13 research outputs found

    Anthropometric Indices of Giardia-Infected Under-Five Children Presenting with Moderate-to-Severe Diarrhea and Their Healthy Community Controls: Data from the Global Enteric Multicenter Study

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    Among all intestinal parasitosis, giardiasis has been reported to be associated with delayed growth in malnourished children under 5 living in low- and middle-income countries. Relevant data on the nutritional status of children aged 0-59 months presenting with moderate-to-severe diarrhea (MSD) and giardia infection were collected from sentinel health facilities of the Global Enteric Multicenter Study's (GEMS) seven field settings, placed in diverse countries of Sub-Saharan Africa and South Asia between, December 2007 and February 2011. Then, this study analyzed a robust dataset of study participants (n = 22,569). Children having giardiasis with MSD constituted as cases (n = 1786), and those without MSD constituted as controls (n = 3470). Among the seven field sites, symptomatic giardiasis was 15% and 22% in Asian and African sites, respectively, whereas asymptomatic giardia infection (healthy without MSD) in Asian and African sites was 21.7% and 30.7%, respectively. Wasting and underweight were more frequently associated and stunting less often associated with symptomatic giardiasis (for all, p < 0.001). Symptomatic giardiasis had a significant association with worsening of nutritional status in under-five children. Improved socio-economic profile along with proper sanitation and hygienic practices are imperative to enhance child nutritional status, particularly in resource limited settings

    Optimizing Regional Business Performance: Leveraging Business and Data Analytics in Logistics & Supply Chain Management for USA's Sustainable Growth

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    The logistics and supply chain management (SCM) sector plays a paramount role in the economic development and growth of countries. In the USA, the effectiveness and efficiency of logistics and SCM functions directly influence regional organizational performance and long-term economic sustainability. The prime objective of this research is to explore the phenomenon of optimizing regional business performance through the application of data and business analytics in logistics and supply chain management for the sustainable growth of the US economy. In this study, the researcher employed machine learning methodologies, specifically ANN, RNN, and SVM, to forecast lead times for purchasing aluminum products. In the research, historical data was collected from the database of one of the aluminum-producing companies in the USA for the last 10 years. In particular, a sample of 38,500 orders of aluminum profiles was adopted for the current study. Retrospectively, the Recurrent Neural Network and the Support Vector Machine displayed the most favorable outcomes in predicting lead time in the supply chain. Particularly, RNN had the least Mean Average Error (MAE) on the testing set (447.72), followed by SVM (453.04), MLR (453.22), and NN (455.41). By deploying these algorithms, the government can optimize inventory degrees, minimize stockouts, and reduce excess inventory. This results in enhanced efficiency, diminished carrying costs, and elevated consumer satisfaction, leading to cost savings and heightened profitability for government companies within the supply chain

    Explainable AI in Credit Card Fraud Detection: Interpretable Models and Transparent Decision-making for Enhanced Trust and Compliance in the USA

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    Credit Card Fraud presents significant challenges across various domains, comprising, healthcare, insurance, finance, and e-commerce.&nbsp; The principal objective of this research was to examine the efficacy of Machine Learning techniques in detecting credit card fraud. Four key Machine Learning techniques were employed, notably, Support Vector Machine, Logistic Regression, Random Forest, and Artificial Neural Network. Subsequently, model performance was evaluated using Precision, Recall, Accuracy, and F-measure metrics. While all models demonstrated high accuracy rates (99%), this was largely due to the dataset's size, with 284,807 attributes and only 492 fraudulent transactions. Nevertheless, accuracy solely did not provide a comprehensive comparison metric. Support Vector Machine showed the highest recall (89.5), correctly identifying the most positive instances, highlighting its efficacy in detecting true positives. On the other hand, the Artificial Neural Network model exhibited the highest precision (79.4, indicating its capability to make accurate identifications, making it proficient in optimistic predictions

    Strategic Employee Performance Analysis in the USA: Deploying Machine Learning Algorithms Intelligently

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    Strategic employee performance assessment assists organizations in steering productivity, affirming employee satisfaction, and accomplishing strategic organizational goals. Machine learning algorithms provide several benefits over mainstream techniques in assessing employee performance. This research paper aimed to explore the deployment of machine learning algorithms in assessing employee performance. The prime objective of employee performance analysis is to assess an employee's achievement during a specific time frame. The dataset for this research revolved around the leadership team of a global retailer's specific store level in the USA, extending over 18 months. The dataset for this study was subjected to Python programming software for intensive and comprehensive data analysis as well as for visualization purposes. From the experiment design, it was evident that XG-Boost seems to be the best-performing model overall. In particular, it had the greatest AUC for both holdout and training data (0.86 and 0.88, respectively), and it has a relatively low runtime (16 minutes) and maximum memory utilization (12%). By contrast, Random Forest displayed an average AUC for training data (0.79) but a lesser AUC for holdout data (0.51), which indicates that it may be overfitting the training data; besides, it had a longer runtime than XG-Boost

