Preprocessing Stage Research Articles

Retinex-based underwater image enhancement via adaptive color correction and hierarchical U-shape transformer

Underwater images can suffer from visibility and quality degradation due to the attenuation of propagated light and other factors unique to the underwater setting. While Retinex-based approaches have shown to be effective in enhancing the underwater image quality, the use of hand-crafted priors and optimization-driven solutions often prevent the adaptivity of these methods to different types of underwater images. Moreover, the commonly-used white balance strategy which often appears in the preprocessing stage of the underwater image enhancement (UIE) algorithms may give rise to unwanted color distortions due to the fact that wavelength-dependent light absorption is not taken into account. To overcome these potential limitations, in this paper, we present an effective UIE model based on adaptive color correction and data-driven Retinex decomposition. Specifically, an adaptive color balance approach which takes into account different attenuation levels for light with different wavelengths is proposed to adaptively enhance the three color channels. Furthermore, deep neural networks are employed for the Retinex decomposition, formulating the optimization problem as an implicit-prior-regularized model which is solved by learning the priors from a large training dataset. Finally, a hierarchical U-shape Transformer network which uses hierarchically-structured multi-scale feature extraction and selective feature aggregation is applied to the decomposed images for contrast enhancement and blur reduction. Experimental results tested on six benchmark underwater image datasets demonstrate the effectiveness of the proposed UIE model.

Application of an ensemble CatBoost model over complex dataset for vehicle classification.

The classification of vehicles presents notable challenges within the domain of image processing. Traditional models suffer from inefficiency, prolonged training times for datasets, intricate feature extraction, and variable assignment complexities for classification. Conventional methods applied to categorize vehicles from extensive datasets often lead to errors, misclassifications, and unproductive outcomes. Consequently, leveraging machine learning techniques emerges as a promising solution to tackle these challenges. This study adopts a machine learning approach to alleviate image misclassifications and manage large quantities of vehicle images effectively. Specifically, a contrast enhancement technique is employed in the pre-processing stage to highlight pixel values in vehicle images. In the feature segmentation stage, Mask-R-CNN is utilized to categorize pixels into predefined classes. VGG16 is then employed to extract features from vehicle images, while an autoencoder aids in selecting features by learning non-linear input features and compressing representation features. Finally, the CatBoost (CB) algorithm is implemented for vehicle classification (VC) in diverse critical environments, such as inclement weather, twilight, and instances of vehicle blockage. Extensive experiments are conducted using different large-scale datasets with various machine learning platforms. The findings indicate that CB (presumably a specific method or algorithm) attains the highest level of performance on the large-scale dataset named UFPR-ALPR, with an accuracy rate of 98.89%.

Bark frequency cepstral coefficient based sadness emotion level recognition system

Based on the adverse physiological and social consequences associated with sadness, recognizing this emotion is crucial to mitigate its negative impacts. To address this issue, an efficient system was proposed for recognizing different level of sadness using embedded technology. The system captured speech input through a microphone and then processed it on Raspberry Pi microcomputer. The key feature extraction technique used was Bark-Frequency Cepstral Coefficient (BFCC) method. After selecting a set of features, a thorough analysis of the computational algorithms was conducted, and dimensionality reduction method was applied to reduce computational costs, including noise. These features served as a pre-processing stage for Machine Learning (ML) model based on K-Nearest Neighbors (KNN) architecture. The results were then shown on a smartphone application through Bluetooth connectivity. Based on experimental results, the system showed an impressive improvement of 11.97 dB in the signal-to-noise ratio (SNR) compared to feature extraction using Mel-frequency Cepstral Coefficient (MFCC) method. The results also showed a favorable accuracy rate, implying the effectiveness of KNN with a k-value of 5 in accurately identifying different level of sadness. The system particularly excelled in correctly recognizing high and low level of sadness. Therefore, the effective categorization of sadness emotion level could be applied to the early diagnosis of mental disorders such as depression and anxiety, contributing to the reduction of the adverse impact of sadness.

