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Neutrosophic set and optimized deep learning for classification of chicken Eimeria species: a practical solution for poultry industry

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2024年 1-28页

作者： Sayed, Gehad Ismail Hassanien, Aboul Ella Basha, Sameh H. Canadian Int Coll CIC Sch Comp Sci Cairo Egypt Cairo Univ Fac Comp & Artificial Intelligence Giza 12613 Egypt Cairo Univ Fac Sci Giza 12613 Egypt Galala Univ Fac Sci Suez 43511 Egypt Sci Res Sch Egypt SRSEG Cairo Egypt

To optimize infection control and bolster productivity within the poultry industry, it is imperative to accurately classify Chicken Eimeria species. There are several methods for determining Eimeria disease in chickens. Traditional methods involve watching for clinical symptoms, and macroscopic lesions, and studying the parasite's biology and oocyst morphology. These methods are frequently time-consuming and labor-intensive, necessitating the manual collection and analysis of samples, which can be especially difficult in large chicken farms. Deep learning algorithms, on the other hand, provide automated, accurate, and non-invasive methods for the detection of Eimeria. This paper proposed a classification model for the automatic classification of chicken Eimeria species. The proposed model is mainly based on integrating neutrosophic set theory and InceptionV3 deep-learning architecture. Three primary phases make up the proposed chicken Eimeria species classification model: the data preprocessing phase, the neutrosophic image conversion phase, and the image classification phase. To address the issue of class imbalance in the adopted dataset and enhance the model's generalizability, the random oversampling method, and data augmentation techniques are employed during the data preprocessing phase. The preprocessed data is considered to feed the neutrosophic set-based segmentation algorithm, where true, false, and intermediate subsets are extracted. Finally, the true subset is utilized to feed the optimized InceptionV3. To determine the optimal hyperparameter values for InceptionV3, a modified version of the Brown Bear optimization algorithm is proposed in this paper. To evaluate the effectiveness of the proposed model, a real benchmark dataset comprising images of different Eimeria species is adopted. The experimental results revealed that the proposed model offers a more efficient and accurate alternative to traditional methods and state-of-the-art models, enabling fast

关键词： Sustainable development goals Poultry industry Neutrosophic set theory Deep learning Swarm optimization algorithms hyperparameters optimization

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Cloud-load forecasting via decomposition-aided attention recurrent neural network tuned by modified particle swarm optimization

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COMPLEX & INTELLIGENT SYSTEMS 2024年第2期10卷 2249-2269页

作者： Predic, Bratislav Jovanovic, Luka Simic, Vladimir Bacanin, Nebojsa Zivkovic, Miodrag Spalevic, Petar Budimirovic, Nebojsa Dobrojevic, Milos Univ Nis Fac Elect Engn Nis Aleksandra Medvedeva 14 Nish 18000 Serbia Singidunum Univ Fac Tech Sci Danijelova 32 Belgrade 11000 Serbia Univ Belgrade Fac Transport & Traff Engn Vojvode Stepe 305 Belgrade 161904 Serbia Singidunum Univ Dept Informat & Comp Danijelova 32 Belgrade 11000 Serbia Univ Pristina Kosovska Mitrov Fac Tech Sci Filipa Visnj bb Kosovska Mitrovica 38220 Serbia

Recent improvements in networking technologies have led to a significant shift towards distributed cloud-based services. However, adequate management of computation resources by providers is vital to maintain the costs of operations and quality of services. A robust system is needed to forecast demand and prevent excessive resource allocations. Extensive literature review suggests that the potential of recurrent neural networks with attention mechanisms is not sufficiently explored and applied to cloud computing. To address this gap, this work proposes a methodology for forecasting load of cloud resources based on recurrent neural networks with and without attention layers. Utilized deep learning models are further optimized through hyperparameter tuning using a modified particle swarm optimization metaheuristic, which is also introduced in this work. To help models deal with complex non-stationary data sequences, the variational mode decomposition for decomposing complex series has also been utilized. The performance of this approach is compared to several state-of-the-art algorithms on a real-world cloud-load dataset. Captured performance metrics (R-2, mean square error, root mean square error, and index of agreement) strongly indicate that the proposed method has great potential for accurately forecasting cloud load. Further, models optimized by the introduced metaheuristic outperformed competing approaches, which was confirmed by conducted statistical validation. In addition, the best-performing forecasting model has been subjected to SHapley Additive exPlanations analysis to determine the impact each feature has on model forecasts, which could potentially be a very useful tool for cloud providers when making decisions.

