71 research outputs found
Data augmentation for recommender system: A semi-supervised approach using maximum margin matrix factorization
Collaborative filtering (CF) has become a popular method for developing
recommender systems (RS) where ratings of a user for new items is predicted
based on her past preferences and available preference information of other
users. Despite the popularity of CF-based methods, their performance is often
greatly limited by the sparsity of observed entries. In this study, we explore
the data augmentation and refinement aspects of Maximum Margin Matrix
Factorization (MMMF), a widely accepted CF technique for the rating
predictions, which have not been investigated before. We exploit the inherent
characteristics of CF algorithms to assess the confidence level of individual
ratings and propose a semi-supervised approach for rating augmentation based on
self-training. We hypothesize that any CF algorithm's predictions with low
confidence are due to some deficiency in the training data and hence, the
performance of the algorithm can be improved by adopting a systematic data
augmentation strategy. We iteratively use some of the ratings predicted with
high confidence to augment the training data and remove low-confidence entries
through a refinement process. By repeating this process, the system learns to
improve prediction accuracy. Our method is experimentally evaluated on several
state-of-the-art CF algorithms and leads to informative rating augmentation,
improving the performance of the baseline approaches.Comment: 20 page
Assessment of Coronary Artery Calcium Score among Asymptomatic Individuals at Intermediate Risk of Developing Coronary Artery Disease
Background and Aim:The present study was designed to estimate the coronary artery calcium score (CACS) and its association with the incidence of major adverse cardiovascular events (MACE) in asymptomatic patients who are at the risk of coronary artery disease (CAD).Materials and Methods:In this prospective cross-sectional observational study, 108 consecutive patients were enrolled. The patients at intermediate risk of cardiovascular disease, atypical chest pain, and a positive family history of CAD were included. Demographic details and clinical data including lipid profile, systolic blood pressure, electrocardiography, 2D echocardiography, and routine blood investigations were reported. CACS was derived from computed tomography using a 256-slice scanner with a rotation time of 270 milliseconds. MACE was recorded at 1-year follow-up.Results:The mean age was 54.55 ± 7.7 years with male predominance (62%). CACS categories 0, 1-99, 100-399, 400-999, and more than 1000 constituted 43.5%, 28.7%, 17.6%, 9.3%, 0.9%, respectively. The correlation between the groups of positive and negative CACS and presence or absence of standard risk factors was found to be statistically significant in diabetes mellitus (P = 0.001), hypertension (P = 0.001), and history of CAD in the family (P= 0.029). Although the association between smokers and calcium was statistically insignificant, it had clinical significance (P = 0.212). Out of 108 patients, MACE was observed in 16 (14.81%) patients with positive CACS at 1-year follow-up.Conclusion:CACS measurement is often regarded as the primary non-invasive approach for risk stratification, MACE estimation, and promptly identifying high-risk asymptomatic individuals
UniRecSys: A Unified Framework for Personalized, Group, Package, and Package-to-Group Recommendations
Recommender systems aim to enhance the overall user experience by providing
tailored recommendations for a variety of products and services. These systems
help users make more informed decisions, leading to greater user satisfaction
with the platform. However, the implementation of these systems largely depends
on the context, which can vary from recommending an item or package to a user
or a group. This requires careful exploration of several models during the
deployment, as there is no comprehensive and unified approach that deals with
recommendations at different levels. Furthermore, these individual models must
be closely attuned to their generated recommendations depending on the context
to prevent significant variation in their generated recommendations. In this
paper, we propose a novel unified recommendation framework that addresses all
four recommendation tasks, namely personalized, group, package, or
package-to-group recommendation, filling the gap in the current research
landscape. The proposed framework can be integrated with most of the
traditional matrix factorization-based collaborative filtering models. The idea
is to enhance the formulation of the existing approaches by incorporating
components focusing on the exploitation of the group and package latent
factors. These components also help in exploiting a rich latent representation
of the user/item by enforcing them to align closely with their corresponding
group/package representation. We consider two prominent CF techniques,
Regularized Matrix Factorization and Maximum Margin Matrix factorization, as
the baseline models and demonstrate their customization to various
recommendation tasks. Experiment results on two publicly available datasets are
reported, comparing them to other baseline approaches that consider individual
rating feedback for group or package recommendations.Comment: 25 page
The Role of the Internet of Things in Health Care: A Systematic and Comprehensive Study
The Internet of Things (IoT) is becoming an emerging trend and has significant potential to replace other technologies, where researchers consider it as the future of the internet. It has given tremendous support and become the building blocks in the development of important cyber-physical systems and it is being severed in a variety of application domains, including healthcare. A methodological evolution of the Internet of Things, enabled it to extend to the physical world beyond the electronic world by connecting miscellaneous devices through the internet, thus making everything is connected. In recent years it has gained higher attention for its potential to alleviate the strain on the healthcare sector caused by the rising and aging population along with the increase in chronic diseases and global pandemics. This paper surveys about various usages of IoT healthcare technologies and reviews the state of the art services and applications, recent trends in IoT based healthcare solutions, and various challenges posed including security and privacy issues, which researchers, service providers and end users need to pay higher attention. Further, this paper discusses how innovative IoT enabled technologies like cloud computing, fog computing, blockchain, and big data can be used to leverage modern healthcare facilities and mitigate the burden on healthcare resources
Analysis of Behavioral Characteristics of Multiple Blackhole Attacks with TCP and UDP Connections in Mobile ADHOC Networks based on Machine Learning Algorithms
In Mobile Adhoc Networks (MANET’s), a suit of nodes which are under mobility work together to transmit data packets in a multiple-hop manner without relying on any fixed or centralized infrastructure. A significant obstacle in managing these networks is identifying malicious nodes, or "black holes". To detect black holes, we proposed a method involves broadcasting a Cseq to the neighboring nodes and awaiting the node's response is utilized. This Network is simulated with 25 number of nodes connected with TCP connection and observed the different behavioural characteristics of nodes. Then the connections are changed to UDP and observed the characteristics. Then characteristics are analyzed with different machine learning algorithms. The network is simulated in NS2 environment
Conformal Group Recommender System
Group recommender systems (GRS) are critical in discovering relevant items
from a near-infinite inventory based on group preferences rather than
individual preferences, like recommending a movie, restaurant, or tourist
destination to a group of individuals. The traditional models of group
recommendation are designed to act like a black box with a strict focus on
improving recommendation accuracy, and most often, they place the onus on the
users to interpret recommendations. In recent years, the focus of Recommender
Systems (RS) research has shifted away from merely improving recommendation
accuracy towards value additions such as confidence and explanation. In this
work, we propose a conformal prediction framework that provides a measure of
confidence with prediction in conjunction with a group recommender system to
augment the system-generated plain recommendations. In the context of group
recommender systems, we propose various nonconformity measures that play a
vital role in the efficiency of the conformal framework. We also show that
defined nonconformity satisfies the exchangeability property. Experimental
results demonstrate the effectiveness of the proposed approach over several
benchmark datasets. Furthermore, our proposed approach also satisfies validity
and efficiency properties.Comment: 23 page
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