Abstract

Recommendation systems can be considered as one of the most popular tools to raise the profit and retain users. In most of the fields Recommender systems are being used. The aim of the recommender system is that it suggests content for users based on their previous choices or what type of taste they are having. When there is a need for implementing an effective recommender system, it should always be diverse in content and it is not supposed to be biased towards the most popular content. In this perspective, the content-based filtering will provide well-suited results for the user. This research study attempts to propose a Recommender system for suggesting consecutive appropriate books for the user to read. The proposed recommender system is designed by using item-based collaborative filtering, content-based collaborative filtering (using Title, Author, Publisher, Category as features), Content-Based Collaborative Filtering (using Summary as a feature), Custom Recommender and at the end different recommenders are compared.

Abstract

Data mining is a process which involves sorting large data sets and spots patterns and relationships which will help solve problems through data analysis. Data mining techniques plus tools enable big enterprises to forecast future coming trends and is then able to make better decisions make more-informed business decisions hence this study. Thyroid disease is a medical problem that occurs when one’s thyroid fails to produce enough hormones. This disease is known to affect everyone of all ages and all genders.in order to identify these disorders are detected by blood tests are taken, and are however difficult to analyze because of the vast amounts of blood samples of data to be forecasted. because of this barrier this study allows us to compare two algorithms to determine the best in results output enabling us to have a quick reaction to these disorders. Thyroid diseases have become the most common especially amongst African in the African continent with continent with 68- 72%population affected while 4-6% affected yearly are women between the age of 18-25. The causes of thyroid diseases are different which further leads to different types of thyroid diseases and resulting disorders from just the popular known goiter to a cancerous goiter. The diseases are further classified into two the normal thyroid and the abnormal thyroid. This paper will be of comparison analysis of thyroid diseases using the unsupervised algorithms k means and fuzzy c in the African continent. In addition, the imagining in medical systems for thyroid diseases has a lot of research today. Effects caused by thyroid diseases are known to be uncomfortable and when managed well they may result positively. Hence when it is a simple goiter is can be cured naturally, but when it becomes cancerous then it may result in diseases like myxema coma. In order to cure this the measures like k means or fuzzy c keywords cluster, fuzzy, kmeans, Africa, thyroid, diseases

Abstract

Healthcare is very important to lead a good life. However, it is very difficult to obtain the consultation with the doctor for every health problem. The idea is to create a medical chatbot using Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor. This will help to reduce healthcare costs and improve accessibility to medical knowledge through medical chatbot. The chatbots are computer programs that use natural language to interact with users. The chatbot stores the data in the database to identify the sentence keywords and to make a query decision and answer the question. Ranking and sentence similarity calculation is performed using n-gram, TFIDF and cosine similarity. The score will be obtained for each sentence from the given input sentence and more similar sentences will be obtained for the query given. The third party, the expert program, handles the question presented to the bot that is not understood or is not present in the database.

Abstract

Diabetes is one of the significant diseases directly or indirectly responsible for fatalities across the world. Data mining techniques are used in the health care sector to diagnose and early detection of ailments. The factors which contribute to diabetes are considered in the present work. We are investigating the features which are contributing towards individuals getting affected by diabetes. The independence or dependence of existing features in any individual has been investigated. The supervised learning algorithm shows an accuracy of 94.65%.

Abstract

In India, the crime rate is increasing each day. In the current situation, recent technological influence, effects of social media and modern approaches help the offenders to achieve their crimes. Both analysis and prediction of crime is a systematized method that classifies and examines the crime patterns. There exist various clustering algorithms for crime analysis and pattern prediction but they do not reveal all the requirements. Among these, K means algorithm provides a better way for predicting the results. The proposed research work mainly focused on predicting the region with higher crime rates and age groups with more or less criminal tendencies. We propose an optimized K means algorithm to lower the time complexity and improve efficiency in the result.

Abstract

Clustering techniques have been widely adopted in many real world data analysis applications, such as customer behavior analysis, targeted marketing, digital forensics, etc. With the explosion of data in today’s big data era, a major trend to handle a clustering over large-scale datasets is outsourcing it to public cloud platforms. This is because cloud computing offers not only reliable services with performance guarantees, but also savings on in-house IT infrastructures. However, as datasets used for clustering may contain sensitive information, e.g., patient health information, commercial data, and behavioral data, etc, directly outsourcing them to public cloud servers inevitably raise privacy concerns. In this paper, we propose a practical privacy-preserving K-means clustering scheme that can be efficiently outsourced to cloud servers. Our scheme allows cloud servers to perform clustering directly over encrypted datasets, while achieving comparable computational complexity and accuracy compared with clusterings over unencrypted ones. We also investigate secure integration of MapReduce into our scheme, which makes our scheme extremely suitable for cloud computing environment. Thorough security analysis and numerical analysis carry out the performance of our scheme in terms of security and efficiency. Experimental evaluation over a 5 million objects dataset further validates the practical performance of our scheme.

