Effect of nutritional status on Rohingya under-five children in Bangladesh

The extent of nutritional status affecting Rohingya refugee under-five children has become a major health issues in Bangladesh. This study aimed to investigate the nutritional status and its effect on under-five Rohingya children in comparison with the areas of Bangladesh. A cross-sectional study of 300 under-five children were conducted by structured questionnaire from Rohingya camp (100), Cox’s Bazar (100) and Dumki (100) applying simple random method. Anthropometric indices (weight, height, mid-upper arm circumference (MUAC)) were measured in children aged 6-59 months. Indices were reported in z-scores and compared with WHO 2005 reference population. Data were analyzed by WHO Anthro-Plus Software and SPSS. About 41% Rohingya, 43% surrounding areas, and 46% Dumki were stunting in height-for-age z-score (HAZ) score respectively. Near about 13%, 11% and 4% were wasting in weight-for-height z-score (WHZ) score and 18%, 15% and 10% were underweight in weight for age Z-score (WAZ) score respectively. Food groups, Disease, worm infestation among 3 study areas were statistically significant (P< .05). Moreover, handwashing practice, vitamin-A consumption and worm infestation effects among diseases were statistically significant. In this study population, there was high prevalence of malnutrition among Rohingya children, especially wasting and underweight compared to other areas. Prevention of malnutrition plays an important role for having a healthy society of Rohingya Refugees

A Comparative Study of AHP and Fuzzy AHP Method for Inconsistent Data

In various cases of decision analysis we use two popular methods – Analytical Hierarchical Process (AHP) and Fuzzy based AHP or Fuzzy AHP. Both the methods deal with stochastic data and can determine decision result through Multi Criteria Decision Making (MCDM) process. Obviously resulting values of the two methods are not same though same set of data is fed into them. In this research work, we have tried to observe similarities and dissimilarities between two methods’ outputs. Almost same trend or fluctuations in outputs have been seen for both methods’ for same set of input data which are not consistent. Both method outputs’ ups and down fluctuations are same for fifty percent cases

Addressing Uncertainty in Imbalanced Histopathology Image Classification of HER2 Breast Cancer: An interpretable Ensemble Approach with Threshold Filtered Single Instance Evaluation (SIE)

Breast Cancer (BC) is among women's most lethal health concerns. Early diagnosis can alleviate the mortality rate by helping patients make efficient treatment decisions. Human Epidermal Growth Factor Receptor (HER2) has become one the most lethal subtype of BC. According to the College of American Pathologists/American Society of Clinical Oncology (CAP/ASCO), the severity level of HER2 expression can be classified between 0 and 3+ range. HER2 can be detected effectively from immunohistochemical (IHC) and, hematoxylin \& eosin (HE) images of different classes such as 0, 1+, 2+, and 3+. An ensemble approach integrated with threshold filtered single instance evaluation (SIE) technique has been proposed in this study to diagnose BC from the multi-categorical expression of HER2 subtypes. Initially, DenseNet201 and Xception have been ensembled into a single classifier as feature extractors with an effective combination of global average pooling, dropout layer, dense layer with a swish activation function, and l2 regularizer, batch normalization, etc. After that, extracted features has been processed through single instance evaluation (SIE) to determine different confidence levels and adjust decision boundary among the imbalanced classes. This study has been conducted on the BC immunohistochemical (BCI) dataset, which is classified by pathologists into four stages of HER2 BC. This proposed approach known as DenseNet201-Xception-SIE with a threshold value of 0.7 surpassed all other existing state-of-art models with an accuracy of 97.12\%, precision of 97.15\%, and recall of 97.68\% on H\&E data and, accuracy of 97.56\%, precision of 97.57\%, and recall of 98.00\% on IHC data respectively, maintaining momentous improvement. Finally, Grad-CAM and Guided Grad-CAM have been employed in this study to interpret, how TL-based model works on the histopathology dataset and make decisions from the data

