The Sixth Generation network (6G) can support autonomous driving along with various vehicular applications like Vehicular Edge Computing (VEC), a distributed computing architecture for connected vehicles. Computational offloading and resource management of help sort out some issues, such as high communication costs, privacy protection, an excessively long training process, etc., by proposing efficient model the Federated Learning computational in environment. Two research issues are...

Abstract In computer vision applications like surveillance and remote sensing, to mention a few, deep learning has had considerable success. Medical imaging still faces number of difficulties, including intra‐class similarity, scarcity training data, poor contrast skin lesions, notably in the case cancer. An optimisation‐aided learning‐based system is proposed for accurate multi‐class lesion identification. The sequential procedures start with preprocessing end categorisation. step where...

AI-driven precision agriculture applications can benefit from the large data source that remote sensing provides, as it gather agricultural monitoring at various scales throughout year. Numerous advantages for sustainable applications, including yield prediction, crop monitoring, and climate change adaptation, be obtained artificial intelligence. In this work, we proposed a fully automated Optimized Self-Attention Fused Convolutional Neural Network (CNN) architecture land use cover...

Wireless sensor networks (WSNs) play a major role in increasing the pervasiveness of Internet Thing (IoT) with smart sensors for Consumer electronics, which are low cost and easy to install. In WSN, data is collected from transmitted sink further operation used by IoT applications. However, resource restrictions on sensitivity radio links these causes serious routing issues affects application performance. Routing protocols designed provide reliable route effective communication between...

Diseases impact the rates of production many agricultural goods. These diseases require detection, which is difficult to do manually. Therefore, creation some automated illness detection systems urgently required. Deep learning showed significant success in area precision agriculture for recognition plant disease. Compared traditional techniques, deep architecture automatically extracts features from deeper layer. In this work, we proposed a new method classifying apple and grapefruit leaf...

The massive yearly population growth is causing hazards to spread swiftly around the world and have a detrimental impact on both human life economy. By ensuring early prediction accuracy, remote sensing enters scene safeguard globe against weather-related threats natural disasters. Convolutional neural networks, which are reflection of deep learning, been used more recently reliably identify land use in images. This work proposes novel bottleneck residual self-attention fusion-assisted...

In contemporary society, the proliferation of online hateful messages has emerged as a pressing concern, inflicting deleterious consequences on both societal fabric and individual well-being. The automatic detection such malevolent content using models designed to recognize it, holds promise in mitigating its harmful impact. However, advent "Hateful Memes" poses fresh challenges paradigm, particularly within realm deep learning models. These memes, constituting textual element associated...

Smart UAVs have been developed under the consumer Internet of Drone Things (CIoDTs) framework to improve quality service (QoS) for several commercial and applications. Artificial intelligence (AI)-inspired algorithms are employed here make these remote sensing devices more intelligent agile perform task most effectively. However, AI-based techniques may suffer from obtaining required feature space, leading poor performance AI system. Thus, address intrinsic issues, this manuscript presents a...

Globally, pests and plant diseases severely threaten forestry agriculture. Plant protection could be substantially enhanced by using non-contact, extremely effective, reasonably priced techniques for identifying tracking across large geographic areas. Precision agriculture is the study of other technologies, such as hyperspectral remote sensing (RS), to increase cultivation instead traditional agricultural methods with less negative environmental effects. In this work, we proposed a novel...

Blockchain technology is widely adopted in the Internet of Medical Things (IoMT) for information storage and retrieval. The integration blockchain with IoMT systems enhances security; however, it raises privacy security data searching storage. This study proposes a novel Binary Spring Search (BSS) technique based on group theory integrated hybrid deep neural network approach to enhance trustworthiness IoMT. proposed method incorporates secure key revocation dynamic policy updates. framework...

Classifying medical images is essential in computer-aided diagnosis (CAD). Although the recent success of deep learning classification tasks has proven advantages over traditional feature extraction techniques, it remains challenging due to inter and intra-class similarity caused by diversity imaging modalities (i.e., dermoscopy, mammography, wireless capsule endoscopy, CT). In this work, we proposed a novel deep-learning framework for classifying several modalities. training phase models,...

Deep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification diagnosis. However, challenges such as inter- intra-class similarities, class imbalance, computational inefficiencies due numerous hyperparameters persist. This study aims address these by presenting a novel deep-learning framework for classifying localizing gastrointestinal (GI) diseases from wireless capsule endoscopy (WCE) images. The proposed begins...