    Algorithmic Trading Strategies: Leveraging Machine Learning Models for Enhanced Performance in the US Stock Market

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    In the recent past, algorithmic trading has become exponentially predominant in the American stock market. The principal objective of this research was to explore the employment of machine learning frameworks in formulating algorithmic trading strategies tailored for the US stock market. For this investigation, an array of software tools was employed, comprising the Pandas library for data manipulation and analysis, the Python programming language, the Scikit-learn library for machine learning algorithms and analysis metrics, and the LIME library for explainable AI. In this study, the researcher gathered an extensive dataset from the Amazon Stock Exchange, spanning from October 19, 2018, to October 16, 2022. The dataset comprised a wide range of parameters related to Amazon's stock data, facilitating a rigorous analysis of its market performance. Five models were subjected to the experiment, notably Ridge Regression, Ada-Boost, Light-GBM, XG-Boost, Linear Regression, and Cat-Boost. From the experiment result, it was evident that the XG-Boost attained the highest R-squared (99.24%) and accuracy (99.23%) among all the algorithms. From the above results, the analyst inferred that the XG-Boost was able to learn a more complex and accurate model of the stock exchange data compared to the other algorithms. XG-Boost algorithm can be utilized to back-test distinct trading strategies on historical data, enabling investors to evaluate their efficiency before risking real capital. By assessing a wide array of factors, the XG-Boost algorithm can assist investors in selecting stocks with a higher probability of outperforming the market

    Ethical Considerations in AI-driven Dynamic Pricing in the USA: Balancing Profit Maximization with Consumer Fairness and Transparency

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    Organizations in the USA are progressively employing AI-driven dynamic pricing as a strategic intervention to flexibly modify their prices based on competition, market demand, and various other factors. This research paper focused on the ethical dimensions of AI-driven dynamic pricing and the crucial interplay between profitability and the establishment of unwavering consumer transparency and fairness. The recommended models for dynamic pricing solutions entailed ensemble learning methods, notably, XG-Boost, Light-GBM, Cat-Boost, and X-NGBoost models. Particularly, the proposed model consolidated the XG-Boost algorithm and the NG-Boost model, resulting in a novel methodology termed the X-NGBoost. To compare and contrast the performance of the proposed models, these algorithms were trained and subjected to the same dataset. The comparison between the models was mainly grounded on the root-mean-square error (RMSE) metric, which was quantified in meters. The results indicated that X-NGBoost had the lowest RMSE on both the testing and training sets, at 4.23 and 5.34 respectively. This indicated that X-NGBoost performed very well on both seen and unseen data. Therefore, from the outcomes it was deduced that, for the provided data set, the X-NGBoost model provided the accurate pricing solution

    Novel AI-Powered Dynamic Inventory Management Algorithm in the USA: Machine Learning Dimension

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    Dynamic inventory management revolves around the practice of progressively modifying inventory degrees to adapt to fluctuations in client demand, production, and supply chain dynamics. At the center, inventory management focuses on upholding enhanced levels of stock to balance consumer service via availability with the costs related to holding excess inventory. This research paper aimed to explore the dynamic inventory management activities employed by organizations in the USA, shedding light on the machine learning strategies that can be deployed and their implications. The performance of the algorithms was empirically evaluated in a Python program experiment utilizing real-world data. To facilitate the data for input into the Neural Network, feature engineering, and selection were imposed to affirm its suitability. This study proposes the Sequence-to-Sequence (Seq2Quant) algorithm, a neural network-powered technique for demand prediction in inventory management.  The current experiment compared and contrasted the performance of the Neural Networks against the following baselines, most notably, Naïve Seasonal Forecast, Moving Average Forecast, ARIMA, Naïve Seasonal Forecast with Averaging over four periods, SARIMAX. From the experiment, it was evident that the Seq2Seq had the lowest MAE (17.44) and the lowest SMAPE (66.91), suggesting that it was the best-performing algorithm overall. Besides, SARIMAX and ARIMAX also performed well, with MAE values of 18.33 and 18.09, respectively

    AI-Based Customer Churn Prediction Model for Business Markets in the USA: Exploring the Use of AI and Machine Learning Technologies in Preventing Customer Churn

