Applied Sciences

Research

19 pages, 6572 KiB

Open AccessArticle

UAV Cluster-Assisted Task Offloading for Emergent Disaster Scenarios

by Minglin Shi, Xiaoqi Zhang, Jia Chen and Hongju Cheng

Appl. Sci. 2023, 13(8), 4724; https://doi.org/10.3390/app13084724 - 9 Apr 2023

Cited by 7 | Viewed by 1799

Natural disasters often have an unpredictable impact on human society and can even cause significant problems, such as damage to communication equipment in disaster areas. In such post-disaster emergency rescue situations, unmanned aerial vehicles (UAVs) are considered an effective tool by virtue of [...] Read more.

Natural disasters often have an unpredictable impact on human society and can even cause significant problems, such as damage to communication equipment in disaster areas. In such post-disaster emergency rescue situations, unmanned aerial vehicles (UAVs) are considered an effective tool by virtue of high mobility, easy deployment, and flexible communication. However, the limited size of UAVs leads to bottlenecks in battery capacity and computational power, making it challenging to perform overly complex computational tasks. In this paper, we propose a UAV cluster-assisted task-offloading model for disaster areas, by adopting UAV clusters as aerial mobile edge servers to provide task-offloading services for ground users. In addition, we also propose a deep reinforcement learning-based UAV cluster-assisted task-offloading algorithm (DRL-UCTO). By modeling the energy efficiency optimization problem of the system model as a Markov decision process and jointly optimizing the UAV flight trajectory and task-offloading policy to maximize the reward value, DRL-UCTO can effectively improve the energy use efficiency of UAVs under limited-resource conditions. The simulation results show that the DRL-UCTO algorithm improves the UAV energy efficiency by about 79.6% and 301.1% compared with the DQN and Greedy algorithms, respectively. Full article

(This article belongs to the Special Issue Intelligent Control Using Machine Learning)

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13 pages, 3365 KiB

Open AccessArticle

Bayesian Matrix Learning by Principle Eigenvector for Completing Missing Medical Data

by Mandi Liu, Lei Zhang and Qi Yue

Appl. Sci. 2023, 13(5), 3314; https://doi.org/10.3390/app13053314 - 5 Mar 2023

Viewed by 1694

Abstract

Since machine learning is applied in medicine, more and more medical data for prediction has been produced by monitoring patients, such as symptoms information of diabetes. This paper establishes a frame called the Diabetes Medication Bayes Matrix (DTBM) to structure the relationship between [...] Read more.

Since machine learning is applied in medicine, more and more medical data for prediction has been produced by monitoring patients, such as symptoms information of diabetes. This paper establishes a frame called the Diabetes Medication Bayes Matrix (DTBM) to structure the relationship between the symptoms of diabetes and the medication regimens for machine learning. The eigenvector of the DTBM is the stable distribution of different symptoms and medication regimens. Based on the DTBM, this paper proposes a machine-learning algorithm for completing missing medical data, which provides a theoretical basis for the prediction of a Bayesian matrix with missing medical information. The experimental results show the rationality and applicability of the given algorithms. Full article

(This article belongs to the Special Issue Intelligent Control Using Machine Learning)

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16 pages, 1641 KiB

Open AccessArticle

A Quantum-Based Beetle Swarm Optimization Algorithm for Numerical Optimization

by Lin Yu, Jieqi Ren and Jie Zhang

Appl. Sci. 2023, 13(5), 3179; https://doi.org/10.3390/app13053179 - 1 Mar 2023

Cited by 8 | Viewed by 1792

Abstract

The beetle antennae search (BAS) algorithm is an outstanding representative of swarm intelligence algorithms. However, the BAS algorithm still suffers from the deficiency of not being able to handle high-dimensional variables. A quantum-based beetle swarm optimization algorithm (QBSO) is proposed herein to address [...] Read more.

