Algorithms and Applications regarding Big Data Analytics and Machine Learning

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 9068

Special Issue Editors

College of Business Administration, King Faisal University, Al-Ahsa, Saudi Arabia
Interests: big data; machine learning; artificial intelligence; data analytics; decision making process; innovations adoption; digital accounting management
Cybersecurity Department, President of Irbid National University, 21110 Irbid, Jordan
Interests: computer information systems; e-learning; mobile learning; cybersecurity; mobile applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Now, and in the future, people over the world are demanding smart services, applications, and wearables that will help them to lead better lives and prolong their lifespan. In fact, big data analytics (BDA), artificial intelligence (AI), and machine learning (ML) have come to play a pivotal role in the realm of business from improving the delivery system of services, cutting down costs, and handling data to the development of new procedures, remote monitoring, and so much more. Today, BDA, AI, and ML have permeated rapidly into the business and e-commerce sectors, finding numerous uses and affecting every imaginable domain. With BDA, AI, and ML, there are endless possibilities. Through its cutting-edge applications, they are helping transform business for the better.

This Special Issue aims to cover the advances and application of BDA, AI, and ML and will present new ideas and experimental results in the field of BDA, AI, and ML from design, service, and theory to their practical use. Areas relevant to BDA, AI, and ML include, but are not limited to: big data analytics, computation and data-intensive applications, novel concurrent algorithms and applications, machine learning for management, artificial intelligence, machine and deep learning, and techniques for resources management in the context of systems.

Dr. Abdalwali Lutfi
Prof. Dr. Ahmad Al-Khasawneh
Guest Editors

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • big data
  • machine learning
  • artificial intelligence
  • data analytics
  • decision-making process
  • electronic and mobile applications
  • smart applications
  • cloud based-systems
  • data quality and accessibility

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 4030 KiB  
Article
Soft Semi-Supervised Deep Learning-Based Clustering
by Mona Suliman AlZuhair, Mohamed Maher Ben Ismail and Ouiem Bchir
Appl. Sci. 2023, 13(17), 9673; https://doi.org/10.3390/app13179673 - 27 Aug 2023
Viewed by 1033
Abstract
Semi-supervised clustering typically relies on both labeled and unlabeled data to guide the learning process towards the optimal data partition and to prevent falling into local minima. However, researchers’ efforts made to improve existing semi-supervised clustering approaches are relatively scarce compared to the [...] Read more.
Semi-supervised clustering typically relies on both labeled and unlabeled data to guide the learning process towards the optimal data partition and to prevent falling into local minima. However, researchers’ efforts made to improve existing semi-supervised clustering approaches are relatively scarce compared to the contributions made to enhance the state-of-the-art fully unsupervised clustering approaches. In this paper, we propose a novel semi-supervised deep clustering approach, named Soft Constrained Deep Clustering (SC-DEC), that aims to address the limitations exhibited by existing semi-supervised clustering approaches. Specifically, the proposed approach leverages a deep neural network architecture and generates fuzzy membership degrees that better reflect the true partition of the data. In particular, the proposed approach uses side-information and formulates it as a set of soft pairwise constraints to supervise the machine learning process. This supervision information is expressed using rather relaxed constraints named “should-link” constraints. Such constraints determine whether the pairs of data instances should be assigned to the same or different cluster(s). In fact, the clustering task was formulated as an optimization problem via the minimization of a novel objective function. Moreover, the proposed approach’s performance was assessed via extensive experiments using benchmark datasets. Furthermore, the proposed approach was compared to relevant state-of-the-art clustering algorithms, and the obtained results demonstrate the impact of using minimal previous knowledge about the data in improving the overall clustering performance. Full article
Show Figures

Figure 1

16 pages, 3161 KiB  
Article
Understanding and Predicting Ride-Hailing Fares in Madrid: A Combination of Supervised and Unsupervised Techniques
by Tulio Silveira-Santos, Anestis Papanikolaou, Thais Rangel and Jose Manuel Vassallo
Appl. Sci. 2023, 13(8), 5147; https://doi.org/10.3390/app13085147 - 20 Apr 2023
Viewed by 1604
Abstract
App-based ride-hailing mobility services are becoming increasingly popular in cities worldwide. However, key drivers explaining the balance between supply and demand to set final prices remain to a considerable extent unknown. This research intends to understand and predict the behavior of ride-hailing fares [...] Read more.
App-based ride-hailing mobility services are becoming increasingly popular in cities worldwide. However, key drivers explaining the balance between supply and demand to set final prices remain to a considerable extent unknown. This research intends to understand and predict the behavior of ride-hailing fares by employing statistical and supervised machine learning approaches (such as Linear Regression, Decision Tree, and Random Forest). The data used for model calibration correspond to a ten-month period and were downloaded from the Uber Application Programming Interface for the city of Madrid. The findings reveal that the Random Forest model is the most appropriate for this type of prediction, having the best performance metrics. To further understand the patterns of the prediction errors, the unsupervised technique of cluster analysis (using the k-means clustering method) was applied to explore the variation of the discrepancy between Uber fares predictions and observed values. The analysis identified a small share of observations with high prediction errors (only 1.96%), which are caused by unexpected surges due to imbalances between supply and demand (usually occurring at major events, peak times, weekends, holidays, or when there is a taxi strike). This study helps policymakers understand pricing, demand for services, and pricing schemes in the ride-hailing market. Full article
Show Figures

