Adaptive Mean Filter Research Articles

Social media mining for business inequality: modified spotted hyena optimization based random forest

Businesses may advertise, communicate with customers and research industry trends on social media today. Due to the massive amount of data generated on social media platforms, organizations struggle to acquire valuable data and fix marketing inequalities. To combine Modified Spotted Hyena Optimization with Random Forest (MSHO-RF) for classification in social media mining for a business inequality study. Data-driven solutions to inequality in operations and customer interactions should increase prediction accuracy, lower feature dimensionality and extract actionable insights. We begin by collecting social media mining for business inequality data. Afterward, Adaptive mean filter (AMF) techniques were used to initialize the data preprocessing. With the use of Linear Discriminant Analysis (LDA), we examined the most suitable arrangement of feature extraction. This research offers a unique method MSHO-RF, to deal with the problem of business inequality using social media mining. The MSHO-RF algorithm had higher results than compared to a variety of performance parameters, featuring a 94% of F1-score, a precision rate of 97%, an accuracy rate of 98% and recall rates of 95%. Our findings suggest that firms might benefit from a framework for leveraging social media data to promote more equitable and sustainable practices if they combine MSHO-RF with social media mining for business inequality. This approach benefits companies and their stakeholders by fostering an open, data-driven ecosystem.

Improving migration forecasting for transitory foreign tourists using an Ensemble DNN-LSTM model

The tourism industry is a key component in many nations’ economies, and precise Forecasting of the number of arrivals of transient international visitors is necessary for efficient resource allocation and strategic planning. Traditional approaches for predicting time series need to catch up when capturing the intricate patterns and interdependencies in data about visitor movement. To overcome this obstacle, we have developed a model that combines Ensemble Deep Neural Networks (EDNN) with Long Short-Term Memories (LSTM) to enhance migration predictions. EDNNs learn complex patterns from historical data, whereas LSTMs model sequential relationships. We start with data from the study-based Migration Forecasting for Transitory Foreign Tourists. The data was preprocessed using Adaptive mean filter (AMF) methods. We used Linear Discriminant Analysis (LDA) to look at different configurations for extracting features. The EDNN-LSTM method had better results than its comparison in terms of a wide range of performance measures having an accuracy of 97%, an MAE of 40.75%, an MSE of 33.10%, and an RMSE of 30.10%. According to the results, the EDNN-LSTM model can represent the intricate dynamics of transitory foreign visitor movement. Consequently, this model is a useful resource for tourism stakeholders, policymakers, and enterprises.

An effective IoT-assisted adaptive thresholding and hybrid deep learning model for rice blast fungal detection

ABSTRACT In Asian countries, rice is the major agricultural product. Hence, the initial identification of plant disorder via the IoT (Internet of Things) offers to ignore the rice from critical disorders. To increase crop production, the measurements should be considered to completely destroy the rice plant disorders by an effective system. This paper proposed an Intelligent IoT-aided deep learning model for detecting rice blast fungal along with a hybrid heuristic algorithm. The proposed work encompasses with multiple stages that are explained as follows. At first, the required paddy images are gathered from online data resources. Next, the pre-processing of the collected images is made by adaptive mean filtering and contrast enhancement. Further, the adaptive thresholding and morphological operation are adopted for leaf segmentation purposes, where the threshold value is tuned by a Fitness-based Billiards-inspired Rat Swarm Optimizer (FBRSO). Consequently, from the segmented image, the Region of Interest (ROI) is cropped. Finally, the cropped ROI is subjected to the Optimized MobileNetv2 and Multiscale Residual Attention Network (OMMRAN), where it includes MobileNetV2 and Multiscale Residual Attention Network, in which some of the hyperparameters are tuned by FBRSO approach. The performance is validated and compared with other existing approaches for detecting rice diseases effectively.

