical correlation analysis to map the responses to commands to control drone movement. The system was able to successfully discriminate between the five frequencies and allow a user to control the drone within 5-10 seconds using their brain signals.
This document provides instructions for three exercises using artificial neural networks (ANNs) in Matlab: function fitting, pattern recognition, and clustering. It begins with background on ANNs including their structure, learning rules, training process, and common architectures. The exercises then guide using ANNs in Matlab for regression to predict house prices from data, classification of tumors as benign or malignant, and clustering of data. Instructions include loading data, creating and training networks, and evaluating results using both the GUI and command line. Improving results through retraining or adding neurons is also discussed.
Data Dissemination in Wireless Sensor Networks: A State-of-the Art SurveyCSCJournals
A wireless sensor network is a network of tiny nodes with wireless sensing capacity for data collection processing and further communicating with the Base Station this paper discusses the overall mechanism of data dissemination right from data collection at the sensor nodes, clustering of sensor nodes, data aggregation at the cluster heads and disseminating data to the Base Station the overall motive of the paper is to conserve energy so that lifetime of the network is extended this paper highlights the existing algorithms and open research gaps in efficient data dissemination.
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...IAEME Publication
This paper presents an approach based on applying an aggregated predictor formed by multiple versions of a multilayer neural network with a back-propagation optimization algorithm for helping the engineer to get a list of the most appropriate well-test interpretation models for a given set of pressure/ production data. The proposed method consists of three stages: (1) data decorrelation through principal component analysis to reduce the covariance between the variables and the dimension of the input layer in the artificial neural network, (2) bootstrap replicates of the learning set where the data is repeatedly sampled with a random split of the data into train sets and using these as new learning sets, and (3) automatic reservoir model identification through aggregated predictor formed by a plurality vote when predicting a new class. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 300 for training, 100 for cross-validation, and 200 for testing. Different network structures were tested during this study to arrive at optimum network design. We notice that the single net methodology always brings about confusion in selecting the correct model even though the training results for the constructed networks are close to 1. We notice also that the principal component analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 95% Compared to a previous approach 80%, the combination of the PCA and ANN is more stable and determine the more accurate results with lesser computational complexity than was feasible previously. Clearly, the aggregated predictor is more stable and shows less bad classes compared to the previous approach.
Time Series Forecasting Using Novel Feature Extraction Algorithm and Multilay...Editor IJCATR
Time series forecasting is important because it can often provide the foundation for decision making in a large variety of fields. A tree-ensemble method, referred to as time series forest (TSF), is proposed for time series classification. The approach is based on the concept of data series envelopes and essential attributes generated by a multilayer neural network... These claims are further investigated by applying statistical tests. With the results presented in this article and results from related investigations that are considered as well, we want to support practitioners or scholars in answering the following question: Which measure should be looked at first if accuracy is the most important criterion, if an application is time-critical, or if a compromise is needed? In this paper demonstrated feature extraction by novel method can improvement in time series data forecasting process
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Multi-task learning using non-linear autoregressive models and recurrent neur...IJECEIAES
Tide level forecasting plays an important role in environmental management and development. Current tide level forecasting methods are usually implemented for solving single task problems, that is, a model built based on the tide level data at an individual location is only used to forecast tide level of the same location but is not used for tide forecasting at another location. This study proposes a new method for tide level prediction at multiple locations simultaneously. The method combines nonlinear autoregressive moving average with exogenous inputs (NARMAX) model and recurrent neural networks (RNNs), and incorporates them into a multi-task learning (MTL) framework. Experiments are designed and performed to compare single task learning (STL) and MTL with and without using non-linear autoregressive models. Three different RNN variants, namely, long short- term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are employed together with non-linear autoregressive models. A case study on tide level forecasting at many different geographical locations (5 to 11 locations) is conducted. Experimental results demonstrate that the proposed architectures outperform the classical single-task prediction methods.
Reliable and Efficient Data Acquisition in Wireless Sensor NetworkIJMTST Journal
The sensors in the WSN sense the surrounding, collects the data and transfers the data to the sink node. It
has been observed that the sensor nodes are deactivated or damaged when exposed to certain radiations or
due to energy problems. This damage leads to the temporary isolation of the nodes from the network which
results in the formation of the holes. These holes are dynamic in nature and can grow and shrink depending
upon the factors causing the damage to the sensor nodes. So a solution has been presented in the base paper
where the dual mode i.e. Radio frequency and the Acoustic mode are considered so that the data can be
transferred easily. Based on this a survey has been done where several factors are studied so that the
performance of the system can be increased.
Neural wavelet based hybrid model for short-term load forecastingAlexander Decker
This document summarizes a research paper that proposes a neural-wavelet based hybrid model for short-term load forecasting. The paper introduces neural networks and how they can be used for electric load forecasting. It then proposes a model that uses wavelet transforms for preprocessing the original load signal data into different levels, before inputting these into a neural network for short-term load forecasting. The model is tested and results show the neural-wavelet model provides more accurate forecasts than an artificial neural network alone.
Submission Deadline: 30th September 2022
Acceptance Notification: Within Three Days’ time period
Online Publication: Within 24 Hrs. time Period
Expected Date of Dispatch of Printed Journal: 5th October 2022
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...IAEME Publication
White layer thickness (WLT) formed and surface roughness in wire electric discharge turning (WEDT) of tungsten carbide composite has been made to model through response surface methodology (RSM). A Taguchi’s standard Design of experiments involving five input variables with three levels has been employed to establish a mathematical model between input parameters and responses. Percentage of cobalt content, spindle speed, Pulse on-time, wire feed and pulse off-time were changed during the experimental tests based on the Taguchi’s orthogonal array L27 (3^13). Analysis of variance (ANOVA) revealed that the mathematical models obtained can adequately describe performance within the parameters of the factors considered. There was a good agreement between the experimental and predicted values in this study.
