Content Based Cross-Domain Recommendation Using Linked Open Data

A recommender system, irrespective of theapproach that has been used to implement it suffers fromthe cold-start situation. Not being able to predict items to anew user due to not having access to his previouspreferences, and not being able to recommend a new item tousers due to not having any prior ratings on theparticular item is the two cold-start problems. Even thoughcontent-based recommender systems are immune to itemcold-start problem, they are comparatively less used due tolack of up-to-date data sources that provide item featuresand also due to the high amount of pre-processing requiredwhen using existing data sources for retrieving meta-data.In this paper we present a content-based cross domainrecommendation system using Linked Open Data toaddress the issue of cold-start situation. The evaluationproves that this approach can be used as a solution to a coldstartsituation and also the prevailing issue of content-basedrecommender systems which forced them to take thebackseat will no longer be applicable when Linked OpenData is used

Experience-based Personalized Diversification of Recommendations

Accuracy of the recommendations has long been regarded as the primary quality aspect of Recommender Systems (RS), but there's an increasing cognizance that there are other factors such as diversity that users also value. Despite the increased interest of researchers to improve diversification of recommendations, we find that personalization of diversification has been overlooked. As the preference for diversity changes from person-to-person, we propose a personalized diversification technique which is capable of controlling the trade-off between accuracy and diversity, where personalization is achieved by diversifying the recommendation list with more novel items if the user has shown diverse preferences in the past, and diversifying the recommendation list with more relevant items if the user has shown homogeneous preferences in the past. Moreover, we also introduce a novel recommendation technique which uses the past preferences of a user and the ratings of experienced item category experts in recommendation generation process. As post-filtering approaches generate the final diversified recommendation list by selecting items from a list generated from some RS, we use the recommendation technique we propose in order to generate an initial recommendation list with both novel and relevant items to improve the personalized diversification process. Our experiments and evaluation provides evidence to illustrate the properties of proposed techniques and indicate the proposed approach has comparable results to state-of-art techniques. Moreover, unlike other techniques, our approach can promote both novel and relevant items and also make the diversification process personalized

Analysis on Using Synthesized Singing Techniques in Assistive Interfaces for Visually Impaired to Study Music

Tactile and auditory senses are the basic types of methods that visually impaired people sense the world. Their interaction with assistive technologies also focuses mainly on tactile and auditory interfaces. This research paper discuss about the validity of using most appropriate singing synthesizing techniques as a mediator in assistive technologies specifically built to address their music learning needs engaged with music scores and lyrics. Music scores with notations and lyrics are considered as the main mediators in musical communication channel which lies between a composer and a performer. Visually impaired music lovers have less opportunity to access this main mediator since most of them are in visual format. If we consider a music score, the vocal performer’s melody is married to all the pleasant sound producible in the form of singing. Singing best fits for a format in temporal domain compared to a tactile format in spatial domain. Therefore, conversion of existing visual format to a singing output will be the most appropriate nonlossy transition as proved by the initial research on adaptive music score trainer for visually impaired [1]. In order to extend the paths of this initial research, this study seek on existing singing synthesizing techniques and researches on auditory interfaces

A STATE-OF-THE-ART SURVEY: FOCUSED WEB CRAWLING USING NAMED ENTITY RECOGNITION FOR NARROW DOMAINS

Within recent years the World Wide Web (WWW) has grown enormously to a large extent where generic web crawlers have become unable to keep up with. As a result, focused web crawlers have gained its popularity which is focused only on a particular domain. But these crawlers are based on lexical terms where they ignore the information contained within named entities; named entities can be a very good source of information when crawling on narrow domains. In this paper we discuss a new approach to focus crawling based on named entities for narrow domains. We have conducted experiments in focused web crawling in three narrow domains: baseball, football and American politics. A classifier based on the centroid algorithm is used to guide the crawler which is trained on web pages collected manually from online news articles for each domain. Our results showed that during anytime of the crawl, the collection built with our crawler is better than the traditional focused crawler based on lexical terms, in terms of the harvest ratio. And this was true for all the three domains considered

Ontology Based Approach for Diagnosis in Personalized Medicine

Due to complexity of the human body, overlapping phenotypes and complex disease networks, disease diagnosis is a real challenge for the physicians. Medical diagnostic decision support systems have been developed as an approach to ease the process of disease diagnosis. Improved patient safety, improved quality of care and improved efficiency in health care delivery are potential benefits of MDDSS.This research proposes a novel and generic mathematical model into differential diagnosis of genetic diseases instead of traditional method of analyzing gene mutations to expose genetic diseases. It is achieved by genotype-phenotype correlation thro-ugh a common computer science concept called Ontology. Basically emerging genetic mutations of the patient are mapped to the standardized vocabulary called Human Phenotype Ontology and subsequently differential diagnosis is done using those terms. Differential diagnosis process is achieved by measuring ontology based semantic similarity by combining information theory and fuzzy relational theory. The system is capable of diagnosing the probability of occurrence of five complex diseases namely Lymphedema-Distichiasis Syndrome, Cornelia de Lange syndrome-me, Popliteal pterygium syndrome, Cohen Syndrome and Smith-Lemli-Opitz Syndrome.We evaluate our system by comparing the results obtained from our system with domain expert’s diagnosis. Pre-diagnosed set of real Cornelia de Lange syndrome patients’ data were used in this attempt. According to the results, our system diagnoses Cornelia de Lange Syndrome with an average probability of 78.32%, Smith-Lemli-Opitz Syndrome has 61.35% probability and other three diseases with very low probability values. This thesaurus based approach which considers a correlation of phenotypes and genotype of a patient can be used by a physician to make a better diagnosis of a disease

Enhancing retrieval of images on the web through effective use of associated text and semantics from low-level image features.