Abstract

Sentiment analysis is used for Natural language Processing, text analysis, text preprocessing, Stemming etc. are the major research field in current time. Sentiment analysis using different techniques and tools for analyze the unstructured data in a manner that objective results can be generated from them. Basically, these techniques allow a computer to understand what is being said by humans. Sentiment analysis uses different techniques to determine the sentiment of a text or sentence. The Internet is a large repository of natural language. People share their thoughts and experiences which are subjective in nature. Many a time, getting suitable information about a product can became tedious for customers. Companies may not be fully aware of customer requirements. Product reviews can be analyzed to understand the sentiment of the people towards a particular topic. However, these are voluminous; therefore a summary of positive and negative reviews needs to be generated. In this paper, the main focus is on the review of algorithms and techniques used for extract feature wise summary of the product and analyzed them to form an authentic review. Future work will include more product reviews websites and will focus on higher level natural language processing tasks. Using best and new techniques or tool for more accurate result in which the system except only those keywords which are in dataset rest of the words are eliminated by the system.

Abstract

In today’s world, opinions and reviews accessible to us are one of the most critical factors in formulating our views and influencing the success of a brand, product or service. With the advent and growth of social media in the world, stakeholders often take to expressing their opinions on popular social media, namely Twitter. While Twitter data is extremely informative, it presents a challenge for analysis because of its humongous and disorganized nature. This paper is a thorough effort to dive into the novel domain of performing sentiment analysis of people’s opinions regarding top colleges in India. Besides taking additional preprocessing measures like the expansion of net lingo and removal of duplicate tweets, a probabilistic model based on Bayes’ theorem was used for spelling correction, which is overlooked in other research studies. This paper also highlights a comparison between the results obtained by exploiting the following machine learning algorithms: Naïve Bayes and Support Vector Machine and an Artificial Neural Network model: Multilayer Perceptron. Furthermore, a contrast has been presented between four different kernels of SVM: RBF, linear, polynomial and sigmoid.

Abstract

The price of the stocks is an important indicator for a company and many factors can affect their values. Different events may affect public sentiments and emotions differently, which may have an effect on the trend of stock market prices. Because of dependency on various factors, the stock prices are not static, but are instead dynamic, highly noisy and nonlinear time series data. Due to its great learning capability for solving the nonlinear time series prediction problems, machine learning has been applied to this research area. Learning-based methods for stock price prediction are very popular and a lot of enhanced strategies have been used to improve the performance of the learning based predictors. However, performing successful stock market prediction is still a challenge. News articles and social media data are also very useful and important in financial prediction, but currently no good method exists that can take these social media into consideration to provide better analysis of the financial market. This paper aims to successfully predict stock price through analyzing the relationship between the stock price and the news sentiments. A novel enhanced learning-based method for stock price prediction is proposed that considers the effect of news sentiments. Compared with existing learning-based methods, the effectiveness of this new enhanced learning-based method is demonstrated by using the real stock price data set with an improvement of performance in terms of reducing the Mean Square Error (MSE). The research work and findings of this paper not only demonstrate the merits of the proposed method, but also points out the correct direction for future work in this area.

Abstract

The rapid increase in mountains of unstructured textual data accompanied by proliferation of tools to analyse them has opened up great opportunities and challenges for text mining research. The automatic labelling of text data is hard because people often express opinions in complex ways that are sometimes difficult to comprehend. The labelling process involves huge amount of efforts and mislabelled datasets usually lead to incorrect decisions. In this paper, we design a framework for sentiment analysis with opinion mining for the case of hotel customer feedback. Most available datasets of hotel reviews are not labelled which presents a lot of works for researchers as far as text data pre-processing task is concerned. Moreover, sentiment datasets are often highly domain sensitive and hard to create because sentiments are feelings such as emotions, attitudes and opinions that are commonly rife with idioms, onomatopoeias, homophones, phonemes, alliterations and acronyms. The proposed framework is termed sentiment polarity that automatically prepares a sentiment dataset for training and testing to extract unbiased opinions of hotel services from reviews. A comparative analysis was established with Naïve Bayes multinomial, sequential minimal optimization, compliment Naïve Bayes and Composite hypercubes on iterated random projections to discover a suitable machine learning algorithm for the classification component of the framework.

