This document presents a study that uses machine learning techniques to predict crime rates. Specifically, it aims to analyze crime data using supervised machine learning classification algorithms like decision trees, support vector machines, logistic regression, k-nearest neighbors, and random forests. The document outlines collecting and preprocessing crime data, selecting relevant features, training models on a portion of the data and testing them on the remaining data. It finds that random forest achieved the best prediction accuracy compared to other algorithms tested. The goal is to help law enforcement agencies better predict and reduce crime rates by analyzing historical crime data patterns.
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.
Comparative Performance Analysis of Machine Learning Techniques for Software ...csandit
Machine learning techniques can be used to analyse data from different perspectives and enable
developers to retrieve useful information. Machine learning techniques are proven to be useful
in terms of software bug prediction. In this paper, a comparative performance analysis of
different machine learning techniques is explored for software bug prediction on public
available data sets. Results showed most of the machine learning methods performed well on
software bug datasets.
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.
Robust Fault-Tolerant Training Strategy Using Neural Network to Perform Funct...Eswar Publications
This paper is intended to introduce an efficient as well as robust training mechanism for a neural network which can be used for testing the functionality of software. The traditional setup of neural network architecture is used constituting the two phases -training phase and evaluation phase. The input test cases are to be trained in first phase and consequently they behave like normal test cases to predict the output as untrained test cases. The test oracle measures the deviation between the outputs of untrained test cases with trained test cases and authorizes a final decision. Our framework can be applied to systems where number of test cases outnumbers the
functionalities or the system under test is too complex. It can also be applied to the test case development when the modules of a system become tedious after modification.
An intrusion detection algorithm for amiIJCI JOURNAL
Nowadays, using the smart metering devices for energy users to manage a wide variety of subscribers,
reading devices for measuring, billing, disconnection and connection of subscribers’ connection
management is an important issue. The performance of these intelligent systems is based on information
transfer in the context of information technology, so reported data from network should be managed to
avoid the malicious activities that including the issues that could affect the quality of service the system. In
this paper for control of the reported data and to ensure the veracity of the obtained information, using
intrusion detection system is proposed based on the support vector machine and principle component
analysis (PCA) to recognize and identify the intrusions and attacks in the smart grid. Here, the operation of
intrusion detection systems for different kernel of SVM when using support vector machine (SVM) and PCA
simultaneously is studied. To evaluate the algorithm, based on data KDD99, numerical simulation is done
on five different kernels for an intrusion detection system using support vector machine with PCA
simultaneously. Also comparison analysis is investigated for presented intrusion detection algorithm in
terms of time - response, rate of increase network efficiency and increase system error and differences in
the use or lack of use PCA. The results indicate that correct detection rate and the rate of attack error
detection have best value when PCA is used, and when the core of algorithm is radial type, in SVM
algorithm reduces the time for data analysis and enhances performance of intrusion detection.
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.
Comparative Performance Analysis of Machine Learning Techniques for Software ...csandit
Machine learning techniques can be used to analyse data from different perspectives and enable
developers to retrieve useful information. Machine learning techniques are proven to be useful
in terms of software bug prediction. In this paper, a comparative performance analysis of
different machine learning techniques is explored for software bug prediction on public
available data sets. Results showed most of the machine learning methods performed well on
software bug datasets.
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.
Robust Fault-Tolerant Training Strategy Using Neural Network to Perform Funct...Eswar Publications
This paper is intended to introduce an efficient as well as robust training mechanism for a neural network which can be used for testing the functionality of software. The traditional setup of neural network architecture is used constituting the two phases -training phase and evaluation phase. The input test cases are to be trained in first phase and consequently they behave like normal test cases to predict the output as untrained test cases. The test oracle measures the deviation between the outputs of untrained test cases with trained test cases and authorizes a final decision. Our framework can be applied to systems where number of test cases outnumbers the
functionalities or the system under test is too complex. It can also be applied to the test case development when the modules of a system become tedious after modification.
