BCA Final year projects in Tiruppur
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BCA Final year projects in Tiruppur Syllabus
- Privacy-Preserving Multi-keyword Top-k Similarity Search Over Encrypted Data (Cloud Computing)
Cloud computing provides individuals and enterprises massive computing power and scalable storage capacities to support a variety of big data applications in domains like health care and scientific research, therefore more and more data owners are involved to outsource their data on cloud servers for great convenience in data management and mining. However, data sets like health records in electronic documents usually contain sensitive information, which brings about privacy concerns if the documents are released or shared to partially un-trusted third-parties in cloud. A practical and widely used technique for data privacy preservation is to encrypt data before outsourcing to the cloud servers, which however reduces the data utility and makes many traditional data analytic operators like keyword-based top-k document retrieval obsolete. In this paper, we investigate the multi-keyword top-k search problem for big data encryption against privacy breaches, and attempt to identify an efficient and secure solution to this problem. Specifically, for the privacy concern of query data, we construct a special tree-based index structure and design a random traversal algorithm, which makes even the same query to produce different visiting paths on the index, and can also maintain the accuracy of queries unchanged under stronger privacy. For improving the query efficiency, we propose a group multi-keyword top-k search scheme based on the idea of partition, where a group of tree-based indexes are constructed for all documents. Finally, we combine these methods together into an efficient and secure approach to address our proposed top-k similarity search. Extensive experimental results on real-life data sets demonstrate that our proposed approach can significantly improve the capability of defending the privacy breaches, the scalability and the time efficiency of query processing over the state-of-the-art methods
2. Query Expansion with Enriched User Profiles for Personalized Search Utilizing Folksonomy Data (Data Mining)
Query expansion has been widely adopted in Web search as a way of tackling the ambiguity of queries. Personalized search utilizing folksonomy data has demonstrated an extreme vocabulary mismatch problem that requires even more effective query expansion methods. Co-occurrence statistics, tag-tag relationships and semantic matching approaches are among those favored by previous research. However, user profiles which only contain a user’s past annotation information may not be enough to support the selection of expansion terms, especially for users with limited previous activity with the system. We propose a novel model to construct enriched user profiles with the help of an external corpus for personalized query expansion. Our model integrates the current state-of-the-art text representation learning framework, known as word embeddings, with topic models in two groups of pseudo-aligned documents. Based on user profiles, we build two novel query expansion techniques. These two techniques are based on topical weights-enhanced word embeddings, and the topical relevance between the query and the terms inside a user profile respectively. The results of an in-depth experimental evaluation, performed on two real-world datasets using different external corpora, show that our approach out performs traditional techniques, including existing non-personalized and personalized query expansion methods.
- Robust Big Data Analytics for Electricity Price Forecasting in the Smart Grid
Electricity value prognostication may be a important a part of good grid as a result of it makes smart grid price economical. Withal, existing strategies for value prognostication is also tough to handle with vast price information within the grid, since the redundancy from feature choice cannot be averted associated an integrated infrastructure is additionally lacked for coordinative the procedures in electricity value prognostication. To unravel such a drag, a unique electricity value prognostication model is developed. Specifically, 3 modules are integrated within the planned model. First, by merging of Random Forest (RF) and Relief-F rule, we tend to propose a hybrid feature selector supported gray Correlation Analysis (GCA) to eliminate the feature redundancy. Second, associate integration of Kernel perform and Principle part Analysis (KPCA) is employed in feature extraction method to appreciate the spatiality reduction. Finally, to forecast value classification, we tend to hints a differential evolution (DE) primarily based Support Vector Machine (SVM) classifier. Our planned electricity value prognostication model is complete via these 3 elements. Numerical results show that our proposal has superior performance than alternative strategies.