Abstract

The skyline operator has attracted considerable attention recently due to its broad applications. However, computing a skyline is challenging today since we have to deal with big data. For data-intensive applications, the MapReduce framework has been widely used recently. In this paper, we propose the efficient parallel algorithm SKY-MR + for processing skyline queries using MapReduce. We first build a quadtree-based histogram for space partitioning by deciding whether to split each leaf node judiciously based on the benefit of splitting in terms of the estimated execution time. In addition, we apply the dominance power filtering method to effectively prune non-skyline points in advance. We next partition data based on the regions divided by the quadtree and compute candidate skyline points for each partition using MapReduce. Finally, we check whether each skyline candidate point is actually a skyline point in every partition using MapReduce. We also develop the workload balancing methods to make the estimated execution times of all available machines to be similar. We did experiments to compare SKY-MR + with the state-of-the-art algorithms using MapReduce and confirmed the effectiveness as well as the scalability of SKY-MR .

Abstract

Traditional parallel algorithms for mining frequent itemsets aim to balance load by equally partitioning data among a group of computing nodes. We start this study by discovering a serious performance problem of the existing parallel Frequent Itemset Mining algorithms. Given a large dataset, data partitioning strategies in the existing solutions suffer high communication and mining overhead induced by redundant transactions transmitted among computing nodes. We address this problem by developing a data partitioning approach called FiDoop-DP using the MapReduce programming model. The overarching goal of FiDoop-DP is to boost the performance of parallel Frequent Itemset Mining on Hadoop clusters. At the heart of FiDoop-DP is the Voronoi diagram-based data partitioning technique, which exploits correlations among transactions. Incorporating the similarity metric and the Locality-Sensitive Hashing technique, FiDoop-DP places highly similar transactions into a data partition to improve locality without creating an excessive number of redundant transactions. We implement FiDoop-DP on a 24-node Hadoop cluster, driven by a wide range of datasets created by IBM Quest Market-Basket Synthetic Data Generator. Experimental results reveal that FiDoop-DP is conducive to reducing network and computing loads by the virtue of eliminating redundant transactions on Hadoop nodes. FiDoop-DP significantly improves the performance of the existing parallel frequent-pattern scheme by up to 31 percent with an average of 18 percent.

Abstract

User preferences play a significant role in market analysis. In the database literature, there has been extensive work on query primitives, such as the well known top-k query that can be used for the ranking of products based on the preferences customers have expressed. Still, the fundamental operation that evaluates the similarity between products is typically done ignoring these preferences. Instead products are depicted in a feature space based on their attributes and similarity is computed via traditional distance metrics on that space. In this work, we utilize the rankings of the products based on the opinions of their customers in order to map the products in a user-centric space where similarity calculations are performed. We identify important properties of this mapping that result in upper and lower similarity bounds, which in turn permit us to utilize conventional multidimensional indexes on the original product space in order to perform these user-centric similarity computations. We show how interesting similarity calculations that are motivated by the commonly used range and nearest neighbor queries can be performed efficiently, while pruning significant parts of the data set based on the bounds we derive on the user-centric similarity of products.

Abstract

In this project, we developed a mathematical model to predict the success and failure of the upcoming movies based on several attributes. Some of the criteria in calculating movie success included budget, actors, director, producer, set locations, story writer, movie release day, competing movie releases at the same time, music, release location and target audience. The success prediction of a movie plays a vital role in movie industry because it involves huge investments. However, success cannot be predicted based on a particular attribute. So, we have built a model based on interesting relation between attributes. The movie industry can use this model to modify the movie criteria for obtaining likelihood of blockbusters. Also, this model can be used by movie watchers in determining a blockbuster before purchasing a ticket. Each of the criteria involved was given a weight and then the prediction was made based on these. For example, if a movies budget was below 5 million, the budget was given a lower weight. Depending on the number of actors, directors and producers past successful movies, each of these categories was given a weight. If the movie was to be released on a weekend, it was given higher weight because the chances of success were greater. If with the release of a movie, there was another high success movie released, a lower weight was given to the release time indicating that the chances of movie success were low due to the competition. The criteria were not limited just to the ones mentioned. There were be additional factors discussed in this work. We have conducted our work with simulation data.

