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DeepLeaf: an optimized deep learning approach for automated recognition of grapevine leaf diseases

Neural Computing and Applications 2025年第15期37卷 8799-8823页

作者： Talaat, Fatma M. Shams, Mahmoud Y. Gamel, Samah A. ZainEldin, Hanaa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Faculty of Engineering Horus University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Plant diseases can cause severe losses in agricultural production, impacting food security and safety. Early detection of plant diseases is crucial to minimize crop damage and ensure agricultural sustainability. Manual monitoring is often impractical due to the complexity and time involved, making automated disease recognition essential. This study presents a new Plant Disease Detection Algorithm (PDDA) called DeepLeaf focused on identifying four common grapevine diseases: leaf blight, black rot, stable, and black measles. The PDDA integrates three key modules: an Image Preprocessing Module, a Feature Extraction Module, and an Optimized Convolutional Neural Network (OCNN)-based Classification Module. The OCNN forms the core of the classification system, with its hyperparameters fine-tuned using fuzzy optimization to enhance performance. Preprocessing techniques are applied to analyze diseased leaves, and a logistic regression algorithm is used to downsample the features for better analysis. The CNN is trained on images from the Plant Village dataset, allowing it to detect and classify grapevine leaf diseases accurately. The proposed model's efficiency in the automated diagnosis of grapevine diseases is demonstrated by its remarkable 99.7% accuracy rate. This high accuracy indicates that the PDDA may help with more effective and scalable plant disease monitoring, which will ultimately allow better agricultural practices. © The Author(s) 2025.

关键词： CNN Detection of leaf disease Extracting features Plant disease

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Neuroadaptive Containment Control for Nonlinear Multiagent Systems With Input Saturation: An Event-Triggered Communication Approach

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3163-3173页

作者： Wang, Xin Zhang, Sen Li, Huaqing Zhang, Wei Li, Hongyi Huang, Tingwen Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations. © 2013 IEEE.

关键词： Radial basis function networks

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Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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ADNeuroNet: a neuroevolution-based neural network algorithm for the diagnosis of neurodegenerative diseases

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Neural Computing and Applications 2025年第14期37卷 8593-8627页

作者： Khan, Afreen Zubair, Swaleha Ali, Irfan Department of Computer Application Faculty of Engineering & IT Integral University Lucknow226026 India Department of Statistics and Operations Research Aligarh Muslim University Aligarh India Department of Computer Science Aligarh Muslim University Aligarh India

Neurodegenerative disorders such as dementia and Alzheimer’s disease (AD) have adversely devastated the health and well-being of the older community. Given that early detection might help prevent or delay cognitive disorders associated with AD, developing an effective diagnostic technique is deemed appropriate to control the disease. Despite advances in clinical diagnostic standards and treatment techniques, the global prevalence of cognitive disturbance and behavioral problems remains steep. Besides the above challenge, the scarcity of AD-related open-source raw data prompted us to tailor ADNeuroNet, a neuroevolution-based neural network (NN) designed for predicting cognitively normal, mild cognitive impairment, and AD instances. To construct a robust NN algorithm that can predict the disease with high accuracy and improved efficiency, we worked on the combined cognitive and demographic clinical data to create a comprehensive model. Primarily, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) repository was used to develop this program. We employed three distinct datasets and developed a predictive model that achieved the maximum performance accuracy of 93.42%. Utilizing only baseline details, the as-developed model successfully diagnosed and predicted neurodegenerative disorders and is likely to emerge as an effective clinical tool in future endeavors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Disease control

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Receiver-Agnostic Radio Frequency Fingerprinting Using a Prototypical Contrastive Domain Adaptation Method

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IEEE Signal Processing Letters 2025年 32卷 1910-1914页

作者： Li, Wenhan Wang, Jiangong Liu, Taijun Xu, Gaoming Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China China Coast Guard Academy Ningbo315801 China

Radio frequency (RF) fingerprinting is a technique used to identify different transmitters by analyzing the unique hardware impairments of RF transmitters, known as RF fingerprints. However, many existing studies have primarily focused on transmitter impairments, while the impact of receiver hardware impairments on RF signals has often been overlooked. To alleviate this issue, this letter proposes a receiver-agnostic RF fingerprinting method using unsupervised domain adaptation. The method employs prototypical contrastive learning to align the features of source domain and target domain data, while simultaneously learning the features of both domains. Experimental results on the real-world dataset (WiSig) demonstrate that the proposed method outperforms other receiver-agnostic RF fingerprinting methods. © 1994-2012 IEEE.

