The concept of relevancy is a most blazing topic in information regaining process. In the last few years there is a drastically increase the digital data so there is a need to increase the accuracy of information regaining process .Semantic Similarity measure the similarity between word-pair by using WordNet as ontology.We have analyzed the different category of semantic similarity algorithm to compute semantic closeness between word-pair and evaluate its value by using WordNet.We have compared various algorithms on Miller- Charles data set of 30 word-pair is used to rank them category wise.
Machine Learning has become an important tool in day to day life or we can say it's a powerful tool in most of the fields which we want to automate. Machine Learning is used to develop algorithms which can learn from the data, which is either labeled, unlabeled or learn from the environment. Machine Learning is used in most of the fields and Especially in health care sector it takes much more benefits through proper decision and prediction techniques. Machine Learning in health care is a scientific study, so we have to store, retrieve and proper use of information, data and provide knowledge to the problems facing in the healthcare sector and also knowledge for the proper decision making. Due to these technologies there is a huge development in health care sectors over the years. For analysis of medical data, medical experts use the machine learning tools and techniques to identify the risks and to provide proper diagnosis and treatment. The paper is based on survey in terms of health care management system using different machine learning approaches and techniques.
Electronic payments are rapidly expanding in popularity these days as a result of the hectic pace of modern living. In addition, hackers and fraudsters routinely misuse the personal information funds in a person’s bank account. To solve this issue, we must develop an intelligent system that protects individuals’ personal data and financial resources. As a result, in the place of human beings, today’s machines act think like human beings. Artificial Intelligence (AI) is a key component of electronic payment systems. It offers a novel method of processing electronic payments. This article provides an overview of the prospects, problems, various dangers associated with e-payments, in particular fraud, which is a major threat to the e-payments industry and a major source of financial loss. E-payments, benefits, as well as the future of e-payments are all discussed in the paper.
In computing, Blockchain is a decentralised, point-to-point program that provides a safe yet verifiable technique for secure distributed validation. Blockchain is a type of distributed transaction validation system. It is widely used in a variety of fields, including the finance sector, the Internet - Of - things, big data, virtualization, and edge computing, to name a few. Artificial intelligence technology, on the other hand, is having a substantial impact on the intellectual growth of a wide range of industries. Blockchain is a difficult technology that represents the important and influential vision and provide a comprehensive perspective to internet security. Blockchain is a hard technologies that represent an inventive and influential vision. When it comes to secure communication, Blockchain technology is always evolving and has the opportunity to deliver about substantial changes in how we work and live in the 21st century. Blockchain technology is continually evolving and become the next paradigms shifting technology. Our new problem is to figure out how we will keep up with the technological developments brought about by this revolutionary technology. A general overview of Blockchain technology, as well as its possibility to assist to future development, is presented in this article, which also proposes many study avenues for further investigation.
Our face reflects our feelings towards anything and everything we see, smell, teste or feel through any of our senses. Hence multiple attempts have been made since last few decades towards understanding the facial expressions. Emotion detection has numerous applications since Safe Driving, Health Monitoring Systems, Marketing and Advertising etc. We propose an Automatic Depression Detection (ADD) system based on Facial Expression Recognition (FER). We propose a model to optimize the FER system for understanding seven basic emotions (joy, sadness, fear, anger, surprise, disgust and neutral) and use it for detection of Depression Level in the subject. The proposed model will detect if a person is in depression and if so, up to what extent. Our model will be based on a Deep Convolution Neural Network (DCNN).
Mobile Forensics is now days, increasingly becoming more challenging as it is the field of science that is continuously evolving with respect to the rapidly developing technologies and techniques for the extraction of the mobile data and its decoding. Majority of the crimes are getting committed digitally and especially the criminals are preferring mobile handsets than a laptop or desktop machines, leaving the footprints behind which could be evidence against them. The mobile handsets along with their software applications are getting more advanced and sophisticated mainly due to advances in Cloud computing where clouds are used to store data, Anti-forensics where efforts are made to defeat forensic procedures and Encryption which is used to secure the data during transit. But when compared with the pace of development in mobile hardware and software, the forensic tools and techniques are growing very slowly. Hence the contemporary forensic tools and methodologies are becoming increasingly obsolete and hence urges for the advanced forensic tools, methods which could comply with the need of today’s mobile forensics. Hence, this work presents a detailed survey of the contemporary challenges faced by the forensic experts with the current forensic tools and its methodologies and also the need, scope and opportunities associated with the novel and secure software framework that can address the majority of issues occurring while extraction and decoding of mobile artifacts.
Finding the similarity Ontology-based between two verbs in bio-medical ontology is a difficult task as there is no standard dataset available. This paper is focused on verb-based similarity. So, the similarity between two nouns is taken as the benchmark for working on the verb similarity. There is no exact idea that the verb hierarchy of wordnet is capable to calculate the verb similarity between two verbs. The finding of similarity considers three parameters such as path, link, and depth. But in this paper, in addition to the path, link, and depth parameters, we also considered parameters such as stem-similarity weighting, derivation nouns weighting, and gloss similarity weighting. Moreover, we implemented two algorithms namely Rich Hierarchy Exploration and Shallow Hierarchy Exploration on a dataset and found that there is no significant difference in its performance.
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