International Conference

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    DIABETIC RETINOPATHY LESIONS IDENTIFICATION IN THE COLOR FUNDUS IMAGES USING MULTI-LAYER PERCEPTRON
    (IEEE, 2022-03-25) Geethalakshmi K; Meenakshi V.S
    The prognosis of Diabetic Retinopathy (DR) is characterized by Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). The early stage of DR is known as NPDR. Detecting NPDR in the early stage becomes crucial to avoid blindness. The purpose of this study is to perceive NPDR lesions using image processing techniques and classification methods. The detection of lesions is carried out by pre-processing, feature extraction, feature vector construction, and classification. The vessel network is extracted for feature extraction in the pre-processing stage. Apart from the regular statistical image features, the color layer features are extracted from the smoothened input image. A clustering-based feature extraction method is introduced to capture features from each color layer. The filtered features, which produce the desired output, are combined and fed into Multi-Layer Perceptron (MLP) classifier. The proposed algorithm achieves 100% accuracy in detecting DR. Hence, this study shows that the proposed method can able to find the DR lesions in the early stage itself.
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    A SURVEY ON DEEP LEARNING APPROACHES IN RETINAL VESSEL SEGMENTATION FOR DISEASE IDENTIFICATION
    (Sankara College of Science and Commerce, 2018-10-10) K, Geethalakshmi
    Human retinal image plays a vital role in detection and diagnosis of various eye diseases for ophthalmologist. Automated blood vessel segmentation diagnoses many eye diseases like diabetic retinopathy, hypertension retinopathy, retinopathy of prematurity or glaucoma based on the feature extraction. Automated image analysis tool based on machine learning algorithms are the key point to improve the quality of image analysis. Deep learning (DL) is a subset of machine learning which is completely based on artificial neural network. It helps a machine to analyze the data efficiently. Deep learning is one extensively applied techniques that provides state of the art accuracy. Different types of neural network and platform used for DL also discussed. This paper reviews the different DL approaches for blood vessels segmentation. It concludes that the deep learning methods produces high level of accuracy in disease identification
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    A REVIEW ON CONTENT BASED IMAGE RETRIEVAL SYSTEM TECHNIQUES
    (PKR Arts College for Women, 2018-08-16) K, Geethalakshmi
    A database is a collection of information that is structured for easy storage, retrieval and update. This information is represented in many forms like text, table, image, chart and graph etc. Content Based Image Retrieval (CBIR) technique explores various methodologies in extracting implicit knowledge, patterns and relationships found in the images from the collection of images. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. Nowadays the content based image retrieval (CBIR) are becoming a source of exact and fast retrieval. This paper focuses on the overview of CBIR and various techniques that were proposed in earlier literature.
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    STUDY ON IMAGE PROCESSING AND SEGMENTATION TECHNIQUES
    (St.Aloysius College, 2017-11-16) K, Geethalakshmi
    Digital Image Processing is always an interesting field as it gives improved pictorial information for human interpretation & processing of image data for storage, transmission and representation for machine perception. Image processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-ay life for various applications. This field of image processing significantly improved in recent times and extended to various fields of science & technology. The Image Processing mainly deals with image acquisition, Image Enhancement, Image Segmentation, Feature Extraction, and Image Classification. A number of Image Processing techniques, in addition to enhancement techniques can be applied to improve the data usefulness. Techniques include convolution edge detection, mathematics, filters, trend removal & image analysis. The various image enhancements and image processing techniques will be discussed in this paper.
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    IMAGE MINING AND TECHNOLOGY CAPABILITIES
    (PSG College of Arts and Science, 2017-02-24) Geethalakshmi K; Sri priyanka
    In the area of data mining, Image mining technology has been considered on advanced field for discovering information related to the images. Image mining is the processing and discovering valuable information and knowledge in large volume of data. Image mining draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and soft computing. All techniques and process used for automated analysis of image content on the internet for marketing and advertising purpose. There are many techniques developed in the earlier researches and eventually these techniques can reveal useful information according to the human requirements but image mining still requires more development especially in the area of web images. Image mining is focused on extracting patterns, implicit knowledge, image data relationship or patterns which are not explicitly found in the image from databases or collections of images some of the methods used to gather knowledge are: image retrieval, data mining, image processing and artificial intelligence. This paper presents study on various image mining technology. It also provides an improvement for future research.