The beetle antennae search (BAS) algorithm is an outstanding representative of swarm intelligence algorithms. However, the BAS algorithm still suffers from the deficiency of not being able to handle high-dimensional variables. A quantum-based beetle swarm optimization algorithm (QBSO) is proposed herein to address this deficiency. In order to maintain population diversity and improve the avoidance of falling into local optimal solutions, a novel quantum representation-based position updating strategy is designed. The current best solution is regarded as a linear superposition of two probabilistic states: positive and deceptive. An increase in or reset of the probability of the positive state is performed through a quantum rotation gate to maintain the local and global search ability. Finally, a variable search step strategy is adopted to speed up the ability of the convergence. The QBSO algorithm is verified against several swarm intelligence optimization algorithms, and the results show that the QBSO algorithm still has satisfactory performance at a very small population size. Full article

(This article belongs to the Special Issue Intelligent Control Using Machine Learning)

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14 pages, 3128 KiB

Open AccessArticle

An Expandable Yield Prediction Framework Using Explainable Artificial Intelligence for Semiconductor Manufacturing

by Youjin Lee and Yonghan Roh

Appl. Sci. 2023, 13(4), 2660; https://doi.org/10.3390/app13042660 - 18 Feb 2023

Cited by 12 | Viewed by 4444

Abstract

Enormous amounts of data are generated and analyzed in the latest semiconductor industry. Established yield prediction studies have dealt with one type of data or a dataset from one procedure. However, semiconductor device fabrication comprises hundreds of processes, and various factors affect device [...] Read more.

Enormous amounts of data are generated and analyzed in the latest semiconductor industry. Established yield prediction studies have dealt with one type of data or a dataset from one procedure. However, semiconductor device fabrication comprises hundreds of processes, and various factors affect device yields. This challenge is addressed in this study by using an expandable input data-based framework to include divergent factors in the prediction and by adapting explainable artificial intelligence (XAI), which utilizes model interpretation to modify fabrication conditions. After preprocessing the data, the procedure of optimizing and comparing several machine learning models is followed to select the best performing model for the dataset, which is a random forest (RF) regression with a root mean square error (RMSE) value of 0.648. The prediction results enhance production management, and the explanations of the model deepen the understanding of yield-related factors with Shapley additive explanation (SHAP) values. This work provides evidence with an empirical case study of device production data. The framework improves prediction accuracy, and the relationships between yield and features are illustrated with the SHAP value. The proposed approach can potentially analyze expandable fields of fabrication conditions to interpret multifaceted semiconductor manufacturing. Full article

(This article belongs to the Special Issue Intelligent Control Using Machine Learning)

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24 pages, 2837 KiB

Open AccessArticle

An Improved Multiple-Target Tracking Scheme Based on IGGM–PMBM for Mobile Aquaculture Sensor Networks

by Chunfeng Lv, Jianping Zhu, Naixue Xiong and Zhengsu Tao

Appl. Sci. 2023, 13(2), 926; https://doi.org/10.3390/app13020926 - 9 Jan 2023

Cited by 2 | Viewed by 1627

Abstract

The Poisson multi-Bernoulli Mixture (PMBM) filter, as well as its variants, is a popular and practical multitarget tracking algorithm. There are some pending problems for the standard PMBM filter, such as unknown detection probability, random target newborn distribution, and high energy consumption for [...] Read more.

The Poisson multi-Bernoulli Mixture (PMBM) filter, as well as its variants, is a popular and practical multitarget tracking algorithm. There are some pending problems for the standard PMBM filter, such as unknown detection probability, random target newborn distribution, and high energy consumption for limited computational and processing capacity in sensor networks. For the sake of accommodating these existing problems, an improved multitarget tracking method based on a PMBM filter with adaptive detection probability and adaptive newborn distribution is proposed, accompanied by an associated distributed fusion strategy to reduce the computational complexities. Firstly, gamma (GAM) distribution is introduced to present the augmented state of unknown and changing target detection probability. Secondly, the intensity of newborn targets is adaptively derived from the inverse gamma (IG) distribution based on this augmented state. Then, the measurement likelihood is presented as a gamma distribution for the augmented state. On these bases, the detailed recursion and closed-form solutions to the proposed filter are derived by means of approximating the intensity of target birth and potential targets to an inverse gamma Gaussian mixture (IGGM) form and the density of existing Bernoulli components to a single IGGM form. Moreover, the associated distributed fusion strategy generalized covariance intersection (GCI), whose target states are measured by multiple sensors according to their respective fusion weights, is applied to a large-scale aquaculture tracking network. Comprehensive experiments are presented to verify the effectiveness of this IGGM–PMBM method, and comparisons with other multitarget tracking filters also demonstrate that tracking behaviors are largely improved; in particular, tracking energy consumption is reduced sharply, and tracking accuracy is relatively enhanced. Full article

