• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.31-38
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• 2008

Ad-Hoc network is a network architecture which has no backbone network and is deployed temporarily and rapidly in emergency or war without fixed mobile infrastructures. All communications between network entities are carried in ad-hoc networks over the wireless medium. Due to the radio communications being extremely vulnerable to propagation impairments, connectivity between network nodes is not guaranteed. Therefore, many new algorithms have been studied recently. This study proposes the secondary header approach to the cluster based routing protocol (CBRP). The primary header becomes abnormal status so that the primary header can not participate in the communications between network entities, the secondary header immediately replaces the primary header without selecting process of the new primary header. This improves the routing interruption problem that occurs when a header is moving out from a cluster or in the abnormal status. The performances of proposed algorithm ACBRP(Advanced Cluster Based Routing Protocol) are compared with CBRP. The cost of the primary header reelection of ACBRP is simulated. And results are presented in order to show the effectiveness of the algorithm.

• Journal of Korean Academy of Nursing
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• v.23 no.3
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• pp.467-486
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• 1993

Today, more chronically ill and handicapped people are being cared for at home by a family member caregiver. The task of caring for a family momber may mean that the caregiver has less time and money and more work which may result in increased fatigue and symptoms of illness. This study was done to examine the well-being of family caregivers. Fifty three family caregivers were interviewed. Concepts were measured using existing tools and included : Burden(25 item 5 point scale), Social sup-port (21 item 7 point scale), Health status defined by a symptom checklist(48 item S point scale), and Well -being defined by a quality of life scale (14 item 7 point scale) and caregiving activities. Data collection was done by interview and Q-sort. Social support and well - being were positively correlated as were symptoms and burden. Symptoms and burden were negatively correlated with social support and well-being. Items on the quality of life scale had a mean score range from 3.09 to 4.96. Quality of life related to income was lowest (3.09) but the desire to use more money for the patient was rated 2.90 on the burden scale where the item means ranged from 0.73 to 3.55. The high mean of 3.55 was for obligation to give care and the low 0.73 was (or not feeling that this was helping the patient. Mean scores for symptoms ranged from 0.26 to 2.15 with the 2.15 being for “worry about all the things that have to be done.” Over half of the patients were dependent for help with some activities of daily living. The caregivers reported doing an average of 3.40 out of five patient care activities including bathing (77.4%), shampooing (67.9%), and washing face and hands (49.1％), and 3.74 out of seven home maintenance activities including laundry (98.1％), cooking (83.0%), and arranging bed-ding(75.5%). The caregivers reported their spouse as one of the main sources of social support, including in times of loneliness and anger The mean score for loneliness as burden was 2.15 and ranked fourth and 31 (58.5％) of the sample reported being lonely recently and not being satisfied with the support received. Similarly anger caused by the patient was given a mean score of 2.13, and anger was reported to have been present recently by 38 (71.7%) of the sample and satis-faction with the support given was low. Having someone to help deal with anger ranked twelfth out of 21 items on the social support scale and had a mean score of 3.98 (range 3.49 to 5.98). Spouses were reported as a major source of social support but the fact that 50% of the caregivers were caring for a spouse, may account for the quality of this source of social support having been affected. These caregivers faced the same problems as others at the same stage of life. but because of the situation, there was a strain on their resources, particularly financial and social. In conclusion it was found that burden is correlated negatively to quality of life and positively to symptoms, but in this sample, symptoms and bur-den were scored relatively low. Does this indicate that the caregivers accept caregiving as part of their destiny and accept the quality of their lives with burden and symptoms just being a part of caregiving\ulcorner Does the correlation between the bur-den and symptoms indicate they are a measure of the same phenomenon or that the sample was of a more mobile, less burdened group of caregivers\ulcorner Quality of life was the one variable that was significant in explaining the varience on burden. Further study is needed to validate the conclusions found in this study but they indicate a need for nurses to ap-proach these caregivers with a plan tailored to each individual situation and to give consideration to interventions directed at improving quality of life and expanding social support networks for those caring for spouses.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.57-77
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• 2013

Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.