Algorithms

36 pages, 6469 KiB

Open AccessEditor’s ChoiceArticle

Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook

by Xuan Di, Rongye Shi, Zhaobin Mo and Yongjie Fu

Algorithms 2023, 16(6), 305; https://doi.org/10.3390/a16060305 - 17 Jun 2023

Cited by 9 | Viewed by 4270

For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNNs), has been booming in science and engineering fields. One key [...] Read more.

For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNNs), has been booming in science and engineering fields. One key challenge of applying PIDL to various domains and problems lies in the design of a computational graph that integrates physics and DNNs. In other words, how the physics is encoded into DNNs and how the physics and data components are represented. In this paper, we offer an overview of a variety of architecture designs of PIDL computational graphs and how these structures are customized to traffic state estimation (TSE), a central problem in transportation engineering. When observation data, problem type, and goal vary, we demonstrate potential architectures of PIDL computational graphs and compare these variants using the same real-world dataset. Full article

(This article belongs to the Collection Feature Papers on Artificial Intelligence Algorithms and Their Applications)

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15 pages, 3252 KiB

Open AccessEditor’s ChoiceArticle

An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments

by Lei Chen, Zhihao Wang, Ru Huo and Tao Huang

Algorithms 2023, 16(4), 197; https://doi.org/10.3390/a16040197 - 5 Apr 2023

Cited by 10 | Viewed by 2244

Abstract

As an essential piece of infrastructure supporting cyberspace security technology verification, network weapons and equipment testing, attack defense confrontation drills, and network risk assessment, Cyber Range is exceptionally vulnerable to distributed denial of service (DDoS) attacks from three malicious parties. Moreover, some attackers [...] Read more.

As an essential piece of infrastructure supporting cyberspace security technology verification, network weapons and equipment testing, attack defense confrontation drills, and network risk assessment, Cyber Range is exceptionally vulnerable to distributed denial of service (DDoS) attacks from three malicious parties. Moreover, some attackers try to fool the classification/prediction mechanism by crafting the input data to create adversarial attacks, which is hard to defend for ML-based Network Intrusion Detection Systems (NIDSs). This paper proposes an adversarial DBN-LSTM method for detecting and defending against DDoS attacks in SDN environments, which applies generative adversarial networks (GAN) as well as deep belief networks and long short-term memory (DBN-LSTM) to make the system less sensitive to adversarial attacks and faster feature extraction. We conducted the experiments using the public dataset CICDDoS 2019. The experimental results demonstrated that our method efficiently detected up-to-date common types of DDoS attacks compared to other approaches. Full article

(This article belongs to the Special Issue Artificial Intelligence in Intrusion Detection Systems)

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19 pages, 1087 KiB

Open AccessEditor’s ChoiceArticle

A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks

by Md Ishtyaq Mahmud, Muntasir Mamun and Ahmed Abdelgawad

Algorithms 2023, 16(4), 176; https://doi.org/10.3390/a16040176 - 23 Mar 2023

Cited by 64 | Viewed by 18482

Abstract

Creating machines that behave and work in a way similar to humans is the objective of artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving, computer activities with artificial intelligence include other activities. A group of algorithms called “deep learning” is [...] Read more.

Creating machines that behave and work in a way similar to humans is the objective of artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving, computer activities with artificial intelligence include other activities. A group of algorithms called “deep learning” is used in machine learning. With the aid of magnetic resonance imaging (MRI), deep learning is utilized to create models for the detection and categorization of brain tumors. This allows for the quick and simple identification of brain tumors. Brain disorders are mostly the result of aberrant brain cell proliferation, which can harm the structure of the brain and ultimately result in malignant brain cancer. The early identification of brain tumors and the subsequent appropriate treatment may lower the death rate. In this study, we suggest a convolutional neural network (CNN) architecture for the efficient identification of brain tumors using MR images. This paper also discusses various models such as ResNet-50, VGG16, and Inception V3 and conducts a comparison between the proposed architecture and these models. To analyze the performance of the models, we considered different metrics such as the accuracy, recall, loss, and area under the curve (AUC). As a result of analyzing different models with our proposed model using these metrics, we concluded that the proposed model performed better than the others. Using a dataset of 3264 MR images, we found that the CNN model had an accuracy of 93.3%, an AUC of 98.43%, a recall of 91.19%, and a loss of 0.25. We may infer that the proposed model is reliable for the early detection of a variety of brain tumors after comparing it to the other models. Full article

(This article belongs to the Special Issue Machine Learning in Healthcare and Biomedical Application II)

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13 pages, 1355 KiB

Open AccessEditor’s ChoiceArticle

Model Parallelism Optimization for CNN FPGA Accelerator

by Jinnan Wang, Weiqin Tong and Xiaoli Zhi

Algorithms 2023, 16(2), 110; https://doi.org/10.3390/a16020110 - 14 Feb 2023

Cited by 7 | Viewed by 3185

Abstract

Convolutional neural networks (CNNs) have made impressive achievements in image classification and object detection. For hardware with limited resources, it is not easy to achieve CNN inference with a large number of parameters without external storage. Model parallelism is an effective way to [...] Read more.

Convolutional neural networks (CNNs) have made impressive achievements in image classification and object detection. For hardware with limited resources, it is not easy to achieve CNN inference with a large number of parameters without external storage. Model parallelism is an effective way to reduce resource usage by distributing CNN inference among several devices. However, parallelizing a CNN model is not easy, because CNN models have an essentially tightly-coupled structure. In this work, we propose a novel model parallelism method to decouple the CNN structure with group convolution and a new channel shuffle procedure. Our method could eliminate inter-device synchronization while reducing the memory footprint of each device. Using the proposed model parallelism method, we designed a parallel FPGA accelerator for the classic CNN model ShuffleNet. This accelerator was further optimized with features such as aggregate read and kernel vectorization to fully exploit the hardware-level parallelism of the FPGA. We conducted experiments with ShuffleNet on two FPGA boards, each of which had an Intel Arria 10 GX1150 and 16GB DDR3 memory. The experimental results showed that when using two devices, ShuffleNet achieved a 1.42× speed increase and reduced its memory footprint by 34%, as compared to its non-parallel counterpart, while maintaining accuracy. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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28 pages, 953 KiB

Open AccessEditor’s ChoiceArticle

Enhancing Logistic Regression Using Neural Networks for Classification in Actuarial Learning

by George Tzougas and Konstantin Kutzkov

Algorithms 2023, 16(2), 99; https://doi.org/10.3390/a16020099 - 9 Feb 2023

Cited by 6 | Viewed by 3538

Abstract

We developed a methodology for the neural network boosting of logistic regression aimed at learning an additional model structure from the data. In particular, we constructed two classes of neural network-based models: shallow–dense neural networks with one hidden layer and deep neural networks [...] Read more.

We developed a methodology for the neural network boosting of logistic regression aimed at learning an additional model structure from the data. In particular, we constructed two classes of neural network-based models: shallow–dense neural networks with one hidden layer and deep neural networks with multiple hidden layers. Furthermore, several advanced approaches were explored, including the combined actuarial neural network approach, embeddings and transfer learning. The model training was achieved by minimizing either the deviance or the cross-entropy loss functions, leading to fourteen neural network-based models in total. For illustrative purposes, logistic regression and the alternative neural network-based models we propose are employed for a binary classification exercise concerning the occurrence of at least one claim in a French motor third-party insurance portfolio. Finally, the model interpretability issue was addressed via the local interpretable model-agnostic explanations approach. Full article

(This article belongs to the Special Issue Deep Neural Networks and Optimization Algorithms)

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A multilayer perceptron neural network with three hidden layers. The last neuron in each layer is the intercept term. Full article ">Figure 2
Improvements of deviance loss and AUC metrics over epochs. Full article ">Figure 3
AUC values on the test set achieved using R’s logistic regression function (left) and logistic regression by a shallow neural network (right). Full article ">Figure 4
AUC values on the test set achieved using a neural network with feature normalization (left) and feature normalization + categorical embeddings (right). Full article ">Figure 5
AUC values on the test set achieved using logistic regression with embeddings (left) and frozen weights learned by a neural network (right). Full article ">Figure 6
A tSNE visualization of the 10-dimensional embeddings for the different regions. Full article ">Figure 7
Feature importance for predicting a positive and negative example for logistic regression (top) and neural networks (bottom). Full article ">

30 pages, 3724 KiB

Open AccessEditor’s ChoiceReview

Defect Detection Methods for Industrial Products Using Deep Learning Techniques: A Review

by Alireza Saberironaghi, Jing Ren and Moustafa El-Gindy

Algorithms 2023, 16(2), 95; https://doi.org/10.3390/a16020095 - 8 Feb 2023

Cited by 46 | Viewed by 18553

Abstract

Over the last few decades, detecting surface defects has attracted significant attention as a challenging task. There are specific classes of problems that can be solved using traditional image processing techniques. However, these techniques struggle with complex textures in backgrounds, noise, and differences [...] Read more.

Over the last few decades, detecting surface defects has attracted significant attention as a challenging task. There are specific classes of problems that can be solved using traditional image processing techniques. However, these techniques struggle with complex textures in backgrounds, noise, and differences in lighting conditions. As a solution to this problem, deep learning has recently emerged, motivated by two main factors: accessibility to computing power and the rapid digitization of society, which enables the creation of large databases of labeled samples. This review paper aims to briefly summarize and analyze the current state of research on detecting defects using machine learning methods. First, deep learning-based detection of surface defects on industrial products is discussed from three perspectives: supervised, semi-supervised, and unsupervised. Secondly, the current research status of deep learning defect detection methods for X-ray images is discussed. Finally, we summarize the most common challenges and their potential solutions in surface defect detection, such as unbalanced sample identification, limited sample size, and real-time processing. Full article

(This article belongs to the Special Issue Deep Learning Architecture and Applications)

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Normal samples of industrial products are compared to defective samples. The first row contains good samples, and the second, third, and fourth rows contain defective samples. The first, second, third, fourth, and fifth columns display wood, grid, capsule, leather, and bill, respectively, and there are three types of defects listed below the image. Full article ">Figure 2
An example of the result of wood defect detection using the presented technique in [<a href="#B2-algorithms-16-00095" class="html-bibr">2</a>]. Full article ">Figure 3
A camera lens with several defects: (a) original image and (b) converted result based on inspection result and polar coordinate transformation [<a href="#B9-algorithms-16-00095" class="html-bibr">9</a>]. Full article ">Figure 4
The results of insulator defect detection. The green box represents the non-defective insulator, and the red box represents the defective insulator [<a href="#B39-algorithms-16-00095" class="html-bibr">39</a>]. Full article ">Figure 5
Presenting the results of experiments on six defect samples using four methods. The defect types are listed in the first column and include drops tar, shadow, floating, crush, pitted surface and scratch. The results from traditional manual feature extraction methods (CPICS-LBP, AEC-LBP, HWV and the proposed method in [<a href="#B53-algorithms-16-00095" class="html-bibr">53</a>]) are shown in columns 2–5. The experiment compares the proposed method with current state-of-the-art methods in detecting strip steel surface defects. Full article ">

14 pages, 2116 KiB

Open AccessEditor’s ChoiceArticle

Effective Heart Disease Prediction Using Machine Learning Techniques

by Chintan M. Bhatt, Parth Patel, Tarang Ghetia and Pier Luigi Mazzeo

Algorithms 2023, 16(2), 88; https://doi.org/10.3390/a16020088 - 6 Feb 2023

Cited by 103 | Viewed by 50574

Abstract

The diagnosis and prognosis of cardiovascular disease are crucial medical tasks to ensure correct classification, which helps cardiologists provide proper treatment to the patient. Machine learning applications in the medical niche have increased as they can recognize patterns from data. Using machine learning [...] Read more.

