Mobile users are increasing exponentially to adopt ubiquitous services offered by various sectors. This has attracted attention for a secure communication framework to access e-health data on mobile devices. The wearable sensor device is attached to the patient's body which monitors the blood pressure, body temperature, serum cholesterol, glucose level, etc. In the proposed secure framework, first, the task starts with the patient authentication, after that the sensors device linked to the patient is activated and the sensor values of the patient are transmitted to the cloud server. The patient's biometrics information has been added as a parameter in addition to the user name and password. The authentication scheme is coined with the SHA-512 algorithm that ensures integrity. To securely send the sensor information, the method follows two kinds of encryption: Substitution-Ceaser cipher and improved Elliptical Curve Cryptography (IECC). Whereas in improved ECC, an additional key (secret key) is generated to enhance the system's security. In this way, the intricacy of the two phases is augmented. The computational cost of the scheme in the proposed framework is 4H + Ec + Dc which is less than the existing schemes. The average correlation coefficient value is about 0.045 which is close to zero shows the strength of the algorithm. The obtained encryption and decryption time are 1.032 µs and 1.004µs respectively. The overall performance is analyzed by comparing the proposed improved ECC with existing Rivest-Shamir-Adleman (RSA)and ECC algorithms.

Ensemble learning method is a collaborative decision-making mechanism that implements to aggregate the predictions of learned classifiers in order to produce new instances. Early analysis has shown that the ensemble classifiers are more reliable than any single part classifier, both empirically and logically. While several ensemble methods are presented, it is still not an easy task to find an appropriate configuration for a particular dataset. Several prediction-based theories have been proposed to handle machine learning crime prediction problem in India. It becomes a challenging problem to identify the dynamic nature of crimes. Crime prediction is an attempt to reduce crime rate and deter criminal activities. This work proposes an efficient authentic method called assemble-stacking based crime prediction method (SBCPM) based on SVM algorithms for identifying the appropriate predictions of crime by implementing learning-based methods, using MATLAB. The SVM algorithm is applied to achieve domain-specific configurations compared with another machine learning model J48, SMO Naïve byes bagging and, the Random Forest. The result implies that a model of a performer does not generally work well. In certain cases, the ensemble model outperforms the others with the highest coefficient of correlation, which has the lowest average and absolute errors. The proposed method achieved 99.5% classification accuracy on the testing data. The model is found to produce more predictive effect than the previous researches taken as baselines, focusing solely on crime dataset based on violence. The results also proved that any empirical data on crime, is compatible with criminological theories. The proposed approach also found to be useful for predicting possible crime predictions. And suggest that the prediction accuracy of the stacking ensemble model is higher than that of the individual classifier.

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The rapidly growing, complex and key area of multimedia processing is medical area where the huge amount of patient?s data is disregarded. Further, medial fields used to interrelate with multimedia formats because of their daily real communication. Healthcare associations are renovating themselves into added efficient, coordinated and user-centered methods through several upcoming techniques. Though, the management of large information such as patient information, medical reports escorts to augments the human labors and security hazards. Therefore, to conquer these problems, IoT-healthcare came into existence that enhances the patients care by reducing the cost of resources in an effective way. Along with lot of IoT benefits, associations are afraid to use them because of its compromise initiated by several intruders. For benefiting their own purposes, intruders hack the patient?s online record for selling to third party for doing the researchers analysis. Further, doctors also make the money for their concession by forcing the patients to buy the medicines from their recommended firms. So as to avert these problems, Blockchain technology has been came across in healthcare systems that may keep track of every activity of the entities. The patients and other entities are recognized through bio metric methods so that even if hackers try to compromise the devices or to access the stored information; the patients may easily identify the intruders or alteration in stored record. In this paper, we have projected a secure IoT healthcare mechanism using Blockchain technique that initially identify the patients and other entities using bio metric passwords and further can analyze the modifications in their records. The proposed scheme results have been validated against traditional method by offering 88% success rate over certain parametric scenarios such as grey hole attack, falsification attack and probabilistic authentication scenarios.