The document proposes a Hybrid Bee Colony based Neural Network (HBCNN) technique for performance enhancement in machine learning systems. The HBCNN technique has three phases: 1) data preprocessing, 2) training an artificial neural network using a bee colony algorithm, and 3) dynamic testing of the trained neural network. The proposed technique is tested on a cancer dataset and its performance is compared to a K-nearest neighbors technique. The HBCNN technique aims to address issues with existing approaches like reduced prediction accuracy and high computational costs.
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...ijaia
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
REVIEWING PROCESS MINING APPLICATIONS AND TECHNIQUES IN EDUCATIONijaia
Process Mining (PM) emerged from business process management but has recently been applied to
educational data and has been found to facilitate the understanding of the educational process.
Educational Process Mining (EPM) bridges the gap between process analysis and data analysis, based on
the techniques of model discovery, conformance checking and extension of existing process models. We
present a systematic review of the recent and current status of research in the EPM domain, focusing on
application domains, techniques, tools and models, to highlight the use of EPM in comprehending and
improving educational processes.
Correlation of artificial neural network classification and nfrs attribute fi...eSAT Journals
Abstract
Mostly 5 to 15% of the women in the stage of reproduction face the disease called Polycystic Ovarian Syndrome (PCOS) which is the multifaceted, heterogeneous and complex. The long term consequences diseases like endometrial hyperplasia, type 2 diabetes mellitus and coronary disease are caused by the polycystic ovaries, chronic anovulation and hyperandrogenism are characterized with the resistance of insulin and the hypertension, abdominal obesity and dyslipidemia and hyperinsulinemia are called as Metabolic syndrome (frequent metabolic traits) The above cause the common disease called Anovulatory infertility. Computer based information along with advanced Data mining techniques are used for appropriate results. Classification is a classic data mining task, with roots in machine learning. Naïve Bayesian, Artificial Neural Network, Decision Tree, Support Vector Machines are the classification tasks in the data mining. Feature selection methods involve generation of the subset, evaluation of each subset, criteria for stopping the search and validation procedures. The characteristics of the search method used are important with respect to the time efficiency of the feature selection methods. PCA (Principle Component Analysis), Information gain Subset Evaluation, Fuzzy rough set evaluation, Correlation based Feature Selection (CFS) are some of the feature selection techniques, greedy first search, ranker etc are the search algorithms that are used in the feature selection. In this paper, a new algorithm which is based on Fuzzy neural subset evaluation and artificial neural network is proposed which reduces the task of classification and feature selection separately. This algorithm combines the neural fuzzy rough subset evaluation and artificial neural network together for the better performance than doing the tasks separately.
Keywords: ANN, SVM, PCA, CFS
DATA AUGMENTATION TECHNIQUES AND TRANSFER LEARNING APPROACHES APPLIED TO FACI...ijaia
The face expression is the first thing we pay attention to when we want to understand a person’s state of
mind. Thus, the ability to recognize facial expressions in an automatic way is a very interesting research
field. In this paper, because the small size of available training datasets, we propose a novel data
augmentation technique that improves the performances in the recognition task. We apply geometrical
transformations and build from scratch GAN models able to generate new synthetic images for each
emotion type. Thus, on the augmented datasets we fine tune pretrained convolutional neural networks with
different architectures. To measure the generalization ability of the models, we apply extra-database
protocol approach, namely we train models on the augmented versions of training dataset and test them on
two different databases. The combination of these techniques allows to reach average accuracy values of
the order of 85% for the InceptionResNetV2 model.
MOVIE SUCCESS PREDICTION AND PERFORMANCE COMPARISON USING VARIOUS STATISTICAL...ijaia
Movies are among the most prominent contributors to the global entertainment industry today, and they
are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide
films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety
of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic
Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial
Neural Network. The models stated above were compared on a variety of factors, including their accuracy
on the training and validation datasets as well as the testing dataset, the availability of new movie
characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered
that certain characteristics have a greater impact on the likelihood of a film's success than others. For
example, the existence of the genre action may have a significant impact on the forecasts, although another
genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the
IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best
performing model of all the models discussed.
