Ternary Pattern Research Articles

EFFECTIVE FEATURE EXTRACTION METHOD FOR UNCONSTRAINED ENVIRONMENT: LOCAL BINARY PATTERN OR LOCAL TERNARY PATTERN

In this study, a range of algorithms addressing the challenges posed by noise and illumination were investigated. Two algorithms, namely LTP and LBP, were selected for comparison due to their demonstrated effectiveness. The process becomes time-consuming due to training samples, mainly when dealing with images featuring higher levels of noise and illumination variations, necessitating efficient algorithms for effective recognition. To compare two effective feature extraction methods viz local binary pattern (LBP) and local ternary pattern (LTP) for an unconstraint environment. The impact of noise and illumination factors is particularly pronounced in the iris datasets of non-cooperative subjects, which serve as the input images for this analysis. These algorithms were applied to diverse datasets with distinctive illumination properties to facilitate feature extraction. The results indicated that the LTP exhibited efficiency in comparison, suggesting its efficacy in handling datasets with varying illumination characteristics. A comparative analysis between LBP and LTP was conducted on two distinct datasets, namely UBIRIS and CASIA. The investigation into the sensitivity of LTP revealed heightened sensitivity during the performance analysis test, with consistent accuracy observed at 50 samples and a scale of 0.3. In the case of the CASIA iris dataset, the recital of LTP and LBP exhibited nearly identical accuracy levels, converging after 70 samples for non-cooperative iris datasets compared to the LBP.

Liver Cancer Diagnosis: Enhanced Deep Maxout Model with Improved Feature Set

This work proposed a liver cancer classification scheme that includes Preprocessing, Feature extraction, and classification stages. The source images are pre-processed using Gaussian filtering. For segmentation, this work proposes a LUV transformation-based adaptive thresholding-based segmentation process. After the segmentation, certain features are extracted that include multi-texon based features, Improved Local Ternary Pattern (LTP-based features), and GLCM features during this phase. In the Classification phase, an improved Deep Maxout model is proposed for liver cancer detection. The adopted scheme is evaluated over other schemes based on various metrics. While the learning rate is 60%, an improved deep maxout model achieved a higher F-measure value (0.94) for classifying liver cancer; however, the previous method like Support Vector Machine (SVM), Random Forest (RF), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), K-Nearest Neighbor (KNN), Deep maxout, Convolutional Neural Network (CNN), and DL model holds less F-measure value. An improved deep maxout model achieved minimal False Positive Rate (FPR), and False Negative Rate (FNR) values with the best outcomes compared to other existing models for liver cancer classification.

Diagnosing the MRI brain tumour images through RNN-LSTM

According to the World Health Organization, cancer is the second most common cause of death worldwide (WHO). Death from cancer may sometimes be avoided through early diagnosis, although this is not always practicable. A tumour, unlike cancer, may be benign, pre-cancerous, or malignant. Contrary to malignant tumours, benign tumours may frequently be surgically removed and seldom metastasize to other organs or tissues. The method most often used to differentiate between various types of tumours is magnetic resonance imaging (MRI) (MRI). But because it depends on human subjectivity, it could be challenging for one person to see a lot of data. The radiologist's ability to identify brain tumours early mostly depends on their level of training. Before determining whether the tumour is benign or malignant, the diagnostic process for it could not be finished. A biopsy is often carried out to determine if the tissue is benign or cancerous. The biopsy of a brain tumour is often delayed until the last brain operation, in contrast to tumours discovered elsewhere in the body. An efficient diagnostics tool for tumour segmentation and classification from MRI images is crucial to obtaining accurate diagnoses, avoiding surgery, and eliminating subjectivity. The classification and segmentation of the tumour part of the MRI brain picture is the goal of this study. Prior to feature extraction in the first study, pre-processing is done using the Gray Level Co-occurrence Matrix (GLCM). Based on these features, the data is then separated into normal, glioma, meningioma, and pituitary groups. In prior studies, classification was handled by traditional Deep Neural Networks (DNN), with the best weights selected using optimization methods. Accordingly, a recent study recommends including Long Short-Term Memory based on Recurrent Neural Networks (RNN) (LSTM). The Local Direction Ternary Pattern (LDTP), the Gray Level Co-occurrence Matrix (GLCM), and the Le-Net features are all combined in the proposed Hybrid GLCM-LDTP-Le-Net Feature extraction approach. The hybrid parameters produced by the feature extraction process and the brain MRI data are used by the RNN-LSTM model to categorise the data as benign or malignant. By removing mixed features from the input data, the suggested hybrid feature extraction improves the learnt models' accuracy. The accuracy of 98.99 % of the suggested hybrid feature extraction approach was higher than the current CNN-based model methodologies.

