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Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

computers, Materials & Continua 2024年第4期79卷 695-712页

作者： Fangjun Luan Xuewen Mu Shuai Yuan Department of Computer Technology School of Computer Science and EngineeringShenyang Jianzhu UniversityShenyang110168China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Jianzhu UniversityShenyang110168China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang Jianzhu UniversityShenyang110168China

Online Signature Verification (OSV), as a personal identification technology, is widely used in various ***, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational ***, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

关键词： Online signature verification feature selection ACG block ghost-ACmix residual structure

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Mobility and Behavior based Trustable Routing in Mobile Wireless Sensor Network 7

Mobility and Behavior based Trustable Routing in Mobile Wire...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Praveen Kumar, B. Umamageswaran, J. Kalpana, A.V. Dhanalakshmi, R. Department of Computer Science and Engineering KL University Andhra Pradesh Vijayawada India Department of Information Technology RMK Engineering College Tamil Nadu Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9781665464086

Mobile wireless sensor networks (MWSNs) allow the sensor nodes to move freely and transmit to each other without needing a fixed infrastructure. Usually, the routing process is very complex, and it becomes even more complicated as the sensor nodes are low-power, cost-effective mobile devices with minimum resources. MWSNs raises deployed in security-critical applications due to their resource-constrained features;they are prone to several security attacks that critically affect data transmission. The most significant innovation of trust is that it evades untrustable nodes by activating several detection routes to detect and obtain node trust, enhancing security quickly. Mobility and Behavior based Trustable Routing (MBTR) is proposed to solve these issues. This approach measures sensor node mobility based on the Received Signal Strength (RSS) between the two sensor nodes. The trust manager computes the node trust ability by the node behavior. Here, the node behavior is checked through the node energy, supportive account, and received packet account. The trust manager selects the security algorithm based on distance during data transmission. The simulation examination demonstrates lesser delay and lesser packet loss in the network. Furthermore, it improves the network throughput in the MWSN. © 2023 IEEE.

关键词： Sensor nodes

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Microservices Architecture As a data Governance Tools in Decentralized E-Government

Microservices Architecture As a Data Governance Tools in Dec...

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2022 International Conference on science and Technology, ICOSTECH 2022

作者： Paramita, Adi Suryaputra Gaol, Ford Lomban Ranti, Benny Supangkat, Suhono Harso Graduate Program Universitas Ciputra Surabaya/ Bina Nusantara University Information Systems Department/Doctor of Computer Science Jakarta Surabaya Indonesia Bina Nusantara University Doctor of Computer Science Graduate Program Jakarta Indonesia Institute of Technology Bandung Systems and Information Technology Department Bandung Indonesia

ISBN: (纸本)9781665479851

Electronic government, or e-Government, is the use of information and communication technology by the public sector (ICT). Additionally, it can be viewed as a paradigm shift in terms of how governments operate. The government protects public values, while citizens act as problem solvers, co-creators, and active participants in value creation. On the other hand, decentralized systems have grown in popularity over the last few years. data has become a significant issue in decentralized E-Government;each local entity has their own data, which is private. A suitable data governance tool is required to ensure that data governance is properly implemented and to prevent the organizational entity from leaking information. The purpose of this research is to examine the data governance tools mechanism utilizing microservices. © 2022 IEEE.

关键词： Information use

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InDRI: Intelligent Diagnosis Radiology Images 6

InDRI: Intelligent Diagnosis Radiology Images

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Nugroho, Kuncahyo Setyo Ritonga, Imelda Liana Gultom, Roby Pahala Januario Lubis, Elida Afni Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Universitas Imelda Medan Indonesia Medan20238 Indonesia Universitas Imelda Medan Indonesia Medan20238 Indonesia Clinical Pathology Unit Rumah Sakit Imelda Pekerja Indonesia Medan20239 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

The need for an early screening and computer-Aided Diagnosis (CAD) system based on Artificial Intelligence (AI) for the field of radiology is essential to realize considering the large impact of lung diseases globally. However, developing AI requires a lot of data, which is a challenge because data in the health sector tends to be limited in quantity and has disparities between diagnostic categories (data imbalance). To overcome this issue, DINO ViT models was trained with the publicly available COVID-19 Radiography database in our previous study. The model was subsequently implemented into a web application, with this study focusing on the development of a prototype for the application. The study found this application can be useful for medical personnel and doctors in carrying out early screening and CAD. Meanwhile, it also highlighted the general unfamiliarity with AI applications among medical staff, emphasizing the need for increased education and training on AI's role in healthcare. © 2024 IEEE.

