This document describes a disease prediction system that uses machine learning algorithms like decision trees, random forests and naive Bayes to predict a disease based on symptoms provided by a patient. The researchers developed a logistic regression model to take in symptoms and predict the likely disease. It was created using Python and aims to help busy professionals more easily identify health issues before they become serious. The system was built using techniques like data collection, preprocessing, model training/evaluation and aims to improve performance over iterations. It was found to provide time savings and early disease warnings compared to traditional diagnosis methods.
Propose a Enhanced Framework for Prediction of Heart DiseaseIJERA Editor
Heart disease diagnosis requires more experience and it is a complex task. The Heart MRI, ECG and Stress Test etc are the numbers of medical tests are prescribed by the doctor for examining the heart disease and it is the way of tradition in the prediction of heart disease. Today world, the hidden information of the huge amount of health care data is contained by the health care industry. The effective decisions are made by means of this hidden information. For appropriate results, the advanced data mining techniques with the information which is based on the computer are used. In any empirical sciences, for the inference and categorisation, the new mathematical techniques to be used called Artificial neural networks (ANNs) it also be used to the modelling of the real neural networks. Acting, Wanting, knowing, remembering, perceiving, thinking and inferring are the nature of mental phenomena and these can be understand by using the theory of ANN. The problem of probability and induction can be arised for the inference and classification because these are the powerful instruments of ANN. In this paper, the classification techniques like Naive Bayes Classification algorithm and Artificial Neural Networks are used to classify the attributes in the given data set. The attribute filtering techniques like PCA (Principle Component Analysis) filtering and Information Gain Attribute Subset Evaluation technique for feature selection in the given data set to predict the heart disease symptoms. A new framework is proposed which is based on the above techniques, the framework will take the input dataset and fed into the feature selection techniques block, which selects any one techniques that gives the least number of attributes and then classification task is done using two algorithms, the same attributes that are selected by two classification task is taken for the prediction of heart disease. This framework consumes the time for predicting the symptoms of heart disease which make the user to know the important attributes based on the proposed framework.
An efficient feature selection algorithm for health care data analysisjournalBEEI
Diabete is a silent killer, which will slowly kill the person if it goes undetected. The existing system which uses F-score method and K-means clustering of checking whether a person has diabetes or not are 100% accurate, and anything which isn't a 100% is not acceptable in the medical field, as it could cost the lives of many people. Our proposed system aims at using some of the best features of the existing algorithms to predict diabetes, and combine these and based on these features; This research work turns them into a novel algorithm, which will be 100% accurate in its prediction. With the surge in technological advancements, we can use data mining to predict when a person would be diagnosed with diabetes. Specifically, we analyze the best features of chi-square algorithm and advanced clustering algorithm (ACA). This research work is done using the Pima Indian Diabetes dataset provided by National Institutes of Diabetes and Digestive and Kidney Diseases. Using classification theorems and methods we can consider different factors like age, BMI, blood pressure and the importance given to these attributes overall, and singles these attributes out, and use them for the prediction of diabetes.
Disease prediction in big data healthcare using extended convolutional neural...IJAAS Team
Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.
Propose a Enhanced Framework for Prediction of Heart DiseaseIJERA Editor
Heart disease diagnosis requires more experience and it is a complex task. The Heart MRI, ECG and Stress Test etc are the numbers of medical tests are prescribed by the doctor for examining the heart disease and it is the way of tradition in the prediction of heart disease. Today world, the hidden information of the huge amount of health care data is contained by the health care industry. The effective decisions are made by means of this hidden information. For appropriate results, the advanced data mining techniques with the information which is based on the computer are used. In any empirical sciences, for the inference and categorisation, the new mathematical techniques to be used called Artificial neural networks (ANNs) it also be used to the modelling of the real neural networks. Acting, Wanting, knowing, remembering, perceiving, thinking and inferring are the nature of mental phenomena and these can be understand by using the theory of ANN. The problem of probability and induction can be arised for the inference and classification because these are the powerful instruments of ANN. In this paper, the classification techniques like Naive Bayes Classification algorithm and Artificial Neural Networks are used to classify the attributes in the given data set. The attribute filtering techniques like PCA (Principle Component Analysis) filtering and Information Gain Attribute Subset Evaluation technique for feature selection in the given data set to predict the heart disease symptoms. A new framework is proposed which is based on the above techniques, the framework will take the input dataset and fed into the feature selection techniques block, which selects any one techniques that gives the least number of attributes and then classification task is done using two algorithms, the same attributes that are selected by two classification task is taken for the prediction of heart disease. This framework consumes the time for predicting the symptoms of heart disease which make the user to know the important attributes based on the proposed framework.