关键词： Cloud-load forecasting Recurrent neural networks Attention layers Particle swarm optimization hyperparameters optimization Variational mode decomposition.

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RETRACTED: Breast cancer diagnosis using Orca predation optimization algorithm (Retracted Article)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期45卷 3855-3873页

作者： Kaladevi, P. Punitha, V. V. Muthusankar, D. Praveen, R. KS Rangasamy Coll Technol Dept Comp Sci & Engn Tiruchengode Tamil Nadu India Anna Univ Madras Inst Technol Campus Dept Comp Technol Chennai Tamil Nadu India

Early detection and classification of breast cancer can be facilitated to initiate the most effective treatment. As the second leading cause of death among women, early breast cancer screening is essential for reducing mortality rates. In this context, Convolutional neural networks (CNNs) are the ideal candidate for increasing the rate of identification and classification of tumours with efficiency, particularly in medical imaging. This research proposes a hybridised CNN with the Orca Predation optimization Algorithm (OPOA) as a novel classification model for the effective detection of abnormalities in breast cancer diagnosis. Specifically, the OPOA technique is used to determine the optimal hyperparameter values for the hybrid CNN architecture being deployed. As the pretrained CNN model, the suggested model utilizeds a ResNet50 residual network. It merged OPOA with the ResNet50 residual network to construct the OPOA-ResNet-50 Architecture. The experimental validation of the proposed OPOA-ResNet-50 model utilising the datasets of curated breast imaging subset of DDSM (CBIS-DDSM) shown improved classification accuracy of 99.04%, specificity of 98.56%, and sensitivity of 97.78% in comparison to the baseline techniques. The results also revealed that the proposed under mammographic image analysis society (MIAS) OPOA-ResNet-50 model demonstrated superior classification accuracy of 98.64%, specificity of 98.79%, and sensitivity of 98.82% compared to the benchmarked methods. The adopted OPOA algorithm is determined to achieve more optimal hyperparameter values for the ResNet50 architecture than the comparative algorithms Improved Marine Predator optimization Algorithm (IMPOA), Whale optimization Algorithm (WOA), Harris hawk's optimization (HHO), and gravitational search algorithm (GSA).

关键词： Deep Learning Architecture ResNet-50 model Convolutional neural networks (CNNs) hyperparameters optimization Orca Predation optimization Algorithm (OPOA)

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Automatic search of architecture and hyperparameters of graph convolutional networks for node classification

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APPLIED INTELLIGENCE 2023年第9期53卷 11104-11119页

作者： Liu, Yang Liu, Jing Li, Yifan Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Guangdong Peoples R China

Graph neural networks (GNNs) rely heavily on architecture design and artificial hyperparameters, often resulting in expensive manual effort and poor performance. Recently, automated machine learning (AutoML) on graphs is a promising approach for automated neural network design that is gaining attention from the research community. The existing AutoML methods for GNNs usually preset the number of layers to a small, fixed number, e.g., no more than three layers, possibly due to the oversmoothing problem. However, it is well known that deeper network models facilitate the extraction of high-level information representations. To this end, using the genetic algorithm (GA) as a framework, in this paper we propose a GNN search method (called GCN-GA) that dynamically searches the depth of the model to efficiently handle the node classification tasks. First, a variable-length encoding strategy is proposed to use the constituent units of four graph convolutional network (GCN) structures with different topologies as building blocks for representing the network architecture. Second, considering that the search method of searching network architectures with fixed hyperparameters and then searching hyperparameters independently may result in the obtained network models be not optimal, GCN-GA represents hyperparameters in the form of fixed-length encoding. They form a hybrid encoding strategy for representing the network architecture and hyperparameters. Then, the GCN-GA simultaneously searches both the network architecture and hyperparameters during the evolution. In addition, for the variable-length encoding, an improved two-point crossover operator, and three types of variation operators are designed for the evolutionary process. Finally, experiments are conducted on three widely used node classification datasets, namely, Cora, CiteSeer, and PubMed, in the semi-supervised and supervised tasks. The experimental results show that GCN-GA achieves more effective classification accu