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    IMAGE PROCESSING TECHNIQUES
    (Dr.N.G.P Arts and Science College, 2017-02-24) K, Geethalakshmi; A, Shivyaa
    Digital Image Processing is always an interesting field as it gives improved pictorial information for human interpretation & processing of image data for storage, transmission and representation for machine perception. Image processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-ay life for various applications. This field of image processing significantly improved in recent times and extended to various fields of science & technology. The Image Processing mainly deals with image acquisition, Image Enhancement, Image Segmentation, Feature Extraction, and Image Classification. A number of Image Processing techniques, in addition to enhancement techniques can be applied to improve the data usefulness. Techniques include convolution edge detection, mathematics, filters, trend removal & image analysis. The various image enhancements and image processing techniques will be discussed in this paper.
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    PRIVACY RISK IN RECOMMENDED SYSTEM
    (PSGR Krishnammal College for Women, 2014-01-09) K, Geethalakshmi; R, Divya
    In many on-line applications, the range of content that is offered to users is so wide that a requirement for automatic recommender systems arises. Such systems will give a personalized selection of relevant things to users. In practice, this may facilitate people realize fun movies, boost sales through targeted advertisements, or facilitate social network users meet new friends. To produce correct personalized recommendations, recommender systems depend on detailed personal information on the preferences of users. Ratings, consumption histories and personal profiles are examples. Recommender systems are useful, but the privacy risks associated in aggregation and process personal information are typically underestimated or neglected. Many users are not sufficiently aware if and the way a lot of their information is collected, if such information is sold-out to third parties or how securely it is saved and for how long. This paper aims to provide insight into privacy in recommender systems. First, we shall discuss different varieties of existing recommender systems. Second, an overview of the data that is employed in recommender systems is given. Third, I analyze the associated risks to information privacy. Finally, relevant research areas for privacy-protection techniques and their relevancy to recommender systems are mentioned.
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    ANALYSIS ON ARTIFICIAL NEURAL NETWORK AND ITS APPLICATIONS
    (PKR Arts College for Women, 2018-08) Selvanayaki M; Mohanapriya S
    Network is an approach of gathering simple elements to produce complex system. There are a large number of different types of networks, but they all are characterized by the following components: a set of nodes, and connections between nodes. The nodes can be seen as computational units. They receive inputs, and process them to obtain an output. This processing might be very simple (such as summing the inputs), or quite complex (a node might contain another network). The connections determine the information flow between nodes. They can be unidirectional, when the information flows only in one sense, and bidirectional, when the information flows in either sense. The interactions of nodes though the connections lead to a global behavior of the network, which cannot be observed in the elements of the network. This means that the abilities of the network supercede the ones of its elements, making networks a very powerful tool.
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    BIOMETRICS STANDARDS AND STANDARDIZATION
    (PSGR Krishnammal College for Women, Coimbatore, 2014-01) Selvanayaki M; FemithaParveen M
    Prevailing methods of human identification based on credentials (identification documents and PIN) are not able to meet the growing demands for stringent security in applications such as national ID cards, border crossings, government benefits, and access control. As a result, biometric recognition, or simply biometrics, which is based on physiological and behavioral characteristics of a person, is being increasingly adopted and mapped to rapidly growing person identification applications. Unlike credentials (documents and PIN), biometric traits (e.g., fingerprint, face, and iris) cannot be lost, stolen, or easily forged; they are also considered to be persistent and unique. These requirements are typically specified in terms of identification accuracy, throughput, user acceptance, system security, robustness, and return on investment.
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    KNOWLEDGE DISCOVERY AND DATA MANAGEMENT USING GENERIC ALGORITHMS
    (Sri Ramakrishna College of Arts and Science for Women, 2019-01) T, Saranya; D, Nivetha
    The term Knowledge Discovery in Databases, or KDD for short, refers to the broad process of finding knowledge in data, and emphasizes the "high-level" application of particular data mining methods. It is of interest to researchers in machine learning pattern recognition, databases, statistics,artificial intelligence, knowledge acquisition for expert systems, and data visualization. The unifying goal of the KDD process is to extract knowledge from data in the context of large databases. It does this by using data mining methods (algorithms) to extract (identify) what is deemed knowledge, according to the specifications of measures and thresholds, using a database along with any required pre-processing, sub sampling, and transformations of that database.