IEEE 2021 : A
Method to Apply Convolution Neural Network Model to Detect and Classify
Tuberculosis (TB) Manifestation in X-Ray Portrayals
Abstract: In developing or poor countries, it is not the easy job to discard the Tuberculosis (TB) outbreak by the persistent social inequalities in health. The smaller number of local health care professionals like doctors and the weak healthcare apparatus found in poor expedients settings. The modern computer enlargement strategies has corrected the recognition of TB testificanduming. In this paper, It offer a paperback plan of action using Convolutional Neural Network (CNN) to handle with um-balanced; less-category X-ray portrayals (data sets), by using CNN plan of action, our plan of action boost the efficiency and correctness for stratifying multiple TB demonstration by a large margin It traverse the effectiveness and efficiency of shamble with cross validation in instructing the network and discover the amazing effect in medical portrayal classification. This plan of actions and conclusions manifest a promising path for more accurate and quicker Tuberculosis healthcare facilities recognition.
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IEEE 2020 : Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment
Abstract: COVID-19 outbreak has put
the whole world in an unprecedented difficult situation bringing life around
the world to a frightening halt and claiming thousands of lives. Due to
COVID-19’s spread in 212 countries and territories and increasing numbers of
infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22
2020), it remains a real threat to the public health system. This paper renders
a response to combat the virus through Artificial Intelligence (AI). Some Deep
Learning (DL) methods have been illustrated to reach this goal, including
Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and
Long /Short Term Memory (LSTM). It delineates an integrated bioinformatics
approach in which different aspects of information from a continuum of
structured and unstructured data sources are put together to form the
user-friendly platforms for physicians and researchers. The main advantage of
these AI-based platforms is to accelerate the process of diagnosis and
treatment of the COVID-19 disease. The most recent related publications and
medical reports were investigated with the purpose of choosing inputs and
targets of the network that could facilitate reaching a reliable Artificial
Neural Network-based tool for challenges associated with COVID-19. Furthermore,
there are some specific inputs for each platform, including various forms of
the data, such as clinical data and medical imaging which can improve the
performance of the introduced approaches toward the best responses in practical
applications.
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IEEE 2020 : Deep Learning for the Detection of COVID-19 Using Transfer Learning and Model Integration
Abstract: We researched the diagnostic capabilities of deep learning on chest radiographs and an image classifier based on the COVID-Net was presented to classify chest X-Ray images. In the case of a small amount of COVID-19 data, data enhancement was proposed to expanded COVID-19 data 17 times. Our model aims at transfer learning, model integration and classifies chest XRay images according to three labels: normal, COVID-19 and viral pneumonia. According to the accuracy and loss value, choose the models ResNet-101 and ResNet-152 with good effect for fusion, and dynamically improve their weight ratio during the training process. After training, the model can achieve 96.1% of the types of chest X-Ray images accuracy on the test set. This technology has higher sensitivity than radiologists in the screening and diagnosis of lung nodules. As an auxiliary diagnostic technology, it can help radiologists improve work efficiency and diagnostic accuracy.
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Abstract: Machine learning (ML) based forecasting mechanisms have proved their significance to anticipate in preoperative outcomes to improve the decision making on the future course of actions. The ML models have long been used in many application domains which needed the identification and prioritization of adverse factors for a threat. Several prediction methods are being popularly used to handle forecasting problems. This study demonstrates the capability of ML models to forecast the number of upcoming patients affected by COVID-19 which is presently considered as a potential threat to mankind. In particular, four standard forecasting models, such as linear regression (LR), least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and exponential smoothing (ES) have been used in this study to forecast the threatening factors of COVID-19. Three types of predictions are made by each of the models, such as the number of newly infected cases, the number of deaths, and the number of recoveries in the next 10 days. The results produced by the study prove it a promising mechanism to use these methods for the current scenario of the COVID-19 pandemic. The results prove that the ES performs best among all the used models followed by LR and LASSO which performs well in forecasting the new confirmed cases, death rate as well as recovery rate, while SVM performs poorly in all the prediction scenarios given the available dataset
IEEE 2020 : Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison
Abstract: In today’s economic scenario, credit card use has become extremely commonplace. These cards allow the user to make payments of large sums of money without the need to carry large sums of cash. They have revolutionized the way of making cashless payments and made making any sort of payments convenient for the buyer. This electronic form of payment is extremely useful but comes with its own set of risks. With the increasing number of users, credit card frauds are also increasing at a similar pace. The credit card information of a particular individual can be collected illegally and can be used for fraudulent transactions. Some Machine Learning Algorithms can be applied to collect data to tackle this problem. This paper presents a comparison of some established supervised learning algorithms to differentiate between genuine and fraudulent transactions.
