Thursday 5 January 2017

IEEE-2019: Improving Heart Disease Prediction Using Feature Selection Approaches

IEEE-2019: Improving Heart Disease Prediction Using Feature Selection Approaches
Abstract: Heart Disease is the disorder of heart and blood veins. It is very difficult for medical practitioners and doctors to predict accurate about heart disease diagnosis. Data science is one of the more important things in early prediction and solves large data problems now days. This research paper describes the prediction of heart disease in medical field by using data science. As many researches done research related to that problem but the accuracy of prediction is still needed to be improved. So, this research focuses on feature selection techniques and algorithms where multiple heart disease datasets are used for experimentation analysis and to show the accuracy improvement. By using the Rapid miner as tool; Decision Tree, Logistic Regression, Logistic Regression SVM, Naïve Bayes and Random Forest; algorithms are used as feature selection techniques and improvement is shown in the results by showing the accuracy.

IEEE-2019: 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.




IEEE-2018: A Novel Mechanism for Fast Detection of Transformed Data Leakage
Abstract: Data leakage is a growing insider threat in information security among organizations and individuals. A series of methods have been developed to address the problem of data leakage prevention (DLP). However, large amounts of unstructured data need to be tested in the Big Data era. As the volume of data grows dramatically and the forms of data become much complicated, it is a new challenge for DLP to deal with large amounts of transformed data. We propose an Adaptive weighted Graph Walk model (AGW) to solve this problem by mapping it to the dimension of weighted graphs. Our approach solves this problem in three steps. First, the adaptive weighted graphs are built to quantify the sensitivity of tested data based on its context. Then, the improved label propagation is used to enhance the scalability for fresh data. Finally, a low-complexity score walk algorithm is proposed to determine the ultimate sensitivity. Experimental results show that the proposed method can detect leaks of transformed or fresh data fast and efficiently.



IEEE-2018: Machine Learning Methods for Disease Prediction with Claims Data 
 Abstract: One of the primary challenges of healthcare delivery is aggregating disparate, asynchronous data sources into meaningful indicators of individual health. We combine natural language word embedding and network modeling techniques to learn meaningful representations of medical concepts by using the weighted network adjacency matrix in the GloVe algorithm, which we call Code2Vec. We demonstrate that using our learned embeddings improve neural network performance for disease prediction. However, we also demonstrate that popular deep learning models for disease prediction are not meaningfully better than simpler, more interpretable classifiers such as XGBoost. Additionally, our work adds to the current literature by providing a comprehensive survey of various machine learning algorithms on disease prediction tasks.




IEEE 2017: Privacy and Secure Medical Data Transmission and Analysis for Wireless Sensing Healthcare System
Abstract :The convergence of Internet of Things (IoT), cloud computing and wireless body-area networks (WBANs) has greatly promoted the industrialization of e-/m-healthcare (electronic-/mobile-healthcare). However, the further flourishing of e-/m-Healthcare still faces many challenges including information security and privacy preservation. To address these problems, a healthcare system (HES) framework is designed that collects medical data from WBANs, transmits them through an extensive wireless sensor network infrastructure and finally publishes them into wireless personal area networks (WPANs) via a gateway. Furthermore, HES involves the GSRM (Groups of Send-Receive Model) scheme to realize key distribution and secure data transmission, the HEBM (Homomorphic Encryption Based on Matrix) scheme to ensure privacy and an expert system able to analyze the scrambled medical data and feed back the results automatically.

IEEE 2017: Privacy-Preserving Location-Proximity for Mobile Apps

Abstract :Location Based Services (LBS) have seen alarming privacy breaches in recent years. While there has been much recent progress by the research community on developing privacy-enhancing mechanisms for LBS, their evaluation has been often focused on the privacy guarantees, while the question of whether these mechanisms can be adopted by practical LBS applications has received limited attention. This paper studies the applicability of Privacy-Preserving Location Proximity (PPLP) protocols in the setting of mobile apps. We categorize popular location social apps and analyze the tradeoffs of privacy and functionality with respect to PPLP enhancements. To investigate the practical performance trade-offs, we present an in-depth case study of an Android application that implements Inner Circle, a state-of-the-art protocol for privacy preserving location proximity. This study indicates that the performance of the privacy-preserving application for coarsegrained precision is comparable to real applications with the same feature set.
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IEEE 2017: IoT based Home Security through Digital Image Processing Algorithms
Abstract- This paper gives an outline for automatic system to control and secure the home, based on digital image processing with the help of Internet of Things (IoT). The system consists of a sensor, digital camera, database in the fog and the mobile phone. Sensors are placed in the frame of the door which alerts camera, to capture an image who intends to enter the house, then sends the image to the database or dataset that is stored in the fog. Image analysis is performed to detect and recognize and match the image with the stored dataset of the authenticated people or pets. If the image captured does not match with the dataset then an alert message is send to the owner of the house. The image processing algorithms are considered for the processing spatial and time complexity of the image captured to cross check with the dataset stored in the fog.
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