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DEDDR: early detection of diabetic retinopathy from widefield and fundus images using pyspark framework

Multimedia Tools and Applications 2025年 1-30页

作者： Kotiyal, Bina Pathak, Heman Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun248001 India Department of Computer Science Kanya Gurukul Kangri Uttarakhand Dehradun248001 India

This research introduces a Deep Learning Approach for early Detection of Diabetic Retinopathy (DEDDR), leveraging the Pyspark computational framework. DEDDR addresses challenges in manual evaluation, achieving an exceptional accuracy rate of 99.9% in grading diabetic retinopathy. It incorporates advanced techniques, including image preprocessing and nerve segmentation. Strategic class weight balancing ensures equitable model training in the presence of class imbalance. The Retinoactive activation function, a unique creation, facilitates learning from negative values. Rigorous assessment metrics validate DEDDR’s robust capabilities. Notably, a customized loss function further refines model performance. The proposed approach carries profound implications for healthcare, accelerating early diabetic retinopathy detection and averting irreversible vision loss. Ongoing and future work encompasses model optimization, interpretability techniques, and seamless integration into clinical practice for real-world validation and impact assessment. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep learning

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Integrating deep and handcrafted features for enhanced decision-making assistance in breast cancer diagnosis on ultrasound images

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Multimedia Tools and Applications 2025年 1-23页

作者： Abhisheka, Barsha Biswas, Saroj Kumar Purkayastha, Biswajit Das, Soumen School of Computer Science UPES Bidholi Uttrakhand Dehradun248007 India Computer Science Engineering National Institute of Technology Silchar NIT Road Assam Silchar788010 India

The classification of breast cancer has emerged as a significant concern in the healthcare sector in recent times. This is primarily due to its status as the second leading cause of cancer-related fatalities among women. Prior research in the field of breast cancer classification has mainly focused on either traditional Machine Learning (ML) based models or employing Deep Learning (DL) models. However, both approaches solely fail to capture the complete spectrum of features necessary for accurate classification. Therefore, this paper introduces a classification model named Hybrid Breast Cancer Prediction System (HBCPS), which utilizes a combination of features derived from deep Convolutional Neural Network (CNN) model and handcrafted features, to improve classification performance. The proposed HBCPS uses the pre-trained ResNet50 network to extract the deep features, while the handcrafted features are obtained using Histogram Orientation Gradient (HOG). For classification the proposed approach uses Support Vector Machine (SVM). In addition, the proposed method integrates the Block Matching 3D(BM3D) denoising filter. This filter efficiently reduces multiplicative noise such as speckle noise from Breast Ultrasound (BUS) images, resulting in improved image quality. This enhancement technique has significantly contributed to improve the overall performance of the system. The proposed framework is evaluated using a widely used Breast Ultrasound Image (BUSI) dataset. The proposed HBCPS exhibited satisfactory performance with an accuracy, precision, recall, F1-score, specificity, and AUC values of 89.02%, 87.67%, 87.17%, 87.36%, 83.87%, and 0.8717, respectively. These metrics indicate the robustness and reliability of the proposed method and offer a comprehensive framework for breast cancer classification using BUS images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Orbit-Orbit Interaction in Spatiotemporal Optical Vortex

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engineering 2025年第2期45卷 44-51页

作者： Jian Chen Jie Zhao Xi Shen Dewei Mo Cheng-Wei Qiu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.

关键词： Spatiotemporal optical vortex Orbital angular momentum Momentum interaction Highly confined wavepacket Diffraction

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Leveraging BERT, DistilBERT, and TinyBERT for Rumor Detection

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IEEE Access 2025年 13卷 72918-72929页

作者： Qazi, Aijazahamed Goudar, R.H. Patil, Rudragoud Hukkeri, Geetabai S. Kulkarni, Dhanashree Angadi Institute of Technology and Management Department of Artificial Intelligence and Data Science Belagavi591108 India Visvesvaraya Technological University Department of Computer Science and Engineering Belagavi590018 India KLS Gogte Institute of Technology Department of Computer Science and Engineering Belagavi590008 India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Karnataka Manipal576104 India Angadi Institute of Technology and Management Department of Computer Science and Engineering Belagavi591108 India

The rapid spread of false information on social media has become a major challenge in today’s digital world. This has created a need for an effective rumor detection system that can identify and control the spread of false information in real-time. The proposed work introduces a rumor detection system by integrating transformer-based models such as BERT, DistilBERT, and TinyBERT with traditional Machine Learning (ML) techniques. The classifiers include Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayes (NB) help in categorizing content as either rumor or non-rumor based on the patterns. The proposed work evaluated BERT, DistilBERT, TinyBERT combined with ML models (SVM, DT, RF, NB) across PHEME dataset using 70:30, 60:40, and 80:20 splits. Overall, BERT + DT and TinyBERT + SVM provided significant results, with BERT + RF and DistilBERT + NB demonstrating better classification capabilities across various events and split ratios on the dataset. © 2013 IEEE.

