scholarly journals A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 232
Author(s):  
Janneth Chicaiza ◽  
Priscila Valdiviezo-Diaz
Keyword(s):  
Recommender Systems ◽  
Survey Result ◽  
Knowledge Graph ◽  
Specific Recommendation ◽  
Future Directions ◽  
Recent Developments ◽  
Comprehensive Survey ◽  
System 2 ◽  
And Performance ◽  
Use Of Knowledge

In recent years, the use of recommender systems has become popular on the web. To improve recommendation performance, usage, and scalability, the research has evolved by producing several generations of recommender systems. There is much literature about it, although most proposals focus on traditional methods’ theories and applications. Recently, knowledge graph-based recommendations have attracted attention in academia and the industry because they can alleviate information sparsity and performance problems. We found only two studies that analyze the recommendation system’s role over graphs, but they focus on specific recommendation methods. This survey attempts to cover a broader analysis from a set of selected papers. In summary, the contributions of this paper are as follows: (1) we explore traditional and more recent developments of filtering methods for a recommender system, (2) we identify and analyze proposals related to knowledge graph-based recommender systems, (3) we present the most relevant contributions using an application domain, and (4) we outline future directions of research in the domain of recommender systems. As the main survey result, we found that the use of knowledge graphs for recommendations is an efficient way to leverage and connect a user’s and an item’s knowledge, thus providing more precise results for users.

Narrow and Broad Faculties in System 1 and System 2: Toward Consensus in the Debate on Modularity

2021 ◽  
Vol 21 (3-4) ◽  
pp. 261-279
Author(s):  
Norbert Francis
Keyword(s):  
Future Research ◽  
Domain Specific ◽  
Evolutionary Science ◽  
System 2 ◽  
And Performance ◽  
System 1 ◽  
Use Of Knowledge

Abstract Research on learning, the structure of attained knowledge, and the use of this competence in performance has repeatedly returned to longstanding proposals about how to better understand proficient use of knowledge and how humans acquire it. The following article takes up an exchange between Chiappe & Gardner (2011) and Barrett & Kurzban (2012) on the concept of modularity, one of these proposals. Despite the disagreements expressed, a careful reading of the contributions shows that they also left us with lines of discussion that will eventually sort out the relevant hypotheses and integrate findings for future research. These lines of work will contribute to a clearer understanding of an updated version of the modularity hypothesis that is also compatible with evolutionary science perspectives on learning. How might the categories of domain-specific and domain-general correspond to the distinction between competence and performance and to that of narrow faculty and broad faculty?

scholarly journals The k-means Algorithm: A Comprehensive Survey and Performance Evaluation

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1295 ◽  
Author(s):  
Mohiuddin Ahmed ◽  
Raihan Seraj ◽  
Syed Mohammed Shamsul Islam
Keyword(s):  
Experimental Analysis ◽  
Clustering Algorithm ◽  
Fundamental Problem ◽  
Clustering Algorithms ◽  
Data Types ◽  
Data Mining Algorithms ◽  
Recent Developments ◽  
Comprehensive Survey ◽  
And Performance ◽  
Mining Algorithms

The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions.

scholarly journals Gait Recognition System: A Survey

2021 ◽  
Vol 23 (11) ◽  
pp. 218-227
Author(s):  
Rahul Thour ◽  
◽  
Prof. (Dr.) R. K. Bathla ◽  
Keyword(s):  
Gait Recognition ◽  
Recognition System ◽  
Future Directions ◽  
The Past ◽  
System A ◽  
Gait Classification ◽  
Recent Developments ◽  
Comprehensive Survey ◽  
Feature Dimensionality Reduction ◽  
Work Done

Human identification by gait has created a great deal of interest in computer vision community due to its advantage of inconspicuous recognition at a relatively far distance. This paper provides a comprehensive survey of recent developments on gait recognition approaches. The survey emphasizes on three major issues involved in a general gait recognition system, namely gait image representation, feature dimensionality reduction and gait classification. It also gives, a review of the available public gait datasets. The concluding discussions outline a number of research challenges and provide promising future directions for the field. In this article presents a survey of the work done in gait analysis for re-identification in the past decade, looking at the main approaches, datasets, and evaluation methodologies also discussed.

Green Synthesized Nanomaterials as Theranostic Platforms for Cancer Treatment: Principles, Challenges and the Road Ahead

2019 ◽  
Vol 26 (8) ◽  
pp. 1311-1327 ◽  
Author(s):  
Pala Rajasekharreddy ◽  
Chao Huang ◽  
Siddhardha Busi ◽  
Jobina Rajkumari ◽  
Ming-Hong Tai ◽  
...  
Keyword(s):  
Nanoparticle Synthesis ◽  
Research Field ◽  
Synthesis Methods ◽  
Synthetic Approach ◽  
Future Directions ◽  
The Road ◽  
New Methods ◽  
Recent Developments ◽  
Theranostic Applications ◽  
Insight Into

With the emergence of nanotechnology, new methods have been developed for engineering various nanoparticles for biomedical applications. Nanotheranostics is a burgeoning research field with tremendous prospects for the improvement of diagnosis and treatment of various cancers. However, the development of biocompatible and efficient drug/gene delivery theranostic systems still remains a challenge. Green synthetic approach of nanoparticles with low capital and operating expenses, reduced environmental pollution and better biocompatibility and stability is a latest and novel field, which is advantageous over chemical or physical nanoparticle synthesis methods. In this article, we summarize the recent research progresses related to green synthesized nanoparticles for cancer theranostic applications, and we also conclude with a look at the current challenges and insight into the future directions based on recent developments in these areas.