Impact of Adaptive Mean Filter as the Preprocessing Stage of Histopathological Image Classification of Breast Tumor Using Transfer Learning VGG16 for Various Magnifications

This study assesses the impact of using an Adaptive Mean Filter (AMF) as a preprocessing stage for classification of breast tumor histopathological images at various magnifications. The histopathological image was converted from red-green-blue (RGB) into grayscale before AMF is applied. In this study, AMF was performed with kernel sizes of 3 × 3 and 5 × 5 pixels. The datasets were extracted using transfer learning VGG16 before being classified using Bagging classifier. To obtain unbiased performance of the model, stratified K fold cross-validation with K = 10 was used. The dataset was divided into K-equal-sized folds. For each fold, the model was trained on the remaining K-1 folds then evaluated on the held-out fold. This process was repeated K times, with each fold used once as the validation set. The accuracy of the model was then averaged over the K folds to estimate its generalization performance. The AMF with a kernel size of 3 × 3 pixels improves the multi-class classification accuracy for magnifications of 40× and 200×, resulting in accuracy increases of 0.20% and 0.89%, respectively. However, at a magnification of 100×, the model's performance decreases. While the use of AMF with a kernel size of 3 × 3 pixels did not raise the accuracy at magnification 400×, it resulted in a lower standard deviation by 0.24%. In binary-class classification, the use of the AMF with a kernel size of 3 × 3 pixels improves accuracy by 1.10% for magnification 40× and by 0.85% for magnification 200×. However, when implemented at magnifications of 100× and 400×, the AMF filter results in decreased performance. In conclusion, the use of the AMF with a kernel size of 3 × 3 pixels as a preprocessing stage for the histopathological image classification of breast tumor has shown to have a positive impact on the accuracy of multi-class and binary-class classifications for magnifications of 40× and 200×, but not for magnifications of 100× and 400×. The results also indicate that the use of AMF filter can reduce the standard deviation compared to without AMF for some magnifications. However, caution should be considered when applying the AMF filter, as it can decrease the model performance in some cases.

Optimal ECG Signal Denoising Using DWT with Enhanced African Vulture Optimization

Cardiovascular diseases (CVDs) are the world's leading cause of death; therefore cardiac health of the human heart has been a fascinating topic for decades. The electrocardiogram (ECG) signal is a comprehensive non-invasive method for determining cardiac health. Various health practitioners use the ECG signal to ascertain critical information about the human heart. In this paper, the noisy ECG signal is denoised based on Discrete Wavelet Transform (DWT) optimized with the Enhanced African Vulture Optimization (AVO) algorithm and adaptive switching mean filter (ASMF) is proposed. Initially, the input ECG signals are obtained from the MIT-BIH ARR dataset and white Gaussian noise is added to the obtained ECG signals. Then the corrupted ECG signals are denoised using Discrete Wavelet Transform (DWT) in which the threshold is optimized with an Enhanced African Vulture Optimization (AVO) algorithm to obtain the optimum threshold. The AVO algorithm is enhanced by Whale Optimization Algorithm (WOA). Additionally, ASMF is tuned by the Enhanced AVO algorithm. The experiments are conducted on the MIT-BIH dataset and the proposed filter built using the EAVO algorithm, attains a significant enhancement in reliable parameters, according to the testing results in terms of SNR, mean difference (MD), mean square error (MSE), normalized root mean squared error (NRMSE), peak reconstruction error (PRE), maximum error (ME), and normalized root mean error (NRME) with existing algorithms namely, PSO, AOA, MVO, etc.

A random exploration based fast adaptive and selective mean filter for salt and pepper noise removal in satellite digital images

The digital image is one of the discoveries that play an important role in various aspects of modern human life. These findings are useful in various fields, including defense (military and non-military), security, health, education, and others. In practice, the image acquisition process often suffers from problems, both in the process of capturing and transmitting images. Among the problems is the appearance of noise which results in the degradation of information in the image and thus disrupts further processes of image processing. One type of noise that damages digital images is salt and pepper noise which randomly changes the pixel values to 0 (black) or 255 (white). Researchers have proposed several methods to deal with this type of noise, including median filter, adaptive mean filter, switching median filter, modified decision based unsymmetric trimmed median filter, and different applied median filter. However, this method suffers from a decrease in performance when applied to images with high-intensity noise. Therefore, in this research, a new filtering method is proposed that can improve the image by randomly exploring pixels, then collecting the surrounding pixel data from the processed pixels (kernel). The kernel will be enlarged if there are no free-noise pixels in the kernel. Furthermore, the damaged pixels will be replaced using the mean data centering statistic. Images enhanced using the proposed method have better quality than the previous methods, both quantitatively (SSIM and PSNR) and qualitatively.

