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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 (10 January 2023) | Viewed by 55362

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Special Issue Editors

Dr. Yue Wu

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Guest Editor

Department of Computer Science and Technology, Xidian University, Xi'an 710071, China
Interests: computer vision; image processing and pattern recognition; theory and applications of computational intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Xinglong Zhang

E-Mail Website
Guest Editor

College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: reinforcement learning; intelligent control; autonomous robots; motion planning

Dr. Pengfei Jia

E-Mail Website
Guest Editor

School of Electrical Engineering, Guangxi University, Nanning 530004, China
Interests: electronic nose; pattern recognition; intelligence sensor
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning and computational intelligence have been applied to various areas and witnessed many successes. Researchers explore many intelligent algorithms which are characterized by computational adaptability, robustness, and high performance. These algorithms facilitate intelligent behavior in complex and dynamic environments and the development of technology that enables machines to think, behave, or act more humanely.

This Special Issue aims to present and discuss the most recent innovations, trends, concerns, challenges, solutions, and application fields in machine learning and computational intelligence. The topic of interest include but are not limited to:

Artificial Intelligence in general, and deep learning, machine learning, data mining;
Reinforcement learning and applications;
Intelligent control, neuro-control, and their applications;
Decision making, planning, and control for autonomous robots;
Intelligence sensors, evolutionary computation, fuzzy logic, etc.

Dr. Yue Wu
Dr. Xinglong Zhang
Dr. Pengfei Jia
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

artificial intelligence
deep learning
machine learning
data mining
reinforcement learning
intelligent control
intelligence sensor
autonomous robots
evolutionary computation
fuzzy logic

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Published Papers (11 papers)

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Editorial

Jump to: Research

3 pages, 164 KiB

Open AccessEditorial

Special Issue on Recent Advances in Machine Learning and Computational Intelligence

by Yue Wu, Xinglong Zhang and Pengfei Jia

Appl. Sci. 2023, 13(8), 5078; https://doi.org/10.3390/app13085078 - 19 Apr 2023

Cited by 1 | Viewed by 1734

Abstract

Machine learning and computational intelligence are currently high-profile research areas attracting the attention of many researchers [...] Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Computational Intelligence)

Research

Jump to: Editorial

26 pages, 5869 KiB

Open AccessArticle

IDEINFO: An Improved Vector-Weighted Optimization Algorithm

by Lixin Zhao and Hui Jin

Appl. Sci. 2023, 13(4), 2336; https://doi.org/10.3390/app13042336 - 11 Feb 2023

Cited by 1 | Viewed by 1669

Abstract

This study proposes an improved vector-weighted averaging algorithm (IDEINFO) for the optimization of different problems. The original vector-weighted optimization algorithm (INFO) uses weighted averaging for entity structures and uses three core procedures to update the positions of the vectors. First, the update rule phase is based on the law of averaging and convergence acceleration to generate new vectors. Second, the vector combination phase combines the obtained vectors with the update rules to achieve a promising solution. Third, the local search phase helps the algorithm eliminate low-precision solutions and improve exploitability and convergence. However, this approach pseudo-randomly initializes candidate solutions, and therefore risks falling into local optima. We, therefore, optimize the initial distribution uniformity of potential solutions by using a two-stage backward learning strategy to initialize the candidate solutions, and a difference evolution strategy to perturb these vectors in the combination stage to produce improved candidate solutions. In the search phase, the search range of the algorithm is expanded according to the probability values combined with the t-distribution strategy, to improve the global search results. The IDEINFO algorithm is, therefore, a promising tool for optimal design based on the considerable efficiency of the algorithm in the case of optimization constraints. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Computational Intelligence)

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22 pages, 3077 KiB

Open AccessArticle

Innovative Forward Fusion Feature Selection Algorithm for Sentiment Analysis Using Supervised Classification

by Ayman Mohamed Mostafa, Meeaad Aljasir, Meshrif Alruily, Ahmed Alsayat and Mohamed Ezz

