Sewer systems are used to convey sewage and/or storm water to sewage treatment
plants for disposal by a network of buried sewer pipes, gutters, manholes and pits.
Unfortunately, the sewer pipe deteriorates with time leading to the collapsing of the
pipe with traffic disruption or clogging of the pipe causing flooding and
environmental pollution. Thus, the management and maintenance of the buried pipes
are important tasks that require information about the changes of the current and
future sewer pipes conditions. In this research, the study was carried on in Baghdad,
Iraq and two deteriorations model’s multinomial logistic regression and neural
network deterioration model NNDM are used to predict sewers future conditions. The
results of the deterioration models' application showed that NNDM gave the highest
overall prediction efficiency of 93.6% by adapting the confusion matrix test, while
multinomial logistic regression was inconsistent with the data. The error in prediction
of related model was due to its inability to reflect the dependent variable (condition
classes) ordered nature.
Comparison and Evaluation of Support Vector Machine and Gene Programming in R...AI Publications
Simulation and evaluation of sediment are important issues in water resources management. Common methods for measuring sediment concentration are generally time consuming and costly and sometimes does not have enough accuracy. In this research, we have tried to evaluate sediment amounts, using Support Vector Machine (SVM), for Kashkanriver, Iran, and compare it with common Gene-Expression Programming. The parameter of flow discharge for input in different time lags and the parameter of sediment for output dhuring contour time (1998-2018) considered. Criteria of correlation coefficient, root mean square error, mean absolute error and Nash Sutcliff coefficient were used to evaluate and compare the performance of models. The results showed that two models estimate sediment discharge with acceptable accuracy, but in terms of accuracy, the support vector machine model had the highest correlation coefficient (0.994), minimum root mean square error (0.001ton/day) , mean absolute error(0.001 ton/day) and the Nash Sutcliff (0.988) hence was chosen the prior in the verification stage. Finally, the results showed that the support vector machine has great capability in estimating minimum and maximum sediment discharge values.
CENTROG FEATURE TECHNIQUE FOR VEHICLE TYPE RECOGNITION AT DAY AND NIGHT TIMESijaia
This work proposes a feature-based technique to recognize vehicle types within day and night times. Support vector machine (SVM) classifier is applied on image histogram and CENsus Transformed
histogRam Oriented Gradient (CENTROG) features in order to classify vehicle types during the day and night. Thermal images were used for the night time experiments. Although thermal images suffer from low image resolution, lack of colour and poor texture information, they offer the advantage of being unaffected by high intensity light sources such as vehicle headlights which tend to render normal images unsuitable
for night time image capturing and subsequent analysis. Since contour is useful in shape based categorisation and the most distinctive feature within thermal images, CENTROG is used to capture this feature information and is used within the experiments. The experimental results so obtained were
compared with those obtained by employing the CENsus TRansformed hISTogram (CENTRIST). Experimental results revealed that CENTROG offers better recognition accuracies for both day and night times vehicle types recognition.
A major challenge in hydrological modelling is to identification of optimal
parameter set of different data, catchment characteristics and objectives. Although, the
identification of optimal parameter set is difficult because of conceptual hydrological
models contain more number of parameters and accuracy also depends upon all the
relevant number of parameters influencing in a model. This identification process
cannot estimate directly and therefore it measured based on calibrating the model
which minimizing an objective function. Here, the objective function can depend upon
the sensitivity of model parameters and calibration of model. In this paper, we proposed
the Emulator Based Optimization (EBO) for reducing number of runs and improving
conceptual model efficiency. Where, emulator models are used to represent the
response surface of the simulation models and it can play a valuable role for
optimization. In this study evaluates EBO for calibrating of SWAT hydrological model
with following steps like input design, simulation model, emulator modelling,
convergence criteria and validation. The results show that EBO calibrates the model
with high accuracy and it captured the observed model with consuming less time. This
study helps for decision making, planning and designing of water resources.
Applications of Computer Science in Environmental ModelsIJLT EMAS
Computation is now regarded as an equal and
indispensable partner, along with theory and experiment, in the
advance of scientific knowledge and engineering practice.
Numerical simulation enables the study of complex systems and
natural phenomena that would be too expensive or dangerous, or
even impossible, to study by direct experimentation. The quest
for ever higher levels of detail and realism in such simulations
requires enormous computational capacity, and has provided the
impetus for dramatic breakthroughs in computer algorithms and
architectures. Due to these advances, computational scientists
and engineers can now solve large-scale problems that were once
thought intractable. Computational science and engineering
(CSE) is a rapidly growing multidisciplinary area with
connections to the sciences, engineering, and mathematics and
computer science. CSE focuses on the development of problemsolving
methodologies and robust tools for the solution of
scientific and engineering problems. We believe that CSE will
play an important if not dominating role for the future of the
scientific discovery process and engineering design. The
computation science is now being used widely for environmental
engineering calculations. The behavior of environmental
engineering systems and processes can be studied with the help
of computation science and understanding as well as better
solutions to environmental engineering problems can be
obtained.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcsandit
Rainfall is considered as one of the major components of the hydrological process; it takes
significant part in evaluating drought and flooding events. Therefore, it is important to have an
accurate model for rainfall prediction. Recently, several data-driven modeling approaches have
been investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal
dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square
Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third
of the data was used for training the model and One-third for testing.
Comparison and Evaluation of Support Vector Machine and Gene Programming in R...AI Publications
Simulation and evaluation of sediment are important issues in water resources management. Common methods for measuring sediment concentration are generally time consuming and costly and sometimes does not have enough accuracy. In this research, we have tried to evaluate sediment amounts, using Support Vector Machine (SVM), for Kashkanriver, Iran, and compare it with common Gene-Expression Programming. The parameter of flow discharge for input in different time lags and the parameter of sediment for output dhuring contour time (1998-2018) considered. Criteria of correlation coefficient, root mean square error, mean absolute error and Nash Sutcliff coefficient were used to evaluate and compare the performance of models. The results showed that two models estimate sediment discharge with acceptable accuracy, but in terms of accuracy, the support vector machine model had the highest correlation coefficient (0.994), minimum root mean square error (0.001ton/day) , mean absolute error(0.001 ton/day) and the Nash Sutcliff (0.988) hence was chosen the prior in the verification stage. Finally, the results showed that the support vector machine has great capability in estimating minimum and maximum sediment discharge values.