关键词： Graph convolutional network Automated machine learning Neural architecture search hyperparameters optimization Genetic algorithms

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A novel short-term wind speed prediction method based on hybrid statistical-artificial intelligence model with empirical wavelet transform and hyperparameter optimization

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JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS 2023年第1期240卷

作者： Yang, Qingshan Huang, Guoqing Li, Tian Xu, Yifan Pan, Jie Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Chongqing Key Lab Wind Engn & Wind Energy Utilizat Chongqing 400045 Peoples R China

The short-term wind speed prediction is important for the connection of wind power to the grid. However, it is challenging due to the stochastic and intermittent characteristics of wind speed. In this paper, a novel wind speed prediction method named as EWT-ARIMA-LSSVM-GPR-DE-GWO method is proposed, where the empirical wavelet transform (EWT) is applied to decompose the original wind speed signal into multiple intrinsic mode functions (IMF), and various prediction models including the statistical model of autoregressive integrated moving average (ARIMA), artificial intelligence models of least squares support vector machine (LSSVM) and Gaussian process regression (GPR) are used for predicting the IMF in sequence from low frequency to high frequency based on the superiority of each model. Differential evolution-grey wolf optimizer (DE-GWO) algo-rithm is used to optimize the hyperparameters of artificial intelligence prediction models of LSSVM and GPR. The proposed short-term wind speed prediction method shows superior performance in both accuracy and stability with validation by the measurement data. Both the EWT signal decomposition and DE-GWO hyperparameter optimization are effective to promote the prediction accuracy for wind speeds with large non-stationary.

关键词： Short-term wind speed prediction Empirical wavelet transform Statistical prediction model Artificial intelligence prediction model hyperparameters optimization

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From multi-physics models to neural network for predictive control synthesis

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OPTIMAL CONTROL APPLICATIONS & METHODS 2023年第3期44卷 1394-1411页

作者： Blaud, Pierre Clement Chevrel, Philippe Claveau, Fabien Haurant, Pierrick Mouraud, Anthony CEA Tech Pays de la Loire CEA F-44340 Bouguenais France IMT Atlantique LS2N UMR CNRS 6004 Nantes France IMT Atlantique GEPEA UMR CNRS 6144 Nantes France

The aim of this document is to present an efficient and systematic method of model-based predictive control synthesis. Model predictive control requires using a model of a dynamical system, that can be linear, time-varying, non-linear, or identified from data. Finding a model that is both precise and simulatable at low computational cost can be challenging and time consuming due to requiring extensive knowledge of the system and physics as well as a large volume of data with relevant scenarios and sometimes a complicated identification work. (filtering noises and bias, data formatting, etc.) The proposed methodology begins with fine-scale multi-physics modeling, which is possible thanks to open model libraries (see Modelica). The obtained model is then simulated by considering ad hoc scenarios to generate data, which are then used to identify a neural network, that will support the predictive control syntheses. The systematic methodology is detailed and applied to the widely used control benchmark known as the quadruple tanks process. Results show that the methodology is accurately applied to optimize hyperparameters in finding a neural network model and to control the quadruple tanks process with the predictive controller.