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURSIAEME Publication
The study explores the reasons for a transgender to become entrepreneurs. In this study transgender entrepreneur was taken as independent variable and reasons to become as dependent variable. Data were collected through a structured questionnaire containing a five point Likert Scale. The study examined the data of 30 transgender entrepreneurs in Salem Municipal Corporation of Tamil Nadu State, India. Simple Random sampling technique was used. Garrett Ranking Technique (Percentile Position, Mean Scores) was used as the analysis for the present study to identify the top 13 stimulus factors for establishment of trans entrepreneurial venture. Economic advancement of a nation is governed upon the upshot of a resolute entrepreneurial doings. The conception of entrepreneurship has stretched and materialized to the socially deflated uncharted sections of transgender community. Presently transgenders have smashed their stereotypes and are making recent headlines of achievements in various fields of our Indian society. The trans-community is gradually being observed in a new light and has been trying to achieve prospective growth in entrepreneurship. The findings of the research revealed that the optimistic changes are taking place to change affirmative societal outlook of the transgender for entrepreneurial ventureship. It also laid emphasis on other transgenders to renovate their traditional living. The paper also highlights that legislators, supervisory body should endorse an impartial canons and reforms in Tamil Nadu Transgender Welfare Board Association.
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURSIAEME Publication
Since ages gender difference is always a debatable theme whether caused by nature, evolution or environment. The birth of a transgender is dreadful not only for the child but also for their parents. The pain of living in the wrong physique and treated as second class victimized citizen is outrageous and fully harboured with vicious baseless negative scruples. For so long, social exclusion had perpetuated inequality and deprivation experiencing ingrained malign stigma and besieged victims of crime or violence across their life spans. They are pushed into the murky way of life with a source of eternal disgust, bereft sexual potency and perennial fear. Although they are highly visible but very little is known about them. The common public needs to comprehend the ravaged arrogance on these insensitive souls and assist in integrating them into the mainstream by offering equal opportunity, treat with humanity and respect their dignity. Entrepreneurship in the current age is endorsing the gender fairness movement. Unstable careers and economic inadequacy had inclined one of the gender variant people called Transgender to become entrepreneurs. These tiny budding entrepreneurs resulted in economic transition by means of employment, free from the clutches of stereotype jobs, raised standard of living and handful of financial empowerment. Besides all these inhibitions, they were able to witness a platform for skill set development that ignited them to enter into entrepreneurial domain. This paper epitomizes skill sets involved in trans-entrepreneurs of Thoothukudi Municipal Corporation of Tamil Nadu State and is a groundbreaking determination to sightsee various skills incorporated and the impact on entrepreneurship.
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONSIAEME Publication
The banking and financial services industries are experiencing increased technology penetration. Among them, the banking industry has made technological advancements to better serve the general populace. The economy focused on transforming the banking sector's system into a cashless, paperless, and faceless one. The researcher wants to evaluate the user's intention for utilising a mobile banking application. The study also examines the variables affecting the user's behaviour intention when selecting specific applications for financial transactions. The researcher employed a well-structured questionnaire and a descriptive study methodology to gather the respondents' primary data utilising the snowball sampling technique. The study includes variables like performance expectations, effort expectations, social impact, enabling circumstances, and perceived risk. Each of the aforementioned variables has a major impact on how users utilise mobile banking applications. The outcome will assist the service provider in comprehending the user's history with mobile banking applications.
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONSIAEME Publication
Technology upgradation in banking sector took the economy to view that payment mode towards online transactions using mobile applications. This system enabled connectivity between banks, Merchant and user in a convenient mode. there are various applications used for online transactions such as Google pay, Paytm, freecharge, mobikiwi, oxygen, phonepe and so on and it also includes mobile banking applications. The study aimed at evaluating the predilection of the user in adopting digital transaction. The study is descriptive in nature. The researcher used random sample techniques to collect the data. The findings reveal that mobile applications differ with the quality of service rendered by Gpay and Phonepe. The researcher suggest the Phonepe application should focus on implementing the application should be user friendly interface and Gpay on motivating the users to feel the importance of request for money and modes of payments in the application.
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINOIAEME Publication
The prototype of a voice-based ATM for visually impaired using Arduino is to help people who are blind. This uses RFID cards which contain users fingerprint encrypted on it and interacts with the users through voice commands. ATM operates when sensor detects the presence of one person in the cabin. After scanning the RFID card, it will ask to select the mode like –normal or blind. User can select the respective mode through voice input, if blind mode is selected the balance check or cash withdraw can be done through voice input. Normal mode procedure is same as the existing ATM.
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...IAEME Publication
There is increasing acceptability of emotional intelligence as a major factor in personality assessment and effective human resource management. Emotional intelligence as the ability to build capacity, empathize, co-operate, motivate and develop others cannot be divorced from both effective performance and human resource management systems. The human person is crucial in defining organizational leadership and fortunes in terms of challenges and opportunities and walking across both multinational and bilateral relationships. The growing complexity of the business world requires a great deal of self-confidence, integrity, communication, conflict and diversity management to keep the global enterprise within the paths of productivity and sustainability. Using the exploratory research design and 255 participants the result of this original study indicates strong positive correlation between emotional intelligence and effective human resource management. The paper offers suggestions on further studies between emotional intelligence and human capital development and recommends for conflict management as an integral part of effective human resource management.
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMYIAEME Publication
Our life journey, in general, is closely defined by the way we understand the meaning of why we coexist and deal with its challenges. As we develop the "inspiration economy", we could say that nearly all of the challenges we have faced are opportunities that help us to discover the rest of our journey. In this note paper, we explore how being faced with the opportunity of being a close carer for an aging parent with dementia brought intangible discoveries that changed our insight of the meaning of the rest of our life journey.
