Abstract-In electronic commerce, recommender systems are used to help customers to choose products according to their needs. These systems suggest products automatically to users by learning their requirements. Recommendations provided by these systems depends upon users purchase probability and preferences. In this paper, different techniques used for recommender systems are studied.Keywords-Recommendation Systems, E-commerce, Content Based Filtering, Collaborative Filtering, Hybrid Methods I. INTRODUCTIONRecommender systems touch our lives every day, from searching on Google to shopping on at any major online retailer. Their sophisticated algorithms attempt and often succeed at showing us the information and products we seek. The information consumers give to service providers and retailers is expanding rapidly, which makes recommendation systems both more complex, and potentially more powerful. Online behaviour including customer metadata, transaction histories and communications allows companies to understand shoppers better, sense their similarities, and address needs they may not even know they have. Simultaneously, we are better able to analyse information about the items sought, including images, sounds in the case of music, and descriptions, which allows companies to refine the ways they cluster products, as well as whom they target as potential buyers. Recommendation systems are at the center of retail both online and off, showing ads on web sites as well as through dynamic displays in brick and mortar stores. By using facial recognition and in-store video cameras, retailers are able to group their customers instantly by gender, age and other demographics in order to show them immediately the goods they are most likely to buy. Given the explosion of consumer goods as well as the rapid increase in the number of vendors on the Internet, the main problem facing customers is how to find the object that they seek, when it is buried beneath a mountain of irrelevant information. A recommender system that can instantly personalize ads in order to solve the customer's search problem is doing both the customer, and the vendor, a favour. New goods are constantly introduced, and new fads sweep the nation, altering shoppers' behaviour. Only a recommender system that is constantly able to learn new patterns can serve consumer needs.

. Caltrans is the first transportation agency to embrace IRIS and make use of its innovative, collaborative, shared development model known as open-source. Working with Caltrans and Mn/DOT, the researchers collaboratively developed enhancements, and extended IRIS to be compatible with the Caltrans District 10 infrastructure and field devices as well as adapting it to match the district's specific nuances and operational aspects. The enhanced IRIS system was integrated with existing Caltrans hardware and software systems. Enhancements were contributed back to Mn/DOT for use by other public and private agencies. The researchers modified IRIS to assume the functions of middleware and Automated Warning System for District 10. Extensive user acceptance and operational testing were performed, leading to deployment in Caltrans District 10. Pilot testing was performed in Districts 1, 2, and 5. The Caltrans urban Transportation Management Centers (TMCs) use the Advanced Traffic Management System (ATMS) software tool, which provides real-time information on highway conditions to detect traffic incidents, manage the flow of traffic, and disseminate traveler information [3]. ATMS helps Caltrans reduce commuting times, maximize roadway capacity, and generally provide safer traveling routes. It also provides operators with unified access and control to multiple types of roadway devices rather than having to operate disparate systems. ATMS is composed of several proprietary software solutions that are expensive to acquire. The recurring maintenance costs have also been increasing. Caltrans rural districts often cannot afford the initial setup cost, let alone the recurring costs associated with development and operation. In addition, rural districts do not have the same mobility needs as large metropolitan regions and therefore do not require many of the advanced features and capabilities that ATMS provides. As a result, Caltrans rural districts have addressed traffic management by developing disparate solutions with non-uniform management, administration, and operating protocols [4].

Abstract-Part-of-Speech tagging is the way to tag every word in a text as a particular part of speech, e.g. proper verb, adverb etc. POS tagging is the first important step in the processing of NLP applications. This paper reports the survey on POS tagging for various Languages. Various techniques used for POS tagging also described in this paper. Due to complex structural effect, the number of problems occurs when tagging the sentences written in various languages. A lot of work has been done by the researchers in this field for various languages using various techniques HMM (Hidden Marcov Model), SVM (Support Vector Machine), ME (Maximum Entropy) etc.Keywords-Natural Language Processing, Part of speech Processing, Tagset, Indian Languages I. INTRODUCTIONThe NLP (natural language processing) is the process that provides the facility of interaction between human and machine. It is a component of computer science, linguistics and artificial intelligence. It is difficult task to build NLP application because human speech is not always specific. The main objective of NLP is to develop such a system that can understand text and translate between human language and another. The work in area of Part-of-Speech (POS) tagging has begun in the early 1960s. Part of Speech tagging is an important tool for NLP. It is one of the simplest as well as statistical models for many NLP applications. POS Tagging is an initial step of information extraction, summarization, retrieval, machine translation, speech conversion [2].POS tagging is the process of assigning the best grammar tag to each word of text like verb, noun, pronoun , adjective , adverb, conjunction , preposition etc. some unknown words exist in every language so it is very difficult task to assign the appropriate POS tag to each word in a sentence [3]. The mostly work that has been done for Indian languages was one of the rule based approaches and other empirical based POS tagging Approach. But the fact was that rule-based approach requires proper language knowledge and hand written rule. Due to morphological effect of Indian languages, researchers faced a great problem to write proper linguistic rules and many cases it was noticed that results were not good. Most of natural language processing work has been done for Hindi, Tamil, Malayalam and Marathi and several part-of-speech taggers have been applied for these languages. After this, researchers moved to stochastic based approach. However the stochastic methods requires large corpora to be effective, but still many successful POS were developed and used in various natural language processing tasks for Indian language. The main issue after morphological richness of Indian Languages is Ambiguity. It is very time consuming process to assign a correct POS tag to different context words. Due to this reason, POS Tagging is becoming a challenging problem for study in the field of NLP [1].

Due to continuously increase of fraud in whole world, affects the daily life of people's and also economic conditions of each country. Different modern fraud detection techniques have been applied to prevent frauds in various fields. In today's world, education is one of the famous fields where cases of fraud admissions, fraud degree certificates are occurring continuously. Fraudsters are continuously playing with life of students. However many techniques have been applied but still it needs to be improve. In this paper, the survey of different fraud detection techniques has been presented. The goal of this paper is to review different existing techniques used to detect frauds.