An intrusion detection algorithm for amiIJCI JOURNAL
Nowadays, using the smart metering devices for energy users to manage a wide variety of subscribers,
reading devices for measuring, billing, disconnection and connection of subscribers’ connection
management is an important issue. The performance of these intelligent systems is based on information
transfer in the context of information technology, so reported data from network should be managed to
avoid the malicious activities that including the issues that could affect the quality of service the system. In
this paper for control of the reported data and to ensure the veracity of the obtained information, using
intrusion detection system is proposed based on the support vector machine and principle component
analysis (PCA) to recognize and identify the intrusions and attacks in the smart grid. Here, the operation of
intrusion detection systems for different kernel of SVM when using support vector machine (SVM) and PCA
simultaneously is studied. To evaluate the algorithm, based on data KDD99, numerical simulation is done
on five different kernels for an intrusion detection system using support vector machine with PCA
simultaneously. Also comparison analysis is investigated for presented intrusion detection algorithm in
terms of time - response, rate of increase network efficiency and increase system error and differences in
the use or lack of use PCA. The results indicate that correct detection rate and the rate of attack error
detection have best value when PCA is used, and when the core of algorithm is radial type, in SVM
algorithm reduces the time for data analysis and enhances performance of intrusion detection.
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
In recent times, various machine learning classifiers are used to improve network intrusion detection. The researchers have proposed many solutions for intrusion detection in the literature. The machine learning classifiers are trained on older datasets for intrusion detection, which limits their detection accuracy. So, there is a need to train the machine learning classifiers on the latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. The selected classifiers such as K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), and Naïve Bayes (NB) classifiers are used for training from the taxonomy of classifiers based on lazy and eager learners. In this paper, Chi-Square, a filter-based feature selection technique, is applied to the UNSW-NB15 dataset to reduce the irrelevant and redundant features. The performance of classifiers is measured in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, F1-Score, True Positive Rate (TPR) and False Positive Rate (FPR) with or without feature selection technique and comparative analysis of these machine learning classifiers is carried out.
Optimization of network traffic anomaly detection using machine learning IJECEIAES
In this paper, to optimize the process of detecting cyber-attacks, we choose to propose 2 main optimization solutions: Optimizing the detection method and optimizing features. Both of these two optimization solutions are to ensure the aim is to increase accuracy and reduce the time for analysis and detection. Accordingly, for the detection method, we recommend using the Random Forest supervised classification algorithm. The experimental results in section 4.1 have proven that our proposal that use the Random Forest algorithm for abnormal behavior detection is completely correct because the results of this algorithm are much better than some other detection algorithms on all measures. For the feature optimization solution, we propose to use some data dimensional reduction techniques such as information gain, principal component analysis, and correlation coefficient method. The results of the research proposed in our paper have proven that to optimize the cyberattack detection process, it is not necessary to use advanced algorithms with complex and cumbersome computational requirements, it must depend on the monitoring data for selecting the reasonable feature extraction and optimization algorithm as well as the appropriate attack classification and detection algorithms.
an error in that computer program. In order to improve the software quality, prediction of faulty modules is
necessary. Various Metric suites and techniques are available to predict the modules which are critical and
likely to be fault prone. Genetic Algorithm is a problem solving algorithm. It uses genetics as its model of
problem solving. It’s a search technique to find approximate solutions to optimization and search
problems.Genetic algorithm is applied for solving the problem of faulty module prediction and as well as
for finding the most important attribute for fault occurrence. In order to perform the analysis, performance
validation of the Genetic Algorithm using open source software jEdit is done. The results are measured in
terms Accuracy and Error in predicting by calculating probability of detection and probability of false
Alarms
Improving the performance of Intrusion detection systemsyasmen essam
Intrusion detection systems (IDS) are widely studied by
researchers nowadays due to the dramatic growth in
network-based technologies. Policy violations and
unauthorized access is in turn increasing which makes
intrusion detection systems of great importance. Existing
approaches to improve intrusion detection systems focus on feature selection or reduction since some features are
irrelevant or redundant which when removed improve the
accuracy as well as the learning time.