A Modified Bio-Inspired Optimizer with Capsule Network for Diagnosis of Alzheimer Disease

Recently, Alzheimer’s disease (AD) is one of the common neurodegenerative disorders, which primarily occurs in old age. Structural magnetic resonance imaging (sMRI) is an effective imaging technique used in clinical practice for determining the period of AD patients. An efficient deep learning framework is proposed in this paper for AD detection, which is inspired from clinical practice. The proposed deep learning framework significantly enhances the performance of AD classification by requiring less processing time. Initially, in the proposed framework, the sMRI images are acquired from a real-time dataset and two online datasets including Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL), and Alzheimer’s Disease Neuroimaging Initiative (ADNI). Next, a fuzzy-based superpixel-clustering algorithm is introduced to segment the region of interest (RoI) in sMRI images. Then, the informative deep features are extracted in segmented RoI images by integrating the probabilistic local ternary pattern (PLTP), ResNet-50, and Visual Geometry Group (VGG)-16. Furthermore, the dimensionality reduction is accomplished by through the modified gorilla troops optimizer (MGTO). This process not only enhances the classification performance but also diminishes the processing time of the capsule network (CapsNet), which is employed to classify the classes of AD. In the MGTO algorithm, a quasi-reflection-based learning (QRBL) process is introduced for generating silverback’s quasi-refraction position for further improving the optimal position’s quality. The proposed fuzzy based superpixel-clustering algorithm and MGTO-CapsNet model obtained a pixel accuracy of 0.96, 0.94, and 0.98 and a classification accuracy of 99.88%, 96.38%, and 99.94% on the ADNI, real-time, and AIBL datasets, respectively.

Generative adversarial network for Multimodal Contrastive Domain Sharing based on efficient invariant feature-centric growth analysis improved brain tumor classification

ABSTRACT Efficient and accurate classification of brain tumor categories remains a critical challenge in medical imaging. While existing techniques have made strides, their reliance on generic features often leads to suboptimal results. To overcome these issues, Multimodal Contrastive Domain Sharing Generative Adversarial Network for Improved Brain Tumor Classification Based on Efficient Invariant Feature Centric Growth Analysis (MCDS-GNN-IBTC-CGA) is proposed in this manuscript.Here, the input imagesare amassed from brain tumor dataset. Then the input images are preprocesssed using Range – Doppler Matched Filter (RDMF) for improving the quality of the image. Then Ternary Pattern and Discrete Wavelet Transforms (TPDWT) is employed for feature extraction and focusing on white, gray mass, edge correlation, and depth features. The proposed method leverages Multimodal Contrastive Domain Sharing Generative Adversarial Network (MCDS-GNN) to categorize brain tumor images into Glioma, Meningioma, and Pituitary tumors. Finally, Coati Optimization Algorithm (COA) optimizes MCDS-GNN’s weight parameters. The proposed MCDS-GNN-IBTC-CGA is empirically evaluated utilizing accuracy, specificity, sensitivity, Precision, F1-score,Mean Square Error (MSE). Here, MCDS-GNN-IBTC-CGA attains 12.75%, 11.39%, 13.35%, 11.42% and 12.98% greater accuracy comparing to the existingstate-of-the-arts techniques, likeMRI brain tumor categorization utilizing parallel deep convolutional neural networks (PDCNN-BTC), attention-guided convolutional neural network for the categorization of braintumor (AGCNN-BTC), intelligent driven deep residual learning method for the categorization of braintumor (DCRN-BTC),fully convolutional neural networks method for the classification of braintumor (FCNN-BTC), Convolutional Neural Network and Multi-Layer Perceptron based brain tumor classification (CNN-MLP-BTC) respectively.

