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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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Lower Bounds and Accelerated Algorithms for Bilevel Optimization

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Journal of Machine Learning Research 2023年第1期24卷 1-56页

作者： Ji, Kaiyi Liang, Yingbin Department of Computer Science and Engineering University at Buffalo BuffaloNY14260-2500 United States Department of Electrical and Computer Engineering The Ohio State University ColumbusOH98195-4322 United States

Bilevel optimization has recently attracted growing interests due to its wide applications in modern machine learning problems. Although recent studies have characterized the convergence rate for several such popular algorithms, it is still unclear how much further these convergence rates can be improved. In this paper, we address this fundamental question from two perspectives. First, we provide the first-known lower complexity bounds of (Formula presented) respectively for strongly-convex-strongly-convex and convex-strongly-convex bilevel optimizations. Second, we propose an accelerated bilevel optimizer named AccBiO, for which we provide the first-known complexity bounds without the gradient boundedness assumption (which was made in existing analyses) under the two aforementioned geometries. We also provide significantly tighter upper bounds than the existing complexity when the bounded gradient assumption does hold. We show that AccBiO achieves the optimal results (i.e., the upper and lower bounds match up to logarithmic factors) when the inner-level problem takes a quadratic form with a constant-level condition number. Interestingly, our lower bounds under both geometries are larger than the corresponding optimal complexities of minimax optimization, establishing that bilevel optimization is provably more challenging than minimax optimization. Our theoretical results are validated by numerical experiments. ©2022 Kaiyi Ji and Yingbin Liang.

关键词： Optimization algorithms

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Distributed field mapping for mobile sensor teams using a derivative‐free optimisation algorithm

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IET Cyber-Systems and Robotics 2024年第2期6卷 20-34页

作者： Tony X.Lin Jia Guo Said Al-Abri Fumin Zhang School of Electrical&Computer Engineering Georgia Institute of TechnologyAtlantaGeorgiaUSA College of Engineering Sultan Qaboos UniversityMuscatOman Department of Electronic&Computer Engineering Hong Kong University of Science and TechnologyHong KongChina

The authors propose a distributed field mapping algorithm that drives a team of robots to explore and learn an unknown scalar field using a Gaussian Process(GP).The authors’strategy arises by balancing exploration objectives between areas of high error and high *** computing high error regions is impossible since the scalar field is unknown,a bio-inspired approach known as Speeding-Up and Slowing-Down is leveraged to track the gradient of the GP *** approach achieves global field-learning convergence and is shown to be resistant to poor hyperparameter tuning of the *** approach is validated in simulations and experiments using 2D wheeled robots and 2D flying mini-ature autonomous blimps.

关键词： environment sensing multi-robot systems

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DemoCraft: Using In-Context Learning to Improve Code Generation in Large Language Models 3

DemoCraft: Using In-Context Learning to Improve Code Generat...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, electrical and Electronics, IITCEE 2025

作者： Joshua, Kapu Nirmal Sreejith, Mihit Indian Institute of Technology Department of Electrical Engineering Kanpur India Indian Institute of Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331515911

Producing executable code from natural-language directives via Large Language Models (LLMs) involves obstacles like semantic uncertainty and the requirement for task-focused context interpretation. To resolve these difficulties, we introduce DemoCraft, a framework that refines code generation through in-context learning and demonstration selection, in tandem with latent concept learning. Latent concept learning appends extra concept tokens, which act as trainable embeddings encapsulating task-oriented insights. We evaluate our approach on two promi- nent datasets: MBPP and Humaneval. Our experimental findings reveal that this method attains roughly a 2x boost in the pass@k metric compared to baseline models. Moreover, we propose two new evaluation measurements: correctness@k and similarity@k. Empirical analyses indicate that our system achieves nearly a 3x advancement in these measures as well. © 2025 IEEE.

关键词： Semantics

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End-To-End Encryption Enabled Lightweight Mutual Authentication Scheme for Resource Constrained IoT Network

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computers, Materials & Continua 2025年第2期82卷 3223-3249页