关键词： Lung cancer

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Token2vec: A Joint Self-Supervised Pre-Training Framework Using Unpaired Speech and Text 48

Token2vec: A Joint Self-Supervised Pre-Training Framework Us...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yue, Xianghu Ao, Junyi Gao, Xiaoxue Li, Haizhou National University of Singapore Department of Electrical and Computer Engineering Singapore Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of HongKong School of Data Science Shenzhen China

ISBN: (纸本)9781728163277

Self-supervised pre-Training has been successful in both text and speech processing. Speech and text offer different but complementary information. The question is whether we are able to perform a speech-Text joint pre-Training on unpaired speech and text. In this paper, we take the idea of self-supervised pre-Training one step further and propose token2vec, a novel joint pre-Training framework for unpaired speech and text based on discrete representations of speech. Specifically, we introduce two modality-specific tokenizers for speech and text. Based on these tokenizers, we convert speech/text sequences into discrete speech/text token sequences consisting of similar language units, thus mitigating the domain mismatch problem and length mismatch problem, which are caused by the distinct characteristics between speech and text. Finally, we feed the discrete speech and text tokens into a modality-Agnostic Transformer encoder and pre-Train with token-level masking language modeling (tMLM). Experiments show that token2vec is significantly superior to various speech-only pre-Training baselines, with up to 17.7% relative WER reduction. Token2vec model is also validated on a non-ASR task, i.e., spoken intent classification, and shows good transferability. © 2023 IEEE.

关键词： Modeling languages

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Unsupervised Person and Vehicle Re-identification via Relative Hard Samples in Industrial Surveillance System 3

Unsupervised Person and Vehicle Re-identification via Relati...

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3rd International Workshop on Intelligent Systems, IWIS 2023

作者： Tang, Qing Cao, Ge Jo, Kang-Hyun Jung, Hail Data Science Group INTERX Ulsan Korea Republic of University of Ulsan Department of Electrical Electronic and Computer Engineering Korea Republic of Seoul National University of Science and Technology Department of Business Administration Seoul Korea Republic of

ISBN: (纸本)9798350305043

Person and Vehicle Re-Identification (Re-ID) is a critical task in the realm of intelligent industrial surveillance systems. It aims to identify the same person or vehicle across different camera views or scenes, facilitating individual tracking across multiple cameras within industrial environments. Re-Id in an industrial environment can be more challenging than in a general environment due to its unique setting and limited annotated dataset. Hence, this paper focuses on addressing the fully unsupervised re-ID problem, aiming to develop a re-ID solution that can learn without the need for any human-annotated labeled data. Furthermore, recent studies have demonstrated the effectiveness of self-supervised Momentum Contrastive learning (MoCo) as an unsupervised object re-ID method. However, MoCo neglects the hard sample learning. Here, we introduced Relative Hard Samples (RHS) learning to ensure selection in an adaptive and stable way by considering the characteristics of each sample. Experimental results confirm the effectiveness of our proposed hard sample learning strategy RHS selection and RHS learning. Comprehensive experiments have been conducted on one vehicle re-ID dataset and two person re-ID datasets. © 2023 IEEE.

关键词： Vehicles

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A general framework for adversarial label learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 5254-5286页

作者： Chidubem Arachie Bert Huang Department of Computer Science Virginia Tech Blacksburg VA Department of Computer Science Data Intensive Studies Center Tufts University Medford MA

We consider the task of training classifiers without fully labeled data. We propose a weakly supervised method--adversarial label learning--that trains classifiers to perform well when noisy and possibly correlated labels are provided. Our framework allows users to provide different weak labels and multiple constraints on these labels. Our model then attempts to learn parameters for the data by solving a zero-sum game for the binary problems and a non-zero sum game optimization for multi-class problems. The game is between an adversary that chooses labels for the data and a model that minimizes the error made by the adversarial labels. The weak supervision constrains what labels the adversary can choose. The method therefore minimizes an upper bound of the classifier's error rate using projected primal-dual subgradient descent. Minimizing this bound protects against bias and dependencies in the weak supervision. We first show the performance of our framework on binary classification tasks then we extend our algorithm to show its performance on multiclass datasets. Our experiments show that our method can train without labels and outperforms other approaches for weakly supervised learning.