The diagnosis and prognosis of cardiovascular disease are crucial medical tasks to ensure correct classification, which helps cardiologists provide proper treatment to the patient. Machine learning applications in the medical niche have increased as they can recognize patterns from data. Using machine learning to classify cardiovascular disease occurrence can help diagnosticians reduce misdiagnosis. This research develops a model that can correctly predict cardiovascular diseases to reduce the fatality caused by cardiovascular diseases. This paper proposes a method of k-modes clustering with Huang starting that can improve classification accuracy. Models such as random forest (RF), decision tree classifier (DT), multilayer perceptron (MP), and XGBoost (XGB) are used. GridSearchCV was used to hypertune the parameters of the applied model to optimize the result. The proposed model is applied to a real-world dataset of 70,000 instances from Kaggle. Models were trained on data that were split in 80:20 and achieved accuracy as follows: decision tree: 86.37% (with cross-validation) and 86.53% (without cross-validation), XGBoost: 86.87% (with cross-validation) and 87.02% (without cross-validation), random forest: 87.05% (with cross-validation) and 86.92% (without cross-validation), multilayer perceptron: 87.28% (with cross-validation) and 86.94% (without cross-validation). The proposed models have AUC (area under the curve) values: decision tree: 0.94, XGBoost: 0.95, random forest: 0.95, multilayer perceptron: 0.95. The conclusion drawn from this underlying research is that multilayer perceptron with cross-validation has outperformed all other algorithms in terms of accuracy. It achieved the highest accuracy of 87.28%. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms for Healthcare)

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42 pages, 655 KiB

Open AccessEditor’s ChoiceReview

Inverse Reinforcement Learning as the Algorithmic Basis for Theory of Mind: Current Methods and Open Problems

by Jaime Ruiz-Serra and Michael S. Harré

Algorithms 2023, 16(2), 68; https://doi.org/10.3390/a16020068 - 19 Jan 2023

Cited by 5 | Viewed by 5058

Abstract

Theory of mind (ToM) is the psychological construct by which we model another’s internal mental states. Through ToM, we adjust our own behaviour to best suit a social context, and therefore it is essential to our everyday interactions with others. In adopting an [...] Read more.

Theory of mind (ToM) is the psychological construct by which we model another’s internal mental states. Through ToM, we adjust our own behaviour to best suit a social context, and therefore it is essential to our everyday interactions with others. In adopting an algorithmic (rather than a psychological or neurological) approach to ToM, we gain insights into cognition that will aid us in building more accurate models for the cognitive and behavioural sciences, as well as enable artificial agents to be more proficient in social interactions as they become more embedded in our everyday lives. Inverse reinforcement learning (IRL) is a class of machine learning methods by which to infer the preferences (rewards as a function of state) of a decision maker from its behaviour (trajectories in a Markov decision process). IRL can provide a computational approach for ToM, as recently outlined by Jara-Ettinger, but this will require a better understanding of the relationship between ToM concepts and existing IRL methods at the algorthmic level. Here, we provide a review of prominent IRL algorithms and their formal descriptions, and discuss the applicability of IRL concepts as the algorithmic basis of a ToM in AI. Full article

(This article belongs to the Special Issue Advancements in Reinforcement Learning Algorithms)

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13 pages, 378 KiB

Open AccessEditor’s ChoiceArticle

Solving the Parallel Drone Scheduling Traveling Salesman Problem via Constraint Programming

by Roberto Montemanni and Mauro Dell’Amico

Algorithms 2023, 16(1), 40; https://doi.org/10.3390/a16010040 - 8 Jan 2023

Cited by 11 | Viewed by 2574

Abstract

Drones are currently seen as a viable way of improving the distribution of parcels in urban and rural environments, while working in coordination with traditional vehicles, such as trucks. In this paper, we consider the parallel drone scheduling traveling salesman problem, where a [...] Read more.

Drones are currently seen as a viable way of improving the distribution of parcels in urban and rural environments, while working in coordination with traditional vehicles, such as trucks. In this paper, we consider the parallel drone scheduling traveling salesman problem, where a set of customers requiring a delivery is split between a truck and a fleet of drones, with the aim of minimizing the total time required to serve all the customers. We propose a constraint programming model for the problem, discuss its implementation and present the results of an experimental program on the instances previously cited in the literature to validate exact and heuristic algorithms. We were able to decrease the cost (the time required to serve customers) for some of the instances and, for the first time, to provide a demonstrated optimal solution for all the instances considered. These results show that constraint programming can be a very effective tool for attacking optimization problems with traveling salesman components, such as the one discussed. Full article

(This article belongs to the Collection Feature Papers in Combinatorial Optimization, Graph, and Network Algorithms)

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18 pages, 2875 KiB

Open AccessEditor’s ChoiceArticle

Image-to-Image Translation-Based Data Augmentation for Improving Crop/Weed Classification Models for Precision Agriculture Applications

by L. G. Divyanth, D. S. Guru, Peeyush Soni, Rajendra Machavaram, Mohammad Nadimi and Jitendra Paliwal

Algorithms 2022, 15(11), 401; https://doi.org/10.3390/a15110401 - 30 Oct 2022

Cited by 28 | Viewed by 7435

Abstract

Applications of deep-learning models in machine visions for crop/weed identification have remarkably upgraded the authenticity of precise weed management. However, compelling data are required to obtain the desired result from this highly data-driven operation. This study aims to curtail the effort needed to [...] Read more.

Applications of deep-learning models in machine visions for crop/weed identification have remarkably upgraded the authenticity of precise weed management. However, compelling data are required to obtain the desired result from this highly data-driven operation. This study aims to curtail the effort needed to prepare very large image datasets by creating artificial images of maize (Zea mays) and four common weeds (i.e., Charlock, Fat Hen, Shepherd’s Purse, and small-flowered Cranesbill) through conditional Generative Adversarial Networks (cGANs). The fidelity of these synthetic images was tested through t-distributed stochastic neighbor embedding (t-SNE) visualization plots of real and artificial images of each class. The reliability of this method as a data augmentation technique was validated through classification results based on the transfer learning of a pre-defined convolutional neural network (CNN) architecture—the AlexNet; the feature extraction method came from the deepest pooling layer of the same network. Machine learning models based on a support vector machine (SVM) and linear discriminant analysis (LDA) were trained using these feature vectors. The F1 scores of the transfer learning model increased from 0.97 to 0.99, when additionally supported by an artificial dataset. Similarly, in the case of the feature extraction technique, the classification F1-scores increased from 0.93 to 0.96 for SVM and from 0.94 to 0.96 for the LDA model. The results show that image augmentation using generative adversarial networks (GANs) can improve the performance of crop/weed classification models with the added advantage of reduced time and manpower. Furthermore, it has demonstrated that generative networks could be a great tool for deep-learning applications in agriculture. Full article

(This article belongs to the Special Issue Deep Learning Architecture and Applications)

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Sample images of (a) Charlock, (b) Fat Hen, (c) Shepherd’s purse, (d) Small-flowered Cranesbill, and (e) Maize. Full article ">Figure 2
Training procedure for image generator through L1 and GAN loss functions. Full article ">Figure 3
Illustration of the original AlexNet [<a href="#B45-algorithms-15-00401" class="html-bibr">45</a>] architecture. Full article ">Figure 4
Visualization of activations of the first Conv-layer of Alexnet. Full article ">Figure 5
Workflow for the artificial image synthesis through adversarial network and crop/weeds classification. Full article ">Figure 6
Sample ground truth images and generated images at different epochs during GAN training. Column-wise from left to right: Charlock, Fat Hen, Shepherd’s purse, Small-flowered Cranesbill, Maize. Full article ">Figure 7
t-SNE visualization plots for real (green points) and synthetic (red) images for all five classes—(a) Maize, (b) Charlock, (c) Shepherd’s purse, (d) Fat Hen, and (e) Small-flowered Cranesbill. Full article ">Figure 8
Best testing confusion matrices based on—(a) Transfer learning (GAN-TL), (b) Feature extraction—SVM (GAN-SVM), and (c) Feature extraction—LDA (GAN-LDA), using both real and GAN-synthesized datasets. Full article ">

24 pages, 2504 KiB

Open AccessEditor’s ChoiceReview

A Survey on Fault Diagnosis of Rolling Bearings

by Bo Peng, Ying Bi, Bing Xue, Mengjie Zhang and Shuting Wan

Algorithms 2022, 15(10), 347; https://doi.org/10.3390/a15100347 - 26 Sep 2022

Cited by 33 | Viewed by 5387

Abstract

The failure of a rolling bearing may cause the shutdown of mechanical equipment and even induce catastrophic accidents, resulting in tremendous economic losses and a severely negative impact on society. Fault diagnosis of rolling bearings becomes an important topic with much attention from [...] Read more.