Abstract: Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naïve bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyper parameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system (FCMIM-SVM) achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.
IEEE 2020 : ENGLISH FOOTBALL PREDICTION USING MACHINE LEARNING CLASSIFIERS
Abstract: Sports Analysis and Betting have been on the rise lately with the ever increasing ease of Internet accessibility and popularity of Machine Learning. This is an interesting area of research for football, as football is regarded as much more complex and dynamic when compared to a few other sports. It is also the world’s most popular sport, played in over 200 countries. Several methodologies and approaches are being taken to develop prediction systems. In this paper, we predict the match outcomes of the English Premier League, by performing a detailed study of past football matches and observing the most important attributes that are likely to decide the conclusion. We use algorithms such as Support Vector Machines, XGBoost and Logistic Regression and then select the best one to give us the target label. This model is applied on real team data and fixture results gathered from for the past few season.
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Abstract: Rainfall prediction is the one of the important technique to predict the climatic conditions in any country. This paper proposes a rainfall prediction model using Multiple Linear Regression (MLR) for Indian dataset. The input data is having multiple meteorological parameters and to predict the rainfall in more precise. The Mean Square Error (MSE), accuracy, correlation are the parameters used to validate the proposed model. From the results, the proposed machine learning model provides better results than the other algorithms in the literature.
IEEE 2020:Predicting the Impact of Android Malicious Samples via Machine Learning : Recently, Android malicious samples threaten billions of mobile end users' security or privacy. The community researchers have designed many methods to automatically and accurately identify Android malware samples. However, the rapid increase of Android malicious samples out powers the capabilities of traditional Android malware detectors and classifiers with respect to the cyber security risk management needs. It is important to identify the small proportion of Android malicious samples that may produce high cyber-security or privacy impact. In this paper, we propose a light-weight solution to automatically identify the Android malicious samples with high security and privacy impact. We manually check a number of Android malware families and corresponding security incidents and defense two impact metrics for Android malicious samples. Our investigation results in a new Android malware dataset with impact ground truth (low impact or high impact). This new dataset is employed to empirically investigate the intrinsic characteristics of low-impact as well as high-impact malicious samples. To characterize and capture Android malicious samples' pattern, reverse engineering is performed to extract semantic features to represent malicious samples. The leveraged features are parsed from both the AndroidManifest.xml _les as well as the disassembled binary classes.dex codes. Then, the extracted features are embedded into numerical vectors. Furthermore, we train highly accurate support vector machine and deep neural network classifiers to categorize the candidate Android malicious samples into low impact or high impact. The empirical results validate the effectiveness of our designed light-weight solution. This method can be further utilized for identifying those high-impact Android malicious samples in the wild.
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IEEE 2020:Predicting the Impact of Android Malicious Samples via Machine Learning
Abstract: Recently,
Android malicious samples threaten billions of mobile end users' security or
privacy.
