scholarly journals Evaluation of Data Mining Techniques and Its Fusion with IoT Enabled Smart Technologies for Effective Prediction of Available Parking Space

2021 ◽  
Vol 12 (4) ◽  
pp. 187-197
Author(s):  
Anchal Dahiya ◽  
Pooja Mittal
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Support Vector ◽  
Learning Approaches ◽  
Parking Space ◽  
Data Mining Technique ◽  
Data Set ◽  
Data Mining Techniques ◽  
Hard Times ◽  
Smart Technologies

After experiencing the hard times of pandemic situations we learned that if we could have a smart system that can help us in automatic parking of the vehicles then it could be a great help to society. This idea motivated us to carry out this current work. Though, nowadays, in almost every application domain, IoT techniques are the buzzword. IoT techniques can also be used to achieve efficacy in predicting free available parking space in advance. But the biggest challenge with IoT techniques is that they generate numerous data, which makes its analysis intangible. It was realized that if IoT techniques can be fused with outperforming data mining techniques, more efficient predictions can be performed. Thus, for this purpose, the main objective of our paper is to firstly, select the most appropriate data mining technique, based on performance evaluation, and then to perform prediction of available parking space in advance by fusing it with IoT techniques. Due to the busy schedule, the drivers need to get information about free parking spaces in advance by using smart phones. With the help of this information, it will be easy for the drivers to park their vehicle in the exact location without wasting their precious time and will maintain social distancing in crowded areas too. Data mining techniques can play an important role in the prediction of available parking space, by extracting only relevant and important information when applied to the given dataset. For this purpose, a comparative analysis of five data mining techniques such as the Support Vector Machine, K- Nearest approach, Decision Tree, Random Forest, and Ensemble learning approaches are applied on PK lot data set by using Python language. For calculation of result anaconda (spyder) is used as a supportive tool. The main outcome of the paper is to find the technique that will give better results for the prediction of the available space and if we fused data mining techniques with IoT technologies results are improvised. Evaluation parameters that are used for finding the best technique are precision, recall, accuracy, and F1-Score. For numerical calculation of the results, the k-fold cross-validation method is used. As the empirical results are calculated using the Pk lot dataset, the decision tree outperformed the best among all the techniques that are selected for analysis.

scholarly journals Diagnosis of Various Thyroid Ailments using Data Mining Classification Techniques

2019 ◽  
pp. 131-136
Author(s):  
Umar Sidiq ◽  
Syed Mutahar Aaqib ◽  
Rafi Ahmad Khan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Research Work ◽  
Support Vector ◽  
Data Sets ◽  
Data Mining Technique ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Classification Techniques ◽  
Using Data

Classification is one of the most considerable supervised learning data mining technique used to classify predefined data sets the classification is mainly used in healthcare sectors for making decisions, diagnosis system and giving better treatment to the patients. In this work, the data set used is taken from one of recognized lab of Kashmir. The entire research work is to be carried out with ANACONDA3-5.2.0 an open source platform under Windows 10 environment. An experimental study is to be carried out using classification techniques such as k nearest neighbors, Support vector machine, Decision tree and Naïve bayes. The Decision Tree obtained highest accuracy of 98.89% over other classification techniques.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

scholarly journals Applying Data Mining Techniques on Continuous Sensed Data for Daily Living Activity Recognition

2015 ◽  
Vol 738-739 ◽  
pp. 191-196
Author(s):  
Yun Jie Li ◽  
Hui Song
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Decision Tree ◽  
Daily Living ◽  
Tree Model ◽  
Data Set ◽  
Data Mining Techniques ◽  
Daily Living Activity ◽  
The Neural Network ◽  
Better Than

In this paper, several data mining techniques were discussed and analyzed in order to achieve the objective of human daily activities recognition based on a continuous sensing data set. The data mining techniques of decision tree, Naïve Bayes and Neural Network were successfully applied to the data set. The paper also proposed an idea of combining the Neural Network with the Decision Tree, the result shows that it works much better than the typical Neural Network and the typical Decision Tree model.

scholarly journals Utilization of Machine Learning in Brain Tumor Classification

2019 ◽  
pp. 325-330
Author(s):  
Sanjeet Pandey ◽  
Brijesh Bharadwaj ◽  
Himanshu Pandey ◽  
Vineet Kr. Singh
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Early Stage ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Mining Technique ◽  
Diabetes Prediction ◽  
System Data ◽  
Health System Data ◽  
Prediction Of Diabetes

