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HDL-ACO hybrid deep learning and ant colony optimization for ocular optical coherence tomography image classification

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SCIENTIFIC REPORTS 2025年第1期15卷 1-12页

作者： Agarwal, Shivani Dohare, Anand Kumar Saxena, Pranshu Singh, Jagendra Singh, Indrasen Sahu, Umesh Kumar Ajay Kumar Garg Engn Coll Dept Informat Technol Ghaziabad India Greater Noida Inst Technol Engn Inst Dept Informat Technol Greater Noida India Bennett Univ Sch Comp Sci Engn & Technol Greater Noida India Vellore Inst Technol Sch Elect Engn Vellore 632014 Tamil Nadu India Manipal Inst Technol Manipal Acad Higher Educ Dept Mechatron Manipal 576104 Karnataka India

Optical Coherence Tomography (OCT) plays a crucial role in diagnosing ocular diseases, yet conventional CNN-based models face limitations such as high computational overhead, noise sensitivity, and data imbalance. This paper introduces HDL-ACO, a novel Hybrid deep learning (HDL) framework that integrates Convolutional Neural Networks with Ant Colony Optimization (ACO) to enhance classification accuracy and computational efficiency. The proposed methodology involves pre-processing the OCT dataset using discrete wavelet transform and ACO-optimized augmentation, followed by multiscale patch embedding to generate image patches of varying sizes. The hybrid deep learning model leverages ACO-based hyperparameter optimization to enhance feature selection and training efficiency. Furthermore, a Transformer-based feature extraction module integrates content-aware embeddings, multi-head self-attention, and feedforward neural networks to improve classification performance. Experimental results demonstrate that HDL-ACO outperforms state-of-the-art models, including ResNet-50, VGG-16, and XGBoost, achieving 95% training accuracy and 93% validation accuracy. The proposed framework offers a scalable, resource-efficient solution for real-time clinical OCT image classification.

关键词： Optical coherence tomography Hybrid deep learning Ant colony optimization Hyperparameter tuning Data Imbalance OCT image classification

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DL-DARE: deep learning-based different activity recognition for the human-robot interaction environment

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NEURAL COMPUTING & APPLICATIONS 2023年第16期35卷 12029-12037页

作者： Kansal, Sachin Jha, Sagar Samal, Prathamesh Thapar Inst Engn Technol Patiala Comp Sci Engn Dept Patiala 147004 Punjab India

This paper proposes a deep learning-based activity recognition for the Human-Robot Interaction environment. The observations of the object state are acquired from the vision sensor in the real-time scenario. The activity recognition system examined in this paper comprises activities labeled as classes (pour, rotate, drop objects, and open bottles). The image processing unit processes the images and predicts the activity performed by the robot using deep learning methods so that the robot will do the actions (sub-actions) according to the predicted activity.

关键词： RGB sensor VGG-16 Resnet-50 Hyper-parameters deep learning Visual tracking Feature extraction

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real-time traffic data: estimating noise and air pollution, comparative ML techniques analysis

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PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT 2025年

作者： Madhu, Kavitha Athira, P. R. Rohini, S. Sikhin, V. C. Balakrishnan, Srijith TKM Coll Engn Dept Civil Engn Kollam India Rockwell Automat Co Bengaluru Karnataka India Delft Univ Technol Delft Netherlands

Motor vehicles significantly contribute to the escalating levels of air and noise pollution in urban centers worldwide. Numerous studies have established a strong correlation between vehicle exhaust emissions, noise levels, and various factors such as traffic flow rate, vehicle composition, fleet speed, as well as deceleration and acceleration speeds. This research monitors ambient air quality and noise levels in diverse city centers during peak hours, shedding light on the impact of vehicular activities. The study investigates into the intricate relationship between vehicular composition and the concentration of particulate matter (PM). Furthermore, it conducts a comprehensive analysis of how traffic composition influences roadside noise pollution, identifying key factors contributing to this environmental concern. Employing an efficient deep learning process, the research employs image detection and tracking of vehicles to enhance understanding. Additionally, various machine learning tools are applied for the prediction of traffic-related air and noise pollution. This research makes a significant contribution to sustainable transportation planning, offering valuable insights into the complex dynamics of vehicular impact on urban environments. The findings not only enhance our understanding of pollution sources but also pave the way for informed decision-making in developing strategies to mitigate the adverse effects of motor vehicle activities.

