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Enhancing Red Palm Weevil Detection Using Bird Swarm Algorithm With Deep Learning Model

IEEE ACCESS 2024年 12卷 1542-1551页

作者： Arasi, Munya A. Almuqren, Latifah Issaoui, Imene Almalki, Nabil Sharaf Mahmud, Ahmed Assiri, Mohammed King Khalid Univ Coll Sci & Arts Rijal Almaa Dept Comp Sci Abha 61421 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia Qassim Univ Appl Coll Unit Sci Res Buraydah Saudi Arabia King Saud Univ Coll Educ Dept Special Educ Riyadh 12372 Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11835 Egypt Prince Sattam bin Abdulaziz Univ Coll Sci & Humanities Dept Comp Sci Al Kharj 16273 Saudi Arabia

In recent times, mostly in the Middle East region, Red Palm Weevils (RPW) are one of the most dangerous pests of palm trees worldwide. The RPW causes significant harm to several palm species. The existing detection method includes the symptoms detection of RPW through sound or visual assessment and chemical recognition of volatile signatures created by diseased palm trees. However, an effective recognition of RPW disease at earlier stages is assumed that a very complex problem for cultivating date palms. This is another reason why the use of state-of-the-art technologies is supported in the avoidance of the spread of the RPW on palm trees. Several researchers are working on determining the correct process for the localization, classification, and detection of RPW pests. Therefore, this paper presents an intelligent Red Palm Weevil Detection using the Bird Swarm Algorithm with Deep Learning (IRPWD-BSADL) model. The major aim of the IRPWD-BSADL technique focuses on the identification and classification of RPW using CV and DL models. Primarily, the bilateral filtering (BF) approach can be utilized to remove the noise that exists in the images. In the presented IRPWD-BSADL technique, an improved ShuffleNet model can be applied for feature extraction purposes. To enhance the recognition results, the IRPWD-BSADL technique makes use of BSA for the hyperparameter tuning process. For RPW detection and classification, an extreme gradient boosting (XGBoost) classifier can be used. The simulation analysis of the IRPWD-BSADL method can be tested on the RPW dataset. An extensive comparison study stated the improved performance of the IRPWD-BSADL algorithm on the RPW detection method.

关键词： Feature extraction classification algorithms Convolutional neural networks Tuning Diseases Computational modeling Insects Computer vision machine learning pest detection agriculture hyperparameter tuning crop productivity

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An Ensemble Learning Algorithm for Cognitive Evaluation by an Immersive Virtual Reality Supermarket

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2024年 32卷 3761-3772页

作者： Wang, Yifan Yang, Ping Yu, Jiangtao Zhang, Shang Gong, Liang Liu, Chunfeng Zhou, Wenjun Peng, Bo Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu 610500 Peoples R China Chengdu Agr Coll Sch Mech & Elect Informat Chengdu 611130 Peoples R China North Sichuan Med Coll Nanchong 637199 Peoples R China Chengdu Second Peoples Hosp Dept Neurol Chengdu 610017 Peoples R China Shanghai Thoven Intelligent Technol Co Ltd Shanghai 200032 Peoples R China

Early screening for Mild Cognitive Impairment (MCI) is crucial in delaying cognitive deterioration and treating dementia. Conventional neuropsychological tests, commonly used for MCI detection, often lack ecological validity due to their simplistic and quiet testing environments. To address this gap, our study developed an immersive VR supermarket cognitive assessment program (IVRSCAP), simulating daily cognitive activities to enhance the ecological validity of MCI detection. This program involved elderly participants from Chengdu Second People's Hospital and various communities, comprising both MCI patients (N=301) and healthy elderly individuals (N=1027). They engaged in the VR supermarket cognitive test, generating complex datasets including User Behavior Data, Tested Cognitive Dimension Game Data, Trajectory Data, and Regional Data. To analyze this data, we introduced an adaptive ensemble learning method for imbalanced samples. Our study's primary contribution is demonstrating the superior performance of this algorithm in classifying MCI and healthy groups based on their performance in IVRSCAP. Comparative analysis confirmed its efficacy over traditional imbalanced sample processing methods and classic ensemble learning voting algorithms, significantly outperforming in metrics such as recall, F1-score, AUC, and G-mean. Our findings advocate the combined use of IVRSCAP and our algorithm as a technologically advanced, ecologically valid approach for enhancing early MCI detection strategies. This aligns with our broader aim of integrating realistic simulations with advanced computational techniques to improve diagnostic accuracy and treatment efficacy in cognitive health assessments.

