Numerical Study of Kermack-Mckendrik SIR Model to Predict the Outbreak of Ebola Virus Diseases Using Euler and Fourth Order Runge-Kutta Methods

Mathematical Modeling has emerged as a vital tool for understanding the dynamics of the spread of many infectious diseases, one amongst is Ebola virus. The main focus of this paper is to model mathematically the transmission dynamics of Ebola virus. For this purpose we tend to use basic SIR model of Ebola Virus to predict the outbreak of the diseases. As we cannot fully solve the 3 basic equations of SIR model with a certain formula solution, we introduce Euler and fourth-order Runge-Kutta methods (RK4). These two proposed strategies are quite efficient and practically well suited for solving initial value problem (IVP) for ordinary differential equations (ODE).We discuss the numerical comparisons between Euler method and Runge-Kutta methods and also discuss regarding their performances with the actual data. The population that we used for this model had roughly a similar number of individuals as the number was living in Republic of Liberia during 2014

Magnetic-Mixed Convection in Nanofluid-Filled Cavity Containing Baffles and Rotating Hollow-Cylinders with Roughness Components

Mixed convective heat transfer in a nanofluid-filled lid-driven square cavity equipped with a rotating cylinder, horizontal baffles, and an external magnetic field is numerically examined in this study. A cylinder with triangular components is set at the centre of the cavity while two horizontal baffles are fixed to its vertical walls. The cavity is under the impact of the external magnetic field. Modified Maxwell’s model is taken into consideration to estimate the thermal conductivity of nanofluids. Galerkin FEM is applied to simulate nondimensional governing equations. The computations are carried out for specific ranges of physical parameters, and the results are illustrated through streamlines, isotherms, and average Nusselt number bar charts. Contours plotting indicate that flow circulation and distribution of temperature are significantly affected by the speed of a rotating rough cylinder. The fluid velocity remarkably increases with an increase in speed ratio and Reynolds number but it declines with Hartmann number, baffle length, and volume fraction. Heat transfer rate is substantially augmented by increasing the rotational speed of the rough cylinder, heights of triangular components, and suspended-nanoparticles which are also optimized for increasing baffle’s length and its horizontal arrangement. The findings of this investigation can be applied to improve the cooling efficiency of engineering equipment such as heat exchangers, energy storage systems, electronic equipment, solar collectors, and nuclear reactor safety devices

BenSignNet: Bengali Sign Language Alphabet Recognition Using Concatenated Segmentation and Convolutional Neural Network

Sign language recognition is one of the most challenging applications in machine learning and human-computer interaction. Many researchers have developed classification models for different sign languages such as English, Arabic, Japanese, and Bengali; however, no significant research has been done on the general-shape performance for different datasets. Most research work has achieved satisfactory performance with a small dataset. These models may fail to replicate the same performance for evaluating different and larger datasets. In this context, this paper proposes a novel method for recognizing Bengali sign language (BSL) alphabets to overcome the issue of generalization. The proposed method has been evaluated with three benchmark datasets such as ‘38 BdSL’, ‘KU-BdSL’, and ‘Ishara-Lipi’. Here, three steps are followed to achieve the goal: segmentation, augmentation, and Convolutional neural network (CNN) based classification. Firstly, a concatenated segmentation approach with YCbCr, HSV and watershed algorithm was designed to accurately identify gesture signs. Secondly, seven image augmentation techniques are selected to increase the training data size without changing the semantic meaning. Finally, the CNN-based model called BenSignNet was applied to extract the features and classify purposes. The performance accuracy of the model achieved 94.00%, 99.60%, and 99.60% for the BdSL Alphabet, KU-BdSL, and Ishara-Lipi datasets, respectively. Experimental findings confirmed that our proposed method achieved a higher recognition rate than the conventional ones and accomplished a generalization property in all datasets for the BSL domain

Multi-objective optimization to the transportation problem considering non-linear fuzzy membership functions

Considering the uncertainty of transporting goods from numerous origins to diverse destinations is a critical task for the decision-maker (DM). The ultimate goal of the DM is to make the right decisions that optimize the profit or loss of the organization under the vagueness of the uncontrollable effects. In this paper, mathematical models are proposed using fuzzy non-linear membership functions for the transportation problem considering the parameters' uncertainty that can help the DM to optimize the multi-objective transportation problems (MOTP) and to achieve the desired goals by choosing a confidence level of the uncertain parameters. Based on DM's selection of the confidence level, a compromise solution of the uncertain multi-objective transportation (UMOTP) is obtained along with the satisfaction level in percent for the DM. Two non-linear fuzzy membership functions are considered: the exponential and the hyperbolic functions. Using both membership functions, the sensitivity analysis was implemented by considering different confidence levels. According to the experimental results, the hyperbolic membership function gives 100% DM's satisfaction in many instances. Moreover, it shows stability against the exponential and linear functions

Stochastic Neighbor Embedding Feature-Based Hyperspectral Image Classification Using 3D Convolutional Neural Network