关键词： traffic engineering environmental aspects sustainability image processing real-time vehicle detection air pollution air quality meter noise pollution sound level meter sustainable development goal (SDG) 11: sustainable cities and communities

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Enhanced SCNN-Based Hybrid Spatial-Temporal Lane Detection Model for Intelligent Transportation Systems

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IEEE ACCESS 2024年 12卷 40075-40091页

作者： Li, Jingang Ma, Chenxu Han, Yonghua Mu, Haibo Jiang, Lurong Zhejiang Sci Tech Univ Sch Informat Sci & Engn Hangzhou 310018 Peoples R China Hangzhou Hikvis Digital Technol Co Ltd Hangzhou 310018 Peoples R China

Accurate and timely lane detection is imperative for the seamless operation of autonomous driving systems. In this study, leveraging the gradual variation of lane features within a defined range of width and length, we introduce an enhanced Spatial-Temporal Recurrent Neural Network (SCNN) framework. This framework serves as the cornerstone of an innovative hybrid spatial-temporal model for lane detection, which is tailored to address the prevalent issues of substandard detection performance and insufficient real-time processing in intricate scenarios, such as those involving lane erosion and inconsistent lighting conditions, which often challenge conventional models. With the foundational understanding that lanes manifest as continuous lines, we employ a temporal sequence of lane imagery as the input to our model, thereby ensuring a rich provision of feature information. The model adopts an encoder-decoder structure and integrates a Spatial-Temporal Recurrent Neural Network module for the extraction of interrelated information from the image sequence. The model culminates in the output of the lane detection results for the terminal frame. The proposed lane detection model exhibits a commendable synthesis of accuracy and real-time efficiency, attaining an Accuracy of 97.87%, an F-1 -score of 0.943, and a FPS of 19.342 on the tvtLANE dataset and an Accuracy of 98.21%, an F-1 -score of 0.957 on the Tusimple dataset. These metrics signify a superior performance over a majority of the current lane detection methods.

关键词： Computer vision deep learning Lane detection Autonomous driving Hybrid power systems Spatiotemporal phenomena Intelligent transportation systems Recurrent neural networks deep learning lane detection

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MangoLeafXNet: An Explainable deep learning Model for Accurate Mango Leaf Disease Classification

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IEEE ACCESS 2025年 13卷 93977-94008页

作者： Rayed, Md. Eshmam Jim, Jamin Rahman Islam, Md Juniadul Mridha, M. F. Kabir, Md Mohsin Hossen, Md. Jakir Amer Int Univ Bangladesh Dept Comp Sci & Engn Dhaka 1229 Bangladesh Univ Girona Super Polytech Sch Girona 17001 Spain Malardalens Univ Div Comp Sci & Software Engn S-72220 Vasteras Sweden Multimedia Univ COE Artificial Intelligence Fac Engn & Technol FET Ctr Adv Analyt CAA Melaka 75450 Malaysia

Addressing the global challenge of ensuring a consistent and abundant supply of fresh fruit, particularly in the context of fruit crops, is hindered by the prevalence of plant diseases. These diseases directly impact the quality of fruits, leading to a decline in overall agricultural production. Mango leaf diseases pose significant threats to global mango production, necessitating accurate and efficient classification techniques for timely disease management. Our study focuses on introducing MangoLeafXNet, a customized Convolutional Neural Network (CNN) architecture specifically tailored for the classification of mango leaf diseases, along with a healthy class. Our proposed model comprises six layers optimized to capture intricate disease patterns, demonstrating superior performance compared with prevalent pre-trained models. The model is trained and evaluated on three publicly available datasets: MangoLeafBD (4000 images across 8 classes), MangoPest (16 pest classes including healthy leaves), and MLDID (3000 high-resolution images across 5 classes). Our model demonstrated exceptional classification performance, attaining 99.8% accuracy, 99.62% recall, 99.5% precision, and an F1-score of 99.56%. Further validation on the MangoPest dataset and the Mango Leaf Disease Identification Dataset (MLDID) resulted in accuracies of 96.31% and 96.33%, respectively, confirming the robustness and adaptability of MangoLeafXNet across different datasets. Additionally, we incorporate Explainable AI techniques, including GRAD-CAM, Saliency Map, and LIME to enhance the interpretability of our model. We deployed Gradio web interface to create an interactive interface that allows users to upload images of mango leaves and get real-time classification and validation results along with confidence scores. This contribution not only advances the state-of-the-art in mango leaf disease classification but also offers promising prospects for real-time disease diagnosis and precision agriculture

