DIABETIC RETINOPATHY IMAGE CLASSIFICATION USING DEEP NEURAL NETWORK

Healthcare is an important field where image classification has an excellent value. An alarming healthcare problem recognized by the WHO that theworld suffers is diabetic retinopathy (DR). DR is a global epidemic which leads to the vision loss. Diagnosing the disease using fundus images is a timeconsuming task and needs experience clinicians to detect the small changes. Here, we are proposing an approach to diagnose the DR and its severity levels from fundus images using convolutional neural network algorithm (CNN). Using CNN, we are developing a training model which identifies the features through iterations. Later, this training model will classify the retina images of patients according to the severity levels. In healthcare field, efficiency and accuracy is important, so using deep learning algorithms for image classification can address these problems efficiently

PROSODY PREDICTION FOR TAMIL TEXT-TO-SPEECH SYNTHESIZER USING SENTIMENT ANALYSIS

A speech synthesizer which sounds similar to a human voice is preferred over a robotic voice, and hence to increase the naturalness of a speech synthesizer an efficacious prosody model is imperative. Hence, this paper is focused on developing a prosody prediction model using sentiment analysis for a Tamil speech synthesizer. Two variations of prosody prediction models using SentiWordNet are experimented: one without a stemmer and the other with a stemmer. The prosody prediction model with a stemmer performs much more efficiently than the one without a stemmer as it tackles the highly agglutinative and inflectional words in Tamil language in a better way and is exemplified clearly, in this paper. The performance of the prosody prediction model with a stemmer has a higher classification accuracy of 77% on the test set in comparison to the 57% accuracy by the prosody model without a stemmer.Â

WORKING OF ACONTEXT-AWARE CONVERSATIONAL ENTITY

Abstract —  Introduction of new technologies in to the world is increasing rapidly and in order to assist the users to get equipped with such technologies industries are providing customer care services. Contacting a customer care service is subjective to several overheads of selecting options from a listed set, waiting for the switching between selections and awaiting the support of a customer care executive as the process usually requires a human intervention. Hence, a substitute for a personnel is required by the IT industries in order to automate the communication process in assisting the customers. Chatbots with context aware question-answering capabilities can be viewed as a good solution to such customer-care assistance. Development of a chatbot and the complexities involved in getting it to work effectively is delineated in this paper

A book recommendation system based on named entities

Recommendation systems are extensively used for suggesting new items to users and play an important role in the discovery of relevant new items, be it books, movies or music. An effective recommendation system should provide heterogeneous results and should not be biased towards only the most popular items. Books are particularly well-suited to content-based filtering as they are now widely available in digital formats which can allow various text mining approaches to dig out content related information. This paper presents a framework to develop a content-based recommendation system for books which can further be integrated with a collaborative filtering model. The proposed content-based recommender will use the Named Entities as the basic criteria to rank books and give recommendations

Ultrasonic intensification as a tool for enhanced microbial biofuel yields

peer-reviewedUltrasonication has recently received attention as a novel bioprocessing tool for process intensification in many areas of downstream processing. Ultrasonic intensification (periodic ultrasonic treatment during the fermentation process) can result in a more effective homogenization of biomass and faster energy and mass transfer to biomass over short time periods which can result in enhanced microbial growth. Ultrasonic intensification can allow the rapid selective extraction of specific biomass components and can enhance product yields which can be of economic benefit. This review focuses on the role of ultrasonication in the extraction and yield enhancement of compounds from various microbial sources, specifically algal and cyanobacterial biomass with a focus on the production of biofuels. The operating principles associated with the process of ultrasonication and the influence of various operating conditions including ultrasonic frequency, power intensity, ultrasonic duration, reactor designs and kinetics applied for ultrasonic intensification are also described

A book recommendation system based on named entities

77-82Recommendation systems are extensively used for suggesting new items to users and play an important role in the discovery of relevant new items, be it books, movies or music. An effective recommendation system should provide heterogeneous results and should not be biased towards only the most popular items. Books are particularly well suited to content based filtering as they are now widely available in digital formats which can allow various text mining approaches to dig out content related information. This paper presents a framework to develop a content based recommendation system for books which can further be integrated with a collaborative filtering model. The proposed content based recommender will use the Named Entities as the basic criteria to rank books and give recommendations