Publications by authors named "M F Ahsan"

Background: Physical activity refers to all bodily movement performed by an individual from morning to night. Physical activity benefits not only physical health but also mental health. Physical activity benefits university students in many ways.

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Melatonin is considered an effective bio-stimulant that is crucial in managing several abiotic stresses including drought. However, its potential mechanisms against drought stress in fragrant roses are not well understood. Here, we aim to investigate the role of melatonin on plants cultivated under drought stress (40 % field capacity) and normal irrigation (80 % field capacity).

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Pyrazoline is a 5-membered ring that has two adjacent nitrogen. It has gained advanced attention from medical and organic chemists due to very low cytotoxic activities. It is applicable and more applied in research fields and has various pharmacological activities, including cardiovascular, anti-tumor, and anti-cancer properties.

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Article Synopsis
  • PM2.5 air pollution in urban areas like Jakarta is a major health concern, often exceeding safe levels due to rapid urbanization, prompting this study to evaluate specific modeling techniques.
  • The study investigates autocorrelation in air quality data and finds that while the Support Vector Regression (SVR) effectively handles autocorrelation, the combination of XGBoost and an Exponentially Weighted Moving Average (EWMA) chart shows superior monitoring performance.
  • The integration of XGBoost and EWMA successfully identified changes in air quality, detecting only one out-of-control point during the monitoring phases, thus improving accuracy and reducing false alarms in air quality assessments.
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Brain tumors present a significant global health challenge, and their early detection and accurate classification are crucial for effective treatment strategies. This study presents a novel approach combining a lightweight parallel depthwise separable convolutional neural network (PDSCNN) and a hybrid ridge regression extreme learning machine (RRELM) for accurately classifying four types of brain tumors (glioma, meningioma, no tumor, and pituitary) based on MRI images. The proposed approach enhances the visibility and clarity of tumor features in MRI images by employing contrast-limited adaptive histogram equalization (CLAHE).

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