An efficient feature selection algorithm for health care data analysisjournalBEEI
Diabete is a silent killer, which will slowly kill the person if it goes undetected. The existing system which uses F-score method and K-means clustering of checking whether a person has diabetes or not are 100% accurate, and anything which isn't a 100% is not acceptable in the medical field, as it could cost the lives of many people. Our proposed system aims at using some of the best features of the existing algorithms to predict diabetes, and combine these and based on these features; This research work turns them into a novel algorithm, which will be 100% accurate in its prediction. With the surge in technological advancements, we can use data mining to predict when a person would be diagnosed with diabetes. Specifically, we analyze the best features of chi-square algorithm and advanced clustering algorithm (ACA). This research work is done using the Pima Indian Diabetes dataset provided by National Institutes of Diabetes and Digestive and Kidney Diseases. Using classification theorems and methods we can consider different factors like age, BMI, blood pressure and the importance given to these attributes overall, and singles these attributes out, and use them for the prediction of diabetes.
Disease prediction in big data healthcare using extended convolutional neural...IJAAS Team
Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.
ARTIFICIAL INTELLIGENCE BASED DATA GOVERNANCE FOR CHINESE ELECTRONIC HEALTH R...IJDKP
Electronic health record (EHR) analysis can leverage great insights to improve the quality of human
healthcare. However, the low data quality problems of missing values, inconsistency, and errors in the
data setseverely hinder buildingrobust machine learning models for data analysis. In this paper, we
develop a methodology ofartificial intelligence (AI)-based data governance to predict the missing values
or verify if the existing values are correct and what they should be when they are wrong. We demonstrate
the performance of this methodology through a case study ofpatient gender prediction and verification.
Experimental resultsshow that the deep learning algorithm of convolutional neural network (CNN) works
very wellaccording to the testing performance measured by the quantitative metric of F1-Score, and it
outperformsthe support vector machine (SVM) models with different vector representations for documents.
Chronic Kidney Disease prediction is one of the most important issues in healthcare analytics. The most interesting and challenging tasks in day to day life is prediction in medical field. In this paper, we employ some machine learning techniques for predicting the chronic kidney disease using clinical data. We use three machine learning algorithms such as Decision Tree(DT) algorithm, Naive Bayesian (NB) algorithm. The performance of the above models are compared with each other in order to select the best classifier in predicting the chronic kidney disease for given dataset.
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.
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
MACHINE LEARNING ALGORITHMS FOR HETEROGENEOUS DATA: A COMPARATIVE STUDYIAEME Publication
In the present digital era massive amount of data is being continuously generated
at exceptional and increasing scales. This data has become an important and
indispensable part of every economy, industry, organization, business and individual.
Further handling of these large datasets due to the heterogeneity in their formats is
one of the major challenge. There is a need for efficient data processing techniques to
handle the heterogeneous data and also to meet the computational requirements to
process this huge volume of data. The objective of this paper is to review, describe
and reflect on heterogeneous data with its complexity in processing, and also the use
of machine learning algorithms which plays a major role in data analytics
We conducted comparative analysis of different supervised dimension reduction techniques by integrating a set of different data splitting algorithms and demonstrate the relative efficacy of learning algorithms dependence of sample complexity. The issue of sample complexity discussed in the dependence of data splitting algorithms. In line with the expectations, every supervised learning classifier demonstrated different capability for different data splitting algorithms and no way to calculate overall ranking of techniques was directly available. We specifically focused the classifier ranking dependence of data splitting algorithms and devised a model built on weighted average rank Weighted Mean Rank Risk Adjusted Model (WMRRAM) for consent ranking of learning classifier algorithms.
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.
EDGE DETECTION IN DIGITAL IMAGE USING MORPHOLOGY OPERATIONIJEEE
This paper shows a method in digital image processing technique to find the defects in tablets. In this paper we use mathematical manipulation, to detect the defected tablet packet.
Comparative Analysis: Effective Information Retrieval Using Different Learnin...RSIS International
Information Retrieval is the activity of searching meaningful information from a collection of information resources such as Documents, relational databases and the World Wide Web. Information retrieval system mainly consists of two phases, storing indexed documents and retrieval of relevant result. Retrieving information effectively from huge data storage, it requires Machine Learning for computer systems. Machine learning has objective to instruct computers to use data or past experience to solve a given problem. Machine learning has number of applications, including classifier to be trained on email messages to learn in order to distinguish between spam and non-spam messages, systems that analyze past sales data to predict customer buying behavior, fraud detection etc. Machine learning can be applied as association analysis through supervised learning, unsupervised learning and Reinforcement Learning. The goal of these three learning is to provide an effective way of information retrieval from data warehouse to avoid problems such as ambiguity. This study will compare the effectiveness and impuissance of these learning approaches.