Shapes2Toon: Generating Cartoon Characters from Simple Geometric Shapes

Cartoons are an important part of our entertainment culture. Though drawing a cartoon is not for everyone, creating it using an arrangement of basic geometric primitives that approximates that character is a fairly frequent technique in art. The key motivation behind this technique is that human bodies - as well as cartoon figures - can be split down into various basic geometric primitives. Numerous tutorials are available that demonstrate how to draw figures using an appropriate arrangement of fundamental shapes, thus assisting us in creating cartoon characters. This technique is very beneficial for children in terms of teaching them how to draw cartoons. In this paper, we develop a tool - shape2toon - that aims to automate this approach by utilizing a generative adversarial network which combines geometric primitives (i.e. circles) and generate a cartoon figure (i.e. Mickey Mouse) depending on the given approximation. For this purpose, we created a dataset of geometrically represented cartoon characters. We apply an image-to-image translation technique on our dataset and report the results in this paper. The experimental results show that our system can generate cartoon characters from input layout of geometric shapes. In addition, we demonstrate a web-based tool as a practical implication of our work.Comment: Accepted as a full paper in AICCSA2022 (19th ACS/IEEE International Conference on Computer Systems and Applications

Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive Study

In an era where the silent struggle of underdiagnosed depression pervades globally, our research delves into the crucial link between mental health and social media. This work focuses on early detection of depression, particularly in extroverted social media users, using LLMs such as GPT 3.5, GPT 4 and our proposed GPT 3.5 fine-tuned model DepGPT, as well as advanced Deep learning models(LSTM, Bi-LSTM, GRU, BiGRU) and Transformer models(BERT, BanglaBERT, SahajBERT, BanglaBERT-Base). The study categorized Reddit and X datasets into "Depressive" and "Non-Depressive" segments, translated into Bengali by native speakers with expertise in mental health, resulting in the creation of the Bengali Social Media Depressive Dataset (BSMDD). Our work provides full architecture details for each model and a methodical way to assess their performance in Bengali depressive text categorization using zero-shot and few-shot learning techniques. Our work demonstrates the superiority of SahajBERT and Bi-LSTM with FastText embeddings in their respective domains also tackles explainability issues with transformer models and emphasizes the effectiveness of LLMs, especially DepGPT, demonstrating flexibility and competence in a range of learning contexts. According to the experiment results, the proposed model, DepGPT, outperformed not only Alpaca Lora 7B in zero-shot and few-shot scenarios but also every other model, achieving a near-perfect accuracy of 0.9796 and an F1-score of 0.9804, high recall, and exceptional precision. Although competitive, GPT-3.5 Turbo and Alpaca Lora 7B show relatively poorer effectiveness in zero-shot and few-shot situations. The work emphasizes the effectiveness and flexibility of LLMs in a variety of linguistic circumstances, providing insightful information about the complex field of depression detection models

MCNN-LSTM: Combining CNN and LSTM to classify multi-class text in imbalanced news data

Searching, retrieving, and arranging text in ever-larger document collections necessitate more efficient information processing algorithms. Document categorization is a crucial component of various information processing systems for supervised learning. As the quantity of documents grows, the performance of classic supervised classifiers has deteriorated because of the number of document categories. Assigning documents to a predetermined set of classes is called text classification. It is utilized extensively in a wide range of data-intensive applications. However, the fact that real-world implementations of these models are plagued with shortcomings begs for more investigation. Imbalanced datasets hinder the most prevalent high-performance algorithms. In this paper, we propose an approach name multi-class Convolutional Neural Network (MCNN)-Long Short-Time Memory (LSTM), which combines two deep learning techniques, Convolutional Neural Network (CNN) and Long Short-Time Memory, for text classification in news data. CNN's are used as feature extractors for the LSTMs on text input data and have the spatial structure of words in a sentence, paragraph, or document. The dataset is also imbalanced, and we use the Tomek-Link algorithm to balance the dataset and then apply our model, which shows better performance in terms of F1-score (98%) and Accuracy (99.71%) than the existing works. The combination of deep learning techniques used in our approach is ideal for the classification of imbalanced datasets with underrepresented categories. Hence, our method outperformed other machine learning algorithms in text classification by a large margin. We also compare our results with traditional machine learning algorithms in terms of imbalanced and balanced datasets

Quantifying the protective capacity of mangroves from storm surges in coastal Bangladesh.