The community researchers have designed many methods to automatically and accurately identify Android malware samples. However, the rapid increase of Android malicious samples out powers the capabilities of traditional Android malware detectors and classifiers with respect to the cyber security risk management needs. It is important to identify the small proportion of Android malicious samples that may produce high cyber-security or privacy impact. In this paper, we propose a light-weight solution to automatically identify the Android malicious samples with high security and privacy impact. We manually check a number of Android malware families and corresponding security incidents and defense two impact metrics for Android malicious samples. Our investigation results in a new Android malware dataset with impact ground truth (low impact or high impact). This new dataset is employed to empirically investigate the intrinsic characteristics of low-impact as well as high-impact malicious samples. To characterize and capture Android malicious samples' pattern, reverse engineering is performed to extract semantic features to represent malicious samples. The leveraged features are parsed from both the AndroidManifest.xml _les as well as the disassembled binary classes.dex codes. Then, the extracted features are embedded into numerical vectors. Furthermore, we train highly accurate support vector machine and deep neural nork classifiers to categorize the candidate Android malicious samples into low impact or high impact. The empirical results validate the effectiveness of our designed light-weight solution. This method can be further utilized for identifying those high-impact Android malicious samples in the wild
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IEEE 2020: Automated Mark Correction Using Natural Language Processing
Abstract: In previous time, students whatever from junior or senior have lots of examination in school. As the competition for learning grows, the frequency of exams increases. It takes time for teachers to mark these exam papers, students also need to wait for a long time to get their score. This paper is focus on estimating the score by using Machine learning methods. This method will work based on NLP and Machine Learning we first take actual answer for all the questions in test then we collect student answers make use of our model we are compare both answers actual and written based on similarity we are going to assign marks for each question finally we are calculating overall marks. In this model we are using Natural Language Process for text processing.
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IEEE2020:Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders
Abstract: Autism
Spectrum Disorder (ASD) is a group of neuro developmental disabilities that are
not curable but may be ameliorated by early interventions. We gathered
early-detected ASD datasets relating to toddlers, children, adolescents and
adults, and applied several feature transformation methods, including log,
Z-score and sine functions to these datasets. Various classification techniques
were then implemented with these transformed ASD datasets and assessed for
their performance. We found SVM showed the best performance for the toddler
dataset, while Adaboost gave the best results for the children dataset,
Glmboost for the adolescent and Adaboost for the adult datasets. The feature
transformations resulting in the best classifications was sine function for
toddler and Z-score for children and adolescent datasets. After these analyses,
several feature selection techniques were used with these Z-score-transformed
datasets to identify the significant ASD risk factors for the toddler, child,
adolescent and adult subjects. The results of these analytical approaches
indicate that, when appropriately optimised, machine learning methods can
provide good predictions of ASD status. This suggests that it may possible to
apply these models for the detection of ASD in its early stages.
IEEE2020:Analysis of Women Safety in Indian Cities Using Machine Learning on Tweets
Abstract: Women and girls have been experiencing a lot of violence and harassment in public places in various cities starting from stalking and leading to sexual harassment or sexual assault. This research paper basically focuses on the role of social media in promoting the safety of women in Indian cities with special reference to the role of social media websites and applications including Twitter platform Facebook and Instagram. This paper also focuses on how a sense of responsibility on part of Indian society can be developed the common Indian people so that we should focus on the safety of women surrounding them. Tweets on Twitter which usually contains images and text and also written messages and quotes which focus on the safety of women in Indian cities can be used to read a message amongst the Indian Youth Culture and educate people to take strict action and punish those who harass the women. Twitter and other Twitter handles which include hash tag messages that are widely spread across the whole globe sir as a platform for women to express their views about how they feel while we go out for work or travel in a public transport and what is the state of their mind when they are surrounded by unknown men and whether these women feel safe or not?
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IEEE2020:Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
Abstract: Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88:7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).
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IEEE2020: A Prediction Approach for Stock
Market Volatility Based on Time Series Data
IEEE2020:Wine Quality Prediction using Machine Learning Algorithms
Abstract: Wine classification is a difficult task since taste is the least understood of the human senses. A good wine quality prediction can be very useful in the certification phase, since currently the sensory analysis is performed by human tasters, being clearly a subjective approach. An automatic predictive system can be integrated into a decision support system, helping the speed and quality of the performance. Furthermore, a feature selection process can help to analyze the impact of the analytical tests. If it is concluded that several input variables are highly relevant to predict the wine quality, since in the production process some variables can be controlled, this information can be used to improve the wine quality. Classification models used here are 1) Random Forest 2) Stochastic Gradient Descent 3) SVC 4) Logistic Regression.
