Data Mining Practical Machine Learning Tools and Techniques

This book is concerned with the importance of Human Computer Interaction (HCI), Usability, user participants, and Sustainability in the Information Communication Technology (ICT) industry throughout the world. ICT has become a crucial instrument for communication, entertainment, commerce and research and this increased usage is presenting new environmental and sustainability issues as we try and meet the ever growing needs of both businesses and individuals. Sustainability must become central to the design of new technologies to make a concerted effort to tackle the environmental concerns we face now and for the future. Development frameworks, tools and models are used and explored and the New Participative Methodology for Sustainable Design (NPMSD) is introduced as a way of identifying key factors needed in developing more sustainable systems including new smart technology and portable devices. It is hoped additional research will be carried out in the future to assess these sustainable design steps using larger, more diverse countries with developed and developing economies to further refine such methods. Sustainable Design will be an invaluable resource for students and researchers, designers and business managers who are interested in the human-centred, environmental concerns of sustainable technologies

Sustainable Design: HCI, Usability and Environmental Concerns

Language and representation in information retrieval

The past decade has seen significant development in the number of personalized recommendation applications on the World Wide Web. It aims to assist users to retrieve relevant items from a large repository of contents by providing items or services of likely interest based on examined evidence of the users’ preferences and desires. However, this vision is complex due to the huge amount of information aka media-rich information available on the web. Most of the systems formulated so far use the metadata linked with the digital contents, but such systems fail to generate significant recommendations results. In these circumstances, a semantic personalized recommendation system (SPRS) plays an important role to take away the semantic gap between high-level semantic contents and low-level media features. The proposed system recommends personalized sets of videos to users depending on their previous activity on the site and exploits a domain ontology and user items content to the domain concepts. To evaluate the performance of the framework, items’ prediction is executed by utilizing the proposed framework, and performance is determined by comparing the predicted and actual ratings of the items in terms of Predictive Accuracy Metrics, precision, and recall.

Deep learning based semantic personalized recommendation system

Web recommendation systems are ubiquitous in the world used to overcome the product overload on e-commerce websites. Among various filtering algorithms, Collaborative Filtering and Content Based Filtering are the best recommendation approaches. Being popular, these filtering approaches still suffer from various limitations such as Cold Start Problem, Sparsity and Scalability all of which lead to poor recommendations. In this paper, we propose a hybrid system-based book recommendation system that anticipates recommendations. The proposed system is a mixture of collaborative filtering and content based filtering which can be explained in three phases: In the first phase, it identifies the users who are analogous to the active user by matching users' profiles. In the second phase, it chooses the candidate's item for every similar user by obtaining vectors Vc and Vm corresponding to the user's profile and the item contents. After calculating the prediction value for each item using the Resnick prediction equation, items are suggested to the target user in the final phase. We compared our proposed system to current state-of-the-art recommendation models, such as collaborative filtering and content-based filtering. It is shown in the experimental section that the proposed hybrid filtering approach outperforms conventional collaborative filtering and content-based filtering.

Automatic recommendation system based on hybrid filtering algorithm

Versatility of fuzzy logic in chronic diseases: A review.

Web search engines assist users to discover useful information on the World Wide Web. However, when the same query is submitted by different users, typical search engines provide the identical results regardless of who submitted the query. The search engines usually provide search results without considering user context or interests. Ecommerce web search personalization is an emerging area in the research which has already been achieved the curiosity. Anticipating the web user needs is one of the key factors of this framework and provides the qualitative results based on web user behavior. In this manuscript; we propose an approach for personalizing the web search using techniques, applications and opportunities of Data mining and web mining. Personalization of ecommerce has become a trend of current business scenario to overcome the problem of product overload, to appease the customers with special treatment offerings. Over the last 10 years it has been adopted as a skillful strategy to give enhanced service to the web clients and sustain in the market.

A semantic framework for ecommerce search engine optimization

In this manuscript we are presenting an efficient framework for retrieving the information from the Semantic Web using an Ontology-based approach. The present web is comprised of unstructured information which is extremely difficult to be retrieved by the search engine. The semantic web covers up this bug by exploiting the ontologies on the web and making the information machine understandable to be effectively retrieved by the search engine.

Ontology Based Informational Retrieval System on the Semantic Web: Semantic Web Mining

eless sensor network is an emerging technology that may greatly affect the human life which seems effective everywhere for high level inference and surveillance. This network is composed of hundreds and thousands of sensor nodes of small size with ubiquitous sensing and computing capabilities. Here each node individually senses the environment but collaboratively achieves complex information gathering task. Recently mobile agents have been proposed for efficient data transmission in wireless sensor networks. In the traditional client/server based computing architecture, data is collected from multiple sources and forwarded to destination for further processing whereas in the mobile agent based computing architecture only task specific executable code traverses to the relevant source for gathering data. Mobile agents can be used for reducing communication cost, for efficient energy conservation, for achieving progressive fusion accuracy etc. Deploying mobile agents in wireless sensor networks provides it extra flexibilities as well as new capabilities to network in contrast with conventional wireless sensor network operation based on client server computing architecture.

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A Hybrid Approach for Deploying Mobile Agents in Wireless Sensor Network

Nowadays, wisdom information retrieval system is available on the web, which enables a new generation of intelligent web that utilizes this semantic information as source of knowledge. The vision of semantic web crafts an environment where the information available on the web can be semantically interpreted. However, with abundance of information on the web, it is complicated to interpret the information with their intended meaning. Achieving this by interoperability among different information systems is very laborious, tedious and error-prone task which results out of semantic heterogeneity. Semantic web suggested solution of retrieval of specific information through the use of ontology matching technique and intelligent agent technology. In this paper we have proposed a knowledge-based grid for retrieving valuable information according to users necessitates. This paper also conducts a review of research in the area of intelligent communities and identifies the major theoretical and practical issues which need to be addressed.

Vijay Rana

Papers

Versatility of fuzzy logic in chronic diseases: A review.

A semantic framework for ecommerce search engine optimization

Ontology Based Informational Retrieval System on the Semantic Web: Semantic Web Mining

A Hybrid Approach for Deploying Mobile Agents in Wireless Sensor Network

An analysis of semantic heterogeneity issues and their countermeasures prevailing in semantic web