MCA Final year projects in Tirupur

Why Glim Technologies, Best MCA Final year projects institute in Tirupur?

Best MCA Final year projects in Tirupur provides IEEE projects on .Net, Java, Python, Hadoop, etc. Best MCA Final year projects in Tirupur provided by certified professional with more than 15 years of experience. MCA Projects is given by our real time experts who have more experience in that Particular domain. The method will be based on the real time project scenarios with 100% of IEEE projects. MCA Projects content is framed to cover both experienced and Non-experienced students in Computers.

What we do at Glim Technologies, best MCA Final year projects in Tirupur?
Glim Technologies, provides latest IEEE Projects for MCA students. Our Project faculty has lot of Real time experience and well qualified professional to deliver quality projects. The faculty has 10+ years of experience and handled many corporate projects in India. Glim Technologies is recognized as Best MCA Final year projects in Tirupur by the students. For all the students, Domain basics is covered to understand the fundamental concepts.

Whom we do projects at GLIM, best MCA Final year projects in Tirupur?
We are more experienced and providing IEEE projects and real time projects across all experience 0 -18 years and we have separate Projects content for MCA students. We customize the syllabus covered according to the role requirements in the IEEE.

MCA final year projects in Tirupur

  1. Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites (Artificial Intelligence)
  2. Online reviews became a vital supply of data for users before creating associate informed purchase call. Early reviews of a product tend to own a high impact on the following product sales. During this paper, we tend to take the initiative to check the behavior characteristics of early reviewers through their announce reviews on 2 real-world giant e-commerce platforms, i.e., Amazon and Yelp. In specific, we tend to divide product life into 3consecutive stages, particularly early, majority and laggards. A user United Nations agency has announced a review within the early stage is taken into account as associate early reviewer. We tend to quantitatively characterize early reviewers supported their rating behaviors, the helpfulness scores received from others and also the correlation of their reviews with product quality. We’ve found that (1) associate early reviewer tends to assign the next average rating score; associated (2) an early reviewer tends to post a lot of useful reviews. Our analysis of product reviews additionally indicates that early reviewer’ ratings and their received helpfulness scores square measure probably to influence product quality. By viewing review posting method as a multiplayer competition game, we tend to propose a completely unique margin-based embedding model for early reviewer prediction. Intensive experiments on 2 totally different e-commerce datasets have shown that our planned approach outperforms variety of competitive baselines.
    1. Frequent Itemsets Mining With Differential Privacy Over Large-Scale Data (Data Mining)

    Frequent itemsets mining with differential privacy refers to the matter of mining all frequent itemsets whose supports ar higher than a given threshold in an exceedingly given transactional dataset, with the constraint that the strip-mined results mustn’t break the privacy of any single dealing. Current solutions for this drawback cannot well balance potency, privacy, and information utility over large-scale information. Toward this finish, we have a tendency to propose associate economical, differential personal frequent itemsets mining formula over large-scale information. supported the concepts of sampling and dealing truncation victimization length constraints, our formula reduces the computation intensity, reduces mining sensitivity, and therefore improves information utility given a set privacy budget. Experimental results show that our formula achieves higher performance than previous approaches on multiple datasets.

    1. Dynamic Facet Ordering for Faceted Product Search Engines(Data Mining)

    Faceted browsing is widely used in Web shops and product comparison sites. In these cases, a fixed ordered list off acets is often employed. This approach suffers from two main issues. First, one needs to invest a significant amount of time to devise an effective list. Second, with a fixed list of facets it can happen that a facet becomes useless if all products that match the query are associated to that particular facet. In this work, we present a framework for dynamic facet ordering in e-commerce. Based on measures for specificity and dispersion of facet values, the fully automated algorithm ranks those properties and facetson top that lead to a quick drill-down for any possible target product. In contrast to existing solutions, the framework addresses-commerce specific aspects, such as the possibility of multiple clicks, the grouping of facets by their corresponding properties, and the abundance of numeric facets. In a large-scale simulation and user study, our approach was, in general, favorably compared to a facet list created by domain experts, a greedy approach as baseline, and a state-of-the-art entropy-based solution.