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...ijaia
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
REVIEWING PROCESS MINING APPLICATIONS AND TECHNIQUES IN EDUCATIONijaia
Process Mining (PM) emerged from business process management but has recently been applied to
educational data and has been found to facilitate the understanding of the educational process.
Educational Process Mining (EPM) bridges the gap between process analysis and data analysis, based on
the techniques of model discovery, conformance checking and extension of existing process models. We
present a systematic review of the recent and current status of research in the EPM domain, focusing on
application domains, techniques, tools and models, to highlight the use of EPM in comprehending and
improving educational processes.
Correlation of artificial neural network classification and nfrs attribute fi...eSAT Journals
Abstract
Mostly 5 to 15% of the women in the stage of reproduction face the disease called Polycystic Ovarian Syndrome (PCOS) which is the multifaceted, heterogeneous and complex. The long term consequences diseases like endometrial hyperplasia, type 2 diabetes mellitus and coronary disease are caused by the polycystic ovaries, chronic anovulation and hyperandrogenism are characterized with the resistance of insulin and the hypertension, abdominal obesity and dyslipidemia and hyperinsulinemia are called as Metabolic syndrome (frequent metabolic traits) The above cause the common disease called Anovulatory infertility. Computer based information along with advanced Data mining techniques are used for appropriate results. Classification is a classic data mining task, with roots in machine learning. Naïve Bayesian, Artificial Neural Network, Decision Tree, Support Vector Machines are the classification tasks in the data mining. Feature selection methods involve generation of the subset, evaluation of each subset, criteria for stopping the search and validation procedures. The characteristics of the search method used are important with respect to the time efficiency of the feature selection methods. PCA (Principle Component Analysis), Information gain Subset Evaluation, Fuzzy rough set evaluation, Correlation based Feature Selection (CFS) are some of the feature selection techniques, greedy first search, ranker etc are the search algorithms that are used in the feature selection. In this paper, a new algorithm which is based on Fuzzy neural subset evaluation and artificial neural network is proposed which reduces the task of classification and feature selection separately. This algorithm combines the neural fuzzy rough subset evaluation and artificial neural network together for the better performance than doing the tasks separately.
Keywords: ANN, SVM, PCA, CFS
DATA AUGMENTATION TECHNIQUES AND TRANSFER LEARNING APPROACHES APPLIED TO FACI...ijaia
The face expression is the first thing we pay attention to when we want to understand a person’s state of
mind. Thus, the ability to recognize facial expressions in an automatic way is a very interesting research
field. In this paper, because the small size of available training datasets, we propose a novel data
augmentation technique that improves the performances in the recognition task. We apply geometrical
transformations and build from scratch GAN models able to generate new synthetic images for each
emotion type. Thus, on the augmented datasets we fine tune pretrained convolutional neural networks with
different architectures. To measure the generalization ability of the models, we apply extra-database
protocol approach, namely we train models on the augmented versions of training dataset and test them on
two different databases. The combination of these techniques allows to reach average accuracy values of
the order of 85% for the InceptionResNetV2 model.
MOVIE SUCCESS PREDICTION AND PERFORMANCE COMPARISON USING VARIOUS STATISTICAL...ijaia
Movies are among the most prominent contributors to the global entertainment industry today, and they
are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide
films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety
of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic
Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial
Neural Network. The models stated above were compared on a variety of factors, including their accuracy
on the training and validation datasets as well as the testing dataset, the availability of new movie
characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered
that certain characteristics have a greater impact on the likelihood of a film's success than others. For
example, the existence of the genre action may have a significant impact on the forecasts, although another
genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the
IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best
performing model of all the models discussed.