Sentiment analysis technique on product reviews using Inception Recurrent Convolutional Neural Network with ResNet Transfer Learning

ABSTRACT Nowadays, online shopping has become a typical option for customers to make purchases due to the rapid technological development of the Internet. Sentiment analysis (SA) of a huge count of user reviews on e-commerce sites can successfully increase the fulfillment of user, but it is difficult so far to predict the precise sentiment polarizations of the user evaluations owing to the variations on textual arrangement, sequence length, and complex logic. In this manuscript, Inception Recurrent Convolutional Neural Network with ResNet Transfer Learning by Red Fox Optimization Algorithm using sentiment analysis on product review (IRCNN-ResNet-RFOA-SA-PR) is proposed. Here, the input data are collected through Amazon Product Reviews database and then preprocessing the input data by using whitespace tokenization, Snowball stemming, and Gensim lemmatization. The preprocessing data are supplied to the ternary pattern and discrete wavelet transform for feature extraction. Then the optimal features are selected by Fire Hawk Optimization Algorithm. After that, IRCNN-ResNet recommends the product based on SA. Finally, IRCNN-ResNet parameter is optimized by RFOA. The proposed method attains 22.37%, 31.08%, and 21.90% greater accuracy and 19.37%, 21.08%, and 25.40% greater precision when compared with existing models, such as machine learning-based SA-PR including new term weighting with feature selection mode, weighted word embedding with deep neural network-based SA-PR, and SA for e-commerce PRs in Chinese utilizing sentiment lexicon with deep learning, respectively.

Classification of cotton crop disease using hybrid model and MDFC feature extraction method

AbstractA novel Modified Deep Fuzzy Clustering (MDFC) based classification model involves four major phases. They are preprocessing, segmentation, feature extraction and finally, detection and classification phase. To reduce noise and smooth the edges of the input image of the cotton crop, bilateral filtering is first used as a preprocessing approach. Next, a modified deep fuzzy clustering is suggested for the segmentation procedure that creates a collection of segments from the preprocessed image. The segmented image is then processed to extract relevant characteristics by using an enhanced Pyramid of Histogram Orientation Gradient (PHOG), Local Directional Ternary Pattern (LDTP), and statistical‐based features. In order to detect and classify cotton crop diseases more effectively, this paper proposes a hybrid system. Here, the features are put through a detection phase, after which the extracted features are trained in the Bidirectional Gated Recurrent Unit (Bi‐GRU) model to determine whether or not the cotton crop is infected. Once it is detected to be diseased, the type of disease is classified via an improved Recurrent Neural Network (RNN). In terms of several performance metrics, the proposed model is validated in comparison with the traditional approaches. The MDFC‐based classification model outperforms existing models with a specificity of 0.9687 at a learning rate of 90. In contrast, other models achieve lower specificities: Bi‐GRU (0.8436), RNN (0.8359), CNN (0.8654), LSTM (0.8769), SVM (0.7983), VGG16 (0.8619), DCNN (0.8725), BI‐RNN + BI‐LSTM (0.7869), and NN + CNN (0.85478).

Bidirectional gated recurrent unit with auto encoders for detecting arrhythmia using ECG data