关键词： classification algorithms Ensemble learning Older adults Dementia Accuracy Games Virtual reality Training Solid modeling Usability Mild cognitive impairment virtual reality imbalanced sample processing ensemble learning

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Rethinking Shallow and Deep Learnings for Transformer Dissolved Gas Analysis: A Review

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2025年第1期32卷 3-10页

作者： Chen, Hong Cai Zhang, Yang Southeast Univ Sch Automat Minist Educ Nanjing 210096 Peoples R China Southeast Univ Key Lab Measurement & Control Complex Syst Engn Minist Educ Nanjing 210096 Peoples R China Natl Univ Def Technol Coll Adv Interdisciplinary Studies Changsha 410073 Peoples R China

Dissolved gas analysis (DGA) of power transformers has attracted attention for years. Extensive machine learning techniques have been adopted to DGA for fault classification. Recently, deep learning (DL) techniques have been brought to deal with DGA issues, while their performances are not significantly improved compared to shallow learning (SL) algorithms. For a comprehensive investigation, this article tests popular SL algorithms and reports DL algorithms on four different DGA datasets. The results show that SL algorithms have efficient capacity for DGA analysis, while DL algorithms may not as great as they expect. In addition of complex structure and numerous parameters to tune, DL algorithms may even perform worse than SL algorithms. This work can be a reference for future DGA algorithm development.

关键词： Power transformer insulation Convolution classification algorithms Oil insulation Feature extraction Dielectrics and electrical insulation Oils Gases Neurons Discharges (electric) Deep learning (DL) dissolved gas analysis (DGA) machine learning power transformer diagnosis

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Enhancing Semi-Supervised Learning With Concept Drift Detection and Self-Training: A Study on Classifier Diversity and Performance

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IEEE ACCESS 2025年 13卷 24681-24697页

作者： Perez, Jose L. M. Barros, Roberto S. M. Santos, Silas G. T. C. Univ Fed Pernambuco Ctr Informat Cidade Univ BR-50740560 Recife Brazil SiDi Inst BR-51110160 Recife Brazil

Machine learning algorithms that assist in decision-making are becoming crucial in several areas, such as healthcare, finance, marketing, etc. algorithms exposed to a larger and more relevant amount of training data tend to perform better. However, the availability of labeled data without human expert intervention is a challenging task, especially in data stream learning with concept drifts, where data is generated rapidly, in real-time, with the possibility of changes in the data distribution. Concept drift occurs in supervised, semi-supervised and unsupervised learning environments, and is addressed through different approaches such as statistics, machine learning, among others. Currently, the use of drift detectors with base classifiers in semi-supervised learning is uncommon. Semi-supervised classifiers often consume a lot of memory and run-time, and the addition of a detection mechanism increases the computational cost. Furthermore, classification in semi-supervised environments can lead to problems related to labeling data to training: an error in this process can negatively impact model performance. This article investigates the use of supervised concept drift detectors in semi-supervised learning problems, highlighting how the detectors can improve the performance of classification. It also explores the influence of diversity in classifier ensembles, showing that increased diversity contributes to enhanced accuracy and robustness of models in concept drift scenarios. Additionally, it introduces a self-training approach to provide more labels and optimize model learning and adaptation. This research also details updates in the Massive Online Analysis (MOA) framework, that supports the simulation of semi-supervised scenarios. The experiments conducted to test the proposed approach used Hoeffding Tree (HT) and Naive Bayes (NB) as base classifiers, which were also employed as members of the ensembles used in this research. These classifiers were combined with se

关键词： Concept drift Detectors Training Semisupervised learning classification algorithms Machine learning algorithms Adaptation models Accuracy Stacking Predictive models Data stream concept drift detectors semi-supervised learning ensembles self-training

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A Variant-Sensitive Malware Detection Method Based on Feature Contrast Enhancement