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...IAEME Publication
The main objective of this study is to analyze the impact of aspects of Organizational Culture on the Effectiveness of the Performance Management System (PMS) in the Health Care Organization at Thanjavur. Organizational Culture and PMS play a crucial role in present-day organizations in achieving their objectives. PMS needs employees’ cooperation to achieve its intended objectives. Employees' cooperation depends upon the organization’s culture. The present study uses exploratory research to examine the relationship between the Organization's culture and the Effectiveness of the Performance Management System. The study uses a Structured Questionnaire to collect the primary data. For this study, Thirty-six non-clinical employees were selected from twelve randomly selected Health Care organizations at Thanjavur. Thirty-two fully completed questionnaires were received.
Living in 21st century in itself reminds all of us the necessity of police and its administration. As more and more we are entering into the modern society and culture, the more we require the services of the so called ‘Khaki Worthy’ men i.e., the police personnel. Whether we talk of Indian police or the other nation’s police, they all have the same recognition as they have in India. But as already mentioned, their services and requirements are different after the like 26th November, 2008 incidents, where they without saving their own lives has sacrificed themselves without any hitch and without caring about their respective family members and wards. In other words, they are like our heroes and mentors who can guide us from the darkness of fear, militancy, corruption and other dark sides of life and so on. Now the question arises, if Gandhi would have been alive today, what would have been his reaction/opinion to the police and its functioning? Would he have some thing different in his mind now what he had been in his mind before the partition or would he be going to start some Satyagraha in the form of some improvement in the functioning of the police administration? Really these questions or rather night mares can come to any one’s mind, when there is too much confusion is prevailing in our minds, when there is too much corruption in the society and when the polices working is also in the questioning because of one or the other case throughout the India. It is matter of great concern that we have to thing over our administration and our practical approach because the police personals are also like us, they are part and parcel of our society and among one of us, so why we all are pin pointing towards them.
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...IAEME Publication
The goal of this study was to see how talent management affected employee retention in the selected IT organizations in Chennai. The fundamental issue was the difficulty to attract, hire, and retain talented personnel who perform well and the gap between supply and demand of talent acquisition and retaining them within the firms. The study's main goals were to determine the impact of talent management on employee retention in IT companies in Chennai, investigate talent management strategies that IT companies could use to improve talent acquisition, performance management, career planning and formulate retention strategies that the IT firms could use. The respondents were given a structured close-ended questionnaire with the 5 Point Likert Scale as part of the study's quantitative research design. The target population consisted of 289 IT professionals. The questionnaires were distributed and collected by the researcher directly. The Statistical Package for Social Sciences (SPSS) was used to collect and analyse the questionnaire responses. Hypotheses that were formulated for the various areas of the study were tested using a variety of statistical tests. The key findings of the study suggested that talent management had an impact on employee retention. The studies also found that there is a clear link between the implementation of talent management and retention measures. Management should provide enough training and development for employees, clarify job responsibilities, provide adequate remuneration packages, and recognise employees for exceptional performance.
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...IAEME Publication
Globally, Millions of dollars were spent by the organizations for employing skilled Information Technology (IT) professionals. It is costly to replace unskilled employees with IT professionals possessing technical skills and competencies that aid in interconnecting the business processes. The organization’s employment tactics were forced to alter by globalization along with technological innovations as they consistently diminish to remain lean, outsource to concentrate on core competencies along with restructuring/reallocate personnel to gather efficiency. As other jobs, organizations or professions have become reasonably more appropriate in a shifting employment landscape, the above alterations trigger both involuntary as well as voluntary turnover. The employee view on jobs is also afflicted by the COVID-19 pandemic along with the employee-driven labour market. So, having effective strategies is necessary to tackle the withdrawal rate of employees. By associating Emotional Intelligence (EI) along with Talent Management (TM) in the IT industry, the rise in attrition rate was analyzed in this study. Only 303 respondents were collected out of 350 participants to whom questionnaires were distributed. From the employees of IT organizations located in Bangalore (India), the data were congregated. A simple random sampling methodology was employed to congregate data as of the respondents. Generating the hypothesis along with testing is eventuated. The effect of EI and TM along with regression analysis between TM and EI was analyzed. The outcomes indicated that employee and Organizational Performance (OP) were elevated by effective EI along with TM.
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...IAEME Publication
By implementing talent management strategy, organizations would have the option to retain their skilled professionals while additionally working on their overall performance. It is the course of appropriately utilizing the ideal individuals, setting them up for future top positions, exploring and dealing with their performance, and holding them back from leaving the organization. It is employee performance that determines the success of every organization. The firm quickly obtains an upper hand over its rivals in the event that its employees having particular skills that cannot be duplicated by the competitors. Thus, firms are centred on creating successful talent management practices and processes to deal with the unique human resources. Firms are additionally endeavouring to keep their top/key staff since on the off chance that they leave; the whole store of information leaves the firm's hands. The study's objective was to determine the impact of talent management on organizational performance among the selected IT organizations in Chennai. The study recommends that talent management limitedly affects performance. On the off chance that this talent is appropriately management and implemented properly, organizations might benefit as much as possible from their maintained assets to support development and productivity, both monetarily and non-monetarily.
