2021

Jump to: 2020, 2019

15 pages, 874 KiB

Open AccessArticle

Online Textual Symptomatic Assessment Chatbot Based on Q&A Weighted Scoring for Female Breast Cancer Prescreening

by Jen-Hui Chen, Obinna Agbodike, Wen-Ling Kuo, Lei Wang, Chiao-Hua Huang, Yu-Shian Shen and Bing-Hong Chen

Appl. Sci. 2021, 11(11), 5079; https://doi.org/10.3390/app11115079 - 30 May 2021

Cited by 9 | Viewed by 3407

Abstract

The increasing number of female breast cancer (FBC) incidences in the East predominated by Chinese language speakers has generated concerns over women’s medicare. To minimize the mortality rate associated with FBC in the region, governments and health experts are jointly encouraging women to [...] Read more.

The increasing number of female breast cancer (FBC) incidences in the East predominated by Chinese language speakers has generated concerns over women’s medicare. To minimize the mortality rate associated with FBC in the region, governments and health experts are jointly encouraging women to undergo mammography screening at the earliest suspicion of FBC symptoms. However, studies show that a huge number of women affected by FBC tend to delay medical consultation at its early stage as a result of factors such as complacency due to unawareness of FBC symptoms, procrastination due to lifestyle, and the feeling of embarrassment in discussing private matters especially with medical personnel of the opposite gender. To address these issues, we propose a symptomatic assessment chatbot (SAC) based on artificial intelligence (AI) designed to prescreen women for FBC symptoms via a textual question-and-answer (Q&A) approach. The purpose of our chatbot is to assist women in engaging in communication regarding FBC symptoms, so as to subsequently initiate formal medical consultations for early FBC diagnosis and treatment. We implemented the SAC systematically with some of the latest natural language processing (NLP) techniques suitable for Chinese word segmentation (CWS) and trained the model with real-world FBC Q&A data obtained from a major hospital in Taiwan. The results from our experiments showed that the SAC achieved very high accuracy in FBC assessment scoring in comparison to FBC patients’ screening benchmark scores obtained from doctors. Full article

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35 pages, 593 KiB

Open AccessReview

A Review of Power System Fault Diagnosis with Spiking Neural P Systems

by Yicen Liu, Ying Chen, Prithwineel Paul, Songhai Fan, Xiaomin Ma and Gexiang Zhang

Appl. Sci. 2021, 11(10), 4376; https://doi.org/10.3390/app11104376 - 12 May 2021

Cited by 9 | Viewed by 2197

Abstract

With the advancement of technologies it is becoming imperative to have a stable, secure and uninterrupted supply of power to electronic systems as well as to ensure the identification of faults occurring in these systems quickly and efficiently in case of any accident. [...] Read more.

With the advancement of technologies it is becoming imperative to have a stable, secure and uninterrupted supply of power to electronic systems as well as to ensure the identification of faults occurring in these systems quickly and efficiently in case of any accident. Spiking neural P system (SNPS) is a popular parallel distributed computing model. It is inspired by the structure and functioning of spiking neurons. It belongs to the category of neural-like P systems and is well-known as a branch of the third generation neural networks. SNPS and its variants can perform the task of fault diagnosis in power systems efficiently. In this paper, we provide a comprehensive survey of these models, which can perform the task of fault diagnosis in transformers, power transmission networks, traction power supply systems, metro traction power supply systems, and electric locomotive systems. Furthermore, we discuss the use of these models in fault section estimation of power systems, fault location identification in distribution network, and fault line detection. We also discuss a software tool which can perform the task of fault diagnosis automatically. Finally, we discuss future research lines related to this topic. Full article

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17 pages, 2141 KiB

Open AccessArticle

Binary Spring Search Algorithm for Solving Various Optimization Problems

by Mohammad Dehghani, Zeinab Montazeri, Ali Dehghani, Om P. Malik, Ruben Morales-Menendez, Gaurav Dhiman, Nima Nouri, Ali Ehsanifar, Josep M. Guerrero and Ricardo A. Ramirez-Mendoza

Appl. Sci. 2021, 11(3), 1286; https://doi.org/10.3390/app11031286 - 30 Jan 2021

Cited by 34 | Viewed by 2941

Abstract

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural [...] Read more.

