State-of-the-Art in Performance Metrics and Future Directions for Data Science Algorithms

Ajay Sharma; Promad Kumar Mishra

doi:10.37398/jsr.2020.640232

A Survey on Challenges and Progresses in Blockchain Technologies: A Performance and Security Perspective

Applied Sciences ◽

10.3390/app9224731 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4731 ◽

Cited By ~ 9

Author(s):

Xiaoying Zheng ◽

Yongxin Zhu ◽

Xueming Si

Keyword(s):

Performance Metrics ◽

State Of The Art ◽

The State ◽

Lessons Learned ◽

Future Directions ◽

Research Directions ◽

Scientific Databases ◽

Potential Applications ◽

Industry Sectors ◽

A Performance

Blockchain naturally fits multiple industry sectors due its characteristics of decentralization, enhanced security, tamper-proof, improved traceability and transparency. However, there is a significant concern of blockchain’s performance, since blockchain trades off its performance for a completely distributed feature, which enhances its security. In this paper, we investigate the state-of-the-art progress of blockchain, mainly from a performance and security perspective. We extracted 42 primary papers from major scientific databases and 34 online technical articles. The objective is to understand the current research trends, challenges and future directions. We briefly introduce the key technologies of blockchain including distributed ledger, cryptography, consensus, smart contracts and benchmarks. We next summarize the performance and security concerns raised in the investigation. We discuss the architectural choices, performance metrics, database management enhancements, and hybrid blockchains, and try to identify the effort that the state-of-the-art has made to balance between the performance and security. We also make experiments on Ethereum and survey other popular blockchain platforms on the scalability feature of blockchain. We later discuss the potential applications and present the lessons learned. Finally, we attempt to identify the open issues and possible research directions.

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Data-Driven Design-by-Analogy: State of the Art and Future Directions

Journal of Mechanical Design ◽

10.1115/1.4051681 ◽

2021 ◽

pp. 1-55

Author(s):

Shuo Jiang ◽

Jie Hu ◽

Kristin L. Wood ◽

Jianxi Luo

Keyword(s):

Design Methodology ◽

Data Science ◽

State Of The Art ◽

Data Driven ◽

Target Domain ◽

Future Directions ◽

Structured Analysis ◽

Design By Analogy ◽

Conceptual Data ◽

Design Databases

Abstract Design-by-Analogy (DbA) is a design methodology wherein new solutions, opportunities or designs are generated in a target domain based on inspiration drawn from a source domain; it can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. In this study, we survey existing data-driven DbA studies and categorize individual studies according to the data, methods, and applications in four categories, namely, analogy encoding, retrieval, mapping, and evaluation. Based on both nuanced organic review and structured analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field. Finally, we propose a future conceptual data-driven DbA system that integrates all propositions.

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International Journal Population Data Science: development and future directions

International Journal for Population Data Science ◽

10.23889/ijpds.v3i4.692 ◽

2018 ◽

Vol 3 (4) ◽

Author(s):

Kerina Jones ◽

Kim McGrail ◽

Rainer Schnell ◽

Claudia Coeli ◽

Stephanie Lee

Keyword(s):

Board Member ◽

Data Science ◽

Performance Metrics ◽

Focal Point ◽

Population Data ◽

Editorial Board Member ◽

General Reader ◽

Future Directions ◽

Founding Editor

Background and rationaleThe International Journal of Population Data Science (IJPDS) was launched in April 2017. It is an electronic, open-access, peer reviewed journal, publishing articles on all aspects of research, development and evaluation connected with about people and populations. It represents an internationally unique vehicle for publishing a broader range of articles than most journals in related fields by including in scope: working papers, methodological developments, informative reports, and other pieces of interest, in addition to more traditional manuscripts. As such, it provides a focal point (and a home) for all areas of Population Data Science. The creation of the IJPDS was inspired by the IPDLN, and places great importance on the viewpoints and activities of Network members to guide the development of the journal. ObjectiveInformation dissemination – stakeholder consultation – informing future directionsThe main objective of this collaborative session is to present the audience with an up-to-date summary of journal strategy and progress to date, and to use this forum to gain further viewpoints to better target future directions of the journal to meet the needs of those working in Population Data Science. PlanThe session will comprise 5 sections: A short presentation overviewing the journal, its historical origins, its remit and relationship to the IPDLN and Network members. Primary objectives of the journal and performance metrics from the first 18 months of operation will be presented. Gauging audience opinion on: What they like/dislike about the journal What is working well/not so well Feedback and discussion on the survey results Discussions with audience in groups, each focusing on some/all of the following questions, and a question of their own choice if something arises: How can we make IJPDS articles more accessible to the general reader/non-specialist researcher?Example options: short video/audio; general reader summaries; infographics; other How can we increase the reach of, and interest in, the journal? Open answers What would you suggest as a special issue?These topics for group discussion will be posed to the session audience with the aim of using the feedback in defining our priorities for the coming year. Feedback and summing up The groups will be asked to give their feedback. Feedback from the discussions and voting will be used to inform the next steps for IJPDS. Facilitators Kerina Jones, Founding Editor-in-Chief, Swansea University, Wales Kim McGrail, Deputy Editor, University of British Columbia, Canada Claudia Medina Coeli, Editorial Board member, Rio de Janeiro Federal University, Brazil Rainer Schnell, Editorial Board member, University of Duisburg-Essen, Germany Stephanie Lee, Journal Director of Operations, Swansea University, Wales

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Data science for engineering design: State of the art and future directions

Computers in Industry ◽

10.1016/j.compind.2021.103447 ◽

2021 ◽

Vol 129 ◽

pp. 103447

Author(s):

Filippo Chiarello ◽

Paola Belingheri ◽

Gualtiero Fantoni

Keyword(s):

Engineering Design ◽

Data Science ◽

State Of The Art ◽

Future Directions

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Effects of Psychotherapy on Brain Activation Patterns in Anxiety Disorders

Zeitschrift für Psychologie ◽

10.1027/2151-2604/a000240 ◽

2016 ◽

Vol 224 (2) ◽

pp. 62-70 ◽

Cited By ~ 2

Author(s):

Thomas Straube

Keyword(s):

Anxiety Disorders ◽

Neuronal Plasticity ◽

Functional Neuroimaging ◽

State Of The Art ◽

Methodological Issues ◽

Future Directions ◽

Activation Patterns ◽

New Learning ◽

Analytical Strategies ◽

Sample Characteristics

Abstract. Psychotherapy is an effective treatment for most mental disorders, including anxiety disorders. Successful psychotherapy implies new learning experiences and therefore neural alterations. With the increasing availability of functional neuroimaging methods, it has become possible to investigate psychotherapeutically induced neuronal plasticity across the whole brain in controlled studies. However, the detectable effects strongly depend on neuroscientific methods, experimental paradigms, analytical strategies, and sample characteristics. This article summarizes the state of the art, discusses current theoretical and methodological issues, and suggests future directions of the research on the neurobiology of psychotherapy in anxiety disorders.

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Data science in economics: comprehensive review of advanced machine learning and deep learning methods

10.31232/osf.io/4pxq2 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Ferdinand Filip ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Data Science ◽

State Of The Art ◽

Science Methods ◽

Learning Models ◽

Diverse Range ◽

Hybrid Machine ◽

Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

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