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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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Cryptanalysis in e-commerce: A novel arrangement procedure using DHEC-SAT upon elliptic curve cryptography

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Multimedia Tools and Applications 2025年第8期84卷 5377-5395页

作者： Fauziyah Wang, Zhaoshun School of Computer and Communication Engineering University of Science and Technology Beijing 30 Xueyuan Road Haidian District Beijing China

Cryptanalytic innovations are considered the most economical and useful method for guaranteeing information security across shared networks. Despite the tinier key magnitude of Elliptic Curve Cryptography (ECC) compared to common cryptanalysis schemes, the majority of scholars have employed cryptanalysis methods to deliver data reliability and verification. Besides, the computation attempt needed by Diffie Hellman-Elliptic Curve (DH-EC) for equal key dimensions is similar and delivers extra security advantages. The research offers a novel key arrangement procedure using DH-EC in the secure automatic transactions (SAT) setting (DHEC-SAT) to enhance data safety. The DHEC-SAT procedure effectively applied the keys in the SAT setting for online payment and was employed to decode and encode the validation data. The DHEC-SAT procedure is implemented for an entry of e-commerce payments to deliver great-degree security examination, fast verification, and the safety of the suggested Cryptanalytic scheme. The suggested DHEC-SAT generates highly secure systems regarding data size (168 bytes), computational period (4.34 ms), and communication costs (1271 bits), as contrasted to extant ECC procedures and PIN-enabled key arrangements. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic commerce

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OSSIoT: An ontology-based Operational Security model for Social Internet of Things using Machine Learning Techniques

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第10期51卷 1440-1453页

作者： Kumar, K.S. Santhosh Hanumanthappa, J. Prakash, S.P. Shiva Krinkin, Kirill Department of Studies in Computer Science University of Mysore Karnataka Mysuru India Department of Information Science and Engineering JSS Science and Technology University Karnataka Mysuru India School of Computer Science and Engineering Constructor University Bremen Germany

The Social Internet of Things (SIoT) is an innovative fusion of IoT and smart devices that enable them to establish dynamic relationships. Securing sensitive data in a smart environment requires a model to determine the relationships between devices through object profiling and ontological models. To address this need, we have proposed an ontology-based operational security model for the SIoT. In our approach, the interpolation method is used to establish relationships, while fiduciary relationships are employed to detect threats. Furthermore, the encryption of heterogeneous device data, coupled with the implementation of an operation-based intrusion detection system, proves highly effective in identifying potential *** relationships are identified as intruders and validated using machine learning techniques. Encrypting heterogeneous device data, along with an operation-based intrusion detection system, efficiently identifies threats in the ever-evolving dynamic nature of the SIoT environment. the encryption of heterogeneous device data, coupled with an operation-based intrusion detection system, effectively identifies threats. Invalid relationships are promptly identified as attackers and machine learning techniques are used to validate relationships encryption and machine learning stand as indispensable tools in the endeavor to secure sensitive information within the realm of the SIoT. The suggested model outperformed the results of the current model, as evidenced by its average accuracy of 85.67%, precision of 90.37%, recall of 92.06%, and F1-score of 91.03% when compared to the existing model. © (2024), (International Association of Engineers). All rights reserved.

关键词： Network intrusion

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Mutual Retinex: Combining Transformer and CNN for Image Enhancement

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

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Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

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Light(science & Applications) 2024年第3期13卷 466-477页

作者： Xinyuan Fang Xiaonan Hu Baoli Li Hang Su Ke Cheng Haitao Luan Min Gu Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Machine learning with optical neural networks has featured unique advantages of the information processing including high speed,ultrawide bandwidths and low energy consumption because the optical dimensions(time,space,wavelength,and polarization)could be utilized to increase the degree of ***,due to the lack of the capability to extract the information features in the orbital angular momentum(OAM)domain,the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network ***,we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network(CNN)based on Laguerre-Gaussian(LG)beam modes with diverse diffraction *** proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction,and deep-learning diffractive layers as a *** resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding,leading to an accuracy as high as 97.2%for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes,as well as a resistance to eavesdropping in point-to-point free-space ***,through extending the target encoded modes into multiplexed OAM states,we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%.Our work provides a deep insight to the mechanism of machine learning with spatial modes basis,which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.

