Living analytics methods for the social web

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Investigation of Effective Classification Method for Online Health Service Recommendation System

Hospital Recommendation Services have been gaining popularity these days. There are many applications and systems that are recommending hospitals based on the user’s requirements and to meet the patient satisfaction. These applications take the reviews of the patients and the users and based on these reviews, they recommend the hospitals. Also if a person is new to the location that he is currently residing, when the speciality is given as input by him, then these applications recommend the hospitals. But the problem is that everyone is not aware of the medical terms like specialities. For those people, “Health Service Recommendation System” comes handy. “Health Service Recommendation System” is an Android Application for finding hospitals within a specified range of distance and requirements provided by the client using the Naïve Bayes classification algorithm. Naïve Bayes algorithm classifies the speciality and thus helps in achieving the maximum accuracy compared to the other algorithms used. This application is helpful even for the people who are not aware of the specialities of the hospitals

A Survey on Various Techniques in Internet of Things (IoT) Implementation: A Comparative Study

As per the current trends in computing research socialization and Personalization in Internet of Things (IOT) environment are quite trending and they are being widely used. The main aim of research work is to provide socialized and personalized services along with creating awareness of predicting the service. Here various kind of methods are discussed which can be used for predicting user intention in large variety of IOT based applications such as smart mobile, smart television, web mining, weather forecasting, health-care/medical, robotics, road-traffic, educational data mining, natural calamities, retail banking, e-commerce, wireless networks & social networking. By common consent it is found that the prediction is made usually for finding techniques that can be accessed by the mobile user, predicting the next page that is most likely to be used by web user, predicting favorite and most likely TV program that can be viewed by user, getting a list of browsing usage and need of user and also predicting user navigational patterns, predicting future climate conditions, predicting the health and welfare of user, predicting user intention so that implicit could be made and human-like interactions could be possible by accepting implicit commands, predicting the exact amount of traffic at a particular location, predicting curricular performance of student in schools & colleges, having prediction of frequency of natural calamities and their occurrences such as floods, earthquakes over a long period of time & also the required time in which precautionary measures could be adopted, predicting & detecting the frauds in which false user try to make transaction in the name of genuine user, predicting the steps and work done by the user to improve the business, predicting & detecting the intruder acting in the network, by the help of context history predicting the mood transition information of the user, etc. Here in this topic of discussion, different techniques such as Decision Tree algorithm, Artificial Intelligence and Data Mining based Machine learning techniques, Content and Collaborative based Recommender algorithms are used for prediction

Prediction Techniques in Internet of Things (IoT) Environment: A Comparative Study

Socialization and Personalization in Internet of Things (IOT) environment are the current trends in computing research. Most of the research work stresses the importance of predicting the service & providing socialized and personalized services. This paper presents a survey report on different techniques used for predicting user intention in wide variety of IOT based applications like smart mobile, smart television, web mining, weather forecasting, health-care/medical, robotics, road-traffic, educational data mining, natural calamities, retail banking, e-commerce, wireless networks & social networking. As per the survey made the prediction techniques are used for: predicting the application that can be accessed by the mobile user, predicting the next page to be accessed by web user, predicting the users favorite TV program, predicting user navigational patterns and usage needs on websites & also to extract the users browsing behavior, predicting future climate conditions, predicting whether a patient is suffering from a disease, predicting user intention to make implicit and human-like interactions possible by accepting implicit commands, predicting the amount of traffic occurring at a particular location, predicting student performance in schools & colleges, predicting & estimating the frequency of natural calamities occurrences like floods, earthquakes over a long period of time & also to take precautionary measures, predicting & detecting false user trying to make transaction in the name of genuine user, predicting the actions performed by the user to improve the business, predicting & detecting the intruder acting in the network, predicting the mood transition information of the user by using context history, etc. This paper also discusses different techniques like Decision Tree algorithm, Artificial Intelligence and Data Mining based Machine learning techniques, Content and Collaborative based Recommender algorithms used for prediction

FARS: Fuzzy Ant based Recommender System for Web Users

Recommender systems are useful tools which provide an adaptive web environment for web users. Nowadays, having a user friendly website is a big challenge in e-commerce technology. In this paper, applying the benefits of both collaborative and content based filtering techniques is proposed by presenting a fuzzy recommender system based on collaborative behavior of ants (FARS). FARS works in two phases: modeling and recommendation. First, user’s behaviors are modeled offline and the results are used in second phase for online recommendation. Fuzzy techniques provide the possibility of capturing uncertainty among user interests and ant based algorithms provides us with optimal solutions. The performance of FARS is evaluated using log files of “Information and Communication Technology Center” of Isfahan municipality in Iran and compared with ant based recommender system (ARS). The results shown are promising and proved that integrating fuzzy Ant approach provides us with more functional and robust recommendations

Genetic Algorithm based Cluster Head Selection for Optimimized Communication in Wireless Sensor Network

Wireless Sensor Network (WSNs) utilizes conveyed gadgets sensors for observing physical or natural conditions. It has been given to the steering conventions which may contrast contingent upon the application and system design. Vitality administration in WSN is of incomparable significance for the remotely sent vitality sensor hubs. The hubs can be obliged in the little gatherings called the Clusters. Clustering is done to accomplish the vitality effectiveness and the versatility of the system. Development of the group likewise includes the doling out the part to the hub based on their borders. In this paper, a novel strategy for cluster head selection based on Genetic Algorithm (GA) has been proposed. Every person in the GA populace speaks to a conceivable answer for the issue. Discovering people who are the best proposals to the enhancement issue and join these people into new people is a critical phase of the transformative procedure. The Cluster Head (CH) is picked using the proposed technique Genetic Algorithm based Cluster Head (GACH). The performance of the proposed system GACH has been compared with Particle Swarm Optimization Cluster Head (PSOCH). Simulations have been conducted with 14 wireless sensor nodes scattered around 8 kilometers. Results proves that GACH outperforms than PSOCH in terms of throughput, packet delivery ratio and energy efficiency

Investigation of Effective Classification Method for Online Health Service Recommendation System

Hospital Recommendation Services have been gaining popularity these days. There are many applications and systems that are recommending hospitals based on the user’s requirements and to meet the patient satisfaction. These applications take the reviews of the patients and the users and based on these reviews, they recommend the hospitals. Also if a person is new to the location that he is currently residing, when the speciality is given as input by him, then these applications recommend the hospitals. But the problem is that everyone is not aware of the medical terms like specialities. For those people, “Health Service Recommendation System” comes handy. “Health Service Recommendation System” is an Android Application for finding hospitals within a specified range of distance and requirements provided by the client using the Naïve Bayes classification algorithm. Naïve Bayes algorithm classifies the speciality and thus helps in achieving the maximum accuracy compared to the other algorithms used. This application is helpful even for the people who are not aware of the specialities of the hospitals