Wireless sensor networks and green networking have been major research areas in the field of communication technology for past few decades. In particular, development 6G systems has brought renewed focus on these areas, as demands higher data rates, more diverse applications, lower energy consumption continue to increase. The use wireless widely explored literature, shown great potential a wide range including environmental monitoring, industrial process control, healthcare. However, key...

The Convolutional Neural Network (CNN) model used in this study uses federated learning to identify and categorize grape leaf diseases into four severity categories. may learn from several data sources while maintaining privacy thanks the technique. Four customers are assess model's performance, each contributing local weights global model. findings show consistent performance across clients levels for detecting classifying diseases. With precision, recall, F1-Score, accuracy values as high...

The lumpy skin disease epidemic in India claimed the lives of over 97,000 cattle three months from July to September 2022. It is spread by ticks and other blood-feeding insects, including some species mosquito, ticks, flies. can also cause fever, nodules, mortality, especially animals who have never been exposed virus before. illness might "substantial" "severe" economic damage, according FAO WOAH. For dairy business, current has proven be problematic. severity Lumpy Skin Disease (LSD) will...

The devastating crop losses and economic damage caused by mango leaf spot disease pose a danger to the global industry. To classify seriousness across six categories, we present hybrid deep learning (DL) model that combines Convolutional Neural Network (CNN) Support Vector Machine (SVM). is trained tested using dataset of 20,000 photos diseased leaves gathered from variety sources, including internet repositories field surveys in plantations. In suggested model, CNN utilized for feature...

Pepper Leaf Blight Disease (PLBD) is a widespread plant ailment that has severe impact on pepper cultivation across the globe. The rapid detection and precise classification of PLBD severity levels are crucial for efficient disease control optimal agricultural productivity. present study introduces novel model based Faster region-based convolutional neural network (R-CNN) multi-classification in leaves. dataset used training testing consisted 10,000 images. model's performance was evaluated...

Development And Manufacturing Of Virtual 3d Printers Through Digital Twin Sensor Ecosystem With Artificial Intellence Involving Control Parameters

Heritage coin identification and categorization is a complex undertaking that requires much knowledge. For the automated recognition of six heritage coins, including ancient Greek, Roman, Byzantine, Islamic, Indian, Chinese we suggest deep learning-based method in this research study employing Convolutional Neural Networks (CNN). To boost size variety dataset, pre-processed dataset 8230 photos historical coins by normalizing pixel values using several data augmentation methods. CNN model,...

Manual vulnerability evaluation tools produce erroneous data and lead to difficult analytical thinking. Such security concerns are exacerbated by the variety, imperfection, redundancies of modern repositories. These problems were common traits producers public disclosures, which make it more identify flaws through direct analysis Internet Things (IoT). Recent breakthroughs in Machine Learning (ML) methods promise new solutions each these infamous diversification asymmetric information...

Tea leaf diseases have become a significant problem in the vast field of agricultural research, calling for sophisticated diagnostic methods. This research takes cutting-edge approach, carefully classifying tea illnesses using power Federated Learning combined with Convolutional Neural Networks (CNN). project included records from six customers, each representing four levels illness severity. The eliminated requirement centralized data aggregation by decentralized architecture federated...

This study introduces a unified method for illness detection in rice by integrating Convolutional Neural Networks (CNNs) and Random Forest. Brown Spot (accuracy of 93.19%) False Smut recall (89.80%) stand out as particular points strength the model, which was trained on dataset that also included Bacterial Blight, Sheath Smut, Stem Rot, Spot. Table 3 outlines architecture, features combination three layers convolution later integration with Forest to strike good balance between complexity...

The abstract summarizes the important findings & results of this research study, which focuses on categorization various hair conditions with a machine-learning model. Precision, recall, and F1-Score metrics for many illness classes, like Alopecia Areata, Tinea Capitis, Telogen Effluvium, Scarring Alopecia, Trichotillomania, Folliculitis, Head Lice, or Psoriasis, are quite promising. demonstrate model's abilities to reliably categorize diseases, accuracy values fluctuating between 88.04%...

Machine learning has the ability to dramatically improve sustainable systems by anticipating needs, maximizing resource use, raising output, and lowering waste. An overview of earlier studies on incorporation machine into is presented together with a case study how was used lower energy use in residential structure. The results show that may be increase efficiency save lot money. Wearable technology added whole new dimension already vast field personal electronics. mobile phone gave devices...

Categorizing cultural heritage photographs into many categories, such as historical relevance, architectural features, and preservation status, is necessary for retrieving information from sites. This research investigates categorising sites using deep learning methods like Convolutional Neural Networks (CNN) Support Vector Machines (SVM). For classification, we created a system that used CNN model with three convolutional layers, four max-pooling fully connected layer, an SVM model. Our...

This study dives into the classification of turmeric leaf diseases to enhance field agriculture and crop health management. Turmeric, a major agricultural commodity, is sensitive variety illnesses, causing problems for both yield quality. In production, accurate prompt disease identification critical efficient The data analysis shows meticulously developed categorization model that has produced impressive results. Precision, Recall, F1-Score diseases, including Leaf Spot, Bacterial Diseases,...

This study uses a CNN architecture to provide deep learning strategy for the detection and categorization of eight common rice illnesses. Three layers convolution, three maximum pooling layers, including two fully linked make up proposed model. The photos numerous diseases were gathered from various sources included in dataset this study. A 2,830 picture-labeled with an 80/20 split between both testing training sets is used train model that was trained then assessed using Fl-score metrics...

Identifying plant diseases early is crucial because they effect the development of affected plants.Despite fact that a wide variety ML models have already been put to use in disease detection and classification, recent developments branch known as Deep Learning (DL) given this area research lot hope for improved precision.On other hand, there currently no reliable quick detector can be used guarantee plant's healthy growth development.In paper we proposed deep learning model detect leaf...