DEEP-LEARNING-BASED HUMAN INTENTION PREDICTION WITH DATA AUGMENTATIONijaia
Data augmentation has been broadly applied in training deep-learning models to increase the diversity of
data. This study ingestigates the effectiveness of different data augmentation methods for deep-learningbased human intention prediction when only limited training data is available. A human participant pitches
a ball to nine potential targets in our experiment. We expect to predict which target the participant pitches
the ball to. Firstly, the effectiveness of 10 data augmentation groups is evaluated on a single-participant
data set using RGB images. Secondly, the best data augmentation method (i.e., random cropping) on the
single-participant data set is further evaluated on a multi-participant data set to assess its generalization
ability. Finally, the effectiveness of random cropping on fusion data of RGB images and optical flow is
evaluated on both single- and multi-participant data sets. Experiment results show that: 1) Data
augmentation methods that crop or deform images can improve the prediction performance; 2) Random
cropping can be generalized to the multi-participant data set (prediction accuracy is improved from 50%
to 57.4%); and 3) Random cropping with fusion data of RGB images and optical flow can further improve
the prediction accuracy from 57.4% to 63.9% on the multi-participant data set.
MITIGATION TECHNIQUES TO OVERCOME DATA HARM IN MODEL BUILDING FOR MLijaia
Given the impact of Machine Learning (ML) on individuals and the society, understanding how harm might
be occur throughout the ML life cycle becomes critical more than ever. By offering a framework to
determine distinct potential sources of downstream harm in ML pipeline, the paper demonstrates the
importance of choices throughout distinct phases of data collection, development, and deployment that
extend far beyond just model training. Relevant mitigation techniques are also suggested for being used
instead of merely relying on generic notions of what counts as fairness.
PREDICTION OF MALIGNANCY IN SUSPECTED THYROID TUMOUR PATIENTS BY THREE DIFFER...cscpconf
In the present study, the abilities of three classification methods of data mining namely artificial
neural networks with feed-forward back propagation algorithm, J48 decision tree method and
logistic regression analysis are compared in a medical real dataset. The prediction of
malignancy in suspected thyroid tumour patients is the objective of the study. The accuracy of
the correct predictions (the minimum error rate), the amount of time consuming in the
modelling process and the interpretability and simplicity of the results for clinical experts are
the factors considered to choose the best method
Software Bug Detection Algorithm using Data mining TechniquesAM Publications
The main aim of software development is to develop high quality software and high quality software is
developed using enormous amount of software engineering data. The software engineering data can be used to gain
empirically based understanding of software development. The meaning full information can be extracted using
various data mining techniques. As Data Mining for Secure Software Engineering improves software productivity and
quality, software engineers are increasingly applying data mining algorithms to various software engineering tasks.
However mining software engineering data poses several challenges, requiring various algorithms to effectively mine
sequences, graphs and text from such data. Software engineering data includes code bases, execution traces,
historical code changes, mailing lists and bug data bases. They contains a wealth of information about a projectsstatus,
progress and evolution. Using well established data mining techniques, practitioners and researchers can
explore the potential of this valuable data in order to better manage their projects and do produce higher-quality
software systems that are delivered on time and within budget
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
Performance analysis of binary and multiclass models using azure machine lear...IJECEIAES
Network data is expanding and that too at an alarming rate. Besides, the sophisticated attack tools used by hackers lead to capricious cyber threat landscape. Traditional models proposed in the field of network intrusion detection using machine learning algorithms emphasize more on improving attack detection rate and reducing false alarms but time efficiency is often overlooked. Therefore, in order to address this limitation, a modern solution has been presented using Machine Learning-as-a-Service platform. The proposed work analyses the performance of eight two-class and three multiclass algorithms using UNSW NB-15, a modern intrusion detection dataset. 82,332 testing samples were considered to evaluate the performance of algorithms. The proposed two class decision forest model exhibited 99.2% accuracy and took 6 seconds to learn 1,75,341 network instances. Multiclass classification task was also undertaken wherein attack types like generic, exploits, shellcode and worms were classified with a recall percentage of 99%, 94.49%, 91.79% and 90.9% respectively by the multiclass decision forest model that also leapfrogged others in terms of training and execution time.
