MediGuard: Protecting Sensitive Healthcare Data with Privacy-Preserving Language Models.

The integration of large language models (LLMs) into digital healthcare has the potential to significantly improve access to accurate and timely medical advice, especially in underserved areas. However, serious privacy concerns hinder the widespread adoption of LLM-based medical consultation systems, as they often require users to disclose private health information, risking unauthorized exposure and non-compliance with regulations. To address these issues, we introduce MediGuard, a new privacy-preserving LLM framework that dynamically protects sensitive healthcare data throughout the consultation process.

Towards precision agriculture: metaheuristic model compression for enhanced pest recognition.

Crop diseases and insect pests pose significant challenges to agricultural productivity, often resulting in considerable yield losses. Traditional pest recognition methods, which rely heavily on manual feature extraction, are not only time consuming and labor intensive but also lack robustness in diverse conditions. While deep learning (DL) models have improved performance over conventional approaches, they typically suffer from high computational demands and large model sizes, limiting their real-world applicability.

Deep-ProBind: binding protein prediction with transformer-based deep learning model.

Binding proteins play a crucial role in biological systems by selectively interacting with specific molecules, such as DNA, RNA, or peptides, to regulate various cellular processes. Their ability to recognize and bind target molecules with high specificity makes them essential for signal transduction, transport, and enzymatic activity. Traditional experimental methods for identifying protein-binding peptides are costly and time-consuming.

Computer-Aided Diagnosis in Spontaneous Abortion: A Histopathology Dataset and Benchmark for Products of Conception.

Spontaneous abortion, commonly known as miscarriage, is a significant concern during early pregnancy. Histopathological examination of tissue samples is a widely used method to diagnose and classify tissue phenotypes found in products of conception (POC) after spontaneous abortion. Histopathological examination is subjective and dependent on the skill and experience of the examiner.

Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features.

Sumoylation is a post-translation modification (PTM) mechanism that involves many critical biological processes, such as gene expression, localizing and stabilizing proteins, and replicating the genome. Moreover, sumoylation sites are associated with different diseases, including Parkinson's and Alzheimer's. Due to its vital role in the biological process, identifying sumoylation sites in proteins is significant for monitoring protein functions and discovering multiple diseases.

Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers.

In order for a country's economy to grow, agricultural development is essential. Plant diseases, however, severely hamper crop growth rate and quality. In the absence of domain experts and with low contrast information, accurate identification of these diseases is very challenging and time-consuming.