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Applied Sciences
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Revolutionary Innovation in Artificial Intelligence: Modern Application and Its Impact
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Revolutionary Innovation in Artificial Intelligence: Modern Application and Its Impact

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 December 2023) | Viewed by 2989

Special Issue Editor

Dr. Pauline Ong

E-Mail Website
Guest Editor
Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), 86400 Parit Raja, Malaysia
Interests: evolutionary computation; machine learning; optimization

Special Issue Information

Dear Colleagues,

Over the past few decades, the rise of artificial intelligence (AI) has rapidly revolutionalized the face of everyday life. AI-enabled technologies have introduced new ways to alleviate problems across many domains, including developing antiviral drug therapy for COVID-19, face recognition technology for use in surveillance systems, chatbots that enable 24/7 customer service automation, combating deforesting through monitoring forests aerially, and creating AI robots for cleaning and surgical purposes, to name a few. There is no doubt that AI is poised to transform the way we live for the better.

This Special Issue of Applied Sciences aims to disseminate current research findings in the broad spectrum of AI, with particular emphasis placed on the practical applications of AI in vast domains, including but not limited to agriculture, art, business analysis, economy, engineering, manufacturing, music, medical, and robotics. We welcome researchers and practitioners to contribute high-quality original work to this Special Issue.

Dr. Pauline Ong
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. 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.

Published Papers (2 papers)

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Research

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14 pages, 7646 KiB  
Article
Taxi Demand and Fare Prediction with Hybrid Models: Enhancing Efficiency and User Experience in City Transportation
by Ka Seng Chou, Kei Long Wong, Boliang Zhang, Davide Aguiari, Sio Kei Im, Chan Tong Lam, Rita Tse, Su-Kit Tang and Giovanni Pau
Appl. Sci. 2023, 13(18), 10192; https://doi.org/10.3390/app131810192 - 11 Sep 2023
Viewed by 1577
Abstract
An essential part of a city’s transportation infrastructure, taxis allow for regular encounters between drivers and customers. Nevertheless, there are issues with efficiency since there is an imbalance in the supply and demand for taxis. This study describes the creation of a platform [...] Read more.
An essential part of a city’s transportation infrastructure, taxis allow for regular encounters between drivers and customers. Nevertheless, there are issues with efficiency since there is an imbalance in the supply and demand for taxis. This study describes the creation of a platform that serves both customers and taxi drivers by offering immediate forecasts of demand and fare. Root mean squared error (RMSE) of 3.31 and a negative log-likelihood of −3.84, the long short-term memory recurrent neural network (LSTM-RNN) with the mixture density network (MDN) is employed to forecast taxi demand. The best RMSE of 3.24 is obtained for fare prediction via an ensemble learning model that integrates linear regression (LR), ridge regression (RR), and multilayer perceptron (MLP). To ensure peak performance, the models are systematically created, implemented, trained, and improved. By integrating these models into a web application interface, the taxi service system offers a better overall user experience, which improves urban mobility. Full article
(This article belongs to the Special Issue Revolutionary Innovation in Artificial Intelligence: Modern Application and Its Impact)
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Review

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16 pages, 288 KiB  
Review
Can Neural Networks Do Arithmetic? A Survey on the Elementary Numerical Skills of State-of-the-Art Deep Learning Models
by Alberto Testolin
Appl. Sci. 2024, 14(2), 744; https://doi.org/10.3390/app14020744 - 15 Jan 2024
Viewed by 980
Abstract
Creating learning models that can exhibit sophisticated reasoning abilities is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction. In the past few years there has [...] Read more.
Creating learning models that can exhibit sophisticated reasoning abilities is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction. In the past few years there has been an explosion of neural network architectures, datasets, and benchmarks specifically designed to tackle mathematical problems, reporting impressive achievements in disparate fields such as automated theorem proving, numerical integration, and the discovery of new conjectures or matrix multiplication algorithms. However, despite this notable success it is still unclear whether deep learning models possess an elementary understanding of quantities and numbers. This survey critically examines the recent literature, concluding that even state-of-the-art architectures and large language models often fall short when probed with relatively simple tasks designed to test basic numerical and arithmetic knowledge. Full article
(This article belongs to the Special Issue Revolutionary Innovation in Artificial Intelligence: Modern Application and Its Impact)
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