Heartbeat irregularity or arrhythmia of a patient with serious congestive cardiovascular breakdown is distinguished in this article, and the class not really settled utilizing long haul ECG information. Several existing methods on arrhythmia detection are utilized, which does not accurately classify the Arrhythmia and it takes higher computational time. Morphological filtering and Extended Empirical wavelet transformation preprocess the input data to remove noises and base line. Then these pre-processed data is fed to Ternary Pattern and Discrete Wavelet Transform (TP-DWT) related feature extracting to take relevant feature. Then, the extracted features are given to Bidirectional Gated Recurrent Unit with Auto Encoder (BiGRU-AE) to classify Arrhythmia into Normal, Atrial Fibrillation (AF), First-degree atrioventricular block (1-AVB), Left Bundle Branch (LBBB), Right Bundle Branch Block (RBBB), Premature Atrial Contraction (PAC), Premature Ventricular Contraction (PVC), ST-segment depression (STD) and ST-segment elevation (STE). The proposed BiGRU-AE-AD-ECG is activated in Python and its efficiency is measured under certain metrics, like sensitivity, precision, f-measure, specificity, accuracy, error rate, ROC and computational time. Here, CPSC 2018 dataset is used to validate the efficiency of the BiGRU-AE-AD-ECG method. The performance of BiGRU-AE-AD-ECG method provides 23.40 %, 18.78 % and 28.05 % higher accuracy, 20.41 %, 27.07 % and 18.80 % higher precision, 32.01 %, 27.40 % and 28.95 % higher sensitivity and 28.05 %, 23.41 % and 31.34 % higher specificity analyzed with existing models, like automatic cardiac arrhythmias categorization based on CNN and attention-based RNN network (CNN-RNN-AD-ECG), a multiple lead-branch fusion network under 12-Lead ECG for multiple class arrhythmia categorization (MLBFNet-AD-ECG), analysis of ECG-based arrhythmia identification scheme using machine learning (SVM-AD-ECG)respectively.

Recalling-Enhanced Recurrent Neural Network optimized with Chimp Optimization Algorithm based speech enhancement for hearing aids

Background noise often distorts the speech signals obtained in a real-world environment. This deterioration occurs in certain applications, like speech recognition, hearing aids. The aim of Speech enhancement (SE) is to suppress the unnecessary background noise in the obtained speech signal. The existing approaches for speech enhancement (SE) face more challenges like low Source-distortion ratio and memory requirements. In this manuscript, Recalling-Enhanced Recurrent Neural Network (R-ERNN) optimized with Chimp Optimization Algorithm based speech enhancement is proposed for hearing aids (R-ERNN-COA-SE-HA). Initially, the clean speech and noisy speech are amassed from MS-SNSD dataset. The input speech signals are encoded using vocoder analysis, and then the Sample RNN decode the bit stream into samples. The input speech signals are extracted using Ternary pattern and discrete wavelet transforms (TP-DWT) in the training phase. In the enhancement stage, R-ERNN forecasts the associated clean speech spectra from noisy speech spectra, then reconstructs a clean speech waveform. Chimp Optimization Algorithm (COA) is considered for optimizing the R-ERNN which enhances speech. The proposed method is implemented in MATLAB, and its efficiency is evaluated under some metrics. The R-ERNN-COA-SE-HA method provides 23.74%, 24.81%, and 19.33% higher PESQ compared with existing methods, such as RGRNN-SE-HA, PACDNN-SE-HA, ARN-SE-HA respectively.

Balancing composite motion optimization using R-ERNN with plant disease

Soybean is a crop with a long cultivation history that plays a significant role on agricultural field. Early diagnosis of Soybean Mosaic Virus Disease (SMVD) is must require, because it causes a fast reduction and significant losses in soybean yields. Therefore, this paper proposes a Balancing Composite Motion optimization utilizing Recalling-Enhanced Recurrent Neural Network with Plant Disease for separating its severity into 0, 1 and 2 grades. Initially, hyper spectral image data was taken from Spec View software. Then, the spectral imageries of soybean leaves are processed to remove the redundancy in frequency bands that emerges owing to hyper spectral camera does not present any important information are filtered by Dual Tree Complex Wavelet Transform (DTCWT). In this, hyper spectral image features are extracted using Ternary pattern and discrete wavelet transform (TP-DWT) method. After completing the process of feature extraction, the feature extracted imageries are given to Recalling-Enhanced Recurrent Neural Network (RE-RNN) and it is used to classify the Soybean mosaic disease. Here, Balancing Composite Motion Optimization (BCMO) algorithm is employed for tuning the RE-RNN hyper parameters. The proposed PDI-RE-RNN-BCMO-HSI method provides 27.03%, 28.94% and 39.04% higher precision compared with existing method like grading model of soybean mosaic infection depending upon hyper spectral imaging technology (PDI-CNN-SVM-HSI), recognization of soybean varieties under hyper spectral imaging technology and one-dimensional CNN (PDI-1D-CNN-HSI) and hyper spectral imaging technology combined with multivariate models to recognize soybean disease (PDI-SNV-ELM-HSI) respectively.

