215,315 research outputs found
An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders
The data mining along with emerging computing techniques have astonishingly
influenced the healthcare industry. Researchers have used different Data Mining
and Internet of Things (IoT) for enrooting a programmed solution for diabetes
and heart patients. However, still, more advanced and united solution is needed
that can offer a therapeutic opinion to individual diabetic and cardio
patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced
healthcare system for proficient diabetes and cardiovascular diseases have been
proposed. The hybridization of data mining and IoT with other emerging
computing techniques is supposed to give an effective and economical solution
to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining,
Internet of Things, chatbots, contextual entity search (CES), bio-sensors,
semantic analysis and granular computing (GC). The bio-sensors of the proposed
system assist in getting the current and precise status of the concerned
patients so that in case of an emergency, the needful medical assistance can be
provided. The novelty lies in the hybrid framework and the adequate support of
chatbots, granular computing, context entity search and semantic analysis. The
practical implementation of this system is very challenging and costly.
However, it appears to be more operative and economical solution for diabetes
and cardio patients.Comment: 11 PAGE
Multiangle social network recommendation algorithms and similarity network evaluation
Multiangle social network recommendation algorithms (MSN) and a new assessmentmethod, called similarity network evaluation (SNE), are both proposed. From the viewpoint of six dimensions, the MSN are classified into six algorithms, including user-based algorithmfromresource point (UBR), user-based algorithmfromtag point (UBT), resource-based algorithm fromtag point (RBT), resource-based algorithm from user point (RBU), tag-based algorithm from resource point (TBR), and tag-based algorithm from user point (TBU). Compared with the traditional recall/precision (RP) method, the SNE is more simple, effective, and visualized. The simulation results show that TBR and UBR are the best algorithms, RBU and TBU are the worst ones, and UBT and RBT are in the medium levels
Finding Influential Users in Social Media Using Association Rule Learning
Influential users play an important role in online social networks since
users tend to have an impact on one other. Therefore, the proposed work
analyzes users and their behavior in order to identify influential users and
predict user participation. Normally, the success of a social media site is
dependent on the activity level of the participating users. For both online
social networking sites and individual users, it is of interest to find out if
a topic will be interesting or not. In this article, we propose association
learning to detect relationships between users. In order to verify the
findings, several experiments were executed based on social network analysis,
in which the most influential users identified from association rule learning
were compared to the results from Degree Centrality and Page Rank Centrality.
The results clearly indicate that it is possible to identify the most
influential users using association rule learning. In addition, the results
also indicate a lower execution time compared to state-of-the-art methods
- …