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IEEE2020:Machine Learning Techniques for Stress Prediction in Working Employees
Abstract: Stress disorders are a common issue among working IT professionals in the industry today. With changing lifestyle and work cultures, there is an increase in the risk of stress among the employees. Though many industries and corporates provide mental health related schemes and try to ease the workplace atmosphere, the issue is far from control. In this paper, we would like to apply machine learning techniques to analyze stress patterns in working adults and to narrow down the factors that strongly determine the stress levels. Towards this, data from the OSMI mental health survey 2017 responses of working professionals within the tech-industry was considered. Various Machine Learning techniques were applied to train our model after due data cleaning and preprocessing. The accuracy of the above models was obtained and studied comparatively. Boosting had the highest accuracy among the models implemented. By using Decision Trees, prominent features that influence stress were identified as gender, family history and availability of health benefits in the workplace. With these results, industries can now narrow down their approach to reduce stress and create a much comfortable workplace for their employees.
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IEEE2020: Network Intrusion Detection using Supervised Machine Learning Technique with Feature Selection
Abstract: A novel supervised machine learning system is developed to classify network traffic whether it is malicious or benign. To find the best model considering detection success rate, combination of supervised learning algorithm and feature selection method have been used. Through this study, it is found that Artificial Neural Network (ANN) based machine learning with wrapper feature selection outperform support vector machine (SVM) technique while classifying network traffic. To evaluate the performance, NSL-KDD dataset is used to classify network traffic using SVM and ANN supervised machine learning techniques. Comparative study shows that the proposed model is efficient than other existing models with respect to intrusion detection success rate.
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IEEE2020:Prediction of Quality for Different Type of Wine based on Different Feature Sets Using Supervised Machine Learning Techniques
Abstract: In
recent years, most of the industries promoting their products based on the
quality certification they received on the products. The traditional way of
assessing the product quality is time consuming, however with the invent of
machine learning techniques the processes has become more efficient and
consumed less time than before. In this paper we have explored, some of the
machine learning techniques to assess the quality of wine based on the
attributes of wine that depends on quality. We have used white wine and red
wine quality dataset for this research work. We have used different feature
selection technique such as genetic algorithm (GA) based feature selection and
simulated annealing (SA) based feature selection to check the prediction
performance. We have used different performance measure such as accuracy,
sensitivity, specificity, positive predictive value, negative predictive value
for comparison using different feature sets and different supervised machine
learning techniques. We have used nonlinear, linear and probabilistic
classifiers. We have found that feature selection-based feature sets able to
provide better prediction than considering all the features for performance prediction.
We have found accuracy ranging from 95.23% to 98.81% with different feature
sets. This analysis will help the industries to access the quality of the
products at less time and more efficient way.
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IEEE2020:Using Natural Language Processing to Extract Health-Related Causality from Twitter Messages
Abstract: Twitter
messages (tweets) contain various types of information, which include
health-related information. Analysis of health-related tweets would help us
understand health conditions and concerns encountered in our daily life. In
this work, we evaluated an approach to extracting causal relations from tweets
using natural language processing (NLP) techniques. We focused on three
health-related topics: stress", "insomnia", and
"headache". We proposed a set of lexico-syntactic patterns based on
dependency parser outputs to extract causal information. A large dataset
consisting of 24 million tweets were used. The results show that our approach
achieved an average precision between 74.59% and 92.27%. Analysis of extracted
relations revealed interesting findings about health-related in Twitter".