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...IAEME Publication
Banking regulations act of India, 1949 defines banking as “acceptance of deposits for the purpose of lending or investment from the public, repayment on demand or otherwise and withdrawable through cheques, drafts order or otherwise”, the major participants of the Indian financial system are commercial banks, the financial institution encompassing term lending institutions. Investments institutions, specialized financial institution and the state level development banks, non banking financial companies (NBFC) and other market intermediaries such has the stock brokers and money lenders are among the oldest of the certain variants of NBFC and the oldest market participants. The asset quality of banks is one of the most important indicators of their financial health. The Indian banking sector has been facing severe problems of increasing Non- Performing Assets (NPAs). The NPAs growth directly and indirectly affects the quality of assets and profitability of banks. It also shows the efficiency of banks credit risk management and the recovery effectiveness. NPA do not generate any income, whereas, the bank is required to make provisions for such as assets that why is a double edge weapon. This paper outlines the concept of quality of bank loans of different types like Housing, Agriculture and MSME loans in state Haryana of selected public and private sector banks. This study is highlighting problems associated with the role of commercial bank in financing Small and Medium Scale Enterprises (SME). The overall objective of the research was to assess the effect of the financing provisions existing for the setting up and operations of MSMEs in the country and to generate recommendations for more robust financing mechanisms for successful operation of the MSMEs, in turn understanding the impact of MSME loans on financial institutions due to NPA. There are many research conducted on the topic of Non- Performing Assets (NPA) Management, concerning particular bank, comparative study of public and private banks etc. In this paper the researcher is considering the aggregate data of selected public sector and private sector banks and attempts to compare the NPA of Housing, Agriculture and MSME loans in state Haryana of public and private sector banks. The tools used in the study are average and Anova test and variance. The findings reveal that NPA is common problem for both public and private sector banks and is associated with all types of loans either that is housing loans, agriculture loans and loans to SMES. NPAs of both public and private sector banks show the increasing trend. In 2010-11 GNPA of public and private sector were at same level it was 2% but after 2010-11 it increased in many fold and at present there is GNPA in some more than 15%. It shows the dark area of Indian banking sector.
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...IAEME Publication
An experiment conducted in this study found that BaSO4 changed Nylon 6's mechanical properties. By changing the weight ratios, BaSO4 was used to make Nylon 6. This Researcher looked into how hard Nylon-6/BaSO4 composites are and how well they wear. Experiments were done based on Taguchi design L9. Nylon-6/BaSO4 composites can be tested for their hardness number using a Rockwell hardness testing apparatus. On Nylon/BaSO4, the wear behavior was measured by a wear monitor, pinon-disc friction by varying reinforcement, sliding speed, and sliding distance, and the microstructure of the crack surfaces was observed by SEM. This study provides significant contributions to ultimate strength by increasing BaSO4 content up to 16% in the composites, and sliding speed contributes 72.45% to the wear rate
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...IAEME Publication
The majority of the population in India lives in villages. The village is the back bone of the country. Village or rural industries play an important role in the national economy, particularly in the rural development. Developing the rural economy is one of the key indicators towards a country’s success. Whether it be the need to look after the welfare of the farmers or invest in rural infrastructure, Governments have to ensure that rural development isn’t compromised. The economic development of our country largely depends on the progress of rural areas and the standard of living of rural masses. Village or rural industries play an important role in the national economy, particularly in the rural development. Rural entrepreneurship is based on stimulating local entrepreneurial talent and the subsequent growth of indigenous enterprises. It recognizes opportunity in the rural areas and accelerates a unique blend of resources either inside or outside of agriculture. Rural entrepreneurship brings an economic value to the rural sector by creating new methods of production, new markets, new products and generate employment opportunities thereby ensuring continuous rural development. Social Entrepreneurship has the direct and primary objective of serving the society along with the earning profits. So, social entrepreneurship is different from the economic entrepreneurship as its basic objective is not to earn profits but for providing innovative solutions to meet the society needs which are not taken care by majority of the entrepreneurs as they are in the business for profit making as a sole objective. So, the Social Entrepreneurs have the huge growth potential particularly in the developing countries like India where we have huge societal disparities in terms of the financial positions of the population. Still 22 percent of the Indian population is below the poverty line and also there is disparity among the rural & urban population in terms of families living under BPL. 25.7 percent of the rural population & 13.7 percent of the urban population is under BPL which clearly shows the disparity of the poor people in the rural and urban areas. The need to develop social entrepreneurship in agriculture is dictated by a large number of social problems. Such problems include low living standards, unemployment, and social tension. The reasons that led to the emergence of the practice of social entrepreneurship are the above factors. The research problem lays upon disclosing the importance of role of social entrepreneurship in rural development of India. The paper the tendencies of social entrepreneurship in India, to present successful examples of such business for providing recommendations how to improve situation in rural areas in terms of social entrepreneurship development. Indian government has made some steps towards development of social enterprises, social entrepreneurship, and social in- novation, but a lot remains to be improved.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...IAEME Publication
Manufacturing industries have witnessed an outburst in productivity. For productivity improvement manufacturing industries are taking various initiatives by using lean tools and techniques. However, in different manufacturing industries, frugal approach is applied in product design and services as a tool for improvement. Frugal approach contributed to prove less is more and seems indirectly contributing to improve productivity. Hence, there is need to understand status of frugal approach application in manufacturing industries. All manufacturing industries are trying hard and putting continuous efforts for competitive existence. For productivity improvements, manufacturing industries are coming up with different effective and efficient solutions in manufacturing processes and operations. To overcome current challenges, manufacturing industries have started using frugal approach in product design and services. For this study, methodology adopted with both primary and secondary sources of data. For primary source interview and observation technique is used and for secondary source review has done based on available literatures in website, printed magazines, manual etc. An attempt has made for understanding application of frugal approach with the study of manufacturing industry project. Manufacturing industry selected for this project study is Mahindra and Mahindra Ltd. This paper will help researcher to find the connections between the two concepts productivity improvement and frugal approach. This paper will help to understand significance of frugal approach for productivity improvement in manufacturing industry. This will also help to understand current scenario of frugal approach in manufacturing industry. In manufacturing industries various process are involved to deliver the final product. In the process of converting input in to output through manufacturing process productivity plays very critical role. Hence this study will help to evolve status of frugal approach in productivity improvement programme. The notion of frugal can be viewed as an approach towards productivity improvement in manufacturing industries.