CENTROG FEATURE TECHNIQUE FOR VEHICLE TYPE RECOGNITION AT DAY AND NIGHT TIMESijaia
This work proposes a feature-based technique to recognize vehicle types within day and night times. Support vector machine (SVM) classifier is applied on image histogram and CENsus Transformed
histogRam Oriented Gradient (CENTROG) features in order to classify vehicle types during the day and night. Thermal images were used for the night time experiments. Although thermal images suffer from low image resolution, lack of colour and poor texture information, they offer the advantage of being unaffected by high intensity light sources such as vehicle headlights which tend to render normal images unsuitable
for night time image capturing and subsequent analysis. Since contour is useful in shape based categorisation and the most distinctive feature within thermal images, CENTROG is used to capture this feature information and is used within the experiments. The experimental results so obtained were
compared with those obtained by employing the CENsus TRansformed hISTogram (CENTRIST). Experimental results revealed that CENTROG offers better recognition accuracies for both day and night times vehicle types recognition.
A major challenge in hydrological modelling is to identification of optimal
parameter set of different data, catchment characteristics and objectives. Although, the
identification of optimal parameter set is difficult because of conceptual hydrological
models contain more number of parameters and accuracy also depends upon all the
relevant number of parameters influencing in a model. This identification process
cannot estimate directly and therefore it measured based on calibrating the model
which minimizing an objective function. Here, the objective function can depend upon
the sensitivity of model parameters and calibration of model. In this paper, we proposed
the Emulator Based Optimization (EBO) for reducing number of runs and improving
conceptual model efficiency. Where, emulator models are used to represent the
response surface of the simulation models and it can play a valuable role for
optimization. In this study evaluates EBO for calibrating of SWAT hydrological model
with following steps like input design, simulation model, emulator modelling,
convergence criteria and validation. The results show that EBO calibrates the model
with high accuracy and it captured the observed model with consuming less time. This
study helps for decision making, planning and designing of water resources.
Applications of Computer Science in Environmental ModelsIJLT EMAS
Computation is now regarded as an equal and
indispensable partner, along with theory and experiment, in the
advance of scientific knowledge and engineering practice.
Numerical simulation enables the study of complex systems and
natural phenomena that would be too expensive or dangerous, or
even impossible, to study by direct experimentation. The quest
for ever higher levels of detail and realism in such simulations
requires enormous computational capacity, and has provided the
impetus for dramatic breakthroughs in computer algorithms and
architectures. Due to these advances, computational scientists
and engineers can now solve large-scale problems that were once
thought intractable. Computational science and engineering
(CSE) is a rapidly growing multidisciplinary area with
connections to the sciences, engineering, and mathematics and
computer science. CSE focuses on the development of problemsolving
methodologies and robust tools for the solution of
scientific and engineering problems. We believe that CSE will
play an important if not dominating role for the future of the
scientific discovery process and engineering design. The
computation science is now being used widely for environmental
engineering calculations. The behavior of environmental
engineering systems and processes can be studied with the help
of computation science and understanding as well as better
solutions to environmental engineering problems can be
obtained.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcsandit
Rainfall is considered as one of the major components of the hydrological process; it takes
significant part in evaluating drought and flooding events. Therefore, it is important to have an
accurate model for rainfall prediction. Recently, several data-driven modeling approaches have
been investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal
dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square
Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third
of the data was used for training the model and One-third for testing.
Classification accuracy analyses using Shannon’s EntropyIJERA Editor
There are many methods for determining the Classification Accuracy. In this paper significance of Entropy of
training signatures in Classification has been shown. Entropy of training signatures of the raw digital image
represents the heterogeneity of the brightness values of the pixels in different bands. This implies that an image
comprising a homogeneous lu/lc category will be associated with nearly the same reflectance values that would
result in the occurrence of a very low entropy value. On the other hand an image characterized by the
occurrence of diverse lu/lc categories will consist of largely differing reflectance values due to which the
entropy of such image would be relatively high. This concept leads to analyses of classification accuracy.
Although Entropy has been used many times in RS and GIS but its use in determination of classification
accuracy is new approach.
A Threshold Fuzzy Entropy Based Feature Selection: Comparative StudyIJMER
Feature selection is one of the most common and critical tasks in database classification. It
reduces the computational cost by removing insignificant and unwanted features. Consequently, this
makes the diagnosis process accurate and comprehensible. This paper presents the measurement of
feature relevance based on fuzzy entropy, tested with Radial Basis Classifier (RBF) network,
Bagging(Bootstrap Aggregating), Boosting and stacking for various fields of datasets. Twenty
benchmarked datasets which are available in UCI Machine Learning Repository and KDD have been
used for this work. The accuracy obtained from these classification process shows that the proposed
method is capable of producing good and accurate results with fewer features than the original
datasets.
Applying Genetic Algorithms to Information Retrieval Using Vector Space ModelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
Magnetic resonance imaging as a tool to assess reliability in simulating hemodynamics in cerebral aneurysms with a dedicated computational fluid dynamics prototype: preliminary results
Christof Karmonik, Y. Jonathan. Zhang, Orlando Diaz, Richard Klucznik, Sasan Partovi, Robert G. Grossman, Gavin W. Britz
Applying genetic algorithms to information retrieval using vector space modelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on
mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in
1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used
cosine similarity and jaccards to compute similarity between the query and documents, and used two
proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at
evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study
concluded that we might have several improvements when using adaptive genetic algorithms.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
BEARINGS PROGNOSTIC USING MIXTURE OF GAUSSIANS HIDDEN MARKOV MODEL AND SUPPOR...IJNSA Journal
Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical
systems or components based on their current health state. RUL can be estimated by using three main
approaches: model-based, experience-based and data-driven approaches. This paper deals with a datadriven
prognostics method which is based on the transformation of the data provided by the sensors into
models that are able to characterize the behavior of the degradation of bearings.
For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the
recently published data base taken from the platform PRONOSTIA clearly show the superiority of the
proposed approach compared to well established method in literature like Mixture of Gaussian Hidden
Markov Models (MoG-HMMs).
BEARINGS PROGNOSTIC USING MIXTURE OF GAUSSIANS HIDDEN MARKOV MODEL AND SUPPOR...IJNSA Journal
Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical systems or components based on their current health state. RUL can be estimated by using three main approaches: model-based, experience-based and data-driven approaches. This paper deals with a data driven prognostics method which is based on the transformation of the data provided by the sensors into
models that are able to characterize the behavior of the degradation of bearings.
For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the recently published data base taken from the platform PRONOSTIA clearly show the superiority of the proposed approach compared to well established method in literature like Mixture of Gaussian Hidden Markov Models (MoG-HMMs).