关键词： economic model predictive control feedforward neural network hyperparameters optimization model identification Modelica

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A novel sample and feature dependent ensemble approach for Parkinson's disease detection

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NEURAL COMPUTING & APPLICATIONS 2023年第22期35卷 15997-16010页

作者： Ali, Liaqat Chakraborty, Chinmay He, Zhiquan Cao, Wenming Imrana, Yakubu Rodrigues, Joel J. P. C. Univ Sci & Technol Dept Elect Engn Bannu Pakistan Birla Inst Technol Dept Elect & Commun Engn Jharkhand India Shenzhen Univ Guangdong Multimedia Informat Serv Engn Technol R Shenzhen 518000 Peoples R China Univ Elect Sci & Technol China UESTC Sch Comp Sci & Engn Chengdu Peoples R China China Univ Petr East China Coll Comp Sci & Technol Qingdao 266555 Peoples R China Inst Telecomunicacoes Covilha Delegat Covilha Portugal

Parkinson's disease (PD) is a neurological disease that has been reported to have affected most people worldwide. Recent research pointed out that about 90% of PD patients possess voice disorders. Motivated by this fact, many researchers proposed methods based on multiple types of speech data for PD prediction. However, these methods either face the problem of low rate of accuracy or lack generalization. To develop an approach that will be free of these issues, in this paper we propose a novel ensemble approach. These paper contributions are two folds. First, investigating feature selection integration with deep neural network (DNN) and validating its effectiveness by comparing its performance with conventional DNN and other similar integrated systems. Second, development of a novel ensemble model namely EOFSC (Ensemble model with Optimal Features and Sample Dependant Base Classifiers) that exploits the findings of recently published studies. Recent research pointed out that for different types of voice data, different optimal models are obtained which are sensitive to different types of samples and subsets of features. In this paper, we further consolidate the findings by utilizing the proposed integrated system and propose the development of EOFSC. For multiple types of vowel phonations, multiple base classifiers are obtained which are sensitive to different subsets of features. These features and sample-dependent base classifiers are integrated, and the proposed EOFSC model is constructed. To evaluate the final prediction of the EOFSC model, the majority voting methodology is adopted. Experimental results point out that feature selection integration with neural networks improves the performance of conventional neural networks. Additionally, feature selection integration with DNN outperforms feature selection integration with conventional machine learning models. Finally, the newly developed ensemble model is observed to improve PD detection accuracy by 6.5%.

关键词： Ensemble learning F-score based feature selection hyperparameters optimization deep neural networks Parkinson's disease

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Enhanced Deep Deterministic Policy Gradient Algorithm Using Grey Wolf Optimizer for Continuous Control Tasks

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IEEE ACCESS 2023年 11卷 139771-139784页

作者： Sumiea, Ebrahim Hamid Hasan Abdulkadir, Said Jadid Ragab, Mohammed Gamal Al-Selwi, Safwan Mahmood Fati, Suliamn Mohamed Alqushaibi, Alawi Alhussian, Hitham Univ Teknol PETRONAS Dept Comp & Informat Sci Seri Iskandar 32610 Malaysia Univ Teknol PETRONAS Ctr Res Data Sci CeRDaS Seri Iskandar 32610 Malaysia Prince Sultan Univ Informat Syst Dept Riyadh 11586 Saudi Arabia

Deep Reinforcement Learning (DRL) allows agents to make decisions in a specific environment based on a reward function, without prior knowledge. Adapting hyperparameters significantly impacts the learning process and time. Precise estimation of hyperparameters during DRL training poses a major challenge. To tackle this problem, this study utilizes Grey Wolf optimization (GWO), a metaheuristic algorithm, to optimize the hyperparameters of the Deep Deterministic Policy Gradient (DDPG) algorithm for achieving optimal control strategy in two simulated Gymnasium environments provided by OpenAI. The ability to adapt hyperparameters accurately contributes to faster convergence and enhanced learning, ultimately leading to more efficient control strategies. The proposed DDPG-GWO algorithm is evaluated in the 2DRobot and MountainCarContinuous simulation environments, chosen for their ease of implementation. Our experimental results reveal that optimizing the hyperparameters of the DDPG using the GWO algorithm in the Gymnasium environments maximizes the total rewards during testing episodes while ensuring the stability of the learning policy. This is evident in comparing our proposed DDPG-GWO agent with optimized hyperparameters and the original DDPG. In the 2DRobot environment, the original DDPG had rewards ranging from -150 to -50, whereas, in the proposed DDPG-GWO, they ranged from -100 to 100 with a running average between 1 and 800 across 892 episodes. In the MountainCarContinuous environment, the original DDPG struggled with negative rewards, while the proposed DDPG-GWO achieved rewards between 20 and 80 over 218 episodes with a total of 490 timesteps.