Multimodal emotion recognition based on the fusion of vision, EEG, ECG, and EMG signals

This paper presents a novel approach for emotion recognition (ER) based on Electroencephalogram (EEG), Electromyogram (EMG), Electrocardiogram (ECG), and computer vision. The proposed system includes two different models for physiological signals and facial expressions deployed in a real-time embedded system. A custom dataset for EEG, ECG, EMG, and facial expression was collected from 10 participants using an Affective Video Response System. Time, frequency, and wavelet domain-specific features were extracted and optimized, based on their Visualizations from Exploratory Data Analysis (EDA) and Principal Component Analysis (PCA). Local Binary Patterns (LBP), Local Ternary Patterns (LTP), Histogram of Oriented Gradients (HOG), and Gabor descriptors were used for differentiating facial emotions. Classification models, namely decision tree, random forest, and optimized variants thereof, were trained using these features. The optimized Random Forest model achieved an accuracy of 84%, while the optimized Decision Tree achieved 76% for the physiological signal-based model. The facial emotion recognition (FER) model attained an accuracy of 84.6%, 74.3%, 67%, and 64.5% using K-Nearest Neighbors (KNN), Random Forest, Decision Tree, and XGBoost, respectively. Performance metrics, including Area Under Curve (AUC), F1 score, and Receiver Operating Characteristic Curve (ROC), were computed to evaluate the models. The outcome of both results, i.e., the fusion of bio-signals and facial emotion analysis, is given to a voting classifier to get the final emotion. A comprehensive report is generated using the Generative Pretrained Transformer (GPT) language model based on the resultant emotion, achieving an accuracy of 87.5%. The model was implemented and deployed on a Jetson Nano. The results show its relevance to ER. It has applications in enhancing prosthetic systems and other medical fields such as psychological therapy, rehabilitation, assisting individuals with neurological disorders, mental health monitoring, and biometric security.

Toward Efficient Convolutional Neural Networks With Structured Ternary Patterns.

High-efficiency deep learning (DL) models are necessary not only to facilitate their use in devices with limited resources but also to improve resources required for training. Convolutional neural networks (ConvNets) typically exert severe demands on local device resources and this conventionally limits their adoption within mobile and embedded platforms. This brief presents work toward utilizing static convolutional filters generated from the space of local binary patterns (LBPs) and Haar features to design efficient ConvNet architectures. These are referred to as Structured Ternary Patterns (STePs) and can be generated during network initialization in a systematic way instead of having learnable weight parameters thus reducing the total weight updates. The ternary values require significantly less storage and with the appropriate low-level implementation, can also lead to inference improvements. The proposed approach is validated using four image classification datasets, demonstrating that common network backbones can be made more efficient and provide competitive results. It is also demonstrated that it is possible to generate completely custom STeP-based networks that provide good trade-offs for on-device applications such as unmanned aerial vehicle (UAV)-based aerial vehicle detection. The experimental results show that the proposed method maintains high detection accuracy while reducing the trainable parameters by 40%-80%. This work motivates further research toward good priors for nonlearnable weights that can make DL architectures more efficient without having to alter the network during or after training.

Local binary pattern and its derivatives to handwriting-based gender classification

Several studies by psychologists and computer scientists have verified the link between handwriting and writer gender. The texture of the writing image is a major indicator of whether it is male or female writing. This paper conducts a comparison analysis to examine the effectiveness of various local binary patterns (LBPs) techniques in detecting gender from scanned images of handwriting. We study different LBP variants, including complete local binary pattern (CLBP), local ternary pattern (LTP), local configuration pattern (LCP), rotated local binary pattern (RLBP), local binary pattern variance (LBPV), and multi-scale local binary pattern (MLBP), as features for representing handwriting images. A support vector machine (SVM) is trained using features from male and female writing. The method achieves encouraging classification rates of 76.68 when tested on subsets of the Qatar University writer identification (QUWI) dataset containing English and Arabic writing samples when using the experimental protocols of the International Conference on Document Analysis and Recognition (ICDAR) 2013 gender classification competitions.