(This article belongs to the Special Issue Intelligent Control Using Machine Learning)

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12 pages, 2305 KiB

Open AccessArticle

Research on Image Mosaic Method Based on Fracture Edge Contour of Bone Tag

by Ting Wang, Huiqin Wang, Ke Wang and Zhe Yang

Appl. Sci. 2023, 13(2), 756; https://doi.org/10.3390/app13020756 - 5 Jan 2023

Cited by 2 | Viewed by 1519

Abstract

Damaged edges of bone tag images contain external factors such as impurities and damage, which affect the stitching process and lead to repair errors. Therefore, this paper proposes a stitching method based on image edge position feature matching. The objective is to improve [...] Read more.

Damaged edges of bone tag images contain external factors such as impurities and damage, which affect the stitching process and lead to repair errors. Therefore, this paper proposes a stitching method based on image edge position feature matching. The objective is to improve the accuracy of image stitching by matching feature points based on the position of the image edge pixel so as to solve the accurate stitching of broken edge contour. In the first step of this method, the image containing the broken edge is preprocessed by edge detection, and the location of the broken edge pixel is proposed. Secondly, the feature descriptors were calculated to extract the shape and texture information of the feature points on the fracture edge. Finally, the feature points are optimized by minimum correction and image mosaic is carried out. In terms of image stitching, pre-registration is performed by finding the feature descriptors that are most similar to the edge of the optimum fracture surface profile. The matching operator is added to the overlapping region to obtain the corrected image, and the panoramic image mosaic of the image fracture surface is performed. The experimental results show that feature descriptor matching can ensure the integrity of the fracture, improve the matching accuracy, optimize the uneven deformation of the fracture, ensure the quality of image stitching, and reduce the degree of image distortion. Full article

(This article belongs to the Special Issue Intelligent Control Using Machine Learning)

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11 pages, 359 KiB

Open AccessArticle

Training Autoencoders Using Relative Entropy Constraints

by Yanjun Li and Yongquan Yan

Appl. Sci. 2023, 13(1), 287; https://doi.org/10.3390/app13010287 - 26 Dec 2022

Cited by 3 | Viewed by 1501

Abstract

Autoencoders are widely used for dimensionality reduction and feature extraction. The backpropagation algorithm for training the parameters of the autoencoder model suffers from problems such as slow convergence. Therefore, researchers propose forward propagation algorithms. However, the existing forward propagation algorithms do not consider [...] Read more.

Autoencoders are widely used for dimensionality reduction and feature extraction. The backpropagation algorithm for training the parameters of the autoencoder model suffers from problems such as slow convergence. Therefore, researchers propose forward propagation algorithms. However, the existing forward propagation algorithms do not consider the characteristics of the data itself. This paper proposes an autoencoder forward training algorithm based on relative entropy constraints, called relative entropy autoencoder (REAE). When solving the feature map parameters, REAE imposes different constraints on the average activation value of the hidden layer outputs obtained by the feature map for different data sets. In the experimental section, different forward propagation algorithms are compared by applying the features extracted from the autoencoder to an image classification task. The experimental results on three image classification datasets show that the classification performance of the classification model constructed by REAE is better than that of the classification model constructed by other forward propagation algorithms. Full article

(This article belongs to the Special Issue Intelligent Control Using Machine Learning)

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14 pages, 691 KiB

Open AccessArticle

FNNS: An Effective Feedforward Neural Network Scheme with Random Weights for Processing Large-Scale Datasets

by Zhao Zhang, Feng Feng and Tingting Huang

Appl. Sci. 2022, 12(23), 12478; https://doi.org/10.3390/app122312478 - 6 Dec 2022

Cited by 10 | Viewed by 4445

Abstract

The size of datasets is growing exponentially as information technology advances, and it is becoming more and more crucial to provide efficient learning algorithms for neural networks to handle massive amounts of data. Due to their potential for handling huge datasets, feed-forward neural [...] Read more.

