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Streamlining plant disease diagnosis with convolutional neural networks and edge devices

Neural Computing and Applications 2024年第29期36卷 18445-18477页

作者： Ahamed, Md. Faysal Salam, Abdus Nahiduzzaman, Md. Abdullah-Al-Wadud, M. Islam, S. M. Riazul Department of Computer Science and Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical and Computer Engineering Rajshahi University of Engineering & ampTechnology Rajshahi6204 Bangladesh Department of Electrical Engineering Qatar University Doha Qatar Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Natural and Computing Sciences University of Aberdeen AberdeenAB24 3FX United Kingdom

In addressing labor-intensive process of manual plant disease detection, this article introduces an innovative solution—the lightweight parallel depthwise separable convolutional neural network (PDSCNN) coupled with a hybrid ridge regression extreme learning machine classifier. This combined approach automates plant disease classification, significantly expediting the learning process and reducing computational complexity. The PDSCNN model excels in feature capture, boasting a mere 9 layers and 0.053 million parameters, resulting in exceptional computational efficiency. On top of that, the classification performances of traditional ELM increased by introducing a hybrid ridge ELM model. Its accelerated operations, reduced memory usage, and optimized parallel processing render it lightweight and ideal for edge devices. The proposed framework exhibits remarkable efficacy, showcasing a precision of 95.76%, a recall of 89.29%, an F1-score of 91.25%, an accuracy of 94.22%, and an AUC of 99.72%. Moreover, training and testing times clock in at just 0.2314 and 0.0259 s, respectively. A comparative analysis against state-of-the-art transfer learning methods underscores its real-time analytical prowess. To demonstrate real-world applicability, we successfully validated our model on the Jetson Nano hardware, making it suitable for resource-constrained settings. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Computational efficiency

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A Secure and Efficient Framework for Multi-Round Data Trading Over the Internet of Artificially Intelligent Things

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IEEE Internet of Things Magazine 2022年第1期5卷 119-124页

作者： Zheng, Xu Zhang, Lizong Hui, Bei Tian, Ling Cai, Zhipeng School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Information and Software Engineering University of Electronic Science and Technology of China China Georgia State University Department of Computer Science United States

IoT data trading has greatly benefited the popularization of both the Internet of Things (IoT) and Artificial Intelligence of Things (AIoT). Current solutions mainly treat the dataset owned by each device as a commodity and focus on maintaining a data market. Meanwhile, inspired by recent achievements in distributed machine learning and federated learning, AIoT can actually realize its services by retaining the data locally on devices and only exchanging model parameters. However, existing IoT data trading methods fail to meet this secure and efficient process. Therefore, this article proposes a novel framework for data trading over AIoT. It allows the service provider to trade on deep learning model training instead of purchasing the full datasets. Key factors for the security issues and data pricing are addressed for the framework, including the design of the learning parameters and the comprehensive privacy concerns. Then a novel data trading strategy is proposed for the service provider to jointly maintain the model performance and the budget consumption. Evaluation results reveal the improvements on the model performance. Finally, some open problems in the design of data trading in AIoTs are discussed. © 2018 IEEE.

关键词： Internet of things

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MFTN: A Multi-scale Feature Transfer Network Based on IMatchFormer for Hyperspectral Image Super-Resolution 41

MFTN: A Multi-scale Feature Transfer Network Based on IMatch...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Shuying Ren, Mingyang Yang, Yong Wang, Xiaozheng Wei, Yingzhi School of Software Tiangong University Tianjin China School of Computer Science and Technology Tiangong University Tianjin China School of Control Science and Engineering Tiangong University Tianjin China

Hyperspectral image super-resolution (HISR) aims to fuse a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to obtain a high-resolution hyperspectral image (HR-HSI). Due to some existing HISR methods ignoring the significant feature difference between LR-HSI and HR-MSI, the reconstructed HR-HSI typically exhibits spectral distortion and blurring of spatial texture. To solve this issue, we propose a multi-scale feature transfer network (MFTN) for HISR. Firstly, three multi-scale feature extractors are constructed to extract features of different scales from the input images. Then, a multi-scale feature transfer module (MFTM) consisting of three improved feature matching Transformers (IMatchFormers) is designed to learn the detail features of different scales from HR-MSI by establishing the cross-model feature correlation between LR-HSI and degraded HR-MSI. Finally, a multiscale dynamic aggregation module (MDAM) containing three spectral aware aggregation modules (SAAMs) is constructed to reconstruct the final HR-HSI by gradually aggregating features of different scales. Extensive experimental results on three commonly used datasets demonstrate that the proposed model achieves better performance compared to state- of-the-art (SOTA) methods. Copyright 2024 by the author(s)

关键词： Image resolution

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Evolutionary Optimization with a Simplified Helper Task for High-Dimensional Expensive Multiobjective Problems

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-32页

作者： Wu, Xunfeng Lin, Qiuzhen Zhou, Junwei Liu, Songbai Coello Coello, Carlos A. Leung, Victor C. M. College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China Mexico City07300 Mexico Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

