A multi-modal geospatial-temporal LSTM based deep learning framework for predictive modeling of urban mobility patterns.

Urban mobility prediction is crucial for optimizing resource allocation, managing transportation systems, and planning urban development. We propose a novel framework, GeoTemporal LSTM (GT-LSTM), designed to address the intricate spatiotemporal dynamics of urban environments. GT-LSTM integrates temporal dependencies with geographic information through a multi-modal approach that combines attention mechanisms and Recurrent Neural Networks (RNNs).

Improved Birthweight Prediction With Feature-Wise Linear Modulation, GRU, and Attention Mechanism in Ultrasound Data.

Objectives: Birthweight prediction in fetal development presents a challenge in direct measurement and often depends on empirical formulas based on the clinician's experience. Existing methods suffer from low accuracy and high execution times, limiting their clinical effectiveness. This study aims to introduce a novel approach integrating feature-wise linear modulation (FiLM), gated recurrent unit (GRU), and Attention network to improve birthweight prediction using ultrasound data.

Advancements in urban scene segmentation using deep learning and generative adversarial networks for accurate satellite image analysis.

In the urban scene segmentation, the "image-to-image translation issue" refers to the fundamental task of transforming input images into meaningful segmentation maps, which essentially involves translating the visual information present in the input image into semantic labels for different classes. When this translation process is inaccurate or incomplete, it can lead to failed segmentation results where the model struggles to correctly classify pixels into the appropriate semantic categories. The study proposed a conditional Generative Adversarial Network (cGAN), for creating high-resolution urban maps from satellite images.

Design and Analysis of Multilayered Neural Network-Based Intrusion Detection System in the Internet of Things Network.

A large array of objects is networked together under the sophisticated concept known as the Internet of Things (IoT). These connected devices collect crucial information that could have a big impact on society, business, and the entire planet. In hostile settings like the internet, the IoT is particularly susceptible to multiple threats.

Multiconvolutional Transfer Learning for 3D Brain Tumor Magnetic Resonance Images.

The difficulty or cost of obtaining data or labels in applications like medical imaging has progressed less quickly. If deep learning techniques can be implemented reliably, automated workflows and more sophisticated analysis may be possible in previously unexplored areas of medical imaging. In addition, numerous characteristics of medical images, such as their high resolution, three-dimensional nature, and anatomical detail across multiple size scales, can increase the complexity of their analysis.

An Empirical Analysis of an Optimized Pretrained Deep Learning Model for COVID-19 Diagnosis.

As a result of the COVID-19 outbreak, which has put the world in an unprecedented predicament, thousands of people have died. Data from structured and unstructured sources are combined to create user-friendly platforms for clinicians and researchers in an integrated bioinformatics approach. The diagnosis and treatment of COVID-19 disease can be accelerated using AI-based platforms.