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Deep learning based computer vision approaches for smart agricultural applications

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Artificial Intelligence in Agriculture 2022年第1期6卷 211-229页

作者： V.G.Dhanya A.Subeesh N.L.Kushwaha Dinesh Kumar Vishwakarma T.Nagesh Kumar G.Ritika A.N.Singh ICAR-Indian Institute of Seed Science MauUttar Pradesh 275101India ICAR-Central Institute of Agricultural Engineering BhopalMadhya Pradesh 462038India ICAR-Indian Agricultural Research Institute New Delhi 110012India Govind Ballabh Pant University of Agriculture and Technology PantnagarUttarakhand 263145India ICAR-National Institute of Natural Fibre Engineering and Technology Kolkata 700040India

The agriculture industry is undergoing a rapid digital transformation and is growing powerful by the pillars of cutting-edge approaches like artificial intelligence and allied *** the core of artificial intelligence,deep learning-based computer vision enables various agriculture activities to be performed automatically with utmost precision enabling smart agriculture into *** vision techniques,in conjunction with high-quality image acquisition using remote cameras,enable non-contact and efficient technology-driven solutions in *** review contributes to providing state-of-the-art computer vision technologies based on deep learning that can assist farmers in operations starting from land preparation to *** works in the area of computer vision were analyzed in this paper and categorized into(a)seed quality analysis,(b)soil analysis,(c)irrigation water management,(d)plant health analysis,(e)weed management(f)livestock management and(g)yield *** paper also discusses recent trends in computer vision such as generative adversarial networks(GAN),vision transformers(ViT)and other popular deep learning ***,this study pinpoints the challenges in implementing the solutions in the farmer’s field in *** overall finding indicates that convolutional neural networks are the corner stone of modern computer vision approaches and their various architectures provide high-quality solutions across various agriculture activities in terms of precision and ***,the success of the computer vision approach lies in building the model on a quality dataset and providing real-time solutions.

关键词： Agriculture automation Computer vision Deep learning Machine learning Smart agriculture vision transformers

Interpretable Detection of Malicious Behavior in Windows Portable Executables Using Multi-Head 2D transformers

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Big Data Mining and Analytics 2024年第2期7卷 485-499页

作者： Sohail Khan Mohammad Nauman Computer Science Department Effat College of EngineeringEffat UniversityJeddah 23341Kingdom of Saudi Arabia

Windows malware is becoming an increasingly pressing problem as the amount of malware continues to grow and more sensitive information is stored on *** of the major challenges in tackling this problem is the complexity of malware analysis,which requires expertise from human *** developments in machine learning have led to the creation of deep models for malware ***,these models often lack transparency,making it difficult to understand the reasoning behind the model’s decisions,otherwise known as the black-box *** address these limitations,this paper presents a novel model for malware detection,utilizing vision transformers to analyze the Operation Code(OpCode)sequences of more than 350000 Windows portable executable malware samples from real-world *** model achieves a high accuracy of 0.9864,not only surpassing the previous results but also providing valuable insights into the reasoning behind the *** model is able to pinpoint specific instructions that lead to malicious behavior in malware samples,aiding human experts in their analysis and driving further advancements in the *** report our findings and show how causality can be established between malicious code and actual classification by a deep learning model,thus opening up this black-box problem for deeper analysis.

关键词： machine learning malware vision transformers Windows Protable Executable(PE)

SwinVid:Enhancing Video Object Detection Using Swin Transformer

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Computer Systems Science & Engineering 2024年第2期48卷 305-320页

作者： Abdelrahman Maharek Amr Abozeid Rasha Orban Kamal ElDahshan Computer Science Department Faculty of Artificial Intelligence and InformaticsBenhaEgypt Mathematics Department Faculty of SciencesAl-Azhar UniversityCairoEgypt Department of Computer Science College of Science and Arts in QurayyatJouf UniversitySakakaSaudi Arabia

What causes object detection in video to be less accurate than it is in still images?Because some video frames have degraded in appearance from fast movement,out-of-focus camera shots,and changes in *** reasons have made video object detection(VID)a growing area of research in recent *** object detection can be used for various healthcare applications,such as detecting and tracking tumors in medical imaging,monitoring the movement of patients in hospitals and long-term care facilities,and analyzing videos of surgeries to improve technique and ***,it can be used in telemedicine to help diagnose and monitor patients *** VID techniques are based on recurrent neural networks or optical flow for feature aggregation to produce reliable features which can be used for *** of those methods aggregate features on the full-sequence level or from nearby *** create feature maps,existing VID techniques frequently use Convolutional Neural Networks(CNNs)as the backbone *** the other hand,vision transformers have outperformed CNNs in various vision tasks,including object detection in still images and image *** propose in this research to use Swin-Transformer,a state-of-the-art vision Transformer,as an alternative to CNN-based backbone networks for object detection in *** proposed architecture enhances the accuracy of existing VID *** ImageNet VID and EPIC KITCHENS datasets are used to evaluate the suggested *** have demonstrated that our proposed method is efficient by achieving 84.3%mean average precision(mAP)on ImageNet VID using less memory in comparison to other leading VID *** source code is available on the website https://***/amaharek/SwinVid.

