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Abstract The loss of electronic medical records has seriously affected the practical application of biomedical data. Therefore, it is a meaningful research effort to effectively fill these lost data. Currently, state-of-the-art methods focus on using Generative Adversarial Networks (GANs) to fill the missing values of electronic medical records, achieving breakthrough progress. However, when facing datasets with high missing rates, the imputation accuracy of these methods sharply deceases. This motivates us to explore the uncertainty of GANs and improve the GAN-based imputation methods. In this paper, the GRUD (Gate Recurrent Unit Decay) network and the UGAN (Uncertainty Generative Adversarial Network) are proposed and organically combined, called UGAN-GRUD. In UGAN-GRUD, it highlights using GAN to generate imputation values and then leveraging GRUD to compensate them. We have designed the UGAN and the GRUD network. The former is employed to learn the distribution pattern and uncertainty of data through the Generator and Discriminator, iteratively. The latter is exploited to compensate the former by leveraging the GRUD based on time decay factor, which can learn the specific temporal relations in electronic medical records. Through experimental research on publicly available biomedical datasets, the results show that UGAN-GRUD outperforms the current state-of-the-art methods, with average 13% RMSE (Root Mean Squared Error) and 24.5% MAPE (Mean Absolute Percentage Error) improvements.

The prevalence of low birth weight (LBW) is high in Bangladesh, but no study has collated recent estimates of LBW prevalence from throughout the country. The aim of this meta-analysis was to evaluate the prevalence of LBW and birth weight status in Bangladesh. We searched PubMed, Medline, Ovo and Google Scholar to find published articles in national and international journals from 2000–2020 and reviewed for relevance. Meta-analysis and Q test were performed to estimate the prevalence and heterogeneity of LBW from all included articles. Meta-regression was done to quantify associations with sample size and study year. Stratified analysis was conducted and effect size calculated for differences in LBW prevalence by sex, division and urban/rural area. In total 48 studies with 166,520 births were found and included in this meta-analysis. The pooled prevalence of LBW was 29.1% (95% CI, 28.9–29.3%) in overall, 29.9% (29.7–30.2%) in rural and 15.7% (14.9–16.6%) in urban areas. There was no significant difference in average birth weight between boys and girls (SD, 0.71; 95% CI, −0.43–1.83). Prevalence of LBW was higher in north-east Bangladesh compared to other zones (p

With its novel bidirectional communication method, Neuralink, the brain-reading gadget created by Elon Musk, is poised to transform human-machine relations. It represents a revolutionary combination of health science, neurology, and artificial intelligence. Neuralink is a potentially beneficial brain implant that consists of tiny electrodes placed behind the ear and a small chip. It can be used to treat neurological conditions and improve cognitive function. Important discussions are nevertheless sparked by ethical worries about abuse, privacy, and security. It is important to maintain a careful balance between the development of technology and moral issues, as seen by the imagined future in which people interact with computers through thinking processes. In order for Neuralink to be widely accepted and responsibly incorporated into the fabric of human cognition and connectivity, ongoing discussions about ethical standards, regulatory frameworks, and societal ramifications are important. Meanwhile, new advancements in Brain-Chip-Interfaces (BCHIs) bring the larger context into focus. By enhancing signal transmission between nerve cells and chips, these developments offer increased signal fidelity and improved spatiotemporal resolution. The potential revolutionary influence of these innovations on neuroscience and human-machine symbiosis raises important considerations about the ethical and societal consequences of these innovations. [ABSTRACT FROM AUTHOR]

In medical care, side effect trial and error processes are utilized for the discovery of hidden reasons for ailments and the determination of conditions. In our exploration, we used a crossbreed strategy to refine our optimal model, improving the Pearson relationship for highlight choice purposes. The underlying stage included the choice of ideal models through a careful survey of the current writing. Hence, our proposed halfand-half model incorporated a blend of these models. The base classifiers utilized included XGBoost, Arbitrary Woods, Strategic Relapse, AdaBoost, and the Crossover model classifiers, while the Meta classifier was the Irregular Timberland classifier. The essential target of this examination was to evaluate the best AI grouping techniques and decide the best classifier concerning accuracy. This approach resolved the issue of overfitting and accomplished the most elevated level of exactness. The essential focal point of the assessment was precision, and we introduced a far-reaching examination of the significant writing in even configuration. To carry out our methodology, we used four top-performing AI models and fostered another model named "half and half," utilizing the UCI Persistent Kidney Disappointment dataset for prescient purposes. In our experiment, we found out that the AI model XGBoost classifier gains almost 94% accuracy, a random forest gains 93% accuracy, Logistic Regression about 90% accuracy, AdaBoost gains 91% accuracy, and our proposed new model named hybrid gains the highest 95% accuracy, and performance of Hybrid model is best on this equivalent dataset. Various noticeable AI models have been utilized to foresee the event of persistent kidney disappointment (CKF). These models incorporate Naïve Bayes, Random Forest, Decision Tree, Support Vector Machine, K-nearest neighbor, LDA (Linear Discriminant Analysis), GB (Gradient Boosting), and neural networks. In our examination, we explicitly used XGBoost, AdaBoost, Logistic Regression, Random Forest, and Hybrid models with the equivalent dataset of highlights to analyze their accuracy scores. [ABSTRACT FROM AUTHOR]