关键词： Transmitters

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Network Bandwidth Optimization Using Software Defined Networking (SDN) 5

Network Bandwidth Optimization Using Software Defined Networ...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Haidar, Gaby Abou Zangana, Ali Noaman Khasro Owayjan, Michel Daou, Roy Abi Zeid Achkar, Roger American University of Science and Technology Faculty of Engineering and Computer Science Beriut Lebanon France La Sagesse University Faculty of Engineering Beirut Lebanon

ISBN: (纸本)9798331519957

This paper provides a detailed comparison of traditional networking architectures and Software Defined Networking (SDN) approaches, with a focus on bandwidth optimization and traffic management. Simulations were conducted under various networking scenarios, utilizing Cisco Packet Tracer for traditional networks and Mininet with the OpenDaylight (ODL) controller for SDN. The evaluated scenarios included single, dual, and triple PC setups, as well as high-Traffic conditions. Traditional networks demonstrated packet loss rates of up to 5%, limited bandwidth utilization (~2.443%), and increasing latency as more devices were added. In contrast, SDN-based setups showed no packet loss and significantly higher bandwidth utilization, reaching up to 94.478%. This study highlights the potential of SDN to enhance network efficiency and scalability, addressing a gap in the literature through scenario-based comparative analysis. Future research directions include exploring large-scale topologies, security enhancements, and advanced optimization techniques. © 2025 IEEE.

关键词： Packet loss

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Design and Implementation of a Slant Polarized Wideband Vivaldi Antenna Array Feed for Monopulse Radar Reflectors

Design and Implementation of a Slant Polarized Wideband Viva...

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2025 IEEE Radio and Wireless Symposium, RWS 2025

作者： Masoumi, Reza Kazemi, Robab Fathy, Aly E. University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN United States

ISBN: (纸本)9798350377743

Traditionally, conical ridge horn antennas are used for feeding large reflectors, but they can cause grating lobes in arrays. This paper introduces a compact Vivaldi antenna for monopulse radar, featuring a planar feed structure that offers a wider bandwidth and simplifies manufacturing. The innovative design of the Vivaldi antenna incorporates strategically placed grooves and a circular dielectric lens to enhance gain and reduce sidelobe levels. Operating from 6 GHz to 18 GHz with proper impedance matching, the antenna is 23 mm wide (0.9λ at 12 GHz), making it over 66% smaller than traditional designs. The antennas are angled in 2 × 2 array configuration to overlap, with a center-to-center spacing of 11.3 mm (0.68λ at 18 GHz), ensuring that no grating lobes are present across the entire bandwidth. This innovative antenna effectively feeds an elliptical reflector, providing high gain, broadband capabilities, and significant null depth, ensuring accurate and efficient performance in monopulse radar applications. © 2025 IEEE.

关键词： Monopulse radar

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Particle-based Data-driven Nonlinear State Estimation of Model-free Process from Nonlinear Measurements

Particle-based Data-driven Nonlinear State Estimation of Mod...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ghosh, Anubhab Eldar, Yonina C. Chatterjee, Saikat Digital Futures Centre School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Faculty of Mathematics and Computer Science The Weizmann Institute of Science Israel

ISBN: (纸本)9798350368741

We consider the problem of causal filtering of a model-free process from (noisy) nonlinear measurements. The 'model-free process' means that we do not have a state-space model (SSM) of the process dynamics, limiting the use of traditional model-driven filters, such as unscented Kalman filter (UKF) and particle filter (PF). To address the problem we propose a particle-based data-driven nonlinear state estimation (pDANSE) method. In pDANSE, a recurrent neural network (RNN) provides the statistical parameters of a Gaussian prior of the underlying state, and particles are then drawn from the prior to compute the posterior moments. pDANSE is typically trained in a semi-supervised fashion. For our experiments we study the use of half-wave rectification as a nonlinear transformation of measurements. We first show that an unsupervised learning-based method under-performs, and subsequently the semi-supervised learning-based pDANSE performs satisfactorily. Using Lorenz-63 system as benchmark, pDANSE is found to be competitive against a model-driven PF that knows the exact SSM. © 2025 IEEE.