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

Multistage Spatial Attention-Based Neural Network for Hand Gesture Recognition

The definition of human-computer interaction (HCI) has changed in the current year because people are interested in their various ergonomic devices ways. Many researchers have been working to develop a hand gesture recognition system with a kinetic sensor-based dataset, but their performance accuracy is not satisfactory. In our work, we proposed a multistage spatial attention-based neural network for hand gesture recognition to overcome the challenges. We included three stages in the proposed model where each stage is inherited the CNN; where we first apply a feature extractor and a spatial attention module by using self-attention from the original dataset and then multiply the feature vector with the attention map to highlight effective features of the dataset. Then, we explored features concatenated with the original dataset for obtaining modality feature embedding. In the same way, we generated a feature vector and attention map in the second stage with the feature extraction architecture and self-attention technique. After multiplying the attention map and features, we produced the final feature, which feeds into the third stage, a classification module to predict the label of the correspondent hand gesture. Our model achieved 99.67%, 99.75%, and 99.46% accuracy for the senz3D, Kinematic, and NTU datasets

Investigating Feature Selection Techniques to Enhance the Performance of EEG-Based Motor Imagery Tasks Classification

Analyzing electroencephalography (EEG) signals with machine learning approaches has become an attractive research domain for linking the brain to the outside world to establish communication in the name of the Brain-Computer Interface (BCI). Many researchers have been working on developing successful motor imagery (MI)-based BCI systems. However, they still face challenges in producing better performance with them because of the irrelevant features and high computational complexity. Selecting discriminative and relevant features to overcome the existing issues is crucial. In our proposed work, different feature selection algorithms have been studied to reduce the dimension of multiband feature space to improve MI task classification performance. In the procedure, we first decomposed the MI-based EEG signal into four sets of the narrowband signal. Then a common spatial pattern (CSP) approach was employed for each narrowband to extract and combine effective features, producing a high-dimensional feature vector. Three feature selection approaches, named correlation-based feature selection (CFS), minimum redundancy and maximum relevance (mRMR), and multi-subspace randomization and collaboration-based unsupervised feature selection (SRCFS), were used in this study to select the relevant and effective features for improving classification accuracy. Among them, the SRCFS feature selection approach demonstrated outstanding performance for MI classification compared to other schemes. The SRCFS is based on the multiple k-nearest neighbour graphs method for learning feature weight based on the Laplacian score and then discarding the irrelevant features based on the weight value, reducing the feature dimension. Finally, the selected features are fed into the support vector machines (SVM), linear discriminative analysis (LDA), and multi-layer perceptron (MLP) for classification. The proposed model is evaluated with two benchmark datasets, namely BCI Competition III dataset IVA and dataset IIIB, which are publicly available and mainly used to recognize the MI tasks. The LDA classifier with the SRCFS feature selection algorithm exhibits better performance. It proves the superiority of our proposed study compared to the other state-of-the-art BCI-based MI task classification systems

An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

Handwriting is a unique and significant human feature that distinguishes them from one another. There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification. However, such systems are susceptible to forgery, posing security risks. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures or symbols. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures or symbols. Our innovative method is intricately designed, encompassing five distinct phases: data collection, preprocessing, feature extraction, significant feature selection, and classification. One key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting (BHW), setting the foundation for our comprehensive approach. Post-preprocessing, we embarked on an exhaustive feature extraction process, encompassing integration with kinematic, statistical, spatial, and composite features. This meticulous amalgamation resulted in a robust set of 91 features. To enhance the efficiency of our system, we employed an analysis of variance (ANOVA) F test and mutual information scores approach, meticulously selecting the most pertinent features. In the identification phase, we harnessed the power of cutting-edge deep learning models, notably the Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM). These models underwent rigorous training and testing to accurately discern individuals based on their handwriting characteristics. Moreover, our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM, capitalizing on fine motor features for enhanced individual classifications. Crucially, our experimental results underscore the superiority of our approach. The CNN, BiLSTM, and hybrid models exhibited superior performance in individual classification when compared to prevailing stateof-the-art techniques. This validates our method’s efficacy and underscores its potential to outperform existing technologies, marking a significant stride forward in the realm of individual identification through handwriting analysis

A Multilingual Handwriting Learning System for Visually Impaired People

Visually impaired people have previously been brought into learning and educational systems through various forms of assistive technology, such as haptic feedback systems. Haptic systems generally need expensive equipment and support from sighted teachers. Moreover, the learning has always been carried out with letters of different alphabets mapped into some tactile pattern. Writing is a big concern for the visually impaired as most official work, like signing, is still carried out by conventional handwriting methods. Most of the existing systems are limited to teaching a single language’s alphabet and basic grammar or may not provide feedback to let the learners know of their learning progress. Therefore, the objectives of this research are to develop an efficient system that includes voice-over guidance to teach writing in multiple alphabets to visually impaired people and to evaluate the performance of the proposed system. As such, a system was developed for teaching multilingual alphabets to visually impaired people with voice instructions. With the aid of a voice-over guide, learners were able to write letters with a stylus on a graphics pad. The progress assessment of the learners is carried out by an image processing algorithm and scored by a machine learning (ML) model. The Random Forest model was used due to its high accuracy (f1-score of 99.8% on test data) among the existing ten different ML algorithms. Finally, the performance and usability of this system were evaluated through an empirical study replicated with 16 participants, including four teachers and twelve visually impaired people. It was found that visually impaired people made fewer attempts to learn handwriting with the proposed system than with the normal handwriting teaching system. 100% of the participants agreed to recommend the system in the future