Analisis Sentimen Terhadap Kinerja Kepolisian Indonesia Menggunakan Metode Multinomial Naive Bayes, Long Short-Term Memory, dan Lexicon-Based

According to the Indonesian Survey Institute, the public trust index in the Indonesian Police decreased to 53% as of October 2022. The level of public satisfaction and trust in police can be measured by examining how the police handle each case that occurs. This research focuses on analyzing and measuring the public’s sentiment toward the police’s performance among Twitter users in Indonesia. After scraping (collecting) data from Twitter, out of the 14,000 collected tweets, only 12,222 tweets were for analysis after the preprocessing stage. Then, the data was labeled into two categories, negative sentiment and positive sentiment using InSet Lexicon. The results showed that 53% (6,478) tweets have a negative sentiment, while 47% (5,744) tweets have a positive sentiment. The topics frequently appear and discussed related to such as the shooting case of Brigadier J, the Kanjuruhan tragedy, the security of the G20 Bali Summit, securing Christmas 2022 and securing New Year 2023. Subsequently, classification was performed on the test data using the Multinomial Naïve Bayes and Long Short-Term Memory (LSTM) methods. In the testing phase, a confusion matrix used to calculate accuracy, recall, precision, and f1-score. The results showed that the Multinomial Naïve Bayes method achieved an accuracy score of 78%, precision of 78.5%, recall of 77%, and an f1-score of 77%, with an average score of 77.6%. Similarly, the LSTM method resulted in an equal accuracy, precision, recall, and f1-score of 87%.

A Bio-Inspired Retinal Model as a Prefiltering Step Applied to Letter and Number Recognition on Chilean Vehicle License Plates

This paper presents a novel use of a bio-inspired retina model as a scene preprocessing stage for the recognition of letters and numbers on Chilean vehicle license plates. The goal is to improve the effectiveness and ease of pattern recognition. Inspired by the responses of mammalian retinas, this retinal model reproduces both the natural adjustment of contrast and the enhancement of object contours by parvocellular cells. Among other contributions, this paper provides an in-depth exploration of the architecture, advantages, and limitations of the model; investigates the tuning parameters of the model; and evaluates its performance when integrating a convolutional neural network and a spiking neural network into an optical character recognition (OCR) algorithm, using 40 different genuine license plate images as a case study and for testing. The results obtained demonstrate the reduction of error rates in character recognition based on convolutional neural networks (CNNs), spiking neural networks (SNNs), and OCR. It is concluded that this bio-inspired retina model offers a wide spectrum of potential applications to further explore, including motion detection, pattern recognition, and improvement of dynamic range in images, among others.

Intelligent Detection of Tunnel Leakage Based on Improved Mask R-CNN

The instance segmentation model based on deep learning has addressed the challenges in intelligently detecting water leakage in shield tunneling. Due to the limited generalization ability of the baseline model, occurrences of missed detections, false detections, and repeated detections are encountered during the actual detection of tunnel water leakage. This paper adopts Mask R-CNN as the baseline model and introduces a mask cascade strategy to enhance the quality of positive samples. Additionally, the backbone network in the model is replaced with RegNetX to enlarge the model’s receptive field, and MDConv is introduced to enhance the model’s feature extraction capability in the edge receptive field region. Building upon these improvements, the proposed model is named Cascade-MRegNetX. The backbone network MRegNetX features a symmetrical block structure, which, when combined with deformable convolutions, greatly assists in extracting edge features from corresponding regions. During the dataset preprocessing stage, we augment the dataset through image rotation and classification, thereby improving both the quality and quantity of samples. Finally, by leveraging pre-trained models through transfer learning, we enhance the robustness of the target model. This model can effectively extract features from water leakage areas of different scales or deformations. Through instance segmentation experiments conducted on a dataset comprising 766 images of tunnel water leakage, the experimental results demonstrate that the improved model achieves higher precision in tunnel water leakage mask detection. Through these enhancements, the detection effectiveness, feature extraction capability, and generalization ability of the baseline model are improved. The improved Cascade-MRegNetX model achieves respective improvements of 7.7%, 2.8%, and 10.4% in terms of AP, AP0.5, and AP0.75 compared to the existing Cascade Mask R-CNN model.