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENTIAEME Publication
In this paper, we investigated a queuing model of fuzzy environment-based a multiple channel queuing model (M/M/C) ( /FCFS) and study its performance under realistic conditions. It applies a nonagonal fuzzy number to analyse the relevant performance of a multiple channel queuing model (M/M/C) ( /FCFS). Based on the sub interval average ranking method for nonagonal fuzzy number, we convert fuzzy number to crisp one. Numerical results reveal that the efficiency of this method. Intuitively, the fuzzy environment adapts well to a multiple channel queuing models (M/M/C) ( /FCFS) are very well.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Home security is of paramount importance in today's world, where we rely more on technology, home
security is crucial. Using technology to make homes safer and easier to control from anywhere is
important. Home security is important for the occupant’s safety. In this paper, we came up with a low cost,
AI based model home security system. The system has a user-friendly interface, allowing users to start
model training and face detection with simple keyboard commands. Our goal is to introduce an innovative
home security system using facial recognition technology. Unlike traditional systems, this system trains
and saves images of friends and family members. The system scans this folder to recognize familiar faces
and provides real-time monitoring. If an unfamiliar face is detected, it promptly sends an email alert,
ensuring a proactive response to potential security threats.
Open Channel Flow: fluid flow with a free surfaceIndrajeet sahu
Open Channel Flow: This topic focuses on fluid flow with a free surface, such as in rivers, canals, and drainage ditches. Key concepts include the classification of flow types (steady vs. unsteady, uniform vs. non-uniform), hydraulic radius, flow resistance, Manning's equation, critical flow conditions, and energy and momentum principles. It also covers flow measurement techniques, gradually varied flow analysis, and the design of open channels. Understanding these principles is vital for effective water resource management and engineering applications.
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
2. Arseniy Aleksandrovich Lebedev
http://www.iaeme.com/IJCIET/index.asp 1675 editor@iaeme.com
2. the error signal disappeared.
At the same time, an exponential relationship was observed between changes in the error
signal time and the number of weights fitted. Problem 1 led to an undesirable effect of constant
oscillation of the scales, problem 2 led to a situation where, with a sufficiently large time
distance between the states affected by the error signal, network training either took an
unacceptably long time or did not occur at all [3].
2. LITERATURE REVIEW
Researchers have proposed a variety of different RNN (recurrent neural network) architectures
aimed at solving these problems.
2.1. Altinay
The work by Altinay in 2017 [4] provides an overview of a number of approaches that use
various modified gradient descent solutions for solving problems with error signal propagation,
but none of the proposed options solves both problems at once.
2.2. Zagami
In work by Jason Zagami in 2018 [5], the architecture of a with a time delay was proposed for
solving both problems, but only for cases of relatively short sequences of states. In such a
network, the neuron weights are updated with a weighted sum of old weights.
2.3. Fox-Turnbull
The idea of time-delayed recurrent networks formed the basis of the NARX neural network
described in work by Fox-Turnbull in 2016 [6].
2.4. Campbell
To solve problems with the propagation of an error signal in cases of relatively long sequences
of states in work by Campbell in 2015 [7], it was proposed to use a set of time constants
governing the updating of the weights.
2.5. Baines and Chen
An attempt to combine the neural network approach with a time delay and a time regulation
constant for updating the weights was undertaken in the work (Mapotse, 2018). However, as
in work by Baines in 2018 [8], and in work by Chen in 2018 [9], in the case of long sequences
of states, a neat and time-consuming process of selecting time constants was required.
2.6. Hsu
An alternative solution to both problems for short and long sequences of events was described
in work by Hsu in 2016 [10]. The authors proposed to update the weights of the recurrent cell
by summing the old weight and the current normalized input value. At the same time, the
normalized current input value gradually distorted (supplanted) the stored information about
past states, which made it impossible to work with long sequences of states.
2.7. Fletcher-Watson
In work by Fletcher-Watson in 2015 [11], to solve problems with the error signal on long
sequences of events, it was proposed to use special, separate network cells that affect the
weights. Such cells are added only if conflicting error signals occur on the network. In a limited
3. Use of Recurrent Neural Network Architectures for Data Verification in the System of Distance
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number of cases, this approach can significantly reduce the number of calculations on the
network, however, in unfavorable cases, the number of additional cells can be equal to the
number of states in the sequence, which can lead to problems similar to the infinite oscillation
of weights.
2.8. Hochreiter and Schmidhuber
In the paper by Hochreiter and Schmidhuber [12] the LSTM recurrent neural network
architecture was proposed. It solves both problems with the propagation of an error signal for
almost arbitrary sequences of states. The backpropagation of the error signal in this architecture
is set automatically by a constant, obtained by applying an efficient algorithm based on a
gradient descent. In this case, the signal propagation occurs through the states of the network
cells that have specific 4-layer architecture. As a result, LSTM is capable of maintaining a
temporal relationship of more than 1000 states even in the case of fairly “noisy” input data and
at the same time does not lose this property on short sequences of states.
3. MATERIALS AND METHODS
In contrast to the classical machine learning methods, in which they find a point estimate for
the parameters of the neural network w, in Bayesian neural networks objects, target variables
and parameters are treated as random variables. Accordingly, the neural network models the
dependence p (y | x, w). The prior distribution p (w) sets the initial knowledge and expectations
about the parameters. For example, in thinning models, the prior distribution encourages zero
parameter values. The learning process consists in finding the posterior distribution of the
parameters p (w | D). Then the predictions of the model will be given as
p(y|x) = Ep(w|D)p(y|x,w) (1)
To find the posterior distribution on the parameters according to the Bayes formula
(2)
This fails due to the non-calculated integral in the denominator. Therefore, an approximate
a posteriori distribution qλ (w) in a certain parametric family of distributions is sought, and λ
is the parameters of an approximate a posteriori distribution. Parameters λ are chosen so as to
minimize the KL divergence:
KL(qλ(w)||p(w|D)) → min, w (3)
Which is tantamount to maximizing the variational lower bound on the likelihood
logarithm:
N L(λ) = X
Eqλ(w) logp(yi|xi,w) − KL(qλ(w)||p(w)) → max λ i=1 (4)
This expression is essentially the sum of the term responsible for the quality of the solution
of the problem, and the regularizer, showing that the a posteriori distribution of the parameters
should be close to the a priori one.