关键词： Tweets

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GET3DGS: Generate 3D Gaussians Based on Points Deformation Fields

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4437-4449页

作者： Yu, Haochen Gong, Weixi Chen, Jiansheng Ma, Huimin University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

The 3D Gaussian Splatting method has recently shown significant advancements in rendering speed and scene composition quality, enhancing its industrial applications and boosting the demand for 3D Gaussian asset generation. However, existing mature 3D generation technologies predominantly rely on implicit representations, which often struggle to balance geometric quality with editability. The production of 3D Gaussian assets generally involves diffusion models that require a dual-stage process of reconstruction and generation, resulting in substantial training and inference costs. To overcome these challenges, we introduce GET3DGS, an innovative approach that combines 3D-aware GANs with 3D Gaussian Splatting representations. This method facilitates the manipulation of the physical attributes of 3D Gaussians, such as geometry and texture, via point deformation fields. Offering faster inference speeds and end-to-end training capabilities, our model outperforms existing diffusion model-based methods. By deriving high-quality Gaussian point cloud geometric representations from 2D images, our approach reduces material accumulation costs and produces data compatible with 3D Gaussian rendering engines. We have evaluated the generative performance of our model on ShapeNet and OmniObject3D and demonstrate competitive results in terms of image and geometric quality relative to previous methods. © 1991-2012 IEEE.

关键词： Rendering (computer graphics)

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Pentago SnW:An Improved Spray and Wait Protocol for Delay Tolerant Wireless Sensor Networks

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China Communications 2025年第3期22卷 104-114页

作者： Idris Afzal Shah Mushtaq Ahmed Raghavendra Singh Department of Computer Science and Engineering Malaviya National Institute of TechnologyJaipur302017India

Delay tolerant wireless sensor networks(DTWSN)is a class of wireless network that finds its deployment in those application scenarios which demand for high packet delivery ratio while maintaining minimal overhead in order to prolong network lifetime;owing to resource-constrained nature of *** fundamental requirement of any network is routing a packet from its source to *** of a routing algorithm depends on the number of network parameters utilized by that routing *** the recent years,various routing protocol has been developed for the delay tolerant networks(DTN).A routing protocol known as spray and wait(SnW)is one of the most widely used routing algorithms for *** this paper,we study the SnW routing protocol and propose a modified version of it referred to as Pentago SnW which is based on pentagonal number *** to binary SnW shows promising results through simulation using real-life scenarios of cars and pedestrians randomly moving on a map.

关键词： binary SnW delay tolerant network Pentago SnW spray and wait routing

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Programmer Visual Attention During Context-Aware Code Summarization

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IEEE Transactions on Software engineering 2025年第5期51卷 1524-1537页

作者： Wallace, Robert Bansal, Aakash Karas, Zachary Tang, Ningzhi Huang, Yu Jia-Jun Li, Toby McMillan, Collin University of Notre Dame Department of Computer Science and Engineering Notre DameIN46556 United States Vanderbilt University Department of Computer Science NashvilleTN37235 United States

Programmer attention represents the visual focus of programmers on parts of the source code in pursuit of programming tasks. The focus of current research in modeling this programmer attention has been on using mouse cursors, keystrokes, or eye tracking equipment to map areas in a snippet of code. These approaches have traditionally only mapped attention for a single method. However, there is a knowledge gap in the literature because programming tasks such as source code summarization require programmers to use contextual knowledge that can only be found in other parts of the project, not only in a single method. To address this knowledge gap, we conducted an in-depth human study with 10 Java programmers, where each programmer generated summaries for 40 methods from five large Java projects over five one-hour sessions. We used eye tracking equipment to map the visual attention of programmers while they wrote the summaries. We also rate the quality of each summary. We found eye-gaze patterns and metrics that define common behaviors between programmer attention during context-aware code summarization. Specifically, we found that programmers need to read up to 35% fewer words (p © 1976-2012 IEEE.

