15 papers · ranked by Valyu relevance
Xiaona Xie, Zeqian Liu, Yuanshuai Wang, Haoyue Fu + 4 more
'Yingqin Zhang' 'Jinbo Xu' 'Sotiris Kotsiantis'] Traditional image classification usually relies on manual feature extraction; however, with the rapid development of artificial intelligence and intelligent vision technology, deep learning models such as CNNs can automatically extract key features from input images to…
Hafza Eman, Syed M. Adnan, Wakeel Ahmad, Abid Ghaffar + 1 more
Citrus farming plays an essential role in agriculture; however, diseases like canker, greening, black spot, and melanose significantly reduce yield and fruit quality. Efficient classification of citrus leaf diseases is important for crop health maintenance and optimal crop yield. Traditional methods for leaf disease…
Jingsi Zhang, Xiaosheng Yu, Xiaoliang Lei, Chengdong Wu
Image classification indicates that it classifies the images into a certain category according to the information in the image. Therefore, extracting image feature information is an important research content in image classification. Traditional image classification mainly uses machine learning methods to extract…
Yongqi Xu, Dongcheng Li, Changcheng Li, Zheming Yuan + 1 more
In the context of intelligent agriculture in China, rapid and accurate identification of crop diseases is essential for ensuring food security and improving crop yield. Although lightweight convolutional neural networks (CNNs) are widely adopted for plant disease recognition due to their computational efficiency, they…
Rovin Tiwari, Jaideep Patel, Nikhat Raza Khan, Ajay Dadhich + 1 more
Rice is grown almost everywhere in the world but is notably prevalent in Asian nations where it serves as the main food source for nearly half of the world’s population. Yet, enduring agricultural problems like various rice diseases have been a problem for farmers and planting specialists for ages. A fast, efficient…
Liquan Zhao, Leilei Wang, Yanfei Jia, Ying Cui + 1 more
To improve accuracy of the MobileNet network, a new lightweight deep neural network is designed based on the MobileNetV2 network. Firstly, it modifies the network depth of MobileNetV2 to balance the image resolution, network width and depth to keep the gradient stable, which reduces the generation of gradient vanishing…
Kostiantyn Khabarlak
Many applications require high accuracy of neural networks as well as low latency and user data privacy guaranty. Face anti-spoofing is one of such tasks. However, a single model might not give the best results for different device performance categories, while training multiple models is time consuming. In this work…
Jiuqi Yan
Artificial intelligence is constantly evolving and can provide effective help in all aspects of people's lives. The experiment is mainly to study the use of artificial intelligence in the field of medicine. The purpose of this experiment was to compare which of MobileNetV1 and MobileNetV2 models was better at detecting…
Bogdan Ivanyuk-Skulskiy, Andrew Garrett Kurbis, Alex Mihailidis, Brokoslaw Laschowski
Robotic prosthetic legs and exoskeletons require real-time and accurate estimation of the walking environment for smooth transitions between different locomotion mode controllers. However, previous studies have mainly been limited to static image classification, therein ignoring the temporal dynamics of human-robot…
Najeebullah, Maaz Salman, Zar Nawab Khan Swati
Digital image spoofing has emerged as a significant security threat in biometric authentication systems, particularly those relying on facial recognition. This study evaluates the performance of three vision-based models, MobileNetV2, ResNET50, and Vision Transformer (ViT), for spoof detection in image classification…
Arthur Gonsales da Silva, Roger Pinho de Oliveira, Caio de Oliveira Bastos, Elena Almeida de Carvalho + 1 more
Image classification is a highly significant field in machine learning (ML), especially when applied to address longstanding and challenging issues in the biological sciences. In this study, we present the development of a hybrid deep learning-based tool suitable for deployment on mobile devices. This tool is aimed at…
Ellie Zontou
The evolution of cellular networks has played a pivotal role in shaping the modern telecommunications landscape. This paper explores the journey of cellular network generations, beginning with the introduction of Japan's first commercial 1G network by Nippon Telegraph and Telephone (NTT) Corporation in 1979. This…
Utkarsh Goel, Mike P. Wittie, kc claffy, Andrew Le
—Mobile (cellular) networks enable innovation, but can also stifle it and lead to user frustration when network performance falls below expectations. As mobile networks become the predominant method of Internet access, developer, research, network operator, and regulatory communities have taken an increased interest in…
Hamza Kheddar
The second-generation (2G) mobile systems were developed in response to the growing demand for a system that met mobile communication demands while also providing greater interoperability with other systems. International organizations were crucial in the development of a system that would offer better services, be…
Andrea Brunello, Andrea Dalla Torre, Paolo Gallo, Donatella Gubiani + 4 more
'Angelo Montanari' 'Nicola Saccomanno' 'Chris Rizos' 'Jari Nurmi'] Positioning via outdoor fingerprinting, which exploits the radio signals emitted by cellular towers, is fundamental in many applications. In most cases, the localization performance is affected by the availability of information about the emitters, such…