MEDOIDS AS A PACKING OF ORB IMAGE DESCRIPTORS

The aim of the research. The paper presents the research about the feasibility to use matching medoids obtained from the set of ORB descriptors instead matching the full set of binary descriptors for image classification problem. Research results. Different methods that include direct brute force medoids matching, grouping of medoids for separate classes, and grouping of descriptors followed by calculation of medoids amongst them were proposed. Numerical experiments were performed for all these methods in order to compare the classification accuracy and inference time. It has been shown that using of medoids allowed us to redistribute processing time in order to perform more calculations during preprocessing rather than during classification. According to modelling performed on the Leeds Butterly dataset matching images based on medoids could have the same accuracy as matching of descriptors (0.69–0.88 for different number of features). Medoids require additional time for the calculation during preprocessing stage but classification time becomes faster: in our experiments we have obtained about 9–10 times faster classification and same 9–10 times increasing preprocessing time for the models that have comparable accuracies. Finally, the efficiency of the proposed ideas was compared to the CNN trained and evaluated on the same data. As expected, CNN required much more preprocessing (training) time but the result is worth it: this approach provides the best classification accuracy and inference time. Conclusion. Medoids matching could have the same accuracy as direct descriptors matching, but the usage of medoids allows us to redistribute the overall modeling time with the increasing preprocessing time and making inference faster.

Coronary Vessel Segmentation in X-ray Angiography Images Using Edge-Based Tracking Method

Automated extraction of coronary arteries is an essential process in the diagnosis of treatment for coronary artery disease (CAD) with computer assistance. Accurately outlining the coronary artery is difficult when using X-ray coronary angiography (XCA) because of the low signal-to-noise ratio and the presence of interfering background structures. In this paper, a new approach for segmenting vessels in angiograms is presented, specifically designed to tackle the difficulties arising from non-uniform illumination, artifacts, and noise present in angiographic images. The proposed method employs an edge-based tracking tool to generate an initial probability map for segmentation. A segmentation method based on coronary vessel tracking is presented for finding the border and centerline of the vessel. The proposed method is designed based on two main components: preprocessing and tracking. In the preprocessing stage, a guided filter and edge-sharpening algorithms are used to enhance the features of the original image. In the tracking stage, an initial point is selected, and using the Gaussian property, a semi-circle operator is applied to track the line perpendicular to the vessel. The proposed method demonstrated remarkable performance in terms of sensitivity and specificity, achieving values of 86.93 and 99.61, respectively. Additionally, the method achieved an accuracy rate of 97.81. Notably, the proposed method outperformed existing state-of-the-art segmentation methods, as indicated by its higher dice score. These impressive results signify a significant advancement in the field of vessel segmentation, highlighting the effectiveness and superiority of the proposed approach.

Evaluation of a Voltametric E-Tongue Combined with Data Preprocessing for Fast and Effective Machine Learning-Based Classification of Tomato Purées by Cultivar.

The potential of a voltametric E-tongue coupled with a custom data pre-processing stage to improve the performance of machine learning techniques for rapid discrimination of tomato purées between cultivars of different economic value has been investigated. To this aim, a sensor array with screen-printed carbon electrodes modified with gold nanoparticles (GNP), copper nanoparticles (CNP) and bulk gold subsequently modified with poly(3,4-ethylenedioxythiophene) (PEDOT), was developed to acquire data to be transformed by a custom pre-processing pipeline and then processed by a set of commonly used classifiers. The GNP and CNP-modified electrodes, selected based on their sensitivity to soluble monosaccharides, demonstrated good ability in discriminating samples of different cultivars. Among the different data analysis methods tested, Linear Discriminant Analysis (LDA) proved to be particularly suitable, obtaining an average F1 score of 99.26%. The pre-processing stage was beneficial in reducing the number of input features, decreasing the computational cost, i.e., the number of computing operations to be performed, of the entire method and aiding future cost-efficient hardware implementation. These findings proved that coupling the multi-sensing platform featuring properly modified sensors with the custom pre-processing method developed and LDA provided an optimal tradeoff between analytical problem solving and reliable chemical information, as well as accuracy and computational complexity. These results can be preliminary to the design of hardware solutions that could be embedded into low-cost portable devices.

