Mulberry leaf dataset for image classification task.

This manuscript presents a mulberry leaf dataset collected from five provinces within three regions in Thailand. The dataset contains ten categories of mulberry leaves. We proposed this dataset due to the challenges of classifying leaf images taken in natural environments arising from high inter-class similarity and variations in illumination and background conditions (multiple leaves from a mulberry tree and shadows appearing in the leaf images). We highlight that our research team recorded mulberry leaves independently from various perspectives during our data acquisition using multiple camera types. The mulberry leaf dataset can serve as vital input data passed to computer vision algorithms (conventional deep learning and vision transformer algorithms) for creating image recognition systems. The dataset will allow other researchers to propose novel computer vision techniques to approach mulberry recognition challenges.

Metformin mitigates dementia risk among individuals with type 2 diabetes.

This mini-narrative review explores the relationship between diabetes and dementia, focusing on the potential mitigating role of metformin in reducing cognitive decline among individuals with type 2 diabetes. The interplay of factors such as glycemic control, diabetic complications, and lifestyle influences characterises diabetes-related dementia. This review emphasises the significance of comprehensive diabetes management in addressing the heightened risk of dementia in this population. Methodologically, the review synthesises evidence from 23 studies retrieved through searches on PubMed, Embase, Google Scholar, and Scopus. Current evidence suggests a predominantly positive association between metformin use and a reduced risk of dementia in individuals with diabetes. However, the review shows the complex nature of these outcomes, revealing variations in results in some studies. These discrepancies show the importance of exploring dose-response relationships, long-term effects, and demographic diversity to unravel the complexities of metformin's impact on cognitive health. Limitations in the existing body of research, including methodological disparities and confounding variables, necessitate refined approaches in future studies. Large-scale prospective longitudinal studies and randomised controlled trials focusing specifically on cognitive effects are recommended. Propensity score matching and exploration of molecular mechanisms can enhance the validity of findings in clinical practice. From a clinical perspective, metformin can serve as a potential adjunctive therapy for individuals with diabetes at risk of cognitive decline.

Vehicle image datasets for image classification.

Vehicle image recognition is a critical research area with diverse traffic management, surveillance, and autonomous driving systems applications. Accurately classifying and identifying vehicles from images play a crucial role in these domains. This work presents two vehicle image datasets: the vehicle type image dataset version 2 (VTID2) and the vehicle make image dataset (VMID). The VTID2 Dataset comprises 4,356 images of Thailand's five most used vehicle types, which enhances diversity and reduces the risk of overfitting problems. This expanded dataset offers a more extensive and varied collection for robust model training and evaluation. This dataset will be valuable for researchers focusing on vehicle image recognition tasks. With an emphasis on sedans, hatchbacks, pick-ups, SUVs, and other vehicles, the dataset allows for developing and evaluating algorithms that accurately classify different types of vehicles. The VMID Dataset contains 2,072 images of logos (called vehicle make) from eleven prominent vehicle brands in Thailand. The proposed dataset will facilitate the development of computer vision algorithms and the evaluation of learning algorithm model performance metrics. These two datasets provide valuable resources to the research community that will foster possible research advancements in vehicle recognition, vehicle logo detection or localization, and vehicle segmentation, contributing to the development of intelligent transportation systems.

Explainable deep learning diagnostic system for prediction of lung disease from medical images.

Around the globe, respiratory lung diseases pose a severe threat to human survival. Based on a central goal to reduce contiguous transmission from infected to healthy persons, several technologies have evolved for diagnosing lung pathologies. One of the emerging technologies is the utility of Artificial Intelligence (AI) based on computer vision for processing wide varieties of medical imaging but AI methods without explainability are often treated as a black box. Based on a view to demystifying the rationale influencing AI decisions, this paper designed and developed a novel low-cost explainable deep-learning diagnostic tool for predicting lung disease from medical images. For this, we investigated explainable deep learning (DL) models (conventional DL and vision transformers (ViTs)) for performing prediction of the existence of pneumonia, COVID19, or no-disease from both original and data augmentation (DA)-based medical images (from two chest X-ray datasets). The results show that our experimental consideration of the DA that combines the impact of cropping, rotation, and horizontal flipping (CROP+ROT+HF) for transforming input images and then passed as input to an Inception-V3 architecture yielded a performance that surpasses all the ViTs and other conventional DL approaches in most of the evaluated performance metrics. Overall, the results suggest that the utility of data augmentation schemes aided the DL methods to yield higher classification accuracies. Furthermore, we compared five different class activation mapping (CAM) algorithms (GradCAM, GradCAM++, EigenGradCAM, AblationCAM, and RandomCAM). The result shows that most of the examined CAM algorithms were effective in identifying the attention region containing the existence of pneumonia or COVID-19 from the medical images (chest X-rays). Our developed low-cost AI diagnostic tool (pilot system) can assist medical experts and radiographers in proffering early diagnosis of lung disease. For this, we selected five to seven deep learning models and the explainable algorithms were deployed on a novel web interface implemented via a Gradio framework.

Synthesis, Characterization, and Biological Activity of N (')-[(Z)-(3-Methyl-5-oxo-1-phenyl-1,5-dihydro-4H-pyrazol-4-ylidene)(phenyl)methyl]benzohydrazide and Its Co(II), Ni(II), and Cu(II) Complexes.

Reaction of 1-phenyl-3-methyl-4-benzoyl-pyrazol-5-one and benzoyl hydrazide in refluxing ethanol gave N (')-[(Z)-(3-methyl-5-oxo-1-phenyl-1,5-dihydro-4H-pyrazol-4-ylidene)(phenyl)methyl]benzohydrazide (HL(1)), which was characterized by NMR spectroscopy and single-crystal X-ray structure study. X-ray diffraction analyses of the crystals revealed a nonplanar molecule, existing in the keto-amine form, with intermolecular hydrogen bonding forming a seven-membered ring system. The reaction of HL(1) with Co(II), Ni(II), and Cu(II) halides gave the corresponding complexes, which were characterized by elemental analysis, molar conductance, magnetic measurements, and infrared and electronic spectral studies. The compounds were screened for their in vitro cytotoxic activity against HL-60 human promyelocytic leukemia cells and antimicrobial activity against some bacteria and yeasts. Results showed that the compounds are potent against HL-60 cells with the IC50 value ≤5 µM, while some of the compounds were active against few studied Gram-positive bacteria.