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Background: Vitamin D3 supplementation showed significant improvement in lung functions and exercise tolerance in both of asthma and COPD patients. Objectives: To evaluate the effects of vitamin D3 supplementation on lung functions and exercise tolerance in D3 deficient, stable patients with ACO. Methods: A double blinded randomized placebo controlled trial was carried out on 60 (sixty) D3 deficient [serum 25-hydroxycholecalciferol, 25(OH)D

Introduction: The study compares the surgical treatment options utilized to treat temporomandibular joint ankylosis at a tertiary level hospital in Bangladesh from 2016 to 2017. Materials and Methods: In this retrospective analysis, 18 patients (28 joints) were studied. History, radiological and physical examinations, and mouth opening were all part of the preoperative and postoperative evaluation. Age, sex, underlying cause, affected joint (s), surgery modality, complications, and follow-up durations were all taken into consideration. The data was analyzed using the SPSS 22.0 statistical software. The degree of mouth opening before and after different surgical techniques in different patient categories was compared using a one-way ANOVA followed by Tuky's HSD test. Results: Temporomandibular joint ankylosis was found to be in several forms (fibrous, fibro-osseous, and osseous), with trauma (61.11%) being the most common cause. The patients ranged in age from 5 to 30 years old, with females accounting for 55.55% of the total. The mean mouth opening significantly increased from 3.11 mm pre-operatively to 31.17 mm in the first month following surgery (p = 0.00001). In 55.55% of the instances, temporary facial nerve paresis occurred. Conclusions: Temporomandibular joint ankylosis can be successfully managed with early excision of the ankylotic mass, restoration of the ramus height by bone grafting, interpositional arthroplasty, and intensive physiotherapy. Update Dent. Coll. j: 2022; 12(2): 7-13

The ample amount of information from hyperspectral image (HSI) bands allows the non-destructive detection and recognition of earth objects. However, dimensionality reduction (DR) of hyperspectral images (HSI) is required before classification as the classifier may suffer from the curse of dimensionality. Therefore, dimensionality reduction plays a significant role in HSI data analysis (e.g., effective processing and seamless interpretation). In this article, a sophisticated technique established as t-Distributed Stochastic Neighbor Embedding (tSNE) following the dimension reduction along with a blended CNN was implemented to improve the visualization and characterization of HSI. In the procedure, first, we employed principal component analysis (PCA) to reduce the HSI dimensions and remove non-linear consistency features between the wavelengths to project them to a smaller scale. Then we proposed tSNE to preserve the local and global pixel relationships and check the HSI information visually and experimentally. Lastly, it yielded two-dimensional data, improving the visualization and classification accuracy compared to other standard dimensionality-reduction algorithms. Finally, we employed deep-learning-based CNN to classify the reduced and improved HSI intra- and inter-band relationship-feature vector. The evaluation performance of 95.21% accuracy and 6.2% test loss proved the superiority of the proposed model compared to other state-of-the-art DR reduction algorithms.

Hyperspectral image (HSI) usually holds information of land cover classes as a set of many contiguous narrow spectral wavelength bands. For its efficient thematic mapping or classification, band (f...

Subspace detection from high dimensional hyperspectral image (HSI) data cube has become an important area of research for efficient identification of ground objects. Standard feature extraction met...

Background: As asthma-COPD overlap (ACO) and COPD share some common clinical features, this makes difficult to distinguish them from each other. Since, both of them are characterized by airway inflammation, any inflammatory biomarker such as total count (TC) of white blood cells (WBC), neutrophil, eosinophil and platelet can be used for that purpose. Objective: To assess the role of TC of WBC, neutrophil, eosinophil and platelet in diagnosis of ACO among patients with COPD. Methods: This cross sectional study was conducted in the Department of Physiology, Bangabandhu Sheikh Mujib Medical University, Dhaka from 2019 March to Feb 2020. In this study, 63 male stable patients (age range: 40 to 80 years) of COPD were enrolled. Among them, 51 patients were finally selected according to exclusion criteria and divided into two groups, ACO (n=26) and COPD (n=25). TC of WBC, neutrophil, eosinophil and platelet of all patients were measured by standard lab procedure. For statistical analysis, Chi-Square and Fisher exact test, Shapiro- Wilk test, Mann-Whitney U test were done as applicable. More over, receiver operating characteristic (ROC) curve (AUC) and optimal cutoff values, ROC curve analysis was performed. Results: In the present study, TC of WBC and neutrophil count were significantly (p

The hyperspectral remote sensing images (HSIs) are acquired to encompass the essential information of land objects through contiguous narrow spectral wavelength bands. The classification accuracy i...