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive. Full article

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2020

Jump to: 2021, 2019

14 pages, 338 KiB

Open AccessArticle

A Bio-Inspired Model of Picture Array Generating P System with Restricted Insertion Rules

by Gexiang Zhang, G. Samdanielthompson, N. Gnanamalar David, Atulya K. Nagar and K.G. Subramanian

Appl. Sci. 2020, 10(22), 8306; https://doi.org/10.3390/app10228306 - 23 Nov 2020

Viewed by 1821

Abstract

In the bio-inspired area of membrane computing, a novel computing model with a generic name of P system was introduced around the year 2000. Among its several variants, string or array language generating P systems involving rewriting rules have been considered. A new [...] Read more.

In the bio-inspired area of membrane computing, a novel computing model with a generic name of P system was introduced around the year 2000. Among its several variants, string or array language generating P systems involving rewriting rules have been considered. A new picture array model of array generating P system with a restricted type of picture insertion rules and picture array objects in its regions, is introduced here. The generative power of such a system is investigated by comparing with the generative power of certain related picture array grammar models introduced and studied in two-dimensional picture language theory. It is shown that this new model of array P system can generate picture array languages which cannot be generated by many other array grammar models. The theoretical model developed is for handling the application problem of generation of patterns encoded as picture arrays over a finite set of symbols. As an application, certain floor-design patterns are generated using such an array P system. Full article

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18 pages, 3352 KiB

Open AccessArticle

Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification

by Daniel Octavian Melinte, Ana-Maria Travediu and Dan N. Dumitriu

Appl. Sci. 2020, 10(20), 7301; https://doi.org/10.3390/app10207301 - 19 Oct 2020

Cited by 48 | Viewed by 6712

Abstract

This paper presents an extensive research carried out for enhancing the performances of convolutional neural network (CNN) object detectors applied to municipal waste identification. In order to obtain an accurate and fast CNN architecture, several types of Single Shot Detectors (SSD) and Regional [...] Read more.

This paper presents an extensive research carried out for enhancing the performances of convolutional neural network (CNN) object detectors applied to municipal waste identification. In order to obtain an accurate and fast CNN architecture, several types of Single Shot Detectors (SSD) and Regional Proposal Networks (RPN) have been fine-tuned on the TrashNet database. The network with the best performances is executed on one autonomous robot system, which is able to collect detected waste from the ground based on the CNN feedback. For this type of application, a precise identification of municipal waste objects is very important. In order to develop a straightforward pipeline for waste detection, the paper focuses on boosting the performance of pre-trained CNN Object Detectors, in terms of precision, generalization, and detection speed, using different loss optimization methods, database augmentation, and asynchronous threading at inference time. The pipeline consists of data augmentation at the training time followed by CNN feature extraction and box predictor modules for localization and classification at different feature map sizes. The trained model is generated for inference afterwards. The experiments revealed better performances than all other Object Detectors trained on TrashNet or other garbage datasets with a precision of 97.63% accuracy for SSD and 95.76% accuracy for Faster R-CNN, respectively. In order to find the optimal higher and lower bounds of our learning rate where the network is actually learning, we trained our model for several epochs, updating the learning rate after each epoch, starting from 1 × 10⁻¹⁰ and decreasing it until reaching 1 × 10⁻¹. Full article

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26 pages, 998 KiB

Open AccessReview

On Applications of Spiking Neural P Systems

by Songhai Fan, Prithwineel Paul, Tianbao Wu, Haina Rong and Gexiang Zhang

Appl. Sci. 2020, 10(20), 7011; https://doi.org/10.3390/app10207011 - 09 Oct 2020

Cited by 18 | Viewed by 2729

Abstract

Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems [...] Read more.

Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems fault diagnoses with fuzzy reasoning SNPS. We also study the structure and workings of these models, their comparisons along with their advantages and disadvantages. We also study the implementation of these models in hardware. Finally, we discuss some new ideas which can further expand the scope of applications of SNPS models as well as their implementations. Full article

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27 pages, 575 KiB

Open AccessArticle

Estimation of Distribution Algorithms with Fuzzy Sampling for Stochastic Programming Problems

by Abdel-Rahman Hedar, Amira A. Allam and Alaa Fahim

Appl. Sci. 2020, 10(19), 6937; https://doi.org/10.3390/app10196937 - 03 Oct 2020

Cited by 2 | Viewed by 1979

Abstract

Generating practical methods for simulation-based optimization has attracted a great deal of attention recently. In this paper, the estimation of distribution algorithms are used to solve nonlinear continuous optimization problems that contain noise. One common approach to dealing with these problems is to [...] Read more.

Generating practical methods for simulation-based optimization has attracted a great deal of attention recently. In this paper, the estimation of distribution algorithms are used to solve nonlinear continuous optimization problems that contain noise. One common approach to dealing with these problems is to combine sampling methods with optimal search methods. Sampling techniques have a serious problem when the sample size is small, so estimating the objective function values with noise is not accurate in this case. In this research, a new sampling technique is proposed based on fuzzy logic to deal with small sample sizes. Then, simulation-based optimization methods are designed by combining the estimation of distribution algorithms with the proposed sampling technique and other sampling techniques to solve the stochastic programming problems. Moreover, additive versions of the proposed methods are developed to optimize functions without noise in order to evaluate different efficiency levels of the proposed methods. In order to test the performance of the proposed methods, different numerical experiments were carried out using several benchmark test functions. Finally, three real-world applications are considered to assess the performance of the proposed methods. Full article

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27 pages, 3143 KiB

Open AccessArticle

Comparison and Ranking of Metaheuristic Techniques for Optimization of PI Controllers in a Machine Drive System

by Omar Aguilar-Mejía, Hertwin Minor-Popocatl and Ruben Tapia-Olvera

Appl. Sci. 2020, 10(18), 6592; https://doi.org/10.3390/app10186592 - 21 Sep 2020

Cited by 5 | Viewed by 2561

Abstract

Proportional integral (PI) control is still the most widely deployed controller in the industrial drives due to its simplicity and the fact that it is easy to understand and implement. Nevertheless, they are successes applied to systems with a complex behavior with a [...] Read more.

Proportional integral (PI) control is still the most widely deployed controller in the industrial drives due to its simplicity and the fact that it is easy to understand and implement. Nevertheless, they are successes applied to systems with a complex behavior with a nonlinear representation, but a disadvantage is the procedure to find the optimal PI controller gains. The optimal values of PI parameters must be computed during the tuning process. However, traditional tuning techniques are based on model and do not provide optimal adjustment parameters for the PI controllers because the transient response could produce oscillations and a large overshoot. In this paper, six swarm intelligence-based algorithms (whale, moth-flame, flower pollination, dragonfly, cuckoo search, and modified flower pollination), are correctly conditioned and delimited to tune the PI controllers, the results are probed in a typical industry actuator. Also, a rigorous study is developed to evaluate the quality and reliability of these algorithms by a statistical analysis based on non-parametric test and post-hoc test. Finally, with the obtained results, some time simulations are carried out to corroborate that the nonlinear system performance is improved for high precision industrial applications subjected to endogenous and exogenous uncertainties in a wide range of operating conditions. Full article

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29 pages, 3806 KiB

Open AccessArticle

BSM-Data Reuse Model Based on In-Vehicular Computing

by Khireddine Benaissa, Salim Bitam and Abdelhamid Mellouk

Appl. Sci. 2020, 10(16), 5452; https://doi.org/10.3390/app10165452 - 06 Aug 2020

Cited by 3 | Viewed by 3886

Abstract

Basic Safety Messages that are frequently generated from multiple connected vehicles can play a primordial role in providing transport data see credible and reliable information they contain. Otherwise, when considering the way Basic Safety Messages (BSMs) are treated, multiple deficiencies prevent the latter [...] Read more.

