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ESDNN: A novel ensembled stack deep neural network for mango leaf disease classification and detection

MULtimeDIA TOOLS AND APPLICATIONS 2024年第4期83卷 10989-11015页

作者： Gautam, Vinay Ranjan, Ranjeet Kumar Dahiya, Priyanka Kumar, Anil Chitkara Univ Inst Engn & Technol Rajpura Punjab India Thapar Inst Engn & Technol CSE Dept Patiala India DIT Univ Sch Comp Data Sci Res Grp Dehra Dun India

The mango crop production and quality are affected by several factors. Plant disease is one of the prime factors that impact crop yield. The disease impacts all parts of plants such as leaves, roots, and stems. Farmers waste most of their energy, time, and effort dealing with it manually and lead to heavy loss in the yield of the crops. Mango production is affected by several types of diseases. Prior recognition of disease would ease and reduce the diagnostic process that enhances the production of quality crops. A huge investigation has been carried out in the field of identifying and classifying leaf disease. Hence, an appropriate artificial intelligence (AI) based solution is needed to support farmers. The objective of the work is to provide an effective and efficient AI-based solution to detect and classify leaf disease earlier. Earlier disease detection in crops is the most prominent way to prevent loss of money and time. The plant leaf image is mainly used as a source to detect disease in the plant. This paper employs an ensemble stacked deep learning model to resolve the problem of automatic identification of mango-leaf diseases. In the proposed approach, initially, the images are segmented for the region of interest and input to a stack of various deep neural networks. The outcome of the deep neural network is aggregated with a machine learning model to identify leaf disease. This model is used to identify various mango leaf diseases such as Powdery mildew, Anthracnose, etc. In the experiment, the deep learning models are stacked and aggregated with machine learning to identify mango leaf diseases. The proposed model is compared with state-of-the-art models and outperforms with 98.57% accuracy.

关键词： Classification Convolutional Neural Network Ensemble learning image processing Leaf disease detection

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A Customizable Face Generation Method Based on Stable Diffusion Model

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IEEE ACCESS 2024年 12卷 195307-195318页

作者： Xiang, Wenlong Xu, Shuzhen Lv, Cuicui Wang, Shuo Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China

Facial generation technology uses computer algorithms and artificial intelligence techniques to generate realistic facial images. This technology typically employs deep learning models such as Generative Adversarial Networks (GANs) and diffusion models, learning from a large dataset of real facial images to generate new virtual facial images. Text-to-image model combines textual and visual information by generating image content based on corresponding input text descriptions. The development of text-to-image models provides new insights into cross-modal learning, promotes interaction and fusion between textual and visual information, thereby supporting the advancement of multimodal intelligent systems. This paper aims to apply text-to-image models to design a customizable facial generation model. The model is improved upon the Stable Diffusion model by incorporating LoRA (Low-Rank Adaptation) principles for style constraints. In addition, we modify the structure of Variational Autoencoder to enhance generation efficiency. Upon obtaining initial generation results, local refinements can be performed without altering the main structure, allowing for localized adjustments. Through pre-generation and post-processing, our model can accurately generate semantically guided face images.

关键词： Diffusion models Noise Computational modeling Adaptation models Noise reduction Training Text to image Semantics image synthesis Computer architecture Diffusion model facial image generation text-to-image U-Net

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Improved Potato Crop Disease Classification Using Ensembled Convolutional Neural Network

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POTATO RESEARCH 2024年 1-27页

作者： Singh, Gurpreet Kasana, Geeta Singh, Karamjeet Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala 147004 Punjab India

Potatoes are an essential crop cultivated in numerous regions around the globe, but they frequently get impacted by diseases that lower their production and quality. To ensure the crop reaches its maximum potential, controlling the diseases in the initial or early stages is necessary. Recent developments in deep learning algorithms have demonstrated significant improvements in predicting agricultural diseases at various stages. However, contemporary deep learning models frequently exhibit real-world performance and generalization capabilities limitations. This study proposes an ensemble convolutional neural network model that combines the three most widely used models, VGG16, MobileNetV2, and ResNet50, to increase generalizability and improve accuracy in the classification of potato crop diseases. The proposed model is trained on a large dataset containing 6644 images of potato leaves, which is constructed by merging three different publicly available datasets. These datasets are originally collected from three distinct locations around the globe (the USA, Ethiopia, and Pakistan). The model aims to achieve improvement in accuracy and maintain generalizability for classifying potato fungal diseases. The proposed ensemble architecture achieved an accuracy of 98.49%, surpassing the individual models. In this study, a web-based interface is developed for the evaluation of the model. The proposed model is tested on this web interface with the images obtained through the Google image Search Engine. A plant pathologist supervised the selection of images and the pre-processing of the dataset. The results of the evaluation indicate that the model will perform better when deployed in real-world situations.