Identification of important features and data mining classification technique...IJECEIAES
Employees absenteeism at the work costs organizations billions a year. Prediction of employees’ absenteeism and the reasons behind their absence help organizations in reducing expenses and increasing productivity. Data mining turns the vast volume of human resources data into information that can help in decision-making and prediction. Although the selection of features is a critical step in data mining to enhance the efficiency of the final prediction, it is not yet known which method of feature selection is better. Therefore, this paper aims to compare the performance of three well-known feature selection methods in absenteeism prediction, which are relief-based feature selection, correlation-based feature selection and information-gain feature selection. In addition, this paper aims to find the best combination of feature selection method and data mining technique in enhancing the absenteeism prediction accuracy. Seven classification techniques were used as the prediction model. Additionally, cross-validation approach was utilized to assess the applied prediction models to have more realistic and reliable results. The used dataset was built at a courier company in Brazil with records of absenteeism at work. Regarding experimental results, correlationbased feature selection surpasses the other methods through the performance measurements. Furthermore, bagging classifier was the best-performing data mining technique when features were selected using correlation-based feature selection with an accuracy rate of (92%).
Biometric Identification and Authentication Providence using Fingerprint for ...IJECEIAES
The raise in the recent security incidents of cloud computing and its challenges is to secure the data. To solve this problem, the integration of mobile with cloud computing, Mobile biometric authentication in cloud computing is presented in this paper. To enhance the security, the biometric authentication is being used, since the Mobile cloud computing is popular among the mobile user. This paper examines how the mobile cloud computing (MCC) is used in security issue with finger biometric authentication model. Through this fingerprint biometric, the secret code is generated by entropy value. This enables the person to request for accessing the data in the desk computer. When the person requests the access to the authorized user through Bluetooth in mobile, the Authorized user sends the permit access through fingerprint secret code. Finally this fingerprint is verified with the database in the Desk computer. If it is matched, then the computer can be accessed by the requested person.
Feature Selection : A Novel Approach for the Prediction of Learning Disabilit...csandit
Feature selection is a problem closely related to dimensionality reduction. A commonly used
approach in feature selection is ranking the individual features according to some criteria and
then search for an optimal feature subset based on an evaluation criterion to test the optimality.
The objective of this work is to predict more accurately the presence of Learning Disability
(LD) in school-aged children with reduced number of symptoms. For this purpose, a novel
hybrid feature selection approach is proposed by integrating a popular Rough Set based feature
ranking process with a modified backward feature elimination algorithm. The approach follows
a ranking of the symptoms of LD according to their importance in the data domain. Each
symptoms significance or priority values reflect its relative importance to predict LD among the
various cases. Then by eliminating least significant features one by one and evaluating the
feature subset at each stage of the process, an optimal feature subset is generated. The
experimental results shows the success of the proposed method in removing redundant
attributes efficiently from the LD dataset without sacrificing the classification performance.
The Optimization of choosing Investment in the capital markets using artifici...inventionjournals
Optimization is one of crucial items in behavioural sciences. These daystheuse of Meta heuristic has grown considerably in all fields. In this study, we will look for optimization of selection in a portfolio of investment opportunities. We’ve been looking for a selection logic using a meta-heuristic algorithm Called artificial neural networks. The results showed that using artificial neural network algorithm had an optimization in decision-making and selection of investment opportunities. The research is applied one considering the purpose and is looking for developing knowledge in a particular field.
An Architecture for Simplified and Automated Machine Learning IJECEIAES
Recently, machine learning has been adopted by businesses to analyze their vast data in order to make strategic decision. However, knowledge in machine learning and technical skill are usually required to prepare data and perform machine learning tasks. This obstacle prevents smaller businesses with no technical knowledge to utilize machine learning. In this paper, we propose an architecture for simplified and automated machine learning process currently supporting the data classification task. The architecture includes a method for characterizing datasets, which allows for simplifying and automating machine learning model and hyperparameter selection based on historical execution configurations. Users can simply upload their datasets via a web browser, and the system will determine the possible models and their hyperparameter configurations for the users to choose from. The prototype shows the feasibility of the proposed architecture. Although the accuracy is still limited by the small execution history and the cleanliness of the input datasets, the architecture can minimize user involvement in the machine learning process so that non-technical users can perform data classification through a web browser without installing any software.
Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Sched...IJECEIAES
Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling.
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...ijcsa
The work is about using Simulated Annealing Algorithm for the effort estimation model parameter
optimization which can lead to the reduction in the difference in actual and estimated effort used in model
development.