Mangroves are an important ecosystem-based protection against cyclonic storm surge. As the surge moves through the mangrove forest, the tree roots, trunks, and leaves obstruct the flow of water. Damage to adjacent coastal lands is attenuated mainly by reducing (i) surge height, which determines the area and depth of inundation and (ii) water flow velocity. But the extent of mangrove protection depends on the density of tree plantings and the diameter of trunks and roots, along with an array of other forest characteristics (e.g., floor shape, bathymetry, spectral features of waves, and tidal stage at which waves enter the forest). Making efficient use of mangroves' protective capacity has been hindered by a lack of location-specific information. This study helps to fill that gap by estimating reduction in storm surge height and water flow velocity from mangroves at selected sites in cyclone-prone, coastal Bangladesh. A hydrodynamic model for the Bay of Bengal, based on the MIKE21FM system, was run multiple times to simulate the surge of cyclone Sidr (2007) at the Barisal coast. Estimates of surge height and water flow velocity were recorded first without mangroves and then with mangroves of various forest widths and planting densities, including specific information on local topography, bathymetry, and Manning's coefficients estimated from species' root and trunk systems. The results show a significant reduction in water flow velocity (29-92%) and a modest reduction in surge height (4-16.5 cm). These findings suggest that healthy mangroves can contribute to significant savings in rehabilitation and maintenance costs by protecting embankments from breaching, toe-erosion, and other damage

Assessing Political Inclination of Bangla Language Models

Natural language processing has advanced with AI-driven language models (LMs), that are applied widely from text generation to question answering. These models are pre-trained on a wide spectrum of data sources, enhancing accuracy and responsiveness. However, this process inadvertently entails the absorption of a diverse spectrum of viewpoints inherent within the training data. Exploring political leaning within LMs due to such viewpoints remains a less-explored domain. In the context of a low-resource language like Bangla, this area of research is nearly non-existent. To bridge this gap, we comprehensively analyze biases present in Bangla language models, specifically focusing on social and economic dimensions. Our findings reveal the inclinations of various LMs, which will provide insights into ethical considerations and limitations associated with deploying Bangla LMs

Convolutional Auto-Encoder and Independent Component Analysis Based Automatic Place Recognition for Moving Robot in Invariant Season Condition

Abstract Building up a map is essential for mobile robots to localize their position and perfect autonomous navigation which is known as Simultaneous Localization and Mapping (SLAM). The map has become very important when the weather is inappropriate for the robot. However, the map becomes inconsistent when the robot moves in the environment and detects errors in its detection accuracy. The robot had difficulty identifying its previously visited path, which is called loop-closure detection when the climate changed immensely e.g. seasonal changes. The main goal of this work is to apply Independent Component Analysis (ICA) and Auto-Encoder (Convolutional Auto-Encoder and Fundamental Auto-Encoder) to understand the route through the robot. During the operation of robots across a wide range of environmental changing conditions, the ICA has auspicious potential to extract descriptors of condition-invariant images. On the other hand, Auto-Encoder has the capability to differentiate condition variant and condition invariant characteristics of a site and identify the most possible route for the robot. In order to complete this work perfectly, we used three seasonal datasets, they are Summer–Fall, Spring–Fall, and Summer–Spring datasets. This work uses the baseline method with a precision-recall curve and evaluates the performance of our proposed algorithm, especially the ICA algorithm. In short, the proposed algorithm ICA showed a 91.05% accuracy rate which is better than the baseline algorithm