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An efficient cloud based face recognition system for e-health secured login using steerable pyramid transform and local directional pattern

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1017-1027页

作者： Dosaj, Ayush Satapathy, Suresh Chandra Soundrapandiyan, Rajkumar Kaur, Mannat Hannoon, Naeem School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Kalinga Institute of Industrial Technology Deemed to be University Odisha Bhubaneswar India Faculty of Electrical Engineering Universiti Teknologi MARA Shah Alam Malaysia

This paper proposes a face recognition system based on steerable pyramid transform (SPT) and local directional pattern (LDP) for e-health secured login in cloud domain. In an e-health login, patients periodically forget their self-made login username and password. This face recognition system can replace the traditional login system. In the proposed system, SPT can decompose a face image into several sub-bands of different scales and orientations, and LDP can encode the sub-bands in binary texture pattern. The LDP binary pattern was obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Therefore, SPT–LDP design represents a face image in a robust way which includes multiple information sources from different scales and orientations. The proposed system is tested on standard benchmark facial recognition technology (FERET) and extended Yale-B databases. Further, the efficiency of the proposed system is proved by comparing recognition rates with other existing descriptor based methods. From the experimental results it is observed that the proposed system achieves 99.79% recognition in fb set, 90.72% in fc set, 84.97% in dup I set, and 87.69% in dup II set for FERET database and 100% in sub2 set, 100% in sub3 set, 96% in sub4 set and 95% in sub5 set for extended Yale-B database which is better than the existing methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Face recognition

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Breaking demodulation limitations:AWG and Deep Learning in Dynamic Spectral Reconstruction for Differential FBG Accelerometers

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IEEE Sensors Journal 2025年第11期25卷 19210-19222页

作者： Yang, Qian Liu, Lei Niu, Ning Quan, ChenYi Ren, SuFen Chen, ShengChao Zhang, JiaLin Yan, YuMeng Xiang, LongTeng Wang, YiPing Liao, ChangRui He, Jun Wang, GuanJun School of Information and Communication Engineering Hainan University College of Electronic Science and Technology China Henan Mechanical and Electrical Vocational College School of Electrical Engineering Henan451100 China Hainan University College of Electronic Science and Technology School of Information and Communication Engineering Haikou570228 China University of Technology Sydney School of Computer Science Faculty of Engineering and Information Technology SydneyNSW Australia Hainan University College of Electronic Science and Technology Haikou570228 China Shenzhen University School of Physics and Optoelectronic Engineering Shenzhen518060 China

Conventional fiber Bragg grating (FBG) accelerometer demodulation often suffers from high environmental sensitivity, complexity, and cost. To address these issues, this paper presents two arrayed waveguide grating (AWG)-based dynamic spectral reconstruction models. First, a novel differential FBG accelerometer with an equal-intensity triangular cantilever beam and a symmetric string-like design enhances sensitivity and mitigates temperature interference. A power ratio-nonlinear logarithmic regression (PR-NLR) model derived from static calibration then enables spectral reconstruction and wavelength demodulation across varying accelerations, achieving 4-82pm accuracy, slightly below that of high-speed demodulators (HSD). Next, by integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), attention mechanisms, and a generative adversarial network (GAN) for data augmentation, prediction errors drop to 1-26pm at 20Hz and 0.2-21pm at 40Hz-a reduction of at least 60% over PR-NLR. This outcome underscores the advantages of deep learning (DL) in managing complex spectral shifts. By overcoming frequency-only demodulation limits, the proposed AWG approach achieves precise wavelength demodulation through dynamic spectral reconstruction. Its compact design, low manufacturing cost, and robust performance facilitate scalable deployment in structural health monitoring, particularly for bridge vibration analysis and industrial machinery diagnostics. © 2001-2012 IEEE.

关键词： Arrayed waveguide gratings

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Chinese Relation Extraction Based on Cross-Attention and Multi-feature Perception

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1837-1853页

作者： Xu, Shiao Sun, Shuihua Zhang, Zhiyuan Zhou, Huan School of Electronics and Information Engineering Jingchu University of Technology Jingmen448000 China School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China

Relation extraction aims at extracting semantic relations between given pairs of entities from unstructured textual data and is a critical task in information extraction. A relation extraction model based on cross-attention and multi-feature perception is proposed to address the limitations of Chinese relation extraction models that rely solely on characters, which struggle to capture rich semantic information, and the inability to ful ly utilize the textual structural information during neural network feature extraction. First, the model uses BERT to encode sentences and entities to obtain character vector sequences and word vector sequences of sentences, and uses word cross-attention mechanism to capture the key word and word information in sentences to form character and word cross-fused sentence sequences. Second, uses graph convolutional network and deep separable convolutional network to capture syntactic information between nodes and multi-granularity local semantic information in sentence sequences, respectively, and forms sentence features for classification by attention mechanism. Final ly, combines with Softmax classifier for relation classification. Experimental results on the Chinese SanWen dataset demonstrate the superiority of the proposed method over mainstream neural network-based relation extraction methods, with an F1-score of 71.57%. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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An intrusion attack classification using bio-inspired optimization technique and ensemble learning model for edge computing environments