ABCNN-IDS: Attention-Based Convolutional Neural Network for Intrusion Detection in IoT Networks

This paper proposes an attention-based convolutional neural network (ABCNN) for intrusion detection in the Internet of Things (IoT). The proposed ABCNN employs an attention mechanism that aids in the learning process for low-instance classes. On the other hand, the Convolutional Neural Network (CNN) employed in the ABCNN framework converges toward the most important parameters and effectively detects malicious activities. Furthermore, the mutual information technique is employed during the pre-processing stage to filter out the most significant features from the datasets, thereby improving the effectiveness of the ABCN model. To assess the effectiveness of the ABCNN approach, we utilized the Edge-IoTset, IoTID20, ToN_IoT, and CIC-IDS2017 datasets. The performance of the proposed architecture was assessed using various evaluation metrics, such as precision, recall, F1-score, and accuracy. Additionally, the performance of the proposed model was compared to multiple ML and DL methods to evaluate its effectiveness. The proposed model exhibited impressive performance on all the utilized datasets, achieving an average accuracy of 99.81%. Furthermore, it demonstrated excellent scores for other evaluation metrics, including 98.02% precision, 98.18% recall, and 98.08% F1-score, which outperformed other ML and DL models.

Driver Distraction Detection and Alert System Using Convolutional Neural Networks

Abstract: The Driver Distraction and Alert System Using Convolutional Neural Networks provide a real-time machine learning and webcam-based monitoring system for driver distraction detection. The system uses a sophisticated Convolutional Neural Network (CNN) architecture trained on the State Form of Distracted Driver Detection dataset, which is made possible by utilizing the OpenCV, TensorFlow, and Pandas packages. With improvements like dropout layers and Xavier weight initialization, the model efficiently classifies ten driver attention states through preprocessing stages like image scaling and normalization. The results of the experiment show how well the suggested method works to identify distracted driving behaviors; a revised model on the Kaggle competition platform achieved a competitive public score of 2.67118

Implementasi Metode K-Nearest Neighbor Untuk Klasifikasi Penyakit Tanaman Mentimun Pada Citra Daun

Cucumber is a vegetable that is widely consumed by Indonesian people. However, cucumber plants are susceptible to disease attack which causes substantial yield loss. Examples of disease in cucumber plants are downy mildew, powdery mildew, and cucumber mozaic virus. This disease can be recognized visually because it has a characteristic color and texture. Through an image, information can be learned about the cucumber plant disease. This study aims to build a disease classification system on cucumber leaf images so that it can provide information on the type of disease. The application of the system consisting of pre-processing, feature extraction, classification, and evaluation stages. The pre-processing stages resizes the RGB image and then converts it to Grayscale. The feature extraction stage uses the GLCM (Gray Level Co-Occurence) method. The classification stage uses the K-NN (K-Nearest Neighbor) algorithm. Evaluation stage is a confusion matrix. The results of the cucumber leaf disease classification test used the K-Nearest Neighbor algorithm, produced the best accuracy value by using the neighborhood value k=1 reaching 90%.

