Significance of AI-assisted techniques for epiphyte plant monitoring and identification from drone images.

Monitoring forest canopies is vital for ecological studies, particularly for assessing epiphytes in rain forest ecosystems. Traditional methods for studying epiphytes, such as climbing trees and building observation structures, are labor, cost intensive and risky. Unmanned Aerial Vehicles (UAVs) have emerged as a valuable tool in this domain, offering botanists a safer and more cost-effective means to collect data.

Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer.

Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis.

Panoramic view of key cross-talks underpinning the oral squamous cell carcinoma stemness - unearthing the future opportunities.

The clinical management of oral cancer is often frequented with challenges that arise from relapse, recurrence, invasion and resistance towards the cornerstone chemo and radiation therapies. The recent conceptual advancement in oncology has substantiated the role of cancer stem cells (CSC) as a predominant player of these intricacies. CSC are a sub-group of tumor population with inherent adroitness to self-renew with high plasticity.

Stacked ensemble learning based on deep convolutional neural networks for pediatric pneumonia diagnosis using chest X-ray images.

Pneumonia is an acute respiratory infection caused by bacteria, viruses, or fungi and has become very common in children ranging from 1 to 5 years of age. Common symptoms of pneumonia include difficulty breathing due to inflamed or pus and fluid-filled alveoli. The United Nations Children's Fund reports nearly 800,000 deaths in children due to pneumonia.

Pediatric pneumonia diagnosis using stacked ensemble learning on multi-model deep CNN architectures.

Pediatric pneumonia has drawn immense awareness due to the high mortality rates over recent years. The acute respiratory infection caused by bacteria, viruses, or fungi infects the lung region and hinders oxygen transport, making breathing difficult due to inflamed or pus and fluid-filled alveoli. Being non-invasive and painless, chest X-rays are the most common modality for pediatric pneumonia diagnosis.