Optimal ECG Signal Denoising Using DWT with Enhanced African Vulture Optimization

Cardiovascular diseases (CVDs) are the world's leading cause of death; therefore cardiac health of the human heart has been a fascinating topic for decades. The electrocardiogram (ECG) signal is a comprehensive non-invasive method for determining cardiac health. Various health practitioners use the ECG signal to ascertain critical information about the human heart. In this paper, the noisy ECG signal is denoised based on Discrete Wavelet Transform (DWT) optimized with the Enhanced African Vulture Optimization (AVO) algorithm and adaptive switching mean filter (ASMF) is proposed. Initially, the input ECG signals are obtained from the MIT-BIH ARR dataset and white Gaussian noise is added to the obtained ECG signals. Then the corrupted ECG signals are denoised using Discrete Wavelet Transform (DWT) in which the threshold is optimized with an Enhanced African Vulture Optimization (AVO) algorithm to obtain the optimum threshold. The AVO algorithm is enhanced by Whale Optimization Algorithm (WOA). Additionally, ASMF is tuned by the Enhanced AVO algorithm. The experiments are conducted on the MIT-BIH dataset and the proposed filter built using the EAVO algorithm, attains a significant enhancement in reliable parameters, according to the testing results in terms of SNR, mean difference (MD), mean square error (MSE), normalized root mean squared error (NRMSE), peak reconstruction error (PRE), maximum error (ME), and normalized root mean error (NRME) with existing algorithms namely, PSO, AOA, MVO, etc.

Automatic Adaptive Filtering Technique for Removal of Impulse Noise Using Gabor Filter

Tremendous development in Internet of Things (IoT) and mobile devices lead to several images pooled on social media websites and communicated through networking channels. These images are mostly corrupted with impulse noises due to hot pixels generated in the camera sensors and communication channels. Adaptive mean filter technique removes impulse noise at low density but is unsuccessful as noise density increases and computationally expensive. In this paper, automatic adaptive filtering technique for removal of impulse (salt and pepper) noise is demonstrated. The proposed algorithm consists of impulse noise detection and noise removal modules. An automatic impulse noise detection module is based on mean and variance technique that selects the noisy pixels among the entire image. The noise removal module is based on replacement of noisy pixel through mean and edge direction using Gabor filter. The proposed technique demonstrated better robustness compared with existing techniques.

COMPARATIVE ANALYSIS OF VARIOUS FILTERS FOR DENOISING OF THE SPINAL CORD MRIs

Magnetic Resonance Imaging (MRI) techniques are a fundamental and imperative part of the medical image processing field. The images acquired from the MRI machines are affected by the noise. This noise degrades the quality of the images. Acquisition of MRI with noise may give erroneous results. Hence, to enhance the image quality, it is necessary to reduce or remove this noise. To enhance the image quality of MRI, a plethora of filtering algorithms are available along with the morphological operations. In this paper, we have implemented numerous filters like Adaptive Median filter, Median filter, Mean filter, bilateral filter, NLM filter, Gaussian filter, Weiner filter, and morphological operations to eliminate the noise in the MRI of the spinal cord. The scenarios considered are 1. Application of filters, 2. Application of filters followed by morphological operations, and 3. Morphological operations followed by the application of filters. Statistical parameters like Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) are found for all three approaches and are used to analyze the performance of these techniques. NLM filters are found to give the best performance when compared to other filters. Morphological operations affect the performance of the filters. Application of morphological operations before filtering degrades the filter performance while applying them after improves the performance. The dataset comprises of 250 spinal cord MRIs with noise. The author inferred that the performance of the filters is improved by applying the filtering techniques after the morphological operation.

Machine Learning and Image Processing Enabled Evolutionary Framework for Brain MRI Analysis for Alzheimer's Disease Detection.

Alzheimer's disease is characterized by the presence of abnormal protein bundles in the brain tissue, but experts are not yet sure what is causing the condition. To find a cure or aversion, researchers need to know more than just that there are protein differences from the usual; they also need to know how these brain nerves form so that a remedy may be discovered. Machine learning is the study of computational approaches for enhancing performance on a specific task through the process of learning. This article presents an Alzheimer's disease detection framework consisting of image denoising of an MRI input data set using an adaptive mean filter, preprocessing using histogram equalization, and feature extraction by Haar wavelet transform. Classification is performed using LS-SVM-RBF, SVM, KNN, and random forest classifier. An adaptive mean filter removes noise from the existing MRI images. Image quality is enhanced by histogram equalization. Experimental results are compared using parameters such as accuracy, sensitivity, specificity, precision, and recall.

