Digital Interaction and Machine Intelligence

doi:10.1007/978-3-030-74728-2

Advances in Intelligent Systems and Computing

2021

DOI: 10.1007/978-3-030-74728-2

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Digital Interaction and Machine Intelligence

Abstract: The series "Lecture Notes in Networks and Systems" publishes the latest developments in Networks and Systems-quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS.Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems.The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-

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2023

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Cited by 5 publications

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Deep Learning for Detecting Verticillium Fungus in Olive Trees: Using YOLO in UAV Imagery

Mamalis,

Kalampokis,

Kalfas

et al. 2023

Algorithms

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The verticillium fungus has become a widespread threat to olive fields around the world in recent years. The accurate and early detection of the disease at scale could support solving the problem. In this paper, we use the YOLO version 5 model to detect verticillium fungus in olive trees using aerial RGB imagery captured by unmanned aerial vehicles. The aim of our paper is to compare different architectures of the model and evaluate their performance on this task. The architectures are evaluated at two different input sizes each through the most widely used metrics for object detection and classification tasks (precision, recall, mAP@0.5 and mAP@0.5:0.95). Our results show that the YOLOv5 algorithm is able to deliver good results in detecting olive trees and predicting their status, with the different architectures having different strengths and weaknesses.

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Deep Learning for Detecting Verticillium Fungus in Olive Trees: Using YOLO in UAV Imagery

Mamalis,

Kalampokis,

Kalfas

et al. 2023

Algorithms

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Contemporary Approaches in Evolving Language Models

Oralbekova,

Mamyrbayev,

Othman

et al. 2023

Applied Sciences

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This article provides a comprehensive survey of contemporary language modeling approaches within the realm of natural language processing (NLP) tasks. This paper conducts an analytical exploration of diverse methodologies employed in the creation of language models. This exploration encompasses the architecture, training processes, and optimization strategies inherent in these models. The detailed discussion covers various models ranging from traditional n-gram and hidden Markov models to state-of-the-art neural network approaches such as BERT, GPT, LLAMA, and Bard. This article delves into different modifications and enhancements applied to both standard and neural network architectures for constructing language models. Special attention is given to addressing challenges specific to agglutinative languages within the context of developing language models for various NLP tasks, particularly for Arabic and Turkish. The research highlights that contemporary transformer-based methods demonstrate results comparable to those achieved by traditional methods employing Hidden Markov Models. These transformer-based approaches boast simpler configurations and exhibit faster performance during both training and analysis. An integral component of the article is the examination of popular and actively evolving libraries and tools essential for constructing language models. Notable tools such as NLTK, TensorFlow, PyTorch, and Gensim are reviewed, with a comparative analysis considering their simplicity and accessibility for implementing diverse language models. The aim is to provide readers with insights into the landscape of contemporary language modeling methodologies and the tools available for their implementation.

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Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey

Zualkernan

Abuhani

Hussain

et al. 2023

Drones

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Unmanned aerial vehicles (UAVs) are increasingly being integrated into the domain of precision agriculture, revolutionizing the agricultural landscape. Specifically, UAVs are being used in conjunction with machine learning techniques to solve a variety of complex agricultural problems. This paper provides a careful survey of more than 70 studies that have applied machine learning techniques utilizing UAV imagery to solve agricultural problems. The survey examines the models employed, their applications, and their performance, spanning a wide range of agricultural tasks, including crop classification, crop and weed detection, cropland mapping, and field segmentation. Comparisons are made among supervised, semi-supervised, and unsupervised machine learning approaches, including traditional machine learning classifiers, convolutional neural networks (CNNs), single-stage detectors, two-stage detectors, and transformers. Lastly, future advancements and prospects for UAV utilization in precision agriculture are highlighted and discussed. The general findings of the paper demonstrate that, for simple classification problems, traditional machine learning techniques, CNNs, and transformers can be used, with CNNs being the optimal choice. For segmentation tasks, UNETs are by far the preferred approach. For detection tasks, two-stage detectors delivered the best performance. On the other hand, for dataset augmentation and enhancement, generative adversarial networks (GANs) were the most popular choice.

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Digital Interaction and Machine Intelligence

Cited by 5 publications

References 14 publications

Deep Learning for Detecting Verticillium Fungus in Olive Trees: Using YOLO in UAV Imagery

Deep Learning for Detecting Verticillium Fungus in Olive Trees: Using YOLO in UAV Imagery

Contemporary Approaches in Evolving Language Models

Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey

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