关键词： deep neural network Kalman filter particle filter semi-supervised learning State estimation

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Leveraging Large Language Models to Identify the Values Behind Arguments 2nd

Leveraging Large Language Models to Identify the Values Be...

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2nd International Workshop on Value engineering in Artificial Intelligence, VALE 2024

作者： Senthilkumar, Rithik Appachi Homayounirad, Amir Siebert, Luciano Cavalcante Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031854620

Human values capture what people and societies perceive as desirable, transcend specific situations and serve as guiding principles for action. People’s value systems motivate their positions on issues concerning the economy, society and politics among others, influencing the arguments they make. Identifying the values behind arguments can therefore help us find common ground in discourse and uncover the core reasons behind disagreements. Transformer-based large language models (LLMs) have exhibited remarkable performance across language generation and analysis. However, leveraging LLMs in sociotechnical systems that assist with discourse and argumentation necessitates systematically evaluating their ability to analyse and identify the values behind arguments, an under-explored research direction. Using a multi-level human value taxonomy inspired by the Schwartz Theory of Basic Human Values, we present a systematic and critical evaluation of GPT-3.5-turbo in human value identification from a dataset of multi-cultural arguments, across the zero-shot, few-shot and chain-of-thought prompting strategies, carrying forward from prior research on this task which leveraged a fine-tuned BERT model. We observe that prompting strategies exhibit performance levels close to, but still behind fine-tuning for value classification. We also detail some challenges associated with value classification with LLMs, offering potential directions for future research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Human Values Large Language Models Prompting

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Machine Translation Performance for Low-Resource Languages: A Systematic Literature Review

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IEEE Access 2025年 13卷 72486-72505页

作者： Tafa, Taofik O. Hashim, Siti Zaiton Mohd Othman, Mohd Shahizan Alhussian, Hitham Nasser, Maged Abdulkadir, Said Jadid Huspi, Sharin Hazlin Adeyemo, Sarafa O. Bena, Yunusa Adamu Universiti Teknologi Malaysia Faculty of Computing Johor Bahru81310 Malaysia Department of Computer Science at Gusau Gusau632101 Nigeria Universiti Teknologi PETRONAS Department of Computer and Information Sciences Perak Seri Iskandar32610 Malaysia Kebbi State University of Science and Technology Faculty of Engineering Aliero Nigeria

Machine translation (MT) for low-resource languages continues to face significant challenges because of limited digital resources and parallel corpora, despite remarkable developments in neural machine translation (NMT). Addressing these challenges requires a thorough review of existing research to identify effective strategies and methods. To achieve this, a systematic literature review (SLR) is conducted following PRISMA guidelines and systematically analysing studies published in various academic databases in the last five years (between 2020 and 2024). A total of 69 relevant articles were examined to evaluate the performance of MT, explore persistent challenges and assess the effectiveness of proposed or used solutions. The analysis shows that while NMT has emerged as the predominant approach, its effectiveness is often reduced by the scarcity of training data and the structural complexity of low-resource languages. Strategies such as active learning, data augmentation, multilingual models and transfer learning are identified as critical for improving translation performance. Additionally, emerging research trends, including data pre-processing, optimization of decoder and rule-based approach demonstrate promising directions for addressing existing limitations. In terms of evaluation, most of the studies used Character n-gram F-score (ChrF), Translation Edit Rate (TER), Metric for Evaluation of Translation with Explicit Ordering (METEOR), Word Error Rate (WER) and Bilingual Evaluation Underscore (BLEU) as techniques’ validation metrics. This review provides a detailed evaluation of the current state of MT for low-resource languages and emphasizes the need for further research into underrepresented languages and the development of comprehensive datasets. © 2013 IEEE.

关键词： computer aided language translation

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