Multi-feature based automatic facial expression recognition using deep convolutional neural network

<p><span>Deep multi-task learning is one of the most challenging research topics widely explored in the field of recognition of facial expression. Most deep learning models rely on the class labels details by eliminating the local information of the sample data which deteriorates the performance of the recognition system. This paper proposes multi-feature-based deep convolutional neural networks (D-CNN) that identify the facial expression of the human face. To enhance the accuracy of recognition systems, the multi-feature learning model is employed in this study. The input images are preprocessed and enhanced via three filtering methods i.e., Gaussian, Wiener, and adaptive mean filtering. The preprocessed image is then segmented using a face detection algorithm. The detected face is further applied with local binary pattern (LBP) that extracts the facial points of each facial expression. These are then fed into the D-CNN that effectively recognizes the facial expression using the features of facial points. The proposed D-CNN is implemented, and the results are compared to the existing support vector machine (SVM). The analysis of deep features helps to extract the local information from the data without incurring a higher computational effort.</span></p>

ECG Denoising Methodology using Intrinsic Time Scale Decomposition and Adaptive Switching Mean Filter

Electrocardiogram (ECG) is a widely employed tool for the analysis of cardiac disorders. A clean ECG is often desired for proper treatment of cardiac ailments. However, in the real scenario, ECG signals are corrupted with various noises during acquisition and transmission. In this article, an efficient ECG de-noising methodology using combined intrinsic time scale decomposition (ITD) and adaptive switching mean filter (ASMF) is proposed. The standard performance metric namely output SNR improvement measure the efficacy of the proposed technique at various signal to noise ratio (SNR). The proposed de-noising methodology is compared with other existing ECG de-noising approaches. A detail qualitative and quantitative study and analysis indicate that the proposed technique can be used as an effective tool for de-noising of ECG signals and hence can serve for better diagnostic in computer-based automated medical system. The performance of the proposed work is compared with existing ECG de-noising techniques namely wavelet soft thresholding based filter (DWT) [16], EMD with DWT technique [18], DWT with ADTF technique [19]. The effectiveness of the presented work has been evaluated in both qualitative and quantitative analysis. All the simulations are carried out using MATLAB software environment.

ECG Denoising Methodology using Intrinsic Time Scale Decomposition and Adaptive Switching Mean Filter

Electrocardiogram (ECG) is a widely employed tool for the analysis of cardiac disorders. A clean ECG is often desired for proper treatment of cardiac ailments. However, in the real scenario, ECG signals are corrupted with various noises during acquisition and transmission. In this article, an efficient ECG de-noising methodology using combined intrinsic time scale decomposition (ITD) and adaptive switching mean filter (ASMF) is proposed. The standard performance metric namely output SNR improvement measure the efficacy of the proposed technique at various signal to noise ratio (SNR). The proposed de-noising methodology is compared with other existing ECG de-noising approaches. A detail qualitative and quantitative study and analysis indicate that the proposed technique can be used as an effective tool for de-noising of ECG signals and hence can serve for better diagnostic in computer-based automated medical system. The performance of the proposed work is compared with existing ECG de-noising techniques namely wavelet soft thresholding based filter (DWT) [16], EMD with DWT technique [18], DWT with ADTF technique [19]. The effectiveness of the presented work has been evaluated in both qualitative and quantitative analysis. All the simulations are carried out using MATLAB software environment.