The failure of a rolling bearing may cause the shutdown of mechanical equipment and even induce catastrophic accidents, resulting in tremendous economic losses and a severely negative impact on society. Fault diagnosis of rolling bearings becomes an important topic with much attention from researchers and industrial pioneers. There are an increasing number of publications on this topic. However, there is a lack of a comprehensive survey of existing works from the perspectives of fault detection and fault type recognition in rolling bearings using vibration signals. Therefore, this paper reviews recent fault detection and fault type recognition methods using vibration signals. First, it provides an overview of fault diagnosis of rolling bearings and typical fault types. Then, existing fault diagnosis methods are categorized into fault detection methods and fault type recognition methods, which are separately revised and discussed. Finally, a summary of existing datasets, limitations/challenges of existing methods, and future directions are presented to provide more guidance for researchers who are interested in this field. Overall, this survey paper conducts a review and analysis of the methods used to diagnose rolling bearing faults and provide comprehensive guidance for researchers in this field. Full article

(This article belongs to the Special Issue Artificial Intelligence for Fault Detection and Diagnosis)

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19 pages, 2574 KiB

Open AccessEditor’s ChoiceArticle

GA−Reinforced Deep Neural Network for Net Electric Load Forecasting in Microgrids with Renewable Energy Resources for Scheduling Battery Energy Storage Systems

by Chaoran Zheng, Mohsen Eskandari, Ming Li and Zeyue Sun

Algorithms 2022, 15(10), 338; https://doi.org/10.3390/a15100338 - 21 Sep 2022

Cited by 19 | Viewed by 3474

Abstract

The large−scale integration of wind power and PV cells into electric grids alleviates the problem of an energy crisis. However, this is also responsible for technical and management problems in the power grid, such as power fluctuation, scheduling difficulties, and reliability reduction. The [...] Read more.

The large−scale integration of wind power and PV cells into electric grids alleviates the problem of an energy crisis. However, this is also responsible for technical and management problems in the power grid, such as power fluctuation, scheduling difficulties, and reliability reduction. The microgrid concept has been proposed to locally control and manage a cluster of local distributed energy resources (DERs) and loads. If the net load power can be accurately predicted, it is possible to schedule/optimize the operation of battery energy storage systems (BESSs) through economic dispatch to cover intermittent renewables. However, the load curve of the microgrid is highly affected by various external factors, resulting in large fluctuations, which makes the prediction problematic. This paper predicts the net electric load of the microgrid using a deep neural network to realize a reliable power supply as well as reduce the cost of power generation. Considering that the backpropagation (BP) neural network has a good approximation effect as well as a strong adaptation ability, the load prediction model of the BP deep neural network is established. However, there are some defects in the BP neural network, such as the prediction effect, which is not precise enough and easily falls into a locally optimal solution. Hence, a genetic algorithm (GA)−reinforced deep neural network is introduced. By optimizing the weight and threshold of the BP network, the deficiency of the BP neural network algorithm is improved so that the prediction effect is realized and optimized. The results reveal that the error reduction in the mean square error (MSE) of the GA–BP neural network prediction is 2.0221, which is significantly smaller than the 30.3493 of the BP neural network prediction. Additionally, the error reduction is 93.3%. The error reductions of the root mean square error (RMSE) and mean absolute error (MAE) are 74.18% and 51.2%, respectively. Full article

(This article belongs to the Special Issue Optimization and Coordination Algorithms for Energy Management Systems)

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23 pages, 3231 KiB

Open AccessEditor’s ChoiceArticle

Social Media Hate Speech Detection Using Explainable Artificial Intelligence (XAI)

by Harshkumar Mehta and Kalpdrum Passi

Algorithms 2022, 15(8), 291; https://doi.org/10.3390/a15080291 - 17 Aug 2022

Cited by 30 | Viewed by 7703

Abstract

Explainable artificial intelligence (XAI) characteristics have flexible and multifaceted potential in hate speech detection by deep learning models. Interpreting and explaining decisions made by complex artificial intelligence (AI) models to understand the decision-making process of these model were the aims of this research. [...] Read more.

Explainable artificial intelligence (XAI) characteristics have flexible and multifaceted potential in hate speech detection by deep learning models. Interpreting and explaining decisions made by complex artificial intelligence (AI) models to understand the decision-making process of these model were the aims of this research. As a part of this research study, two datasets were taken to demonstrate hate speech detection using XAI. Data preprocessing was performed to clean data of any inconsistencies, clean the text of the tweets, tokenize and lemmatize the text, etc. Categorical variables were also simplified in order to generate a clean dataset for training purposes. Exploratory data analysis was performed on the datasets to uncover various patterns and insights. Various pre-existing models were applied to the Google Jigsaw dataset such as decision trees, k-nearest neighbors, multinomial naïve Bayes, random forest, logistic regression, and long short-term memory (LSTM), among which LSTM achieved an accuracy of 97.6%. Explainable methods such as LIME (local interpretable model—agnostic explanations) were applied to the HateXplain dataset. Variants of BERT (bidirectional encoder representations from transformers) model such as BERT + ANN (artificial neural network) with an accuracy of 93.55% and BERT + MLP (multilayer perceptron) with an accuracy of 93.67% were created to achieve a good performance in terms of explainability using the ERASER (evaluating rationales and simple English reasoning) benchmark. Full article

(This article belongs to the Section Evolutionary Algorithms and Machine Learning)

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23 pages, 14110 KiB

Open AccessEditor’s ChoiceArticle

Design of Multi-Objective-Based Artificial Intelligence Controller for Wind/Battery-Connected Shunt Active Power Filter

by Srilakshmi Koganti, Krishna Jyothi Koganti and Surender Reddy Salkuti

Algorithms 2022, 15(8), 256; https://doi.org/10.3390/a15080256 - 25 Jul 2022

Cited by 24 | Viewed by 5113

Abstract

Nowadays, the integration of renewable energy sources such as solar, wind, etc. into the grid is recommended to reduce losses and meet demands. The application of power electronics devices (PED) to control non-linear, unbalanced loads leads to power quality (PQ) issues. This work [...] Read more.

Nowadays, the integration of renewable energy sources such as solar, wind, etc. into the grid is recommended to reduce losses and meet demands. The application of power electronics devices (PED) to control non-linear, unbalanced loads leads to power quality (PQ) issues. This work presents a hybrid controller for the self-tuning filter (STF)-based Shunt active power filter (SHAPF), integrated with a wind power generation system (WPGS) and a battery storage system (BS). The SHAPF comprises a three-phase voltage source inverter, coupled via a DC-Link. The proposed neuro-fuzzy inference hybrid controller (NFIHC) utilizes both the properties of Fuzzy Logic (FL) and artificial neural network (ANN) controllers and maintains constant DC-Link voltage. The phase synchronization was generated by a self-tuning filter (STF) for the effective working of SHAPF during unbalanced and distorted supply voltages. In addition, STF also does the work of low-pass filters (LPFs) and HPFs (high-pass filters) for splitting the Fundamental component (FC) and Harmonic component (HC) of the current. The control of SHAPF works on d-q theory with the advantage of eliminating low-pass filters (LPFs) and phase-locked loop (PLL). The prime objective of the projected work is to regulate the DC-Link voltage during wind uncertainties and load variations, and minimize the total harmonic distortion (THD) in the current waveforms, thereby improving the power factor (PF).Test studies with various combinations of balanced/unbalanced loads, wind velocity variations, and supply voltage were used to evaluate the suggested method’s superior performance. In addition, the comparative analysis was carried out with those of the existing controllers such as conventional proportional-integral (PI), ANN, and FL. Full article

(This article belongs to the Special Issue Optimization and Coordination Algorithms for Energy Management Systems)

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28 pages, 1344 KiB

Open AccessEditor’s ChoiceReview

Overview of Distributed Machine Learning Techniques for 6G Networks

by Eugenio Muscinelli, Swapnil Sadashiv Shinde and Daniele Tarchi

Algorithms 2022, 15(6), 210; https://doi.org/10.3390/a15060210 - 15 Jun 2022

Cited by 24 | Viewed by 4897

Abstract

The main goal of this paper is to survey the influential research of distributed learning technologies playing a key role in the 6G world. Upcoming 6G technology is expected to create an intelligent, highly scalable, dynamic, and programable wireless communication network able to [...] Read more.

The main goal of this paper is to survey the influential research of distributed learning technologies playing a key role in the 6G world. Upcoming 6G technology is expected to create an intelligent, highly scalable, dynamic, and programable wireless communication network able to serve many heterogeneous wireless devices. Various machine learning (ML) techniques are expected to be deployed over the intelligent 6G wireless network that provide solutions to highly complex networking problems. In order to do this, various 6G nodes and devices are expected to generate tons of data through external sensors, and data analysis will be needed. With such massive and distributed data, and various innovations in computing hardware, distributed ML techniques are expected to play an important role in 6G. Though they have several advantages over the centralized ML techniques, implementing the distributed ML algorithms over resource-constrained wireless environments can be challenging. Therefore, it is important to select a proper ML algorithm based upon the characteristics of the wireless environment and the resource requirements of the learning process. In this work, we survey the recently introduced distributed ML techniques with their characteristics and possible benefits by focusing our attention on the most influential papers in the area. We finally give our perspective on the main challenges and advantages for telecommunication networks, along with the main scenarios that could eventuate. Full article

(This article belongs to the Special Issue Algorithms for Communication Networks)

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22 pages, 22712 KiB

Open AccessEditor’s ChoiceArticle

Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm

by Yuan Luo, Jiakai Lu, Qiong Qin and Yanyu Liu

Algorithms 2022, 15(6), 198; https://doi.org/10.3390/a15060198 - 8 Jun 2022

Cited by 8 | Viewed by 3219

Abstract

The Jump Point Search (JPS) algorithm ignores the possibility of any-angle walking, so the paths found by the JPS algorithm under the discrete grid map still have a gap with the real paths. To address the above problems, this paper improves the path [...] Read more.

The Jump Point Search (JPS) algorithm ignores the possibility of any-angle walking, so the paths found by the JPS algorithm under the discrete grid map still have a gap with the real paths. To address the above problems, this paper improves the path optimization strategy of the JPS algorithm by combining the viewable angle of the Angle-Propagation Theta* (AP Theta*) algorithm, and it proposes the AP-JPS algorithm based on an any-angle pathfinding strategy. First, based on the JPS algorithm, this paper proposes a vision triangle judgment method to optimize the generated path by selecting the successor search point. Secondly, the idea of the node viewable angle in the AP Theta* algorithm is introduced to modify the line of sight (LOS) reachability detection between two nodes. Finally, the paths are optimized using a seventh-order polynomial based on minimum snap, so that the AP-JPS algorithm generates paths that better match the actual robot motion. The feasibility and effectiveness of this method are proved by simulation experiments and comparison with other algorithms. The results show that the path planning algorithm in this paper obtains paths with good smoothness in environments with different obstacle densities and different map sizes. In the algorithm comparison experiments, it can be seen that the AP-JPS algorithm reduces the path by 1.61–4.68% and the total turning angle of the path by 58.71–84.67% compared with the JPS algorithm. The AP-JPS algorithm reduces the computing time by 98.59–99.22% compared with the AP-Theta* algorithm. Full article

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22 pages, 848 KiB

Open AccessEditor’s ChoiceReview

A Survey on Network Optimization Techniques for Blockchain Systems

by Robert Antwi, James Dzisi Gadze, Eric Tutu Tchao, Axel Sikora, Henry Nunoo-Mensah, Andrew Selasi Agbemenu, Kwame Opunie-Boachie Obour Agyekum, Justice Owusu Agyemang, Dominik Welte and Eliel Keelson

Algorithms 2022, 15(6), 193; https://doi.org/10.3390/a15060193 - 4 Jun 2022

Cited by 14 | Viewed by 5512

Abstract

The increase of the Internet of Things (IoT) calls for secure solutions for industrial applications. The security of IoT can be potentially improved by blockchain. However, blockchain technology suffers scalability issues which hinders integration with IoT. Solutions to blockchain’s scalability issues, such as [...] Read more.