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年

作者： Yang, Shumian Hu, Jiarui Li, Xin Zhao, Dawei Xu, Lijuan Yu, Fuqiang Wang, Chunhui Qilu Univ Technol Shandong Acad Sci Shandong Comp Sci Ctr Key Lab Comp Power Network & Informat SecurMinist Jinan 250014 Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Ind Network & Informat Syst Jinan 250014 Peoples R China Shandong Shen Qi Informat Technol Co Ltd Jinan 250013 Peoples R China

Malware poses a great threat to information security such as user data, privacy, and assets. Early detection before it has a real impact is the main countermeasure. However, the diversity of carriers and technologies has led to a huge gap between the training scenarios and actual scenarios of detection methods. This makes it difficult for supervision-based detection frameworks to identify new malware variants and complicates threat response. We propose a novel method that integrates frequency domain techniques with feature alignment to enhance variant malware detection, reducing distribution differences between labeled (source) and new (target) samples. By converting malware into grayscale images and applying discrete cosine transform (DCT) for improved feature extraction, followed by feature extraction via a deep residual network from both domains, our model systematically aligns features. This alignment is achieved through a tailored domain adaptation technique involving the minimization of classification and domain alignment losses, which ensures the consistent learning of features across varied domains. Such rigorous alignment not only enhances detection accuracy for both known and variant malware but also supports simultaneous detection across significant distribution differences. We conduct extensive experiments on two real-world datasets to evaluate the performance of various deep learning models under consistent and inconsistent domain distributions. Compared to existing methods, our approach improves accuracy by an average of 1.4% on the BIG2015 dataset, 3.2% on the MDA dataset, 2.75% on the Malimg dataset, and also achieves the best performance on the MaleVis dataset, with similar gains in precision, recall, and F1-score across all datasets.

关键词： Malware Feature extraction Accuracy Gray-scale classification algorithms Training Frequency-domain analysis Supervised learning Convolutional neural networks Computer science Malware variants malware detection feature alignment

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Application a Committee of Kohonen Neural Networks to Training of Image Classifier Based on Description of Descriptors Set

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IEEE ACCESS 2024年 12卷 73376-73385页

作者： Gorokhovatskyi, Volodymyr Tvoroshenko, Iryna Yakovleva, Olena Hudakova, Monika Gorokhovatskyi, Oleksii Kharkiv Natl Univ Radio Elect Dept Informat UA-61166 Kharkiv Ukraine Bratislava Univ Econ & Management Econ & Management Inst Dept Econ & Finance Bratislava 85104 Slovakia SYTOSS Ltd Dept AI Res & Dev Bratislava 83104 Slovakia Simon Kuznets Kharkiv Natl Univ Econ Dept Informat & Comp Engn UA-61166 Kharkiv Ukraine

The research aims to improve structural methods of image classification based on a description as a set of keypoint descriptors. The focus is on implementing a classifier training using a committee of Kohonen networks separately for each description in an etalon database. The training result is a fixed set of data centroids, which ensures high classification speed. The improvement comprises implementing independent training for each etalon, which increases the accuracy of approximating descriptions with a set of centroids and, in general, guarantees that the classification efficiency remains at a decent level. Calculating the cluster centroids for each class prevents the influence of descriptors from other classes. In addition, independent training is effective in cases when the powers of etalon descriptions differ. The classification speed of the proposed method based on training a committee of Kohonen networks, compared to the traditional linear search method, increases proportionally to the ratio of the description power and the number of generated centroids. The presented results of experimental modeling of the proposed methods comprise a database of images of coins. The test sample is formed as a set of images from the etalon database by applying geometric transformations of shift, scale, and rotation. The testing stage has shown a high level of classification accuracy after a proposed training and revealed a practical opportunity to choose a network structure and parameters capable of providing the required level of accuracy and speed criteria for classification for an applied task.