Data-driven adaptive predictive control for an activated sludge processjournalBEEI
Data-driven control requires no information of the mathematical model of the controlled process. This paper proposes the direct identification of controller parameters of activated sludge process. This class of data-driven control calculates the predictive controller parameters directly using subspace identification technique. By updating input-output data using receding window mechanism, the adaptive strategy can be achieved. The robustness test and stability analysis of direct adaptive model predictive control are discussed to realize the effectiveness of this adaptive control scheme. The applicability of the controller algorithm to adapt into varying kinetic parameters and operating conditions is evaluated. Simulation results show that by a proper and effective excitation of direct identification of controller parameters, the convergence and stability of the implicit predictive model can be achieved.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScscpconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line diagnosis. An application example is presented to illustrate and confirm the effectiveness and the accuracy of the proposed approach.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScsitconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
In literature, there are two categories for the analysis of Water Distribution Networks (WDN). The first is Demand Driven Analysis (DDA) at which engineers satisfies the demand at each node and then calculate the pressure in the design of new networks. Softwares like EPANET and other commercial ones comprises the DDA methodologies. Normally, engineers do not take into consideration the sudden events (i.e excessive firefighting demand, excessive demand in some junctions, pipe failure, or pump failure). These events may produce negative pressure problems to the network leading to deficient nodes. In the second category named Pressure Driven Analysis (PDA), researchers attempted to solve the negative pressure problem. Indeed, the PDA methods are treated into three different ways. (i) Modifying the hydraulic solver source code by introducing a new PDA method, or (ii) adding artificial elements like check valve, internal dummy node, flow control valve, reservoir or emitter to network demand nodes, or (iii) adding some of the previous explained artificial elements to demand nodes which are suffering from pressure deficiency. Many researchers try to take into consideration the extended period simulation (EPS) in the water network. Until now, there are many challenges facing researchers to come over the problem of deficient nodes. In this paper, a comparison between results (Demand & Pressure) of a case study when using different PDA methods.
State and fault estimation based on fuzzy observer for a class of Takagi-Suge...IJEECSIAES
Singular nonlinear systems have received wide attention in recent years, and can be found in various applications of engineering practice. On the basis of the Takagi-Sugeno (T-S) formalism, which represents a powerful tool allowing the study and the treatment of nonlinear systems, many control and diagnostic problems have been treated in the literature. In this work, we aim to present a new approach making it possible to estimate simultaneously both non-measurable states and unknown faults in the actuators and sensors for a class of continuous-time Takagi-Sugeno singular model (CTSSM). Firstly, the considered class of CTSSM is represented in the case of premise variables which are non-measurable, and is subjected to actuator and sensor faults. Secondly, the suggested observer is synthesized based on the decomposition approach. Next, the observer’s gain matrices are determined using the Lyapunov theory and the constraints are defined as linear matrix inequalities (LMIs). Finally, a numerical simulation on an application example is given to demonstrate the usefulness and the good performance of the proposed dynamic system.
State and fault estimation based on fuzzy observer for a class of Takagi-Suge...nooriasukmaningtyas
Singular nonlinear systems have received wide attention in recent years, and can be found in various applications of engineering practice. On the basis of the Takagi-Sugeno (T-S) formalism, which represents a powerful tool allowing the study and the treatment of nonlinear systems, many control and diagnostic problems have been treated in the literature. In this work, we aim to present a new approach making it possible to estimate simultaneously both non-measurable states and unknown faults in the actuators and sensors for a class of continuous-time Takagi-Sugeno singular model (CTSSM). Firstly, the considered class of CTSSM is represented in the case of premise variables which are non-measurable, and is subjected to actuator and sensor faults. Secondly, the suggested observer is synthesized based on the decomposition approach. Next, the observer’s gain matrices are determined using the Lyapunov theory and the constraints are defined as linear matrix inequalities (LMIs). Finally, a numerical simulation on an application example is given to demonstrate the usefulness and the good performance of the proposed dynamic system.
EFFICACY OF NEURAL NETWORK IN RAINFALL-RUNOFF MODELLING OF BAGMATI RIVER BASINIAEME Publication
In this paper, rainfall-runoff model of Bagmati river basin has been developed
using the ANN Technique. Three-layered fced forward network structure with back
propagation algorithm was used to train the ANN model. Different combinations of
rainfall and runoff were considered as input to the network and trained by BP
algorithm with different error tolerance, learning parameter, number of cycles and
number of hidden layers. The sensitivity of the prediction accuracy to the number of
hidden layer neurons in a back error propagation algorithm was used for the study.
The monthly rainfall and runoff data from 2000 to 2009 of Bagmati river basin has
been considered for the development of ANN model. Performance evaluation of the
model has been done by using statistical parameters. Three sets of data have been
used to make several combination of year keeping in view the highest peaks of
hydrographs. First set of data used was from 2000 to 2006 for the calibration and
from 2007 to 2009 for validation. The second set of data was from 2004 to 2009 for
calibration and from 2000 to 2003 for validation. The Third set of data was from 2000
to 2009 for calibration and from 2007 to 2009 for validation. It was found that the
third set of data gave better result than other two sets of data. The study demonstrates
the applicability of ANN approach in developing effective non-linear models of
Rainfall-Runoff process without the need to explicitly representing the internal
hydraulic structure of the watershed
Classification accuracy analyses using Shannon’s EntropyIJERA Editor
There are many methods for determining the Classification Accuracy. In this paper significance of Entropy of
training signatures in Classification has been shown. Entropy of training signatures of the raw digital image
represents the heterogeneity of the brightness values of the pixels in different bands. This implies that an image
comprising a homogeneous lu/lc category will be associated with nearly the same reflectance values that would
result in the occurrence of a very low entropy value. On the other hand an image characterized by the
occurrence of diverse lu/lc categories will consist of largely differing reflectance values due to which the
entropy of such image would be relatively high. This concept leads to analyses of classification accuracy.
Although Entropy has been used many times in RS and GIS but its use in determination of classification
accuracy is new approach.
A Threshold Fuzzy Entropy Based Feature Selection: Comparative StudyIJMER
Feature selection is one of the most common and critical tasks in database classification. It
reduces the computational cost by removing insignificant and unwanted features. Consequently, this
makes the diagnosis process accurate and comprehensible. This paper presents the measurement of
feature relevance based on fuzzy entropy, tested with Radial Basis Classifier (RBF) network,
Bagging(Bootstrap Aggregating), Boosting and stacking for various fields of datasets. Twenty
benchmarked datasets which are available in UCI Machine Learning Repository and KDD have been
used for this work. The accuracy obtained from these classification process shows that the proposed
method is capable of producing good and accurate results with fewer features than the original
datasets.