Pneumonia screening on chest X-rays with optimized ensemble model

Pneumonia is a lung illness that may result from a variety of various viral diseases and may be lethal. It might be difficult to diagnose and treat pneumonia on chest X-ray pictures because pneumonia and other lung illnesses are closely connected. As a result, the present methods for forecasting pneumonia cannot achieve meaningful levels of accuracy. This study aids in identifying diseases from chest X-ray images that don't exhibit the same symptoms as pneumonia. Five stages will be included in the projected model: (a) pre-processing, (b) segmentation, (c) feature extraction, (d) optimal feature selection, and (e) detection. The gathered image is initially pre-processed using Dynamic Histogram Equalization (DHE), followed in this case by median filtering. At the segmentation phase, by utilizing the Enhanced Watershed Segmentation, the ROI is separated from the background region of the pre-processed image. Additionally, in the feature extraction step, features based on Local Gradient Increasing Pattern (LGIP), Harmonic Local Gradient Pattern (hLGP), Improved Local Ternary Pattern (ILTP), and Improved Local Gradient Pattern (ILGP) will be derived from the isolated ROI areas. Using the proposed AHGOA (Archimedes-assisted Henry Gas Optimization Algorithm) model, the best characteristics from these retrieved features were selected. This is the best option for avoiding the dimensionality curse. The selected EC + AHGOA method obtain good accuracy (∼0.95) for tuning percentage 70 in pneumonia diagnosis from Chest X-ray images than some other previous techniques includes EC + AOA (∼0.92), EC + HGSO (∼0.93), EC + HGS (∼0.88), EC + PRO (∼0.90), and EC + BES (∼0.89).

Gannet devil optimization-based deep learning for skin lesion segmentation and identification

Skin lesion identification is a way of identifying abnormal skin tissues from normal skin. The affected area of the skin are segmented in the skin lesion segmentation process, which helps to improve the identification task. In this paper, the skin lesion segmentation and identification process are completed through efficient deep-learning networks. Here, the Wiener filter is employed for preprocessing and is used to cleans up the noise present in the image. The UNet++ model completes the skin lesion segmentation and it separates the affected region from the noise-free images. By considering the segmented portion, the prevalent features namely Convolutional Neural Network (CNN) features, Local Optimal Oriented Pattern (LOOP), statistical features, Gray-Level Co-Occurrence Matrix (GLCM) features, Local ternary pattern (LTP), and Shape Local Binary Texture (SLBT) features are mined out. Moreover, the LeNet model completes the skin lesion segmentation and the weight of both UNet++ and LeNet is tuned by the devised Gannet Devil Optimization Algorithm (GDO), which is the incorporation of Gannet Optimization Algorithm (GOA) and Tasmanian Devil Optimization algorithm (TDO). To segment and classify the lesions from the medical images, the dataset SIIM-ISIC Melanoma Identification data is taken into consideration. Besides, the experimental result indicates the GDO-LeNet provided improved performance regarding the accuracy, True Positive Rate (TPR), True Negative Rate (TNR), positive predictive value (PPV), and Negative predictive value (NPV) of 0.924, 0.925, 0.937, 0.896 and 0.890.