The size of datasets is growing exponentially as information technology advances, and it is becoming more and more crucial to provide efficient learning algorithms for neural networks to handle massive amounts of data. Due to their potential for handling huge datasets, feed-forward neural networks with random weights (FNNRWs) have drawn a lot of attention. In this paper, we introduced an efficient feed-forward neural network scheme (FNNS) for processing massive datasets with random weights. The FNNS divides large-scale data into subsets of the same size, and each subset derives the corresponding submodel. According to the activation function, the optimal range of input weights and biases is calculated. The input weight and biases are randomly generated in this range, and the iterative scheme is used to evaluate the output weight. The MNIST dataset was used as the basis for experiments. The experimental results demonstrate that the algorithm has a promising future in processing massive datasets. Full article

(This article belongs to the Special Issue Intelligent Control Using Machine Learning)

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14 pages, 2564 KiB

Open AccessArticle

Face Gender and Age Classification Based on Multi-Task, Multi-Instance and Multi-Scale Learning

by Haibin Liao, Li Yuan, Mou Wu, Liangji Zhong, Guonian Jin and Neal Xiong

Appl. Sci. 2022, 12(23), 12432; https://doi.org/10.3390/app122312432 - 5 Dec 2022

Cited by 4 | Viewed by 1982

Abstract

Automated facial gender and age classification has remained a challenge because of the high inter-subject and intra-subject variations. We addressed this challenging problem by studying multi-instance- and multi-scale-enhanced multi-task random forest architecture. Different from the conventional single facial attribute recognition method, we designed [...] Read more.

Automated facial gender and age classification has remained a challenge because of the high inter-subject and intra-subject variations. We addressed this challenging problem by studying multi-instance- and multi-scale-enhanced multi-task random forest architecture. Different from the conventional single facial attribute recognition method, we designed effective multi-task architecture to learn gender and age simultaneously and used the dependency between gender and age to improve its recognition accuracy. In the study, we found that face gender has a great influence on face age grouping; thus, we proposed a random forest face age grouping method based on face gender conditions. Specifically, we first extracted robust multi-instance and multi-scale features to reduce the influence of various intra-subject distortion types, such as low image resolution, illumination and occlusion, etc. Furthermore, we used a random forest classifier to recognize facial gender. Finally, a gender conditional random forest was proposed for age grouping to address inter-subject variations. Experiments were conducted by using two popular MORPH-II and Adience datasets. The experimental results showed that the gender and age recognition rates in our method can reach 99.6% and 96.14% in the MORPH-II database and 93.48% and 63.72% in the Adience database, reaching the state-of-the-art level. Full article

(This article belongs to the Special Issue Intelligent Control Using Machine Learning)

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21 pages, 2860 KiB

Open AccessArticle

Performance Evaluation of Convolutional Neural Network for Multi-Class in Cross Project Defect Prediction

by Sundas Noreen, Rizwan Bin Faiz, Sultan Alyahya and Mohamed Maddeh

Appl. Sci. 2022, 12(23), 12269; https://doi.org/10.3390/app122312269 - 30 Nov 2022

Cited by 2 | Viewed by 1694

Abstract

Cross-project defect prediction (CPDP) is a practical approach for finding software defects in projects which have incomplete or fewer data. Improvements to the defect prediction accuracy of CPDP—such as the PROMISE repository, the correct classification of the source data, removing the noise, reducing [...] Read more.