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IEEE2020:A Machine Learning Approach to Predict Autism Spectrum Disorder
Abstract: In present day Autism Spectrum Disorder (ASD)
is gaining its momentum faster than ever. Detecting autism traits through
screening tests is very expensive and time consuming. With the advancement of
artificial intelligence and machine learning (ML), autism can be predicted at
quite early stage. Though number of studies has been carried out using
different techniques, these studies didn't provide any definitive conclusion
about predicting autism traits in terms of different age groups. Therefore this
paper aims to propose an effective prediction model based on ML technique and
to develop a mobile application for predicting ASD for people of any age. As
outcomes of this research, an autism prediction model was developed by merging
Random Forest-CART (Classification and Regression Trees) and Random
Forest-Id3(Iterative Dichotomiser 3) and also a mobile application was
developed based on the proposed prediction model. The proposed model was
evaluated with AQ-10 dataset and 250 real dataset collected from people with
and without autistic traits. The evaluation results showed that the proposed
prediction model provide better results in terms of accuracy, specificity,
sensitivity, precision and false positive rate (FPR) for both kinds of
datasets.
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IEEE2020:Sentiment Analysis of Comment Texts Based on BiLSTM
Abstract: With the rapid development of Internet
technology and social networks, a large number of comment texts are generated
on the Web. In the era of big data, mining the emotional tendency of comments
through artificial intelligence technology is helpful for the timely
understanding of network public opinion. The technology of sentiment analysis
is a part of artificial intelligence, and its research is very meaningful for
obtaining the sentiment trend of the comments. The essence of sentiment
analysis is the text classification task, and different words have different
contributions to classification. In the current sentiment analysis studies,
distributed word representation is mostly used. However, distributed word
representation only considers the semantic information of word, but ignore the
sentiment information of the word. In this paper, an improved word
representation method is proposed, which integrates the contribution of
sentiment information into the traditional TF-IDF algorithm and generates
weighted word vectors. The weighted word vectors are input into bidirectional
long short term memory (BiLSTM) to capture the context information effectively,
and the comment vectors are better represented. The sentiment tendency of the
comment is obtained by feed forward neural network classifier. Under the same
conditions, the proposed sentiment analysis method is compared with the
sentiment analysis methods of RNN, CNN, LSTM, and NB. The experimental results
show that the proposed sentiment analysis method has higher precision, recall,
and F1 score. The method is proved to be effective with high accuracy on
comments.
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IEEE2020:Prediction of thyroid Disease Using Data Mining Techniques
Abstract: Classification based Data mining plays
important role in various healthcare services. In healthcare field, the
important and challenging task is to diagnose health conditions and proper
treatment of disease at the early stage. There are various diseases that can be
diagnosed early and can be treated at the early stage. As for example, Thyroid
diseases. the traditional ways of diagnosing thyroid diseases depends on
clinical examination and many blood tests. The Main task is to detect disease
diagnosis at the early stages with higher accuracy. Data mining techniques
plays an important role in healthcare field for making decision, disease
diagnosis and providing better treatment for the patients at low cost. Thyroid
disease Classification is an important task. The purpose of this study is
predication of thyroid disease using different classification techniques and
also to find the TSH, T3,T4 correlation towards hyperthyroidism and
hyporthyroidism and also to finding the TSH, T3,T4 correlation with gender
towards hyperthyroidism and hyporthyroidism.
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IEEE2020:Risk Prediction of Ischemic Heart Disease Using Artificial Neural Network
Abstract: The fatty plaque deposits narrow artery walls leading
to the heart Ischemic Heart Disease (IHD). For that, the flowing of blood is
reduced. Hyperpiesia polygenic disorder, drug addiction, and lipidemia are the
initiators of tachycardia. From the healthcare unit, 835 IHD patients data with
14 features like Age, Sex, Diabetes Mellitus (DM), Electrocardiograph (ECG), Exercise
Tolerance Test (ETT) etc. have been collected. By implementing artificial
neural network (ANN) techniques in our collected dataset the risk prediction is
determined around 84.47%. The precision, sensitivity, specificity, and
f1-score, for the ANN, are 79.97, 82.69, 72.63, and 85.17 respectively. The significant
contingency of tachycardia is anticipated.