Appl. Sci. 2023, 13(4), 2074; https://doi.org/10.3390/app13042074 - 5 Feb 2023

Cited by 10 | Viewed by 3222

Abstract

Sentiment analysis is considered one of the significant trends of the recent few years. Due to the high importance and increasing use of social media and electronic services, the need for reviewing and enhancing the provided services has become crucial. Revising the user services is based mainly on sentiment analysis methodologies for analyzing users’ polarities to different products and applications. Sentiment analysis for Arabic reviews is a major concern due to high morphological linguistics and complex polarity terms expressed in the reviews. In addition, the users can present their orientation towards a service or a product by using a hybrid or mix of polarity terms related to slang and standard terminologies. This paper provides a comprehensive review of recent sentiment analysis methods based on lexicon or machine learning (ML). The comparison provides a clear vision of the number of classes, the used dialect, the annotated algorithms, and their performance. The proposed methodology is based on cross-validation of Arabic data using a k-fold mechanism that splits the dataset into training and testing folds; subsequently, the data preprocessing is executed to clean sentiments from unwanted terms that can affect data analysis. A vectorization of the dataset is then applied using TF–IDF for counting word and polarity terms. Furthermore, a feature selection stage is processed using Pearson, Chi², and Random Forest (RF) methods for mapping the compatibility between input and target features. This paper also proposed an algorithm called the forward fusion feature for sentiment analysis (FFF-SA) to provide a feature selection that applied different machine learning (ML) classification models for each chunk of k features and accumulative features on the Arabic dataset. The experimental results measured and scored all accuracies between the feature importance method and ML models. The best accuracy is recorded with the Naïve Bayes (NB) model with the RF method. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Computational Intelligence)

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16 pages, 6669 KiB

Open AccessArticle

RJA-Star Algorithm for UAV Path Planning Based on Improved R5DOS Model

by Jian Li, Weijian Zhang, Yating Hu, Shengliang Fu, Changyi Liao and Weilin Yu

Appl. Sci. 2023, 13(2), 1105; https://doi.org/10.3390/app13021105 - 13 Jan 2023

Cited by 13 | Viewed by 2677

Abstract

To improve the obstacle avoidance ability of agricultural unmanned aerial vehicles (UAV) in farmland settings, a three-dimensional space path planning model based on the R5DOS model is proposed in this paper. The direction layer of the R5DOS intersection model is improved, and the RJA-star algorithm is constructed with the improved jump point search A-star algorithm in our paper. The R5DOS model is simulated in MATLAB. The simulation results show that this model can reduce the computational complexity, computation time, the number of corners and the maximum angles of the A-star algorithm. Compared with the traditional algorithm, the model can avoid obstacles effectively and reduce the reaction times of the UAV. The final fitting results show that compared with A-star algorithm, the RJA-star algorithm reduced the total distance by 2.53%, the computation time by 97.65%, the number of nodes by 99.96% and the number of corners by 96.08% with the maximum corners reduced by approximately 63.30%. Compared with the geometric A-star algorithm, the running time of the RJA-star algorithm is reduced by 95.84%, the number of nodes is reduced by 99.95%, and the number of turns is reduced by 67.28%. In general, the experimental results confirm the effectiveness and feasibility of RJA star algorithm in three-dimensional space obstacle avoidance. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Computational Intelligence)

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24 pages, 5525 KiB

Open AccessArticle

High Performing Facial Skin Problem Diagnosis with Enhanced Mask R-CNN and Super Resolution GAN

by Mira Kim and Myeong Ho Song

Appl. Sci. 2023, 13(2), 989; https://doi.org/10.3390/app13020989 - 11 Jan 2023

Cited by 11 | Viewed by 17487

Abstract

Facial skin condition is perceived as a vital indicator of the person’s apparent age, perceived beauty, and degree of health. Machine-learning-based software analytics on facial skin conditions can be a time- and cost-efficient alternative to the conventional approach of visiting facial skin care shops or dermatologist’s offices. However, the conventional CNN-based approach is shown to be limited in the diagnosis performance due to the intrinsic characteristics of facial skin problems. In this paper, the technical challenges in facial skin problem diagnosis are first addressed, and a set of 5 effective tactics are proposed to overcome the technical challenges. A total of 31 segmentation models are trained and applied to the experiments of validating the proposed tactics. Through the experiments, the proposed approach provides 83.38% of the diagnosis performance, which is 32.58% higher than the performance of conventional CNN approach. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Computational Intelligence)