Applying Genetic Algorithms to Information Retrieval Using Vector Space ModelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
Magnetic resonance imaging as a tool to assess reliability in simulating hemodynamics in cerebral aneurysms with a dedicated computational fluid dynamics prototype: preliminary results
Christof Karmonik, Y. Jonathan. Zhang, Orlando Diaz, Richard Klucznik, Sasan Partovi, Robert G. Grossman, Gavin W. Britz
Applying genetic algorithms to information retrieval using vector space modelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on
mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in
1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used
cosine similarity and jaccards to compute similarity between the query and documents, and used two
proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at
evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study
concluded that we might have several improvements when using adaptive genetic algorithms.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
BEARINGS PROGNOSTIC USING MIXTURE OF GAUSSIANS HIDDEN MARKOV MODEL AND SUPPOR...IJNSA Journal
Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical
systems or components based on their current health state. RUL can be estimated by using three main
approaches: model-based, experience-based and data-driven approaches. This paper deals with a datadriven
prognostics method which is based on the transformation of the data provided by the sensors into
models that are able to characterize the behavior of the degradation of bearings.
For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the
recently published data base taken from the platform PRONOSTIA clearly show the superiority of the
proposed approach compared to well established method in literature like Mixture of Gaussian Hidden
Markov Models (MoG-HMMs).
BEARINGS PROGNOSTIC USING MIXTURE OF GAUSSIANS HIDDEN MARKOV MODEL AND SUPPOR...IJNSA Journal
Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical systems or components based on their current health state. RUL can be estimated by using three main approaches: model-based, experience-based and data-driven approaches. This paper deals with a data driven prognostics method which is based on the transformation of the data provided by the sensors into
models that are able to characterize the behavior of the degradation of bearings.
For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the recently published data base taken from the platform PRONOSTIA clearly show the superiority of the proposed approach compared to well established method in literature like Mixture of Gaussian Hidden Markov Models (MoG-HMMs).
Data-driven adaptive predictive control for an activated sludge processjournalBEEI
Data-driven control requires no information of the mathematical model of the controlled process. This paper proposes the direct identification of controller parameters of activated sludge process. This class of data-driven control calculates the predictive controller parameters directly using subspace identification technique. By updating input-output data using receding window mechanism, the adaptive strategy can be achieved. The robustness test and stability analysis of direct adaptive model predictive control are discussed to realize the effectiveness of this adaptive control scheme. The applicability of the controller algorithm to adapt into varying kinetic parameters and operating conditions is evaluated. Simulation results show that by a proper and effective excitation of direct identification of controller parameters, the convergence and stability of the implicit predictive model can be achieved.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScscpconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line diagnosis. An application example is presented to illustrate and confirm the effectiveness and the accuracy of the proposed approach.
NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUEScsitconf
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
In literature, there are two categories for the analysis of Water Distribution Networks (WDN). The first is Demand Driven Analysis (DDA) at which engineers satisfies the demand at each node and then calculate the pressure in the design of new networks. Softwares like EPANET and other commercial ones comprises the DDA methodologies. Normally, engineers do not take into consideration the sudden events (i.e excessive firefighting demand, excessive demand in some junctions, pipe failure, or pump failure). These events may produce negative pressure problems to the network leading to deficient nodes. In the second category named Pressure Driven Analysis (PDA), researchers attempted to solve the negative pressure problem. Indeed, the PDA methods are treated into three different ways. (i) Modifying the hydraulic solver source code by introducing a new PDA method, or (ii) adding artificial elements like check valve, internal dummy node, flow control valve, reservoir or emitter to network demand nodes, or (iii) adding some of the previous explained artificial elements to demand nodes which are suffering from pressure deficiency. Many researchers try to take into consideration the extended period simulation (EPS) in the water network. Until now, there are many challenges facing researchers to come over the problem of deficient nodes. In this paper, a comparison between results (Demand & Pressure) of a case study when using different PDA methods.
State and fault estimation based on fuzzy observer for a class of Takagi-Suge...IJEECSIAES
Singular nonlinear systems have received wide attention in recent years, and can be found in various applications of engineering practice. On the basis of the Takagi-Sugeno (T-S) formalism, which represents a powerful tool allowing the study and the treatment of nonlinear systems, many control and diagnostic problems have been treated in the literature. In this work, we aim to present a new approach making it possible to estimate simultaneously both non-measurable states and unknown faults in the actuators and sensors for a class of continuous-time Takagi-Sugeno singular model (CTSSM). Firstly, the considered class of CTSSM is represented in the case of premise variables which are non-measurable, and is subjected to actuator and sensor faults. Secondly, the suggested observer is synthesized based on the decomposition approach. Next, the observer’s gain matrices are determined using the Lyapunov theory and the constraints are defined as linear matrix inequalities (LMIs). Finally, a numerical simulation on an application example is given to demonstrate the usefulness and the good performance of the proposed dynamic system.
State and fault estimation based on fuzzy observer for a class of Takagi-Suge...nooriasukmaningtyas
Singular nonlinear systems have received wide attention in recent years, and can be found in various applications of engineering practice. On the basis of the Takagi-Sugeno (T-S) formalism, which represents a powerful tool allowing the study and the treatment of nonlinear systems, many control and diagnostic problems have been treated in the literature. In this work, we aim to present a new approach making it possible to estimate simultaneously both non-measurable states and unknown faults in the actuators and sensors for a class of continuous-time Takagi-Sugeno singular model (CTSSM). Firstly, the considered class of CTSSM is represented in the case of premise variables which are non-measurable, and is subjected to actuator and sensor faults. Secondly, the suggested observer is synthesized based on the decomposition approach. Next, the observer’s gain matrices are determined using the Lyapunov theory and the constraints are defined as linear matrix inequalities (LMIs). Finally, a numerical simulation on an application example is given to demonstrate the usefulness and the good performance of the proposed dynamic system.
EFFICACY OF NEURAL NETWORK IN RAINFALL-RUNOFF MODELLING OF BAGMATI RIVER BASINIAEME Publication
In this paper, rainfall-runoff model of Bagmati river basin has been developed
using the ANN Technique. Three-layered fced forward network structure with back
propagation algorithm was used to train the ANN model. Different combinations of
rainfall and runoff were considered as input to the network and trained by BP
algorithm with different error tolerance, learning parameter, number of cycles and
number of hidden layers. The sensitivity of the prediction accuracy to the number of
hidden layer neurons in a back error propagation algorithm was used for the study.