Since past few years data mining lot of attention related to knowledge like extracting methods in health care system like diabetes, cancer, CVS etc. There are lot of technique of data mining like decision tree, Naive base, KNN; J48 etc. are being used for prediction of diabetes. Diabetes is metabolic disorder related to poor absorption of insulin into body mussels or poor lowered secretion of insulin from pancreases. As this disease, this is main death causes disease in the world. So, prediction of these diseases with the help of data mining technique may help to protect many lives. In this study, we have to discuss various data mining technique, types of diabetes, application of these data mining technique. Prediction of diabetes or any other disease could play a significant role in health system. Data mining are very useful in the scenario. These techniques help in selection, understanding and designing of large size data to analysis the chances of diseases occurrence. Recently who has announced diseases a major cause of death worldwide. The prediction and identification early stage of diabetes can play major role to treat this disease significantly. Various data mining techniques like KNN, Decision tree, Naïve Bays etc. would be a significant asset for the researcher for gaining various data about diabetes, its causes, symptoms and possible treatment that have been using in the past and currently used by various physician. In this study we have briefly discussed various data mining techniques/models. Which have been currently used for diabetes prediction? Along with this discussion, we have also focused on performance and short coming of existing models/techniques time to time evaluated by researchers.

scholarly journals Prediction of active debt in the State of Pernambuco, Brazil

2020 ◽  
Vol 5 (1) ◽  
pp. 88-95
Author(s):  
Álvaro Farias Pinheiro ◽  
João Alberto Da Silva Amaral ◽  
Geraldo Torres Galindo Neto ◽  
José Nilo Martins Sampaio ◽  
Wedson Lino Soares
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
The State ◽  
Support Vector ◽  
Data Mining Techniques ◽  
Collection Process ◽  
Mining Model

Application of data mining (DM) techniques to optimize the process of collection of Active Debt (AD) of the State of Pernambuco, Brazil. We apply the following data mining techniques: Decision Tree (DT), Logistic regression (LR), Nayve bayes (NB), Support vector machine (SVM), also applied to the Random Forest technique which is considered an essemble method. We observed that the RF technique obtained better results than all the techniques of classification, reaching higher values in all metrics analyzed. We note that the creation of a data mining model to choose which debts can succeed in the collection process can bring benefits to the pernambuco government. With the application of RF technique, we obtained indexes above 85% in the evaluation of the metrics.

scholarly journals PENERAPAN DECISION TREE C4.5 SEBAGAI SELEKSI FITUR DAN SUPPORT VECTOR MACHINE (SVM) UNTUK DIAGNOSA KANKER PAYUDARA

2019 ◽  
Vol 19 (1) ◽  
pp. 54-61
Author(s):  
Pakarti Riswanto ◽  
RZ. Abdul Aziz ◽  
Sriyanto -
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Cancer Cells ◽  
Support Vector ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
New Findings ◽  
Tree Classifier

In the field of medicine, the use of data mining has a quite important and evolutionary role that can change the perspective of doctors, practitioners and health researchers in the process of detecting breast cancer in a patient. There are 2 classification applications in it, namely the process of diagnosing (diagnosing) cancer cells that distinguishes between tumors (benign cancer) or malignant cancer and prognosis (prognosis) to determine the possibility of reappearance of cancer cells in patients who have been operated on in the future. Data mining aims to describe new findings in the dataset and explain a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and related knowledge from the database.Classification with data mining can be done using several methods, namely Decision Tree, K-Nearest Neighbor, Naive Bayes, ID3, CART, Linear Discriminant Analysis, etc., which certainly have advantages and disadvantages of each. But in this study, the author focuses on the classification of data mining using the Support Vector Mechine and Deccision Tree algorithms.This study will analyze the Breast Cancer Wisconsin Original data set obtained from the UCI Machine Learning Repository (repository of research data) to classify breast cancer malignancies. This time the author correlates between the Decision Tree classifier algorithm which has good ability to process large databases as a feature selection, then with a proper and relevant SVM Method used in analyzing and diagnosing breast breast cancer patients because it has accurate results for existing problems and several bases . Keywords— Data Mining, diagnosis, Decision Tree, SVM Method