关键词： Accuracy Plant diseases Computational modeling deep learning Computer architecture Crops Mathematical models Robustness real-time systems image classification image processing convolutional neural network deep learning mango leaf disease image classification explainable AI

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PlantView: Integrating deep learning with 3D modeling for indoor plant augmentation

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ECOLOGICAL INFORMATICS 2024年 84卷

作者： Afzal, Sitara Khan, Haseeb Ali Lee, Jong Weon Sejong Univ Dept Software Mixed Real & Interact Lab Seoul 05006 South Korea

Indoor plant recognition poses significant challenges due to the variability in lighting conditions, plant species, and growth stages. Despite the growing interest in applying deep learning techniques to plant data, there still needs to be more research focused on the automatic recognition of indoor plant species, highlighting the need for real-time, automated solutions. To address this gap, this study introduces a novel approach for real-time identification and visualization of indoor plants using a Convolutional Neural Network (CNN)-based model called PlantView, integrated with Augmented reality (AR) for enhanced user interaction. The proposed PlantView model not only accurately classifies the plant species but also visualizes them in a 3D AR environment, allowing users to interact with virtual plant models seamlessly integrated into their real-world surroundings. We developed a custom dataset comprising over 28,000 images of 48 different plant species at various growth stages, captured under diverse lighting conditions and camera settings. Our proposed approach achieves an impressive accuracy of 98.20 %. To validate the effectiveness of PlantView model, we conduct extensive experiments and compared its performance against state-of-the-art methods, demonstrating its superior accuracy and processing speed. The results indicate that our method is not only highly effective for real-time indoor plant recognition but also offers practical applications for enhancing indoor plant care and visualization. This research offers a comprehensive solution for indoor plant enthusiasts and professionals, combining advanced computer vision techniques with immersive AR visualization to revolutionize the way indoor plants are identified, visualized, and integrated into living spaces.

关键词： Indoor plant Computer vision image processing deep learning Augmented reality

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deep learning for High Speed Optical Coherence Elastography With a Fiber Scanning Endoscope

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2025年第3期44卷 1445-1453页

作者： Neidhardt, Maximilian Latus, Sarah Eixmann, Tim Huttmann, Gereon Schlaefer, Alexander Hamburg Univ Technol Inst Med Technologyand Intelligent Syst D-21073 Hamburg Germany Interdisciplinary CompetenceCenter Interface Res I D-20251 Hamburg Germany Univ Lubeck Inst Biomed Opt D-23562 Lubeck Germany SustAInLivWork Ctr Excellence LT-44249 Kaunas Lithuania Interdisciplinary Competence Ctr Interface Res ICC D-20251 Hamburg Germany

Tissue stiffness is related to soft tissue pathologies and can be assessed through palpation or via clinical imaging systems, e.g., ultrasound or magnetic resonance imaging. Typically, the image based approaches are not suitable during interventions, particularly for minimally invasive surgery. To this end, we present a miniaturized fiber scanning endoscope for fast and localized elastography. Moreover, we propose a deep learning based signal processing pipeline to account for the intricate data and the need for real-time estimates. Our elasticity estimation approach is based on imaging complex and diffuse wave fields that encompass multiple wave frequencies and propagate in various directions. We optimize the probe design to enable different scan patterns. To maximize temporal sampling while maintaining three-dimensional information we define a scan pattern in a conical shape with a temporal frequency of 5.05kHz. To efficiently process the image sequences of complex wave fields we consider a spatio-temporal deep learning network. We train the network in an end-to-end fashion on measurements from phantoms representing multiple elasticities. The network is used to obtain localized and robust elasticity estimates, allowing to create elasticity maps in real-time. For 2D scanning, our approach results in a mean absolute error of 6.31(576)kPa compared to 11.33(1278)kPa for conventional phase tracking. For scanning without estimating the wave direction, the novel 3D method reduces the error to 4.48(363)kPa compared to 19.75(2182)kPa for the conventional 2D method. Finally, we demonstrate feasibility of elasticity estimates in ex-vivo porcine tissue.