The model has been tested using OOP’s dataset, obtained from NASA for research purpose.The data set
based model equation parameters have been found that consists of two independent variables, viz. Lines of
Code (LOC) along with one more attribute as a dependent variable related to software development effort
(DE). The results have been compared with the earlier work done by the author on Artificial Neural
Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) and it has been observed that the
developed SA based model is more capable to provide better estimation of software development effort than
ANN and ANFIS
DEEP-LEARNING-BASED HUMAN INTENTION PREDICTION WITH DATA AUGMENTATIONijaia
Data augmentation has been broadly applied in training deep-learning models to increase the diversity of
data. This study ingestigates the effectiveness of different data augmentation methods for deep-learningbased human intention prediction when only limited training data is available. A human participant pitches
a ball to nine potential targets in our experiment. We expect to predict which target the participant pitches
the ball to. Firstly, the effectiveness of 10 data augmentation groups is evaluated on a single-participant
data set using RGB images. Secondly, the best data augmentation method (i.e., random cropping) on the
single-participant data set is further evaluated on a multi-participant data set to assess its generalization
ability. Finally, the effectiveness of random cropping on fusion data of RGB images and optical flow is
evaluated on both single- and multi-participant data sets. Experiment results show that: 1) Data
augmentation methods that crop or deform images can improve the prediction performance; 2) Random
cropping can be generalized to the multi-participant data set (prediction accuracy is improved from 50%
to 57.4%); and 3) Random cropping with fusion data of RGB images and optical flow can further improve
the prediction accuracy from 57.4% to 63.9% on the multi-participant data set.
MITIGATION TECHNIQUES TO OVERCOME DATA HARM IN MODEL BUILDING FOR MLijaia
Given the impact of Machine Learning (ML) on individuals and the society, understanding how harm might
be occur throughout the ML life cycle becomes critical more than ever. By offering a framework to
determine distinct potential sources of downstream harm in ML pipeline, the paper demonstrates the
importance of choices throughout distinct phases of data collection, development, and deployment that
extend far beyond just model training. Relevant mitigation techniques are also suggested for being used
instead of merely relying on generic notions of what counts as fairness.
PREDICTION OF MALIGNANCY IN SUSPECTED THYROID TUMOUR PATIENTS BY THREE DIFFER...cscpconf
In the present study, the abilities of three classification methods of data mining namely artificial
neural networks with feed-forward back propagation algorithm, J48 decision tree method and
logistic regression analysis are compared in a medical real dataset. The prediction of
malignancy in suspected thyroid tumour patients is the objective of the study. The accuracy of
the correct predictions (the minimum error rate), the amount of time consuming in the
modelling process and the interpretability and simplicity of the results for clinical experts are
the factors considered to choose the best method
Software Bug Detection Algorithm using Data mining TechniquesAM Publications
The main aim of software development is to develop high quality software and high quality software is
developed using enormous amount of software engineering data. The software engineering data can be used to gain
empirically based understanding of software development. The meaning full information can be extracted using
various data mining techniques. As Data Mining for Secure Software Engineering improves software productivity and
quality, software engineers are increasingly applying data mining algorithms to various software engineering tasks.
However mining software engineering data poses several challenges, requiring various algorithms to effectively mine
sequences, graphs and text from such data. Software engineering data includes code bases, execution traces,
historical code changes, mailing lists and bug data bases. They contains a wealth of information about a projectsstatus,
progress and evolution. Using well established data mining techniques, practitioners and researchers can
explore the potential of this valuable data in order to better manage their projects and do produce higher-quality
software systems that are delivered on time and within budget
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
A Defect Prediction Model for Software Product based on ANFISIJSRD
Artificial intelligence techniques are day by day getting involvement in all the classification and prediction based process like environmental monitoring, stock exchange conditions, biomedical diagnosis, software engineering etc. However still there are yet to be simplify the challenges of selecting training criteria for design of artificial intelligence models used for prediction of results. This work focus on the defect prediction mechanism development using software metric data of KC1.We have taken subtractive clustering approach for generation of fuzzy inference system (FIS).The FIS rules are generated at different radius of influence of input attribute vectors and the developed rules are further modified by ANFIS technique to obtain the prediction of number of defects in software project using fuzzy logic system.
