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DFNet: Dense fusion convolution neural network for plant leaf disease classification

Agronomy Journal 2024年第3期116卷 826-838页

作者： Faisal, Muhamad Leu, Jenq-Shiou Avian, Cries Prakosa, Setya Widyawan Köppen, Mario Department of Electronic and Computer Engineering (ECE) National Taiwan University of Science and Technology Taipei City Taiwan Department of Computer Science and Systems Engineering (CSSE) Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka-shi Japan

The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. However, most approaches depend on a single convolutional neural network (CNN) to extract the leaf features, ignoring the opportunity to take full advantage of the feature richness available in the images. This paper explores a novel CNN model with multiple automated feature extractors, namely, dense fusion CNN (DFNet), for classifying plant leaf diseases. DFNet aims to increase the diversity of extracted features in order to improve discrimination. Instead of using a single-CNN model, DFNet relies on a double-pretrained CNN model, MobileNetV2 and NASNetMobile, as the feature extractor. The features extracted from each CNN are fused in the fusion layer using a fully connected network. The proposed method was evaluated using corn (Zea mays L.) and coffee (Coffea canephora) leaf disease datasets and compared to the existing models. The experiment showed that DFNet is superior and consistent to other CNN methods by achieving an accuracy of 97.53% for corn leaf diseases and 94.65% for coffee leaf diseases. © 2023 The Authors. Agronomy Journal © 2023 American Society of Agronomy.

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Innovative Approach for Early Heart Disease Prediction: Gaussian Mixture Model and K-Nearest Neighbour 3

Innovative Approach for Early Heart Disease Prediction: Gaus...

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3rd International Conference on Intelligent Data communication Technologies and Internet of Things, IDCIoT 2025

作者： Surendran, R. Anandaraj, B. Deepa, P. Mageshwari, M. Balamurugan, K.S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Madnapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Electronics and communication engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

An Electrocardiogram (ECG) contributes significantly to early diagnosis and classification of heart diseases, arrhythmia which means irregular heart rate. Regrettably, the process became difficult due to asymmetric and minimal amplitude from recordings of ECG and cannot do the extraction from multiple signals. By using machine learning technology, it can classify and extract multiple samples, but cannot extract the data features from complex data structures. To overcome the limitations, previously introduced metaheuristic optimization (MHO) to improve the classification and extraction. Proposed a method which supports and K-Nearest Neighbour (KNN) Gaussian Mixture Model (GMM) algorithms for determine the abnormalities, predicting cardiac events, and improve diagnostic accuracy. As a result, this can help us to get results more quickly and accurately with sensitivity 99.7% and accuracy 99.2%, for helping healthcare professionals in diagnosing arrythmias, myocardial infarction and any other heart conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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Statistical Test for Distributed Cluster-based Intermittent Fault Detection in Wireless Sensor Networks 14

Statistical Test for Distributed Cluster-based Intermittent ...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Gouda, Bhabani Sankar Das, Sudhakar Panigrahi, Trilochan Computer Science and Engineering BPUT Rourkela Rourkela India NIST Institute of Science and Technology Department of Electronic and Communication Berhampur India

Due to the regular growing number of applications, distributed WSN enhanced to enjoy very large appeal. Occasionally, a small number of distributed nodes are drop some data value or unable to transmit accurate data to the respective or specific fusion Centre or to a nearby node. In the process of distributed network's performance suffers significantly because it is not aware of the node's malfunction. Data from the defective or affected node might be treated as outliers. So that for identify the data sent by the problematic sensor node can be found using a different statistical test. In this paper, proposed a DSFDOA method that a clustering-based statistical Z-test and a self-detectable distributed fault detection system are bringing forward. Put forward in DSFDOA and distributed sensor node algorithm is perform in Python and MATLAB, and staging is measured in terms of different parameter like false data alarm rate and detection data accuracy (DDA) & (FDAR). The results initiate demonstrate that the proposed approach shown performs better than the current algorithms and when compared to the execute results of the previous algorithm. © Grenze Scientific Society, 2023.

关键词： Statistical tests

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Denoising of MRI and Brain Tumor Classification using Local Binary Pattern and SVM Classifier 3

Denoising of MRI and Brain Tumor Classification using Local ...

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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Kavin Kumar, K. Kumar, Neelam Sanjeev Senthilkumar, C. Nanmaran, R. Sangar, G. Chandrasekaran, Gokul Kongu Engineering College Department of Electronics and Communication Engineering Erode India Vadapalani Chennai India Simats University Simats Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai602 105 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Chennai India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798331509675

All data collection systems, including magnetic resonance imaging, are susceptible to noise. Modern high-field scanners can achieve high signal-to-noise ratios;however, noise becomes a significant issue that requires careful management when signals become extremely faint due to specific acquisition processes. The proposed scheme consists of steps which include denoising, feature extraction and classification. For denoising, the Non-Local Means filtering approach is used, and the performance of the denoising method is analyzed using PSNR values. The major objective here is to study texture classification methods to distinguish between various types of brain cancers, such as gliomas, meningiomas, and pituitary tumors. We then evaluate its performance on an additional dataset. The obtained photos serve both educational and evaluative purposes. The accuracy of two methods is computed and compared. The feature extraction performance is compared between two separate classifiers using standard deviation, variance, and mean as performance measures. Among the methods tested, SVM achieved the best accuracy rate of 96.67%. © 2025 IEEE.