Duality beyondPersons and Groups

2020 ◽  
pp. 391-413
Author(s):  
Sophie Mützel ◽  
Ronald Breiger
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Data ◽  
Future Directions ◽  
Affiliation Networks ◽  
The Social ◽  
Recent Developments ◽  
Principle Of Duality ◽  
Seminal Article

This chapter focuses on the general principle of duality, which was originally introduced by Simmel as the intersection of social circles. In a seminal article, Breiger formalized Simmel’s idea, showing how two-mode types of network data can be transformed into one-mode networks. This formal translation proved to be fundamental for social network analysis, which no longer needed data on who interacted with whom but could work with other types of data. In turn, it also proved fundamental for the analysis of how the social is structured in general, as many relations are dual (e.g. persons and groups, authors and articles, organizations and practices), and are thus susceptible to an analysis according to duality principles. The chapter locates the concept of duality within past and present sociology. It also discusses the use of duality in the analysis of culture as well as in affiliation networks. It closes with recent developments and future directions.

scholarly journals Array databases: concepts, standards, implementations

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Peter Baumann ◽  
Dimitar Misev ◽  
Vlad Merticariu ◽  
Bang Pham Huu
Keyword(s):  
Service Quality ◽  
Ad Hoc ◽  
Query Language ◽  
Distributed Processing ◽  
Database Systems ◽  
Database Technology ◽  
Comprehensive Survey ◽  
Spatio Temporal ◽  
And Performance ◽  
Array Databases

AbstractMulti-dimensional arrays (also known as raster data or gridded data) play a key role in many, if not all science and engineering domains where they typically represent spatio-temporal sensor, image, simulation output, or statistics “datacubes”. As classic database technology does not support arrays adequately, such data today are maintained mostly in silo solutions, with architectures that tend to erode and not keep up with the increasing requirements on performance and service quality. Array Database systems attempt to close this gap by providing declarative query support for flexible ad-hoc analytics on large n-D arrays, similar to what SQL offers on set-oriented data, XQuery on hierarchical data, and SPARQL and CIPHER on graph data. Today, Petascale Array Database installations exist, employing massive parallelism and distributed processing. Hence, questions arise about technology and standards available, usability, and overall maturity. Several papers have compared models and formalisms, and benchmarks have been undertaken as well, typically comparing two systems against each other. While each of these represent valuable research to the best of our knowledge there is no comprehensive survey combining model, query language, architecture, and practical usability, and performance aspects. The size of this comparison differentiates our study as well with 19 systems compared, four benchmarked to an extent and depth clearly exceeding previous papers in the field; for example, subsetting tests were designed in a way that systems cannot be tuned to specifically these queries. It is hoped that this gives a representative overview to all who want to immerse into the field as well as a clear guidance to those who need to choose the best suited datacube tool for their application. This article presents results of the Research Data Alliance (RDA) Array Database Assessment Working Group (ADA:WG), a subgroup of the Big Data Interest Group. It has elicited the state of the art in Array Databases, technically supported by IEEE GRSS and CODATA Germany, to answer the question: how can data scientists and engineers benefit from Array Database technology? As it turns out, Array Databases can offer significant advantages in terms of flexibility, functionality, extensibility, as well as performance and scalability—in total, the database approach of offering “datacubes” analysis-ready heralds a new level of service quality. Investigation shows that there is a lively ecosystem of technology with increasing uptake, and proven array analytics standards are in place. Consequently, such approaches have to be considered a serious option for datacube services in science, engineering and beyond. Tools, though, vary greatly in functionality and performance as it turns out.

scholarly journals The Past, Present and Future of Cannabis sativa Tissue Culture

Plants ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 185
Author(s):  
Adrian S. Monthony ◽  
Serena R. Page ◽  
Mohsen Hesami ◽  
Andrew Maxwell P. Jones
Keyword(s):  
Tissue Culture ◽  
Disease Virus ◽  
Cannabis Sativa ◽  
Clonal Plants ◽  
Future Directions ◽  
The Past ◽  
Recent Developments ◽  
History Of ◽  
Drug Type

The recent legalization of Cannabis sativa L. in many regions has revealed a need for effective propagation and biotechnologies for the species. Micropropagation affords researchers and producers methods to rapidly propagate insect-/disease-/virus-free clonal plants and store germplasm and forms the basis for other biotechnologies. Despite this need, research in the area is limited due to the long history of prohibitions and restrictions. Existing literature has multiple limitations: many publications use hemp as a proxy for drug-type Cannabis when it is well established that there is significant genotype specificity; studies using drug-type cultivars are predominantly optimized using a single cultivar; most protocols have not been replicated by independent groups, and some attempts demonstrate a lack of reproducibility across genotypes. Due to culture decline and other problems, the multiplication phase of micropropagation (Stage 2) has not been fully developed in many reports. This review will provide a brief background on the history and botany of Cannabis as well as a comprehensive and critical summary of Cannabis tissue culture. Special attention will be paid to current challenges faced by researchers, the limitations of existing Cannabis micropropagation studies, and recent developments and future directions of Cannabis tissue culture technologies.

scholarly journals Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

2021 ◽  
Vol 11 (4) ◽  
pp. 1627
Author(s):  
Yanbin Li ◽  
Gang Lei ◽  
Gerd Bramerdorfer ◽  
Sheng Peng ◽  
Xiaodong Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Optimization Methods ◽  
Machine Learning Algorithms ◽  
Cloud Services ◽  
Robust Design Optimization ◽  
Support Vector ◽  
Future Directions ◽  
Electromagnetic Devices ◽  
Recent Developments

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

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