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Multimedia Tools and Applications 2024年 1-28页

作者： Rahamathulla, Mohemmed Yousuf Ramaiah, Mangayarkarasi School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamilnadu Vellore India

The phenomenal rise in network traffic across various sectors, driven by advancements in network communication, has led to an explosion of connected devices. While internet-based service providers have enhanced smart environments, these environments remain highly vulnerable to security risks. Due to their resource- constrained nature, Internet of Things (IoT) systems rely on edge servers for processing. Cutting-edge machine learning algorithms have significantly improved the security of edge computing systems by enabling early intrusion detection. However, the dynamic nature of our society demands more sophisticated Network Intrusion Detection Systems (NIDS) that can balance the challenge of managing the explosive growth of network traffic data with effectively responding to attacks. This paper proposes a novel NIDS that can train and learn from massive amounts of data in a centralized environment. To identify the best subset of features for improved data representation and classification, a bio-inspired algorithm, the Improved Firefly Optimization Algorithm (IFOA), is employed. Furthermore, a hyper-tuned Extremely Randomized Trees (ERT) attack detection model is developed to accurately identify malicious behavior while minimizing false positives. The experimental results demonstrate that the proposed approach achieves superior accuracy and execution speed compared to alternative solutions and its baseline versions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Network intrusion

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An Efficient Approach for EEG Seizure Detection using CNN with Feature Extraction 7

An Efficient Approach for EEG Seizure Detection using CNN wi...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Joseph, Liya Izudheen, Sminu Narayanana, Uma Nair, Abhinav R Ashok Gopal, K.A. Rajagiri School of Engineering & Technology Department of Computer Science and Engineering Ernakulam India CVV Institute of Science and Technology Department of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9798350372816

Epileptic seizures are a significant agony for those who suffer from them. Epileptic studies primarily focus on understanding the abnormal behavior of brain signals. Detecting seizures in EEG signals manually is a very chaotic and challenging task. Therefore, we propose an automatic seizure detection system that drastically reduces the manual effort required for seizure detection while providing accurate results. The proposed method involves extracting twelve statistical features from segmented data and adjusting labels to facilitate binary classification using a Convolutional Neural Network (CNN). The dataset is normalized and balanced using SMOTE to address class imbalance. Evaluation metrics such as accuracy, precision and recall are computed on the test set with results visualized through accuracy and loss curves. User input allows for predictions on specific test data, determining the presence of seizures based on model predictions. This structured approach ensures reliable seizure detection through the use of advanced deep learning techniques and comprehensive model evaluation. Furthermore, a comparative study was conducted to assess the effectiveness of the proposed model against conventional machine learning algorithms such as Logistic Regression, Support Vector Machine (SVM), Gradient Boosting and Decision Tree. Our analysis reveals that CNN consistently demonstrates superior performance compared to these methods. © 2024 IEEE.

关键词： Convolutional neural networks

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Blockchain-based internet of things security architecture and applications

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16703-16714页

作者： Chen, Hongsong Lei, Shi Zhang, Yiying Han, Xintong Cao, Yongrui Zhang, Yongpeng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

The Internet of Things (IoT) and blockchain are two emerging information technologies that will significantly impact the lives and production patterns of people. When they meet together, blockchain services can be used to solve some IoT challenges, however some new risks will be introduced. Therefore, how to use blockchain services in IoT environment and which key security and privacy challenges in IoT environment can be addressed by blockchain services are two critical issues. Thus, we propose a novel hybrid IoT architecture based on blockchain services for the first issue. To address the second issue, we design system architecture for implementing blockchain-as-a-service in the IoT environment, select the dimensions of fog computing, edge computing and software vulnerability to classify the challenges and solutions. Then practical application scenarios are summarized. The single on-chain time is between 15 and 20 s and the single off-chain in IOTA is about 2 s. The on-chain time is longer than the off-chain time. Finally, seven challenges and the related solution for blockchain-based IoT are proposed to promote future blockchain and IoT research. The proposed architecture can improve the structural security and functional security for IoT and Blockchain. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Internet of things

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Inspires effective alternatives to backpropagation:predictive coding helps understand and build learning

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Neural Regeneration Research 2025年第11期20卷 3215-3216页

作者： Zhenghua Xu Miao Yu Yuhang Song School of Health Sciences and Biomedical Engineering Hebei University of TechnologyTianjinChina Department of Computer Science University of OxfordOxfordUK

Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human *** enable learning by brain and machine,it is essential to accurately identify and correct the prediction errors,referred to as credit assignment(Lillicrap et al.,2020).It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience.