The monthly rainfall and runoff data from 2000 to 2009 of Bagmati river basin has
been considered for the development of ANN model. Performance evaluation of the
model has been done by using statistical parameters. Three sets of data have been
used to make several combination of year keeping in view the highest peaks of
hydrographs. First set of data used was from 2000 to 2006 for the calibration and
from 2007 to 2009 for validation. The second set of data was from 2004 to 2009 for
calibration and from 2000 to 2003 for validation. The Third set of data was from 2000
to 2009 for calibration and from 2007 to 2009 for validation. It was found that the
third set of data gave better result than other two sets of data. The study demonstrates
the applicability of ANN approach in developing effective non-linear models of
Rainfall-Runoff process without the need to explicitly representing the internal
hydraulic structure of the watershed
Performance improvement of a Rainfall Prediction Model using Particle Swarm O...ijceronline
The performances of the statistical methods of time series forecast can be improved by precise selection of their parameters. Various techniques are being applied to improve the modeling accuracy of these models. Particle swarm optimization is one such technique which can be conveniently used to determine the model parameters accurately. This robust optimization technique has already been applied to improve the performance of artificial neural networks for time series prediction. This study uses particle swarm optimization technique to determine the parameters of an exponential autoregressive model for time series prediction. The model is applied for annual rainfall prediction and it shows a fairly good performance in comparison to the statistical ARIMA model
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.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
2. Condition Prediction Models of Deteriorated Trunk Sewer Using Multinomial Logistic Regression and
Artificial Neural Network
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1. INTRODUCTION
Sewer networks are subsurface infrastructure systems, in which sewers collect sewage and/or
storm water to sewage treatment plants or other places for disposal. Buried sewer pipes
deteriorate with time due to several deterioration factors (e.g. environmental and operational
factors) leading to structural deterioration (e.g. breakage or deformation of pipes) and
hydraulic deterioration (e.g. blockages or tree intrusion that reduces pipe’s hydraulic
efficiency) (Micevski et al., 2002). Thus, deterioration causes cities to be susceptible to its
effect of collapse and flooding due to huge length of the sewers making it more difficult to
monitor them. Alternatively, sewers future condition can be predicted using deterioration
models. Then, the predicted information is used by utilities to make optimal decisions on
repairing, overhauling or replacing pipes in poor condition (Tran, 2007).
Many researchers wrote in various fields related to the issue of this research, as Salman
(2010) applied several deterioration models (binary logistic regression, ordinal regression and
multinomial logistic regression) on inspection data of Cincinnati city (USA). The binary
logistic regression analysis showed the best performance in predicting sewer deterioration; the
total model efficiency was 66%. Prediction efficiency for good condition was 78% and for
bad condition 46%.
Khan et al., (2010) and Kadhim Naief Kadhim, 2018 developed deterioration models
using data from Pierrefonds, Canada. They used neural network modeling with back
propagation (BPNN) and probabilistic (PNN) approaches. Taking about 20% of the available
dataset to test the model, the coefficient of determination (R2
) ranged within 71 and 86 %
depending on the deterioration factors considered.
The aim of this study is the developing of two deteriorations model's multinomial logistic
regression and NNDM using available data. Then, the developed models are used to identify
the most important factors that influence the deterioration of sewers.
2. MATERIAL AND METHOD
2.1. Data Acquisition
The effectiveness of condition prediction models depends upon the quality and quantity of the
collected data and selection of predictors. Zublin trunk sewer with data from Al-Rusafa side
in Baghdad, Iraq was used as a case study to illustrate the applicability of the developed
models. For this study, data are collected from site investigation, information from different
departments of Baghdad Mayoralty (design, implementation, planning, operation and
maintenance and geographic information systems) and also from questionnaire distributed on
different sections in the different municipalities of Al-Rusafa that Zublin sewer serves them.
The data included: sewer condition, age, diameter, depth, length, slope, traffic intensity and
material. A condition rating system with five classes, from 1 (excellent) to4 (poor) and 5 (very
poor) was used to describe sewers conditions. There were no sewers in condition 1 (excellent)
and few sewers in condition 2 (very good) that have been combined with sewers in condition
3 (good). The dataset available for this trunk sewer contained 97 records corresponding to
individual manhole-to-manhole sewer length. Out of the 97 useful sewer data, 77 were set
aside for calibration and 20 were for validation (These data numbers were chosen after several
attempts through the SPSS programs to obtain the best results with high coefficient of
correlation). The statistical analysis software SPSS was selected to perform calibrations for
the selected deterioration models. A part of the calibration sample used in developing these
models is shown in Table 1.
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Table 1. Dataset portion for the calibration of the deterioration models
NAME M.H CONDITION
AGE
(YEAR)
DIAMETER
(M)
LENGTH
(M)
DEPTH
(M)
SLOPE
(M/M)
TRAFFIC MATERIAL
TH 49A 4 35 1.8 19.09 3.84 0.0005 3 2
TH 50 3 28 1.8 43.19 3.96 0.0007 3 2
TH 34 4 36 2.4 165.57 5.81 0.0005 1 1
TH 35 5 37 2.4 100.36 5.82 0.0005 1 1
NT 39 4 34 3.0 193.35 7.16 0.0005 2 1
NT 40 3 33 3.0 197.23 6.99 0.0006 1 1
Note: Traffic 1 = low, 2 = medium, 3 = high, Material 1 = concrete, 2 = PVC, M.H:
Manhole, TH: Al-Thawra Trunk, NT:North Trunk
2.2. Sewer Deterioration Models
2.2.1. Multinomial logistic regression
Multinomial logistic regression is used to model categorical dependent variable with more
than two categories. As there are m categories of the dependent variable, one of them is
selected as the reference category. Then, (m – 1) log its are generated using the remaining (m
– 1) categories as in Eq.1 (Salman, 2010):
( ( ))
( ( ))
(1)
Where: j = 1, 2, …, m – 1 is the dependent variable categories; αj is the intercept for
category j; x1, x2, …, xn are independent variables and βj1, βj2, … , βjn are the regression
coefficients that correspond to n-number of independent variables defined for each dependent
category j.