In the present time, a large number of modified estimators have been proposed by authors to obtain efficiency. In this study, we suggested an alternative regression type estimator for estimating finite population means when there is either a positive or negative correlation between study variables and auxiliary variables. We obtained bias and mean square error equation of the proposed estimator ignoring the first-order approximation and found the theoretical conditions that make proposed estimator more efficient than simple random sampling mean estimator, product estimator and ratio estimator. In addition, these conditions are supported by a numerical example and it has been concluded that the proposed estimator performed better comparing with the usual simple random sampling mean estimator, ratio estimator and product estimator.

A newly invented post-translational modification (PTM), phosphoglycerylation, has shown its essential role in the construction and functional properties of proteins and dangerous human diseases. Hence, it is very urgent to know about the molecular mechanism behind the phosphoglycerylation process to develop the drugs for related diseases. But accurately identifying of phosphoglycerylation site from a protein sequence in a laboratory is a very difficult and challenging task. Hence, the construction of an efficient computation model is greatly sought for this purpose. A little number of computational models are currently available for identifying the phosphoglycerylation sites, which are not able to reach their prediction capability at a satisfactory level. Therefore, an effective predictor named PLP_FS has been designed and constructed to identify phosphoglycerylation sites in this study. For the training purpose, an optimal number of feature sets was obtained by fusion of multiple F_Score feature selection techniques from the features generated by three types of sequence-based feature extraction methods and fitted with the support vector machine classification technique to the prediction model. On the other hand, the k-neighbor near cleaning and SMOTE methods were also implemented to balance the benchmark dataset. The suggested model in 10-fold cross-validation obtained an accuracy of 99.22%, a sensitivity of 98.17% and a specificity of 99.75% according to the experimental findings, which are better than other currently available predictors for accurately identifying the phosphoglycerylation sites.

A hyperspectral image (HSI), which contains a number of contiguous and narrow spectral wavelength bands, is a valuable source of data for ground cover examinations. Classification using the entire original HSI suffers from the “curse of dimensionality” problem because (i) the image bands are highly correlated both spectrally and spatially, (ii) not every band can carry equal information, (iii) there is a lack of enough training samples for some classes, and (iv) the overall computational cost is high. Therefore, effective feature (band) reduction is necessary through feature extraction (FE) and/or feature selection (FS) for improving the classification in a cost-effective manner. Principal component analysis (PCA) is a frequently adopted unsupervised FE method in HSI classification. Nevertheless, its performance worsens when the dataset is noisy, and the computational cost becomes high. Consequently, this study first proposed an efficient FE approach using a normalized mutual information (NMI)-based band grouping strategy, where the classical PCA was applied to each band subgroup for intrinsic FE. Finally, the subspace of the most effective features was generated by the NMI-based minimum redundancy and maximum relevance (mRMR) FS criteria. The subspace of features was then classified using the kernel support vector machine. Two real HSIs collected by the AVIRIS and HYDICE sensors were used in an experiment. The experimental results demonstrated that the proposed feature reduction approach significantly improved the classification performance. It achieved the highest overall classification accuracy of 94.93% for the AVIRIS dataset and 99.026% for the HYDICE dataset. Moreover, the proposed approach reduced the computational cost compared with the studied methods.

In recent years, the demand for prompt detection and classification of breast cancer is rising sharply as breast cancer has become leading cancer type among women throughout the world. Convolutional Neural Networks(CNNs) are widely being used for performing mentioned tasks.However, they need a large number of labeled images which may appear to be infeasible for some kinds of medical images data such as mammographic tumor images. To address this difficulty, Transfer Learning becomes convenient. In this paper, we proposed a deep learning model for classifying Benign and Malignant types of breast tumor that trains an InceptionV3 model which pulls out features from the histopathological images of various magnification. These features are then used for classification. Introduced system is validated on BreakHis dataset and gains average validation set accuracy of 99.50%, 98.90%, 98.96% and 98.51% for magnification factor 40X, 100X, 200X and 400X respectively which outperforms all studied baseline models. Different performance metrices such as precision, recall, F 1 score, Specificity have additionally been used for performance estimation purposes.

The remote sensing hyperspectral images (HSIs) usually comprise many important information of the land covers capturing through a set of hundreds of narrow and contiguous spectral wavelength bands....

