Abstract In recent years, deep learning has significantly reshaped numerous fields and applications, fundamentally altering how we tackle a variety of challenges. Areas such as natural language processing (NLP), computer vision, healthcare, network security, wide-area surveillance, precision agriculture have leveraged the merits era. Particularly, improved analysis remote sensing images, with continuous increase in number researchers contributions to field. The high impact development is...

The growth of wireless networks has been remarkable in the last few years. One main reasons for this is massive use portable and stand-alone devices with network connectivity. These have become essential on daily basis consumer electronics. As dependency increased, attacks against them over time increased as well. To detect these attacks, a intrusion detection system (NIDS) high accuracy low needed. In work, we propose machine learning (ML) based (WNIDS) Wi-Fi to efficiently them. proposed...

Artificial Intelligence (AI) has become one of the most recurrent topics nowadays, due to its many industrial applications and wide range research in academia. Within AI, Machine Learning (ML) is prominent sub-fields, popularity which complemented with high demand as a professional skill for different industries. In last few years, teaching ML thus increasingly common levels education. However, curricula implemented several courses or programs learn are not always appropriate backgrounds...

Hyperspectral image (HSI) classification is the most vibrant area of research in hyperspectral community due to rich spectral information contained HSI can greatly aid identifying objects interest. However, inherent non-linearity between materials and corresponding profiles brings two major challenges classification: interclass similarity intraclass variability. Many advanced deep learning methods have attempted address these issues from perspective a region/patch-based approach, instead...

Martian landslide segmentation is a challenging task compared to the same on Earth. One of reasons that vegetation typically lost or significantly less its surroundings in regions In contrast, Mars desert planet, and there no aid detection segmentation. Recent work has demonstrated strength vision transformer (ViT) based deep learning models for various computer tasks. Inspired by multi-head attention mechanism ViT, which can model global longrange spatial correlation between local input...

Landslide segmentation on Earth has been a challenging computer vision task, in which the lack of annotated data or limitation computational resources major obstacle development accurate and scalable artificial intelligence-based models. However, accelerated progress deep learning techniques availability data-sharing initiatives have enabled significant achievements landslide Earth. With current capabilities technology availability, replicating similar task other planets, such as Mars, does...

A recent revision to the Nurse Practitioner Role Core Competencies will lead robust changes in graduate education. Incorporating innovative technology into core courses of advanced practice nursing prepare practice-ready providers with a high level competence leading successful health outcomes, improved patient satisfaction, and decreased care costs.

The objective of this work is to predict corn grain yield and seed compositions (i.e., protein, oil starch concentration) using Unmanned Aerial Vehicles (UAV)-based multispectral imagery deep learning models. UAV was acquired throughout the growing season 2022 over a cornfield near Brookings, South Dakota, USA. Deep methods such as Convolutional Neural Network (CNN), Long Short-Term Memory networks (LSTM), Transformer were used based on imagery. results show that: (1) both original...

The rapid progress in deep learning, particularly convolutional neural networks (CNNs), has significantly enhanced the effectiveness and efficiency of hyperspectral image (HSI) classification. While CNN-based approaches excel enriching local features, they often struggle to capture long-range dependencies sequential data. To address this limitation, an attention mechanism can be integrated with CNN architectures both global rich representations. Transformer their variations, known for...

Hyperspectral image (HSI) classification is the most vibrant area of research in hyperspectral community due to rich spectral information contained HSI can greatly aid identifying objects interest. However, inherent non-linearity between materials and corresponding profiles brings two major challenges classification: interclass similarity intraclass variability. Many advanced deep learning methods have attempted address these issues from perspective a region/patch-based approach, instead...