2.2.1.1. Model calibration
Maximum Likelihood Estimation method is used to estimate the parameters αj and βjnin
multinomial logistic regression due to this method handles (m – 1) equations simultaneously
and determines the parameters that maximize the likelihood function (Agresti, 2002). For ith
observation with independent variables = ( , , …., ), the Log Likelihood function is
as follows:
[∏ ( ) ] ∑ ( ) ( ∑ ) ⌈ ∑ ( )⌉(2)
Where: for i = 1, 2, …, p; = ( , , …, ) = multinomial trial for subject i; = 1
if the response is in category j, and otherwise 0. Eq. 2 may be shortly written, if all
observations are considered as follows (Agresti, 2002):
∏ [∏ ( ) ] (3)
2.2.1.2. Model significance
To evaluate this model significance, a comparison is made between the values of log
likelihood of the base model (defined by eq. 4) and final model (defined as a model that
approved after different statistical significant using chi-square test). After multiplying these
log likelihood values by -2, a chi-square distribution is used to evaluate the statistical
significance of the difference between these values. The critical chi-square value is compared
with the test value with DOF (i.e. degree of freedom) equals to the model estimated number
of the coefficients of regression. The log likelihood equation of the base model is as follows
(Menard, 2002):
4. Condition Prediction Models of Deteriorated Trunk Sewer Using Multinomial Logistic Regression and
Artificial Neural Network
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( ) [ ( ) ( ) ( )](4)
Where, n1, n2, …, nm is the observations number in each category level and p1, p2, …, pm
is the observations proportion corresponding to the respective category.
2.2.1.3. Significance of regression coefficients
The likelihood ratio method can be used to determine the variables significance. In this
method, the difference in the values of -2 Log likelihood between a final model and a reduced
model (is formed by omitting the variable of interest from the final model) should be
calculated (Menard, 2002). The critical chi-square value is compared with the resultant value
with DOF equals to the regression coefficients number that are omitted from the model. The
DOF would be equal to m – 1, if the omitted variable is categorical of values equal to m.
2.2.1.4. Assumptions of multinomial logistic regression
If an ordinal relationship has been found between the dependent variable categories, this
model cannot reflect the dependent variable ordered nature. When using an ordinal regression
model, independent variables coefficients are required to be constant for each dependent
variable level, which represents the proportional odds assumption (McCullagh, 1980).This
assumption is restrictive in this model application compared to the multinomial logistic
regression that provides more flexibility to the independent variables coefficients.
2.2.2. NNDM
Neural networks can be used to predict outcome data from input data in a manner that
simulates the operation of the human nervous system. Unlike statistical models, NNs have no
assumptions related with the model structure because it is determined by data (Tran, 2007).
Generally, the model can simulate nonlinear relationships within the deterioration process and
can handle ordinal outputs such as condition classes.
2.2.2.1. Model structure
Generally, a neural network consists of several layers containing artificial neurons connected
together (Al-Barqawi and Zayed, 2008) as shown in Fig. 1. The connection weights, which
attach the connections between the neurons are calculated using the observed data when the
difference (i.e. error) between the values of the actual output and predicted output is small
(Salman, 2010). The NNs have always a special input signal values equal to 1, with a bias
weight. They are not shown in Fig. 1 in order to reduce the complexity. The function of bias
weight is to allow or stop the input signals going through by (being non-zero) or (being zero)
value respectively. The general form of a neural network function is as follows:
(∑ ) (5)
Where, Y the output signal, Xi the input signal, K the number of input signals, Wi the
connection weights, f the activation function.
5. Dr. Basim Hussein Khudair, Dr Ghassan Khalaf Khalid and Rehab Karim Jbbar
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Figure 1 Structure of the NNs (Al-Barqawi and Zayed, 2008)
2.2.2.2. Activation functions
The values of units in the succeeding layer are linked to the weighted sums of units in a
preceding layer by the activation function. The hyperbolic tangent function was used for the
neurons of hidden layer and the soft max function was used for the neurons of output layer in
this study, since using automatic architecture and the output is categorical (IBM® SPSS®
Statistics 20 User Guide).
3. RESULTS AND DISCUSSION
3.1. Development of Multinomial Logistic Regression
3.1.1. Model Parameters
The generated equations of multinomial logistic regression are two, since the outcome
variable has three possible states (the procedure of predicting three outcome variables from
two equations is written in section 3.1.4). Mathematically, multinomial logistic regression
equations can be written as below:
(
( )
( )
)
(6)
Where: CR= Condition Rating, j = 3 and 4 indicates the condition level, and j=5 was
selected as reference category; αj and βj1, βj2,…, βj8 are the intercept and regression
coefficients for condition level j respectively, Age, Diameter, Length, Depth and Slope are the
numerical independent variables and the different values of the categorical variables are
defined by dummy variables Zi (Ariaratnam et al., 2001), for which, a value of either 1 or 0
are assigned as follows:
, if traffic intensity = 1 (low), otherwise 0
, if traffic intensity = 2 (medium), otherwise 0
, if material type = 1 (concrete), otherwise 0
Table 2 provides the intercept term and regression coefficients for the first and second
equations. In addition, this table gives Exp ( ), which explains how much the odds of Y
change for a unit increase of an independent variable.
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Artificial Neural Network
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Table 2. Parameter estimates of multinomial logistic regression for condition ratings 3 and 4.
Conditiona
Std. Error Wald df Sig. Exp( )
95% Confidence Interval for
Exp( )
Lower Bound Upper Bound
3.0 Intercept 95.890 28.769 11.109 1 .001
age -3.074- .868 12.538 1 .000 .046 .008 .253
diameter(m) 3.314 5.737 .334 1 .563 27.504 .000 2101282.970
length .014 .031 .213 1 .644 1.014 .955 1.078
depth 1.672 1.626 1.058 1 .304 5.323 .220 128.891
slope -519.433- 2186.123 .056 1 .812 2.588E-226 .000 .b
[traffic=1] -10.570- 3.806 7.714 1 .005 2.566E-5 1.478E-8 .045
[traffic=2] -6.585- 4.542 2.102 1 .147 .001 1.881E-7 10.143
[traffic=3] 0c
. . 0 . . . .
[material=1] 2.794 7.709 .131 1 .717 16.342 4.480E-6 59604937.147
[material=2] 0c
. . 0 . . . .
4.0 Intercept 67.426 24.661 7.475 1 .006
age -2.042- .755 7.323 1 .007 .130 .030 .570
diameter(m) 4.795 5.014 .914 1 .339 120.863 .007 2241546.931
length .031 .027 1.308 1 .253 1.032 .978 1.089
depth -.450- 1.389 .105 1 .746 .637 .042 9.706
slope 1371.297 1364.346 1.010 1 .315 .b
.000 .b
[traffic=1] -2.685- 2.586 1.078 1 .299 .068 .000 10.836
[traffic=2] 1.717 3.020 .323 1 .570 5.569 .015 2072.682
[traffic=3] 0c
. . 0 . . . .
[material=1] -4.596- 4.558 1.017 1 .313 .010 1.331E-6 76.488
[material=2] 0c
. . 0 . . . .
a. The reference category is: 5.0.
b. Floating point overflow occurred while computing this statistic. Its value is therefore set to system missing.
c. This parameter is set to zero because it is redundant.
