Many problems can reduce handwritten character recognition performance, such as image degradation, light conditions, low-resolution images, and even the quality of the capture devices. However, in ...this research, we have focused on the noise in the character images that could decrease the accuracy of handwritten character recognition. Many types of noise penalties influence the recognition performance, for example, low resolution, Gaussian noise, low contrast, and blur. First, this research proposes a method that learns from the noisy handwritten character images and synthesizes clean character images using the robust deblur generative adversarial network (DeblurGAN). Second, we combine the DeblurGAN architecture with a convolutional neural network (CNN), called DeblurGAN-CNN. Subsequently, two state-of-the-art CNN architectures are combined with DeblurGAN, namely DeblurGAN-DenseNet121 and DeblurGAN-MobileNetV2, to address many noise problems and enhance the recognition performance of the handwritten character images. Finally, the DeblurGAN-CNN could transform the noisy characters to the new clean characters and recognize clean characters simultaneously. We have evaluated and compared the experimental results of the proposed DeblurGAN-CNN architectures with the existing methods on four handwritten character datasets: n-THI-C68, n-MNIST, THI-C68, and THCC-67. For the n-THI-C68 dataset, the DeblurGAN-CNN achieved above 98% and outperformed the other existing methods. For the n-MNIST, the proposed DeblurGAN-CNN achieved an accuracy of 97.59% when the AWGN+Contrast noise method was applied to the handwritten digits. We have evaluated the DeblurGAN-CNN on the THCC-67 dataset. The result showed that the proposed DeblurGAN-CNN achieved an accuracy of 80.68%, which is significantly higher than the existing method, approximately 10%.
This study presents a methodology that combines artificial multiple intelligence systems (AMISs) and machine learning to forecast the ultimate tensile strength (UTS), maximum hardness (MH), and heat ...input (HI) of AA-5083 and AA-6061 friction stir welding. The machine learning model integrates two machine learning methods, Gaussian process regression (GPR) and a support vector machine (SVM), into a single model, and then uses the AMIS as the decision fusion strategy to merge SVM and GPR. The generated model was utilized to anticipate three objectives based on seven controlled/input parameters. These parameters were: tool tilt angle, rotating speed, travel speed, shoulder diameter, pin geometry, type of reinforcing particles, and tool pin movement mechanism. The effectiveness of the model was evaluated using a two-experiment framework. In the first experiment, we used two newly produced datasets, (1) the 7PI-V1 dataset and (2) the 7PI-V2 dataset, and compared the results with state-of-the-art approaches. The second experiment used existing datasets from the literature with varying base materials and parameters. The computational results revealed that the proposed method produced more accurate prediction results than the previous methods. For all datasets, the proposed strategy outperformed existing methods and state-of-the-art processes by an average of 1.35% to 6.78%.
This research develops the TB/non-TB detection and drug-resistant categorization diagnosis decision support system (TB-DRC-DSS). The model is capable of detecting both TB-negative and TB-positive ...samples, as well as classifying drug-resistant strains and also providing treatment recommendations. The model is developed using a deep learning ensemble model with the various CNN architectures. These architectures include EfficientNetB7, mobileNetV2, and Dense-Net121. The models are heterogeneously assembled to create an effective model for TB-DRC-DSS, utilizing effective image segmentation, augmentation, and decision fusion techniques to improve the classification efficacy of the current model. The web program serves as the platform for determining if a patient is positive or negative for tuberculosis and classifying several types of drug resistance. The constructed model is evaluated and compared to current methods described in the literature. The proposed model was assessed using two datasets of chest X-ray (CXR) images collected from the references. This collection of datasets includes the Portal dataset, the Montgomery County dataset, the Shenzhen dataset, and the Kaggle dataset. Seven thousand and eight images exist across all datasets. The dataset was divided into two subsets: the training dataset (80%) and the test dataset (20%). The computational result revealed that the classification accuracy of DS-TB against DR-TB has improved by an average of 43.3% compared to other methods. The categorization between DS-TB and MDR-TB, DS-TB and XDR-TB, and MDR-TB and XDR-TB was more accurate than with other methods by an average of 28.1%, 6.2%, and 9.4%, respectively. The accuracy of the embedded multiclass model in the web application is 92.6% when evaluated with the test dataset, but 92.8% when evaluated with a random subset selected from the aggregate dataset. In conclusion, 31 medical staff members have evaluated and utilized the online application, and the final user preference score for the web application is 9.52 out of a possible 10.
