The Grid 2: Blueprint for a New Computing Infrastructure

Computational Analysis of Microarray Data

1. Introduction to Probability 2. Conditional Probability 3. Random Variables and Distributions 4. Expectation 5. Special Distributions 6. Large Random Samples 7. Estimation 8. Sampling Distributions of Estimators 9. Testing Hypotheses 10. Categorical Data and Nonparametric Methods 11. Linear Statistical Models 12. Simulation

Probability and Statistics

Sentiment Analysis (SA) is an application of Natural Language Processing (NLP) to extract the sentiments expressed in the text. In this paper, we experimented five approaches to perform SA, namely, Lexicon based approach, Supervised Machine learning based approach, Hybrid approach, K-means with Bag of Word (BoW) approach and K-modes with BoW approach. We have experimented these approaches using five corpora with different feature representation techniques to predict the best approach to perform SA in Tamil texts. In this research we used Basic features such as word count and punctuation count in addition to traditional features such as Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) included to check their influence in the prediction. We have compared these approaches, features and the corpora. From the evaluation the highest accuracy of 79% is obtained for UJ_Corpus_Opinions_Nouns corpus with fastText for supervised Machine learning based approach.

Sentiment Analysis in Tamil Texts: A Study on Machine Learning Techniques and Feature Representation

Recently, neural networks have been widely used in e-commerce recommender systems, owing to the rapid development of deep learning. We formalize the recommender system as a sequential recommendation problem, intending to predict the next items that the user might be interacted with. Recent works usually give an overall embedding from a user's behavior sequence. However, a unified user embedding cannot reflect the user's multiple interests during a period. In this paper, we propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec. Our multi-interest module captures multiple interests from user behavior sequences, which can be exploited for retrieving candidate items from the large-scale item pool. These items are then fed into an aggregation module to obtain the overall recommendation. The aggregation module leverages a controllable factor to balance the recommendation accuracy and diversity. We conduct experiments for the sequential recommendation on two real-world datasets, Amazon and Taobao. Experimental results demonstrate that our framework achieves significant improvements over state-of-the-art models. Our framework has also been successfully deployed on the offline Alibaba distributed cloud platform.

Controllable Multi-Interest Framework for Recommendation

Design of cognitive image filters for suppression of noise level in medical images

Search queries on large databases, often return a large number of results, only a small subset of which is relevant to the user. When the user want to search the result for a particular query he or she find lot of difficulties when query results are large in size. To overcome the searching and navigation difficulty the following contributions are made. Design very good user interface to search the query using front end tools like ASP.NET and it will fetch the result from database like SQL SERVER 2005.For personalized recommendation system Advanced Encryption Standard algorithm is used to get the user feedback in secured format. Query results are organized into a tree format using tree control. Using several real-world ratings the comprehensive empirical evaluation shows diversity gains of proposed techniques. Ranking concept is used to display the concepts in order based on more number of times that concept is accessed. Edge cut algorithm is used to display the query result mostly related to the user expected results in tree format. Graph is generated based on spatial attributes. Ranking and categorization, which can also be combined, have been proposed to alleviate this information overload problem.

Recommendation system based on statistical analysis of ranking from user

The healthcare sectors have many difficulties and challenges in finding diseases. Healthcare organizations are collecting bulk amount of patient data. The Data mining methods are utilized to decide covered data that is valuable to healthcare specialists with effective analytic decision making. Data mining strategies are utilized in the field of the healthcare industry for different purposes. The objective of this paper is to assess and analyze using three unique data mining arrangement methods, for example, Naive Bayes (NB), Support Vector Machine (SVM) and Decision Tree to decide the potential approaches to predict the possibility of heart disease for diabetic patients dependent on their predictive accuracy.

Comparative Analysis of Data Mining Techniques to Predict Heart Disease for Diabetic Patients

With the coming of anomaly based intrusion detection systems, numerous methodologies and strategies have been produced to track novel assaults on the systems. High detection rate of 98% at a low caution rate of 1% can be accomplished by utilizing these procedures. In spite of the fact that anomaly-based methodologies are productive, signature-based detection is favored for standard usage of intrusion detection systems. As an assortment of anomaly detection procedures were recommended, it is hard to look at the qualities, shortcomings of these strategies. The motivation behind why ventures don’t support the anomaly-based intrusion detection techniques can be surely knew by approving the efficiencies of the every one of the strategies. To explore this issue, the present condition of the examination hone in the field of anomaly-based intrusion detection is surveyed moreover. In this paper, we utilize Deep learning strategies to actualize an anomaly based Novel-IDS. These procedures demonstrate the touchy intensity of generative models with great arrangement, capacities to reason some portion of its knowledge from inadequate data and the versatility.

Anomaly Based Novel Intrusion Detection System For Network Traffic Reduction

Web services is software entity allows machine to machine communication, operates as standalone unit and interoperability over network using standard web technologies such as Hypertext transfer pro...

K. Vengatesan

Papers

Design of cognitive image filters for suppression of noise level in medical images

Recommendation system based on statistical analysis of ranking from user

Comparative Analysis of Data Mining Techniques to Predict Heart Disease for Diabetic Patients

Anomaly Based Novel Intrusion Detection System For Network Traffic Reduction

An investigation on adaptive HTTP media streaming Quality-of-Experience (QoE) and agility using cloud media services