3.1.2. Significance of the Model
The model significance was evaluated using the likelihood ratio of the final to the intercept
only models. Table 3 provides the model significance and values of likelihood ratio. Based on
the results, the– 2 log likelihood values difference between the final and intercept only models
is 130.808 that corresponds to a significant result as p-value was 0.00 which is less than
significance level of 0.05.
Table 3 Significance test results for multinomial logistic regression analysis.
Model
Model Fitting
Criteria
Likelihood Ratio Tests
-2 Log Likelihood Chi-Square df Sig.
Intercept Only 168.763
Final 37.955 130.808 16 .000
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3.1.3. Significance of Model Parameters
Table 4 shows the significance of each model parameter and differences between -2 Log
Likelihood values. Based on the results, excluding diameter variable from the overall model
results in the lowest difference in -2 log likelihood value (Chi-Square that is 1.096); while, the
exclusion of age from the model corresponds the highest difference in the -2 log likelihood
value (i.e. 90.921). According to the significance values shown in Table 4, the parameters of
age, traffic and depth are significant as they have p-values less than 0.05.
Table 4 Significance test results of multinomial logistic regression for independent variables.
Effect
Model Fitting Criteria Likelihood Ratio Tests
-2 Log Likelihood of
Reduced Model
Chi-Square df Sig.
Intercept 37.955 .000 0 .
Age 128.876 90.921 2 .000
diameter(m) 39.051 1.096 2 .578
Length 40.407 2.452 2 .293
Depth 44.654 6.699 2 .035
Slope 40.576 2.621 2 .270
traffic 65.562 27.607 4 .000
material 41.787 3.832 2 .147
3.1.4. Prediction Rate
The prediction procedure involves two steps. The first step involves entering the parameter
estimates and independent variables into the equations of the multinomial logistic regression
and computing odds ratios. Second step includes using the values of the odds ratio to compute
the probabilities associated with each class. By inserting the predicted parameters of the
model, the equations of multinomial logistic regression can be rewritten as follows (Salman,
2010):
(
( )
( )
)
(7)
(
( )
( )
)
(8)
G1 and G2 are the odds ratios and once they are determined, the following equations are
used to calculate the probabilities associated with each condition level:
( )
( )
[ ( ) ( )]
( )
( )
[ ( ) ( )]
(9)
8. Condition Prediction Models of Deteriorated Trunk Sewer Using Multinomial Logistic Regression and
Artificial Neural Network
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( )
[ ( ) ( )]
To make a classification, the observed values of the predictors are inserted into the Eq. 7
and Eq. 8. Then, classification probabilities are calculated from Eq. 9. The observation is
assigned to the class with the highest classification probability.
3.2. Development of NNDM
In this model, approximately 61% of the data were assigned for training, 19.5% for testing
and 19.5% to a holdout sample as this configuration gave high overall prediction efficiency.
Furthermore, values of all the scale input factors are rescaled using normalized method
according to Eq. 10 to improve network training (IBM® SPSS® Statistics 20 User Guide).
(10)
3.2.1. Training of NNDM
NNDM training in this study is used to calculate the model structure (i.e. the network weights
and the hidden neurons numbers). Three neurons in the hidden layer have been chosen by
automatic architecture selection.
3.2.2. Sample prediction
The model architecture is listed in Table 5, where the condition of a sewer with a particular
characteristic can then be predicted.
The non-linear relationship between the input and output data can be written as follows:
∑ (11)
( ) (12)
( )
∑
( ) (13)
Where: n the number of the predictors, bias weight, the output of the hidden
neurons, the output of the output neuron, Hk is the input for .
To make a classification, the observed values of the predictors are inserted into the
equations above to calculate probabilities, which are values between 0-1. The observation is
assigned to the class with the highest probability.
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Table 5. Estimation of hidden and output parameters
Predictor
Predicted
Hidden Layer 1 Output Layer
H(1:1) H(1:2) H(1:3)
[Condition=3
.0]
[Condition=4
.0]
[Condition=5
.0]
Input
Layer
(Bias) .207 .499 -1.453-
[traffic=1] -1.196- .067 -.207-
[traffic=2] 1.021 .092 -.739-
[traffic=3] -.432- -1.219- -.620-
[material=1] -.536- -.768- -1.396-
[material=2] 1.287 -.089- -.985-
Age -.447- 3.834 5.020
Diameter m -.815- -.607- -.208-
Length -.275- -.444- -.554-
Depth -.758- -.507- .064
Slope .465 .931 -.325-
Hidden
Layer 1
(Bias) -1.196- .844 .273
H(1:1) .206 1.456 -1.949-
H(1:2) -3.780- 1.746 1.411
H(1:3) -2.760- -1.088- 3.987
3.2.3. Independent Variable Importance
The importance of each independent variable in determining the neural network is computed
based on both training and testing samples (IBM® SPSS® Statistics 20 User Guide). It
appears from Fig. 2 that the variable age has the greatest effect on how the network classifies
sewers followed by traffic, material, slope, length, diameter and depth respectively. To
determine the variation in the model-predicted value for various independent variable values,
the importance of the independent variable is used. In addition, when dividing the importance
values on the largest importance values, normalized importance is obtained, which is
expressed as percentages.
Figure 2 Independent variable importance chart
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Artificial Neural Network
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3.3. Model Performance Evaluation
For evaluating model performance, the model error (i.e. the difference between predicted and
observed values) must be quantified (Wright et al., 2006). With high model error, the
performance of the model is low. The confusion matrix is often used for ordinal and
categorical outputs. The validation dataset should be used to effectively test the model (Baik
et al., 2006). When comparing the observed values with model prediction, four possible
situations can be observed: (1) true positive (TP) when the model correctly predicts the sewer
in good condition, (2) true negative (TN) when the model correctly predicts the sewer in poor
condition, (3) false positive (FP) when the model incorrectly predicts the sewer in good class
as being in bad class, and (4) false negative (FN) when the model incorrectly predicts the
sewer in poor condition as being in good condition as shown in Table 6 (Tran, 2007).
In Table 6, the TP11 means the pipes number which are observed and correctly predicted
in condition 1. In addition, O1, O2 and O3 represent the total pipes numbers that are
Titiladunayo, I. F, Akinnuli, B.O, Ibikunle, R. A, Agboola, O.O, Ogunsemi, B.T
Titiladunayo, I. F, Akinnuli, B.O, Ibikunle, R. A, Agboola, O.O, Ogunsemi, B.T C. O.