In recent years, surrogate-Assisted evolutionary algorithms (SAEAs) have been sufficiently studied for tackling computationally expensive multiobjective optimization problems (EMOPs), as they can quickly estimate the qualities of solutions by using surrogate models to substitute for expensive evaluations. However, most existing SAEAs only show promising performance for solving EMOPs with no more than 10 dimensions, and become less efficient for tackling EMOPs with higher dimensionality. Thus, this article proposes a new SAEA with a simplified helper task for tackling high-dimensional EMOPs. In each generation, one simplified task will be generated artificially by using random dimension reduction on the target task (i.e., the target EMOPs). Then, two surrogate models are trained for the helper task and the target task, respectively. Based on the trained surrogate models, evolutionary multitasking optimization is run to solve these two tasks so that the experiences of solving the helper task can be transferred to speed up the convergence of tackling the target task. Moreover, an effective model management strategy is designed to select new promising samples for training the surrogate models. When compared to five competitive SAEAs on four well-known benchmark suites, the experiments validate the advantages of the proposed algorithm on most test cases. © 2024 held by the owner/author(s).

关键词： Multitasking

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Fair and Communication-Efficient Personalized Federated Learning 18th

Fair and Communication-Efficient Personalized Federated Lea...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zheng, Yifu Zheng, Xu Wang, Tingqi Mu, Chong Zhakiyev, Nurkhat School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Science and Innovation Astana IT University Astana Kazakhstan

ISBN: (纸本)9783031714665

Personalized Federated Learning (pFL) is among the most popular tasks in distributed deep learning, which compensates for mutual knowledge and enables device-specific model personalization. However, the effectiveness of pFL is severely impeded by challenges on fairness and significant communication overhead, where devices holding essential samples have to devote extensive resources for model training. To address these issues, we introduce the Fair and Communication-Efficient Personalized Federated Learning (FCE-PFL) framework, which harmonizes performance and device fairness while maintaining communication costs. Based on the Dempster-Shafer Theory, FCE-PFL employs assistance and contribution metrics to quantify the auxiliary information a client receives and provides. Then FCE-PFL balances and adjusts the involvement of devices in each training iteration, so as to facilitate fair training by constraining the maximum resource consumption per device and reduce the overall communication overhead. Our framework has been proven to be superior to existing methods in accuracy through rigorous experiments on the CIFAR10 and CIFAR100 datasets, demonstrating its potential as a fair and efficient pFL solution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Lake Water Level Forecasting Using LSTM and GRU: A Deep Learning Approach 9th

Lake Water Level Forecasting Using LSTM and GRU: A Deep Lear...

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9th Future Technologies Conference, FTC 2024

作者： Du, Yuxin Fan, Jing Happonen, Ari Paulraj, Dassan Tuape, Micheal Department of Software Engineering School of Engineering Sciences Lappeenranta-Lahti University of Technology Mukkulankatu 19 Lahti15210 Finland Department of Software Engineering School of Engineering Sciences Lappeenranta-Lahti University of Technology Yliopistonkatu 34 Lappeenranta53850 Finland Department of Computer Science and Engineering R.M.K. Engineering College Chennai India

ISBN: (纸本)9783031731242

Artificial Intelligence and Deep Learning-based methods show constant promise in addressing time series forecasting challenges. Lake water level forecasting is an essential & significant environmental and societal impact related time series forecasting problem, with climate change connections. Lakes’ diverse hydrological characteristics make high-performance forecasting models to be lake-specific. Thus, comprehensive experiments are necessary to develop accurate deep learning-based forecasting models. We propose an approach for effective and efficient systematic search conduction for high-performance Deep Learning forecasting models. The method is applicable across various time series forecasting challenges. The research was structured around three experimental groups, each focusing on predicting the water levels of Lake Vesijärvi in Lahti, Finland, over periods of 1 day, 3 days, and 7 days, respectively with Long Short-Term Memory and Gated Recurrent Unit. The results are highly promising. All models achieved a Nash-Sutcliffe Efficiency above 0.95 and a Root Mean Squared Error below 0.025. The best-performing model achieved a Nash-Sutcliffe Efficiency above 0.99 and a Root Mean Squared Error below 0.0011. All evaluation metrics were calculated from testing data without signs of overfitting. This research provides insights into Deep Learning-based time series forecasting and a replicable method to conduct such studies effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lakes

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Defense Mechanisms Against Audio Adversarial Attacks: Recent Advances and Future Directions 1

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3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Li, Routing Xue, Meng School of Software Engineering Huazhong University of Science and Technology Wuhan China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

With the popularity of speech and speaker recognition systems in recent years, voice interfaces are increasingly integrated into various Internet of Things (IoT) devices. However, studies have demonstrated that such systems are vulnerable to attacks using manipulated inputs. During the last few years, defense mechanisms have been studied and discussed from various aspects to protect voice systems from such attacks. Notwithstanding, there is lacking survey focus on the defense mechanism of audio adversarial examples. In this paper, we provide a comprehensive survey on state-of-the-art defense methods by illuminating their main concepts, reviewing the recent progress with a novel taxonomy, and discussing the future directions. It promises to bring awareness to the security problems in speech and speaker recognition systems and encourages people to propose more robust defenses against audio adversarial examples. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Speech recognition