关键词： Convolutional neural network deep learning Ensemble learning image processing Potato disease classification

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Economical Electric Vehicle Charging Scheduling via deep Imitation learning

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第11期25卷 18196-18210页

作者： Huang, Zhenhao Wang, Jing Fan, Xuezhong Yue, Renfeng Xiang, Cheng Gao, Shuhua Natl Univ Singapore Dept Elect & Comp Engn Singapore 119077 Singapore Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China Shandong Aipu Elect Equipment Co Ltd Jinan 250061 Peoples R China State Grid Shandong Elect Power Co Jinan Licheng Dist Power Supply Co Jinan 250100 Peoples R China

This study investigates economical scheduling of charging for an electric vehicle (EV) in a typical household with an intelligent charging management system. The problem formulation considers rooftop solar power generation, time-varying domestic energy consumption, real-time pricing of electricity, and user preferences. This task traditionally takes the form of a mixed-integer linear programming (MILP) problem, but we demonstrate its equivalence to linear programming (LP) to reduce computational complexity. The LP problem can be solved to global optimality if all future information is known, which is unrealistic in practice and replaced with forecasting. learning-based methods such as deep reinforcement learning (DRL) eliminate the need for a forecaster and make online decisions rapidly using a learned policy. We propose an approach based on imitation learning that leverages the knowledge of an LP expert by learning from its optimal demonstrations instead of learning from scratch in DRL. Our approach trains a deep neural network (DNN) based policy efficiently in a supervised manner and incorporates a safety post-processing mechanism that enforces strict constraint satisfaction. Numerical studies on real-world data show that the proposed approach achieves 23 similar to 220 times speedup compared to DRL for DNN training, and the total electricity cost is far lower than DRL as well, which is strikingly close to the lower bound in theory.

关键词： Electric vehicle charging Electricity Costs Optimization State of charge Training Imitation learning Electric vehicle charging scheduling imitation learning deep neural network linear programming reinforcement learning

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real-time estimation model for magnetic arc blow angle based on auxiliary task learning

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JOURNAL OF MANUFACTURING PROCESSES 2024年 131卷 682-693页

作者： Wang, Zhenmin Dong, Ying Li, Liuyi Chi, Peng Zhou, Danhuan Zhu, Zeguang Wu, Xiangmiao Zhang, Qin South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

Magnetic arc blow during the welding of submersible pressure hulls can severely impact the weld quality. real-time estimation of the arc posture by the teaching-replay welding robot is a crucial and challenging step for further correction of the magnetic blow. This paper proposes a lightweight deep learning model capable of real-time estimation of the welding arc posture. The model takes arc images as input and outputs the angle of arc magnetic blow end-to-end. Specifically, to enable the model to learn the prior knowledge of human perception of arc magnetic blow angles, a training strategy utilizing keypoint detection as an auxiliary task has been proposed. This approach enhances the estimation accuracy of the model without incurring additional inference costs. Additionally, an efficient multi-scale attention (EMA) mechanism was integrated into the angle prediction branch to facilitate the learning of long-range feature dependencies. To confirm the effectiveness of the model, an arc magnetic blow image dataset was constructed for training and testing. The experimental results show that the model achieves a cumulative score (CS3) of 98.47 % and a mean absolute error (MAE) of 0.9985 degrees during testing. The model achieves an inference speed of 70.49 FPS on an Intel (R) Core (TM) i7-8750H CPU, which satisfies the criteria for real-time monitoring.