Figure 1

16 pages, 430 KiB  
Article
Multi-Label Classification Based on Associations
by Raed Alazaidah, Ghassan Samara, Sattam Almatarneh, Mohammad Hassan, Mohammad Aljaidi and Hasan Mansur
Appl. Sci. 2023, 13(8), 5081; https://doi.org/10.3390/app13085081 - 19 Apr 2023
Cited by 6 | Viewed by 1258
Abstract
Associative classification (AC) has been shown to outperform other methods of single-label classification for over 20 years. In order to create rules that are both more precise and simpler to grasp, AC combines the rules of mining associations with the task of classification. [...] Read more.
Associative classification (AC) has been shown to outperform other methods of single-label classification for over 20 years. In order to create rules that are both more precise and simpler to grasp, AC combines the rules of mining associations with the task of classification. However, the current state of knowledge and the views of various specialists indicate that the issue of multi-label classification (MLC) cannot be solved by any AC method. Since this is the case, adapting or using an AC algorithm to manage multi-label datasets is one of the most pressing issues. To solve the MLC issue, this research proposes modifying the classification based on associations (msCBA) method by extending its capabilities to consider more than one class label in the consequent of its rules and modifying its rules order procedure to fit the nature of the multi-label dataset. The proposed algorithm outperforms several other MLC algorithms from various learning techniques across a variety of performance measuresand using six datasets with different domains. The main findings of this research are the significance of utilizing the local dependencies among labels compared to global dependencies, and the important rule of AC in solving the problem of MLC. Full article
Show Figures

Figure 1

21 pages, 8711 KiB  
Article
Deep Learning Algorithms to Identify Autism Spectrum Disorder in Children-Based Facial Landmarks
by Hasan Alkahtani, Theyazn H. H. Aldhyani and Mohammed Y. Alzahrani
Appl. Sci. 2023, 13(8), 4855; https://doi.org/10.3390/app13084855 - 12 Apr 2023
Cited by 10 | Viewed by 4239
Abstract
People with autistic spectrum disorders (ASDs) have difficulty recognizing and engaging with others. The symptoms of ASD may occur in a wide range of situations. There are numerous different types of functions for people with an ASD. Although it may be possible to [...] Read more.
People with autistic spectrum disorders (ASDs) have difficulty recognizing and engaging with others. The symptoms of ASD may occur in a wide range of situations. There are numerous different types of functions for people with an ASD. Although it may be possible to reduce the symptoms of ASD and enhance the quality of life with appropriate treatment and support, there is no cure. Developing expert systems for identifying ASD based on the facial landmarks of children is the main contribution for improvements in the healthcare system in Saudi Arabia for detecting ASD at an early stage. However, deep learning algorithms have provided outstanding performances in a variety of pattern-recognition studies. The use of techniques based on convolutional neural networks (CNNs) has been proposed by several scholars to use in investigations of ASD. At present, there is no diagnostic test available for ASD, making this diagnosis challenging. Clinicians focus on a patient’s behavior and developmental history. Therefore, using the facial landmarks of children has become very important for detecting ASDs as the face is thought to be a reflection of the brain; it has the potential to be used as a diagnostic biomarker, in addition to being an easy-to-use and practical tool for the early detection of ASDs. This study uses a variety of transfer learning approaches observed in deep CNNs to recognize autistic children based on facial landmark detection. An empirical study is conducted to discover the ideal settings for the optimizer and hyperparameters in the CNN model so that its prediction accuracy can be improved. A transfer learning approach, such as MobileNetV2 and hybrid VGG19, is used with different machine learning programs, such as logistic regression, a linear support vector machine (linear SVC), random forest, decision tree, gradient boosting, MLPClassifier, and K-nearest neighbors. The deep learning models are examined using a standard research dataset from Kaggle, which contains 2940 images of autistic and non-autistic children. The MobileNetV2 model achieved an accuracy of 92% on the test set. The results of the proposed research indicate that MobileNetV2 transfer learning strategies are better than those developed in existing systems. The updated version of our model has the potential to assist physicians in verifying the accuracy of their first screening for ASDs in child patients. Full article
Show Figures

Figure 1

Back to TopTop