scholarly journals Graph Learning based Recommender Systems: A Review

2021 ◽  
Author(s):  
Shoujin Wang ◽  
Liang Hu ◽  
Yan Wang ◽  
Xiangnan He ◽  
Quan Z. Sheng ◽  
...  
Keyword(s):  
Systematic Review ◽  
Recommender Systems ◽  
Rapid Development ◽  
Research Area ◽  
Learning Approaches ◽  
Graph Learning ◽  
Promising Direction ◽  
Learning Techniques ◽  
Fast Development ◽  
Content Based Filtering

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ advanced graph learning approaches to model users’ preferences and intentions as well as items’ characteristics and popularity for Recommender Systems (RS). Differently from other approaches, including content based filtering and collaborative filtering, GLRS are built on graphs where the important objects, e.g., users, items, and attributes, are either explicitly or implicitly connected. With the rapid development of graph learning techniques, exploring and exploiting homogeneous or heterogeneous relations in graphs is a promising direction for building more effective RS. In this paper, we provide a systematic review of GLRS, by discussing how they extract knowledge from graphs to improve the accuracy, reliability and explainability of the recommendations. First, we characterize and formalize GLRS, and then summarize and categorize the key challenges and main progress in this novel research area.

scholarly journals Sequential Recommender Systems: Challenges, Progress and Prospects

2019 ◽  
Author(s):  
Shoujin Wang ◽  
Liang Hu ◽  
Yan Wang ◽  
Longbing Cao ◽  
Quan Z. Sheng ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Research Area ◽  
Research Progress ◽  
Important Research ◽  
Research Directions ◽  
User Behaviors ◽  
Precise Characterization ◽  
Content Based Filtering ◽  
Over Time

The emerging topic of sequential recommender systems (SRSs) has attracted increasing attention in recent years. Different from the conventional recommender systems (RSs) including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users’ preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations. In this paper, we provide a systematic review on SRSs. We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic. Finally, we discuss the important research directions in this vibrant area.

scholarly journals Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review

2018 ◽  
Vol 8 (12) ◽  
pp. 2570 ◽  
Author(s):  
Yves Rybarczyk ◽  
Rasa Zalakeviciute
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Deterministic Models ◽  
Learning Techniques ◽  
Vector Machines ◽  
Estimation Problems ◽  
Selection Of

Current studies show that traditional deterministic models tend to struggle to capture the non-linear relationship between the concentration of air pollutants and their sources of emission and dispersion. To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques. Nevertheless, it is puzzling why a certain algorithm is chosen over another for a given task. This systematic review intends to clarify this question by providing the reader with a comprehensive description of the principles underlying these algorithms and how they are applied to enhance prediction accuracy. A rigorous search that conforms to the PRISMA guideline is performed and results in the selection of the 46 most relevant journal papers in the area. Through a factorial analysis method these studies are synthetized and linked to each other. The main findings of this literature review show that: (i) machine learning is mainly applied in Eurasian and North American continents and (ii) estimation problems tend to implement Ensemble Learning and Regressions, whereas forecasting make use of Neural Networks and Support Vector Machines. The next challenges of this approach are to improve the prediction of pollution peaks and contaminants recently put in the spotlights (e.g., nanoparticles).

scholarly journals A Bibliometric Insight of Genetic Factors in ASD: Emerging Trends and New Developments

2020 ◽  
Vol 11 (1) ◽  
pp. 33
Author(s):  
Kang Wang ◽  
Weicheng Duan ◽  
Yijie Duan ◽  
Yuxin Yu ◽  
Xiuyi Chen ◽  
...  
Keyword(s):  
Genetic Factors ◽  
De Novo ◽  
Rapid Development ◽  
The United States ◽  
Research Area ◽  
Scientific Output ◽  
Autism Spectrum ◽  
De Novo Mutations ◽  
Emerging Trends ◽  
Genetic Abnormalities

