A survey on data‐efficient algorithms in big data era

Adadi, Amina

doi:10.1186/s40537-021-00419-9

Cited by 148 publications

(81 citation statements)

References 195 publications

(201 reference statements)

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“…Generally, data is considered a scarce resource, let alone accurately labeled data that is far from abundant. 111 Data has to be annotated manually according to human judgment, which is an extremely costly and time-consuming process. Crowdsourcing, on the other hand, is an alternative approach, which exploits the crowd to annotate data and thus significantly reduces human labor and therefore cost.…”

Section: Properties Of Ml-based Algorithmsmentioning

confidence: 99%

A Guide to Signal Processing Algorithms for Nanopore Sensors

2021

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Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.

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Section: Properties Of Ml-based Algorithmsmentioning

confidence: 99%

A Guide to Signal Processing Algorithms for Nanopore Sensors

2021

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“…An algorithm is a sequence or method of solving a problem or in other words, it is a series of instructions for carrying out an operation. It is mostly used in data processing, calculation, and other related computer and mathematical operations that suggests future opportunities to advance the research on data-efficiency in machine learning (Adadi, 2021). Four studies are related before this research with different methods and results.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysisof Students’ Web Browsing Behaviours Using Data Miningat a Campus Network

Kassim¹,

Roslan²

2021

TURCOMAT

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Analytics provides insight to people based on the analytics of past usage by using techniques such as statistics, data mining, machine learning and artificial intelligence. Lack of monitoring system of browsing causes low engagements that reduce the growth of certain businesses caused by unnecessary browsing for students learning time. This paper presents an analysis on browsing behavior that classifies browsed words followed their ethical word-groups browsing. An Analytic platform is created as a monitoring system of browsing behavior. Data mining, indexing and classification method are used in this research as data is the essential key of creating a predictive model and four types of ethical groups have been filtered based on the browsing behaviors. The browsed words are categorized into four types of browsing called queries, applications, social media, Campus-related sites. The research method uses software tools and data mining process on the browsing data and analytics is presented on the development of the dashboard mainly using the R programming language. Few unethical words using the indexing method are generated in analytic graphs based on the type of browsing versus time. Data collected from the browsing behaviors of students’analysis taken from browsing database of personal computer and laboratory computer in the campus network. The result shows that othercategories are the highest categories which reached79.6% for personals' computer browsing compared to72.4% browsing at the laboratory computers. It is identified that about 21% of the browsing behavior was filtered during the data mined processed. The other category is still on the research portfolio where these libraries must be filtered in detail to identify whether they are learning or non-learning activities. This research is significant in that helps to increase the effectiveness of suggestions applications, optimize the internet usage by blocking unnecessary words or webpages, and even campus guide systems by monitoring the surrounding browsing behavior of the students’ usages of the campus network computer labs.

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“…The ability to learn quickly in a sample-efficient manner is essential in these data-constrained situations. Collectively, these issues underscore the need for data-efficient deep learning [116] with the ability to learn in diverse domains with limited data.…”

Section: Active Research Problems In Deep Learningmentioning

confidence: 99%

On Deep Research Problems in Deep Learning

Ulhaq¹

2021

Preprint

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The subject of deep learning has emerged in the last decade as one of the most promising approaches to machine learning. Today, certainly, much of the recent progress in artificial intelligence is due to it, but research challenges are still unresolved and remain open to the research community. This paper attempts to offer a comprehensive review of deep learning progress in active research frontiers. On the one side, by presenting a brief overview of deep learning success, we inspire researchers to work in deep learning. On the other hand, we examine a range of technical issues, and open research issues that we believe are relevant topics for exploratory research. As deep learning applies to various fields, we restrict this paper’s scope to visual recognition tasks to analyze these problems with a specific lens. However, these problems will be broadly applicable to other fields. It will make it easier for new researchers to recognize outstanding research problems in the deep learning domain.

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A survey on data‐efficient algorithms in big data era

Cited by 148 publications

References 195 publications

A Guide to Signal Processing Algorithms for Nanopore Sensors

A Guide to Signal Processing Algorithms for Nanopore Sensors

Analysisof Students’ Web Browsing Behaviours Using Data Miningat a Campus Network

On Deep Research Problems in Deep Learning

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