Cross-project defect prediction (CPDP) is a practical approach for finding software defects in projects which have incomplete or fewer data. Improvements to the defect prediction accuracy of CPDP—such as the PROMISE repository, the correct classification of the source data, removing the noise, reducing the distribution gap, and balancing the output classes—are an ongoing challenge, as is the selection of an optimal feature set. This research paper aims to achieve a higher defect prediction accuracy for multi-class CPDP by selecting an optimal feature set through XGBoost combined with an automatic feature extraction using a convolutional neural network (CNN). This research type is explanatory, and this research method is controlled experimentation, for which the independent variable prediction accuracy was dependent upon two variables, XGBoost and CNN. The Softmax layer was added to the output layers of the CNN classifier to classify the output into multiple classes. In our experimentation with CPDP, we selected all 28 versions of the multi-class, in which 11 versions were selected as the source projects, against which we predicted 28 target versions with an average AUC of 75.57%. We validated this research paper’s results through the Wilcoxon test. Therefore, after removing the noise, class imbalances, and the data distribution gap, and treating the PROMISE dataset as multi-class, the optimal features selected through XGBoost and classified through the CNN can substantially increase the prediction accuracy in CPDP as evident from our exploratory data analysis (EDA). Full article

(This article belongs to the Special Issue Intelligent Control Using Machine Learning)

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20 pages, 4989 KiB

Open AccessArticle

Impact of Optimal Feature Selection Using Hybrid Method for a Multiclass Problem in Cross Project Defect Prediction

by Abeer Jalil, Rizwan Bin Faiz, Sultan Alyahya and Mohamed Maddeh

Appl. Sci. 2022, 12(23), 12167; https://doi.org/10.3390/app122312167 - 28 Nov 2022

Cited by 3 | Viewed by 1756

Abstract

The objective of cross-project defect prediction (CPDP) is to develop a model that trains bugs on current source projects and predicts defects of target projects. Due to the complexity of projects, CPDP is a challenging task, and the precision estimated is not always [...] Read more.

The objective of cross-project defect prediction (CPDP) is to develop a model that trains bugs on current source projects and predicts defects of target projects. Due to the complexity of projects, CPDP is a challenging task, and the precision estimated is not always trustworthy. Our goal is to predict the bugs in the new projects by training our model on the current projects for cross-projects to save time, cost, and effort. We used experimental research and the type of research is explanatory. Our research method is controlled experimentation, for which our independent variable is prediction accuracy and dependent variables are hyper-parameters which include learning rate, epochs, and dense layers of neural networks. Our research approach is quantitative as the dataset is quantitative. The design of our research is 1F1T (1 factor and 1 treatment). To obtain the results, we first carried out exploratory data analysis (EDA). Using EDA, we found that the dataset is multi-class. The dataset contains 11 different projects consisting of 28 different versions of all the projects in total. We also found that the dataset has significant issues of noise, class imbalance, and distribution gaps between different projects. We pre-processed the dataset for experimentation by resolving all these issues. To resolve the issue of noise, we removed duplication from the dataset by removing redundant rows. We then covered the data distribution gap between different sources and target projects using the min-max normalization technique. After covering the data distribution gap, we generated synthetic data using a CTGANsynthesizer to solve class imbalance issues. We solved the class imbalance issue by generating an equal number of instances, as well as an equal number of output classes. After carrying out all of these steps, we obtained normalized data. We applied the hybrid feature selection technique on the pre-processed data to optimize the feature set. We obtained significant results of an average AUC of 75.98%. From the empirical study, it was demonstrated that feature selection and hyper-parameter tuning have a significant impact on defect prediction accuracy in cross-projects. Full article

(This article belongs to the Special Issue Intelligent Control Using Machine Learning)

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19 pages, 3384 KiB

Open AccessArticle

RSVN: A RoBERTa Sentence Vector Normalization Scheme for Short Texts to Extract Semantic Information

by Lei Gao, Lijuan Zhang, Lei Zhang and Jie Huang

Appl. Sci. 2022, 12(21), 11278; https://doi.org/10.3390/app122111278 - 7 Nov 2022

Cited by 3 | Viewed by 2452

Abstract

With the explosive growth in short texts on the Web and an increasing number of Web corpora consisting of short texts, short texts are playing an important role in various Web applications. Entity linking is a crucial task in knowledge graphs and a [...] Read more.