Autism spectrum disorder (ASD) cases have increased rapidly in recent decades, which is associated with various genetic abnormalities. To provide a better understanding of the genetic factors in ASD, we assessed the global scientific output of the related studies. A total of 2944 studies published between 1997 and 2018 were included by systematic retrieval from the Web of Science (WoS) database, whose scientific landscapes were drawn and the tendencies and research frontiers were explored through bibliometric methods. The United States has been acting as a leading explorer of the field worldwide in recent years. The rapid development of high-throughput technologies and bioinformatics transferred the research method from the traditional classic method to a big data-based pipeline. As a consequence, the focused research area and tendency were also changed, as the contribution of de novo mutations in ASD has been a research hotspot in the past several years and probably will remain one into the near future, which is consistent with the current opinions of the major etiology of ASD. Therefore, more attention and financial support should be paid to the deciphering of the de novo mutations in ASD. Meanwhile, the effective cooperation of multi-research centers and scientists in different fields should be advocated in the next step of scientific research undertaken.

scholarly journals An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1788
Author(s):  
Gomatheeshwari Balasekaran ◽  
Selvakumar Jayakumar ◽  
Rocío Pérez de Prado
Keyword(s):  
Execution Time ◽  
Autonomous Vehicles ◽  
Rapid Development ◽  
Single Layer ◽  
Battery Life ◽  
Learning Approaches ◽  
Task Management ◽  
Inference System ◽  
Safe Driving ◽  
Self Driving Cars

With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms).Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods.

423 Massive Open Online Courses in Anatomy Education: A Systematic Review

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
J Cheong ◽  
I Keenan
Keyword(s):  
Systematic Review ◽  
Medical Education ◽  
Online Courses ◽  
Massive Open Online Courses ◽  
Learning Approaches ◽  
Discussion Forums ◽  
Massive Open Online ◽  
Pubmed Database ◽  
Literature Search Strategy ◽  
Learning Development

Abstract Introduction Massive open online courses (MOOCs) have become increasingly popular for remotely delivering education to global audiences. MOOCs can provide an online platform for participants coming from a variety of professional backgrounds and international locations. Our systematic review investigated current literature surrounding MOOCs, and identified the value of such courses with respect to anatomical and medical education. Method Our literature search strategy involved exploring the PubMed database for the terms ’MOOC + Anatomy’ and ‘MOOC + Medical Education’ identified 290 relevant articles. Following implementation of the PRISMA criteria, 24 articles out of 290 were selected for inclusion in our systematic review. Results Participants completing MOOCs in anatomical and medical education generally showed increased knowledge and skills. We found that MOOC discussion forums encourage participants’ social learning development. MOOCs have shown to address participants’ needs and expectations while supplementing traditional learning approaches. However, the majority of experimental research studies did not include pre-post testing or appropriate controls. Furthermore, high levels of participant attrition, inability to address all learning approaches were identified as limitations with respect to MOOCs. Conclusions Although MOOCs have shown success in providing an excellent platform for learning, it has yet to show definitive improvements when compared to traditional teaching methods.

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

scholarly journals A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5248
Author(s):  
Aleksandra Pawlicka ◽  
Marek Pawlicki ◽  
Rafał Kozik ◽  
Ryszard S. Choraś
Keyword(s):  
Systematic Review ◽  
Recommender Systems ◽  
Recommender System ◽  
State Of The Art ◽  
The State ◽  
Advantages And Disadvantages ◽  
Comprehensive Survey ◽  
Security Concerns ◽  
Valuable Role

This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two–three types at once and neglect the others.

Applicability of machine learning techniques in food intake assessment: A systematic review

2021 ◽  
pp. 1-18
Author(s):  
Larissa Oliveira Chaves ◽  
Ana Luiza Gomes Domingos ◽  
Daniel Louzada Fernandes ◽  
Fabio Ribeiro Cerqueira ◽  
Rodrigo Siqueira-Batista ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Food Intake ◽  
Machine Learning Techniques ◽  
Intake Assessment ◽  
Learning Techniques

scholarly journals Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 667
Author(s):  
Wei Chen ◽  
Qiang Sun ◽  
Xiaomin Chen ◽  
Gangcai Xie ◽  
Huiqun Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Deep Learning ◽  
Heart Sounds ◽  
Learning Approaches ◽  
Automated Classification ◽  
Learning Methods ◽  
Artificial Intelligence Technology ◽  
Medical Big Data ◽  
Effective Models

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

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