Osueke, T. M. A. Olayanju, C. A. Ezugwu, A. O. Onokwai, I. Ikpotokin, D. C. Uguru-Okorie
nd F.C. Nnaji observed in condition 1, 2 and 3 respectively and P1, P2 and P3 represent the
total pipes numbers which are predicted in condition 1, 2 and 3 respectively.
Table 6 Confusion matrix (Tran, 2007)
Predicted condition
Total1
(good)
2
(fair)
3
(poor)
Observed
condition
1 (good) TP11 FP12 FP13 O1
2 (fair) FP21 TP22 FP23 O2
3 (poor) FN31 FN32 TN33 O3
Total P1 P2 P3
The overall predicted efficiency (OPE) was used to evaluate the performance prediction of
multinomial logistic regression and NNDM Salman (2010), which were developed in this
research to predict sewers future conditions. From the confusion matrix, the OPE can be
computed using Eq. 14.
(14)
Tables 7 and 8are the confusion matrices for multinomial logistic regression and NNDM.
According to Table7, the overall predicted efficiency of multinomial regression model was
90.9% for the calibration sample and for all condition classes (3, 4 and 5), prediction
percentages were satisfactory. While, for the validation sample, the overall percentage of
correct estimations was 55%, which is lower than the calibration sample. For condition
classes 3 and 5, the overall predicted efficiencies were satisfactory, but the prediction rate
remained low for condition class 4. A higher threat to the model validity is caused by the low
prediction rate for condition class 4, which confirms the finding of Salman (2010).
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Table 7. Prediction efficiencies for the calibration and validation of the multinomial logistic
regression
Condition Predicted Group Membership Percent
Correct3 4 5
(a) Calibration Count 3 23 4 0 85.2%
4 2 25 0 92.6%
5 1 0 22 95.7%
(b) Validation Count 3 5 2 0 71.4%
4 6 1 0 14.3%
5 0 1 5 83.3%
(a) 90.9% of original grouped cases are correctly classified.
(b) 55% of original grouped cases are correctly classified.
Table 8 shows that the deterioration model based on the NNDM provides the highest
overall prediction efficiency in the calibration and validation samples as compared with the
multinomial logistic regression. The high overall prediction efficiency by the NNDM could
be attributed to its inherent ability to model complex processes. The NNDM was used by Tran
(2007), which gave the highest prediction efficiency of 82%.
Table 8. Prediction efficiencies for the calibration and validation of the NNDM
Condition
Predicted Group Membership Percent
Correct3 4 5
(a) Training Count 3 13 0 1 92.9%
4 0 16 1 94.1%
5 1 0 15 93.8%
(b) Testing Count 3 6 1 1 75.0%
4 0 4 0 100.0%
5 0 0 3 100.0%
(c) Holdout Count 3 4 1 0 80.0%
4 1 5 0 83.3%
5 0 1 3 75.0%
(d) Validation Count 3 6 1 0 85.7%
4 5 2 0 28.6%
5 0 0 6 100%
(a) Prediction efficiency is 93.6%
(b) Prediction efficiency is 86.7%
(c) Prediction efficiency is 80.0%
(d) Prediction efficiency is 70.0%
4. CONCLUSIONS AND RECOMMENDATIONS
In order to achieve the research objectives (sewers future condition prediction to make
optimal decisions on repairing, overhauling or replacing pipes in poor condition), two
deteriorations models' multinomial logistic regression and NNDM were developed, tested and
evaluated using the available data for Zublin trunk sewer for predicting the sewer's future
conditions. This type of information is extremely important in projecting budgetary
requirements for sewer system maintenance and rehabilitation, and the success of proactive
pipe maintenance strategies.
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The confusion matrix test showed that multinomial logistic regression was inconsistent
with the data and the error in prediction of this model was due to its inability to reflect the
dependent variable ordered nature. While, NNDM was found to have high overall prediction
efficiency, which could be attributed to its inherent ability to model complex processes. In
addition, based on the application of these two models, pipe age was found to be highly
significant in the deterioration of the Zublin trunk sewer.
In addition, other factors like type of soil backfill, presence of H2S and groundwater level
should be investigated and gathered to understand further the sewer deterioration mechanism
and, consequently, to develop a more effective model.
REFERENCES
[1] Agresti, A., 2002. Categorical data analysis, 2nd Ed., Wiley, Hoboken, New Jersey.
[2] Al-Barqawi, H. and Zayed, T., 2008. Infrastructure Management: Integrated AHP/ANN
Model to Evaluate Municipal Water Mains’ Performance, J. Infrastructure Systems, 14
(4): 305-318.
[3] Ariaratnam, S.T., El-Assaly, A. & Tang, Y., 2001. Assessment of infrastructure inspection
needs using logistic models. Journal of Infrastructure Systems, 7(4), 160-165.
[4] Baik, H. S., Jeong, H. S. and Abraham, D. M., 2006. Estimating Transition Probabilities
in Markov Chain-Based Deterioration Models for Management of Wastewater Systems,
Journal of Water Resources Planning and Management, ASCE, 132 (1): 15-24.
[5] IBM® SPSS® Statistics 20 User Guide.
[6] Kadhim Naief Kadhim (Estimating of Consumptive Use of Water in Babylon
Governorate-Iraq by Using Different Methods). (IJCIET), Volume 9, Issue 2, (Feb 2018)
[7] Khan, Z., Zayed, T. and Moselhi, O., 2010. Structural Condition Assessment of Sewer
Pipelines. Journal of Performance of Constructed Facilities 24: 170-179.
[8] McCullagh, P., 1980. Regression models for ordinal data. Journal of the Royal Statistics
Society, Series B (Methodological), 42(2), 109 – 142.
[9] Menard, S., 2002. Applied logistic regression analysis. Sage University Papers Series
onQuantitative Applications in the Social Sciences, 07-106, Thousand Oaks, CA.
[10] Micevski, T., Kuszera, G. and Coombes, P., 2002. Markov model for storm water pipe
deterioration. Journal of Infrastructure Systems 8(2): 49-56.
[11] Salman, B., 2010. Infrastructure Management and Deterioration Risk Assessment of
Wastewater Collection Systems, Ph.D. thesis, University of Cincinnati. Ohio.
[12] Tran, D.H., 2007. Investigation of deterioration models for stormwater pipe systems.
Doctoral dissertation, School of Architectural, Civil and Mechanical Engineering, Faculty
of Health, Engineering and Science, Victoria University, Australia.
[13] Wright, L.T., Heaney, J.P. and Dent, S., 2006. Prioritizing Sanitary Sewers for
Rehabilitation Using Least-Cost Classifiers. Journal of Infrastructure Systems, ASCE, 12
(3): 174-183.