关键词： Training Interferometers Vectors Neural networks Mathematical models Economics Visualization classification algorithms Image classification classification accuracy classifier training image classification Kohonen network set of descriptors

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Fully Automatic Karyotyping via Deep Convolutional Neural Networks

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IEEE ACCESS 2024年 12卷 46081-46092页

作者： Wang, Chengyu Yu, Limin Su, Jionglong Shen, Juming Selis, Valerio Yang, Chunxiao Ma, Fei Xian Jiaotong Liverpool Univ Coll Ind Entrepreneurs HeXie Management Res Ctr Suzhou 215123 Jiangsu Peoples R China Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215000 Jiangsu Peoples R China Xian Jiaotong Liverpool Univ XJTLU Entrepreneur Coll Taicang Sch AI & Adv Comp Suzhou 215000 Jiangsu Peoples R China Xian Jiaotong Liverpool Univ Inst Leadership & Educ Adv Dev Acad Future Educ Suzhou 215000 Jiangsu Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England Suzhou Precis Med Technol Co Ltd Suzhou 215100 Jiangsu Peoples R China Xian Jiaotong Liverpool Univ Sch Math & Phys Suzhou 215000 Jiangsu Peoples R China

Chromosome karyotyping is an important yet labor-intensive procedure for diagnosing genetic diseases. Automating such a procedure drastically reduces the manual work of cytologists and increases congenital disease diagnosing precision. Researchers have contributed to chromosome segmentation and classification for decades. However, very few studies integrate the two tasks as a unified, fully automatic procedure or achieved a promising performance. This paper addresses the gap by presenting: 1) A novel chromosome segmentation module named ChrRender, with the idea of rendering the chromosome instances by combining rich global features from the backbone and coarse mask prediction from Mask R-CNN;2) A devised chromosome classification module named ChrNet4 that pays more attention to channel-wise dependencies from aggregated informative features and calibrating the channel interdependence;3) An integrated Render-Attention-Architecture to accomplish fully automatic karyotyping with segmentation and classification modules;4) A strategy for eliminating differences between training data and segmentation output data to be classified. These proposed methods are implemented in three ways on the public Q-band BioImLab dataset and a G-band private dataset. The results indicate promising performance: 1) on the joint karyotyping task, which predicts a karyotype image by first segmenting an original microscopical image, then classifying each segmentation output with a precision of 89.75% and 94.22% on the BioImLab and private dataset, respectively;2) On the separate task with two datasets, ChrRender obtained AP50 of 96.652% and 96.809% for segmentation, ChrNet4 achieved 95.24% and 94.07% for classification, respectively. The COCO format annotation files of BioImLab used in this paper are available at https://***/Alex17swim/BioImLab The study introduces an integrated workflow to predict a karyotyping image from a Microscopical Chromosome Image. With state-of-the-art performance

关键词： Biological cells Task analysis Image segmentation Feature extraction Biomedical imaging Computer architecture Training Chromosome mapping classification algorithms Cell therapy Diseases Microscopy Rendering (computer graphics) Genetics Recurrent neural networks Convolutional neural networks Chromosome karyotyping fully automatic classification instance segmentation

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Performance-Oriented and Sustainability-Oriented Design of an Effective Android Malware Detector

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IEEE ACCESS 2024年 12卷 159036-159055页

作者： Qadir, Sana Naeem, Amna Hussain, Mehdi Ghafoor, Huma Hashim, Aisha Hassan Abdalla Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Islamabad 44000 Pakistan Natl Univ Sci & Technol Sch Interdisciplinary Engn & Sci Islamabad 44000 Pakistan Int Islamic Univ Malaysia Kuala Lumpur 50728 Malaysia

Effective Android malware detection is a complex problem because of the rapidly-evolving, complicated, and diverse nature of malware. The design of malware detectors should prioritise high detection rate, efficient use of computational resources, and sustainability. Keeping these design priorities in mind, we develop and empirically evaluate four different classifiers. Firstly, to ensure high detection rate, we use a dataset compiled using hybrid analysis of a diverse set of apps. Unlike most publicly-available Android datasets, the dynamic analysis of each app was carried out on a real device and not on a virtual setup. This means that this dataset contains a comprehensive profile of sophisticated malware capable of changing its behaviour on a virtual setup. Secondly, to enhance efficiency, we explore the use of a GPU-based setup and different feature selection techniques. Lastly, we emphasize sustainability by training the models using apps that date back to the beginning of the Android ecosystem i.e. from 2008 until 2020. Our results show that Random Forest (RF) is the most effective classifier with the highest accuracy of 97.86%. This accuracy is 2.78% higher than the best accuracy reported in existing literature. The data also shows that RF is the most sustainable classifier with minimal decrease in F1 score for over-time performance. With regard to efficiency, we find that Logistic Regression (LR) is the best option and that the training time of most models improves significantly when a GPU-based setup instead of a CPU-based setup.

