In healthcare machine learning is used mainly for disease diagnosis or acute condition detection based on patient data analysis. In the proposed work diabetic patient dataset analysis is done for hypoglycemia detection which means the lowering of blood glucose level. Often in healthcare it is observed that the dataset is imbalanced. Therefore an Ensemble Approach using imbalanced dataset techniques Synthetic Minority Over-sampling Technique and Adaptive Synthetic oversampling methods with different evaluation methods like train-test, k-fold, Stratified K-Fold and repeat train-test were used. This ensemble approach was implemented on diabetic dataset using K-Nearest Neighbor, Support Vector Machine, Random Forest, Naïve Bayes and Logistic Regression classifiers with average Stacking-C method thereafter to conclude. Comparative analysis was done using three different considerations. The results showed that KNN and Random forest gives more stable metric values both on balanced and imbalanced dataset. The confusion matrix consideration concluded that KNN and Random Forest were found to be better with least false negative and maximum true positive count. But if average train and test time is taken into consideration then Naïve Bayes and Random forest had least average train-test time. Thus the three different considerations concluded that the proposed ensemble approach gives better clarity for different classifier implementation using machine learning.
Multimedia Messaging Service (MMS) is a new standard in mobile messaging. Like SMS, MMS is a way to send a message from one mobile to another. MMS can include not just text, but also sound, images and video. For making MMS secure, steganography can be used with it. Without having privacy of data there is no meaning of doing communication using extremely high end technologies like SMS or MMS. This can be achieved by using steganography, which is the process of hiding secret information inside some carrier. SMS and MMS are can be used as carrier for hiding information on mobile devices. For insisting more security, encrypted data will be hidden inside MMS. As mobile devices have less memory and less processing power, we cannot use computation intensive encryption algorithms like AES, DES, and RSA. Elliptic Curve Cryptography (ECC) is emerging as an attractive alternative to traditional public-key cryptosystems. ECC offers equivalent security with smaller key sizes resulting in faster computations, lower power consumption, as well as memory and bandwidth savings. In my paper, I have proposed a method of encrypting text with ECC and then hiding encrypted text in MMS. SMS are limited to 160 character messages while MMS has no size limit. Biggest use of MMS is likely to be for companies for sending MMS messages to subscribers, enquirers or customers or for banks for sending secret information like PINS/Passwords etc. The computational burden of ECC can be minimized by executing ECC with multiple threads.
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