Data Mining - Concepts and Techniques.

Emotion and Adaptation

Traffic control and vehicle owner identification has become major problem in every country. Sometimes it becomes difficult to identify vehicle owner who violates traffic rules and drives too fast. Therefore, it is not possible to catch and punish those kinds of people because the traffic personal might not be able to retrieve vehicle number from the moving vehicle because of the speed of the vehicle. Therefore, there is a need to develop Automatic Number Plate Recognition (ANPR) system as a one of the solutions to this problem. There are numerous ANPR systems available today. These systems are based on different methodologies but still it is really challenging task as some of the factors like high speed of vehicle, non-uniform vehicle number plate, language of vehicle number and different lighting conditions can affect a lot in the overall recognition rate. Most of the systems work under these limitations. In this paper, different approaches of ANPR are discussed by considering image size, success rate and processing time as parameters. Towards the end of this paper, an extension to ANPR is suggested.

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Automatic Number Plate Recognition System (ANPR): A Survey

Infrastructure-based vehicular networks (consisting of a group of Base Stations (BSs) along the road) will be widely deployed to support Wireless Access in Vehicular Environment (WAVE) and a series of safety and non-safety related applications and services for vehicles on the road. As an important measure of user satisfaction level, uplink connectivity probability is defined as the probability that messages from vehicles can be received by the infrastructure (i.e., BSs) through multi-hop paths. While on the system side, downlink connectivity probability is defined as the probability that messages can be broadcasted from BSs to all vehicles through multi-hop paths, which indicates service coverage performance of a vehicular network. This paper proposes an analytical model to predict both uplink and downlink connectivity probabilities. Our analytical results, validated by simulations and experiments, reveal the trade-off between these two key performance metrics and the important system parameters, such as BS and vehicle densities, radio coverage (or transmission power), and maximum number of hops. This insightful knowledge enables vehicular network engineers and operators to effectively achieve high user satisfaction and good service coverage, with necessary deployment of BSs along the road according to traffic density, user requirements and service types.

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Multi-Hop Connectivity Probability in Infrastructure-Based Vehicular Networks

Yield prediction benefits the farmers in reducing their losses and to get best prices for their crops. The objective of this work is to analyze the environmental parameters like Area under Cultivation (AUC), Annual Rainfall (AR) and Food Price Index (FPI) that influences the yield of crop and to establish a relationship among these parameters. In this research, Regression Analysis (RA) is used to analyze the environmental factors and their infliction on crop yield. RA is a multivariate analysis technique which analyzes the factors groups them into explanatory and response variables and helps to obtain a decision. A sample of environmental factors like AR, AUC, FPI are considered for a period of 10 years from 1990-2000. Linear Regression (LR) is used to establish relationship between explanatory variables (AR, AUC, FPI) and the crop yield as response variable. R 2 value clearly shows that yield is mainly dependent on AR. AUC and FPI are the other two factors influencing the crop yield. This research can be extended by considering other factors like Minimum Support Price (MSP), Cost Price Index (CPI), Wholesale Price Index (WPI) etc. and their relationship with crop yield.

Prediction of Crop Yield using Regression Analysis

Optical character recognition (OCR) method has been used in converting printed text into editable text. OCR is very useful and popular method in various applications. Accuracy of OCR can be dependent on text preprocessing and segmentation algorithms. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation, complex background of image etc. We begin this paper with an introduction of Optical Character Recognition (OCR) method, History of Open Source OCR tool Tesseract, architecture of it and experiment result of OCR performed by Tesseract on different kinds images are discussed. We conclude this paper by comparative study of this tool with other commercial OCR tool Transym OCR by considering vehicle number plate as input. From vehicle number plate we tried to extract vehicle number by using Tesseract and Transym and compared these tools based on various parameters. explained.Keywords like: Desktop OCR, Server OCR, Web OCR etc.

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Optical Character Recognition by Open source OCR Tool Tesseract: A Case Study

As with many other sectors the amount of agriculture data based are increasing on a daily basis. However, the application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel research area. In this paper we provide a brief review of a variety of Data Mining techniques that have been applied to model data from or about the agricultural domain. The Data Mining techniques applied on Agricultural data include k-means, bi clustering, k nearest neighbor, Neural Networks (NN) Support Vector Machine (SVM), Naive Bayes Classifier and Fuzzy cmeans. As can be seen the appropriateness of data mining techniques is to a certain extent determined by the different types of agricultural data or the problems being addressed. This survey summarize the application of data mining techniques and predictive modeling application in the agriculture field.

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A Brief survey of Data Mining Techniques Applied to Agricultural Data

India is an agriculture based developing country. Information dissemination to the knowledge intensive agriculture sector is upgraded by mobile-enabled information services and rapid growth of mobile
telephony. It bridge the gap between the availability of agricultural input and delivery of agricultural
outputs and agriculture infrastructure. Mobile computing, cloud computing, machine learning and soft
computing are the immerging techniques which are being used in almost all fields of research. Apart from
this, they are also useful in our day-to-day activities such as education, medical and agriculture. This
paper explores how Android Apps of agricultural services have impacted the farmers in their farming
activities.

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Survey of android apps for agriculture sector

Wallet Payments Recent Potential Threats and Vulnerabilities with its possible security Measures

Objectives: To compare different data mining algorithms with the same parameters on the 10fold cross validation test to predict the crop yield. Methods/Analysis: Different data mining classification algorithms like K-nearest Neighbor, K-means, Neural Network, Support Vector Machine, Case-based Reasoning, Decision Tree algorithm, etc. are applied for various application of agriculture domain. A comparative study is done by using J48, Naive Bayes and Simple Cart algorithms to determine which classification algorithm is best fitted for crop prediction. Findings: In this study, this work reveals the superior performance of J48 classification algorithm with accuracy 89.33% for crop prediction than the other two classification algorithms Simple Cart and Naive Bayes. Novelty /Improvement: This study first time demonstrates the application of different data mining classification techniques (as discussed above) in the domain of agriculture for yield prediction.

Dharmendra Patel

Papers

Optical Character Recognition by Open source OCR Tool Tesseract: A Case Study

A Brief survey of Data Mining Techniques Applied to Agricultural Data

Survey of android apps for agriculture sector

Wallet Payments Recent Potential Threats and Vulnerabilities with its possible security Measures

A Comparative Study on Various Data Mining Algorithms with Special Reference to Crop Yield Prediction