Performance analysis of binary and multiclass models using azure machine lear...IJECEIAES
Network data is expanding and that too at an alarming rate. Besides, the sophisticated attack tools used by hackers lead to capricious cyber threat landscape. Traditional models proposed in the field of network intrusion detection using machine learning algorithms emphasize more on improving attack detection rate and reducing false alarms but time efficiency is often overlooked. Therefore, in order to address this limitation, a modern solution has been presented using Machine Learning-as-a-Service platform. The proposed work analyses the performance of eight two-class and three multiclass algorithms using UNSW NB-15, a modern intrusion detection dataset. 82,332 testing samples were considered to evaluate the performance of algorithms. The proposed two class decision forest model exhibited 99.2% accuracy and took 6 seconds to learn 1,75,341 network instances. Multiclass classification task was also undertaken wherein attack types like generic, exploits, shellcode and worms were classified with a recall percentage of 99%, 94.49%, 91.79% and 90.9% respectively by the multiclass decision forest model that also leapfrogged others in terms of training and execution time.
Identification of important features and data mining classification technique...IJECEIAES
Employees absenteeism at the work costs organizations billions a year. Prediction of employees’ absenteeism and the reasons behind their absence help organizations in reducing expenses and increasing productivity. Data mining turns the vast volume of human resources data into information that can help in decision-making and prediction. Although the selection of features is a critical step in data mining to enhance the efficiency of the final prediction, it is not yet known which method of feature selection is better. Therefore, this paper aims to compare the performance of three well-known feature selection methods in absenteeism prediction, which are relief-based feature selection, correlation-based feature selection and information-gain feature selection. In addition, this paper aims to find the best combination of feature selection method and data mining technique in enhancing the absenteeism prediction accuracy. Seven classification techniques were used as the prediction model. Additionally, cross-validation approach was utilized to assess the applied prediction models to have more realistic and reliable results. The used dataset was built at a courier company in Brazil with records of absenteeism at work. Regarding experimental results, correlationbased feature selection surpasses the other methods through the performance measurements. Furthermore, bagging classifier was the best-performing data mining technique when features were selected using correlation-based feature selection with an accuracy rate of (92%).
Biometric Identification and Authentication Providence using Fingerprint for ...IJECEIAES
The raise in the recent security incidents of cloud computing and its challenges is to secure the data. To solve this problem, the integration of mobile with cloud computing, Mobile biometric authentication in cloud computing is presented in this paper. To enhance the security, the biometric authentication is being used, since the Mobile cloud computing is popular among the mobile user. This paper examines how the mobile cloud computing (MCC) is used in security issue with finger biometric authentication model. Through this fingerprint biometric, the secret code is generated by entropy value. This enables the person to request for accessing the data in the desk computer. When the person requests the access to the authorized user through Bluetooth in mobile, the Authorized user sends the permit access through fingerprint secret code. Finally this fingerprint is verified with the database in the Desk computer. If it is matched, then the computer can be accessed by the requested person.
Feature Selection : A Novel Approach for the Prediction of Learning Disabilit...csandit
Feature selection is a problem closely related to dimensionality reduction. A commonly used
approach in feature selection is ranking the individual features according to some criteria and
then search for an optimal feature subset based on an evaluation criterion to test the optimality.
The objective of this work is to predict more accurately the presence of Learning Disability
(LD) in school-aged children with reduced number of symptoms. For this purpose, a novel
hybrid feature selection approach is proposed by integrating a popular Rough Set based feature
ranking process with a modified backward feature elimination algorithm. The approach follows
a ranking of the symptoms of LD according to their importance in the data domain. Each
symptoms significance or priority values reflect its relative importance to predict LD among the
various cases. Then by eliminating least significant features one by one and evaluating the
feature subset at each stage of the process, an optimal feature subset is generated. The
experimental results shows the success of the proposed method in removing redundant
attributes efficiently from the LD dataset without sacrificing the classification performance.
The Optimization of choosing Investment in the capital markets using artifici...inventionjournals
Optimization is one of crucial items in behavioural sciences. These daystheuse of Meta heuristic has grown considerably in all fields. In this study, we will look for optimization of selection in a portfolio of investment opportunities. We’ve been looking for a selection logic using a meta-heuristic algorithm Called artificial neural networks. The results showed that using artificial neural network algorithm had an optimization in decision-making and selection of investment opportunities. The research is applied one considering the purpose and is looking for developing knowledge in a particular field.