Leaf abnormalities pose a significant threat to agricultural productivity, particularly in medicinal plants such as Centella asiatica (Linn.) Urban (CAU), where they can severely impact both the ...yield and the quality of leaf-derived substances. In this study, we focus on the early detection of such leaf diseases in CAU, a critical intervention for minimizing crop damage and ensuring plant health. We propose a novel parallel-Variable Neighborhood Strategy Adaptive Search (parallel-VaNSAS) ensemble deep learning method specifically designed for this purpose. Our approach is distinguished by a two-stage ensemble model, which combines the strengths of advanced image segmentation and Convolutional Neural Networks (CNNs) to detect leaf diseases with high accuracy and efficiency. In the first stage, we employ U-net, Mask-R-CNN, and DeepNetV3++ for the precise image segmentation of leaf abnormalities. This step is crucial for accurately identifying diseased regions, thereby facilitating a focused and effective analysis in the subsequent stage. The second stage utilizes ShuffleNetV2, SqueezeNetV2, and MobileNetV3, which are robust CNN architectures, to classify the segmented images into different categories of leaf diseases. This two-stage methodology significantly improves the quality of disease detection over traditional methods. By employing a combination of ensemble segmentation and diverse CNN models, we achieve a comprehensive and nuanced analysis of leaf diseases. Our model’s efficacy is further enhanced through the integration of four decision fusion strategies: unweighted average (UWA), differential evolution (DE), particle swarm optimization (PSO), and Variable Neighborhood Strategy Adaptive Search (VaNSAS). Through extensive evaluations of the ABL-1 and ABL-2 datasets, which include a total of 14,860 images encompassing eight types of leaf abnormalities, our model demonstrates its superiority. The ensemble segmentation method outperforms single-method approaches by 7.34%, and our heterogeneous ensemble model excels by 8.43% and 14.59% compared to the homogeneous ensemble and single models, respectively. Additionally, image augmentation contributes to a 5.37% improvement in model performance, and the VaNSAS strategy enhances solution quality significantly over other decision fusion methods. Overall, our novel parallel-VaNSAS ensemble deep learning method represents a significant advancement in the detection of leaf diseases in CAU, promising a more effective approach to maintaining crop health and productivity.
The classification of certain agricultural species poses a formidable challenge due to their inherent resemblance and the absence of dependable visual discriminators. The accurate identification of ...these plants holds substantial importance in industries such as cosmetics, pharmaceuticals, and herbal medicine, where the optimization of essential compound yields and product quality is paramount. In response to this challenge, we have devised an automated classification system based on deep learning principles, designed to achieve precision and efficiency in species classification. Our approach leverages a diverse dataset encompassing various cultivars and employs the Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning model (P-AMIS-E). This model integrates ensemble image segmentation techniques, including U-Net and Mask-R-CNN, alongside image augmentation and convolutional neural network (CNN) architectures such as SqueezeNet, ShuffleNetv2 1.0x, MobileNetV3, and InceptionV1. The culmination of these elements results in the P-AMIS-E model, enhanced by an Artificial Multiple Intelligence System (AMIS) for decision fusion, ultimately achieving an impressive accuracy rate of 98.41%. This accuracy notably surpasses the performance of existing methods, such as ResNet-101 and Xception, which attain 93.74% accuracy on the testing dataset. Moreover, when applied to an unseen dataset, the P-AMIS-E model demonstrates a substantial advantage, yielding accuracy rates ranging from 4.45% to 31.16% higher than those of the compared methods. It is worth highlighting that our heterogeneous ensemble approach consistently outperforms both single large models and homogeneous ensemble methods, achieving an average improvement of 13.45%. This paper provides a case study focused on the Centella Asiatica Urban (CAU) cultivar to exemplify the practical application of our approach. By integrating image segmentation, augmentation, and decision fusion, we have significantly enhanced accuracy and efficiency. This research holds theoretical implications for the advancement of deep learning techniques in image classification tasks while also offering practical benefits for industries reliant on precise species identification.
This study aims to develop a web application, TB-DRD-CXR, for the categorization of tuberculosis (TB) patients into subgroups based on their level of drug resistance. The application utilizes an ...ensemble deep learning model that classifies TB strains into five subtypes: drug sensitive tuberculosis (DS-TB), drug resistant TB (DR-TB), multidrug-resistant TB (MDR-TB), pre-extensively drug-resistant TB (pre-XDR-TB), and extensively drug-resistant TB (XDR-TB).
The ensemble deep learning model employed in the TB-DRD-CXR web application incorporates novel fusion techniques, image segmentation, data augmentation, and various learning rate strategies. The performance of the proposed model is compared with state-of-the-art techniques and standard homogeneous CNN architectures documented in the literature.