The increase of the Internet of Things (IoT) calls for secure solutions for industrial applications. The security of IoT can be potentially improved by blockchain. However, blockchain technology suffers scalability issues which hinders integration with IoT. Solutions to blockchain’s scalability issues, such as minimizing the computational complexity of consensus algorithms or blockchain storage requirements, have received attention. However, to realize the full potential of blockchain in IoT, the inefficiencies of its inter-peer communication must also be addressed. For example, blockchain uses a flooding technique to share blocks, resulting in duplicates and inefficient bandwidth usage. Moreover, blockchain peers use a random neighbor selection (RNS) technique to decide on other peers with whom to exchange blockchain data. As a result, the peer-to-peer (P2P) topology formation limits the effective achievable throughput. This paper provides a survey on the state-of-the-art network structures and communication mechanisms used in blockchain and establishes the need for network-based optimization. Additionally, it discusses the blockchain architecture and its layers categorizes existing literature into the layers and provides a survey on the state-of-the-art optimization frameworks, analyzing their effectiveness and ability to scale. Finally, this paper presents recommendations for future work. Full article

(This article belongs to the Special Issue Advances in Blockchain Architecture and Consensus)

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22 pages, 878 KiB

Open AccessEditor’s ChoiceArticle

Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder

by Mousumi Bala, Mohammad Hanif Ali, Md. Shahriare Satu, Khondokar Fida Hasan and Mohammad Ali Moni

Algorithms 2022, 15(5), 166; https://doi.org/10.3390/a15050166 - 16 May 2022

Cited by 30 | Viewed by 5729

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that severely impairs an individual’s cognitive, linguistic, object recognition, communication, and social abilities. This situation is not treatable, although early detection of ASD can assist to diagnose and take proper steps for mitigating its effect. [...] Read more.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that severely impairs an individual’s cognitive, linguistic, object recognition, communication, and social abilities. This situation is not treatable, although early detection of ASD can assist to diagnose and take proper steps for mitigating its effect. Using various artificial intelligence (AI) techniques, ASD can be detected an at earlier stage than with traditional methods. The aim of this study was to propose a machine learning model that investigates ASD data of different age levels and to identify ASD more accurately. In this work, we gathered ASD datasets of toddlers, children, adolescents, and adults and used several feature selection techniques. Then, different classifiers were applied into these datasets, and we assessed their performance with evaluation metrics including predictive accuracy, kappa statistics, the f1-measure, and AUROC. In addition, we analyzed the performance of individual classifiers using a non-parametric statistical significant test. For the toddler, child, adolescent, and adult datasets, we found that Support Vector Machine (SVM) performed better than other classifiers where we gained 97.82% accuracy for the RIPPER-based toddler subset; 99.61% accuracy for the Correlation-based feature selection (CFS) and Boruta CFS intersect (BIC) method-based child subset; 95.87% accuracy for the Boruta-based adolescent subset; and 96.82% accuracy for the CFS-based adult subset. Then, we applied the Shapley Additive Explanations (SHAP) method into different feature subsets, which gained the highest accuracy and ranked their features based on the analysis. Full article

(This article belongs to the Special Issue Interpretability, Accountability and Robustness in Machine Learning)

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18 pages, 523 KiB

Open AccessEditor’s ChoiceArticle

Closed-Form Solution of the Bending Two-Phase Integral Model of Euler-Bernoulli Nanobeams

by Efthimios Providas

Algorithms 2022, 15(5), 151; https://doi.org/10.3390/a15050151 - 28 Apr 2022

Cited by 7 | Viewed by 3442

Abstract

Recent developments have shown that the widely used simplified differential model of Eringen’s nonlocal elasticity in nanobeam analysis is not equivalent to the corresponding and initially proposed integral models, the pure integral model and the two-phase integral model, in all cases of loading [...] Read more.

Recent developments have shown that the widely used simplified differential model of Eringen’s nonlocal elasticity in nanobeam analysis is not equivalent to the corresponding and initially proposed integral models, the pure integral model and the two-phase integral model, in all cases of loading and boundary conditions. This has resolved a paradox with solutions that are not in line with the expected softening effect of the nonlocal theory that appears in all other cases. In addition, it revived interest in the integral model and the two-phase integral model, which were not used due to their complexity in solving the relevant integral and integro-differential equations, respectively. In this article, we use a direct operator method for solving boundary value problems for nth order linear Volterra–Fredholm integro-differential equations of convolution type to construct closed-form solutions to the two-phase integral model of Euler–Bernoulli nanobeams in bending under transverse distributed load and various types of boundary conditions. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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30 pages, 937 KiB

Open AccessEditor’s ChoiceReview

A Review on the Performance of Linear and Mixed Integer Two-Stage Stochastic Programming Software

by Juan J. Torres, Can Li, Robert M. Apap and Ignacio E. Grossmann

Algorithms 2022, 15(4), 103; https://doi.org/10.3390/a15040103 - 22 Mar 2022

Cited by 12 | Viewed by 4830

Abstract

This paper presents a tutorial on the state-of-the-art software for the solution of two-stage (mixed-integer) linear stochastic programs and provides a list of software designed for this purpose. The methodologies are classified according to the decomposition alternatives and the types of the variables [...] Read more.

This paper presents a tutorial on the state-of-the-art software for the solution of two-stage (mixed-integer) linear stochastic programs and provides a list of software designed for this purpose. The methodologies are classified according to the decomposition alternatives and the types of the variables in the problem. We review the fundamentals of Benders decomposition, dual decomposition and progressive hedging, as well as possible improvements and variants. We also present extensive numerical results to underline the properties and performance of each algorithm using software implementations, including DECIS, FORTSP, PySP, and DSP. Finally, we discuss the strengths and weaknesses of each methodology and propose future research directions. Full article

(This article belongs to the Special Issue Stochastic Algorithms and Their Applications)

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Number of iterations for PH to solve SSLP instances using different cost proportional multipliers. Full article ">Figure 2
Walltime for PH to solve SSLP instances using different cost proportional multipliers. Full article ">Figure 3
Solution time improvement by using WW heuristics for SSLP and SSLPR instances. Full article ">Figure 4
Comparison of optimality gaps from PySP, DSP, and literature for SSLP and SSLPR library—instances with only binary in the first stage. Full article ">Figure 5
Comparison of the optimality gaps from PySP, DSP, and literature for DCAP library—instances with mixed-integer variables in the first stage. Full article ">Figure A1
Maximum relative improvement of the solution time by using FortSP’s solvers over DECIS’s solvers. Full article ">Figure A2
Cumulative solution time of masters problem in BD, where **, *** means the algorithm fails to solve the instance in 10,800 CPU seconds. Full article ">Figure A3
Cumulative solution time of scenario instances in BD, where **, *** means the algorithm fails to solve the instance in 10,800 CPU seconds. Full article ">

18 pages, 576 KiB

Open AccessEditor’s ChoiceArticle

Evolutionary Optimization of Spiking Neural P Systems for Remaining Useful Life Prediction

by Leonardo Lucio Custode, Hyunho Mo, Andrea Ferigo and Giovanni Iacca

Algorithms 2022, 15(3), 98; https://doi.org/10.3390/a15030098 - 19 Mar 2022

Cited by 8 | Viewed by 4158

Abstract

Remaining useful life (RUL) prediction is a key enabler for predictive maintenance. In fact, the possibility of accurately and reliably predicting the RUL of a system, based on a record of its monitoring data, can allow users to schedule maintenance interventions before faults [...] Read more.

Remaining useful life (RUL) prediction is a key enabler for predictive maintenance. In fact, the possibility of accurately and reliably predicting the RUL of a system, based on a record of its monitoring data, can allow users to schedule maintenance interventions before faults occur. In the recent literature, several data-driven methods for RUL prediction have been proposed. However, most of them are based on traditional (connectivist) neural networks, such as convolutional neural networks, and alternative mechanisms have barely been explored. Here, we tackle the RUL prediction problem for the first time by using a membrane computing paradigm, namely that of Spiking Neural P (in short, SN P) systems. First, we show how SN P systems can be adapted to handle the RUL prediction problem. Then, we propose the use of a neuro-evolutionary algorithm to optimize the structure and parameters of the SN P systems. Our results on two datasets, namely the CMAPSS and new CMAPSS benchmarks from NASA, are fairly comparable with those obtained by much more complex deep networks, showing a reasonable compromise between performance and number of trainable parameters, which in turn correlates with memory consumption and computing time. Full article

(This article belongs to the Special Issue Algorithms in Decision Support Systems Vol. 2)

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37 pages, 1000 KiB

Open AccessEditor’s ChoiceArticle

Deterministic Approximate EM Algorithm; Application to the Riemann Approximation EM and the Tempered EM

by Thomas Lartigue, Stanley Durrleman and Stéphanie Allassonnière

Algorithms 2022, 15(3), 78; https://doi.org/10.3390/a15030078 - 25 Feb 2022

Cited by 5 | Viewed by 3184

Abstract

The Expectation Maximisation (EM) algorithm is widely used to optimise non-convex likelihood functions with latent variables. Many authors modified its simple design to fit more specific situations. For instance, the Expectation (E) step has been replaced by Monte Carlo (MC), Markov Chain Monte [...] Read more.