关键词： Arc welding Magnetic arc blow deep learning Angle estimation Auxiliary task learning

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Adaptive Video Streaming in Multi-Tier Computing Networks: Joint Edge Transcoding and Client Enhancement

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第4期23卷 2657-2670页

作者： Wang, Shuoyao Yang, Junyan Bi, Suzhi Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Shenzhen Univ Guangdong Hong Kong Joint Lab Big Data Imagingand Shenzhen 518060 Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen 518066 Peoples R China

With the advancement of multimedia technology and wireless networks, there is a growing demand for high-quality video streaming. Delivering stable video streaming in extremely dynamic wireless networks, nevertheless, is still an open problem. Recent developments in client computing and mobile edge computing (MEC) technologies have both shown promise in enhancing the adaptive bitrate (ABR) streaming services. In this paper, we consider a video streaming system in multi-tier computing networks, enabled by joint edge-side video transcoding and client-side video enhancement. By "enhancement," we mean that the client improves the video chunk quality via client-side image processing modules. In particular, we aim to design a joint bitrate adaptation, edge transcoding, and client image-processing algorithm, maximizing the quality of experience (QoE) of streaming services. The majority of the prior art has concentrated on super-resolution-enabled video streaming. Contrarily, we show that the video enhancement method outperforms the super-resolution approach in terms of signal-to-noise ratio and frames per second, implying a superior alternative for client-side processing in ABR streaming. We formulate the problem as an event-triggered Markov decision process (E-MDP), and propose a deep reinforcement learning (DRL)-based framework, named EDTEA. To deal with the delayed feedback induced by multi-tier computing, the entropy and the expected re-buffering terms are introduced to the objective and the reward, respectively. Extensive simulations based on real-world videos and bandwidth traces manifest that compared with state-of-the-art approaches, EDTEA provides 10.4%similar to 78.4% extra QoE while reducing re-buffering time by 85.5%similar to 91.7%.

关键词： Adaptive video streaming deep reinforcement learning edge computing video enhancement video transcoding

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MSSPG-AL: FEW-SHOT HYPERSPECTRAL image CLASSIFICATION WITH ACTIVE learning UPDATED MULTI-SCALE SUPERPIXEL GRAPH FUSION 31

MSSPG-AL: FEW-SHOT HYPERSPECTRAL IMAGE CLASSIFICATION WITH A...

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2024 International Conference on image processing

作者： Yu, Long Li, Jun Zhuo, Li Sun Yat Sen Univ Sch Geog & Planning Guangzhou 510275 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430078 Peoples R China

ISBN: (纸本)9798350349405;9798350349399

deep learning has been widely used in the field of hyperspectral image (HSI) classification, but existing classification methods generally require a large number of labels. With rarely labeled samples, most deep learning methods have the problem of overfitting. Although few-shot learning has developed in this direction in recent years, many methods are weak in exploring the relationships between samples in the current scene. Graph-based methods have advantages in this respect, but there are some problems in graph learning, such as lack of label information guidance and multi-scale information loss. To solve these problems, in this paper, we propose a new multi-scale superpixel graph fusion method (MSSPG), and the graph structure is dynamically optimized by combining active learning (MSSPG-AL). By incorporating class relationships and multi-scale information, false connections in the graph structure can be iteratively eliminated. Experiments on three real hyperspectral data demonstrate that our method can achieve remarkable performance in the few-shot HSI classification.

关键词： HSI classification superpixel graph multi-scale active learning few-shot

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Bangladeshi Vehicle Classification and Detection Using deep Convolutional Neural Networks With Transfer learning

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IEEE ACCESS 2025年 13卷 26429-26455页

作者： Md Farid, Dewan Kumer Das, Proshanta Islam, Monirul Sina, Ebna United Int Univ Dept Comp Sci & Engn Dhaka 1212 Bangladesh