作者： Shafi Ullah Haidawati Muhammad Nasir Kushsairy Kadir Akbar Khan Ahsanullah Memon Shanila Azhar Ilyas Khan Muhammad Ashraf Department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Computer Engineering Section Malaysian Institute of Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Electrical Engineering Section Universiti Kuala LumpurBritish Malaysian InstituteSalengor53100Malaysia Electrical Engineering Department Mehran UniversityKhairpur-Mirs76062Pakistan Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication networks consist of resource-constrained autonomous devices, also known as autonomous Internet of things (IoTs) or machine-type communication devices (MTCDs) which act as a backbone for Industrial IoT, smart cities, and other autonomous systems. Due to the limited computing and memory capacity, these devices cannot maintain strong security if conventional security methods are applied such as heavy encryption. This article proposed a novel lightweight mutual authentication scheme including elliptic curve cryptography (ECC) driven end-to-end encryption through curve25519 such as (i): efficient end-to-end encrypted communication with pre-calculation strategy using curve25519;and (ii): elliptic curve Diffie-Hellman (ECDH) based mutual authentication technique through a novel lightweight hash function. The proposed scheme attempts to efficiently counter all known perception layer security threats. Moreover, the pre-calculated key generation strategy resulted in cost-effective encryption with 192-bit curve security. It showed comparative efficiency in key strength, and curve strength compared with similar authentication schemes in terms of computational and memory cost, communication performance and encryption robustness.

关键词： Mutual authentication lightweight end-to-end encryption elliptic curve cryptography industrial internet of things curve25519 machine-to-machine communication

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Optimizing Skin Lesion Segmentation with UNet and Attention-Guidance Utilizing Test Time Augmentation 6

Optimizing Skin Lesion Segmentation with UNet and Attention-...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Ahamed, Md. Faysal Hossain, Md. Munawar Mary, Mekhala Mariam Islam, Md. Rabiul Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh International Standard University Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350385779

To properly diagnose and screen for skin disorders, precise segmentation of the affected area is required. Segmenting skin lesions is difficult because of factors such as the form of the lesion, its proximity to surrounding skin, and the presence of hair. To effectively tackle this matter, we present a pioneering technique that utilizes deep learning to achieve comprehensive automation in skin lesion segmentation. This approach incorporates advanced preprocessing methodologies to enhance the overall performance. The architecture Attention-UNet is employed for the purpose of selectively segmenting images of skin lesions. The high detection rate of this design allows for precise melanoma segmentation in real-time. Finally, in the post-processing phase, we applied test-time augmentation (TTA) to further enhance segmentation precision. Both the ISIC 2017 and ISIC 2018 skin lesion datasets were used for training and testing the suggested technique. The model's mDSC, mIoU, recall, and precision were all 91.93%, 88.15%, 92.72%, and 91.15%, respectively, when trained independently on the ISIC 2017 datasets. Separate training on the ISIC 2018 datasets achieved similar results: 92.17% mDSC, 90.26% mIoU, 92.34% recall, and 92.01% precision. After being trained on a dataset consisting ISIC data from both 2017 and 2018, the proposed method achieved mDSC scores of 92.30% percent, mIoU scores of 90.06% percent, recall rates of 93.91% percent, and precision rates of 90.74% percent. Our model demonstrates superior FPS scores compared to other models, indicating effective real-time performance. Due to its scalability and reliability, the suggested methodological framework may be utilized to create a completely automated computer-aided skin lesion diagnostic system. © 2024 IEEE.

关键词： Deep learning

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Optimizing Crisis Management with Advanced UAV Imaging Technology 4

Optimizing Crisis Management with Advanced UAV Imaging Techn...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Reddy, Jambula Snehith Mathi, Sri Chaithanya Reddy, Polu Sai Nadh Mateti, Vedha Pranava Yadav, Sudha Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India Amrita School of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331540364

The UAV systems that are fitted with current technology have helped in boosting the crisis management activities such as disaster relief operations, search and rescues, and policing. This paper integrates the developmental function of advanced detective UAVs into episodes of crises. These UAVs are capable of quickly and effectively assessing the damage, find people and observe post-disaster occurrences through with the help of complicated image analysis. It incorporates cameras with HD resolutions, thermal imagery systems, and numerous ML algorithms that allow entities and people to be identified besides detecting heat through barriers and thus increase the operational effectiveness during response operations. Indeed, the social implication as noticed with detective UAVs is highly useful in that it provides support services in disaster, bringing relief directly to the affected groups and guaranteeing the security of the rescue squads. Therefore, these UAVs enhance crisis consequence management, reducing death rates, the number of casualties and the extent of property damage. They do also help in safety and security to the public through assisting the police in surveillance and the fight against crime. Analyzing UAV's physical and virtual units, the challenges describe the UAV's use cases in different kinds of crises, such as natural ones, industrial failures, and criminal events. It also deals with issues such as restrictive policies, data security, and the requirement of professional personnel. From case studies and more personal accounts, detective UAVs' potential of crisis management is revolutionary: loss of life is avoided, damage to property is limited, and order is maintained. If further developed and properly integrated into the emergency management systems, UAVs will be the part of the equipment of the future for enhancing the efficiency of response to critical situations. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Cogging Torque Minimization in Low-Speed Transverse Flux Permanent Magnet Generator Using Improved Skewing Techniques