A robust ECG denoising technique using variable frequency complex demodulation

Background and objectiveElectrocardiogram (ECG) is widely used for the detection and diagnosis of cardiac arrhythmias such as atrial fibrillation. Most of the computer-based automatic cardiac abnormality detection algorithms require accurate identification of ECG components such as QRS complexes in order to provide a reliable result. However, ECGs are often contaminated by noise and artifacts, especially if they are obtained using wearable sensors, therefore, identification of accurate QRS complexes often becomes challenging. Most of the existing denoising methods were validated using simulated noise added to a clean ECG signal and they did not consider authentically noisy ECG signals. Moreover, many of them are model-dependent and sampling-frequency dependent and require a large amount of computational time. MethodsThis paper presents a novel ECG denoising technique using the variable frequency complex demodulation (VFCDM) algorithm, which considers noises from a variety of sources. We used the sub-band decomposition of the noise-contaminated ECG signals using VFCDM to remove the noise components so that better-quality ECGs could be reconstructed. An adaptive automated masking is proposed in order to preserve the QRS complexes while removing the unnecessary noise components. Finally, the ECG was reconstructed using a dynamic reconstruction rule based on automatic identification of the severity of the noise contamination. The ECG signal quality was further improved by removing baseline drift and smoothing via adaptive mean filtering. ResultsEvaluation results on the standard MIT-BIH Arrhythmia database suggest that the proposed denoising technique provides superior denoising performance compared to studies in the literature. Moreover, the proposed method was validated using real-life noise sources collected from the noise stress test database (NSTDB) and data from an armband ECG device which contains significant muscle artifacts. Results from both the wearable armband ECG data and NSTDB data suggest that the proposed denoising method provides significantly better performance in terms of accurate QRS complex detection and signal to noise ratio (SNR) improvement when compared to some of the recent existing denoising algorithms. ConclusionsThe detailed qualitative and quantitative analysis demonstrated that the proposed denoising method has been robust in filtering varieties of noises present in the ECG. The QRS detection performance of the denoised armband ECG signals indicates that the proposed denoising method has the potential to increase the amount of usable armband ECG data, thus, the armband device with the proposed denoising method could be used for long term monitoring of atrial fibrillation.

Noise Reduction in CT Images Using a Selective Mean Filter.

Background: Noise reduction is a method for reducing CT dose; however, it can reduce image quality.Objective: This study aims to propose a selective mean filter (SMF) and evaluate its effectiveness for noise suppression in CT images.Material and Methods: This experimental study proposed and implemented the new noise reduction algorithm. The proposed algorithm is based on a mean filter (MF), but the calculation of the mean pixel value using the neighboring pixels in a kernel selectively applied a threshold value based on the noise of the image. The SMF method was evaluated using images of phantoms. The dose reduction was estimated by comparing the image noise acquired with a lower dose after implementing the SMF method and the noise in the original image acquired with a higher dose. For comparison, the images were also filtered with an adaptive mean filter (AMF) and a bilateral filter (BF).Results: The spatial resolution of the image filtered with the SMF was similar to the original images and the images filtered with the BF. While using the AMF, spatial resolution was significantly corrupted. The noise reduction achieved using the SMF was up to 75%, while it was up to 50% using the BF. Conclusion: SMF significantly reduces the noise and preserves the spatial resolution of the image. The noise reduction was more pronounced with BF, and less pronounced with AMF.

Adaptive colour‐guided non‐local means algorithm for compound noise reduction of depth maps

Depth maps are used to describe object positioning information in three-dimensional (3D) space, and they are crucial for RGB-D data representation, which is useful for numerous interactive visual applications. In practice, depth maps are often contaminated by compound noise, including intrinsic noise and missing regions owing to active illumination shadows. As existing noise models cannot describe the above-mentioned compound noise effectively, the subsequent filter design is a challenging task. In this study, an adaptive colour-guided non-local mean (NLM) filter is proposed to address such compound noise. First, the authors classify the depth map into hole and non-hole pixels. Then, the proposed filter is designed on the basis of the NLM framework, where the colour image is used as a guide prior for hole-artifact removal. Finally, the authors use a shock filter to effectively address the non-regularisation of the restored depth map edges and remove the remaining noise. Experiments show that the proposed filter qualitatively and quantitatively outperforms existing colour-guided and unguided filters. Moreover, the authors verify the superiority of the proposed filter through virtual view synthesis and 3D scene reconstruction applications.

Denoising Wearable Armband ECG Data Using the Variable Frequency Complex Demodulation Technique.