ARTIFICIAL INTELLIGENCE BASED DATA GOVERNANCE FOR CHINESE ELECTRONIC HEALTH R...IJDKP
Electronic health record (EHR) analysis can leverage great insights to improve the quality of human
healthcare. However, the low data quality problems of missing values, inconsistency, and errors in the
data setseverely hinder buildingrobust machine learning models for data analysis. In this paper, we
develop a methodology ofartificial intelligence (AI)-based data governance to predict the missing values
or verify if the existing values are correct and what they should be when they are wrong. We demonstrate
the performance of this methodology through a case study ofpatient gender prediction and verification.
Experimental resultsshow that the deep learning algorithm of convolutional neural network (CNN) works
very wellaccording to the testing performance measured by the quantitative metric of F1-Score, and it
outperformsthe support vector machine (SVM) models with different vector representations for documents.
Chronic Kidney Disease prediction is one of the most important issues in healthcare analytics. The most interesting and challenging tasks in day to day life is prediction in medical field. In this paper, we employ some machine learning techniques for predicting the chronic kidney disease using clinical data. We use three machine learning algorithms such as Decision Tree(DT) algorithm, Naive Bayesian (NB) algorithm. The performance of the above models are compared with each other in order to select the best classifier in predicting the chronic kidney disease for given dataset.
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.
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
MACHINE LEARNING ALGORITHMS FOR HETEROGENEOUS DATA: A COMPARATIVE STUDYIAEME Publication
In the present digital era massive amount of data is being continuously generated
at exceptional and increasing scales. This data has become an important and
indispensable part of every economy, industry, organization, business and individual.
Further handling of these large datasets due to the heterogeneity in their formats is
one of the major challenge. There is a need for efficient data processing techniques to
handle the heterogeneous data and also to meet the computational requirements to
process this huge volume of data. The objective of this paper is to review, describe
and reflect on heterogeneous data with its complexity in processing, and also the use
of machine learning algorithms which plays a major role in data analytics
We conducted comparative analysis of different supervised dimension reduction techniques by integrating a set of different data splitting algorithms and demonstrate the relative efficacy of learning algorithms dependence of sample complexity. The issue of sample complexity discussed in the dependence of data splitting algorithms. In line with the expectations, every supervised learning classifier demonstrated different capability for different data splitting algorithms and no way to calculate overall ranking of techniques was directly available. We specifically focused the classifier ranking dependence of data splitting algorithms and devised a model built on weighted average rank Weighted Mean Rank Risk Adjusted Model (WMRRAM) for consent ranking of learning classifier algorithms.
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.
EDGE DETECTION IN DIGITAL IMAGE USING MORPHOLOGY OPERATIONIJEEE
This paper shows a method in digital image processing technique to find the defects in tablets. In this paper we use mathematical manipulation, to detect the defected tablet packet.
Comparative Analysis: Effective Information Retrieval Using Different Learnin...RSIS International
Information Retrieval is the activity of searching meaningful information from a collection of information resources such as Documents, relational databases and the World Wide Web. Information retrieval system mainly consists of two phases, storing indexed documents and retrieval of relevant result. Retrieving information effectively from huge data storage, it requires Machine Learning for computer systems. Machine learning has objective to instruct computers to use data or past experience to solve a given problem. Machine learning has number of applications, including classifier to be trained on email messages to learn in order to distinguish between spam and non-spam messages, systems that analyze past sales data to predict customer buying behavior, fraud detection etc. Machine learning can be applied as association analysis through supervised learning, unsupervised learning and Reinforcement Learning. The goal of these three learning is to provide an effective way of information retrieval from data warehouse to avoid problems such as ambiguity. This study will compare the effectiveness and impuissance of these learning approaches.
ICU Patient Deterioration Prediction : A Data-Mining Approachcsandit
A huge amount of medical data is generated every da
y, which presents a challenge in analysing
these data. The obvious solution to this challenge
is to reduce the amount of data without
information loss. Dimension reduction is considered
the most popular approach for reducing
data size and also to reduce noise and redundancies
in data. In this paper, we investigate the
effect of feature selection in improving the predic
tion of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a su
bset of features would mean choosing the
most important lab tests to perform. If the number
of tests can be reduced by identifying the
most important tests, then we could also identify t
he redundant tests. By omitting the redundant
tests, observation time could be reduced and early
treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be av
oided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deteri
oration using the medical lab results. We
apply our technique on the publicly available MIMIC
-II database and show the effectiveness of
the feature selection. We also provide a detailed a
nalysis of the best features identified by our
approach.