关键词： Imaging Probes Elasticity deep learning Phantoms Endoscopes Data acquisition Robots Optical fibers Elastography Elasticity mapping endoscopic imaging neural networks miniaturized probe OCT

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Exploring the Application of deep learning in Multi-View image Fusion in Complex Environments

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TRAITEMENT DU SIGNAL 2023年第6期40卷 2731-2740页

作者： Luo, Xiujuan Shao, Lili Heze Univ Sch Comp Heze 274015 Peoples R China

The advancement of technology has unveiled the immense potential of deep learning across various domains, notably in multi-view image fusion within complex environments. Multiview image fusion aims to merge images from different perspectives to garner more comprehensive and detailed information. Despite this, challenges persist in such fusion under complex conditions, particularly when confronting significant variations in perspective and intricate lighting scenarios. Predominant deep learning approaches, reliant on extensive annotated data, grapple with high computational complexity when processing large-scale and high-dimensional image data, thus hindering real-time applicability. This exploration primarily focuses on two facets: multi-view image registration based on the moment of inertia axis method, and multi-view image fusion utilizing morphological decomposition and attention feature integration. The objective is to enhance the efficiency and effectiveness of multi-view image fusion in complex settings, propelling the practical advancement of deep learning technologies.

关键词： deep learning multi-view image fusion complex environments moment of inertia axis method morphological decomposition attention feature integration

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RT-Droid: a novel approach for real-time android application analysis with transfer learning-based CNN models

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JOURNAL OF real-time image processing 2023年第3期20卷 1-17页

作者： Tasyurek, Murat Arslan, Recep Sinan Kayseri Univ Dept Comp Engn Kayseri Turkiye

Today, the number, type and complexity of malware is increasing rapidly. Convolution neural network (CNN) based networks continue to be used in software classification based on image. In this study, a CNN model named real time-Droid(RT-Droid), which has a very fast malware detection capability and works based on YOLO V5, is introduced. RT-Droid detects android malware with high accuracy and performs this process at near real-time speed. For this process, firstly the features in the android manifest file are extracted and converted to an image in RGB format similar to QR code. Thus, images become processed by CNN-based deep learning models. These images were used to train VGGNet, Faster R-CNN, YOLO V4 and V5 models with the transfer learning technique. The android malware detection performances of the obtained trained models (weights) were examined. In the tests performed with Drebin, Genome and Arslan dataset, the precision value is 98.3%, while the F-score value is 97.0%. In obtaining these values, only 0.019 s per application was needed for analysis. It also requires 25 times less memory space compared to a gray-scale image. Since the small images of the YOLO V5 model can detect objects with very high accuracy and in real time, it provides serious efficiency in processing time. We also compared the results with VGGNet, Faster R-CNN and YOLO V4, which are commonly used CNN models for object detection, and show that it yields results at a higher rate and at least 5.5 times faster than similarly trained networks. Our method detects hacker-generated Android malware very quickly and with high accuracy, while being robust against obfuscated apps.

关键词： image based malware detection Convolutional neural network Transfer learned Android malware Cybersecurity QR-code

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Automatic Quantification of Atmospheric Turbulence Intensity in Space-time Domain

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SENSORS 2025年第5期25卷 1483页

作者： Gulich, Damian Tebaldi, Myrian Sierra-Sosa, Daniel UNLP CONICET La Plata Ctr Invest Opt CIC RA-1897 La Plata Argentina Univ Nacl La Plata UNLP Fac Ingn Dept Ciencias Bas RA-1900 La Plata Argentina Catholic Univ Amer Elect Engn & Comp Sci Washington DC 20064 USA

Quantifying atmospheric turbulence intensity is a challenging task, particularly when assessing real-world scenarios. In this paper, we propose a deep learning method for quantifying atmospheric turbulence intensity based on the space-time domain analysis from videos depicting different turbulence levels. We capture videos of a static image under controlled air turbulence intensities using an inexpensive camera, and then, by slicing these videos in the space-time domain, we extract spatio-temporal representations of the turbulence dynamics. These representations are then fed into a Convolutional Neural Network for classification. This network effectively learns to discriminate between different turbulence regimes based on the spatio-temporal features extracted from a real-world experiment captured in video slices.

关键词： atmospheric turbulence deep learning space-time analysis video analysis turbulence intensity quantification

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