Basic Safety Messages that are frequently generated from multiple connected vehicles can play a primordial role in providing transport data see credible and reliable information they contain. Otherwise, when considering the way Basic Safety Messages (BSMs) are treated, multiple deficiencies prevent the latter to be capable of constituting a precious data source. As we know, data become more useful the more widely are used, which is the exact opposite of what happens with the BSMs that exist only temporarily, used locally, considered disposable, and are never stored. In this paper, we introduce a data reuse model that retains collected BSMs, stores, and processes them inside the vehicle constituting a continuous data source holding retained snapshots along the roadway. Our model provided a primary data source available on a large scale, considered to be a worthy dataset for machine learning tasks, capable of visualizing different traffic-related indicators to enhance analytics and support decisions-making. In the study case, we set up an in-vehicle data platform, where we achieved an 80% of BSMs size reduction and provided a rich set of APIs to serve applications. We also adopted the Artificial Neural Networks (ANN) as an information processing paradigm for performing traffic volume prediction, where the obtained results have reached over 99% of accuracy. Full article

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18 pages, 2991 KiB

Open AccessArticle

Coupling Elephant Herding with Ordinal Optimization for Solving the Stochastic Inequality Constrained Optimization Problems

by Shih-Cheng Horng and Shieh-Shing Lin

Appl. Sci. 2020, 10(6), 2075; https://doi.org/10.3390/app10062075 - 19 Mar 2020

Cited by 8 | Viewed by 1803

Abstract

The stochastic inequality constrained optimization problems (SICOPs) consider the problems of optimizing an objective function involving stochastic inequality constraints. The SICOPs belong to a category of NP-hard problems in terms of computational complexity. The ordinal optimization (OO) method offers an efficient framework for [...] Read more.

The stochastic inequality constrained optimization problems (SICOPs) consider the problems of optimizing an objective function involving stochastic inequality constraints. The SICOPs belong to a category of NP-hard problems in terms of computational complexity. The ordinal optimization (OO) method offers an efficient framework for solving NP-hard problems. Even though the OO method is helpful to solve NP-hard problems, the stochastic inequality constraints will drastically reduce the efficiency and competitiveness. In this paper, a heuristic method coupling elephant herding optimization (EHO) with ordinal optimization (OO), abbreviated as EHOO, is presented to solve the SICOPs with large solution space. The EHOO approach has three parts, which are metamodel construction, diversification and intensification. First, the regularized minimal-energy tensor-product splines is adopted as a metamodel to approximately evaluate fitness of a solution. Next, an improved elephant herding optimization is developed to find N significant solutions from the entire solution space. Finally, an accelerated optimal computing budget allocation is utilized to select a superb solution from the N significant solutions. The EHOO approach is tested on a one-period multi-skill call center for minimizing the staffing cost, which is formulated as a SICOP. Simulation results obtained by the EHOO are compared with three optimization methods. Experimental results demonstrate that the EHOO approach obtains a superb solution of higher quality as well as a higher computational efficiency than three optimization methods. Full article

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16 pages, 943 KiB

Open AccessArticle

An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

by Rodrigo Olivares, Roberto Munoz, Ricardo Soto, Broderick Crawford, Diego Cárdenas, Aarón Ponce and Carla Taramasco

Appl. Sci. 2020, 10(5), 1827; https://doi.org/10.3390/app10051827 - 06 Mar 2020

Cited by 39 | Viewed by 3366

Abstract

During the last years, highly-recognized computational intelligence techniques have been proposed to treat classification problems. These automatic learning approaches lead to the most recent researches because they exhibit outstanding results. Nevertheless, to achieve this performance, artificial learning methods firstly require fine tuning of [...] Read more.

During the last years, highly-recognized computational intelligence techniques have been proposed to treat classification problems. These automatic learning approaches lead to the most recent researches because they exhibit outstanding results. Nevertheless, to achieve this performance, artificial learning methods firstly require fine tuning of their parameters and then they need to work with the best-generated model. This process usually needs an expert user for supervising the algorithm’s performance. In this paper, we propose an optimized Extreme Learning Machine by using the Bat Algorithm, which boosts the training phase of the machine learning method to increase the accuracy, and decreasing or keeping the loss in the learning phase. To evaluate our proposal, we use the Parkinson’s Disease audio dataset taken from UCI Machine Learning Repository. Parkinson’s disease is a neurodegenerative disorder that affects over 10 million people. Although its diagnosis is through motor symptoms, it is possible to evidence the disorder through variations in the speech using machine learning techniques. Results suggest that using the bio-inspired optimization algorithm for adjusting the parameters of the Extreme Learning Machine is a real alternative for improving its performance. During the validation phase, the classification process for Parkinson’s Disease achieves a maximum accuracy of 96.74% and a minimum loss of 3.27%. Full article