关键词： assignment learning enable

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Geometric prior guided hybrid deep neural network for facial beauty analysis

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CAAI Transactions on Intelligence technology 2024年第2期9卷 467-480页

作者： Tianhao Peng Mu Li Fangmei Chen Yong Xu David Zhang The School of Computer Science and Technology Guizhou UniversityGuiyangChina Department of Automation Moutai InstituteRenhuaiGuizhouChina The School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhenChina The Information and Communication Engineering Department Dalian Minzu UniversityDalianChina The School of Data Science The Chinese University of Hong KongShenzhenShenzhenChina

Facial beauty analysis is an important topic in human *** may be used as a guidance for face beautification applications such as cosmetic *** neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable ***,most existing DNN-based models regard facial beauty analysis as a normal classification *** ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty *** be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the *** by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial ***,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two *** model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric *** performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid *** the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

关键词： deep neural networks face analysis face biometrics image analysis

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An Improved Hybrid Deep Learning Approach for Security Requirements Classification

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computers, Materials & Continua 2025年第3期82卷 4041-4067页

作者： Shoaib Hassan QianmuLi Muhammad Zubair Rakan AAlsowail Muhammad Umair School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China Faculty of Information Technology and Computer Science University of Central PunjabLahore5400Pakistan Computer Skills Self-Development Skills DevelopmentDeanship of Common First YearKing Saud UniversityRiyadh11362Saudia Arabia

As the trend to use the latestmachine learning models to automate requirements engineering processes continues,security requirements classification is tuning into the most researched field in the software engineering *** literature studies have proposed numerousmodels for the classification of security ***,adopting those models is constrained due to the lack of essential datasets permitting the repetition and generalization of studies employing more advanced machine learning ***,most of the researchers focus only on the classification of requirements with security *** did not consider other nonfunctional requirements(NFR)directly or indirectly related to *** has been identified as a significant research gap in security requirements *** major objective of this study is to propose a security requirements classification model that categorizes security and other relevant security *** use PROMISE_exp and DOSSPRE,the two most commonly used datasets in the software engineering *** proposed methodology consists of two *** the first step,we analyze all the nonfunctional requirements and their relation with security *** found 10 NFRs that have a strong relationship with security *** the second step,we categorize those NFRs in the security requirements *** proposedmethodology is a hybridmodel based on the ConvolutionalNeural Network(CNN)and Extreme Gradient Boosting(XGBoost)***,we evaluate the model by updating the requirement type column with a binary classification column in the dataset to classify the requirements into security and non-security *** performance is evaluated using four metrics:recall,precision,accuracy,and F1 Score with 20 and 28 epochs number and batch size of 32 for PROMISE_exp and DOSSPRE datasets and achieved 87.3%and 85.3%accuracy,*** proposed study shows an enhancement in metrics

关键词： Requirements engineering security requirements deep learning CNN XGBoost classification

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A Memristive Spiking Neural Network Circuit for Bio-Inspired Navigation Based on Spatial Cognitive Mechanisms

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IEEE Transactions on Biomedical Circuits and Systems 2025年第3期19卷 686-698页

作者： Chen, Zhanfei Wang, Xiaoping Wang, Zilu Yang, Chao Huang, Tingwen Lai, Jingang Zeng, Zhigang School of Mechanical Engineering and Automation Shenzhen518055 China Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Hubei Key Laboratory of Brain-Inspired Intelligent Systems School of Artificial Intelligence and Automation Wuhan430074 China Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Shenzhen University of Advanced Technology School of Computer Science and Control Engineering Shenzhen518055 China

Cognitive navigation, a high-level and crucial function for organisms' survival in nature, enables autonomous exploration and navigation within the environment. However, most existing works for bio-inspired navigation are implemented with non-neuromorphic computing. This work proposes a bio-inspired memristive spiking neural network (SNN) circuit for goal-oriented navigation, capable of online decision-making through reward-based learning. The circuit comprises three primary modules. The place cell module encodes the agent's spatial position in real-time through Poisson spiking;the action cell module determines the direction of subsequent movement;and the reward-based learning module provides a bio-inspired learning method adaptive to delayed and sparse rewards. To facilitate practical application, the entire SNN is quantized and deployed on a real memristive hardware platform, achieving about a 21 × reduction in energy consumption compared to a typical digital acceleration system in the forward computing phase. This work offers an implementation idea of neuromorphic solution for robotic navigation application in low-power scenarios. © 2007-2012 IEEE.

关键词： Intelligent robots

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