- Scalable Uncertainty-Aware Truth Discovery in Big Data Social Sensing Applications for Cyber-Physical Systems
Social sensing may be a new huge knowledge application paradigm for Cyber-Physical Systems (CPS), wherever a gaggle of people volunteer (or are recruited) to report measurements or observations concerning the physical world at scale. A elementary challenge in social sensing applications lies in discovering the correctness of according observations and responsibility of information sources while not previous knowledge on either of them. We have a tendency to check with this downside as truth discovery. Where as previous studies have created progress on addressing this challenge, 2 vital limitations exist: (i) current solutions failed to totally explore the uncertainty side of human according knowledge that results in sub-optimal truth discovery results; (ii) current truth discovery solutions are largely designed as sequent algorithms that don’t scale well to large-scale social sensing events. during this paper, we have a tendency to develop a climbable Uncertainty-Aware Truth Discovery (SUTD) theme to handle the higher than limitations. The SUTD theme solves a constraint estimation downside to collectively estimate the correctness of according data and therefore the responsibility of information sources whereas expressly considering the uncertainty on the reported data. to handle the quantifiability challenge, the SUTD is meant to run a Graphic process Unit (GPU) with thousands of cores, that is shown to run 2 to a few orders of magnitude quicker than the sequent truth discovery solutions. In analysis, we have a tendency to compare our SUTD theme to the progressive solutions victimization 3planet datasets collected from Twitter: Paris Attack, Oregon Shooting, and Baltimore Riots, beat 2015. The analysis results show that our new theme considerably out performs the baselines in terms of each truth discovery accuracy and execution time.
- Privacy-Preserving Social Media Data Publishing for Personalized Ranking-Based Recommendation (Machine learning)
Personalized recommendation is crucial to assist users notice pertinent info. It usually depends on an oversizedassortment of user knowledge, above all users’ on-line activity (e.g., tagging/rating/checking-in) on social media, to mine user preference. However, cathartic such user activity knowledge makes users liable to logical thinkingattacks, as non-public knowledge (e.g., gender) will usually be inferred from the users’ activity knowledge. during this paper, we have a tendency to planned PrivRank, a customizable and continuous privacy-preserving social media knowledge business framework protective users against logical thinking attacks whereas sanctionativecustomized ranking-based recommendations. Its key plan is to endlessly change user activity knowledge such the privacy outflow of user-specified non-public knowledge is decreased beneath a given knowledge distortion budget, that bounds the ranking loss incurred from the info obfuscation method so as to preserve the utility of the info for sanctionative recommendations. Associate in Nursing empirical analysis on each artificial and real-world knowledgesets shows that our framework will expeditiously give effective and continuous protection of user-specified non-public data, whereas still protective the utility of the obfuscated knowledge for customized ranking-based recommendation. Compared to progressive approaches, PrivRank achieves each an improved privacy protection and the next utility altogether the ranking-based recommendation use cases we have a tendency totested.
- Scalable Content-Aware Collaborative Filtering for Location Recommendation (Data Mining)
Location recommendation plays a vital role in serving to folks notice engaging places. tho’ recent analysis has studied the way to advocate locations with social and geographical info, few of them self-addressed the cold-start drawback of recent users. as a result of quality records ar typically shared on social networks, linguistics info may be leveraged to tackle this challenge. A typical methodology is to feed them into explicit-feedback-based content-aware cooperative filtering, however they need drawing negative samples for higher learning performance, as users’ negative preference isn’t discernible in human quality. However, previous studies have through empirical observation shown sampling-based ways don’t perform well. to the present finish, we tend to propose a climbableImplicit-feedback-based Content-aware cooperative Filtering (ICCF) framework to include linguistics content and to steer afar from negative sampling. we tend to then develop associate economical optimisation formula, scaling linearly with information size and have size, and quadratically with the dimension of latent house. we tend toadditional establish its relationship with graph Laplacian regularised matrix factorisation. Finally, we tend to judgeICCF with a large-scale LBSN dataset within which users have profiles and matter content. The results show that ICCF outperforms many competitive baselines, which user info isn’t solely effective for up recommendations however conjointly addressing cold-start situations.
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