Lysine formylation is a newly discovered and mostly interested type of post-translational modification (PTM) that is generally found on core and linker histone proteins of prokaryote and eukaryote and plays various important roles on the regulation of various cellular mechanisms. Hence, it is very urgent to properly identify formylation site in protein for understanding the molecular mechanism of formylation deeply and defining drug for relevant diseases. As experimentally identification of formylation site using traditional processes are expensive and time consuming, a simple and high speedy mathematical model for predicting accurately lysine formylation sites is highly desired. A useful computational model named PLF_SVM is deigned and proposed in this study by using binary encoding (BE), amino acid composition (AAC), reverse position relative incidence matrix (RPRIM), position relative incidence matrix (PRIM), and position specific amino acid propensity (PSAAP) feature generation methods for predicting formylated and non-formylated lysine sites. Besides, the Synthetic Minority Oversampling Technique (SMOTE) and a proposed sample selection strategy named EnSVM are applied to handle the imbalance training dataset problem. Thereafter, the optimal number of features are selected by F-score method to train the model. Finally, it has been seen that PLF_SVM outperforms the state-of-the-art approaches in validation and independent test with an accuracy of 98.61% and 98.77% respectively. At https://plf-svm.herokuapp.com/, a user-friendly web tool is also created for identifying formylation sites. Therefore, the proposed method may be helpful guideline for the analysis and prediction of formylated lysine and knowing the process of cellular regulation.

Document images can have numerous noises that should be filtered to eliminate those unutilized objects. Skewness is liable not to understand the texts clearly in the document image. A morphological technique is being conducted to preprocess the image. Connected component analysis along with the bounding box approach is introduced to figure out a line. A number of longest connected components are extracted from numerous illustrations. In order to detect a precise skewed angle, the least square method is applied to the logically selected regions to acquire the best fitting line. The skewed angle is discovered by averaging angles of best-fitting lines. The proposed system is already applied on the printed document image and got tremendous accuracy during execution.

Being electrocardiogram already an established method for analyzing cardiac health, it gained many researchers' interests to classify heartbeats accurately. In spite of having numerous works in this field, it still lacks obtaining high accuracy scores. In this paper, some well-known machine learning approaches are used by tuning and compared with other state-of-the-art related methodologies. The datasets are used in this research work, are highly imbalanced and handled with penalizing the loss value of the Artificial Neural Network (ANN) by assigning class weights. Two different enriched ECG datasets are selected for this research. They are MIT-BIH Arrhythmia which contains five classes and PTB Diagnostic ECG which contains two classes. About 98.06% and 97.664% accuracy are achieved with proposed approaches for MIT-BIH Arrhythmia and PTB Diagnostic ECG dataset respectively. Both cases this research outperforms all the other state-of-the-art methodologies.

Hyperspectral image classification has become a very important area of research in recent years. However the classif cation become very challenging as the input dimension is very high. This is because the cost increases with the number of input features used to describe the pixel vectors. This problem can be addressed by reducing the irrelevant features for the task of classification. Moreover all the features are no equally important for classification. Principal Component Analysis (PCA) extracts feature however it depends on global variance. To overcome these limitations, a class-oriented subspace detection method is proposed which measures the relevancy of the selected subspace using Normalized Mutual Information (NMI) and Cross Cumulative Residual Entropy (CCRE). The application of NMI and CCRE over PCA images maximize the relevance as well as provided an uncorrelated subspace. Experimental analysis was performed to assess the effectiveness of the proposed method and the selected subspace was evaluated using the Support Vector Machine (SVM). Our proposed method achieves 87.61% and 88.57% classification accuracy respectively.

Technological advancement has been developed immensely in two areas over the past few years; these are computer and communication networks. This breakthrough has provided an opportunity for people around the world to access literature and important information. If the database of these manuscripts can be developed and make them available on the internet, the people will access those information as desired. The documents can be well-preserved in digital library so that they cannot be degraded. They are trying to improve the recognition rate in order to get the precise meaning of texts. Distorted character recognition is complicated in contrast with simple plain text images. Sometimes distorted character is seemed to be other character and the accuracy rate drops down drastically. As a result significant information is misplaced. In this paper, we are proposing an algorithm which will improve character recognition rate however it is distorted document image. The accuracy rate of our recommended algorithm is 97%.

In recent years, the classification of Hyper Spectral Image (HSI) has posed a big challenge for the analysis of multidimensional property of the image. So it is of utmost importance to reduce the dimension of HSIs. There are several ways to reduce the dimension of hyperspectral images such as Principle Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Kernel Entropy Component Analysis (KECA) and so on. Through this article, We proposed an updated variant of KPCA using multiple kernels such as Linear, RBF, Cosine, Sigmoid, etc. We fused their spectral and special properties by classifying the HSIs using Hybrid Spectral Net Model (HybridSN) which is a recently trending modified deep neural network algorithm using Convolutional Neural Network (CNN). This paper presents empirical outcomes of the effects of using different kernels of KPCA algorithm and their performances regarding the classification of well-known hyperspectral data sets.