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
Comparative Study on Machine Learning Algorithms for Network Intrusion Detect...ijtsrd
Network has brought convenience to the earth by permitting versatile transformation of information, however it conjointly exposes a high range of vulnerabilities. A Network Intrusion Detection System helps network directors and system to view network security violation in their organizations. Characteristic unknown and new attacks are one of the leading challenges in Intrusion Detection System researches. Deep learning that a subfield of machine learning cares with algorithms that are supported the structure and performance of brain known as artificial neural networks. The improvement in such learning algorithms would increase the probability of IDS and the detection rate of unknown attacks. Throughout, we have a tendency to suggest a deep learning approach to implement increased IDS and associate degree economical. Priya N | Ishita Popli "Comparative Study on Machine Learning Algorithms for Network Intrusion Detection System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38175.pdf Paper URL : https://www.ijtsrd.com/computer-science/computer-network/38175/comparative-study-on-machine-learning-algorithms-for-network-intrusion-detection-system/priya-n
An unsupervised feature selection algorithm with feature ranking for maximizi...Asir Singh
Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,
efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical,
finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The
predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and
redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be
removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm
namely unsupervised learning with ranking based feature selection (FSULR). It removes redundant features by clustering and eliminates
irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this
proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes (NB),
instance based (IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for
classifiers.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Due to diagnosis problem in detecting lung Cancer, it becomes the most dangerous cancer seen in human being. Because of early diagnosis, the survival rate among people is increased. The prediction of lung cancer is the most challenging cancer problem, due to its structure of cells in human body. In which most of tissues or cells are overlapping on one another. Now-a-days, the use of images processing techniques is increased in growing medical field for its disease diagnosis, where the time factor plays important role. Detecting cancer within a time, increases the survival rate of patients. Many radiologists still use MRI only for assessment of superior sulcus tumors and in cases where invasion of spinal cord canal is suspected. MRI can detect and stage lung cancer and this method would be excellent of lung malignancies and other diseases.
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
A Comprehensive review of Conversational Agent and its prediction algorithmvivatechijri
There is an exponential increase in the use of conversational bots. Conversational bots can be
described as a platform that can chat with people using artificial intelligence. The recent advancement has
made A.I capable of learning from data and produce an output. This learning of data can be performed by using
various machine learning algorithm. Machine learning techniques involves construction of algorithms that can
learn for data and can predict the outcome. This paper reviews the efficiency of different machine learning
algorithm that are used in conversational bot.
An Empirical Comparison and Feature Reduction Performance Analysis of Intrusi...ijctcm
This paper reports on the empirical evaluation of five machine learning algorithm such as J48, BayesNet, OneR, NB and ZeroR using ten performance criteria: accuracy, precision, recall, F-Measure, incorrectly classified instances, kappa statistic, mean absolute error, root mean squared error, relative absolute error, root relative squared error. The aim of this paper is to find out which classifier is better in its performance for intrusion detection system. Machine Learning is one of the methods used in the intrusion detection system (IDS).Based on this study, it can be concluded that J48 decision tree is the most suitable associated algorithm than the other four algorithms. In this paper we compared the performance of Intrusion Detection System (IDS) Classifiers using seven feature reduction techniques.
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
In recent times, various machine learning classifiers are used to improve network intrusion detection. The researchers have proposed many solutions for intrusion detection in the literature. The machine learning classifiers are trained on older datasets for intrusion detection, which limits their detection accuracy. So, there is a need to train the machine learning classifiers on the latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. The selected classifiers such as K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), and Naïve Bayes (NB) classifiers are used for training from the taxonomy of classifiers based on lazy and eager learners. In this paper, Chi-Square, a filter-based feature selection technique, is applied to the UNSW-NB15 dataset to reduce the irrelevant and redundant features. The performance of classifiers is measured in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, F1-Score, True Positive Rate (TPR) and False Positive Rate (FPR) with or without feature selection technique and comparative analysis of these machine learning classifiers is carried out.
Optimization of network traffic anomaly detection using machine learning IJECEIAES
In this paper, to optimize the process of detecting cyber-attacks, we choose to propose 2 main optimization solutions: Optimizing the detection method and optimizing features. Both of these two optimization solutions are to ensure the aim is to increase accuracy and reduce the time for analysis and detection. Accordingly, for the detection method, we recommend using the Random Forest supervised classification algorithm. The experimental results in section 4.1 have proven that our proposal that use the Random Forest algorithm for abnormal behavior detection is completely correct because the results of this algorithm are much better than some other detection algorithms on all measures. For the feature optimization solution, we propose to use some data dimensional reduction techniques such as information gain, principal component analysis, and correlation coefficient method. The results of the research proposed in our paper have proven that to optimize the cyberattack detection process, it is not necessary to use advanced algorithms with complex and cumbersome computational requirements, it must depend on the monitoring data for selecting the reasonable feature extraction and optimization algorithm as well as the appropriate attack classification and detection algorithms.