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13 pages, 4101 KiB

Open AccessArticle

Model Predictive Control of Quadruped Robot Based on Reinforcement Learning

by Zhitong Zhang, Xu Chang, Hongxu Ma, Honglei An and Lin Lang

Appl. Sci. 2023, 13(1), 154; https://doi.org/10.3390/app13010154 - 22 Dec 2022

Cited by 7 | Viewed by 8388

Abstract

For the locomotion control of a legged robot, both model predictive control (MPC) and reinforcement learning (RL) demonstrate powerful capabilities. MPC transfers the high-level task to the lower-level joint control based on the understanding of the robot and environment, model-free RL learns how to work through trial and error, and has the ability to evolve based on historical data. In this work, we proposed a novel framework to integrate the advantages of MPC and RL, we learned a policy for automatically choosing parameters for MPC. Unlike the end-to-end RL applications for control, our method does not need massive sampling data for training. Compared with the fixed parameters MPC, the learned MPC exhibits better locomotion performance and stability. The presented framework provides a new choice for improving the performance of traditional control. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Computational Intelligence)

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14 pages, 2352 KiB

Open AccessArticle

A Deep Learning Approach for Credit Scoring Using Feature Embedded Transformer

by Chongren Wang and Zhuoyi Xiao

Appl. Sci. 2022, 12(21), 10995; https://doi.org/10.3390/app122110995 - 30 Oct 2022

Cited by 11 | Viewed by 8827

Abstract

In this paper, we introduce a transformer into the field of credit scoring based on user online behavioral data and develop an end-to-end feature embedded transformer (FE-Transformer) credit scoring approach. The FE-Transformer neural network is composed of two parts: a wide part and a deep part. The deep part uses the transformer deep neural network. The output of the deep neural network and the feature data of the wide part are concentrated in a fusion layer. The experimental results show that the FE-Transformer deep learning model proposed in this paper outperforms the LR, XGBoost, LSTM, and AM-LSTM comparison methods in terms of area under the receiver operating characteristic curve (AUC) and the Kolmogorov–Smirnov (KS). This shows that the FE-Transformer deep learning model proposed in this paper can accurately predict user default risk. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Computational Intelligence)

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12 pages, 1194 KiB

Open AccessArticle

Vehicle-Following Control Based on Deep Reinforcement Learning

by Yong Huang, Xin Xu, Yong Li, Xinglong Zhang, Yao Liu and Xiaochuan Zhang

Appl. Sci. 2022, 12(20), 10648; https://doi.org/10.3390/app122010648 - 21 Oct 2022

Cited by 2 | Viewed by 2190

Abstract

Intelligent vehicle-following control presents a great challenge in autonomous driving. In vehicle-intensive roads of city environments, frequent starting and stopping of vehicles is one of the important cause of front-end collision accidents. Therefore, this paper proposes a subsection proximal policy optimization method (Subsection-PPO), which divides the vehicle-following process into the start–stop and steady stages and provides control at different stages with two different actor networks. It improves security in the vehicle-following control using the proximal policy optimization algorithm. To improve the training efficiency and reduce the variance of advantage function, the weighted importance sampling method is employed instead of the importance sampling method to estimate the data distribution. Finally, based on the TORCS simulation engine, the advantages and robustness of the method in vehicle-following control is verified. The results show that compared with other deep learning learning, the Subsection-PPO algorithm has better algorithm efficiency and higher safety than PPO and DDPG in vehicle-following control. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Computational Intelligence)

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16 pages, 5083 KiB

Open AccessArticle

A Novel Mixed-Attribute Fusion-Based Naive Bayesian Classifier

by Guiliang Ou, Yulin He, Philippe Fournier-Viger and Joshua Zhexue Huang

Appl. Sci. 2022, 12(20), 10443; https://doi.org/10.3390/app122010443 - 17 Oct 2022

Cited by 11 | Viewed by 2100

Abstract

The Naive Bayesian classifier (NBC) is a well-known classification model that has a simple structure, low training complexity, excellent scalability, and good classification performances. However, the NBC has two key limitations: (1) it is built upon the strong assumption that condition attributes are independent, which often does not hold in real-life, and (2) the NBC does not handle continuous attributes well. To overcome these limitations, this paper presents a novel approach for NBC construction, called mixed-attribute fusion-based NBC (MAF-NBC). It alleviates the two aforementioned limitations by relying on a mixed-attribute fusion mechanism with an improved autoencoder neural network for NBC construction. MAF-NBC transforms the original mixed attributes of a data set into a series of encoded attributes with maximum independence as a pre-processing step. To guarantee the generation of useful encoded attributes, an efficient objective function is designed to optimize the weights of the autoencoder neural network by considering both the encoding error and the attribute’s dependence. A series of persuasive experiments was conducted to validate the feasibility, rationality, and effectiveness of the designed MAF-NBC approach. Results demonstrate that MAF-NBC has superior classification performance than eight state-of-the-art Bayesian algorithms, namely the discretization-based NBC (Dis-NBC), flexible naive Bayes (FNB), tree-augmented naive (TAN) Bayes, averaged one-dependent estimator (AODE), hidden naive Bayes (HNB), deep feature weighting for NBC (DFW-NBC), correlation-based feature weighting filter for NBC (CFW-NBC), and independent component analysis-based NBC (ICA-NBC). Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Computational Intelligence)