In the ever-evolving field of cybersecurity, sophisticated methods—which combine supervised and unsupervised approaches— are used to tackle cybercrime. Strong supervised tools include Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), while well-known unsupervised methods include the K-means clustering model. These techniques are used on the publicly available StatLine dataset from CBS, which is a large dataset that includes the individual attributes of one thousand crime victims. Performance analysis shows the remarkable 91% accuracy of SVM in supervised classification by examining the differences between training and testing data. K-Nearest Neighbors (KNN) models are quite good in the unsupervised arena; their accuracy in detecting criminal activity is impressive, at 79.56%. Strong assessment metrics, such as False Positive (FP), True Negative (TN), False Negative (FN), False Positive (TP), and False Alarm Rate (FAR), Detection Rate (DR), Accuracy (ACC), Recall, Precision, Specificity, Sensitivity, and Fowlkes–Mallow's scores, provide a comprehensive assessment. [ABSTRACT FROM AUTHOR]

A thorough comparison of several machine learning methods is provided in this paper, including gradient boosting, AdaBoost, Random Forest (RF), XGBoost, Artificial Neural Network (ANN), and a unique hybrid framework (RF-XGBoost-LR). The assessment investigates their efficacy in real-time sales data analysis using key performance metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R2 score. The study introduces the hybrid model RF-XGBoost-LR, leveraging both bagging and boosting methodologies to address the limitations of individual models. Notably, Random Forest and XGBoost are scrutinized for their strengths and weaknesses, with the hybrid model strategically combining their merits. Results demonstrate the superior performance of the proposed hybrid model in terms of accuracy and robustness, showcasing potential applications in supply chain studies and demand forecasting. The findings highlight the significance of industry-specific customization and emphasize the potential for improved decision-making, marketing strategies, inventory management, and customer satisfaction through precise demand forecasting. [ABSTRACT FROM AUTHOR]

Raising awareness about Toxoplasma gondii infection among cat owners in Bangladesh is indispensable to formulate persuasive management tactics to avoid zoonotic infections from pet cats. However, to the authors' best knowledge, no studies have been performed in Bangladesh to determine knowledge and practices of toxoplasmosis in cat owners. Therefore, the objectives of the current study were to cover this research gap. We carried out a cross‐sectional study in Bangladesh from June 2020 through December 2021. A structured online questionnaire was distributed to cat owners, which were voluntarily completed by them. The questionnaire included socio‐demographic data, aetiology, transmissions, clinical signs, and preventive practices towards toxoplasmosis. Overall, 1,019 cat owners participated voluntarily in the cross‐sectional survey. Among them, 793 (77.82%) participants showed poor knowledge regarding toxoplasmosis. Under specific knowledge sections, 62.51% of the participants revealed incorrect knowledge that toxoplasmosis was a zoonotic disease. In the same way, (72.03–85.77) % of the cat owners were unaware that the disease could be transmitted from improperly washed vegetables, raw or undercooked meat and fish, and contaminated water and milk with cat faeces. Respondents' age, education, occupation, residence type, and marital status were significantly (p <.05) associated with their knowledge level. Besides, 94.11% of cat owners had a good practice level. They followed good practices in different issues; however, they practiced those activities without knowing their impacts on disease control. Cat owners' age, education, occupation, and residence type had a significant (p <.05) association with the practice level against toxoplasmosis. This is the first study highlighting the low level of knowledge among cat owners about toxoplasmosis in Bangladesh. These knowledge gaps could increase the risk and transmission of Toxoplasma gondii infection among them and their families. The survey recommends the arrangement of educational training and programmes to increase the awareness of toxoplasmosis among cat owners. [ABSTRACT FROM AUTHOR]

Background: Arsenic exposure and micronutrient deficiencies may alter immune reactivity to influenza vaccination in pregnant women, transplacental transfer of maternal antibodies to the foetus, and maternal and infant acute morbidity. Objectives: The Pregnancy, Arsenic, and Immune Response (PAIR) Study was designed to assess whether arsenic exposure and micronutrient deficiencies alter maternal and newborn immunity and acute morbidity following maternal seasonal influenza vaccination during pregnancy. Population: The PAIR Study recruited pregnant women across a large rural study area in Gaibandha District, northern Bangladesh, 2018–2019. Design: Prospective, longitudinal pregnancy and birth cohort. Methods: We conducted home visits to enrol pregnant women in the late first or early second trimester (11–17 weeks of gestational age). Women received a quadrivalent seasonal inactivated influenza vaccine at enrolment. Follow‐up included up to 13 visits between enrolment and 3 months postpartum. Arsenic was measured in drinking water and maternal urine. Micronutrient deficiencies were assessed using plasma biomarkers. Vaccine‐specific antibody titres were measured in maternal and infant serum. Weekly telephone surveillance ascertained acute morbidity symptoms in women and infants. Preliminary Results: We enrolled 784 pregnant women between October 2018 and March 2019. Of 784 women who enrolled, 736 (93.9%) delivered live births and 551 (70.3%) completed follow‐up visits to 3 months postpartum. Arsenic was detected (≥0.02 μg/L) in 99.7% of water specimens collected from participants at enrolment. The medians (interquartile ranges) of water and urinary arsenic at enrolment were 5.1 (0.5, 25.1) μg/L and 33.1 (19.6, 56.5) μg/L, respectively. Water and urinary arsenic were strongly correlated (Spearman's ⍴ = 0.72) among women with water arsenic ≥ median but weakly correlated (⍴ = 0.17) among women with water arsenic < median. Conclusions: The PAIR Study is well positioned to examine the effects of low‐moderate arsenic exposure and micronutrient deficiencies on immune outcomes in women and infants. Registration: NCT03930017. [ABSTRACT FROM AUTHOR]