The Expectation Maximisation (EM) algorithm is widely used to optimise non-convex likelihood functions with latent variables. Many authors modified its simple design to fit more specific situations. For instance, the Expectation (E) step has been replaced by Monte Carlo (MC), Markov Chain Monte Carlo or tempered approximations, etc. Most of the well-studied approximations belong to the stochastic class. By comparison, the literature is lacking when it comes to deterministic approximations. In this paper, we introduce a theoretical framework, with state-of-the-art convergence guarantees, for any deterministic approximation of the E step. We analyse theoretically and empirically several approximations that fit into this framework. First, for intractable E-steps, we introduce a deterministic version of MC-EM using Riemann sums. A straightforward method, not requiring any hyper-parameter fine-tuning, useful when the low dimensionality does not warrant a MC-EM. Then, we consider the tempered approximation, borrowed from the Simulated Annealing literature and used to escape local extrema. We prove that the tempered EM verifies the convergence guarantees for a wider range of temperature profiles than previously considered. We showcase empirically how new non-trivial profiles can more successfully escape adversarial initialisations. Finally, we combine the Riemann and tempered approximations into a method that accomplishes both their purposes. Full article

(This article belongs to the Special Issue Stochastic Algorithms and Their Applications)

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(a) Average values, with standard deviation, over 2000 simulations of the negative log-likelihood along the steps of the Riemann EM. The Riemann EM increases the likelihood. (b) Average and standard deviation of the relative parameter reconstruction errors at the end of the Riemann EM. Full article ">Figure 2
(a) Visual representation of the number of Riemann intervals over the EM steps for each profile <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>i</mi> </msub> </semantics></math>. The total number of Riemann intervals computed over 100 EM iterations are: 5150 for “low”, 14,950 for “medium”, 50,500 for “linear” and 104,950 for “high”. (b) For each profile, average evolution of the negative log-likelihood, with standard deviation, over 50 simulations. The results are fairly similar, in particular between “medium”, “high” and “linear”. Full article ">Figure 3
(a) Visual representation of the number of Riemann intervals over the EM steps for each profile <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>i</mi> </msub> </semantics></math>. In higher dimension, the computational complexity of the profiles are very different. More precisely, the total number of Riemann squares computed over 100 EM iterations are: 4534 for “square root”, 125,662 for “5 square root”, 348,550 for “low” and 2,318,350 for “medium”. (b) For each profile, average evolution of the negative log-likelihood, with standard deviation, over 50 simulations. The “square root” profile performs poorly. However, the other three are comparable despite their different computational complexities. Full article ">Figure 4
500 sample points from a Mixture of Gaussians with 3 classes. The true centroid of each Gaussian are depicted by black crosses, and their true covariance matrices are represented by the confidence ellipses of level 0.8, 0.99 and 0.999 around the centre. Each sub-figure corresponds to one of the three different versions of the true parameters. From (a) to (c): the true <math display="inline"><semantics> <msub> <mi>μ</mi> <mi>k</mi> </msub> </semantics></math> of the two left clusters (<math display="inline"><semantics> <msub> <mi>μ</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>μ</mi> <mn>2</mn> </msub> </semantics></math>) are getting closer while everything else stays identical. Full article ">Figure 5
Typical final positioning of the centroids by EM (first row) and tmp-EM with oscillating profile (second row) when the initialisation is made at the barycenter of all data points (blue crosses). The three columns represent the three gradually more ambiguous parameter sets. Each figure represents the positions of the estimated centroids after convergence of the EM algorithms (orange cross), with their estimated covariance matrices (orange confidence ellipses). In each simulation, 500 sample points were drawn from the real GMM (small green crosses). In those example, tmp-EM managed to correctly identify the position of the three real centroids. Full article ">Figure 6
Typical final positioning of the centroids by EM (first row) and tmp-EM with oscillating profile (second row) when the initialisation is made by selecting two points in the isolated cluster and one in the lower ambiguous cluster (blue crosses). The three columns represent the three gradually more ambiguous parameter sets. Each figure represents the positions of the estimated centroids after convergence of the EM algorithms (orange cross), with their estimated covariance matrices (orange confidence ellipses). In each simulation, 500 sample points were drawn from the real GMM (small green crosses). In those examples, although EM kept two centroids on the isolated cluster, tmp-EM managed to correctly identify the position of the three real centroids. Full article ">Figure 7
Results over many simulations of the Riemann EM and tmp-Riemann EM on the Beta-Gaussian model. The tempered Riemann EM reaches relative errors on the real parameters that are orders of magnitude below the Riemann EM with no temperature. The likelihood reached is also lower with the tempering. Full article ">

19 pages, 5851 KiB

Open AccessEditor’s ChoiceReview

Machine Learning in Cereal Crops Disease Detection: A Review

by Fraol Gelana Waldamichael, Taye Girma Debelee, Friedhelm Schwenker, Yehualashet Megersa Ayano and Samuel Rahimeto Kebede

Algorithms 2022, 15(3), 75; https://doi.org/10.3390/a15030075 - 24 Feb 2022

Cited by 23 | Viewed by 7623

Abstract

Cereals are an important and major source of the human diet. They constitute more than two-thirds of the world’s food source and cover more than 56% of the world’s cultivatable land. These important sources of food are affected by a variety of damaging [...] Read more.

Cereals are an important and major source of the human diet. They constitute more than two-thirds of the world’s food source and cover more than 56% of the world’s cultivatable land. These important sources of food are affected by a variety of damaging diseases, causing significant loss in annual production. In this regard, detection of diseases at an early stage and quantification of the severity has acquired the urgent attention of researchers worldwide. One emerging and popular approach for this task is the utilization of machine learning techniques. In this work, we have identified the most common and damaging diseases affecting cereal crop production, and we also reviewed 45 works performed on the detection and classification of various diseases that occur on six cereal crops within the past five years. In addition, we identified and summarised numerous publicly available datasets for each cereal crop, which the lack thereof we identified as the main challenges faced for researching the application of machine learning in cereal crop detection. In this survey, we identified deep convolutional neural networks trained on hyperspectral data as the most effective approach for early detection of diseases and transfer learning as the most commonly used and yielding the best result training method. Full article

(This article belongs to the Special Issue Mathematical Models and Their Applications III)

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14 pages, 1123 KiB

Open AccessEditor’s ChoiceArticle

Approximation of the Riesz–Caputo Derivative by Cubic Splines

by Francesca Pitolli, Chiara Sorgentone and Enza Pellegrino

Algorithms 2022, 15(2), 69; https://doi.org/10.3390/a15020069 - 21 Feb 2022

Cited by 17 | Viewed by 5099

Abstract

Differential problems with the Riesz derivative in space are widely used to model anomalous diffusion. Although the Riesz–Caputo derivative is more suitable for modeling real phenomena, there are few examples in literature where numerical methods are used to solve such differential problems. In [...] Read more.

Differential problems with the Riesz derivative in space are widely used to model anomalous diffusion. Although the Riesz–Caputo derivative is more suitable for modeling real phenomena, there are few examples in literature where numerical methods are used to solve such differential problems. In this paper, we propose to approximate the Riesz–Caputo derivative of a given function with a cubic spline. As far as we are aware, this is the first time that cubic splines have been used in the context of the Riesz–Caputo derivative. To show the effectiveness of the proposed numerical method, we present numerical tests in which we compare the analytical solution of several boundary differential problems which have the Riesz–Caputo derivative in space with the numerical solution we obtain by a spline collocation method. The numerical results show that the proposed method is efficient and accurate. Full article

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20 pages, 2764 KiB

Open AccessEditor’s ChoiceArticle

A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning

by Ahmed Abdelmoamen Ahmed and Gbenga Agunsoye

Algorithms 2021, 14(8), 250; https://doi.org/10.3390/a14080250 - 21 Aug 2021

Cited by 21 | Viewed by 6628

Abstract

The increasing ubiquity of network traffic and the new online applications’ deployment has increased traffic analysis complexity. Traditionally, network administrators rely on recognizing well-known static ports for classifying the traffic flowing their networks. However, modern network traffic uses dynamic ports and is transported [...] Read more.

The increasing ubiquity of network traffic and the new online applications’ deployment has increased traffic analysis complexity. Traditionally, network administrators rely on recognizing well-known static ports for classifying the traffic flowing their networks. However, modern network traffic uses dynamic ports and is transported over secure application-layer protocols (e.g., HTTPS, SSL, and SSH). This makes it a challenging task for network administrators to identify online applications using traditional port-based approaches. One way for classifying the modern network traffic is to use machine learning (ML) to distinguish between the different traffic attributes such as packet count and size, packet inter-arrival time, packet send–receive ratio, etc. This paper presents the design and implementation of NetScrapper, a flow-based network traffic classifier for online applications. NetScrapper uses three ML models, namely K-Nearest Neighbors (KNN), Random Forest (RF), and Artificial Neural Network (ANN), for classifying the most popular 53 online applications, including Amazon, Youtube, Google, Twitter, and many others. We collected a network traffic dataset containing 3,577,296 packet flows with different 87 features for training, validating, and testing the ML models. A web-based user-friendly interface is developed to enable users to either upload a snapshot of their network traffic to NetScrapper or sniff the network traffic directly from the network interface card in real time. Additionally, we created a middleware pipeline for interfacing the three models with the Flask GUI. Finally, we evaluated NetScrapper using various performance metrics such as classification accuracy and prediction time. Most notably, we found that our ANN model achieves an overall classification accuracy of 99.86% in recognizing the online applications in our dataset. Full article

(This article belongs to the Special Issue Intelligent Optimization for Transportation, Logistics and Vehicle Routing)

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22 pages, 2628 KiB

Open AccessEditor’s ChoiceArticle

COVID-19 Prediction Applying Supervised Machine Learning Algorithms with Comparative Analysis Using WEKA

by Charlyn Nayve Villavicencio, Julio Jerison Escudero Macrohon, Xavier Alphonse Inbaraj, Jyh-Horng Jeng and Jer-Guang Hsieh

Algorithms 2021, 14(7), 201; https://doi.org/10.3390/a14070201 - 30 Jun 2021

Cited by 45 | Viewed by 8195

Abstract

Early diagnosis is crucial to prevent the development of a disease that may cause danger to human lives. COVID-19, which is a contagious disease that has mutated into several variants, has become a global pandemic that demands to be diagnosed as soon as [...] Read more.