关键词： KNN LBP MDC NLM SVM

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Design and Analysis of a UWB Slotted Vivaldi Antenna for Microwave Imaging Applications

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IEIE Transactions on Smart Processing and Computing 2023年第4期12卷 350-357页

作者： Paul, Liton Chandra Majumder, Akash Rani, Tithi Hossain, Md. Najmul Rahim, Md. Abdur Shin, Jungpil Yun, Keun Soo Department of Electrical Electronic and Communication Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh Department of Electronics & Telecommunication Engineering Faculty of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh School of Computer Science and Engineering The University of Aizu / Fukushima Aizuwakamatsu965-8580 Japan School of Computer and Information Technology Ulsan College / Dong-gu Ulsan44610 Korea Republic of

An ultra-wideband (UWB) slotted compact Vivaldi antenna with a microstrip line feed was evaluated for microwave imaging (MI) applications. The recommended FR4 substrate-based Vivaldi antenna is 50×50×1.5 mm3 in size. The proposed compact Vivaldi antenna showed good radiation characteristics and spanned an ultra-wide bandwidth of 10 GHz, ranging from 3.6 to 13.6 GHz, using a microstrip line feed with a radial stub, tapered slots, and resonant cavities. After analyzing the simulation findings, the proposed compact slotted UWB Vivaldi antenna was compared with the basic Vivaldi antenna. The maximum gain and efficiency were 7.35 dB and 82%, respectively. The antenna was simulated in the time domain using the computer simulation technology (CST) microwave studio. The frequency domain solver was also used for cross-validation, and the results were equally good. In conclusion, the proposed compact Vivaldi antenna is a good option for microwave imaging applications. © 2023 The institute of electronics and Information Engineers.

关键词： Cavity resonators

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Analysing Forecasting of Stock Prices: An Explainable AI Approach 2

Analysing Forecasting of Stock Prices: An Explainable AI App...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kumar, Priyanshu Hota, Lopamudra Tikkiwal, Vinay Anand Kumar, Arun Department of Computer Science and Engineering National Institute of Technology Rourkela769008 India Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida201304 India

Predicting stock prices is a well-known and significant problem. We can learn about market behaviour over time and identify trends that might not have been seen without an effective stock prediction model. Machine learning will be a useful approach to solving this issue with the increased processing capacity of computers. Behavioural economics also asserts that the investments made by investors depend on their emotions so psychological theories can also be applied to explain their behaviour and its impact on the market. Combining the analysis of these behavioural patterns with the use of historical financial data sets can result in an approach for accurate stock price predictions. The primary focus of the proposed model is on comparing different models to provide a comparative analysis of the results provided by models used in the literature. The paper provides insight into the improvisation of the current techniques and how different parameters and different error analysis techniques can be implemented. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

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Machine Learning based Modeling of Drugs using Virtual Screening and in Silico Approach 5

Machine Learning based Modeling of Drugs using Virtual Scree...

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5th International Conference on Smart electronics and communication, ICOSEC 2024

作者： Balamurugan, K.S. Appathurai, K. Sathish Kumar, P.J. Kumutha, D. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India Kg College of Arts and Science Department of Computer Technology Coimbatore India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Jeppiaar Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Sriperumbudur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798331504403

The process and implementation of drug development is a complex structure which is a time-consuming process with higher accuracy. A transformative approach to optimize drug discovery is achieved through the integration of drug discovery with a virtual screening process. This is implemented through the in silico machine learning process. The traditional methods involve extracting and testing numerous chemical compounds in vitro and in vivo in the identification of potential drug contenders. These methods lead to various drawbacks that include higher costs with timelines. To overcome the drawbacks of the existing system, the integration of paradigm modeling is initiated. The behavior of molecules with biological targets is determined through In-silico machine learning algorithms. The effective analysis of millions of chemical compounds is done using a virtual screening process through computational simulations and desired pharmacological effects. The machine learning algorithms help in the extraction of intricate patterns from the molecular dataset. Certain properties such as molecule binding affinity, bioavailability, and toxicity are predicted using these algorithms. They help to optimize the molecular structure in silico to improve the interaction with the target proteins. The reliability is achieved through the quality of the training data with the robustness of the algorithm. They help to generalize newer chemical combinations that provide the solution for various diseases with personalized recommendations through the aid of artificial intelligence. © 2024 IEEE.