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23 pages, 1362 KiB

Open AccessArticle

Adapted Binary Particle Swarm Optimization for Efficient Features Selection in the Case of Imbalanced Sensor Data

by Dorin Moldovan, Ionut Anghel, Tudor Cioara and Ioan Salomie

Appl. Sci. 2020, 10(4), 1496; https://doi.org/10.3390/app10041496 - 21 Feb 2020

Cited by 7 | Viewed by 3890

Abstract

Daily living activities (DLAs) classification using data collected from wearable monitoring sensors is very challenging due to the imbalance characteristics of the monitored data. A major research challenge is to determine the best combination of features that returns the best accuracy results using [...] Read more.

Daily living activities (DLAs) classification using data collected from wearable monitoring sensors is very challenging due to the imbalance characteristics of the monitored data. A major research challenge is to determine the best combination of features that returns the best accuracy results using minimal computational resources, when the data is heterogeneous and not fitted for classical algorithms that are designed for balanced low-dimensional datasets. This research article: (1) presents a modification of the classical version of the binary particle swarm optimization (BPSO) algorithm that introduces a particular type of particles called sensor particles, (2) describes the adaptation of this algorithm for data generated by sensors that monitor DLAs to determine the best positions and features of the monitoring sensors that lead to the best classification results, and (3) evaluates and validates the proposed approach using a machine learning methodology that integrates the modified version of the algorithm. The methodology is tested and validated on the Daily Life Activities (DaLiAc) dataset. Full article

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2019

Jump to: 2021, 2020

17 pages, 3049 KiB

Open AccessArticle

Classification of Motor Imagery Using a Combination of User-Specific Band and Subject-Specific Band for Brain-Computer Interface

by Vacius Jusas and Sam Gilvine Samuvel

Appl. Sci. 2019, 9(23), 4990; https://doi.org/10.3390/app9234990 - 20 Nov 2019

Cited by 8 | Viewed by 2505

Abstract

The essential task of a Brain-Computer Interface (BCI) is to extract the motor imagery features from Electro-Encephalogram (EEG) signals for classifying the thought process. It is necessary to analyse these obtained signals in both the time domain and frequency domains. It is observed [...] Read more.

The essential task of a Brain-Computer Interface (BCI) is to extract the motor imagery features from Electro-Encephalogram (EEG) signals for classifying the thought process. It is necessary to analyse these obtained signals in both the time domain and frequency domains. It is observed that the combination of multiple algorithms increases the performance of the feature extraction process. This paper identifies combinations that have not been attempted previously and improves the accuracy of the overall process, although other authors implemented different combinations of the techniques. The focus is given more on the feature extraction process and frequency bands, which are user-specific and subject-specific frequency bands. In both time and frequency domains, after analysing EEG signals with the time domain parameter, we select the frequency band and the timing while using the Fisher ratio of the time domain parameter (TDP). We used Fisher discriminant analysis (FDA)-type F-score to simultaneously select the frequency band and time segment for multi-class classification. We extracted subject-specific TDP features from the training trials to train the classifier when optimal time-frequency areas were selected for each subject. In this paper, various methods are explored for obtaining the features, which are Time Domain Parameters (TDP), Fast Fourier Transform (FFT), Principal Component Analysis (PCA), R², Fast Correlation Based Filter (FCBF), Empirical Mode Decomposition (EMD), and Intrinsic time-scale decomposition (ITD). After the extraction process, PCA is used for dimensionality reduction. An efficient result was obtained with the combination of TDP, FFT, and PCA. We used the multi-class Fisher’s linear discriminant analysis (LDA) as the classifier, which was in line with the FDA-type F-score. It is observed that the combination of feature extraction techniques to the frequency bands that were selected by the Fisher ratio and FDA type F-score along with Fisher’s LDA classifier had higher accuracy than the results obtained other researches. A kappa coefficient accuracy of 0.64 is obtained for the proposed technique. Our method leads to better classification performance when compared to state-of-the-art methods. The novelty of the approach is based on the combination of frequency bands and two feature extraction methods. Full article

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