Autonomous robot visual navigation is a fundamental locomotion task based on extracting relevant features from images taken the surrounded environment to control an independent displacement. In navigation, use of known map helps obtain accurate localization, but in absence this map, guided or free exploration pathway must be executed sequence representing map. This paper presents appearance-bas...

Animal Vehicle Collision, commonly called roadkill, is an emerging threat to drivers and wild animals, increasing fatalities every year. Currently, prevalent methods using visible light cameras are efficient for animal detection in daylight time. This paper focuses on locating wildlife close roads during nocturnal hours by utilizing thermographic obtained images, thus enhancing vehicle safety. ...

The classification of various document images is considered an important step towards building a modern digital library or office automation system. Convolutional Neural Network (CNN) classifiers trained with backpropagation are considered to be the current state of the art model for this task. However, there are two major drawbacks for these classifiers: the huge computational power demand for...

The most prevalent and well-used method for obtaining images from huge, unlabelled image datasets is content-based retrieval. Convolutional Neural Networks are pre-trained deep neural networks which can generate extract accurate features databases. These CNN models have been trained using large databases with thousands of classes that include a huge number images, making it simple to use their ...

Many Indonesians have difficulty reading and learning the Brahmi script. Solving these problems can be done by developing software. Previous research has classified script but not had an output that matches letter. Therefore, letter classification is carried out as part of process recognizing This study uses Convolutional Neural Network (CNN) method with VGG16 architecture for classifying writi...

<span lang="EN-US">Deep learning is currently playing an important role in image analysis and classification. Diseases maize diminish productivity, which a major cause of economic damages the agricultural business throughout world. Researchers have previously utilized hand-crafted characteristics to classify images identify leaf illnesses Maize plants. With advancement deep learning, rese...

This paper analyzes the impact of input sparsity and DFS/DVFS configurations for single-board computers on execution time, power, energy each VGG16 layer as first step towards efficient CNN inference computers. For this purpose, we develop a power time measurement environment perform experiments using Raspberry Pi 4 NVIDIA Jetson Nano. Our results show that clock frequency strongly correlates w...

Listening to lung sounds through auscultation is vital in examining the respiratory system for abnormalities. Automated analysis of can be beneficial health systems low-resource settings where there a lack skilled physicians. In this work, we propose lightweight convolutional neural network (CNN) architecture classify diseases from individual breath cycles using hybrid scalogram-based features ...

This article presents a transfer learning model via convolutional neural networks (CNNs) with skip connection topology, to avoid the vanishing gradient and time complexity, which are usually common in networks. Three pretrained CNN architectures, namely AlexNet, VGG16 GoogLeNet employed equip connections. The is implemented through fine-tuning freezing architectures connections based on magneti...

Monkeypox has been recognized as the next global pandemic after COVID-19 and its potential damage cannot be neglected. Computer vision-based diagnosis detection method with deep learning models have proven effective during period. However, limited samples, are difficult to full trained. In this paper, twelve CNN-based models, including VGG16, VGG19, ResNet152, DenseNet121, DenseNet201, Efficien...