License Plate Recognition (LPR) has been intensively studied in many countries. Due to the different types of number plates being used, the requirements of an automatic number plate recognition system are different for each country. In general, objective of any LPR system is to localize and recognize potential license plate region(s) of the vehicle images captured from handheld device/digital camera/mobile phone camera. The proposed method for License Plate Recognition (LPR) system works in three modules: localization of license plate, segmentation of the characters and recognition of the characters from the license plate. Localization of the license plate is done using morphological operations, horizontal & vertical edge processing. Character segmentation is carried out using connected component labeling. Character recognition is done by using Neural Network classifier. The method is tested on 100 samples of Indian vehicle images. The system achieves 86% accuracy in localization, 81% accuracy in segmentation and 80% accuracy in character recognition.

License plate recognition system for indian vehicles

Research has proved that there is a high correlation between high class attendance and academic performance. Learning is a progressive activity which builds upon those of the previous day(s). Reading material and working independently do not compensate for the loss of insight gained by being physically present in classroom environment. As a result attendance monitoring should be an integral part of any institution especially a tertiary institution.

Developing a Java based RFID application to automate student attendance monitoring

Domain adaptation in sentiment analysis is one of the areas where a classifier trained in one domain often classifies sentiments poorly when applied to another domain due to domain-specific words. Extracting features and their relevant opinion words from different domain sources and mapping them to the target domains are herculean tasks as far as domain adaptation is concerned. In this paper, the feature extraction technique is refined by which the mapping task is enhanced. The feature extraction technique uses both the syntactic and semantic properties of the features for extracting similar words. The features are further refined by merging synonyms and by replacing negative polarity terms with the appropriate antonyms. This refinement in the feature selection improves the mapping functionality of the domain adaptation and also exploits the relationship between domain-specific words and domain-independent words from different domains.

An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis

Analysis of customer relationships based on their satisfaction is a practical and motivating success factor for the growth of every company. Web intelligence describes the scientific development that uses information technology and artificial intelligence for new frameworks, services, and products provided by the web. This chapter aims to present the model of analyzing the users' sentiments from their online reviews on an e-commerce platform using machine-learning classifiers, namely naive bayes, logistic regression, support vector machine, and neural network. For data analysis, latent semantic analysis has been applied to examine the most frequent words used in online reviews. Finally, customers' interest in online shopping analysis has been performed to classify the customers' sentiments from their posted reviews on the e-commerce platform. In addition, the authors compared the performance results of these classifiers on the e-commerce dataset. The results reveal that the naive bayes classifier has performed better than all the other three classifiers. 

Development of Effective Electronic Customer Relationship Management (ECRM) Model by the Applications of Web Intelligence Analytics

AbstractIn agriculture, plant diseases cause significant economic losses every year and are major threats against food security. To control the infestation at the earliest stage and minimize crop loss, precise diagnosis of plant diseases is the only way. The diagnostic process usually relies on the in-field visual identification by agricultural experts with rich experience. However, for a large number of underdeveloped and developing countries, human experts are scarce and expensive. As an alternative, image processing techniques are becoming popular for automated plant disease diagnosis in agriculture. Several image processing systems have been proposed worldwide during the last one and a half decades. Aside from a mere literature survey, this paper presents a statistical study of the application trend of various image processing techniques used to design image processing systems for plant disease diagnosis in agriculture. This study will be very beneficial for aspirant researchers in the future to design such systems.KeywordsImage processingPlant disease diagnosisApplication trendSegmentation techniquesFeature extraction techniquesMachine learning techniques 

Image Processing Techniques in Plant Disease Diagnosis: Application Trend in Agriculture

A new approach for face recognition using wavelet features is presented. Initially, the given image is divided into 12 blocks, each of size 50*60 pixels. Then, discrete wavelet transform is applied to each block and energy features (mean) of horizontal and vertical coefficients are determined. The extracted features from training samples are used to train the neural network. Further, the test face image is processed to obtain wavelet energy features and recognized using neural network classifier.

International journal of graphics and multimedia (ijgm)

In recent years, many web applications play an important role that too online-commerce-based applications are ruling the web-based application by attracting the customer based on their products and providing more discount on the product. Customers buy the products online because of low-cost and the quick delivery of the products, and therefore for measuring the quality of the product, the user’s comments on the different products play an important role which can be used for getting useful information on the product. In this paper, we do extract the data set from various E-commerce applications then by applying various methods like data pre-processing, classification and clustering on the products API we can able to analyse the reviews of the customers on different products. In this paper, we have created the product API for 100 instances of products by considering different attributes like product id, product name, total number of comments, rating and the text analysis. In this paper, first we apply the data pre-processing to clean the data set and then apply the logistic regression-based classification on the attributes taken, then form a cluster based on positive and negative reviews provided by the users and determining the accuracy using logistic regression technique and then comparing with decision tree technique accuracy with proposed logistic regression technique we achieve an improved accuracy.

Logistic Regression-Based Classification for Reviews Analysis on E-Commerce Based Applications

The detection of citrus plant leaf disease generally includes many methods and proposing work gives the detailed information about different Image Processing methods. One of the major sources of nutrients and energy for the human race are citrus plants which are irreplaceable in nature. Bacterial disease Citrus canker is one of the diseases which are caused by Bacterium Xanthomonas Axonopodis PV Citric (XAC) and its infection results in reduced fruit quality. Detecting citrus canker at the initial stage is the key to control and it is difficult to eradicate. K-means clustering is the best method used among all other methods but Color co-occurrence Matrix gave a better analysis based on texture. Proposed paper gives information about different traditional methods which are used in detection of citrus canker leaf.

A Novel Based Approach for Detection of Canker Disease on Citrus Leaves Using Image Processing Methods

/pdf/rfid-concepts-applications-and-issues-4wvtvgpgzq.pdf

RFID Concepts, Applications and Issues

Due to the exponential growth of the digital media and digital communication, providing the security for the data (information) has become one of the crucial issue. This situation made researchers to look towards developing novel algorithms for encryption of biometric data for addressing security issues. In this paper a simple yet effective data encryption model for biometric data for the security issue is addressed. The newly proposed model is composed of two stages like data encryption and data matching. A data matching technique for the proposed encryption algorithm is also designed. Proposed data encryption approach is critically analyzed by conducting huge experiments on the publically available face and knuckle corpuses. The result of the experiments reveals that the proposed algorithm outperforms for two types of the biometric data.

Image encryption approach for security issues

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