关键词： Complex structure Computational simulations Drug development Machine learning process Pharmacological effects Virtual screening process

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Efficient Road Situation Classification Using Long-Term Recurrent Convolutional Network 14th

Efficient Road Situation Classification Using Long-Term Recu...

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14th International Conference on Frontier Computing, FC 2024

作者： Dofitas, Cyreneo Gil, Joon-Min Byun, Yung-Cheol Department of Electronic Engineering Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Institute of Information Science and Technology Jeju National University Jeju63243 Korea Republic of

ISBN: (纸本)9789819623945

The state of a road encompasses various factors, including the presence of vehicles and pedestrians. On-the-road surveillance cameras are crucial in tracking information regarding pedestrians, moving vehicles, and objects in the road vicinity. With the increasing adoption of deep learning models in vehicle monitoring systems, there is a focus on identifying and classifying conditions relevant to driving comfort and road safety. This study addresses the issue of an imbalanced dataset. However, the scarcity of publicly available datasets and efficient detection techniques poses challenges in accurately classifying road situations. In this paper, we propose a long-term recurrent convolutional network (LRCN) comprised of two groups integrated with convolutional neural networks (CNN) and long short-term memory (LSTM) networks with multiple layers of LSTM blocks for road situation detection in videos. Our model for road situation classification achieves an accuracy of 71% when using the original dataset captured from the camera. However, employing data augmentation to balance the dataset resulted in an increased accuracy of 90%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Sustainable AI Systems for Monitoring and Predicting Wildfires in Vulnerable Forest Regions

Sustainable AI Systems for Monitoring and Predicting Wildfir...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Raj, T. Selvin Retna Raj, J. Relin Francis Krishnan, R. Santhana Balamuralikrishnan, G. Vikkiramapandian, D. Jothi, J. Nirmal Dmi College of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Psna College of Engineering and Technology Department of Information Technology Dindigul India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India

ISBN: (纸本)9798331509828

Wildfires pose significant threats to ecological systems, human safety, and economic stability, especially in vulnerable forest regions. To address this critical issue, a sustainable AI-driven framework, WiSEFire (Wildfire Surveillance and Estimation), is proposed for real-time monitoring and prediction of wildfires. The framework integrates a hybrid approach, utilizing Random Forest for feature analysis and Gated Recurrent Units (GRU) for temporal fire prediction. The system processes multi-source data, including satellite imagery, IoT sensors, and meteorological information, to enhance detection accuracy and prediction reliability. Simulation experiments were conducted using benchmark datasets to evaluate WiSEFire against state-of-the-art algorithms, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Performance metrics such as accuracy, latency, and detection efficiency were analyzed. The results demonstrated that WiSE Fire outperformed existing methods, achieving superior detection precision and reduced latency while maintaining energy efficiency. This research highlights the potential of sustainable AI frameworks for mitigating wildfire risks and improving emergency response capabilities © 2025 IEEE.

关键词： Convolutional neural networks

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Next-Gen Manhole Monitoring: Autoencoder-Assisted Anomaly Detection 3

Next-Gen Manhole Monitoring: Autoencoder-Assisted Anomaly De...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Krishnan, R. Santhana Gopikumar, S. Muthu, A. Essaki Raj, J. Relin Francis Kumari, D. Abitha Malar, P. Stella Rose Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Civil Engineering Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical AndTechnical Sciences Department of Electronics and Communication Engineering Chennai India Rmk Engineering College Department of Computer Science and Engineering Thiruvallur India J.P. College of Engineering Department of Electronics and Communication Engineering Thenkasi India

ISBN: (纸本)9798350375190

In various regions, the maintenance of manholes is imperative, given the potential consequences for public health and safety. Neglecting this responsibility can result in severe outcomes, including loss of lives and the spread of diseases within the community. To address these challenges, an advanced anomaly detection system is proposed, leveraging deep learning with Autoencoders. Notably, the entire system operates on solar power, aligning with a commitment to environmentally sustainable practices. At the core of the system's functionality is the Autoencoder, a deep learning model to differentiate complex manhole conditions. The Autoencoder is trained on normative sensor data, capturing nuanced patterns inherent in routine manhole states. This training enables the Autoencoder to perform real-time anomaly detection by swiftly identifying deviations such as the presence of noxious gases, irregular sewage levels, or unauthorized attempts at access. When anomalies are detected, signaling potential hazards, the Autoencoder triggers alert messages disseminated to municipal authorities via a cloud server. This proactive approach empowers timely intervention, particularly critical in scenarios involving toxic gases, ensuring the safeguarding of maintenance personnel. Additionally, the system integrates an NFC reader, streamlining authorized access for designated personnel while upholding robust security measures. In essence, the incorporation of Autoencoders into the system furnishes an intelligent anomaly detection infrastructure for manhole surveillance. By continually adapting to normative patterns, the Autoencoder enhances the system's capacity to identify and promptly communicate potential risks, thereby improving the overall safety and efficiency of manhole maintenance practices. © 2024 IEEE.

关键词： Internet of things

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