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In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We...

In the current digital era, image classification of fruits, particularly apples, has become crucial for various applications, ranging from agriculture to retail. This research focuses on the utilization of Convolutional ...

As part of sustainability, care for the environment is necessary to ensure health of everyone. Sadly, pollution and particularly stray waste have turned out to be a major problem for humans and wildlife. All the litter, ...

- In this study, we investigate the potential of using state-of-the-art neural network architectures to increase the accuracy in classification of Quranic recitations per verse. Using a dataset of more than 4000 audio re...

Global food security relies heavily on rice production, which is continuously threatened by crop diseases. Although deep learning (DL) models have demonstrated strong diagnostic performance, their high computational and ...

Lung diseases such as pneumonia, tuberculosis, COVID-19, and lung cancer remain significant global health challenges that demand rapid and accurate diagnosis to improve patient outcomes. This study proposes NASNet-ViT, a...

Objectives: Deep learning networks have achieved significant progress in caries diagnosis, but automated localization and numbering of carious teeth in cone-beam computed tomography (CBCT) images remain underexplored. Th...

Convolutional Neural Network Twin Arm Robot for Automated Packing of Electronic Components is an intellectual robotic system that is developed as a COCA (Collaborative Operation Control Agent) operational handler of the ...

Background: Medical waste management in resource-limited healthcare facilities remains dominated by manual segregation, which is error-prone and difficult to standardize. Existing automated solutions often rely on cloud-...

Abstract Purpose This study introduces a new Convolutional Neural Network (CNN) architecture, EyeDiagNet, for the classification of fundus images into four classes: normal, cataract, diabetic retinopathy (DR), and glauco...

To address the challenge of extracting multi-scale features from distribution line insulators in complex inspection environments—where varying scales and background interference complicate detection—this study propos...

Biotic and abiotic stresses reduce crop yields, yet existing image‑based diagnostic models are often crop‑specific, computationally intensive, and poorly interpretable. This study present: (i) a multi-crop diagnostic...

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, which seriously affects children’s normal life. Screening potential autistic children before professional diagnose is helpful to early detection and earl...

Objetivo: Desarrollar un sistema automatizado para la detección de estadios de maduración dental en segundos molares inferiores en radiografías panorámicas. Materiales y métodos: Se recopilaron 1142 imágenes radio...

"La ecografía ha revolucionado el cuidado prenatal al proporcionar una herramienta no invasiva para evaluar el desarrollo fetal y detectar anomalías cromosómicas en el primer trimestre, como el síndrome de Down (tris...

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two...