Self-attention based progressive generative adversarial network optimized with momentum search optimization algorithm for classification of brain tumor on MRI image

This manuscript proposes a self-attention based progressive generative adversarial network optimized with momentum search optimization algorithm for brain tumor classification on MRI image (SPGAN-MSOA-CBT-MRI). Initially, the data are gathered through the Brats 2019 dataset. Afterward, the data are given to pre-processing. In pre-processing, it reduces the noise and maximizes the superiority of input image utilizing anisotropic diffusion Kuwahara filtering (ADKF). Then the pre-processing output is fed to Feature extraction segment. Here, six texture features, like homogeneity (Angular Second Moment), contrast, Inverse difference moment, entropy, correlation and variance are extracted based on ternary pattern and discrete wavelet transform (TP-DWT). The extracted features are given to SPGAN for effectively classify the brain tumor on MRI image. The proposed SPGAN-MSOA-CBT-MRI approach is implemented in MATLAB. The performance metrics is evaluated to check the robustness of the proposed technique. The performance of proposed SPGAN-MSOA-CBT-MRI approach attains 6.45%, 9.45%, and 11.67% high accuracy;7.23%, 10.34%, and 12.56% high F-score compared with existing methods, such as GAM-SpCaNet: Gradient awareness minimization-based spinal convolution attention network for brain tumor classification (GAM-SpCaNet-CBT-MRI), Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel (CNN RCNN-CBT-MRI) and Smart brain tumor detection scheme with the help of deep convolutional neural networks (DCNN-CBT-MRI) respectively.

Induction Motor Fault Diagnosis with Local Ternary Pattern and AI Approaches

Induction Motor Fault Diagnosis with Local Ternary Pattern and AI Approaches

Cepstral and acoustic ternary pattern based hybrid feature extraction approach for end-to-end bangla speech recognition

Cepstral and acoustic ternary pattern based hybrid feature extraction approach for end-to-end bangla speech recognition

Skin cancer detection: Improved deep belief network with optimal feature selection

During the past few decades, melanoma has grown increasingly prevalent, and timely identification is crucial for lowering the mortality rates linked to this kind of skin cancer. Because of this, having access to an automated, trustworthy system that can identify the existence of melanoma may be very helpful in the field of medical diagnostics. Because of this, we have introduced a revolutionary, five-stage method for detecting skin cancer. The input images are processed utilizing histogram equalization as well as Gaussian filtering techniques during the initial pre-processing stage. An Improved Balanced Iterative Reducing as well as Clustering utilizing Hierarchies (I-BIRCH) is proposed to provide better image segmentation by efficiently allotting the labels to the pixels. From those segmented images, features such as Improved Local Vector Pattern, local ternary pattern, and Grey level co-occurrence matrix as well as the local gradient patterns will be retrieved in the third stage. We proposed an Arithmetic Operated Honey Badger Algorithm (AOHBA) to choose the best features from the retrieved characteristics, which lowered the computational expense as well as training time. In order to demonstrate the effectiveness of our proposed skin cancer detection strategy, the categorization is done using an improved Deep Belief Network (DBN) with respect to those chosen features. The performance assessment findings are then matched with existing methodologies.

ODQN-Net: Optimized Deep Q Neural Networks for Disease Prediction Through Tongue Image Analysis Using Remora Optimization Algorithm.

Tongue analysis plays the major role in disease type prediction and classification according to Indian ayurvedic medicine. Traditionally, there is a manual inspection of tongue image by the expert ayurvedic doctor to identify or predict the disease. However, this is time-consuming and even imprecise. Due to the advancements in recent machine learning models, several researchers addressed the disease prediction from tongue image analysis. However, they have failed to provide enough accuracy. In addition, multiclass disease classification with enhanced accuracy is still a challenging task. Therefore, this article focuses on the development of optimized deep q-neural network (DQNN) for disease identification and classification from tongue images, hereafter referred as ODQN-Net. Initially, the multiscale retinex approach is introduced for enhancing the quality of tongue images, which also acts as a noise removal technique. In addition, a local ternary pattern is used to extract the disease-specific and disease-dependent features based on color analysis. Then, the best features are extracted from the available features set using the natural inspired Remora optimization algorithm with reduced computational time. Finally, the DQNN model is used to classify the type of diseases from these pretrained features. The obtained simulation performance on tongue imaging data set proved that the proposed ODQN-Net resulted in superior performance compared with state-of-the-art approaches with 99.17% of accuracy and 99.75% and 99.84% of F1-score and Mathew's correlation coefficient, respectively.