2019-2020 IEEE python Machine Learning Projects

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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 data-set are precipitation, temperature, reference crop, evapotranspiration, 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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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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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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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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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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Abstract : Human Activity Recognition (HAR) based on sensor networks is an important research direction in the fields of pervasive computing and body area network. Existing researches often use statistical machine learning methods to manually extract and construct features of different motions. However, in the face of extremely fast-growing waveform data with no obvious laws, the traditional feature engineering methods are becoming more and more incapable. With the development of Deep Learning technology, we do not need to manually extract features and can improve the performance in complex human activity recognition problems. By migrating deep neural network experience in image recognition, we propose a deep learning model (InnoHAR) based on the combination of Inception Neural Network and recurrent neural network. The model inputs the waveform data of multi-channel sensors end-to-end. Multi-dimensional features are extracted by Inception-like modules with using of various kernel-based convolution layers. Combined with GRU, modeling for time series features is realized, making full use of data characteristics to complete classification tasks. Through experimental verification on three most widely used public HAR datasets, our proposed method shows consistent superior performance and has good generalization performance, when compared with state-of-the-arts.

 

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IEEE Python machine learning Projects

Project CODE
TITLES
BASEPAPER
SYNOPSIS
LINKS
1.  IEEE 2018:Predictive Analysis of Sports Data using Google Prediction API Title Title Title
2.  IEEE 2018:Phishing Web Sites Features Classification Based on Extreme Learning Machine Title Title Title
3.  IEEE 2018:Credit card fraud detection using Machine Learning Techniques Title Title Title
4.  IEEE 2018:Animal classification using facial images with score-level fusion Title Title Title
5.  IEEE 2018:Leveraging Deep Preference Learning for Indexing and Retrieval of Biomedical Images Title Title Title
6.  IEEE 2018:Machine learning approach for brain tumor detection Title Title Title
7.  IEEE 2018:Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection: A Comparative Study Title Title Title
8.  IEEE 2018:Zaman Serisi Verilerini Kullanarak Makine Öğrenmesi Yöntemleri ile Bitcoin Fiyat Tahmini Prediction of Bitcoin Prices with Machine Learning Methods using Time Series Data Title Title Title
9.  IEEE 2018:Research on Personalized Referral Service and Big Data Mining for E-commerce with Machine Learning Title Title Title
10.  IEEE 2018:Classifying Depressed Users With Multiple Instance Learning from Social Network Data Title Title Title
11.  IEEE 2018:Breast Cancer Diagnosis Using Adaptive Voting Ensemble Machine Learning Algorithm Title Title Title
12.  IEEE 2018:Application of machine learning in recommendationsystems Title Title Title
13.  IEEE 2017:Point-of-interest Recommendation for Location Promotion in Location-based Social Networks Title Title Title
14. IEEE 2017:NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media Title Title Title
15. IEEE 2017:SocialQ&A: An Online Social Network Based Question and Answer System Title Title Title
16. IEEE 2017:Modeling Urban Behavior by Mining Geotagged Social Data Title Title Title
17. IEEE 2017:A Workflow Management System for Scalable Data Mining on Clouds Title Title Title
18. IEEE 2016:SPORE: A Sequential Personalized Spatial Item Recommender System Title Title Title
19. IEEE 2016:Inverted Linear Quadtree: Efficient Top K Spatial Keyword Search Title Title Title
20. IEEE 2016: Truth Discovery in Crowd sourced Detection of Spatial Events Title Title Title
21. IEEE 2016: Sentiment Analysis of Top Colleges in India Using Twitter Data Title Title Title
22. IEEE 2016: FRAppE: Detecting Malicious Facebook Applications Title Title Title
23. IEEE 2016: Practical Approximate k-Nearest Neighbor Queries with Location and Query Privacy Title Title Title
23. IEEE 2016: A Novel Pipeline Approach for Efficient Big Data Broadcasting Title Title Title
24. IEEE 2016:VoteTrust: Leveraging Friend Invitation Graph to Defend against Social Network Sybils Title Title Title
25. IEEE 2016:A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data Title Title Title
26. IEEE 2016:SmartCrawler: A Two-Stage Crawler for Efficiently Harvesting Deep- Web Interfaces Title Title Title
27. IEEE 2016: FiDoop: Parallel Mining of Frequent Itemsets Using MapReduce Title Title Title

About IEEE python Machine Learning Projects

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.IEEE python Machine Learning Projects Click here.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.IEEE python Machine Learning Projects Click here.

IEEE python Machine Learning Projects
IEEE python Machine Learning Projects
IEEE python Machine Learning Projects