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42 pages, 2492 KiB

Open AccessArticle

A Distributed Bi-Behaviors Crow Search Algorithm for Dynamic Multi-Objective Optimization and Many-Objective Optimization Problems

by Ahlem Aboud, Nizar Rokbani, Bilel Neji, Zaher Al Barakeh, Seyedali Mirjalili and Adel M. Alimi

Appl. Sci. 2022, 12(19), 9627; https://doi.org/10.3390/app12199627 - 25 Sep 2022

Cited by 4 | Viewed by 2680

Abstract

Dynamic Multi-Objective Optimization Problems (DMOPs) and Many-Objective Optimization Problems (MaOPs) are two classes of the optimization field that have potential applications in engineering. Modified Multi-Objective Evolutionary Algorithms hybrid approaches seem to be suitable to effectively deal with such problems. However, the standard Crow Search Algorithm has not been considered for either DMOPs or MaOPs to date. This paper proposes a Distributed Bi-behaviors Crow Search Algorithm (DB-CSA) with two different mechanisms, one corresponding to the search behavior and another to the exploitative behavior with a dynamic switch mechanism. The bi-behaviors CSA chasing profile is defined based on a large Gaussian-like Beta-1 function, which ensures diversity enhancement, while the narrow Gaussian Beta-2 function is used to improve the solution tuning and convergence behavior. Two variants of the proposed DB-CSA approach are developed: the first variant is used to solve a set of MaOPs with 2, 3, 5, 7, 8, 10,15 objectives, and the second aims to solve several types of DMOPs with different time-varying Pareto optimal sets and a Pareto optimal front. The second variant of DB-CSA algorithm (DB-CSA-II) is proposed to solve DMOPs, including a dynamic optimization process to effectively detect and react to the dynamic change. The Inverted General Distance, the Mean Inverted General Distance and the Hypervolume Difference are the main measurement metrics used to compare the DB-CSA approach to the state-of-the-art MOEAs. The Taguchi method has been used to manage the meta-parameters of the DB-CSA algorithm. All quantitative results are analyzed using the non-parametric Wilcoxon signed rank test with 0.05 significance level, which validated the efficiency of the proposed method for solving 44 test beds (21 DMOPs and 23 MaOPS). Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Computational Intelligence)

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11 pages, 1150 KiB

Open AccessArticle

Combining UNet 3+ and Transformer for Left Ventricle Segmentation via Signed Distance and Focal Loss

by Zhi Liu, Xuelin He and Yunhua Lu

Appl. Sci. 2022, 12(18), 9208; https://doi.org/10.3390/app12189208 - 14 Sep 2022

Cited by 9 | Viewed by 2583

Abstract

Left ventricle (LV) segmentation of cardiac magnetic resonance (MR) images is essential for evaluating cardiac function parameters and diagnosing cardiovascular diseases (CVDs). Accurate LV segmentation remains a challenge because of the large differences in cardiac structures in different research subjects. In this work, a network based on an encoder–decoder architecture for automatic LV segmentation of short-axis cardiac MR images is proposed. It combines UNet 3+ and Transformer to jointly predict the segmentation masks and signed distance maps (SDM). UNet 3+ can extract coarse-grained semantics and fine-grained details from full scales, while a Transformer is used to extract global features from cardiac MR images. It solves the problem of low segmentation accuracy caused by blurred LV edge information. Meanwhile, the SDM provides a shape-aware representation for segmentation. The performance of the proposed network is validated on the 2018 MICCAI Left Ventricle Segmentation Challenge dataset. The five-fold cross-validation evaluation was performed on 145 clinical subjects, and the average dice metric, Jaccard coefficient, accuracy, and positive predictive value reached 0.908, 0.834, 0.979, and 0.903, respectively, showing a better performance than that of other mainstream ones. Full article

(This article belongs to the Special Issue Recent Advances in Machine Learning and Computational Intelligence)

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