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High-quality Trajectory Generation for Autonomous Driving: A Lightweight Federated Learning-based Diffusion Model

High-quality Trajectory Generation for Autonomous Driving: A...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Gao, Runquan Kang, Jiawen Lai, Bingkun Xu, Minrui Sun, Geng Zhang, Tao Zhang, Weiting Yang, Dong Guangdong University of Technology School of Automation Guangzhou China Nanyang Technological University School of Computer Science and Engineering Singapore Jilin University College of Computer Science and Technology Changchun China Beijing Jiaotong University School of Software Engineering Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China

ISBN: (纸本)9798350351255

Vehicle trajectory data plays a pivotal role in simulation testing for autonomous driving. Hence, there exist well-established trajectory generation methods employing deep generative models to generate trajectories mapping the distribution of the original dataset, thereby augmenting existing trajectory datasets. However, these methods typically rely on large datasets gathered by governmental or organizational entities for central training, which may pose data privacy, security, and accessibility issues. Therefore, it is challenging to generate high-quality traffic trajectory data while preserving privacy which involves a delicate balance between these two objectives. To deal with this challenge, we introduce Federated Learning into the diffusion model and propose a Federated Learning-based diffusion model (FedDifftraj) to generate traffic trajectory data. Unlike existing central training methods, FedDifftraj aggregates model parameters uploaded by different vehicles and then updates a global model. Additionally, there is a substantial communication overhead incurred during the training of the federated diffusion model. Therefore, we quantize the local diffusion model before uploading it to the parameter server. Through extensive simulations on real-world datasets, FedDifftraj can generate high-quality traffic trajectory data that is consistent with the results of the central training while preserving privacy and reducing communication overhead by 93.74% when utilizing 8-bit quantization. © 2024 IEEE.

关键词： Differential privacy

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Explainable Story and Visualization for Learning Data Structure Concepts 5

Explainable Story and Visualization for Learning Data Struct...

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5th International Conference on computer Science and Technologies in Education, CSTE 2023

作者： Wang, Bingshu Zhu, Xinyi Shi, Cheng Cheng, Yan School of Software Northwestern Polytechnical University Xi'an China School of Computer Science and Engineering Xi'An University of Technology Xi'an China

ISBN: (纸本)9798350325720

The 'Data Structure' course is a fundamental subject within the entire computer course system, and it plays a vital role in the field of computer and information disciplines. However, the abstraction and logic of data structures have made this course challenging for many underestimated students. It involves many concepts, which can be difficult to understand. To enhance teaching efficiency and facilitate students' comprehension of data structure concepts, we propose a method that combines storytelling and visualization. Our goal is to present these concepts clearly and promote a deeper understanding among students. We have developed new expressions that serve as a bridge between data structure concepts and their logical structures. Our teaching practice has shown that this approach is highly effective. It not only increases the teacher's confidence in their ability to teach, but also enhances students' subjective initiative when it comes to learning. © 2023 IEEE.

关键词： Visualization

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Deep Learning Model for Analyzing EEG Signal Analysis

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IEEE Access 2025年 13卷 91034-91045页

作者： Gupta, Varun Kumar, Vivek Prince Singh, Saurabh Lee, Young-Seok Ra, In-Ho Department of Electronics and Communication National Institute of Technology Sikkim India National Institute of Technology Department of Electrical and Electronics Engineering Sikkim India Dongguk University Department of Industrial & Systems Engineering Seoul04620 Korea Republic of Woosong University Department of AI and Bigdata Daejeon34606 Korea Republic of Kunsan National University School of Computer & Software Gunsan Korea Republic of Kunsan National University School of Software Gunsan54150 Korea Republic of

To analyze the physiological information within the acquired EEG signal is very cumbersome due to the possibility of several factors, viz. noise and artifacts, complexity of brain dynamics, and inter-subject variability. To address these issues, this paper compares a U-shaped encoder-decoder network (UNET) and Bat-based UNET signal analysis (BUSA) techniques to classify depression rates in the Electroencephalogram (EEG) datasets. The main objective of including these two techniques is to reveal their effectiveness. It comprises pre-processing, feature extraction, feature selection, and classification stages. The framework excels at noise reduction during pre-processing, enhancing dataset integrity. Feature extraction leverages band power and correlation dimension to extract crucial features. Furthermore, feature selection optimizes classification accuracy by refining the fitness function of bats in the classification layer. The performance of UNET and BUSA are compared based on the following performance evaluating parameters viz. accuracy (Acc), Area Under the Curve (AUC), precision (P), and recall (R) (or sensitivity (Se)). The results indicated that the BUSA technique outperforms the UNET technique. © 2013 IEEE.

关键词： Electroencephalography

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