关键词： weak supervision adversarial learning unsupervised learning constraint learning lagrangian optimization

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Geometric Optimisation of Unmanned Aerial Vehicle Trajectories in Uncertain Environments

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Vehicular Communications 2025年 54卷

作者： Akshya, J. Sundarrajan, M. Amutha, S. Dhanaraj, Rajesh Kumar Khadidos, Adil O. Khadidos, Alaa O. Selvarajan, Shitharth Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Networking and Communications SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia School of Built Environment Engineering and Computing Leeds Beckett University LS6 3HF Leeds United Kingdom Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

The problem of efficient trajectory optimisation for Unmanned Aerial Vehicles (UAVS) in dynamic and constrained environments is one where energy efficiency, spatial coverage, and path smoothness need to be balanced. The existing methods, namely RRT*, A*, and Dijkstra, are popular but generally heuristic and do not provide globally optimal solutions. They face significant limitations while dealing with complex geometries, dynamic obstacles, and multi-objective requirements. These challenges call for a mathematically sound framework that seamlessly integrates convex analysis and computational geometry to provide an optimal trajectory planning framework. This research work introduces a convex optimisation framework for UAV trajectory planning which unifies multiple objectives, like minimising energy consumption, maximising spatial coverage, and ensuring the smoothness of the path, into a single convex objective function. More importantly, it indicates that obstacle dynamics and uncertain environmental conditions are handled better by it, so it is relatively easier for safe and efficient navigation. Proven to converge faster and with higher precision than RRT*, A*, and Dijkstra, the approach proposed here enjoys intrinsic convex properties, which ensure global optimality. Qualitative measurements show the efficiency of the proposed framework. The result is energy efficiency of 90%, with 92% coverage, 98% constraint satisfaction, and 95% path smoothness, which is 15-25% better on all metrics than traditional approaches can offer. By bridging between theory in convex optimisation and practice for solving multi-objective problems in a dynamic setting, this study provides a more robust solution for UAV trajectory planning. © 2025 The Author(s)

关键词： Computational Geometry Convex Optimisation Delaunay Triangulations Spatial Coverage UAV Trajectory Voronoi Diagrams

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Corrigendum to “Molecular subtypes classification of breast cancer in DCE-MRI using deep features” [Expert Syst. Appl. 236 (2024) 121371]

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Expert Systems with Applications 2025年 270卷

作者： Ali M. Hasan Noor K.N. Al-Waely Hadeel K. Aljobouri Hamid A. Jalab Rabha W. Ibrahim Farid Meziane College of Medicine Al-Nahrain University Baghdad Iraq Biomedical Engineering Department College of Engineering Al-Nahrain University Iraq Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nile Street 64001 Thi-Qar Iraq Department of Computer Science and Mathematics Lebanese American University Beirut 1102 2801 Lebanon Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Nicosia/Mersin 10 Turkey Data Science Research Centre School of Computing and Engineering University of Derby United Kingdom

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Cardio-Vascular Disease Prediction using Machine Learning Techniques

Cardio-Vascular Disease Prediction using Machine Learning Te...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Konda, Srinivas Kar, Naresh Kumar Pulicherla, Padmaja Shivakanth, Gandla Ramachandra, C.A. Cmr Technical Campus Department of Data Science Hyderabad India Gitam Deemed to Be University Department of Cse Hyderabad India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

The main goal of this study is to use data Mining Method and Artificial Neural Network to develop a system that can automatically and rapidly predict the risk of coronary heart disease (ANN). The IRT Perundurai Medical College and Hospital's master health checkup data on occupational drivers were used to test this idea (PMCH). Analysis for risk identification is performed in the first stage of the hybrid approach suggested in this study, and level prediction is performed in the second. The sensitivity, specificity, precision, receiver operating curve, area under curve, 10-fold cross validation technique, and the F-measure are used for this investigation. The initial step of the study involves thinking about the most common and changeable dangers. Systolic blood pressure, diastolic blood pressure, and body mass index (BMI) are three biophysical variables, whereas fasting blood sugar, postprandial blood sugar, and triglyceride levels are three blood chemical factors (TG). All of these characteristics have a predetermined margin value that is based on WHO guidelines. Support Vector Machine (SVM), Naive Bayes (NB), and the C4.5 algorithm in Decision Tree are the three approaches used to categorize these variables and forecast the risk (DT). The C4.5 algorithm fared best in forecasting CHD risk when the three approaches were compared using the performance metrics, as discovered by the investigation. The decision tree C4.5 method outperformed the other two classifiers with an improved 99.5% accuracy and 99.67% sensitivity. The increased percentage demonstrates that the Decision tree method delivered consistent results that were better to those produced by the Naive Bayes and SVM models. © 2023 IEEE.

关键词： Decision trees

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