The first addend is usually estimated using the Monte Carlo method with one sample of
weights for each object:
Eqλ(w) logp(yi|xi,w) ≈ logp(yi|xi,w),w ∼ qλ(w). (5)
To avoid gradient bias, a reparameterization trick is applied by Damewood in 2016 [13]:
the weights are given as
. (6)
4. Arseniy Aleksandrovich Lebedev
http://www.iaeme.com/IJCIET/index.asp 1677 editor@iaeme.com
The reparameterization trick is not applicable to all distributions, but it is applicable, for
example, to the normal distribution:
, (7)
elementwise multiplication.
Also, the method of multiple regression, the logistic regression method and the coefficient
of determination were used as a control in the experiment.
4. RESULTS AND DISCUSSIONS
Dropout [14] is a regularization technique for neural networks, which imposes multiplicative
noise on the inputs of each layer. Typically, noise vector elements are generated from the
Bernoulli distribution (binary dropout) or from the normal distribution with a center at 1
(Gaussian dropout), and the parameters of this noise are adjusted using cross-validation. In
work by McLain in 2018 [15], an interpretation of the Gaussian dropout is proposed as a way
to specify the Bayesian neural network. This made it possible to adjust the noise parameters
automatically. In work by Virtanen in 2015 [16], this approach was extended to thin the fully
connected neural networks and was called the thinning variational dropout (TVD).
Consider a fully connected layer h = g (Wx + b) with a weight matrix W. In a TVD, the a
priori distribution of weights is given as a factorized log-uniform distribution:
(8)
This distribution has a large mass at zero and therefore encourages zero weights.
Approximate a posteriori distribution is sought in the family of factorized normal
distributions.
k, nq(W) = Y q(wij), q(wij|θij,αij) = N(θij,αijθij2 )i, j=1 (9)
The use of such a posteriori distribution is equivalent to the imposition of a multiplicative
[17]
wij = θijξij, ξij ∼ N(1,αij), (10)
Or additive [18]:
. (11)
Of normal noise on weight. The parametrization of weights (11) is called additive
reparametrization and makes it possible to reduce the dispersion of gradients L over the average
weights θij. In addition, since the sum of normal distributions is a normal distribution with
calculated parameters, noise can be imposed on Wx pre-activation, rather than separately on
the components of the W matrix. This technique is called local reparametrization [19, 20].
Local reparameterization allows one to reduce the dispersion of gradients even more, and also
saves computations, since sampling noise on weights separately for each object is an expensive
operation.
In the TVD, the variation of the lower estimate (4) is optimized by {θ, logσ} using the trick
of reparametrization, additive reparametrization and local reparametrization to achieve
unbiased low dispersion gradients. Since the a priori and the approximate a posteriori
distributions are factorized by weights, the KL divergence also splits into a sum for individual
5. Use of Recurrent Neural Network Architectures for Data Verification in the System of Distance
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weights, and each term depends only on the noise dispersion αij due to the special choice of
the prior distribution:
KL(q(wij|θij,αij)||p(wij)) = k(αij),
k(α) ≈ 0.64σ(1.87 + 1.49logα) − 0.5log(1 + α−1
) + C. (12)
The last expression is a fairly accurate approximation of the KL divergence and is obtained.
KL divergence (12) encourages large values of αij and small modulo values of θij. If αij →
∞ for the weight wij, then due to the large noise dispersion of the model, it is advantageous to
set θij = 0 and σij = αijθij2 = 0 to avoid large prediction errors. As a result, the distribution q
(wij | θij, αij) approaches the delta function at 0, and this weight is always zero.
The thinning variational dropout model was extended to achieve group thinning of the full-
connected layer. Group dilution refers to the removal of a weight group from a model, for
example, rows or columns of a weight matrix. Group thinning allows you to remove elements
of hidden layers of the neural network, which accelerates the passage forward. As an example,
we will combine the columns of the weights matrix of a fully connected layer into groups and
number them 1 ... k.
The authors propose to introduce group multiplicative weights zi for each weight group and
adjust weights in the following parameterization:
wij = wˆijzi. (13)
In a fully connected layer, this parametrization is equivalent to imposing multiplicative
noise on the input layer:
. (14)
Since the main task is to nullify zi, the authors use for the multiplicative variables the same
pair of a priori-approximate posteriori distribution, as in the TVD:
. (15)
For individual weights, the standard normal a priori distribution is used, and the posterior
distribution, as in the TVD, is approximated in the class of normal distributions:
p(wij) = N(wij|0,1) q(wij) = N(wij|θij,σij2 ). (13)
The prior distribution to individual weights encourages zero averages θij, and this, in turn,
helps bring the group averages θiz to zero, that is, reset the group variables.
The model is trained in the same way as the TVD model by optimizing the variational lower
bound (4). KL-divergence splits into a sum of KL-divergences for group variables and for
weights, with the last term calculated analytically.
For most tasks, recurrent neural networks are defined by dense weights matrices, with most
of the weights being uninformative and not affecting the quality of the solution to the problem.
Despite the existence of heuristic approaches to thinning RNN based on a large number of
hyperparameters, the use of Bayesian thinning techniques has not been previously investigated
for recurrent neural networks. On the other hand, the literature describes various models of
Bayesian regularization of RNN, some features of which are also reflected in the proposed
model.