关键词： polarization mode momentum

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Explaining the better generalization of label distribution learning for classification

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science China(Information sciences) 2025年第5期68卷 348-361页

作者： Jing WANG Xin GENG School of Computer Science and Engineering Southeast University Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications(Southeast University) Ministry of Education

Label distribution learning(LDL) has shown advantages over traditional single-label learning(SLL) in many realworld applications, but its superiority has not been theoretically understood. In this paper, we attempt to explain why LDL generalizes better than SLL. Label distribution has rich supervision information such that an LDL method can still choose the sub-optimal label from label distribution even if it neglects the optimal one. In comparison, an SLL method has no information to choose from when it fails to predict the optimal label. The better generalization of LDL can be credited to the rich information of label distribution. We further establish the label distribution margin theory to prove this explanation; inspired by the theory,we put forward a novel LDL approach called LDL-LDML. In the experiments, the LDL baselines outperform the SLL ones, and LDL-LDML achieves competitive performance against existing LDL methods, which support our explanation and theories in this paper.

关键词： label distribution learning classification generalization label distribution margin label ambiguity neural network

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TransEffiVisNet – an image captioning architecture for auditory assistance for the visually impaired

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Multimedia Tools and Applications 2024年 1-20页

作者： R, Harshitha B, Lakshmipriya Krishnamurthy, Vallidevi School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India School of Engineering Jawaharlal Nehru University New Delhi India

Insights derived out of image captioning systems have potential applications in real life, including providing auditory assistance for the visually impaired. This paper proposes TransEffiVisNet, a novel image captioning system embedded with text-to-speech generation, which can be deployed to aid individuals with visual impairments. The proposed model combines a convolutional neural network (CNN) and a transformer-based encoder and decoder to generate descriptive captions for the visual content provided as an image input. A comparative study was conducted to assess the performance of the proposed model formulated using five prominent CNN models—VGG16, ResNet50, EfficientNetB0, EfficientNetB1, and Inception V3, while maintaining the same transformer architecture. The image captioning model with EfficientNetB1 showcased phenomenal results compared to four other architectures and hence preferred to be used in TransEffiVisNet. Integration of Google Text-to-Speech technology to convert captions into speech further enhances the functionality of the model which in future can be deployed for visually impaired assistance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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A Scalable and Secure Facial Recognition Attendance System Leveraging CNN Algorithms for Real-Time Accuracy and Efficiency

A Scalable and Secure Facial Recognition Attendance System L...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Kumar, Aakash Sharma, Jiya Renjith, P.N. Vellore Institute of Technology School of Computer Science and Engineering India

ISBN: (纸本)9798331529833

The facial recognition attendance system represents a modern approach to attendance tracking, designed to improve efficiency, accuracy, and security in various organizational settings. Advanced biometric technologies are leveraged to capture and encode facial features, creating unique digital identifiers for each individual. The methodology employs cutting-edge facial recognition algorithms powered by convolutional neural networks (CNNs) with a 99.5% recognition accuracy rate. These algorithms extract and encode facial vectors with sub-millisecond latency, which are then compared in real-time against a secure, encrypted database of preregistered individuals. Upon a successful match, the system automatically records the attendance, ensuring an accurate and efficient process. With an intuitive web interface built on ReactJS and a backend supported by *** and MongoDB, the system provides seamless access to attendance records and administrative functionalities. The entire attendance recording process is optimized for speed, typically completing within two seconds per individual. Additionally, advanced anti-spoofing measures and integration support with existing HR management systems redefine traditional attendance management practices with a scalable, robust, and secure solution. (Abstract) © 2025 IEEE.

关键词： Efficiency

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A Decoupling-Type Strategy for the Allen-Cahn Equation on Curved Surfaces

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Journal of Mathematical Research with Applications 2025年第1期45卷 73-83页

作者： Xiaoman XIE Qing PAN School of Computer and Communication Engineering Changsha University of Science & Technology

In this paper, we construct an efficient decoupling-type strategy for solving the Allen-Cahn equation on curved surfaces. It is based on an FEM-EIEQ(Finite Element Method and explicit-Invariant Energy Quadratization) fully discrete scheme with unconditional energy stability. Spatially the FEM is adopted, using a triangular mesh discretization strategy that can be adapted to complex regions. Temporally, the EIEQ approach is considered, which not only linearizes the nonlinear potential but also gives a new variable that we combine with the nonlocal splitting method to achieve the fully decoupled computation. The strategy can successfully transform the Allen-Cahn system into some completely independent algebraic equations and linear elliptic equations with constant coefficients, we only need to solve these simple equations at each time step. Moreover, we conducted some numerical experiments to demonstrate the effectiveness of the strategy.

关键词： FEM-EIEQ Allen-Cahn equation surface

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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