Early diagnosis is crucial to prevent the development of a disease that may cause danger to human lives. COVID-19, which is a contagious disease that has mutated into several variants, has become a global pandemic that demands to be diagnosed as soon as possible. With the use of technology, available information concerning COVID-19 increases each day, and extracting useful information from massive data can be done through data mining. In this study, authors utilized several supervised machine learning algorithms in building a model to analyze and predict the presence of COVID-19 using the COVID-19 Symptoms and Presence dataset from Kaggle. J48 Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes algorithms were applied through WEKA machine learning software. Each model’s performance was evaluated using 10-fold cross validation and compared according to major accuracy measures, correctly or incorrectly classified instances, kappa, mean absolute error, and time taken to build the model. The results show that Support Vector Machine using Pearson VII universal kernel outweighs other algorithms by attaining 98.81% accuracy and a mean absolute error of 0.012. Full article

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12 pages, 994 KiB

Open AccessEditor’s ChoiceArticle

Digital Twins in Solar Farms: An Approach through Time Series and Deep Learning

by Kamel Arafet and Rafael Berlanga

Algorithms 2021, 14(5), 156; https://doi.org/10.3390/a14050156 - 18 May 2021

Cited by 18 | Viewed by 4652

Abstract

The generation of electricity through renewable energy sources increases every day, with solar energy being one of the fastest-growing. The emergence of information technologies such as Digital Twins (DT) in the field of the Internet of Things and Industry 4.0 allows a substantial [...] Read more.

The generation of electricity through renewable energy sources increases every day, with solar energy being one of the fastest-growing. The emergence of information technologies such as Digital Twins (DT) in the field of the Internet of Things and Industry 4.0 allows a substantial development in automatic diagnostic systems. The objective of this work is to obtain the DT of a Photovoltaic Solar Farm (PVSF) with a deep-learning (DL) approach. To build such a DT, sensor-based time series are properly analyzed and processed. The resulting data are used to train a DL model (e.g., autoencoders) in order to detect anomalies of the physical system in its DT. Results show a reconstruction error around 0.1, a recall score of 0.92 and an Area Under Curve (AUC) of 0.97. Therefore, this paper demonstrates that the DT can reproduce the behavior as well as detect efficiently anomalies of the physical system. Full article

(This article belongs to the Special Issue Algorithms and Applications of Time Series Analysis)

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21 pages, 1182 KiB

Open AccessEditor’s ChoiceArticle

Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer

by Felix D. Beacher, Lilianne R. Mujica-Parodi, Shreyash Gupta and Leonardo A. Ancora

Algorithms 2021, 14(5), 147; https://doi.org/10.3390/a14050147 - 5 May 2021

Cited by 11 | Viewed by 7025

Abstract

The ability to predict the individual outcomes of clinical trials could support the development of tools for precision medicine and improve the efficiency of clinical-stage drug development. However, there are no published attempts to predict individual outcomes of clinical trials for cancer. We [...] Read more.

The ability to predict the individual outcomes of clinical trials could support the development of tools for precision medicine and improve the efficiency of clinical-stage drug development. However, there are no published attempts to predict individual outcomes of clinical trials for cancer. We used machine learning (ML) to predict individual responses to a two-year course of bicalutamide, a standard treatment for prostate cancer, based on data from three Phase III clinical trials (n = 3653). We developed models that used a merged dataset from all three studies. The best performing models using merged data from all three studies had an accuracy of 76%. The performance of these models was confirmed by further modeling using a merged dataset from two of the three studies, and a separate study for testing. Together, our results indicate the feasibility of ML-based tools for predicting cancer treatment outcomes, with implications for precision oncology and improving the efficiency of clinical-stage drug development. Full article

(This article belongs to the Special Issue Machine Learning in Healthcare and Biomedical Application)

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17 pages, 503 KiB

Open AccessEditor’s ChoiceArticle

Multiple Criteria Decision Making and Prospective Scenarios Model for Selection of Companies to Be Incubated

by Altina S. Oliveira, Carlos F. S. Gomes, Camilla T. Clarkson, Adriana M. Sanseverino, Mara R. S. Barcelos, Igor P. A. Costa and Marcos Santos

Algorithms 2021, 14(4), 111; https://doi.org/10.3390/a14040111 - 30 Mar 2021

Cited by 48 | Viewed by 3739

Abstract

This paper proposes a model to evaluate business projects to get into an incubator, allowing to rank them in order of selection priority. The model combines the Momentum method to build prospective scenarios and the AHP-TOPSIS-2N Multiple Criteria Decision Making (MCDM) method to [...] Read more.

This paper proposes a model to evaluate business projects to get into an incubator, allowing to rank them in order of selection priority. The model combines the Momentum method to build prospective scenarios and the AHP-TOPSIS-2N Multiple Criteria Decision Making (MCDM) method to rank the alternatives. Six business projects were evaluated to be incubated. The Momentum method made it possible for us to create an initial core of criteria for the evaluation of incubation projects. The AHP-TOPSIS-2N method supported the decision to choose the company to be incubated by ranking the alternatives in order of relevance. Our evaluation model has improved the existing models used by incubators. This model can be used and/or adapted by any incubator to evaluate the business projects to be incubated. The set of criteria for the evaluation of incubation projects is original and the use of prospective scenarios with an MCDM method to evaluate companies to be incubated does not exist in the literature. Full article

(This article belongs to the Special Issue Algorithms and Models for Dynamic Multiple Criteria Decision Making)

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14 pages, 611 KiB

Open AccessEditor’s ChoiceArticle

An Integrated Neural Network and SEIR Model to Predict COVID-19

by Sharif Noor Zisad, Mohammad Shahadat Hossain, Mohammed Sazzad Hossain and Karl Andersson

Algorithms 2021, 14(3), 94; https://doi.org/10.3390/a14030094 - 19 Mar 2021

Cited by 32 | Viewed by 5599

Abstract

A novel coronavirus (COVID-19), which has become a great concern for the world, was identified first in Wuhan city in China. The rapid spread throughout the world was accompanied by an alarming number of infected patients and increasing number of deaths gradually. If [...] Read more.

A novel coronavirus (COVID-19), which has become a great concern for the world, was identified first in Wuhan city in China. The rapid spread throughout the world was accompanied by an alarming number of infected patients and increasing number of deaths gradually. If the number of infected cases can be predicted in advance, it would have a large contribution to controlling this pandemic in any area. Therefore, this study introduces an integrated model for predicting the number of confirmed cases from the perspective of Bangladesh. Moreover, the number of quarantined patients and the change in basic reproduction rate (the R $_{0}$ -value) can also be evaluated using this model. This integrated model combines the SEIR (Susceptible, Exposed, Infected, Removed) epidemiological model and neural networks. The model was trained using available data from 250 days. The accuracy of the prediction of confirmed cases is almost between 90% and 99%. The performance of this integrated model was evaluated by showing the difference in accuracy between the integrated model and the general SEIR model. The result shows that the integrated model is more accurate than the general SEIR model while predicting the number of confirmed cases in Bangladesh. Full article

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12 pages, 393 KiB

Open AccessEditor’s ChoiceArticle

UAV Formation Shape Control via Decentralized Markov Decision Processes

by Md Ali Azam, Hans D. Mittelmann and Shankarachary Ragi

Algorithms 2021, 14(3), 91; https://doi.org/10.3390/a14030091 - 17 Mar 2021

Cited by 17 | Viewed by 3976

Abstract

In this paper, we present a decentralized unmanned aerial vehicle (UAV) swarm formation control approach based on a decision theoretic approach. Specifically, we pose the UAV swarm motion control problem as a decentralized Markov decision process (Dec-MDP). Here, the goal is to drive [...] Read more.

In this paper, we present a decentralized unmanned aerial vehicle (UAV) swarm formation control approach based on a decision theoretic approach. Specifically, we pose the UAV swarm motion control problem as a decentralized Markov decision process (Dec-MDP). Here, the goal is to drive the UAV swarm from an initial geographical region to another geographical region where the swarm must form a three-dimensional shape (e.g., surface of a sphere). As most decision-theoretic formulations suffer from the curse of dimensionality, we adapt an existing fast approximate dynamic programming method called nominal belief-state optimization (NBO) to approximately solve the formation control problem. We perform numerical studies in MATLAB to validate the performance of the above control algorithms. Full article

(This article belongs to the Special Issue Algorithms in Stochastic Models)

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15 pages, 367 KiB

Open AccessEditor’s ChoiceArticle

An Improved Greedy Heuristic for the Minimum Positive Influence Dominating Set Problem in Social Networks

by Salim Bouamama and Christian Blum

Algorithms 2021, 14(3), 79; https://doi.org/10.3390/a14030079 - 28 Feb 2021

Cited by 16 | Viewed by 5299

Abstract

This paper presents a performance comparison of greedy heuristics for a recent variant of the dominating set problem known as the minimum positive influence dominating set (MPIDS) problem. This APX-hard combinatorial optimization problem has applications in social networks. Its aim is to identify [...] Read more.

This paper presents a performance comparison of greedy heuristics for a recent variant of the dominating set problem known as the minimum positive influence dominating set (MPIDS) problem. This APX-hard combinatorial optimization problem has applications in social networks. Its aim is to identify a small subset of key influential individuals in order to facilitate the spread of positive influence in the whole network. In this paper, we focus on the development of a fast and effective greedy heuristic for the MPIDS problem, because greedy heuristics are an essential component of more sophisticated metaheuristics. Thus, the development of well-working greedy heuristics supports the development of efficient metaheuristics. Extensive experiments conducted on a wide range of social networks and complex networks confirm the overall superiority of our greedy algorithm over its competitors, especially when the problem size becomes large. Moreover, we compare our algorithm with the integer linear programming solver CPLEX. While the performance of CPLEX is very strong for small and medium-sized networks, it reaches its limits when being applied to the largest networks. However, even in the context of small and medium-sized networks, our greedy algorithm is only 2.53% worse than CPLEX. Full article

(This article belongs to the Special Issue 2021 Selected Papers from Algorithms Editorial Board Members)

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31 pages, 2431 KiB

Open AccessEditor’s ChoiceArticle

Solution Merging in Matheuristics for Resource Constrained Job Scheduling

by Dhananjay Thiruvady, Christian Blum and Andreas T. Ernst

Algorithms 2020, 13(10), 256; https://doi.org/10.3390/a13100256 - 9 Oct 2020

Cited by 13 | Viewed by 3541

Abstract

Matheuristics have been gaining in popularity for solving combinatorial optimisation problems in recent years. This new class of hybrid method combines elements of both mathematical programming for intensification and metaheuristic searches for diversification. A recent approach in this direction has been to build [...] Read more.