An Architecture for Simplified and Automated Machine Learning IJECEIAES
Recently, machine learning has been adopted by businesses to analyze their vast data in order to make strategic decision. However, knowledge in machine learning and technical skill are usually required to prepare data and perform machine learning tasks. This obstacle prevents smaller businesses with no technical knowledge to utilize machine learning. In this paper, we propose an architecture for simplified and automated machine learning process currently supporting the data classification task. The architecture includes a method for characterizing datasets, which allows for simplifying and automating machine learning model and hyperparameter selection based on historical execution configurations. Users can simply upload their datasets via a web browser, and the system will determine the possible models and their hyperparameter configurations for the users to choose from. The prototype shows the feasibility of the proposed architecture. Although the accuracy is still limited by the small execution history and the cleanliness of the input datasets, the architecture can minimize user involvement in the machine learning process so that non-technical users can perform data classification through a web browser without installing any software.
Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Sched...IJECEIAES
Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling.
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...ijcsa
The work is about using Simulated Annealing Algorithm for the effort estimation model parameter
optimization which can lead to the reduction in the difference in actual and estimated effort used in model
development.
The model has been tested using OOP’s dataset, obtained from NASA for research purpose.The data set
based model equation parameters have been found that consists of two independent variables, viz. Lines of
Code (LOC) along with one more attribute as a dependent variable related to software development effort
(DE). The results have been compared with the earlier work done by the author on Artificial Neural
Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) and it has been observed that the
developed SA based model is more capable to provide better estimation of software development effort than
ANN and ANFIS
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...IAEME Publication
This paper presents an approach based on applying an aggregated predictor formed by multiple versions of a multilayer neural network with a back-propagation optimization algorithm for helping the engineer to get a list of the most appropriate well-test interpretation models for a given set of pressure/ production data. The proposed method consists of three stages: (1) data decorrelation through principal component analysis to reduce the covariance between the variables and the dimension of the input layer in the artificial neural network, (2) bootstrap replicates of the learning set where the data is repeatedly sampled with a random split of the data into train sets and using these as new learning sets, and (3) automatic reservoir model identification through aggregated predictor formed by a plurality vote when predicting a new class. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 300 for training, 100 for cross-validation, and 200 for testing. Different network structures were tested during this study to arrive at optimum network design. We notice that the single net methodology always brings about confusion in selecting the correct model even though the training results for the constructed networks are close to 1. We notice also that the principal component analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 95% Compared to a previous approach 80%, the combination of the PCA and ANN is more stable and determine the more accurate results with lesser computational complexity than was feasible previously. Clearly, the aggregated predictor is more stable and shows less bad classes compared to the previous approach.
Artificial Neural Network and Multi-Response Optimization in Reliability Meas...inventionjournals
Neural network is an important tool for reliability analysis, including estimation of reliability or utility function which are too complicated to be analytical expressed for large or complex system. It has been demonstrated the neural network has significant improvement in the parameter estimation accuracy over the traditional chi-square test. There are many parameters of a neural network that should be determined while training the dataset, since different setups of algorithm parameters affect the estimation performance in either accuracy or computation efficiency. In this paper, neural network training is used to estimate the utility function for the parallel-series redundancy allocation problem, and weighted principal component based multi-response optimization method is applied to find the optimal setting of neural network parameters so that the simultaneous minimizations of training error and computing time are achieved.
Artificial intelligence based pattern recognition is
one of the most important tools in process control to identify
process problems. The objective of this study was to
evaluate the relative performance of a feature-based
Recognizer compared with the raw data-based recognizer.
The study focused on recognition of seven commonly
researched patterns plotted on the quality chart. The
artificial intelligence based pattern recognizer trained using
the three selected statistical features resulted in significantly
better performance compared with the raw data-based
recognizer.
Application of Neural Network for Cell Formation in Group TechnologyIJMER
Group Technology is a method for increasing productivity of manufacturing quality products.