When applying a TVD to a RNN, the features of the recurrent layer should be taken into
account.:
6. Arseniy Aleksandrovich Lebedev
http://www.iaeme.com/IJCIET/index.asp 1679 editor@iaeme.com
• weights in the recurrent layer are related in time, that is, different elements of the
input sequence are multiplied by the same weights matrices;
• in Bayesian regularization of RNN, the current hidden state ht and the matrix of
recurrent weights Wh are not independent random variables, since the second
involved in the expressions for calculating the first.
First, we consider a model of a thinning variational dropout for a recurrent layer, and then
we note the features of applying a TVD to a fully connected layer and a layer of representations
in the RNN.
Following, we use a log-uniform prior distribution on the weights of the recurrent layer
{Wx, Wh} and approximate the posterior distribution in the class of normal distributions:
, (14)
The training model is to optimize the variation lower bound
(15)
On parameters {θ, logσ} using stochastic gradient optimization methods. In expression
(15), the first term is the likelihood of the model, averaged over the distribution over the
weights q (w | θ, σ). In the process of optimization, this plausibility is estimated by the Monte
Carlo method with one sample of weights. As in the model of the TVD model, the
reparameterization trick and additive reparameterization are used here in order to obtain
unbiased gradients with low dispersion.
(16)
In plausibility, the dependence of the target variable yi is expanded in time to emphasize
that the same weights Wx, Wh are used at all times. So normal noise in Bayesian
regularization of RNN, it must be time bound: the same noise sample is used for one object at
a time.
However, in Bayesian regularization of RNN, local reparameterization cannot be applied
to either Wx or Wh scales. Applying local reparameterization to the Wx weights matrix in the
RNN implies using the same noise sample to preactivate Wxxt ∀t, which is not equivalent to
using the same Wx weights sample at all points in time. For Wh, local reparametrization cannot
be applied for another reason: since ht − 1 and Wh are not independent random variables, the
assertion about the sum of normal distributions is not applicable to the product Whht − 1.
Instead of using local reparameterization in order to avoid resource-intensive sampling of
three-dimensional noise matrices, it is proposed to use one sample of Wx and Wh weights for
all objects of one mini-batch.
A similar scheme can be applied to gate architectures, for example, LSTM. In this case, the
prior and approximate a posteriori distribution will be used for each of the parameter matrices.
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At the testing stage, we can use the average values of weights Θx, Θh by analogy with. In
addition, weights with large values get nulled.
The final diagram of one-time step forward along the thinning Bayesian RNN, including
zeroing the scales in test mode, is given in algorithm (4).
When applying a thinning dropout to other RNN layers preceding or following the recurrent
layer, at the training stage one should use the same sample of weights at all times for one object.
Thus, when formulating the TVD model for RNN, the following features of recurrent
neural networks are taken into account:
1. sampling the same noise on the weight at all points in time;
2. unlike the direct distribution networks, local reparametrization is not applicable to
RNN, therefore it is proposed to sample one weights matrix for all mini-batch
objects.
In order for thinning to help speed up the forward path through the recurrent neural network
when performing computations on GPUs, you need to remove weights in groups corresponding
to one neuron. To do this, you can apply the approach described. However, this approach can
be improved to obtain different levels of sparseness in gate recurrent architectures. Consider
this approach for the most popular LSTM gateway architecture.
In LSTM, in addition to the latent state vector, the internal memory ct vector is maintained
at each time instant. At each time step, the memory is first updated using the gate mechanism,
then the hidden state is updated:
(17)
By analogy with (14), in order to achieve group thinning, we introduce into the model
multiplicative group variables on weights. In addition to the group variables zx and zh on the
rows of weights matrices responsible for excluding the elements of the input and hidden
vectors, we also introduce the group variables zi, zf, zg and zo on the columns of the weights
matrices responsible for causing the gates i, f, o and information flow g to input data. For
example, for the matrix Wfx we get the following parameterization of the weights:
wf, ijx = wˆf,ijx zix · zjf. (18)
Such parametrization corresponds to the imposition of multiplicative noise on the input
vector xt and the hidden state ht, as well as separate multiplicative noise on the preactivation
of gates and information flow:
(19)
8. Arseniy Aleksandrovich Lebedev
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When the zx and zh components are zeroed out, the element of the input vector or hidden
state is excluded from the model, respectively. When the components zi
, zf
, zg
, or
zo
are zeroed
out, the element of the corresponding gate or information flow becomes constant, not
determined by the input data xt
and ht
. Note that the appearance of constant gates in the model
simplifies, but does not violate the structure of LSTM, and in addition, it saves the computation
of matrix products.
The standard normal a priori distribution for individual weights wˆij was used, but in
practice this limits the thinning of the model. In this paper, it is proposed to use such a prior-
approximate a posteriori distribution, as in the TVD for the RNN, for all groups of weights (for
example, the distributions for the Wfx
matrix are given):
(20)
Due to the thinning of all three groups of weights, a hierarchical effect is achieved: thinning
individual weights contributes to the appearance of constant gates and simplifies the structure
of the LSTM, which, in turn, helps to eliminate the xt
and ht
elements.
The sampling of the group variables zx
, zh
, zi
, zf
, zg
and zo
is carried out using the trick of
reparametrization and additive reparametrization, as in the model of the TVD for RNN. The
training of the model, the forward passage and the testing phase are similar to the TVD model
for RNN with the addition of only additional sampling of group variables in the forward pass
and the components of KL-divergence responsible for group variables in the variational lower
bound (15).
Group thinning can be applied similarly to the layer of representations. To do this, you need
to introduce group multiplicative variables to the elements of the dictionary and dilute both the
elements of the representation matrix and the multiplicative group variables. The a priorI and
approximate a posteriori representation for weights remain the same as in the group TVD
model described above for RNN. As a result of applying such a model, the effect of thinning
the input dictionary is achieved, that is, the selection of features.
Thus, when formulating the model of group TVD for RNN, the following features of the
gating recurrent neural networks are taken into account:
Introduced multiplicative variables on the preactivation of gates and information flow;
For weights wij, a thinning log-uniform prior distribution is used, which enhances the
thinning of group variables.