Brain tumor classification is one of the most important aspects in the fields of medical image analysis. As tumors are regarded as precursor to cancers, efficient brain tumor classification can prove life saving. For this reason, Convolutional Neural Network(CNN) based approaches are widely being used for classifying brain tumors. However there lies a dilemma, CNNs are accustomed to large amount of training data for giving better result. It is where transfer learning comes useful. In this paper, we propose 3-class deep learning model for classifying Glioma, Meningioma and Pituitary tumors which are regarded as three prominent types of brain tumor. Our proposed model by adopting the concept of transfer learning uses a pre-trained InceptionV3model extracts features from the brain MRI images and deploys softmax classifier for classification. The proposed system is tested on CE-MRI dataset from figshare and achieves an average classification accuracy of 99%, outperforming all previous methods. Few other performance measures such as precision, recall, F-score are also considered while assessing the performance.

Remotely sensed images provide unseen characteristics of the earth's surface as they are composed of hundreds of spectral bands of the electromagnetic spectrum. Both spatial and spectral information of hyperspectral image make it possible to categories vegetation and recognize earth's minerals and materials. But analysis of hyperspectral data suffers from curse of dimensionality due to the huge number of spectral bands or features. Not all features contain useful information and additionally, there is redundant information in some features. This paper proposes a model for detecting effective feature subspace from original hyperspectral data using both Segmented Principal Component Analysis and F-score methods. Depending on the correlation of the spectral bands, the data cube is partitioned into subgroups. Then principal component transform is performed on each subgroup. Finally, the most informative feature subspace is selected by considering discriminative characteristics of the features using F-score method. Two real hyperspectral images are used in the implementation of the proposed model. The classification accuracy of the proposed approach shows the superiority over other studied methods.

An architecture is proposed in this paper, which combines both the U-net and Neural Ordinary Differential Equations for semantic segmentation. This method consumes very lower memory and at the same time in many cases outperforms some state-of-the-art methodologies in terms of very well known performance metrics for semantic segmentation. The proposed approach is tested on three datasets, two of them are medical images and another one is self-supervised dataset. For all the datasets, the proposed approach outperforms the state-of-the-art methods with the same environmental setup.

Hyperspectral Image (HSI) is a rich source of information for the analysis of the earth's surface. HSI produces a rich set of both spectral and spatial information for possible recognition of earth materials, minerals and vegetation categories. Since HSI has high dimensional spectral information so that, feature extraction methods has been used to reduce the dimensions. The most widely used feature extraction method Principal Component Analysis (PCA) is applied in HSI for dimension reduction. The aim of this paper is to analyze PCA and its different variants Segmented-PCA (SPCA), Folded-PCA (FPCA), and its nonlinear approach Kernel-PCA (KPCA) for effective feature extraction and classification of HSI. Moreover, the noise adjusted methods Minimum Noise fraction (MNF) and its variants segmented MNF is also studied for comparing the feature extraction methods. For comparing the robustness of the studied methods, two real HSI is used for the experiments. The experiments show that the classification accuracy of the MNF method are 95.94% and 97.61% for AVIRIS and HYDICE datasets respectively which outperforms that other PCA based methods.

In recent years, the classification of Hyper Spectral Image (HSI) is a big challenge for its multidimensional property. So it is burning question to reduce the dimension of HSIs. There are several ways to reduce the dimension of hyperspectral images like Principle Component Analysis (PCA), Kernel Principle Component Analysis (KPCA), Kernel Entropy Component Analysis (KECA) and so on. In this paper, we proposed a modified version of KPCA using multiple kernels like Linear, Radial Basis Function (RBF), Cosine, Sigmoid. Then fused their spectral and special properties by doing the classification of the HSIs using Hybrid Spectral Net (HybridSN) Model which is a recently trending modified deep neural network algorithm of Convolutional Neural Network (CNN). Finally, this paper demonstrates experimental results to show the effects and performance on classification of using different kernels of KPCA algorithm with other algorithms such as Non-negative Matrix Factorization(NMF), Independent Component Analysis (ICA) and Singular Value Decomposition(SVD) on well-known hyperspectral dataset.

This paper is concerned about the detection procedure of drowsiness that causes fatal road accidents leading to death. Increasing lack of awareness of traffic rules is doubling the number of accidents daily. However, detection and indication of driver fatigue is an active area of research. In this concussion, both inside and outside individuals of the car are victimized. However, prevention of drowsiness requires a technique to detect the deterioration legitimately along with a warning mechanism of the vehicle operator. Although the existing solutions are created using some distinctive methods, there are some problems such as costly sensors and handling of information. The objective of this research is to create an improved, innovative, cost efficient and real time strategy for solving this problem of blinking, yawn, and head bending. A pre-trained model by a histogram-oriented gradient (HOG) and a linear vector support machine (SVM) extracts the eye, nose and mouth position and assesses the eye aspect ratio (EAR), moutt opening ratio (MOR) and nose length ratio (NLR). These pieces of information are then compared with the value threshold adapted from the sleeping or drowsy face models aspect ratio data set.