Analisis Sentimen Pada Media Sosial Instagram Terhadap Akun Presiden Joko Widodo Menggunakan Metode Naïve Bayes Classifier

In the growing digital era, social media, especially Instagram, has become the main platform for people to communicate and express themselves. One of the most influential accounts is President Joko Widodo's official account, @jokowi, which is often in the spotlight with thousands of comments covering a wide range of sentiments, both positive and negative. In the midst of his popularity, sentiment analysis is key to understanding the public's views on Jokowi's leadership. This study aims to analyze public sentiment towards President Joko Widodo (Jokowi) through comments posted on his official Instagram account (@jokowi). By utilizing the Naïve Bayes Classifier method, this study collected data from 1000 comments which were then processed through various stages of the methodology, including data collection, preprocessing, weighting, k-fold cross validation, and method implementation. Through the preprocessing stage involving cleansing, stopword removal, stemming, and tokenizing, the comments were prepared for further analysis. Test results using k-fold cross validation show that the model has an average accuracy of 80.3%. In addition, evaluation using confusion matrix showed an accuracy of 84.1%, with a precision of 85.5% and recall of 92.4%. These results show that the Naïve Bayes Classifier method performs well in classifying positive and negative sentiments in the comments.

Predicting game-induced emotions using EEG, data mining and machine learning

BackgroundEmotion is a complex phenomenon that greatly affects human behavior and thinking in daily life. Electroencephalography (EEG), one of the human physiological signals, has been emphasized by most researchers in emotion recognition as its specific properties are closely associated with human emotion. However, the number of human emotion recognition studies using computer games as stimuli is still insufficient as there were no relevant publicly available datasets provided in the past decades. Most of the recent studies using the Gameemo public dataset have not clarified the relationship between the EEG signal’s changes and the emotion elicited using computer games. Thus, this paper is proposed to introduce the use of data mining techniques in investigating the relationships between the frequency changes of EEG signals and the human emotion elicited when playing different kinds of computer games. The data acquisition stage, data pre-processing, data annotation and feature extraction stage were designed and conducted in this paper to obtain and extract the EEG features from the Gameemo dataset. The cross-subject and subject-based experiments were conducted to evaluate the classifiers’ performance. The top 10 association rules generated by the RCAR classifier will be examined to determine the possible relationship between the EEG signal's frequency changes and game-induced emotions.ResultsThe RCAR classifier constructed for cross-subject experiment achieved highest accuracy, precision, recall and F1-score evaluated with over 90% in classifying the HAPV, HANV and LANV game-induced emotions. The 20 experiment cases’ results from subject-based experiments supported that the SVM classifier could accurately classify the 4 emotion states with a kappa value over 0.62, demonstrating the SVM-based algorithm’s capabilities in precisely determining the emotion label for each participant’s EEG features’ instance.ConclusionThe findings in this study fill the existing gap of game-induced emotion recognition field by providing an in-depth evaluation on the ruleset algorithm’s performance and feasibility of applying the generated rules on the game-induced EEG data for justifying the emotional state prediction result.

#176 System to detect vascular calcification through convolutional neural networks