Abstract

Now a days, huge amount data has generated on the internet and it is important to extract useful information from that huge data. Different data mining techniques are used to extract and implement to solve divers types of problems. In the era of News and blogs, there is need to extract news and need to analyze to determine opinion of that news reviews. Sentiment analysis finds an opinion i.e. positive or negative about particular subject. Negation is a very common morphological creation that affects polarity and therefore, needs to be taken into reflection in sentiment analysis. Automatic detection of negation from News article is a need for different types of text processing applications including Sentiment Analysis and Opinion Mining. Our system uses online news databases from one resources namely BBC news. While handling news articles, we executed three subtasks namely categorizing the objective, separation of good and bad news content from the articles and performed preprocessing of data is cleaned to get only what is required for analysis, Steps like tokenization, stop word removal etc. The currently work focuses on different computational methods modeling negation in sentiment analysis. Especially, on aspects level of representation used for sentiment analysis, negation word recognition and scope of negation and identification.

Abstract

Nowadays, expansion of social media and internet are driving to a whole another level. Most of the users critically review anything on the internet specially foods and services in restaurants to showcase their humble opinion. These opinions are very valuable in decision making process. Analyzing and extracting the actual opinion throughout these reviews manually is practically difficult since there are large numbers of reviews available in the various aspects. So, an automated methodology is needed to solve this problem. Opinion mining or sentiment analysis is such methodology to analysis these reviews and classify topics as positive, negative and neutral. There are three different levels of opinion mining; Document based, Sentence based and Aspect based. Document and Sentence based opinion mining focus on overall polarity of document and sentence respectively and do not describe the important aspects of each opinions which is more accurate. Hence Aspect based opining mining is the trending topic and this paper is specifically focused on it on reviews in the domain of restaurants.

Abstract

In this project, we developed a mathematical model to predict the success and failure of the upcoming movies based on several attributes. Some of the criteria in calculating movie success included budget, actors, director, producer, set locations, story writer, movie release day, competing movie releases at the same time, music, release location and target audience. The success prediction of a movie plays a vital role in movie industry because it involves huge investments. However, success cannot be predicted based on a particular attribute. So, we have built a model based on interesting relation between attributes. The movie industry can use this model to modify the movie criteria for obtaining likelihood of blockbusters. Also, this model can be used by movie watchers in determining a blockbuster before purchasing a ticket. Each of the criteria involved was given a weight and then the prediction was made based on these. For example, if a movies budget was below 5 million, the budget was given a lower weight. Depending on the number of actors, directors and producers past successful movies, each of these categories was given a weight. If the movie was to be released on a weekend, it was given higher weight because the chances of success were greater. If with the release of a movie, there was another high success movie released, a lower weight was given to the release time indicating that the chances of movie success were low due to the competition. The criteria were not limited just to the ones mentioned. There were be additional factors discussed in this work. We have conducted our work with simulation data.

Abstract

Fraud detection is a scenario applicable to many industries such as banking and financial sectors, insurance, healthcare, government agencies and law enforcement and more. There has been a drastic increase in recent years, pushing fraud detection more important than ever. Hundreds of millions of dollars are lost to fraud every year. Upcoding fraud is one such fraud in which a service provider acquires additional financial gain by coding a service by upgrading it even though the lesser service has been performed. Incorporating artificial intelligence with data mining and statistics help to anticipate and detect these frauds and minimize costs. Using sophisticated data mining tools, millions of transcations can be searched to spot patterns and detect fraudulent transactions. This paper gives an insight into the various datamining tools which are efficient in detecting upcoding frauds especially in the healthcare insurance sector in India.

Abstract

Question and Answer (Q&A) systems play a vital role in our daily life for information and knowledge sharing. Users post questions and pick questions to answer in the system. Due to the rapidly growing user population and the number of questions, it is unlikely for a user to stumble upon a question by chance that (s)he can answer. Also, altruism does not encourage all users to provide answers, not to mention high quality answers with a short answer wait time. The primary objective of this paper is to improve the performance of Q&A systems by actively forwarding questions to users who are capable and willing to answer the questions. To this end, we have designed and implemented SocialQ&A, an online social network based Q&A system. SocialQ&A leverages the social network properties of common-interest and mutual-trust friend relationship to identify an asker through friendship who are most likely to answer the question, and enhance the user security. We also improve SocialQ&A with security and efficiency enhancements by protecting user privacy and identifies, and retrieving answers automatically for recurrent questions. We describe the architecture and algorithms, and conducted comprehensive large-scale simulation to evaluate SocialQ&A in comparison with other methods. Our results suggest that social networks can be leveraged to improve the answer quality and asker’s waiting time. We also implemented a real prototype of SocialQ&A, and analyze the Q&A behavior of real users and questions from a small-scale real-world SocialQ&A system.

