Cloud-based configurable data stream processing architecture in rural economic development.

Purpose: This study aims to address the limitations of traditional data processing methods in predicting agricultural product prices, which is essential for advancing rural informatization to enhance agricultural efficiency and support rural economic growth.

Methodology: The RL-CNN-GRU framework combines reinforcement learning (RL), convolutional neural network (CNN), and gated recurrent unit (GRU) to improve agricultural price predictions using multidimensional time series data, including historical prices, weather, soil conditions, and other influencing factors. Initially, the model employs a 1D-CNN for feature extraction, followed by GRUs to capture temporal patterns in the data.

A hybridized feature extraction for COVID-19 multi-class classification on computed tomography images.

COVID-19 has killed more than 5 million individuals worldwide within a short time. It is caused by SARS-CoV-2 which continuously mutates and produces more transmissible new different strains. It is therefore of great significance to diagnose COVID-19 early to curb its spread and reduce the death rate.

Clinical and angiographic characteristics of ruptured and unruptured distal cerebral aneurysms: a review of a large series of cases in a high-volume center.

Objective: To evaluate clinical, angiographic features, and endovascular approach of ruptured and unruptured distal intracranial aneurysms (DIAs).

Methods: From January 2013 to February 2022, details of all consecutive intracranial aneurysms (IAs) treated endovascularly in our center were collected and retrospectively reviewed. IAs involving the anterior cerebral artery, middle cerebral artery, and posterior cerebral artery (distal to anterior communicating artery, limen insula, and P1 segment, respectively), and those distal to superior cerebellar artery, anterior-inferior cerebellar artery, and posterior inferior cerebellar artery's first segment were classified based on their etiology, location, size, and shape.

SegEIR-Net: A Robust Histopathology Image Analysis Framework for Accurate Breast Cancer Classification.

Background: Breast Cancer (BC) is a significant threat affecting women globally. An accurate and reliable disease classification method is required to get an early diagnosis. However, existing approaches lack accurate and robust classification.

Wearable Sensor Data Classification for Identifying Missing Transmission Sequence Using Tree Learning.

Wearable Sensor (WS) data accumulation and transmission are vital in analyzing the health status of patients and elderly people remotely. Through specific time intervals, the continuous observation sequences provide a precise diagnosis result. This sequence is however interrupted due to abnormal events or sensor or communicating device failures or even overlapping sensing intervals.