Content-based indexing and retrieval has emerged as an important area in computer vision and multimedia computing. Current solutions for searching image data primarily deal with associated text and low-level image features. Humans tend to use high-level concepts in everyday life; user queries are typically based on higher-level semantics and not low-level image features. However, what current computer vision techniques can automatically extract from images are mostly low-level visual features. To narrow down this semantic gap, some off-line and on-line processing is needed. The state-of-the-art image retrieval approach is to incorporate image semantics with low-level visual primitives to enhance the retrieval performance. Unfortunately the current mainstream of the image retrieval technologies in most web search engines is keyword-based retrieval; they have not explored the full potential of semantics of an image through effective use of its nearby text. Therefore I propose an image retrieval system that captures semantics of an image through effective use of its associated text and use integrated system architecture for keyword-based retrieval with low-level image features to enhance retrieval of images on the web. I have developed a new image retrieval system that enhances retrieval of images on the web through optimum. I conducted a preliminary study on collection of images obtained from HTML documents on the web. Based on my findings on text associated with the image, I have identified the textual contents of page title, image title, image alternate text, image caption and Meta tags are well related to an embedded image. These keywords lists have different significance in identifying the image semantics. I comparatively evaluate the performance of each keyword list exclusively to study their impact on overall retrieval effectiveness. The major contribution of my work included a full-scale development and implementation of the new image retrieval system I-Search. The system was based on an enhanced image representation that exploits the vast power of image semantics from the text associated with the images and higher-level semantic categories based on low-level image features of the images. The user-interface was designed to allow the user to communicate keywords based query and semantic categories to the image retrieval system. The performance of this new image retrieval system I-Search was compared with GoogleTM and YahooTM. Our analysis of this experiment confirmed that the integration of text associated with an image and low-level image features will lead to efficient retrieval system for content-based indexing of images on the web and will in fact substantially enhance the image searching capabilities on the web

Machine Learning and Natural Language Processing Usage for Psychological Consultation

It`s an obvious fact to claim, that the problem of stress has become a vital issue presently. One of the main root cause of this is the escalation of human desires and complexity of those. Most of those desires would be difficult to achieve or not practical at all. When the reality is so harsh, compared with the imaginations, it will incur for stress. More the gap between reality and the perceptions, stress will escalate. As results of people are running after unrealistic or difficult to achieve greener pastures, most of them will end up with becoming a victim of stress. Stress management is a difficult skill to be developed, but in current context, it has become an essential skill to have. This research is based on the concept of internal self-talk. Thought stream captured in-form of text stream will be segmented, according to the cognitive behavioral therapeutic approach. This is technically implemented via POS tagging of the Stanford NLP library. Afterwards machine learning approach is used to train the WEKA engine, according to the OCEAN model, which is a prominent psychological model. Predictions derived from the trained WEKA model, will be presented inform of a report with the help of itext reporting plugin. This report will be used by the psychologist, before providing the treatment to the patient/client. It`s assumed, that this tool will be a good aiding tool, which can reduce the cognitive effort of a consultant. Respective, problem, technical, executional and all important aspects are addressed in detail, within this paper along with required evidences

Neuro-Fuzzy Approach to Measure Sociological Impact on Education

Education and Society are closely bound, such that they cannot be separated from each other because education builds up society and on the other hand society is the source of education .Education transfers culture, knowledge and etc of a society from one generation to the next. Hence, better education is for a better society and healthier society becomes the foundation of better education. Education could be formal, informal and non-formal and in this research, formal education is the prime concern. The research is aimed at tracing the correlation between the sociological environment of a Sri Lankan student and his/her educational performance during the grades 10 and 11. A thorough survey was conducted covering a selected sample of population to extract data from the students on their social aspects and conduct and their performance in the G.C.E (O Level) Examination results. A Neural Network with a Fuzzy interface was trained on past data and the performance of the network was evaluated using a test data set. The results with nearly 65% accuracy are encouraging in order to further improve the methodology towards better results. However, the final goal of the research is to prepare the ground to develop a tool which helps counselors to make decisions while helping students to enhance performance at G.C.E (O Level) Examination

Audio Music Monitoring: Analyzing Current Techniques for Song Recognition and Identification

when people are attaching or interesting in something, usually they are trying to interact with it frequently. Music is attached to people since the day of they were born. When music repository grows, people faced lots of challenges such as finding a song quickly, categorizing, organizing and even listening again when they want etc. Because of this, people tend to find electronic solutions. To index music, most of the researchers use content based information retrieval mechanism since content based classification doesn’t need any additional information rather than audio features embedded to it. As well as it is the most suitable way to search music, when user don’t know the meta data attached to it, like author of the song. The most valuable application of this audio recognition is copyright infringement detection. Throughout this survey we will present approaches which were proposed by various researchers to detect, recognize music using content base mechanisms. And finally we will conclude this by analyzing the current status of this era