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IEEE2020:A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection
Abstract: Diabetic Retinopathy (DR) is an ophthalmic disease that damages retinal blood vessels. DR causes impaired vision and may even lead to blindness if it is not diagnosed in early stages. DR has five stages or classes, namely normal, mild, moderate, severe and PDR (Proliferative Diabetic Retinopathy). Normally, highly trained experts examine the colored fundus images to diagnose this fatal disease. This manual diagnosis of this condition (by clinicians) is tedious and error-prone. Therefore, various computer vision-based techniques have been proposed to automatically detect DR and its different stages from retina images. However, these methods are unable to encode the underlying complicated features and can only classify DR’s different stages with very low accuracy particularly, for the early stages. In this research, we used the publicly available Kaggle dataset of retina images to train an ensemble of five deep Convolution Neural Network (CNN) models (Resnet50, Inceptionv3, Xception, Dense121, and Dense169) to encode the rich features and improve the classification for different stages of DR. The experimental results show that the proposed model detects all the stages of DR unlike the current methods and performs better compared to state-of-the-art methods on the same Kaggle dataset.
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IEEE2020: Agriculture Analysis Using Data Mining And Machine Learning Techniques
Abstract: Agriculture is an important application in India. The modern technologies can change the situation of farmers and decision making in agricultural field in a better way. Python is used as a front end for analyzing the agricultural data set. Jupyter Notebook is the data mining tool used to predict the crop production. The parameter includes in the dataset are precipitation, temperature, reference crop, evapo transpiration, area, production and yield for the season from January to December for the years 2000 to 2018. The data mining techniques like K-Means Clustering, KNN, SVM, and Bayesian network algorithm where high accuracy can be achieved.
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IEEE2020:Performance Analysis of Machine
Learning Techniques to Predict Diabetes Mellitus
Abstract: Diabetes mellitus is a common disease of human body caused by a group of metabolic disorders where the sugar levels over a prolonged period is very high. It affects different organs of the human body which thus harm a large number of the body's system, in particular the blood veins and nerves. Early prediction in such disease can be controlled and save human life. To achieve the goal, this research work mainly explores various risk factors related to this disease using machine learning techniques. Machine learning techniques provide efficient result to extract knowledge by constructing predicting models from diagnostic medical datasets collected from the diabetic patients. Extracting knowledge from such data can be useful to predict diabetic patients. In this work, we employ four popular machine learning algorithms, namely Support Vector Machine (SVM), Naive Bayes (NB), K-NearestNeighbor (KNN) and C4.5 Decision Tree (DT), on adult population data to predict diabetic mellitus. Our experimental results show that C4.5 decision tree achieved higher accuracy compared to other machine learning techniques.
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IEEE2020:Prediction and Diagnosis of Heart
Disease Patients using Data Mining Technique
Abstract: we are living in a post modern era and there are tremendous changes happening to our daily routines which make an impact on our health positively and negatively. As a result of these changes various kind of diseases are enormously increased. Especially, heart disease has become more common these days. The life of people is at a risk. Variation in Blood pressure, sugar, pulse rate etc. can lead to cardiovascular diseases that include narrowed or blocked blood vessels. It may causes Heart failure, Aneurysm, Peripheral artery disease, Heart attack, Stroke and even sudden cardiac arrest. Many forms of heart disease can be detected or diagnosed with different medical tests by considering family medical history and other factors. But, the prediction of heart diseases without doing any medical tests is quite difficult. The aim of this project is to diagnose different heart diseases and to make all possible precautions to prevent at early stage itself with affordable rate. We follow ‘Data mining’ technique in which attributes are fed in to SVM, Random forest, KNN, and ANN classification Algorithms for the prediction of heart diseases. The preliminary readings and studies obtained from this technique is used to know the possibility of detecting heart diseases at early stage and can be completely cured by proper diagnosis.