Vehicle classification and detection has been a field of application for deep learning and image processing which play a very important role in intelligent transport management and AI-assisted driving systems. In this paper, we have presented a vehicle classification and detection system to detect and classify low-speed and high-speed Bangladeshi vehicles. To begin, we have implemented and tested the performance of the 11 pre-trained deep convolutional neural network (CNN) models: YOLOv8 Classify, MobileNetV2, GoogLeNet, AlexNet, ResNet-50, SqueezeNet, VGG19, DenseNet-121, Xception, InceptionV3, and NASNetMobile on the six vehicle classification and detection datasets: BIT-Vehicle, IDD, DhakaAI, Poribohon-BD, Sorokh-Poth, and VTID2. We have found that YOLOv8 Classify, MobileNetV2, and GoogLeNet models outperform other models in categorising vehicle types (e.g., car, truck, bus) in images where the vehicle is already the main subject. Next, we have customised the LabelImg image annotation tool to improve the following features: (a) Changing Label Font & Border, (b) Detecting Incorrect Labels, (c) Abbreviating Label Names, (d) Improving Crosshair & Bounding Box Guide, (e) Adding Progress Information, and (f) Improving File List Panel. We have collected data from real-world highway conditions in Dhaka city and labelled 54,556 objects from 5,460 images based on 16 Bangladeshi on-road vehicle classes. Furthermore, we have built a Bangladeshi native vehicle detection classifier for locating and identifying vehicles within larger scenes, often with multiple objects using YOLOv8 Detect and SSD-Mobilenet V2 models and later deploying this classifier into NVIDIA Jetson Nano Developer Kit (a small and powerful computer). Finally, we have tested the proposed Bangladeshi vehicle detection system with different timing, lighting, and weather conditions in several areas of Dhaka city. The proposed system can detect and classify low-speed and high-speed vehicles with an average 93%

关键词： Convolutional neural networks deep learning Vehicle detection Accuracy Urban areas Roads Transfer learning real-time systems Automobiles YOLO deep learning transfer learning vehicle classification and detection system

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Robust Training of Social Media image Classification Models

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第1期11卷 546-565页

作者： Alam, Firoj Alam, Tanvirul Ofli, Ferda Imran, Muhammad Hamad Bin Khalifa Univ HBKU Qatar Comp Res Inst Doha Qatar BJIT Ltd Dhaka 1212 Bangladesh

images shared on social media help crisis managers gain situational awareness and assess incurred damages, among other response tasks. As the volume and velocity of such content are typically high, real-time image classification has become an urgent need for faster disaster response. Recent advances in computer vision and deep neural networks have enabled the development of models for image classification for a number of tasks, including detecting crisis incidents, filtering irrelevant images, classifying images into specific humanitarian categories, and assessing the severity of the damage. To develop robust models, it is necessary to understand the capability of the publicly available pretrained models for these tasks, which remains to be underexplored in the crisis informatics literature. In this study, we address such limitations by investigating ten different network architectures for four different tasks using the largest publicly available datasets for these tasks. We also explore various data augmentation strategies, semisupervised techniques, and a multitask learning setup. In our extensive experiments, we achieve promising results.

关键词： Task analysis Social networking (online) Data models Training Benchmark testing real-time systems Pipelines Crisis informatics disaster response humanitarian tasks multitask learning social media image classification

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Leveraging Hybrid deep learning Models for Enhanced Multivariate time Series Forecasting

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NEURAL processing LETTERS 2024年第5期56卷 1-25页

作者： Mahmoud, Amal Mohammed, Ammar Cairo Univ Fac Grad Studies Stat Res Dept Comp Sci Giza Egypt

time series forecasting is crucial in various domains, ranging from finance and economics to weather prediction and supply chain management. Traditional statistical methods and machine learning models have been widely used for this task. However, they often face limitations in capturing complex temporal dependencies and handling multivariate time series data. In recent years, deep learning models have emerged as a promising solution for overcoming these limitations. This paper investigates how deep learning, specifically hybrid models, can enhance time series forecasting and address the shortcomings of traditional approaches. This dual capability handles intricate variable interdependencies and non-stationarities in multivariate forecasting. Our results show that the hybrid models achieved lower error rates and higher R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R<^>2$$\end{document} values, signifying their superior predictive performance and generalization capabilities. These architectures effectively extract spatial features and temporal dynamics in multivariate time series by combining convolutional and recurrent modules. This study evaluates deep learning models, specifically hybrid architectures, for multivariate time series forecasting. On two real-world datasets - Traffic Volume and Air Quality - the TCN-BiLSTM model achieved the best overall performance. For Traffic Volume, the TCN-BiLSTM model achieved an R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R<^>2$$\end{document} score of 0.976, and for Air Quality, it reached an R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasy

关键词： time Series deep learning Temporal Convolutional Network Convolutional Neural Network Long Short-Term Memory Gated Recurrent Network

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