With the explosive growth in short texts on the Web and an increasing number of Web corpora consisting of short texts, short texts are playing an important role in various Web applications. Entity linking is a crucial task in knowledge graphs and a key technology in the field of short texts that affects the accuracy of many downstream tasks in natural language processing. However, compared to long texts, the entity-linking task of Chinese short text is a challenging problem due to the serious colloquialism and insufficient contexts. Moreover, existing methods for entity linking in Chinese short text underutilize semantic information and ignore the interaction between label information and the original short text. In this paper, we propose a RoBERTa sentence vector normalization scheme for short texts to fully extract the semantic information. Firstly, the proposed model utilizes RoBERTa to fully capture contextual semantic information. Secondly, the anisotropy of RoBERTa’s output sentence vectors is revised by utilizing the standard Gaussian of flow model, which enables the sentence vectors to more precisely characterize the semantics. In addition, the interaction between label embedding and text embedding is employed to improve the NIL entity classification. Experimental results demonstrate that the proposed model outperforms existing research results and mainstream deep learning methods for entity linking in two Chinese short text datasets. Full article

(This article belongs to the Special Issue Intelligent Control Using Machine Learning)

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17 pages, 2328 KiB

Open AccessArticle

MDA: An Intelligent Medical Data Augmentation Scheme Based on Medical Knowledge Graph for Chinese Medical Tasks

by Binbin Shi, Lijuan Zhang, Jie Huang, Huilin Zheng, Jian Wan and Lei Zhang

Appl. Sci. 2022, 12(20), 10655; https://doi.org/10.3390/app122010655 - 21 Oct 2022

Cited by 7 | Viewed by 1876

Abstract

Text data augmentation is essential in the field of medicine for the tasks of natural language processing (NLP). However, most of the traditional text data augmentation focuses on the English datasets, and there is little research on the Chinese datasets to augment Chinese [...] Read more.

Text data augmentation is essential in the field of medicine for the tasks of natural language processing (NLP). However, most of the traditional text data augmentation focuses on the English datasets, and there is little research on the Chinese datasets to augment Chinese sentences. Nevertheless, the traditional text data augmentation ignores the semantics between words in sentences, besides, it has limitations in alleviating the problem of the diversity of augmented sentences. In this paper, a novel medical data augmentation (MDA) is proposed for NLP tasks, which combines the medical knowledge graph with text data augmentation to generate augmented data. Experiments on the named entity recognition task and relational classification task demonstrate that the MDA can significantly enhance the efficiency of the deep learning models compared to cases without augmentation. Full article

(This article belongs to the Special Issue Intelligent Control Using Machine Learning)

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16 pages, 2724 KiB

Open AccessArticle

An Intelligent Shooting Reward Learning Network Scheme for Medical Image Landmark Detection

by Kai Huang and Feng Feng

Appl. Sci. 2022, 12(20), 10190; https://doi.org/10.3390/app122010190 - 11 Oct 2022

Viewed by 1689

Abstract

As the need for medical services has grown in recent years, medical image critical point detection has emerged as a new subject of research for academics. In this paper, a search decision network method is proposed for medical image landmark detection. Unlike the [...] Read more.

As the need for medical services has grown in recent years, medical image critical point detection has emerged as a new subject of research for academics. In this paper, a search decision network method is proposed for medical image landmark detection. Unlike the conventional coarse-to-fine methods which generate bias prediction due to poor initialization, our method is to use the neural network structure search strategy to find a suitable network structure and then make reasonable decisions for robust prediction. To achieve this, we formulate medical landmark detection as a Markov decision process and design a shooting reward function to interact with the task. The task aims to maximize the discount of the received value and search for the optimal network architecture over the entire search space. Furthermore, we embed the central difference convolution, which typically extracts the data invariant feature representation, into the architectural search space. In experiments using standard accessible datasets, our approach achieves a detection accuracy of 98.59% in the 4 mm detection range. Our results demonstrate that, on standard datasets, our proposed approach consistently outperforms the majority of methods. Full article

(This article belongs to the Special Issue Intelligent Control Using Machine Learning)

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