Machine Learning in Big Data Modeling

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (15 October 2023) | Viewed by 2020

Special Issue Editor

Centre for Research in Applied Measurement and Evaluation, University of Alberta, 6-110 Education Centre North, 11210 87 Ave NW, Edmonton, AB T6G 2G5, Canada
Interests: psychometrics; psycho-educational assessments; educational data mining; big data modeling; large-scale testing; learning analytics; digital assessments; computerized adaptive testing; statistical programming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The concept of big data refers to structured, semi-structured, and unstructured data that come with greater variety, increasing volumes, and more velocity. With the exponential growth in data storage and computing power, it is now possible to process quantitative and textual data from various sources, such as mobile devices, social media, and the Internet of Things. Although, big data analytics offers great potential for leveraging big data in knowledge discovery and automation. However, traditional tools for managing and using smaller volumes of data may not be suitable for big data. To manage, organize, and model big data, researchers and practitioners need powerful hardware and distributed computing paradigms, as well as strong predictive models.

The focus of this Special Issue is big data analytics, including—but not limited to—data capture and storage, big data technologies, data visualization techniques for big data, architectures for parallel processing of big data, data mining tools and techniques, machine-learning algorithms for big data, and cloud computing platforms designed for processing big data. We encourage submissions that present findings of empirical research, systematic reviews, or theoretical work utilizing big data in social and natural sciences (e.g., education, psychology, business, health, computing science, etc.).

Dr. Okan Bulut
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • reinforcement learning
  • statistical learning
  • data mining
  • data science

Published Papers (1 paper)

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Research

30 pages, 1159 KiB  
Article
An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification
by Nasir Ayub, Tayyaba, Saddam Hussain, Syed Sajid Ullah and Jawaid Iqbal
Algorithms 2023, 16(12), 548; https://doi.org/10.3390/a16120548 - 28 Nov 2023
Viewed by 1214
Abstract
Sentiment analysis holds great importance within the domain of natural language processing as it examines both the expressed and underlying emotions conveyed through review content. Furthermore, researchers have discovered that relying solely on the overall sentiment derived from the textual content is inadequate. [...] Read more.
Sentiment analysis holds great importance within the domain of natural language processing as it examines both the expressed and underlying emotions conveyed through review content. Furthermore, researchers have discovered that relying solely on the overall sentiment derived from the textual content is inadequate. Consequently, sentiment analysis was developed to extract nuanced expressions from textual information. One of the challenges in this field is effectively extracting emotional elements using multi-label data that covers various aspects. This article presents a novel approach called the Ensemble of DenseNet based on Aquila Optimizer (EDAO). EDAO is specifically designed to enhance the precision and diversity of multi-label learners. Unlike traditional multi-label methods, EDAO strongly emphasizes improving model diversity and accuracy in multi-label scenarios. To evaluate the effectiveness of our approach, we conducted experiments on seven distinct datasets, including emotions, hotels, movies, proteins, automobiles, medical, news, and birds. Our initial strategy involves establishing a preprocessing mechanism to obtain precise and refined data. Subsequently, we used the Vader tool with Bag of Words (BoW) for feature extraction. In the third stage, we created word associations using the word2vec method. The improved data were also used to train and test the DenseNet model, which was fine-tuned using the Aquila Optimizer (AO). On the news, emotion, auto, bird, movie, hotel, protein, and medical datasets, utilizing the aspect-based multi-labeling technique, we achieved accuracy rates of 95%, 97%, and 96%, respectively, with DenseNet-AO. Our proposed model demonstrates that EDAO outperforms other standard methods across various multi-label datasets with different dimensions. The implemented strategy has been rigorously validated through experimental results, showcasing its effectiveness compared to existing benchmark approaches. Full article
(This article belongs to the Special Issue Machine Learning in Big Data Modeling)
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