Abstract

With 20 million installs a day , third-party apps are a major reason for the popularity and addictiveness of Facebook. Unfortunately, hackers have realized the potential of using apps for spreading malware and spam. The problem is already significant, as we find that at least 13% of apps in our dataset are malicious. So far, the research community has focused on detecting malicious posts and campaigns. In this paper, we ask the question: Given a Facebook application, can we determine if it is malicious? Our key contribution is in developing FRAppE-Facebook’s Rigorous Application Evaluator-arguably the first tool focused on detecting malicious apps on Facebook. To develop FRAppE, we use information gathered by observing the posting behavior of 111K Facebook apps seen across 2.2 million users on Facebook. First, we identify a set of features that help us distinguish malicious apps from benign ones. For example, we find that malicious apps often share names with other apps, and they typically request fewer permissions than benign apps. Second, leveraging these distinguishing features, we show that FRAppE can detect malicious apps with 99.5% accuracy, with no false positives and a high true positive rate (95.9%). Finally, we explore the ecosystem of malicious Facebook apps and identify mechanisms that these apps use to propagate. Interestingly, we find that many apps collude and support each other; in our dataset, we find 1584 apps enabling the viral propagation of 3723 other apps through their posts. Long term, we see FRAppE as a step toward creating an independent watchdog for app assessment and ranking, so as to warn Facebook users before installing apps.

Abstract

We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques.

Abstract

Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.

Abstract

The increasing influence of social media and enormous participation of users creates new opportunities to study human social behavior along with the capability to analyze large amount of data streams. One of the interesting problems is to distinguish between different kinds of users, for example users who are leaders and introduce new issues and discussions on social media. Furthermore, positive or negative attitudes can also be inferred from those discussions. Such problems require a formal interpretation of social media logs and unit of information that can spread from person to person through the social network. Once the social media data such as user messages are parsed and network relationships are identified, data mining techniques can be applied to group different types of communities. However, the appropriate granularity of user communities and their behavior is hardly captured by existing methods. In this paper, we present a framework for the novel task of detecting communities by clustering messages from large streams of social data. Our framework uses K-Means clustering algorithm along with Genetic algorithm and Optimized Cluster Distance (OCD) method to cluster data. The goal of our proposed framework is twofold that is to overcome the problem of general K-Means for choosing best initial centroids using Genetic algorithm, as well as to maximize the distance between clusters by pairwise clustering using OCD to get an accurate clusters. We used various cluster validation metrics to evaluate the performance of our algorithm. The analysis shows that the proposed method gives better clustering results and provides a novel use-case of grouping user communities based on their activities. Our approach is optimized and scalable for real-time clustering of social media data.

Abstract

Data mining has been a current trend for attaining diagnostic results. Huge amount of unmined data is collected by the healthcare industry in order to discover hidden information for effective diagnosis and decision making. Data mining is the process of extracting hidden information from massive dataset, categorizing valid and unique patterns in data. There are many data mining techniques like clustering, classification, association analysis, regression etc. The objective of our paper is to predict Chronic Kidney Disease(CKD) using classification techniques like Naive Bayes and Artificial Neural Network(ANN). The experimental results implemented in Rapidminer tool show that Naive Bayes produce more accurate results than Artificial Neural Network.

Abstract

The Cloud is increasingly being used to store and process big data for its tenants and classical security mechanisms using encryption are neither sufficiently efficient nor suited to the task of protecting big data in the Cloud. In this paper, we present an alternative approach which divides big data into sequenced parts and stores them among multiple Cloud storage service providers. Instead of protecting the big data itself, the proposed scheme protects the mapping of the various data elements to each provider using a trapdoor function. Analysis, comparison and simulation prove that the proposed scheme is efficient and secure for the big data of Cloud tenants.