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IEEE20: Analysis and Detection of DDoS Attacks on
Cloud Computing Environment using Machine Learning Techniques
Abstract: The primary benefit of the cloud is that it elastically scales to meet variable demand and it provides the environment which scales up and scales down instantly according to the demand, so it needs great protection from DDoS attacks to tackle downtime effects of DDoS Attacks. Distribute Denial of Service attacks fall on the category of critical attacks that compromise the availability of the network. These attacks have become sophisticated and continue to grow at a rapid pace so to detect and counter these attacks have become a challenging task. This work was carried out on the own cloud environment using Tor Hammer as an attacking tool and a new dataset was generated with Intrusion Detection System. This work incorporates various machine learning algorithms: Support Vector Machine, Naïve Bayes, and Random Forest for classification and overall accuracy was 99.7%, 97.6% and 98.0% of Support Vector Machine, Random Forest and Naïve Bayes respectively
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IEEE2020:Prediction of Heart Disease Using Machine Learning Algorithm
Abstract: Health care field has a vast amount of data, for processing those data certain techniques are used. Data mining is one of the techniques often used. Heart disease is the Leading cause of death worldwide. This System predicts the arising possibilities of Heart Disease. The outcomes of this system provide the chances of occurring heart disease in terms of percentage. The datasets used are classified in terms of medical parameters. This system evaluates those parameters using data mining classification technique. The datasets are processed in python programming using two main Machine Learning Algorithm namely Decision Tree Algorithm and Naive Bayes Algorithm which shows the best algorithm among these two in terms of accuracy level of heart disease.
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IEEE2020: Disease Influence Measure Based Diabetic
Prediction with Medical Data Set Using Data Mining
Abstract: The problem of diabetic prediction has been well studied in this paper. The disease predictions have been explored using various methods of data mining. The use of medical data set on the prediction of diabetic mellitus has been analyzed. This paper performs a detailed survey on disease prediction using data mining approaches based on diabetic data set. The presence of disease has been identified using the appearance of various symptoms. However, the methods use different features and produces varying accuracy. The result of prediction differs with the methods/measures/ features being used. Towards diabetic prediction, a Disease Influence Measure (DIM) based diabetic prediction has been presented. The method preprocesses the input data set and removes the noisy records. In the second stage, the method estimates disease influence measure (DIM) based on the features of input data point. Based on the DIM value, the method performs diabetic prediction. Different approaches of disease prediction have been considered and their performance in disease prediction has been compared. The analysis result has been presented in detail towards the development.
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IEEE2020:Diabetes Prediction Using
Different Machine Learning Approaches
Abstract: The diabetes is one of lethal diseases in the world. It is additional a inventor of various varieties of disorders foe example: coronary failure, blindness, urinary organ diseases etc. In such case the patient is required to visit a diagnostic center, to get their reports after consultation. Due to every time they have to invest their time and currency. But with the growth of Machine Learning methods we have got the flexibility to search out an answer to the current issue, we have got advanced system mistreatment information processing that has the ability to forecast whether the patient has polygenic illness or not. Furthermore, forecasting the sickness initially ends up in providing the patients before it begins vital. Information withdrawal has the flexibility to remove unseen data from a large quantity of diabetes associated information. The aim of this analysis is to develop a system which might predict the diabetic risk level of a patient with a better accuracy. Model development is based on categorization methods as Decision Tree, ANN, Naive Bayes and SVM algorithms. For Decision Tree, the models give precisions of 85%, for Naive Bayes 77% and 77.3% for Support Vector Machine. Outcomes show a significant accuracy of the methods.
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IEEE2020:A Survey on Placement prediction system using machine learning
Abstract: The ease of making better choices and making better decisions in terms of selecting colleges is the main aim of this system. Our analysis on colleges for the students makes easier for them to make accurate decision about their preferred colleges. For such analysis, it requires future possibilities from the past record data which can potentially make the predictions and recommendation for students. Our analysis with the machine learning classification methods would help giving probable accuracy and this requires analytical methods for predicting future recommendation. Today, most students make mistakes in their preference list due to lack of knowledge, improper and incorrect analysis of colleges and insecure predictions. Hence repent and regret after allotment. Our project will solve the general issue of the student community by using machine learning technology. In this system Random Forest and Decision Tree machine learning classification algorithm is going to use.
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