MCA Final year projects in Tiruppur
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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 Tiruppur by the students. For all the students, Domain basics is covered to understand the fundamental concepts.
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MCA Final year projects in Tiruppur Syllabus
- Self-Adjusting Slot Configurations for Homogeneous and Heterogeneous Hadoop Clusters
The Map-Reduce framework and its open supply implementation Hadoop became the defector platform for scalable analysis on massive information sets in recent years. One in every of the first issues in Hadoop is the way to minimize the completion length (i.e, create span) of a collection of Map-Reduce jobs. The present Hadoop solely permits static slot configuration, i.e., fastened numbers of map slots and scale back slots throughout the lifespan of a cluster. However, we have a tendency to found that such a static configuration might cause low system resource utilizations further as long completion length. impelled by this, we have a tendency to propose straightforward nevertheless effective schemes that use slot quantitative relation between map and scale back tasks as a tunable knob for reducing the create span of a given set. By leverage the work data of recently completed jobs, our schemes dynamically allocates resources (or slots) to map and scale back tasks. We have a tendency to enforce the bestowed schemes in Hadoop V0.20.2 and evaluated them with representative Map-Reduce benchmarks at Amazon EC2. The experimental results demonstrate the effectiveness and strength of our schemes underneath each straightforward workloads and additional complicated mixed workloads.
- Service Rating Prediction by Exploring Social Mobile Users’ Geographical Locations
Recently, advances in intelligent mobile device and positioning techniques have basically increased social networks that permit users to share their experiences, reviews, ratings, photos, check-ins, etc. The geographical info set by sensible phone bridges the gap between physical and digital worlds. Location knowledge functions because the affiliation between user’s physical behaviors and virtual social networks structured by the sensible phone or internet services. we tend to talk to these social networks involving geographical info as location-based social networks (LBSNs). Such info brings opportunities and challenges for recommender systems to resolve the cold begin, exiguity downside of datasets and rating prediction. During this paper, we tend to change use of the mobile users’ location sensitive characteristics to hold out rating postulation. We tend to mine: 1) the connectedness between user’s ratings and user-item geographical location distances, known as as user-item geographical affiliation,2) the connectedness between users’ rating variations and user-user geographical location distances, known as as user-user geographical affiliation. It’s discovered that humans’ rating behaviors are plagued by geographical location considerably. Moreover, 3 factors: user-item geographical affiliation, user-user geographical affiliation, and social interest similarity, are consolidated into a unified rating prediction model. We tend to conduct a series of experiments on a true social rating network dataset Yelp. Experimental results demonstrate that the planned approach outperforms existing models
3. CompetitiveBike: Competitive Analysis and Popularity Prediction of Bike-Sharing Apps Using Multi-Source Data (Machine learining)
In recent years, bike-sharing systems are wide deployed in several massive cities, which give a cheap and healthy style. With the prevalence of bike-sharing systems, tons of corporations be part of the bike-sharing market, resulting in progressively fierce competition. To be competitive, bike-sharing corporations and app developers ought to create strategic selections and predict the recognition of bike-sharing apps. However, existing works largely concentrate on predicting the recognition of one app, the recognition contest among completely differentapps has not been explored nonetheless. during this paper, we have a tendency to aim to forecast the recognitioncontest between Mobike and Ofo, 2 most well-liked bike-sharing apps in China. we have a tendency to develop CompetitiveBike, a system to predict the recognition contest among bike-sharing apps investment multi-source information. we have a tendency to extract 2 novel styles of options: coarse-grained and fine-grained competitive features, and utilize Random Forest model to forecast the long run aggressiveness. additionally, we have a tendency to read mobile apps competition as a long event and generate the event plot line to complement our competitive analysis. we have a tendency to collect information regarding 2 bike-sharing apps and 2 food ordering & delivery apps from eleven app stores and Sina Weibo, implement intensive experimental studies, and therefore the results demonstrate the effectiveness and generality of our approach.
4. Weakly-supervised Deep Embedding for Product Review Sentiment Analysis (Deep Learning)
Product reviews area unit valuable for approaching consumers in serving to them build choices. to the presentfinish, completely different opinion mining techniques are projected, wherever judgement a review sentence’s orientation (e.g. positive or negative) is one in all their key challenges. Recently, deep learning has emerged as a good suggests that for finding sentiment classification issues. A neural network as such learns a helpfulillustration mechanically while not human efforts. However, the success of deep learning extremely depends on the supply of large-scale coaching information. we tend to propose a unique deep learning framework for product review sentiment classification that employs prevalently obtainable ratings as weak direction signals. The framework consists of 2 steps: (1) learning a high level illustration (an embedding space) that captures the finalsentiment distribution of sentences through rating information; (2) adding a classification layer on prime of the embedding layer and use labelled sentences for supervised fine-tuning. we tend to explore 2 sorts of low level network structure for modeling review sentences, namely, convolutional feature extractors and long immediate memory. to judge the projected framework, we tend to construct a dataset containing one.1M feeble labelledreview sentences and eleven,754 labelled review sentences from Amazon. Experimental results show the efficaciousness of the projected framework and its superiority over baselines.
5. Towards Detecting Compromised Accounts on Social Networks(Secure Computing)
Compromising social network accounts has become a profitable course of action for cybercriminals. By hijacking control of a popular media or business account, attackers can distribute their malicious messages or disseminate fake information to a large user base. The impacts of these incidents range from a tarnished reputation to multi-billion dollar monetary losses on financial markets. In our previous work, we demonstrated how we can detect large-scale compromises (i.e., so-called campaigns) of regular online social network users. In this work, we show how we can use similar techniques to identify compromises of individual high-profile accounts. High-profile accounts frequently have one characteristic that makes this detection reliable – they show consistent behavior over time. We show that our system, were it deployed, would have been able to detect and prevent three real-world attacks against popular companies and news agencies. Furthermore, our system, in contrast to popular media, would not have fallen for a staged compromise instigated by a US restaurant chain for publicity reasons.
6. Understand Short Texts by Harvesting and Analyzing Semantic Knowledge (Data Mining)
Understanding short texts is crucial to many applications, but challenges abound. First, short texts do not always observe the syntax of a written language. As a result, traditional natural language processing tools, ranging from part-of-speech tagging to dependency parsing, cannot be easily applied. Second, short texts usually do not contain sufficient statistical signals to support many state-of-the-art approaches for text mining such as topic modeling. Third, short texts are more ambiguous and noisy, and are generated in an enormous volume, which further increases the difficulty to handle them. We argue that semantic knowledge is required in order to better understand short texts. In this work, we build a prototype system for short text understanding which exploits semantic knowledge provided by a well-known knowledge base and automatically harvested from a web corpus. Our knowledge-intensive approaches disrupt traditional methods for tasks such as text segmentation, part-of-speech tagging, and concept labeling, in the sense that we focus on semantics in all these tasks. We conduct a comprehensive performance evaluation on real-life data. The results show that semantic knowledge is indispensable for short text understanding, and our knowledge-intensive approaches are both effective and efficient in discovering semantics of short texts.
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