Bangla_MER: A unique dataset for Bangla mathematical entity recognition.

Mathematical entity recognition is essential for machines to define and illustrate mathematical substance faultlessly and to facilitate sufficient mathematical operations and reasoning. As mathematical entity recognition in the Bangla language is novel, to our best knowledge, there is no available dataset exists in any repository. In this paper, we present state of the art Bangla mathematical entity dataset containing 13,717 observations. Each record has a mathematical statement, mathematical type and mathematical entity. This dataset can be utilized to conduct research involving the recognition of mathematical operators, renowned mathematical terms (such as complex numbers, real numbers, prime numbers, etc.), and operands as numbers. The findings mentioned above, and their combination are also feasible with a modest tweak to the dataset. Furthermore, we have structured this dataset in raw format and made a CSV file, incorporating three columns: text, math entity, and label. As an outcome, researchers may easily handle the data, facilitating a variety of deep learning and machine learning explorations.

Cognitive Function in Urban and Rural Postmenopausal Women.

BACKGROUND: Menopause is a well-known risk factor for decreasing cognitive function in women. Postmenopausal women are increasing in number but relevant studies are very scarce. This study compared the cognitive function between urban and rural postmenopausal women and assessed the influence of socio-demographic factors on cognitive function. OBJECTIVES: The aim of the study was to assess the association between the cognitive function of urban and rural postmenopausal women. METHODS: This comparative cross-sectional study was conducted among 87 urban and 87 rural postmenopausal women who were selected by purposive sampling method from the Nakhalpara and Dhamrai area of Dhaka district during the period from January to December 2020. Data were collected with a semi-structured questionnaire based on the Bengali version of the Mini-Mental State Examination (MMSE) scale through face-to-face interviews and record reviewing with a checklist. Statistical analyses of the results were obtained using Microsoft Excel (Microsoft Corporation, Redmond, WA) and SPSS version 24 (IBM Corp., Armonk, NY). RESULTS: The mean age of postmenopausal women was 58.09 ± 8.163 years in urban areas and 60.00 ± 7.562 years in rural areas. The majority (31, 35.6%) of urban women were primary school pass whereas 58 (66.7%) rural women were illiterate. The mean family income of the women was 43022.99 ± 10992.57 Bangladeshi taka (BDT) in the urban group and 14022.99 ± 5023.14 BDT in the rural group. The study revealed that 31 (35.6%) women in the urban group and 53 (60.9%) women in the rural group had abnormal cognitive function. CONCLUSION: The percentage of abnormal cognitive function was higher in rural postmenopausal women. Cognitive function has an association with monthly family income, housing condition, family type, age at the time of marriage, lifestyle, and co-morbidities. Policymakers can take the findings as a guide to formulate policies and programs for the improvement of cognitive function of postmenopausal women.

SalmonScan: A novel image dataset for machine learning and deep learning analysis in fish disease detection in aquaculture.

Fish diseases pose a significant threat to food security in aquaculture, as they can lead to considerable reductions in fish production, quality, and profitability. Globally, salmon aquaculture is the quickest-expanding food production system. Detecting and diagnosing fish diseases in their early stages is essential to prevent the spread of diseases and reduce the negative impact on aquaculture's economy and environment. To serve this purpose, we introduce the SalmonScan dataset, a novel and comprehensive collection of images of healthy and infected salmon fish, which can be used for various applications in computer science and aquaculture. Images from online sources and aquaculture salmon firms were gathered to create the dataset. The dataset was then labeled based on the health status of the fish, fresh or infected. Data augmentation methods like rotation, cropping, flipping, and scaling were used to guarantee the dataset's strength and size. The dataset includes 456 images of fresh fish and 752 images of infected fish, both varied and inclusive while maintaining excellent quality. Other researchers and practitioners can use the dataset we have collected for various purposes. They can use it to create and test new or existing machine learning (ML) and deep learning (DL) based computer vision models for identifying, categorizing, counting, and analyzing the behavior and biomass of salmon fish. They can also use it to study how different environmental factors affect the health and growth of salmon fish. Furthermore, they can evaluate the accuracy and performance of different image acquisition and processing methods. Additionally, they can explore the feasibility of using generative adversarial networks (GANs) and transfer learning to improve the training speed and stability of DL models designed for fish detection. This SalmonScan dataset paper describes and documents the dataset in detail, making it publicly available and reusable for the research community.

Prevalence, Knowledge, Causes, and Practices of Self-Medication During the COVID-19 Pandemic in Bangladesh: A Cross-Sectional Survey.

Introduction During the COVID-19 pandemic, self-medication (SM) has become a critical element in the healthcare system. SM can ease the burden on hospitals and medical resources by treating minor illnesses. However, inappropriate SM practices can lead to adverse drug reactions, drug resistance, and incorrect diagnoses, resulting in poor health outcomes. Methods To evaluate the prevalence, knowledge, causes, and practices of SM among the Bangladeshi population during the COVID-19 outbreak, a cross-sectional survey with structured questionnaires was conducted in Chittagong City, Bangladesh, from March to May 2022. The survey included 265 participants, with an average age of 35.09 years, and a multiple-choice questionnaire was used to gather information. Results The study found that 64.15% of the respondents had sufficient knowledge of SM, while 35.8% had insufficient knowledge. The primary reasons for SM during the pandemic were the influence of friends/family (90.74%), fear of infection or contact with COVID-19 cases (73.15%), and fear of quarantine or self-isolation (72.22%). Analgesics/pain relievers (84%) were the most commonly used drugs for SM for COVID-19 prevention and treatment. Antiulcerants/antacid (42%), vitamin C and multivitamins (42%), and antibiotics (32%) were also frequently used. Conclusion This study suggests that SM is prevalent among Chittagong City residents, particularly those with less than a tertiary education. The study highlights the importance of building awareness about SM practices and taking necessary steps to control them.