Shilpi Sharma

The Hidden Truth Anonymity in Cyberspace: Deep Web

The main objective of this paper is to study and illustrate the workings of the invisible Internet and practically determining the Onion Routing relays, bridges and exit nodes. A detailed research has been done on the working of exit nodes and privacy over the deep web through vulnerable nodes. This paper also illustrates a practical study of the depth of data available on the deep web. Emphasis is laid down towards the safe access to deep web without compromising one’s privacy. A survey was conducted in the paper to show the awareness among the technically sound public and results were shown in pie charts.

Improved Exemplar-Based Image Inpainting Approach

Image inpainting is a very common technique which is widely used to bring back or recover an image when it gets destroyed, or when there is an intention to perform a morphological operation on an image. It is a simple process to fill up the pixels of a particular region. The task is very similar to that of a skilled painter who has to draw or remove an object from its painting on its canvas. Inpainting’s application may vary from its usage which includes object removal, object replacement etc. The aim of this process is to change the image properties by removing or adding objects. It has been also used to recover lost pixels or block of pixels of an image during a transmission of image through a noisy channel. It has been a useful method for red-eye removal and default stamped date from photographs clicked through primitive cameras. In this paper, we have built an exemplar-based image inpainting tool using basic functions of MATLAB. We have discussed the results by comparing the output generated by the image inpainting tool with Adobe Photoshop results to compare the results and check how much efficient the tool is.

Extensible Platform of Crowdsourcing on Social Networking Sites: An Analysis

The evolutionary history of the human brain shows advancement in its complexity and creativity during its evolutionary path from early primates to hominids, and finally to Homo sapiens. This most powerful human asset known as the brain is highly capable of solving problems. When a problem arises, humans make use of their intelligence and various methods of finding the solution. No doubt they have come up with the best solutions, but many questions have been raised on how that problem is approached and how the solution is derived. The peculiar thing is that everyone has a different mechanism of thinking and comes up with different patterns of solutions. Can this pattern be mimicked by a machine where a problem can be solved by inputs from multiple individuals? Crowdsourcing and neural networks come into play in this domain. Crowdsourcing deals with the pooling of ideas by people. The more people, the wider the perspective obtained. The data given by them are processed and the field of neural networks plays a vital role in analyzing the data. These data contain various patterns and hidden solutions to many problems.

Impact of Facebook’s Check-in Feature on Users of Social Networking Sites

Social media is infiltrating the planet. Facebook has been the leader in this industry for almost a decade. The social media sites have made many changes over the years regarding security and advertising that have frankly, miffed a lot of people. The paper mainly describes about the impact of Facebook Check-in feature. Also, a new feature is proposed- Check-in-Checker which is presently not yet introduced in Facebook. Check-in Checker is an extension of the existing Facebook feature check-in. It is a helpful tool to know who is going where and at what time. Also, popularity about a particular common place being visited by friends will increase. Data was gathered from 550 people who give their views about how they use Facebook. Also, their opinion is taken in account about the existing feature ‘Check-in’ provided by Facebook. Analyzing the data, the new feature Check-in Checker suits today’s demand and requirement of a user, to be added up in Facebook’s features list. This feature doesn’t violates any user’s privacy and holds good number of positive reasons to be there in the pool of Facebook’s exciting features.

Analysis on data mining models for Internet Of Things

This papers aim is to establish the relationship between Data Mining and the Internet of Things. Four models for data mining have been discussed indicating some challenges of the data for Internet of Things which are multi-layer model, distributed model, grid based model and big data model. The key issues about the models like collection of data, data abstraction and aggregation, event filtering etc. are also discussed. After reviewing the key issues, a new model has been proposed. In addition, a tweet sentiment analyzer project has been made on the concept of data mining using the python language.

A recent trends on Big Data analytics

Society is transforming into logically more instrumented and therefore, associations zone unit fabricating and putting away massive measures of information. Overseeing and picking up bits of knowledge from the made learning might be a test and key to upper hand. Investigation arrangements that procedure organized and unstructured data are vital as they will encourage associations pick up bits of knowledge and concentrate data not exclusively from their in private non-inheritable learning, however furthermore from monster measures of data publically available on the net. One of the real uses of future era parallel and conveyed frameworks is in enormous information investigation. Information storehouses for such applications at present surpass Exabyte and are quickly expanding in size. Past their sheer extent, these datasets and related applications contemplations posture huge difficulties for strategy and programming improvement. Datasets are regularly dispersed and their size and security contemplations warrant conveyed strategies. Information regularly lives on stages with broadly changing computational and system abilities. Contemplations of adaptation to non-critical failure, security, and get to control are basic in numerous applications (Dean and Ghemawat, 2004; Apache hadoop). Investigation undertakings frequently have hard due dates and information quality is a noteworthy worry in yet different applications. For most rising applications, information driven models and techniques, fit for working at scale, are so far obscure. Notwithstanding when known strategies can be scaled, approval of results is a noteworthy issue. Attributes of equipment stages and the product stack generally affect information examination. In this article, we give a diagram of the state of-the-workmanship and concentrate on rising patterns to highlight the equipment, programming, and application scene of enormous information examination.

Novel technique for prediction analysis using normalization for an improvement in K-means clustering

Clustering is the unsupervised classification of spatterns in a dataset. Clustering is widely used to discover distributed patterns and classify them as clusters. Clustering algorithms uses a similarity measure based on distance. In order to cluster data points, k-means uses Euclidean distance measure and central point choice. In the K-means clustering, data points will be stacked and a central point is chosen. From the central point chosen, Euclidean distance will be computed and on that basis clusters will be assigned to the data points. One of the drawbacks of K-means is that numbers of clusters has to be provided due to which some data points remains un-clustered. In this paper, we propose a clustering calculation through which number of clusters can be characterised naturally. The proposed technique will improve accuracy and decrease clustering time moreover cluster quality will also be improved through multiple iterations.

Bang of Social Engineering in Social Networking Sites

This research paper is a brief study on social engineering that explores the internet awareness among males and females of different age groups. In our study, we have researched on how an individual shares his/her identity and sensitive information which directly or indirectly affects them on social networking sites. This information can be user’s personal identification traits, their photos, visited places, etc. The parameters chosen for influence of social engineering in social networking sites are passwords, share ability, and awareness. This research briefly explains how people between age group of 13–40 years share their information over the web and their awareness of netiquettes. This information is then conclusively used to calculate average amount of sensitive information which can be extracted through social engineering for different age groups of males and females.

EncryptPost: A Framework for User Privacy on Social Networking Sites

Social networking sites are gaining popularity among Internet users. As users are enjoying this new style of networking, the privacy concerns are also attracting public attention due to privacy breaches in social networking sites. We propose a framework that protects user privacy on a social networking site by shielding a user’s personal post or messages, other service providers or third parties who are not explicitly authorized by the user to view the content. The architecture maintains the usability of sites services and stores sensitive information in encrypted form on a separate server. Our result shows that the proposed framework successfully conceals a user’s personal information, while allowing the user and his friends to explore Social networking site services as usual.

Modeling the Decline of Orkut with Popularity in Facebook

Social networking services are gaining popularity at a very fast rate. Social networking websites like Facebook, Twitter, Myspace and many more have been active among people for a long time, thus providing users many features to enjoy online. Apart from great success of these networking websites, there have been various reasons for the downfall of a known website- Orkut. This paper concludes various reasons for the downfall of Orkut and helps in understanding the popularity of Facebook. The outcome of this paper will help in understanding whether Facebook will have a downfall like Orkut or there may be some other reasons for its downfall.