Abstract Background and Aims Cardiovascular disease is the major cause of death in patients with chronic kidney disease (CKD). It is increasingly believed that the presence of vascular calcification (VC) increases the risk of mortality due to cardiovascular events. VC is very common in CKD, especially in hemodialysis patient. To improve the life expectancy of patients, it is important to detect VC in the early stages of the disease. There are several ways to discover VC such as computed tomography (CT) scans, echocardiography, and X-Ray among others. However, one of the best options is the usage of X-ray images because is an affordable and simpler process in comparison to using CT scans or echocardiography. However, the problem with X-ray images is the difficulty of finding a well-trained professional that can detect the VC after analyzing the images which leads to possible misleading diagnoses. The goal of this study is to develop a prototype that detects Vascular Calcification from X-ray images in an automated way using the power of Convolutional Neural Networks (CNN). Method To train a CNN to detect VC from lumbosacral spine X-ray images, a set of 700 DICOM files complemented with 236 JPEG images was available. After getting the images, they had to be anonymized, and data, such as name and age was removed from each image. Then, the images were divided into three datasets: Train, Test, and Validation. Given that the dataset was not balanced between the pictures with VC (Positive) and those with no VC (Negative), it had to be reduced (Table 1). The next step was implemented to clean the images and prepare them to feed the CNN. An example of preprocessing is that some X-ray images were of the entire body. For the analysis, the VC detection was assessed at the anterior and the posterior walls of the abdominal aorta adjacent to vertebrae L1-L4. In addition to that, the DICOM images were converted to JPEG. After having the dataset prepared, a CNN architecture had to be selected. To achieve it, a revision of modern CNN architectures was performed. Two items were considered when selecting these: the performance on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and if there was an available pre-trained model. With that criteria, pre-trained EfficientNet-B0 and ResNet50 models were used to build the prototype due to the limited dataset. Results After having the dataset prepared, the architectures selected, and the pre-trained models, the Training, Validation and Test stages were executed. For Training, with EfficientNet-B0, the Accuracy was 86.9%, while for Validation, the Accuracy was 57.4% (Fig. 1). Using the RestNet50 model, the precision was 90.506% during Training, while for Validation, it was 54.8% (Fig. 2). Finally, during the Testing stage, ResNet50 had a better performance, because EfficientNetB0 detected 80% for the negative class and 60% for the positive class. After the CNN models were trained, an additional validation stage was performed to compare the model results with the analysis made by a radiologist and a nephrologist. This comparison determined that the model performed better with images having VC, while it was less accurate with images having no presence of VC. This contrast could have different reasons, one being possible false-positive images. Conclusion This study created a CNN model using pre-trained models on top of modern architectures such as EfficientNet-B0 and ResNet50. Tools were used to prepare the images as well as to train, validate and test the model. The Transfer Learning helped to build a model faster, giving high Accuracy rates with only a few images due to the limited dataset. However, the model could give better results if a wider and balanced set of images is available. Also, a better pre-processing stage can be executed to reduce false-positive images. For future work, this model can be used as the core of a system that not only accurately detects Vascular Calcification from X-Ray images, but also can determine the level of calcification using measures such as the Kauppila Index or Adragao.

An automated geometry generation procedure for CWE applications in OpenFOAM: the case study of a thunderstorm downburst moving over Genoa city

AbstractSince a decade, Computational Fluid Dynamics (CFD) simulations for Wind Engineering (WE) on large-scale environments became a norm. Meso-$$\gamma$$ γ scales and microscale can be characterized by a large number of structures as well as complex terrain topographies. CFD simulations over such an area would commonly imply the involvement of a time-consuming geometry preparation in the pre-processing stage. This is also the innovative goal of the present paper for which an automated geometry generation procedure for Computational Wind Engineering (CWE) applications is developed in open-source environments. This paper further expands on the unsteady Reynolds-averaged Navier–Stokes (URANS) simulation of a thunderstorm downburst immersed in a background Atmospheric Boundary Layer (ABL) wind moving over Genoa city, Italy. Recommendations for geometry and grid generation of the present case study are also proposed. The study considers two simultaneous approaching wind conditions, a background ABL wind and a moving downburst (DB), the latter modeled as an impinging jet. The simulated results are compared with available full-scale LiDAR profiler data in terms of the vertical profiles of outflow velocity, showing relatively good agreement in terms of magnitude and profile shape.

DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI

ABSTRACT A brain tumour is an abnormal mass of tissue. Brain tumours vary in size, from tiny to large. Moreover, they display variations in location, shape, and size, which add complexity to their detection. The accurate delineation of tumour regions poses a challenge due to their irregular boundaries. In this research, these issues are overcome by introducing the DTDO-ZFNet for detection of brain tumour. The input Magnetic Resonance Imaging (MRI) image is fed to the pre-processing stage. Tumour areas are segmented by utilizing SegNet in which the factors of SegNet are biased using DTDO. The image augmentation is carried out using eminent techniques, such as geometric transformation and colour space transformation. Here, features such as GIST descriptor, PCA-NGIST, statistical feature and Haralick features, SLBT feature, and CNN features are extricated. Finally, the categorization of the tumour is accomplished based on ZFNet, which is trained by utilizing DTDO. The devised DTDO is a consolidation of DTBO and CDDO. The comparison of proposed DTDO-ZFNet with the existing methods, which results in highest accuracy of 0.944, a positive predictive value (PPV) of 0.936, a true positive rate (TPR) of 0.939, a negative predictive value (NPV) of 0.937, and a minimal false-negative rate (FNR) of 0.061%.