We propose a novel electrocardiogram (ECG) denoising technique using the variable frequency complex demodulation (VFCDM) algorithm. We used VFCDM to perform the sub-band decomposition of the noise-contaminated ECG to remove the noise components so that accurate QRS complexes could be identified. The ECG quality was further improved by removing baseline drift and smoothing via adaptive mean filtering. The proposed method was validated on the MIT-BIH arrhythmia database (MITDB) and wearable armband ECG data. For the former, we added Gaussian white noise to the ECG signals at different signal-to-noise ratios and the denoising performance of the proposed method was compared with other denoising techniques. The proposed approach showed superior denoising performance compared to the other methods. We compared the QRS complex detection performance of the noisy to the denoised armband ECG. The performance of the proposed denoising method on the armband ECG resulted in comparable QRS complex detection as that obtained when using Holter monitor ECG signals. This demonstrates that the proposed algorithm can significantly increase the amount of usable armband ECG data, which would otherwise have been discarded due to electromyogram contamination especially during arm movements. Hence, the proposed algorithm has the potential to enable long-term monitoring of atrial fibrillation using the armband without the discomfort of skin irritation often experienced with Holter monitors.Clinical Relevance- The proposed ECG denoising method can significantly increase the ECG quality of wearable ECG devices, which are more susceptible to muscle and motion artifacts.

Deep Learning Based Switching Filter for Impulsive Noise Removal in Color Images.

Noise reduction is one of the most important and still active research topics in low-level image processing due to its high impact on object detection and scene understanding for computer vision systems. Recently, we observed a substantially increased interest in the application of deep learning algorithms. Many computer vision systems use them, due to their impressive capability of feature extraction and classification. While these methods have also been successfully applied in image denoising, significantly improving its performance, most of the proposed approaches were designed for Gaussian noise suppression. In this paper, we present a switching filtering technique intended for impulsive noise removal using deep learning. In the proposed method, the distorted pixels are detected using a deep neural network architecture and restored with the fast adaptive mean filter. The performed experiments show that the proposed approach is superior to the state-of-the-art filters designed for impulsive noise removal in color digital images.

Modified decision based unsymmetric adaptive neighborhood trimmed mean filter for removal of very high density salt and pepper noise

In this paper, a novel and efficient algorithm, Modified Decision Based Unsymmetric Adaptive Neighborhood Trimmed Mean Filter, for removal of very high density salt and pepper noise (SPN) is proposed. The proposed technique comprises of two phases. The first phase involves the fusion of decision based partial trimmed global mean filter, decision based unsymmetric trimmed modified Winsorized mean filter and decision based adaptive neighborhood median filter which takes the benefits of partial trimmed global mean, unsymmetric trimmed modified Winsorized mean and neighborhood pixel technique. The second phase is applied to wipe out the left over noisy pixels by replacing the processing pixel with the Winsorized mean of the first ordered neighborhood pixels. The proposed algorithm is examined upto 99% levels of salt and pepper noise for grey scale and color bitmap images and it gives better Peak Signal to Noise Ratio (PSNR), Image Enhancement Factor (IEF) and Structural Similarity Index (SSIM) values for high noise densities.

Adaptive Filter with Type-2 Fuzzy System and Optimization-Based Kernel Interpolation for Satellite Image Denoising

Abstract Satellite image denoising is a recent trend in image processing, but faces many challenges due to the environmental factors. Previous works have developed many filters for denoising the hyperspectral satellite images. Accordingly, this work utilizes an adaptive filter with the type 2 fuzzy system and the optimization-based kernel interpolation for the satellite image denoising. Here, the image denoising has been done through three steps, namely noise identification, noise correction and image enhancement. Initially, the type 2 fuzzy system identifies the noisy pixels in the satellite image and converts the image into a binary image, which is passed through the adaptive nonlocal mean filter (ANLMF) for the noise correction. Finally, the kernel-based interpolation scheme carries out the image enhancement, which is done through the proposed chronological Jaya optimization algorithm (chronological JOA) that is developed by modifying Jaya optimization algorithm (JOA) with the chronological idea. The performance of the proposed denoising scheme is analyzed by considering the satellite images from two standard databases, namely Indian pines database and NRSC/ISRO satellite database. Also, the comparative analysis is performed with the state-of-the-art denoising methods using the evaluation metrics, peak signal to noise ratio (PSNR), structural similarity index (SSIM) and second derivative-like measure of enhancement (SDME). From the results, it is exposed that the proposed adaptive filter with the chronological JOA has the improved performance with the PSNR of 22.0408 dB, SDME of 244.133 dB and SSIM of 0.872.