Data Mining - Concepts and Techniques.

International Journal of Computer and Applications

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

Software engineering: theory and practice

Исследование влияния пола и психологических характеристик автора на количественные параметры его текста с использованием программы Linguistic Inquiry and Word Count

Cloud computing is making big revolution in information technology field since it is advantageous than traditional systems. Outsourcing and remote data storage may stand as obstacle for security of data in cloud. Data security model (DSM) with safe data retrieval framework has been proposed which employs hyperelliptic curve cryptography for data encryption in order to avoid huge computation costs for keys involved. First approach is client-side encryption of data and then storing encrypted data in cloud. Registered users access data with valid username and password provided by client. Initially data categorization is performed with sensitivity levels being provided by clients, based on which data is placed in security sections. Various strategies utilized include 80-bit HECC encryption, SHA-3 (Secure Hash Algorithm-3) for integrity verification, data retrieval by using encrypted index and dividing data into three sections for storing in cloud. An algorithm to search files in server with multiple keywords has been proposed and implemented. We for first time implemented HECC and HECC with parallel executions in cloud with Openstack along with HECC-as-a-Service (HaaS). Detailed security and result analysis of model is provided along with comparison of scheme with existing methods which shows that model is much faster than any other model employing HECC in cloud.

Client-Controlled HECC-as-a-Service (HaaS)

The content generated by user available on social media is huge in size where the aggression in content need to be identified. The issue of aggression has to be detected when it comes to social media. Expression of aggression can either be done directly, overtly or in hidden manner. Social media content is mostly non-aggressive when considered about their nature. The proposed system works on the methods in deep learning such as CNN (Convolutional Neural Network) as well as LSTM (Long Short Term Memory Network). The first method CNN works based on further subdivided layers which are five in number including the input as well as output layer. Along with the above mentioned classifiers, ensemble method based on voting is utilized. The comments from Facebook are being used for training the model which are considered as comments from in domain. Also posts related to other social media are also being used for training that belong to cross domain. F1-score weightage for the posts related to Facebook are found to be 0.702 compared to the 0.603 for the posts related to other social media.

Prediction based on social media dataset using CNN-LSTM to classify the accurate Aggression level

research paper proposes the findings of the accuracy of the result by using the K-Means clustering technique in prediction of heart disease diagnosis with real and artificial datasets. K-Means Clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. Each cluster is assigned a random target number of clusters-k and started from a random initialization. The proposed technique classifies the group of the objects based on attributes into K number of groups. The grouping is done by minimizing the sum of squares of distances between data using Euclidean distance formula and the corresponding cluster centroid. The research result shows that the integration of clustering gives promising results with highest accuracy rate and robustness. General Terms Data mining, Clustering algorithm, Heart disease.

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Development of a Data Clustering Algorithm for Predicting Heart

In the software measurement validations, assessing the validation of software metrics in software engineering is a very difficult task due to lack of theoretical methodology and empirical methodology [41, 44, 45]. During recent years, there have been a number of researchers addressing the issue of validating software metrics. At present, software metrics are validated theoretically using properties of measures. Further, software measurement plays an important role in understanding and controlling software development practices and products. The major requirement in software measurement is that the measures must represent accurately those attributes they purport to quantify and validation is critical to the success of software measurement. Normally, validation is a collection of analysis and testing activities across the full life cycle and complements the efforts of other quality engineering functions and validation is a critical task in any engineering project. Further, validation objective is to discover defects in a system and assess whether or not the system is useful and usable in operational situation. In the case of software engineering, validation is one of the software engineering disciplines that help build quality into software. The major objective of software validation process is to determine that the software performs its intended functions correctly and provides information about its quality and reliability. This paper discusses the validation methodology, techniques and different properties of measures that are used for software metrics validation. In most cases, theoretical and empirical validations are conducted for software metrics validations in software engineering [1-50].

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Software metrics validation methodologies in software engineering

Hypertensive retinopathy (HR)is one of the severe damages caused in retinal vascular due to the hectic and abnormal upset in a human life. These damages are taken in a clinical sample by monitoring the presence and importance of all parameters that shows the increasing rate. Retina as main features for finding the variation which takes a longer duration to fix the diseases and impact in arteriolar construction and optic disk edema. In this work, a enhanced transfer learning-convolutional neural network (E-TLCNN) model is proposed for diagnosing HR using high quality images from fundus images. Therefore for understanding the results in an accurate manner transfer learning is used for classifying its stages. Also a new model using CNN architecture as DenseNet has been proposed for classifying the features to focus on the severity such as reading the diabetic retinopathy and AVR that can be utilized to spot the diseases. Dataset from Kaggle shows a 96% of accuracy in classifying the sensitivity along with its training and testing data whereas the comparison of image classification fetched less compared with K-nearest neighbor algorithm (KNN). Also based on the accuracy achieved validation process also done using the remaining images from the dataset.

T. Devi

Papers

Client-Controlled HECC-as-a-Service (HaaS)

Prediction based on social media dataset using CNN-LSTM to classify the accurate Aggression level

Development of a Data Clustering Algorithm for Predicting Heart

Software metrics validation methodologies in software engineering

E-TLCNN Classification using DenseNet on Various Features of Hypertensive Retinopathy (HR) for Predicting the Accuracy