Traditional RNN maps the input sequence of vectors x1, ..., xT to the output sequence of
vectors y1, ..., yT. This is achieved by calculating the sequence of “hidden” states h1, ..., hT,
considered as sequential registration of information about past states and which is relevant for
predicting future states. These variables are connected by a simple system of equations
presented in formulas (4) and (11):
ht = tanh(Whxxt + Whhht-1 + bh), (21)
yt = σ(Wyhht + by), (22)
9. Use of Recurrent Neural Network Architectures for Data Verification in the System of Distance
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where tanh and sigmoid function σ () are applied element by element, Whx is the matrix of
weights of input values, Whh is the matrix of recurrent weights, Wyh is the matrix of weights
of output values, bh and by are the offset regulators of hidden and output values, respectively.
Traditional RNN has problems associated with the propagation of the error signal when a
large number of analyzed states. The RNN architecture that solves this problem most efficiently
is LSTM. In LSTM, hidden items retain their values until they are completely cleared by
triggering special “forget gate” gates. Due to the mechanism of the LSTM valves, information
on intermediate states is stored much longer than conventional RNN, which greatly facilitates
the process of their learning. In addition, in LSTM, hidden layers are updated using
multiplicative interaction (rather than additive), which allows this architecture to reflect much
more complex transformations with the same number of neurons in hidden layers.
The LSTM variant of the RNN was used in the two most recent experimental studies
devoted to studying the capabilities of the RNN for predicting educational outcomes.
In the work of scientists from Kyushu University, the accuracy of predicting the future
score for the course “Information Science” was investigated on the basis of the actions of 108
students who are journaled by M2B CSR training support. The input to the RNN was obtained
from journals by giving the student a score from 0 to 6 on the following scales:
1. attendance (0 - no, 3 – late attendance, 5 – in-time attendance);
2. point for testing (with a scale breakdown with a step of 20%);
3. the fact of delivery of the report (0 - not submitted, 3 - delivered late, 5 - delivered
on time);
4. the number of views of course materials, materials in the service of electronic
textbooks, actions in the service of electronic textbooks, words in texts sent to the
service of electronic portfolio (broken down by step 10%, below 50% - 0 points).
A total of 9 variables were proposed. Output data served as a final score for the course on
a 5-point scale. The paper does not indicate the specific type of LSTM used for forecasting,
however, based on a brief description, it can be assumed that this is the traditional LSTM
described in the paper.
As one step (state), there were used scores on 9 scales, given to a student for one week of
classes. The maximum number of weeks used in the experiment was 15. The method of
multiple regression and the coefficient of determination were used as a control in the
experiment. Figure 1 shows the accuracy of the prediction of the final score after each week of
the course.
Figure 1. The accuracy prediction of the final score prediction after each week of the course
10. Arseniy Aleksandrovich Lebedev
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From Figure 1 it can be seen that when using LSTM, prediction accuracy of >90% is
achieved at the 6th week step, while for multiple regression only at the 10th
one, and for the
determination coefficient - only at the 14th
one.
Of particular interest in the work is the fact that as input data are used not individual
properties of students, but directly written program code. For vectorization of program code,
the researchers used the method they developed based on building the AST representation of
the student’s program code by analogy with the presentation of texts in natural language.
As a control method, the logistic regression method was used, the input data for which was
a two-dimensional vector. The first element of this vector is calculated as a function inversely
proportional to the number of appearances among the one student sent program codes that are
close to the correct version. The second element is a binary sign of the success / failure of the
assignment.
Figure 2 shows the graphs of the prediction accuracy of the educational outcome of the next
assignment for the control method and LSTM.
Figure 2. The accuracy of the educational result of the next task prediction, depending on the number
of attempts to send the task to students
In Figure 2 it can be seen that the LSTM method has an average of 10 percentage points
higher accuracy compared to the control one. The minimum accuracy of LSTM is more than
80%. The difference in accuracy is explained by the authors of the work by the fact that the
LSTM method allows building a forecast directly based on the meaning of the student’s
response (properties of its program code), while the control method takes into account only the
number of attempts that were close to successful.
5. CONCLUSION
The extremely high prediction accuracy achieved in the experiment (100%) is explained, in our
opinion, by a simple 5-point scale of estimation. It should be noted that in no other work
analyzed in the framework of the analytical review such accuracy has been achieved.
Nevertheless, given the widespread 5-point scale in the Russian Federation, it is extremely
important that the use of the simplest LSTM architecture in combination with a small set of
equally simple input data can provide such prediction accuracy, at least for subject-specific
results.
The paper describes an experiment of using LSTM to predict an educational result based
on the source code of the programs compiled by students during the execution of a single task
of the Hour of Code mass online course on the Code.org platform. The input data set contained
about 1.2 million program codes compiled by 263.5 thousand students. At the disposal of
11. Use of Recurrent Neural Network Architectures for Data Verification in the System of Distance
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researchers there were data only for two tasks of the course. As a single step (state), one attempt
was made by the student to send a program code (students had the opportunity to send several
program codes in response to one task). At the same time, only those students who made from
2 to 10 attempts to complete the task were selected from the total data set. LSTM should have
predicted the educational outcome in the form of the likelihood of a successful assignment,
next to the one whose performance data was used for LSTM training.
The traditional LSTM described was used for training (the authors indicate this explicitly).
Since the output of the LSTM must produce a probabilistic value, the output values of the last
cell of the network pass through a fully connected layer and the next layer with the Softmax
activation function.
FUNDING STATEMENT
Applied research described in this paper is carried out with financial support of the state
represented by the Russian Federation Ministry for Education and Science under the
Agreement #14.576.21.0091 of 26 September 2017 (unique identifier of applied research -
RFMEFI57617X0091).
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