The objective of this paper is to provide an insight on effect of stringency in Covid-19 spread in India especially in Chennai, a city were more lockdown, and restrictions was imposed to control the infection. Even though the restriction was imposed in the country by the end of March 2020, the growth reduction was seen in the mid of June as the awareness was increased. The average Covid-19 case growth was got reduce from 3.43 to 2.62% by July mid. To analysis the impact of stringency, a detailed analysis was done on Chennai city which was imposed with more repeated lockdowns to flatten the curve. We tried to fit a regression line with three difference scenario of data. The results show a promising R -squared and p value, with a right skewed distribution normal probability plot. The impact of lockdown in people’s lives in different sectors were also discussed in this paper.

All our day"s work in this world of science is done with useful data. These useful data or information is extracted from raw facts. From the information, knowledge is gained. This knowledge is used by the customers for beneficial outcome. Here comes the concept of data mining. An important query arises as how to preserve these data. This concept is called as Privacy Preserving with Data Mining (PPDM). Many PPDM techniques are available to protect the data. The PPDM technique is useful in fields like medicine, forensics, defence, etc to preserve the confidential data. The existing techniques protect the secret data either by perturbing or by hiding them. Moreover, most of the techniques focus only on the numerical data. Very few perturbation techniques like translation, multiplicative and rotation perturb the images. But these techniques are very easily attacked by third parties since the transformation is a linear one. The security strength of these perturbation techniques is very low. Other perturbation techniques such as k-Anonymity, Rule Hiding and Data Swapping are applicable to numerical data. Excessive k-Anonymity also leads to data loss. Morphological operations greatly change the shape and structure of an image. But they are also reactive to noise and intrusions on the boundaries of the image. The privacy level of the images with these perturbation techniques is very low. The Research Work aims to overcome the drawbacks of the existing perturbation techniques. The objective of the Research Work is to improve the privacy level of the images and to strengthen the security of images by implementing a Random Projection technique. The scope of the Research Work is confined to medical images. Medical images with various dimensions such as 2D, 3D and 5D are taken. Dimensionality Reduction plays a key role in this Research Work. Dimensionality of the input images is reduced by applying Random Projection technique.

All our day’s work in this world of science is done with useful data. These useful data or information is extracted from raw facts. From the information, knowledge is gained. This knowledge is used by the customers for beneficial outcome. Here comes the concept of data mining. An important query arises as how to preserve these data. This concept is called as Privacy Preserving with Data Mining (PPDM). Many PPDM techniques are available to protect the data. The PPDM technique is useful in fields like medicine, forensics, defence, etc to preserve the confidential data. The existing techniques protect the secret data either by perturbing or by hiding them. Moreover, most of the techniques focus only on the numerical data. Very few perturbation techniques like translation, multiplicative and rotation perturb the images. But these techniques are very easily attacked by third parties since the transformation is a linear one. The Research Work aims to overcome the drawbacks of the existing perturbation techniques. The main objective of the Research Work is to improve the privacy level of the images by implementing a Random Projection (RP) technique. The remarkable features and benefits of the RP technique are pinpointed.

Nowadays, there is a consistently expanding relocation of individuals to urban territories. Human services administrations is a Standout amongst the most difficult perspectives that is extraordinarily influenced by the immense deluge of individuals to downtown areas. In such change a great many homes are being with brilliant gadgets which create huge volume s of indexical information that can be dissected to help savvy city administrations. In this paper, we propose a model that uses savvy home huge information as a methods for learning and finding human movement designs for wellbeing applicati ons.for this we utilize visit design mining, bunch investigation and forecast to gauge and dissect vitality utilization changes the tenants behaviour. Since, individuals propensities are generally disti nguished by ordinary schedules, demonstrates in dividuals’ troubles in taking tend to themselves, for example, not planning nourishment or not utilizing shower/bath.our places of business the need to break down transient vitality utilization at mach ine level, which is straightforwardly identified with human exercises. This exploration utilizes the UK Domestic Appliance Level Electricity dataset(UK-Dale) time arrangement information of energy utilization gathered from 2012-2015 with time determ ination of six seconds for five houses with 109 app aratus from SouthernEngland.The information from shrewd meters are mined in the quantum/information cut of 24hrs, and the outcomes are kept up cro-sswise over progressive mining exercises.the after effect of distinguishing human movement desig-ns from machine use are displayed in points of interest.

Cloud computing has become a significant technology trend, and plenty of consultants expect that cloud computing can reshape data technology (IT) and also the IT marketplace. In this paper, we are suggesting a safety mechanism that provides open analysis on shared knowledge within the cloud. During this mechanism, the individual of the underwriter on every sq. in shared statistics are going to be hold on in private from open verifies, who can proficiently verify the shared records without improving the entire report. This mechanism can play out numerous comparing undertakings at the identical time instead of checking them separately. This framework provides a security saving options of open inspecting part for shared facts at intervals the cloud. we have a tendency to area unit mistreatment ring marks to make homomorphy authentication, so thereto, AN open verify will offer an summary on shared data while not obtaining the entire facts, but it cannot be understanding that who is the underwriter on every piece. To get the effectiveness of confirming numerous examining undertakings, we facilitate to increase our tool for better examining. Certainly, the tractability implies the capacity to gather administrator to find the person of the underwriter in the metadata of few great instances