Enhance micro-Doppler signatures-based human activity classification accuracy of FMCW radar using the threshold method

Nowadays, radar-based human activity classification is being widely adopted in healthcare systems due to its benefits in terms of personal privacy compliance, non-contact sensing, and being unaffected by weather conditions. This study proposes a threshold method in the pre-processing stage to improve human activity classification accuracy by determining the region of meaningful information (RMI) on the spectrogram. Initially, a mask function, which is created by a certain threshold value, is applied to the input spectrogram to highlight the RMI from the micro-Doppler (m-D) signatures. Only the highlighted RMI on the spectrogram is retained as input to the classifiers. Then, five Convolutional Neural Networks (CNNs) of varying complexity are employed to extract features, identify activities, and assess the effectiveness of the suggested approach. The experimental results demonstrate that the suggested approach has enhanced classification accuracy by up to 11% when compared to the original unprocessed dataset.

Feature selection method for banknote dirtiness recognition based on mathematical functions driven slime mould algorithm

In most data dimensionality reduction tasks, feature selection (FS) is an essential preprocessing stage. The slime mould algorithm (SMA) is a novel metaheuristic optimization algorithm. This paper proposes a feature selection method for banknote dirtiness recognition based on mathematical functions driven slime mould algorithm. This method is applied to the feature selection of banknote dirtiness recognition, and the Lévy flight operator is used to replace the control parameter vc in SMA, and a series of mathematical functions are added to replace the control parameter vb in the slime mould algorithm to enhance the global search capability. Test results show that this method achieves the highest accuracy of 76.85 % in recognizing the banknote dirtiness. Additionally, by applying the same method to feature selection and recognition of dirtiness for the left middle white area images of banknotes, the highest accuracy reaches 74.99 %. This suggests that the left middle white area can essentially replace the whole banknote. To verify the performance of the proposed feature selection method, comparative tests with four other optimization algorithms are carried out, and the results demonstrate that three improvement strategies can effectively improve the performance of SMA while maintaining a balance between exploration and exploitation. Tests have revealed that these three algorithms have significant advantages over the past SMA in terms of recognition accuracy and convergence speed when it comes to identifying the dirtiness of banknotes. Therefore, this method is considered to be one of the most effective methods to solve the feature selection problem of banknote dirtiness.

Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models

This study focused on comparing distributed learning models with centralized and local models, assessing their efficacy in predicting specific delivery and patient-related outcomes in obstetrics using real-world data. The predictions focus on key moments in the obstetric care process, including discharge and various stages of hospitalization. Our analysis: using 6 different machine learning methods like Decision Trees, Bayesian methods, Stochastic Gradient Descent, K-nearest neighbors, AdaBoost, and Multi-layer Perceptron and 19 different variables with various distributions and types, revealed that distributed models were at least equal, and often superior, to centralized versions and local versions. We also describe thoroughly the preprocessing stage in order to help others implement this method in real-world scenarios. The preprocessing steps included cleaning and harmonizing missing values, handling missing data and encoding categorical variables with multisite logic. Even though the type of machine learning model and the distribution of the outcome variable can impact the result, we reached results of 66% being superior to the centralized and local counterpart and 77% being better than the centralized with AdaBoost. Our experiments also shed light in the preprocessing steps required to implement distributed models in a real-world scenario. Our results advocate for distributed learning as a promising tool for applying machine learning in clinical settings, particularly when privacy and data security are paramount, thus offering a robust solution for privacy-concerned clinical applications.