Matheuristics have been gaining in popularity for solving combinatorial optimisation problems in recent years. This new class of hybrid method combines elements of both mathematical programming for intensification and metaheuristic searches for diversification. A recent approach in this direction has been to build a neighbourhood for integer programs by merging information from several heuristic solutions, namely construct, solve, merge and adapt (CMSA). In this study, we investigate this method alongside a closely related novel approach—merge search (MS). Both methods rely on a population of solutions, and for the purposes of this study, we examine two options: (a) a constructive heuristic and (b) ant colony optimisation (ACO); that is, a method based on learning. These methods are also implemented in a parallel framework using multi-core shared memory, which leads to improving the overall efficiency. Using a resource constrained job scheduling problem as a test case, different aspects of the algorithms are investigated. We find that both methods, using ACO, are competitive with current state-of-the-art methods, outperforming them for a range of problems. Regarding MS and CMSA, the former seems more effective on medium-sized problems, whereas the latter performs better on large problems. Full article

(This article belongs to the Special Issue Algorithms for Graphs and Networks)

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12 pages, 247 KiB

Open AccessEditor’s ChoiceArticle

The Use of an Exact Algorithm within a Tabu Search Maximum Clique Algorithm

by Derek H. Smith, Roberto Montemanni and Stephanie Perkins

Algorithms 2020, 13(10), 253; https://doi.org/10.3390/a13100253 - 4 Oct 2020

Cited by 7 | Viewed by 2812

Abstract

Let

G = (V, E)

be an undirected graph with vertex set V and edge set E. A clique C of G is a subset of the vertices of V with every pair of vertices of C adjacent. A [...] Read more.

Let

G = (V, E)

be an undirected graph with vertex set V and edge set E. A clique C of G is a subset of the vertices of V with every pair of vertices of C adjacent. A maximum clique is a clique with the maximum number of vertices. A tabu search algorithm for the maximum clique problem that uses an exact algorithm on subproblems is presented. The exact algorithm uses a graph coloring upper bound for pruning, and the best such algorithm to use in this context is considered. The final tabu search algorithm successfully finds the optimal or best known solution for all standard benchmarks considered. It is compared with a state-of-the-art algorithm that does not use exact search. It is slower to find the known optimal solution for most instances but is faster for five instances and finds a larger clique for two instances. Full article

(This article belongs to the Special Issue Algorithms for Graphs and Networks)

18 pages, 404 KiB

Open AccessEditor’s ChoiceArticle

A Survey on Shortest Unique Substring Queries

by Paniz Abedin, M. Oğuzhan Külekci and Shama V. Thankachan

Algorithms 2020, 13(9), 224; https://doi.org/10.3390/a13090224 - 6 Sep 2020

Cited by 4 | Viewed by 3471

Abstract

The shortest unique substring (SUS) problem is an active line of research in the field of string algorithms and has several applications in bioinformatics and information retrieval. The initial version of the problem was proposed by Pei et al. [ICDE’13]. Over the years, [...] Read more.

The shortest unique substring (SUS) problem is an active line of research in the field of string algorithms and has several applications in bioinformatics and information retrieval. The initial version of the problem was proposed by Pei et al. [ICDE’13]. Over the years, many variants and extensions have been pursued, which include positional-SUS, interval-SUS, approximate-SUS, palindromic-SUS, range-SUS, etc. In this article, we highlight some of the key results and summarize the recent developments in this area. Full article

(This article belongs to the Special Issue Algorithms in Bioinformatics)

17 pages, 471 KiB

Open AccessEditor’s ChoiceArticle

Exact Method for Generating Strategy-Solvable Sudoku Clues

by Kohei Nishikawa and Takahisa Toda

Algorithms 2020, 13(7), 171; https://doi.org/10.3390/a13070171 - 16 Jul 2020

Viewed by 10211

Abstract

A Sudoku puzzle often has a regular pattern in the arrangement of initial digits and it is typically made solvable with known solving techniques called strategies. In this paper, we consider the problem of generating such Sudoku instances. We introduce a rigorous framework [...] Read more.

A Sudoku puzzle often has a regular pattern in the arrangement of initial digits and it is typically made solvable with known solving techniques called strategies. In this paper, we consider the problem of generating such Sudoku instances. We introduce a rigorous framework to discuss solvability for Sudoku instances with respect to strategies. This allows us to handle not only known strategies but also general strategies under a few reasonable assumptions. We propose an exact method for determining Sudoku clues for a given set of clue positions that is solvable with a given set of strategies. This is the first exact method except for a trivial brute-force search. Besides the clue generation, we present an application of our method to the problem of determining the minimum number of strategy-solvable Sudoku clues. We conduct experiments to evaluate our method, varying the position and the number of clues at random. Our method terminates within 1 min for many grids. However, as the number of clues gets closer to 20, the running time rapidly increases and exceeds the time limit set to 600 s. We also evaluate our method for several instances with 17 clue positions taken from known minimum Sudokus to see the efficiency for deciding unsolvability. Full article

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24 pages, 1066 KiB

Open AccessEditor’s ChoiceArticle

Binary Time Series Classification with Bayesian Convolutional Neural Networks When Monitoring for Marine Gas Discharges

by Kristian Gundersen, Guttorm Alendal, Anna Oleynik and Nello Blaser

Algorithms 2020, 13(6), 145; https://doi.org/10.3390/a13060145 - 19 Jun 2020

Cited by 12 | Viewed by 4976

Abstract

The world’s oceans are under stress from climate change, acidification and other human activities, and the UN has declared 2021–2030 as the decade for marine science. To monitor the marine waters, with the purpose of detecting discharges of tracers from unknown locations, large [...] Read more.

The world’s oceans are under stress from climate change, acidification and other human activities, and the UN has declared 2021–2030 as the decade for marine science. To monitor the marine waters, with the purpose of detecting discharges of tracers from unknown locations, large areas will need to be covered with limited resources. To increase the detectability of marine gas seepage we propose a deep probabilistic learning algorithm, a Bayesian Convolutional Neural Network (BCNN), to classify time series of measurements. The BCNN will classify time series to belong to a leak/no-leak situation, including classification uncertainty. The latter is important for decision makers who must decide to initiate costly confirmation surveys and, hence, would like to avoid false positives. Results from a transport model are used for the learning process of the BCNN and the task is to distinguish the signal from a leak hidden within the natural variability. We show that the BCNN classifies time series arising from leaks with high accuracy and estimates its associated uncertainty. We combine the output of the BCNN model, the posterior predictive distribution, with a Bayesian decision rule showcasing how the framework can be used in practice to make optimal decisions based on a given cost function. Full article

(This article belongs to the Special Issue Methods and Applications of Uncertainty Quantification in Engineering and Science)

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26 pages, 388 KiB

Open AccessEditor’s ChoiceArticle

Late Acceptance Hill-Climbing Matheuristic for the General Lot Sizing and Scheduling Problem with Rich Constraints

by Andreas Goerler, Eduardo Lalla-Ruiz and Stefan Voß

Algorithms 2020, 13(6), 138; https://doi.org/10.3390/a13060138 - 9 Jun 2020

Cited by 13 | Viewed by 4738

Abstract

This paper considers the general lot sizing and scheduling problem with rich constraints exemplified by means of rework and lifetime constraints for defective items (GLSP-RP), which finds numerous applications in industrial settings, for example, the food processing industry and the pharmaceutical industry. To [...] Read more.

This paper considers the general lot sizing and scheduling problem with rich constraints exemplified by means of rework and lifetime constraints for defective items (GLSP-RP), which finds numerous applications in industrial settings, for example, the food processing industry and the pharmaceutical industry. To address this problem, we propose the Late Acceptance Hill-climbing Matheuristic (LAHCM) as a novel solution framework that exploits and integrates the late acceptance hill climbing algorithm and exact approaches for speeding up the solution process in comparison to solving the problem by means of a general solver. The computational results show the benefits of incorporating exact approaches within the LAHCM template leading to high-quality solutions within short computational times. Full article

(This article belongs to the Special Issue Optimization Algorithms for Allocation Problems)

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30 pages, 871 KiB

Open AccessEditor’s ChoiceArticle

Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods

by Lucky O. Daniel, Caston Sigauke, Colin Chibaya and Rendani Mbuvha

Algorithms 2020, 13(6), 132; https://doi.org/10.3390/a13060132 - 26 May 2020

Cited by 14 | Viewed by 5172

Abstract

Wind offers an environmentally sustainable energy resource that has seen increasing global adoption in recent years. However, its intermittent, unstable and stochastic nature hampers its representation among other renewable energy sources. This work addresses the forecasting of wind speed, a primary input needed [...] Read more.

Wind offers an environmentally sustainable energy resource that has seen increasing global adoption in recent years. However, its intermittent, unstable and stochastic nature hampers its representation among other renewable energy sources. This work addresses the forecasting of wind speed, a primary input needed for wind energy generation, using data obtained from the South African Wind Atlas Project. Forecasting is carried out on a two days ahead time horizon. We investigate the predictive performance of artificial neural networks (ANN) trained with Bayesian regularisation, decision trees based stochastic gradient boosting (SGB) and generalised additive models (GAMs). The results of the comparative analysis suggest that ANN displays superior predictive performance based on root mean square error (RMSE). In contrast, SGB shows outperformance in terms of mean average error (MAE) and the related mean average percentage error (MAPE). A further comparison of two forecast combination methods involving the linear and additive quantile regression averaging show the latter forecast combination method as yielding lower prediction accuracy. The additive quantile regression averaging based prediction intervals also show outperformance in terms of validity, reliability, quality and accuracy. Interval combination methods show the median method as better than its pure average counterpart. Point forecasts combination and interval forecasting methods are found to improve forecast performance. Full article

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19 pages, 570 KiB

Open AccessEditor’s ChoiceArticle

A Survey of Low-Rank Updates of Preconditioners for Sequences of Symmetric Linear Systems

by Luca Bergamaschi

Algorithms 2020, 13(4), 100; https://doi.org/10.3390/a13040100 - 21 Apr 2020

Cited by 14 | Viewed by 3990

Abstract

The aim of this survey is to review some recent developments in devising efficient preconditioners for sequences of symmetric positive definite (SPD) linear systems

A_{k} x_{k} = b_{k}, k = 1, \dots

arising in many scientific applications, such [...] Read more.