Abstract

Sentiment classification is a topic-sensitive task, i.e., a classifier trained from one topic will perform worse on another. This is especially a problem for the tweets sentiment analysis. Since the topics in Twitter are very diverse, it is impossible to train a universal classifier for all topics. Moreover, compared to product review, Twitter lacks data labeling and a rating mechanism to acquire sentiment labels. The extremely sparse text of tweets also brings down the performance of a sentiment classifier. In this paper, we propose a semi-supervised topic-adaptive sentiment classification (TASC) model, which starts with a classifier built on common features and mixed labeled data from various topics. It minimizes the hinge loss to adapt to unlabeled data and features including topic-related sentiment words, authors’ sentiments and sentiment connections derived from“@” mentions of tweets, named as topic-adaptive features. Text and non-text features are extracted and naturally split into two views for co-training. The TASC learning algorithm updates topic-adaptive features based on the collaborative selection of unlabeled data, which in turn helps to select more reliable tweets to boost the performance. We also design the adapting model along a timeline (TASC-t) for dynamic tweets. An experiment on 6 topics from published tweet corpuses demonstrates that TASC outperforms other well-known supervised and ensemble classifiers. It also beats those semi-supervised learning methods without feature adaption. Meanwhile, TASC-t can also achieve impressive accuracy and F-score. Finally, with timeline visualization of “river” graph, people can intuitively grasp the ups and downs of sentiments’ evolvement, and the intensity by color gradation.

Abstract

Heart disease is a most harmful one that will cause death. It has a serious long term disability. This disease attacks a person so instantly. Medical data is still information rich but knowledge poor. Therefore diagnosing patients correctly on the basis of time is an exigent function for medical support. An invalid diagnosis done by the hospital leads for losing reputation. The precise diagnosis of heart disease is the dominant biomedical issue. The motivation of this paper is to develop an efficacious treatment using data mining techniques that can help remedial situations. Further data mining classification algorithms like decision trees, neural networks, Bayesian classifiers, Support vector machines, Association Rule, K- nearest neighbour classification are used to diagnosis the heart diseases. Among these algorithms Support Vector Machine (SVM) gives best result.

Abstract

With the advancement in technology, industry, e-commerce and research a large amount of complex and pervasive digital data is being generated which is increasing at an exponential rate and often termed as big data. Traditional Data Storage systems are not able to handle Big Data and also analyzing the Big Data becomes a challenge and thus it cannot be handled by traditional analytic tools. Cloud Computing can resolve the problem of handling, storage and analyzing the Big Data as it distributes the big data within the cloudlets. No doubt, Cloud Computing is the best answer available to the problem of Big Data storage and its analyses but having said that, there is always a potential risk to the security of Big Data storage in Cloud Computing, which needs to be addressed. Data Privacy is one of the major issues while storing the Big Data in a Cloud environment. Data Mining based attacks, a major threat to the data, allows an adversary or an unauthorized user to infer valuable and sensitive information by analyzing the results generated from computation performed on the raw data. This thesis proposes a secure k-means data mining approach assuming the data to be distributed among different hosts preserving the privacy of the data. The approach is able to maintain the correctness and validity of the existing k-means to generate the final results even in the distributed environment.

Abstract

In our proposed system is identifying reliable information in the medical domain stand as building blocks for a healthcare system that is up-to-date with the latest discoveries. By using the tools such as NLP, ML techniques. In this research, focus on diseases and treatment information, and the relation that exists between these two entities. The main goal of this research is to identify the disease name with the symptoms specified and extract the sentence from the article and get the Relation that exists between Disease-Treatment and classify the information into cure, prevent, side effect to the user.This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.

Abstract

Information extraction (IE) and knowledge discovery in databases (KDD) are both useful approaches for discovering information in textual corpora, but they have some deficiencies. Information extraction can identify relevant sub-sequences of text, but is usually unaware of emerging, previously unknown knowledge and regularities in a text and thus cannot form new facts or new hypotheses. Complementary to information extraction, emerging data mining methods and techniques promise to overcome the deficiencies of information extraction. This research work combines the benefits of both approaches by integrating data mining and information extraction methods. The aim is to provide a new high-quality information extraction methodology and, at the same time, to improve the performance of the underlying extraction system. Consequently, the new methodology should shorten the life cycle of information extraction engineering because information predicted in early extraction phases can be used in further extraction steps, and the extraction rules developed require fewer arduous test-and-debug iterations. Effectiveness and applicability are validated by processing online documents from the areas of eHealth and eRecruitment.

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