关键词： Malware Detectors Feature extraction Accuracy Sustainable development Operating systems Biological system modeling Training Static analysis classification algorithms Malware detection machine learning graphics processing unit performance sustainability

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The State of Retinal Image Analysis: Deep Learning Advances and Applications

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IEEE ACCESS 2025年 13卷 65066-65093页

作者： Soomro, Toufique Ahmed Bin Ubaid, Faisal Zardari, Shehnila Shah, Ahmed Ali Alansari, Marwah M. Nagro, Shimaa A. Charles Univ Prague Artificial Intelligence & Cyber Futures Inst Bathurst NSW 2795 Australia Univ Larkano Dept Elect Engn Larkana 77280 Pakistan Sukkur IBA Univ Comp Sci Dept Sukkur 65200 Pakistan NED Univ Engn & Technol Dept Software Engn Karachi 75270 Pakistan Sukkur IBA Univ Elect Engn Dept Sukkur 65200 Pakistan Saudi Elect Univ Coll Comp & Informat Riyadh 11673 Saudi Arabia

This study explores deep learning techniques that can help diagnose eye conditions, focusing on diabetic retinopathy (DR). By using cutting-edge technology in image processing and artificial intelligence, it aims to better understand and classify various eye issues like microaneurysms, hemorrhages, and others. Despite progress, challenges like poor image quality and noise persist. The paper stresses the need for improved methods to detect small blood vessels while reducing interference. It also addresses the complexity of uneven lighting in extracting blood vessel images from the retina. The main contribution of this study is its thorough examination of existing methods and its detailed investigation into using deep learning for detecting retinal blood vessels. It highlights the effectiveness of different deep learning models and their potential in accurately classifying DR grades. This research underscores the importance of grading methods in analyzing medical images, especially retinal images, to enhance the accuracy of diagnosing eye diseases.

关键词： Retina Biomedical imaging Blood vessels Feature extraction Accuracy Image segmentation Hemorrhaging Deep learning classification algorithms Retinal vessels Diabetic retinopathy retinal blood vessels color fundus images deep learning microaneurysms hemorrhages exudates image processing techniques convolutional neural networks medical image analysis

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Identification of Spectrally Similar Materials From Multispectral Imagery Based on Condition Number of Matrix

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 4751-4766页

作者： Wang, Maozhi Chen, Shu-Hua Feng, Jun Xu, Wenxi Wang, Daming Chengdu Univ Technol Coll Math Sci Geomath Key Lab Sichuan Prov Chengdu 610059 Peoples R China Univ Calif Davis Dept Land Air & Water Resources Davis CA 95616 USA China Geol Survey Tianjin Ctr Tianjin 300170 Peoples R China

Identification of spectrally similar materials from multispectral remote sensing (RS) imagery with only several bands is an important issue that challenges comprehensive applications of the RS of surface characteristics. This study proposes a new method to identify spectrally similar materials from these types of imagery. The method is constructed based on the theory of condition number of matrix, and a theorem is proven as the foundation of the designed identification algorithm. Mathematically, the motivation behind designing this new algorithm is to decrease the condition number of the matrix for a linear system and, by doing so, to change an ill-conditioned system to a well-conditioned one. Technically, this new method achieves the purpose by adding supplementary features to all the original spectra including similar materials, which can be further used as indicative signatures to identify these materials. Thus, the proposed method is named a condition number-based method with supplementary features (SF-CNM). The threshold scheme and supplementary features are two main novelty techniques to ensure the uniqueness and accuracy of the proposed SF-CNM for specified samples. The results for a case study to identify water, ice, snow, shadow, and other materials from Landsat 8 OLI data indicate that SF-CNM can identify the materials specified by the given samples successfully and accurately and that SF-CNM significantly outperforms those of spectral angle mapper algorithm, Mahalanobis classifier, maximum likelihood, and artificial neural network, and produces the performance similar to, even slightly better than that of support vector machine.

关键词： classification algorithms Mathematical models Landsat Hyperspectral imaging Training Support vector machines Reviews Linear systems Data models classification tree analysis Condition number multispectral imagery spectrally similar materials identification supplementary features

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