关键词： Deep deterministic policy gradient deep reinforcement learning grey wolf optimization hyperparameters optimization

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High-speed hyperparameter optimization for deep ResNet models in image recognition

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第5期26卷 2605-2613页

作者： Jafar, Abbas Lee, Myungho Myongji Univ Dept Comp Engn Yongin Kyungki Do South Korea

Convolutional Neural Network (CNN) is one of the most widely used deep learning models in pattern and image recognition. It can train a large number of datasets and get valuable results. The deep Residual Network (ResNet) is one of the most innovative CNN architecture to train thousands of layers or more and leads to high performance for complex problems. This deep model trains the neural networks with the idea of identity shortcut connection that skips layers. In this paper, we built a hyperparameter optimization approach for the ResNet models with different numbers of layers and show that our optimization leads to significant performance improvements. We developed a manual search approach by enhancing the traditional data augmentation proposed by the previous approaches. Using the CIFAR-100 dataset for classification, our approach significantly improves the classification error rate. It also reduces the computing time compared with the previous automatic approaches. Our approach leads to 23.01% error rate and 4 h and 15 min of computing time of ResNet-164 for the CIFAR-100 dataset using the NIVIDIA Tesla P100 GPU.

关键词： Image recognition Convolutional Neural Network Deep Residual Learning hyperparameters optimization

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A Novel Graph Convolutional Gated Recurrent Unit Framework for Network-Based Traffic Prediction

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IEEE ACCESS 2023年 11卷 130102-130118页

作者： Hussain, Basharat Afzal, Muhammad Khalil Anjum, Sheraz Rao, Imran Kim, Byung-Seo COMSATS Univ Islamabad Dept Comp Sci Wah Campus Wah Cantt 47040 Pakistan Riphah Int Univ Fac Comp Islamabad 44000 Pakistan Blue Brackets Technol Pvt Ltd Islamabad 44000 Pakistan Hongik Univ Dept Software & Commun Engn Sejong 30016 South Korea

A Smart City is characterized mainly as an efficient, technologically advanced, green, and socially informed city. An intelligent transportation system (ITS) is a subset area of smart cities that enhances the safety and mobility of road vehicles. It essentially makes travel more convenient, time-efficient and improves the citizens' quality of life. Accurate and real-time traffic prediction enables law enforcement agencies with well-informed about traffic congestion. However, accurate traffic prediction has been considered a challenging issue. Traffic prediction has restrictions on road network topology and the patterns of dynamic change in time-series data. We propose a novel deep learning framework GCST-GRU, called graph convolutional Spatio-temporal gated recurrent unit, to determine the next traffic state from traffic data. The proposed model learns complex topological structures by capturing a) spatial dependencies from data by using the graph convolution operator, and b) temporal dependencies by using the GRU neural network. Experimental results demonstrate that our framework can obtain complex Spatio-temporal correlations efficiently from the traffic network and perform better than state-of-the-art baseline models on a real-world traffic dataset. The graphical visualization by using convolution operation over the neural network shows that the model outperforms the reachability of the 3-hops neighbor effect in the traffic data graph. Additionally, the training time of the proposed framework is better than the existing state-of-the-art deep learning studies.

关键词： Intelligent transportation system traffic flow prediction traffic network graph gated recurrent unit hyperparameters optimization deep learning

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