ICU PATIENT DETERIORATION PREDICTION: A DATA-MINING APPROACHcscpconf
A huge amount of medical data is generated every day, which presents a challenge in analysing
these data. The obvious solution to this challenge is to reduce the amount of data without
information loss. Dimension reduction is considered the most popular approach for reducing
data size and also to reduce noise and redundancies in data. In this paper, we investigate the
effect of feature selection in improving the prediction of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a subset of features would mean choosing the
most important lab tests to perform. If the number of tests can be reduced by identifying the
most important tests, then we could also identify the redundant tests. By omitting the redundant
tests, observation time could be reduced and early treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be avoided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deterioration using the medical lab results. We
apply our technique on the publicly available MIMIC-II database and show the effectiveness of
the feature selection. We also provide a detailed analysis of the best features identified by our
approach.
Enhanced Detection System for Trust Aware P2P Communication NetworksEditor IJCATR
Botnet is a number of computers that have been set up to forward transmissions to other computers unknowingly to the user
of the system and it is most significant to detect the botnets. However, peer-to-peer (P2P) structured botnets are very difficult to detect
because, it doesn’t have any centralized server. In this paper, we deliver an infrastructure of P2P that will improve the trust of the peers
and its data. In order to identify the botnets we provide a technique called data provenance integrity. It will ensure the correct origin or
source of information and prevents opponents from using host resources. A reputation based trust model is used for selecting the
trusted peer. In this model, each peer has a reputation value which is calculated based on its past activity. Here a hash table is used for
efficient file searching and data stored in it is based on the reputation value.
C omparative S tudy of D iabetic P atient D ata’s U sing C lassification A lg...Editor IJCATR
Data mining refers to extracting knowledge from large amount of data. Real life data mining approaches are
interesting because they often present a different se
t of problems for
diabetic
patient’s
data
.
The
research area to solve
various problems and classification is one of main problem in the field. The research describes algorithmic discussion of J48
,
J48 Graft, Random tree, REP, LAD. Here used to compare the
performance of computing time, correctly classified
instances, kappa statistics, MAE, RMSE, RAE, RRSE and
to find the error rate measurement for different classifiers in
weka .In this paper the
data
classification is diabetic patients data set is develope
d by collecting data from hospital repository
consists of 1865 instances with different attributes. The instances in the dataset are two categories of blood tests, urine t
ests.
Weka tool is used to classify the data is evaluated using 10 fold cross validat
ion and the results are compared. When the
performance of algorithms
,
we found J48 is better algorithm in most of the cases
Comparative Study of Diabetic Patient Data’s Using Classification Algorithm i...Editor IJCATR
Data mining refers to extracting knowledge from large amount of data. Real life data mining approaches are
interesting because they often present a different set of problems for diabetic patient’s data. The research area to solve
various problems and classification is one of main problem in the field. The research describes algorithmic discussion of J48,
J48 Graft, Random tree, REP, LAD. Here used to compare the performance of computing time, correctly classified
instances, kappa statistics, MAE, RMSE, RAE, RRSE and to find the error rate measurement for different classifiers in
weka .In this paper the data classification is diabetic patients data set is developed by collecting data from hospital repository
consists of 1865 instances with different attributes. The instances in the dataset are two categories of blood tests, urine tests.
Weka tool is used to classify the data is evaluated using 10 fold cross validation and the results are compared. When the
performance of algorithms, we found J48 is better algorithm in most of the cases.
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTIONIJDKP
Developing predictive modelling solutions for risk estimation is extremely challenging in health-care
informatics. Risk estimation involves integration of heterogeneous clinical sources having different
representation from different health-care provider making the task increasingly complex. Such sources are
typically voluminous, diverse, and significantly change over the time. Therefore, distributed and parallel
computing tools collectively termed big data tools are in need which can synthesize and assist the physician
to make right clinical decisions. In this work we propose multi-model predictive architecture, a novel
approach for combining the predictive ability of multiple models for better prediction accuracy. We
demonstrate the effectiveness and efficiency of the proposed work on data from Framingham Heart study.
Results show that the proposed multi-model predictive architecture is able to provide better accuracy than
best model approach. By modelling the error of predictive models we are able to choose sub set of models
which yields accurate results. More information was modelled into system by multi-level mining which has
resulted in enhanced predictive accuracy.
CLASSIFICATION ALGORITHM USING RANDOM CONCEPT ON A VERY LARGE DATA SET: A SURVEYEditor IJMTER
Data mining environment produces a large amount of data, that need to be
analyses, pattern have to be extracted from that to gain knowledge. In this new period with
rumble of data both ordered and unordered, by using traditional databases and architectures, it
has become difficult to process, manage and analyses patterns. To gain knowledge about the
Big Data a proper architecture should be understood. Classification is an important data mining
technique with broad applications to classify the various kinds of data used in nearly every
field of our life. Classification is used to classify the item according to the features of the item
with respect to the predefined set of classes. This paper provides an inclusive survey of
different classification algorithms and put a light on various classification algorithms including
j48, C4.5, k-nearest neighbor classifier, Naive Bayes, SVM etc., using random concept.
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.
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.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
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.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
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
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.