Cogging Torque Minimization in Low-Speed Transverse Flux Per...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Salehi, Maryam Omidi, Alireza Farahzadi, Mohammad Darabi, Ahmad Hoseintabar, Mohammad Acharya, Vedanadam Mudumbai Manjrekar, Madhav University of North Carolina at Charlotte Department of Electrical and Computer Engineering CharlotteNC United States University of Science and Culture Department of Electrical and Computer Engineering Tehran Iran Shahrood University of Technology Department of Electrical and Computer Engineering Shahrood Iran Powersys Inc CA United States

ISBN: (纸本)9798350376067

The transverse flux permanent magnet machine is known for its high torque density, simple winding, and high pole number. These unique features enable its utilization in a variety of low-speed applications. However, in direct-drive applications and small-scale wind turbines, high-cogging torque is one of the main concerns. In this paper, a transverse flux permanent magnet generator with rotary structure and magnetic shunts is designed and prototyped to validate the FEM results. To study the effects of the cogging torque, the 3D magnetic equivalent circuit of the designed generator is obtained. Then, numerical analysis based on the displacement of the torque waveform is proposed to eliminate the major harmonics of the cogging torque. This displacement can be employed by skewing the stator U-shaped cores. A geometric scheme is proposed to calculate the optimum skew angle to minimize the cogging torque. FEM results indicate that the proposed method for the optimum skewed angle calculation is an effective solution to reduce the cogging torque. According to the results, by applying the skewed angle in U-shaped cores, the cogging torque decreases by 55.85%. © 2024 IEEE.

关键词： Wind turbines

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Anomaly Detection and Classification in Streaming PMU Data in Smart Grids

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computer Systems science & engineering 2023年第9期46卷 3387-3401页

作者： A.L.Amutha R.Annie Uthra J.Preetha Roselyn R.Golda Brunet Department of Computational Intelligence SRM Institute of Science and TechnologyChennai603203India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India Department of Computer Science and Engineering Government College of EngineeringSalem636011India

The invention of Phasor Measurement Units(PMUs)produce synchronized phasor measurements with high resolution real time monitoring and control of power system in smart grids that make *** are used in transmitting data to Phasor Data Concentrators(PDC)placed in control centers for monitoring purpose.A primary concern of system operators in control centers is maintaining safe and efficient operation of the power *** can be achieved by continuous monitoring of the PMU data that contains both normal and abnormal *** normal data indicates the normal behavior of the grid whereas the abnormal data indicates fault or abnormal conditions in power *** a result,detecting anomalies/abnormal conditions in the fast flowing PMU data that reflects the status of the power system is critical.A novel methodology for detecting and categorizing abnormalities in streaming PMU data is presented in this *** proposed method consists of three modules namely,offline Gaussian Mixture Model(GMM),online GMM for identifying anomalies and clustering ensemble model for classifying the *** significant features of the proposed method are detecting anomalies while taking into account of multivariate nature of the PMU dataset,adapting to concept drift in the flowing PMU data without retraining the existing model unnecessarily and classifying the *** proposed model is implemented in Python and the testing results prove that the proposed model is well suited for detection and classification of anomalies on the fly.

关键词： Smart Grid PMU data incremental learning classifying anomalies artificial intelligence

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A low-cost buoyant force measurement device for monitoring sugar concentration in water solution

A low-cost buoyant force measurement device for monitoring s...

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2023 International Conference on electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Kaučić, Luka Kovačević, Ivana Aleksi, Ivan Keser, Tomislav Zidar, Josip Rimac-Drlje, Snježana Computer Science and Information Technology Osijek Faculty of Electrical Engineering Department of Computer Engineering and Automation Osijek Croatia Computer Science and Information Technology Osijek Faculty of Electrical Engineering Department of Communications Osijek Croatia

ISBN: (纸本)9798350322972

Monitoring sugar concentration during fermentation is crucial for producing high-quality alcoholic beverages. Traditional methods for measuring sugar concentration can be costly and time-consuming, especially for small-scale producers. In this study, we developed a low-cost buoyant force measurement device for monitoring sugar concentration in water solutions. The device consists of a buoyant object fully submerged in the solution and connected to a load-cell sensor. As the sugar concentration in the liquid increases, the buoyant force on the object increases, and the load-cell measures this force. Proposed device is calibrated using solutions of known sugar concentrations and its high accuracy and precision is presented. The device is tested in controlled environment to ensure accurate tracking changes in sugar concentration over time. Proposed device can be a valuable tool for small-scale producers looking to optimize their fermentation processes while minimizing costs. © 2023 IEEE.

关键词： Fermentation

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