Computational results indicate that the suggested method outperforms existing methods reported in the literature, providing a 4.0%-33.9% increase in accuracy. Moreover, the proposed model demonstrates superior performance compared to standard CNN models, including DenseNet201, NASNetMobile, EfficientNetB7, EfficientNetV2B3, EfficientNetV2M, and ConvNeXtSmall, with accuracy improvements of 28.8%, 93.4%, 2.99%, 48.0%, 4.4%, and 7.6% respectively.
The TB-DRD-CXR web application was developed and tested with 33 medical staff. The computational results showed a high accuracy rate of 96.7%, time-based efficiency (ET) of 4.16 goals/minutes, and an overall relative efficiency (ORE) of 100%. The system usability scale (SUS) score of the proposed application is 96.7%, indicating user satisfaction and a likelihood of recommending the TB-DRD-CXR application to others based on previous literature.
A person infected with drug-resistant tuberculosis (DR-TB) is the one who does not respond to typical TB treatment. DR-TB necessitates a longer treatment period and a more difficult treatment ...protocol. In addition, it can spread and infect individuals in the same manner as regular TB, despite the fact that early detection of DR-TB could reduce the cost and length of TB treatment. This study provided a fast and effective classification scheme for the four subtypes of TB: Drug-sensitive tuberculosis (DS-TB), drug-resistant tuberculosis (DR-TB), multidrug-resistant tuberculosis (MDR-TB), and extensively drug-resistant tuberculosis (XDR-TB). The drug response classification system (DRCS) has been developed as a classification tool for DR-TB subtypes. As a classification method, ensemble deep learning (EDL) with two types of image preprocessing methods, four convolutional neural network (CNN) architectures, and three decision fusion methods have been created. Later, the model developed by EDL will be included in the dialog-based object query system (DBOQS), in order to enable the use of DRCS as the classification tool for DR-TB in assisting medical professionals with diagnosing DR-TB. EDL yields an improvement of 1.17-43.43% over the existing methods for classifying DR-TB, while compared with classic deep learning, it generates 31.25% more accuracy. DRCS was able to increase accuracy to 95.8% and user trust to 95.1%, and after the trial period, 99.70% of users were interested in continuing the utilization of the system as a supportive diagnostic tool.
This study introduces an innovative approach to sustainable tourist trip planning that aligns tourists’ preferences with vital sustainability factors. Traditional methods often prioritize singular ...objectives, such as minimizing travel time or cost, thereby overlooking the broader sustainability implications associated with travel. To bridge this gap, we propose a multi-objective sustainability tourist trip design model that optimizes travel itineraries across various dimensions, including CO2 emissions, environmental impacts, and socio-economic benefits. To address this problem, we developed a mixed-integer programming model. Subsequently, a novel Artificial Multiple Intelligence System (AMIS) was employed. The AMIS integrates multiple intelligence systems and employs meticulously designed and effective improvement methods facilitated by adaptive heuristic learning selection procedures. We subjected the model to rigorous testing using real-world data, and our analysis underscored the superiority of our approach. It resulted in a noteworthy 22.46%–27.95% reduction in CO2 costs and a substantial 14.20% reduction in waste generation, along with a noteworthy 6.46% increase in community ownership and a 7.07% increase in cultural heritage value, compared to existing models and methods based on differential evolution algorithms, ant colony optimization, and genetic algorithms. This study contributes significantly to enhancing our understanding of the challenges and prospects inherent in integrating sustainability considerations into the design of travel itineraries.
Predicting the sequence pattern of the handwritten text images is a challenging problem due to various writing styles, insufficient training data, and also background noise appearing in the text ...images. The architecture of the combination between convolutional neural network (CNN) and recurrent neural network (RNN), called CRNN architecture, is the most successful sequence learning method for handwritten text recognition systems. For handwritten text recognition in historical Thai document images, we first trained nine different CRNN architectures with both training from scratch and transfer learning techniques to find out the most powerful technique. We discovered that the transfer learning technique does not significantly outperform scratch learning. Second, we examined training the CRNN model by applying the basic transformation data augmentation techniques: shifting, rotation, and shearing. Indeed, the data augmentation techniques provided more accurate performance than without applying data augmentation techniques. However, it did not show significant results. The original training strategy aimed to find the global minima value and not always solve the overfitting problems. Third, we proposed a cyclical data augmentation strategy, called CycleAugment, to discover many local minima values and prevent overfitting. In each cycle, it rapidly decreased the training loss to reach the local minima. The CycleAugment strategy allowed the CRNN model to learn the input images with and without applying data augmentation techniques to learn from many input patterns. Hence, the CycleAugment strategy consistently achieved the best performance when compared with other strategies. Finally, we prevented image distortion by applying a simple technique to the short word images and achieved better performance on the historical Thai document image dataset.
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