The aim of this survey is to review some recent developments in devising efficient preconditioners for sequences of symmetric positive definite (SPD) linear systems

A_{k} x_{k} = b_{k}, k = 1, \dots

arising in many scientific applications, such as discretization of transient Partial Differential Equations (PDEs), solution of eigenvalue problems, (Inexact) Newton methods applied to nonlinear systems, rational Krylov methods for computing a function of a matrix. In this paper, we will analyze a number of techniques of updating a given initial preconditioner by a low-rank matrix with the aim of improving the clustering of eigenvalues around 1, in order to speed-up the convergence of the Preconditioned Conjugate Gradient (PCG) method. We will also review some techniques to efficiently approximate the linearly independent vectors which constitute the low-rank corrections and whose choice is crucial for the effectiveness of the approach. Numerical results on real-life applications show that the performance of a given iterative solver can be very much enhanced by the use of low-rank updates. Full article

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Figure 1
Eigenvalues and angle between eigenvectors of A and IC-preconditioned A. Full article ">Figure 2
Number of iterations to solve each linear system in the sequence by MINRES with: no update, BFGS update with previous solutions, spectral update with previous solutions, BFGS update with leftmost eigenvectors. On the right also the CPU time is reported. Full article ">Figure 3
3D domain and triangulation. Full article ">Figure 4
Number of iterations for each linear system in the sequence and various preconditioning strategies. Initial preconditioner: IC (<math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mn>1</mn> </msub> <mo>,</mo> <mn>1</mn> <mi>e</mi> <mo>−</mo> <mn>5</mn> </mrow> </semantics></math>) (upper plots) and IC (<math display="inline"><semantics> <mrow> <msub> <mi>A</mi> <mn>2</mn> </msub> <mo>,</mo> <mn>1</mn> <mi>e</mi> <mo>−</mo> <mn>4</mn> </mrow> </semantics></math>) lower plots. Full article ">Figure 5
Number of iterations for the Newton phase with fixed, SR1 tuned and generalized block tuned (GBT) preconditioners. In red the (scaled) logarithm of the indicator <math display="inline"><semantics> <msub> <mi>ξ</mi> <mi>j</mi> </msub> </semantics></math>. Full article ">

19 pages, 11395 KiB

Open AccessEditor’s ChoiceArticle

How to Identify Varying Lead–Lag Effects in Time Series Data: Implementation, Validation, and Application of the Generalized Causality Algorithm

by Johannes Stübinger and Katharina Adler

Algorithms 2020, 13(4), 95; https://doi.org/10.3390/a13040095 - 16 Apr 2020

Cited by 3 | Viewed by 5936

Abstract

This paper develops the generalized causality algorithm and applies it to a multitude of data from the fields of economics and finance. Specifically, our parameter-free algorithm efficiently determines the optimal non-linear mapping and identifies varying lead–lag effects between two given time series. This [...] Read more.

This paper develops the generalized causality algorithm and applies it to a multitude of data from the fields of economics and finance. Specifically, our parameter-free algorithm efficiently determines the optimal non-linear mapping and identifies varying lead–lag effects between two given time series. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences—structural breaks in their relationship are also captured. A large-scale simulation study validates the outperformance in the vast majority of parameter constellations in terms of efficiency, robustness, and feasibility. Finally, the presented methodology is applied to real data from the areas of macroeconomics, finance, and metal. Highest similarity show the pairs of gross domestic product and consumer price index (macroeconomics), S&P 500 index and Deutscher Aktienindex (finance), as well as gold and silver (metal). In addition, the algorithm takes full use of its flexibility and identifies both various structural breaks and regime patterns over time, which are (partly) well documented in the literature. Full article

(This article belongs to the Special Issue Mathematical Models and Their Applications)

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9 pages, 2485 KiB

Open AccessEditor’s ChoiceArticle

Beyond Newton: A New Root-Finding Fixed-Point Iteration for Nonlinear Equations

by Ankush Aggarwal and Sanjay Pant

Algorithms 2020, 13(4), 78; https://doi.org/10.3390/a13040078 - 29 Mar 2020

Cited by 3 | Viewed by 5340

Abstract

Finding roots of equations is at the heart of most computational science. A well-known and widely used iterative algorithm is Newton’s method. However, its convergence depends heavily on the initial guess, with poor choices often leading to slow convergence or even divergence. In [...] Read more.

Finding roots of equations is at the heart of most computational science. A well-known and widely used iterative algorithm is Newton’s method. However, its convergence depends heavily on the initial guess, with poor choices often leading to slow convergence or even divergence. In this short note, we seek to enlarge the basin of attraction of the classical Newton’s method. The key idea is to develop a relatively simple multiplicative transform of the original equations, which leads to a reduction in nonlinearity, thereby alleviating the limitation of Newton’s method. Based on this idea, we derive a new class of iterative methods and rediscover Halley’s method as the limit case. We present the application of these methods to several mathematical functions (real, complex, and vector equations). Across all examples, our numerical experiments suggest that the new methods converge for a significantly wider range of initial guesses. For scalar equations, the increase in computational cost per iteration is minimal. For vector functions, more extensive analysis is needed to compare the increase in cost per iteration and the improvement in convergence of specific problems. Full article

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65 pages, 1890 KiB

Open AccessEditor’s ChoiceReview

Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey

by Christos Nakas, Dionisis Kandris and Georgios Visvardis

Algorithms 2020, 13(3), 72; https://doi.org/10.3390/a13030072 - 24 Mar 2020

Cited by 102 | Viewed by 14873

Abstract

Wireless Sensor Networks (WSNs) are among the most emerging technologies, thanks to their great capabilities and their ever growing range of applications. However, the lifetime of WSNs is extremely restricted due to the delimited energy capacity of their sensor nodes. This is why [...] Read more.

Wireless Sensor Networks (WSNs) are among the most emerging technologies, thanks to their great capabilities and their ever growing range of applications. However, the lifetime of WSNs is extremely restricted due to the delimited energy capacity of their sensor nodes. This is why energy conservation is considered as the most important research concern for WSNs. Radio communication is the utmost energy consuming function in a WSN. Thus, energy efficient routing is necessitated to save energy and thus prolong the lifetime of WSNs. For this reason, numerous protocols for energy efficient routing in WSNs have been proposed. This article offers an analytical and up to date survey on the protocols of this kind. The classic and modern protocols presented are categorized, depending on i) how the network is structured, ii) how data are exchanged, iii) whether location information is or not used, and iv) whether Quality of Service (QoS) or multiple paths are or not supported. In each distinct category, protocols are both described and compared in terms of specific performance metrics, while their advantages and disadvantages are discussed. Finally, the study findings are discussed, concluding remarks are drawn, and open research issues are indicated. Full article

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17 pages, 2260 KiB

Open AccessEditor’s ChoiceArticle

Observability of Uncertain Nonlinear Systems Using Interval Analysis

by Thomas Paradowski, Sabine Lerch, Michelle Damaszek, Robert Dehnert and Bernd Tibken

Algorithms 2020, 13(3), 66; https://doi.org/10.3390/a13030066 - 16 Mar 2020

Cited by 10 | Viewed by 4364

Abstract

In the field of control engineering, observability of uncertain nonlinear systems is often neglected and not examined. This is due to the complex analytical calculations required for the verification. Therefore, the aim of this work is to provide an algorithm which numerically analyzes [...] Read more.

In the field of control engineering, observability of uncertain nonlinear systems is often neglected and not examined. This is due to the complex analytical calculations required for the verification. Therefore, the aim of this work is to provide an algorithm which numerically analyzes the observability of nonlinear systems described by finite-dimensional, continuous-time sets of ordinary differential equations. The algorithm is based on definitions for distinguishability and local observability using a rank check from which conditions are deduced. The only requirements are the uncertain model equations of the system. Further, the methodology verifies observability of nonlinear systems on a given state space. In case that the state space is not fully observable, the algorithm provides the observable set of states. In addition, the results obtained by the algorithm allows insight into why the remaining states cannot be distinguished. Full article

(This article belongs to the Special Issue Algorithms for Reliable Estimation, Identification and Control)

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12 pages, 368 KiB

Open AccessEditor’s ChoiceArticle

Optimization of Constrained Stochastic Linear-Quadratic Control on an Infinite Horizon: A Direct-Comparison Based Approach

by Ruobing Xue, Xiangshen Ye and Weiping Wu

Algorithms 2020, 13(2), 49; https://doi.org/10.3390/a13020049 - 24 Feb 2020

Viewed by 3727

Abstract

In this paper we study the optimization of the discrete-time stochastic linear-quadratic (LQ) control problem with conic control constraints on an infinite horizon, considering multiplicative noises. Stochastic control systems can be formulated as Markov Decision Problems (MDPs) with continuous state spaces and therefore [...] Read more.

In this paper we study the optimization of the discrete-time stochastic linear-quadratic (LQ) control problem with conic control constraints on an infinite horizon, considering multiplicative noises. Stochastic control systems can be formulated as Markov Decision Problems (MDPs) with continuous state spaces and therefore we can apply the direct-comparison based optimization approach to solve the problem. We first derive the performance difference formula for the LQ problem by utilizing the state separation property of the system structure. Based on this, we successfully derive the optimality conditions and the stationary optimal feedback control. By introducing the optimization, we establish a general framework for infinite horizon stochastic control problems. The direct-comparison based approach is applicable to both linear and nonlinear systems. Our work provides a new perspective in LQ control problems; based on this approach, learning based algorithms can be developed without identifying all of the system parameters. Full article

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13 pages, 6398 KiB

Open AccessEditor’s ChoiceArticle

Optimal Learning and Self-Awareness Versus PDI

by Brendon Smeresky, Alex Rizzo and Timothy Sands

Algorithms 2020, 13(1), 23; https://doi.org/10.3390/a13010023 - 11 Jan 2020

Cited by 32 | Viewed by 6799

Abstract

This manuscript will explore and analyze the effects of different paradigms for the control of rigid body motion mechanics. The experimental setup will include deterministic artificial intelligence composed of optimal self-awareness statements together with a novel, optimal learning algorithm, and these will be [...] Read more.

This manuscript will explore and analyze the effects of different paradigms for the control of rigid body motion mechanics. The experimental setup will include deterministic artificial intelligence composed of optimal self-awareness statements together with a novel, optimal learning algorithm, and these will be re-parameterized as ideal nonlinear feedforward and feedback evaluated within a Simulink simulation. Comparison is made to a custom proportional, derivative, integral controller (modified versions of classical proportional-integral-derivative control) implemented as a feedback control with a specific term to account for the nonlinear coupled motion. Consistent proportional, derivative, and integral gains were used throughout the duration of the experiments. The simulation results will show that akin feedforward control, deterministic self-awareness statements lack an error correction mechanism, relying on learning (which stands in place of feedback control), and the proposed combination of optimal self-awareness statements and a newly demonstrated analytically optimal learning yielded the highest accuracy with the lowest execution time. This highlights the potential effectiveness of a learning control system. Full article

(This article belongs to the Special Issue Algorithms for PID Controller 2019)

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