an error in that computer program. In order to improve the software quality, prediction of faulty modules is
necessary. Various Metric suites and techniques are available to predict the modules which are critical and
likely to be fault prone. Genetic Algorithm is a problem solving algorithm. It uses genetics as its model of
problem solving. It’s a search technique to find approximate solutions to optimization and search
problems.Genetic algorithm is applied for solving the problem of faulty module prediction and as well as
for finding the most important attribute for fault occurrence. In order to perform the analysis, performance
validation of the Genetic Algorithm using open source software jEdit is done. The results are measured in
terms Accuracy and Error in predicting by calculating probability of detection and probability of false
Alarms
Improving the performance of Intrusion detection systemsyasmen essam
Intrusion detection systems (IDS) are widely studied by
researchers nowadays due to the dramatic growth in
network-based technologies. Policy violations and
unauthorized access is in turn increasing which makes
intrusion detection systems of great importance. Existing
approaches to improve intrusion detection systems focus on feature selection or reduction since some features are
irrelevant or redundant which when removed improve the
accuracy as well as the learning time.
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
Comparative Study on Machine Learning Algorithms for Network Intrusion Detect...ijtsrd
Network has brought convenience to the earth by permitting versatile transformation of information, however it conjointly exposes a high range of vulnerabilities. A Network Intrusion Detection System helps network directors and system to view network security violation in their organizations. Characteristic unknown and new attacks are one of the leading challenges in Intrusion Detection System researches. Deep learning that a subfield of machine learning cares with algorithms that are supported the structure and performance of brain known as artificial neural networks. The improvement in such learning algorithms would increase the probability of IDS and the detection rate of unknown attacks. Throughout, we have a tendency to suggest a deep learning approach to implement increased IDS and associate degree economical. Priya N | Ishita Popli "Comparative Study on Machine Learning Algorithms for Network Intrusion Detection System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38175.pdf Paper URL : https://www.ijtsrd.com/computer-science/computer-network/38175/comparative-study-on-machine-learning-algorithms-for-network-intrusion-detection-system/priya-n
An unsupervised feature selection algorithm with feature ranking for maximizi...Asir Singh
Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,
efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical,
finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The
predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and
redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be
removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm
namely unsupervised learning with ranking based feature selection (FSULR). It removes redundant features by clustering and eliminates
irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this
proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes (NB),
instance based (IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for
classifiers.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Due to diagnosis problem in detecting lung Cancer, it becomes the most dangerous cancer seen in human being. Because of early diagnosis, the survival rate among people is increased. The prediction of lung cancer is the most challenging cancer problem, due to its structure of cells in human body. In which most of tissues or cells are overlapping on one another. Now-a-days, the use of images processing techniques is increased in growing medical field for its disease diagnosis, where the time factor plays important role. Detecting cancer within a time, increases the survival rate of patients. Many radiologists still use MRI only for assessment of superior sulcus tumors and in cases where invasion of spinal cord canal is suspected. MRI can detect and stage lung cancer and this method would be excellent of lung malignancies and other diseases.
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
A Comprehensive review of Conversational Agent and its prediction algorithmvivatechijri
There is an exponential increase in the use of conversational bots. Conversational bots can be
described as a platform that can chat with people using artificial intelligence. The recent advancement has
made A.I capable of learning from data and produce an output. This learning of data can be performed by using
various machine learning algorithm. Machine learning techniques involves construction of algorithms that can
learn for data and can predict the outcome. This paper reviews the efficiency of different machine learning
algorithm that are used in conversational bot.
An Empirical Comparison and Feature Reduction Performance Analysis of Intrusi...ijctcm
This paper reports on the empirical evaluation of five machine learning algorithm such as J48, BayesNet, OneR, NB and ZeroR using ten performance criteria: accuracy, precision, recall, F-Measure, incorrectly classified instances, kappa statistic, mean absolute error, root mean squared error, relative absolute error, root relative squared error. The aim of this paper is to find out which classifier is better in its performance for intrusion detection system. Machine Learning is one of the methods used in the intrusion detection system (IDS).Based on this study, it can be concluded that J48 decision tree is the most suitable associated algorithm than the other four algorithms. In this paper we compared the performance of Intrusion Detection System (IDS) Classifiers using seven feature reduction techniques.
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.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
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
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
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
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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.
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.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.