A Comparative Study on Medical Diagnosis Using Predictive Data Mining

2014 ◽  
pp. 327-360 ◽  
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Hossein Ebrahimpour-Komleh
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Medical Diagnosis ◽  
Medical Data ◽  
Support Vector ◽  
Medical Data Mining ◽  
Data Mining Techniques ◽  
Predictive Data Mining

Medical diagnosis is a most important problem in medical data mining. The possible errors of a physician can reduce with the help of data mining techniques. The goal of this chapter is to analyze and compare predictive data mining techniques in the medical diagnosis. To this purpose, various data mining techniques such as decision tree, neural networks, support vector machine, and lazy modelling are considered. Results show data mining techniques can considerably help a physician.

Top 10 data mining techniques in business applications: a brief survey

Kybernetes ◽  
2017 ◽  
Vol 46 (7) ◽  
pp. 1158-1170 ◽  
Author(s):  
Wei-Chao Lin ◽  
Shih-Wen Ke ◽  
Chih-Fong Tsai
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Supervised Learning ◽  
Bankruptcy Prediction ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Content Type ◽  
Data Mining Techniques ◽  
Business Applications ◽  
Learning Techniques

Purpose Data mining is widely considered necessary in many business applications for effective decision-making. The importance of business data mining is reflected by the existence of numerous surveys in the literature focusing on the investigation of related works using data mining techniques for solving specific business problems. The purpose of this paper is to answer the following question: What are the widely used data mining techniques in business applications? Design/methodology/approach The aim of this paper is to examine related surveys in the literature and thus to identify the frequently applied data mining techniques. To ensure the recent relevance and quality of the conclusions, the criterion for selecting related studies are that the works be published in reputed journals within the past 10 years. Findings There are 33 different data mining techniques employed in eight different application areas. Most of them are supervised learning techniques and the application area where such techniques are most often seen is bankruptcy prediction, followed by the areas of customer relationship management, fraud detection, intrusion detection and recommender systems. Furthermore, the widely used ten data mining techniques for business applications are the decision tree (including C4.5 decision tree and classification and regression tree), genetic algorithm, k-nearest neighbor, multilayer perceptron neural network, naïve Bayes and support vector machine as the supervised learning techniques and association rule, expectation maximization and k-means as the unsupervised learning techniques. Originality/value The originality of this paper is to survey the recent 10 years of related survey and review articles about data mining in business applications to identify the most popular techniques.

A Comparative Study on Medical Diagnosis Using Predictive Data Mining

2016 ◽  
pp. 923-954
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Hossein Ebrahimpour-Komleh
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Medical Diagnosis ◽  
Medical Data ◽  
Support Vector ◽  
Medical Data Mining ◽  
Data Mining Techniques ◽  
Predictive Data Mining

Medical diagnosis is a most important problem in medical data mining. The possible errors of a physician can reduce with the help of data mining techniques. The goal of this chapter is to analyze and compare predictive data mining techniques in the medical diagnosis. To this purpose, various data mining techniques such as decision tree, neural networks, support vector machine, and lazy modelling are considered. Results show data mining techniques can considerably help a physician.

Review of Data Mining Techniques Used in Healthcare

2021 ◽  
pp. 1-26
Author(s):  
Usha Gupta ◽  
Kamlesh Sharma
Keyword(s):  
Data Mining ◽  
Vital Role ◽  
Mining Machine ◽  
Support Vector ◽  
Data Set ◽  
Data Mining Techniques ◽  
Network Support ◽  
Data Mining Algorithms ◽  
Clinical Databases ◽  
Mining Algorithms

Data mining plays a vital role in converting the medical data like text, image, and graphs into meaningful new data, which helps to take the better decision. In this chapter, an overview of the current research is discussed using the data mining techniques for the finding, analysis, and prediction of various diseases. The focus of this study is to identify the well-performing data mining algorithms used on medical and clinical databases. Multiple algorithms have been identified: text-based mining, association rule-based mining, pattern-based mining, keyword-based mining, machine learning, neural network support vector machine, apriori algorithm, k-means clustering, and natural language. Analyses of the algorithm show that there is no single algorithm or model more suitable for diagnosing or predicting diseases. In some scenarios, some algorithms work very well but not in another data set. There are many examples in clinical or medical research where the combination of different algorithms gives good results.

Sign in / Sign up

Export Citation Format

Share Document