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    Understanding consumer churn is pivotal for companies in the USA to develop efficient strategies for consumer retention and reduce its negative effects on revenue and profitability. To start with, understanding client churn entails pinpointing the factors that contribute to it. This research paper delved into the application of machine learning algorithms such as Random Forests and Decision Trees for designing churn prediction models and exploring key factors that churn probabilities. The dataset used in this study was sourced from the prominent UCI repository of machine learning databases, preserved at the University of California, Irvine. This dataset provided extensive information on a total of 3333 clients, facilitating in-depth analysis and insights. Models performance evaluation comprised examining the model's efficiency using a confusion matrix. Random Forest seemed to be a relatively better performing model than Decision Tree for this specific classification task. In particular, Random Forest attained higher accuracy (96.25%), precision (91.49), Recall (83.49%), F-measure (0.87), and Phi coefficient (0.85).&nbsp; By deploying Random Forest and Decision Tree models, government companies can get an in-depth comprehension of the factors that lead to consumer churn. As a result, this information may enable them to tailor targeted retention strategies and interventions. By effectively retaining consumers, government organizations can maintain a stable customer base, leading to sustained revenue and economic growth

    Is Bangladesh on the right path toward sustainable development? An empirical exploration of energy sources, economic growth, and CO2 discharges nexus

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    The sustainability of the recent economic progress of Bangladesh is critically dependent on how it faces envi-ronmental challenges, as the country is one of the primary victims of climate alteration. Taking into account the crucial roles of energy sources in this scenario, we analyze the impacts of non-renewable and renewable energy consumption (NREC and REC) on the growth-environment nexus in Bangladesh from 1980 to 2018. Based on the Auto-Regressive Distributed Lag (ARDL) model with and without structural breaks and policy dummies, our findings show that REC significantly upsurges economic growth, whereas NREC diminishes it. However, NREC leads to environmental deterioration, while REC enhances environmental quality. Besides, our results fail to support the Environmental Kuznets Curve hypothesis for Bangladesh. Interestingly, the policy dummy upsurges CO2 discharges while lessening economic growth, implying that the Bangladesh governments policies do not adequately cut pollution. Our Toda-Yamamoto non-causality test indicates a unidirectional causality running from GDP and its square term and NREC to CO2 emissions. Our findings suggest that policymakers in Bangladesh should adopt and implement strategies like enhancing renewable energy production, investment subsidies, tax credits, quota policies, and technological advancements to boost REC while plunging NREC to achieve economic sustainability.Author´s last draft is self-archived on the author's personal homepage: https://ylifin.wordpress.com/publications/</p

    Clinical risk factors of death from pneumonia in children with severe acute malnutrition in an urban critical care ward of Bangladesh.

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    BACKGROUND: Risks of death are high when children with pneumonia also have severe acute malnutrition (SAM) as a co-morbidity. However, there is limited published information on risk factors of death from pneumonia in SAM children. We evaluated clinically identifiable factors associated with death in under-five children who were hospitalized for the management of pneumonia and SAM. METHODS: For this unmatched case-control design, SAM children of either sex, aged 0-59 months, admitted to the Dhaka Hospital of the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) during April 2011 to July 2012 with radiological pneumonia were studied. The SAM children with pneumonia who had fatal outcome constituted the cases (n = 35), and randomly selected SAM children with pneumonia who survived constituted controls (n = 105). RESULTS: The median (inter-quartile range) age (months) was comparable among the cases and the controls [8.0 (4.9, 11.0) vs. 9.7 (5.0, 18.0); p = 0.210)]. In logistic regression analysis, after adjusting for potential confounders, such as vomiting, abnormal mental status, and systolic hypotension (<70 mm of Hg) in absence of dehydration, fatal cases of severely malnourished under-five children with pneumonia were more often hypoxemic (OR = 23.15, 95% CI = 4.38-122.42), had clinical dehydration (some/severe) (OR = 9.48, 95% CI = 2.42-37.19), abdominal distension at admission (OR = 4.41, 95% CI = 1.12-16.52), and received blood transfusion (OR = 5.50, 95% CI = 1.21-24.99) for the management of crystalloid resistant systolic hypotension. CONCLUSION AND SIGNIFICANCE: We identified hypoxemia, clinical dehydration, and abdominal distension as the independent predictors of death in SAM children with pneumonia. SAM children with pneumonia who required blood transfusion for the management of crystalloid resistant systolic hypotension were also at risk for death. Thus, early identification and prompt management of these simple clinically recognizable predictors of death and discourage the use of blood transfusion for the management of crystalloid resistant systolic hypotension may help reduce deaths in such population
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