    1. Efficient Keyword-aware Representative Travel Route Recommendation (Data Mining)

    With the popularity of social media (e.g., Facebook and Flicker), users can easily share their check-in records and photos during their trips. In view of the huge number of user historical mobility records in social media, we aim to discover travel experiences to facilitate trip planning. When planning a trip, users always have specific preferences regarding their trips. Instead of restricting users to limited query options such as locations, activities or time periods, we consider arbitrary text descriptions as keywords about personalized requirements. Moreover, a diverse and representative set of recommended travel routes is needed. Prior works have elaborated on mining and ranking existing routes from check-in data. To meet the need for automatic trip organization, we claim that more features of Places of Interest (POIs) should be extracted. Therefore, in this paper, we propose an efficient Keyword-aware Representative Travel Route framework that uses knowledge extraction from users’ historical mobility records and social interactions. Explicitly, we have designed a keyword extraction module to classify the POI-related tags, for effective matching with query keywords. We have further designed a route reconstruction algorithm to construct route candidates that fulfill the requirements. To provide befitting query results, we explore Representative Skyline concepts, that is, the Skyline routes which best describe the trade-offs among different POI features. To evaluate the effectiveness and efficiency of the proposed algorithms, we have conducted extensive experiments on real location-based social network datasets, and the experiment results show that our methods do indeed demonstrate good performance compared to state-of-the-art works.

    1. Disease Prediction by Machine Learning over Big Data from Healthcare Communities

    With huge information growth in medicine and health care communities, correct analysis of medical information edges early illness detection, patient care and community services. But the analysis accuracy is reduced once the standard of medical information is incomplete. Moreover, completely different regions exhibit distinctive characteristics of sure regional diseases, which can weaken the prediction of illness outbreaks. During this paper, we have a tendency to contour machine learning algorithms for effective prediction of chronic illness happening in disease-frequent communities. We have a tendency to experiment the changed prediction models over real-life hospital information collected from central China in 2013-2015. to beat the problem of incomplete information, we have a tendency to use a latent issue model to reconstruct the missing information. We have a tendency to experiment on a regional chronic illness of cerebral pathology. We have a tendency to propose a brand new convolution neural network based mostly multimodal illness risk prediction (CNN-MDRP)algorithm victimization structured and unstructured information from hospital. To the simplest of our data, none of the present work centered on each information varieties within the space of medical huge information analytics Compared to many typical prediction algorithms, the prediction accuracy of our planned rule reaches ninety four.8% with a convergence speed that is quicker than that of the CNN-based uni-modal illness risk prediction (CNN-UDRP) rule.

    1. Dy-Scale: a Map-Reduce Job Scheduler for Heterogeneous Multi-core Processors

    The practicality of contemporary multi-core processors is commonly driven by a given power budget that needs designers to judge completely different call trade-offs, e.g., to settle on between several slow, power-efficient cores, or fewer quicker, power-hungry cores, or a mixture of them. Here, we tend to epitome and measure a brand new Hadoop hardware, referred to as Dy-Scale that exploits capabilities offered by heterogeneous cores at intervals one multi-core processor for achieving a spread of performance objectives. A typical Map-Reduce employment contains jobs with completely different performance goals: massive, batch jobs that area unit output orientating, and smaller interactive jobs that area unit interval sensitive. Heterogeneous multi-core processors change making virtual resource pools supported “slow” and “fast” cores for multi-class priority planning. Since a similar information are often accessed with either “slow” or “fast” slots, spare resources (slots)can be shared between completely different resource pools. victimization measurements on associate actual experimental setting and via simulation, we tend to argue in favor of heterogeneous multi-core processors as they deliver the goods “faster” (up to 40%) process of tiny, interactive Map-Reduce jobs, whereas providing improved output (up to 40%) for big, batch jobs. we tend to measure the performance advantages of Dy-Scale versus the inventory accounting and capability job schedulers that area unit loosely utilized in the Hadoop community.

And More…

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