关键词： Economic and social effects

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Proactive Crowdsourced Monitoring and Sensing With Expansible Activity Recognition Based on Internet of Things Localization

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IEEE Internet of Things Journal 2025年第11期12卷 17674-17686页

作者： Chen, Lien-Wu Liao, Chun-Wei Liu, Jun-Xian Feng Chia University Department of Information Engineering and Computer Science Taichung407 Taiwan

This paper proposes a Proactive Crowdsourced Monitoring and Sensing (PCMS) framework with the designed Smart iBeacon device to accurately recognize the activities of an equipped target, exclusively customize the recognition model of a specific target, and actively trigger cooperative tracking of nearby smartphones for an abnormal target based on Internet of Things (IoT) localization. According to our review of relevant research, PCMS is the first framework that provides the following features: i) coarse-grained and fine-grained features can be extracted to accurately recognize target activities through densely connected convolutional networks with improvement design, ii) crowdsourced monitoring and sensing can be actively triggered for a target as the abnormal activity of the target is detected, and iii) deep learning model of target activity recognition can be exclusively customized for a specific target to improve the recognition accuracy based on the dedicated activity data of the target. An Android-based prototype with stationary iBeacon nodes and the Smart iBeacon is implemented to verify the feasibility and superiority of our PCMS framework. Experimental results show that our framework outperforms existing methods and can accurately recognize target activities for abnormal event detection and proactive crowdsourced tracking in a real-time manner. © 2014 IEEE.

关键词： Smartphones

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A Forefront System for Sugarcane Leaf Disease Classification Using Deep Learning 4

A Forefront System for Sugarcane Leaf Disease Classification...

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4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Sakib, Abu Noman Md Partha, Sajal Basak Shojib, Mohammad Anik Islam Hasan, Md Mahamudul Shuvo, Badiuzzaman Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (纸本)9798331530938

The agriculture sector has already seen a favorable advancement because of the involvement of machine learning (ML) methods. Sugarcane industry faces a gruesome drawback because of several types of leaf diseases. Nowadays, ML methods have come handy also to detect the diseases at the very early stage. Our work represents a cutting-edge deep learning method with a goal to classify five popular sugarcane leaf diseases: Bacterial Blight, Mosaic, RedRot, Rust, Yellow, and can also identify which one is Healthy. This study also conducts a comparative analysis of various state-of-the-art models, evaluating their training time, parameters, accuracy and model size using the same dataset. Based on this evaluation, the optimal model was selected for final evaluation. Several pre-trained models were modified to enhance speed and reduce storage requirements. The system can identify the disease category for every given sugarcane leaf image using the EfficientNetB0 model, which demonstrated the best performance, and also provides the probability of correct identification. Several preprocessing steps are involved such as removing noise, addressing missing data, and standardizing images to ensure maximum accuracy. The models were trained, validated, and tested on a dataset of 4,800 sugarcane leaf images from the Sugarcane Leaf Disease Dataset. Among all the models, EfficienNetB0, InceptionV3 and ResNet50 achieved high accuracy (98.75%, 98.54% and 98.54%, respectively). However, EfficienNetB0 was ultimately chosen due to its faster training, smaller size, and fewer parameters, making it ideal for this study. © 2025 IEEE.

关键词： Contrastive Learning

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Enhancing aviation safety: Machine learning for real-time ADS-B injection detection through advanced data analysis

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Alexandria engineering Journal 2025年 126卷 262-276页

作者： Rahman, Md. Atiqur Bhuiyan, Touhid Ali, M. Ameer Department of Computer Science and Engineering East West university Dhaka Bangladesh School of Information Technology Washington University of Science and Technology Alexandria United States Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

Airplanes play a critical role in global transportation, ensuring the efficient movement of people and goods. Although generally safe, aviation systems occasionally encounter incidents and accidents that underscore the need for proactive risk management. This study employs machine learning to detect abnormalities in commercial aircraft operations using Automatic Dependent Surveillance–Broadcast (ADS-B) data. Given the growing reliance on ADS-B technology, concerns regarding its susceptibility to security breaches, such as injection attacks, have intensified. To address these vulnerabilities, we propose a robust ADS-B injection detection system. Employing GridSearchCV for model optimization, it effectively identifies and categorizes injection risks. The system's performance, evaluated using the ADS-B Message Injection Attacks Dataset, achieves outstanding results, including a value of 0.9970 for the accuracy, precision, recall, and F1 score. The proposed classifier also demonstrates a higher area under the curve (0.9999), specificity (0.9956), and Cohen's kappa (0.9954) than existing approaches, while achieving a lower log loss (0.0107). This research significantly enhances aviation security by introducing a highly accurate, computationally efficient, and reliable real-time detection model for ADS-B injection attacks, ensuring the integrity and resilience of modern flight control systems. © 2025 The Authors

关键词： Risk management

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