For improving the flexibility in manufacturing systems, cell formation is the main step in group
technology .Every manufacturing industry faces problem of productivity and their priority is to deliver
product to valuable customer in time. For fulfilling this purpose a proper engineering analysis is needed
which can reduce material handling and wait time. This can be done by cell formation. There are
various techniques which are available for cell formation and discussed by different researchers but
neural network is found the best among them due to its better and fast computation results. Here in this
paper Adaptive Resonance Theory ART1 is analyzed and proven a better way to cope up with the
manufacturing problems.
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...Sarvesh Kumar
The work is carried on the application of differential equation (DE) and its computational technique of genetic algorithm and neural (GANN) in C#, which is frequently used in globalised world by human wings. Diagrammatical and flow chart presentation is the major concerned for easy undertaking of these two concepts with indication of its present and future application is the new initiative taken in this paper along with computational approaches in C#. Little observation has been also pointed during working, functioning and development process of above algorithm in C# under given boundary value condition of DE for genetic and neural. Operations of fitness function and Genetic operations were completed for behavioural transmission of chromosome.
EFFICIENT USE OF HYBRID ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM COMBINED WITH N...csandit
This research study proposes a novel method for automatic fault prediction from foundry data
introducing the so-called Meta Prediction Function (MPF). Kernel Principal Component
Analysis (KPCA) is used for dimension reduction. Different algorithms are used for building the
MPF such as Multiple Linear Regression (MLR), Adaptive Neuro Fuzzy Inference System
(ANFIS), Support Vector Machine (SVM) and Neural Network (NN). We used classical
machine learning methods such as ANFIS, SVM and NN for comparison with our proposed
MPF. Our empirical results show that the MPF consistently outperform the classical methods.
A NOVEL EVALUATION APPROACH TO FINDING LIGHTWEIGHT MACHINE LEARNING ALGORITHM...IJNSA Journal
Building practical and efficient intrusion detection systems in computer network is important in industrial areas today and machine learning technique provides a set of effective algorithms to detect network
intrusion. To find out appropriate algorithms for building such kinds of systems, it is necessary to evaluate various types of machine learning algorithms based on specific criteria. In this paper, we propose a novel evaluation formula which incorporates 6 indexes into our comprehensive measurement, including precision, recall, root mean square error, training time, sample complexity and practicability, in order to
find algorithms which have high detection rate, low training time, need less training samples and are easy
to use like constructing, understanding and analyzing models. Detailed evaluation process is designed to
get all necessary assessment indicators and 6 kinds of machine learning algorithms are evaluated.
Experimental results illustrate that Logistic Regression shows the best overall performance.
A NOVEL EVALUATION APPROACH TO FINDING LIGHTWEIGHT MACHINE LEARNING ALGORITHM...IJNSA Journal
Building practical and efficient intrusion detection systems in computer network is important in industrial areas today and machine learning technique provides a set of effective algorithms to detect network intrusion. To find out appropriate algorithms for building such kinds of systems, it is necessary to evaluate various types of machine learning algorithms based on specific criteria. In this paper, we propose a novel evaluation formula which incorporates 6 indexes into our comprehensive measurement, including precision, recall, root mean square error, training time, sample complexity and practicability, in order to find algorithms which have high detection rate, low training time, need less training samples and are easy to use like constructing, understanding and analyzing models. Detailed evaluation process is designed to get all necessary assessment indicators and 6 kinds of machine learning algorithms are evaluated. Experimental results illustrate that Logistic Regression shows the best overall performance.
Neural network based numerical digits recognization using nnt in matlabijcses
Artificial neural networks are models inspired by human nervous system that is capable of learning. One of
the important applications of artificial neural network is character Recognition. Character Recognition
finds its application in number of areas, such as banking, security products, hospitals, in robotics also.
This paper is based on a system that recognizes a english numeral, given by the user, which is already
trained on the features of the numbers to be recognized using NNT (Neural network toolbox) .The system
has a neural network as its core, which is first trained on a database. The training of the neural network
extracts the features of the English numbers and stores in the database. The next phase of the system is to
recognize the number given by the user. The features of the number given by the user are extracted and
compared with the feature database and the recognized number is displayed.
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.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.