关键词： Video object detection vision transformers convolutional neural networks deep learning

Performance Comparison of vision Transformer- and CNN-Based Image Classification Using Cross Entropy: A Preliminary Application to Lung Cancer Discrimination from CT Images

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Journal of Biomedical Science and Engineering 2024年第9期17卷 157-170页

作者： Eri Matsuyama Haruyuki Watanabe Noriyuki Takahashi Faculty of Informatics The University of Fukuchiyama Kyoto Japan School of Radiological Technology Gunma Prefectural College of Health Sciences Gunma Japan School of Health Sciences Fukushima Medical University Fukushima Japan

This study evaluates the performance and reliability of a vision transformer (ViT) compared to convolutional neural networks (CNNs) using the ResNet50 model in classifying lung cancer from CT images into four categories: lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), large cell carcinoma (LULC), and normal. Although CNNs have made significant advancements in medical imaging, their limited capacity to capture long-range dependencies has led to the exploration of ViTs, which leverage self-attention mechanisms for a more comprehensive global understanding of images. The study utilized a dataset of 748 lung CT images to train both models with standardized input sizes, assessing their performance through conventional metrics—accuracy, precision, recall, F1 score, specificity, and AUC—as well as cross entropy, a novel metric for evaluating prediction uncertainty. Both models achieved similar accuracy rates (95%), with ViT demonstrating a slight edge over ResNet50 in precision and F1 scores for specific classes. However, ResNet50 exhibited higher recall for LULC, indicating fewer missed cases. Cross entropy analysis showed that the ViT model had lower average uncertainty, particularly in the LUAD, Normal, and LUSC classes, compared to ResNet50. This finding suggests that ViT predictions are generally more reliable, though ResNet50 performed better for LULC. The study underscores that accuracy alone is insufficient for model comparison, as cross entropy offers deeper insights into the reliability and confidence of model predictions. The results highlight the importance of incorporating cross entropy alongside traditional metrics for a more comprehensive evaluation of deep learning models in medical image classification, providing a nuanced understanding of their performance and reliability. While the ViT outperformed the CNN-based ResNet50 in lung cancer classification based on cross-entropy values, the performance differences were minor and may not hold c

关键词： Lung Cancer Classification vision transformers Convolutional Neural Networks Cross Entropy Deep Learning

Advancing Crowd Object Detection: A Review of YOLO, CNN and ViTs Hybrid Approach*

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Journal of Intelligent Learning Systems and Applications 2024年第3期16卷 175-221页

作者： Mahmoud Atta Mohammed Ali Tarek Aly Atef Tayh Raslan Mervat Gheith Essam A. Amin Faculty of Graduate Studies for Statistical Research Software Engineering Department Cairo University Giza Egypt

One of the most basic and difficult areas of computer vision and image understanding applications is still object detection. Deep neural network models and enhanced object representation have led to significant progress in object detection. This research investigates in greater detail how object detection has changed in the recent years in the deep learning age. We provide an overview of the literature on a range of cutting-edge object identification algorithms and the theoretical underpinnings of these techniques. Deep learning technologies are contributing to substantial innovations in the field of object detection. While Convolutional Neural Networks (CNN) have laid a solid foundation, new models such as You Only Look Once (YOLO) and vision transformers (ViTs) have expanded the possibilities even further by providing high accuracy and fast detection in a variety of settings. Even with these developments, integrating CNN, YOLO and ViTs, into a coherent framework still poses challenges with juggling computing demand, speed, and accuracy especially in dynamic contexts. Real-time processing in applications like surveillance and autonomous driving necessitates improvements that take use of each model type’s advantages. The goal of this work is to provide an object detection system that maximizes detection speed and accuracy while decreasing processing requirements by integrating YOLO, CNN, and ViTs. Improving real-time detection performance in changing weather and light exposure circumstances, as well as detecting small or partially obscured objects in crowded cities, are among the goals. We provide a hybrid architecture which leverages CNN for robust feature extraction, YOLO for rapid detection, and ViTs for remarkable global context capture via self-attention techniques. Using an innovative training regimen that prioritizes flexible learning rates and data augmentation procedures, the model is trained on an extensive dataset of urban settings. Compared to solo YOLO,

关键词： Object Detection Deep Learning Computer vision YOLO Convolutional Neural Networks (CNN) vision transformers Neural Networks Transfer Learning Autonomous Driving Self-Drive Vehicles