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Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

computer Modeling in engineering & Sciences 2024年第4期139卷 725-739页

作者： Puja S.Prasad Adepu Sree Lakshmi Sandeep Kautish Simar Preet Singh Rajesh Kumar Shrivastava Abdulaziz S.Almazyad Hossam M.Zawbaa Ali Wagdy Mohamed CSE Department Geethanjali College of EngineeringHyderabadTelangana501301India Department of Computer Science LBEF CampusKathmandu44600Nepal School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia CeADAR Ireland’s Centre for AI Technological University DublinDublinD07 EWV4Ireland Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

Pupil dynamics are the important characteristics of face spoofing *** face recognition system is one of the most used biometrics for authenticating individual *** main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,*** proposed model uses pupil characteristics for liveness detection during the authentication *** pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light *** proposed framework consists of two-phase *** the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video *** above measurement is converted into feature space using Kohn and Clynes model-defined *** Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the *** the second phase,we perform the facial recognition ***-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional *** features are scale,rotation,and orientation invariant and are used for recognizing facial *** brute force matching algorithm is used for matching features of two different *** threshold value we considered is 0.08 for good *** analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Err

关键词： SIFT pupil CASIA-SURF pupillary light reflex replay attack dataset brute force

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3D Multi-Object Tracking Employing MS-GLMB Filter for Autonomous Driving 13

3D Multi-Object Tracking Employing MS-GLMB Filter for Autono...

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13th International Conference on Control, Automation and Information Sciences, ICCAIS 2024

作者： Van Ma, Linh Hussain, Muhammad Ishfaq Yow, Kin-Choong Jeon, Moongu School of Electrical Engineering and Computer Science Gist Korea Republic of Large-scale Ai Research Group Korea Republic of University of Regina Faculty of Engineering and Applied Science Canada

ISBN: (纸本)9798331542047

The MS-GLMB filter offers a robust framework for tracking multiple objects through the use of multi-sensor data. Building on this, the MV-GLMB and MV-GLMB-AB filters enhance the MS-GLMB capabilities by employing cameras for 3D multi-sensor multi-object tracking, effectively addressing occlusions. However, both filters depend on overlapping fields of view from the cameras to combine complementary information. In this paper, we introduce an improved approach that integrates an additional sensor, such as LiDAR, into the MS-GLMB framework for 3D multi-object tracking. Specifically, we present a new LiDAR measurement model, along with a multi-camera and LiDAR multi-object measurement model. Our experimental results demonstrate a significant improvement in tracking performance compared to existing MS-GLMB-based methods. Importantly, our method eliminates the need for overlapping fields of view, broadening the applicability of the MS-GLMB filter. Our source code for nuScenes dataset is available at https://***/linh-gist/ms-glmb-nuScenes. © 2024 IEEE.

关键词： Object tracking

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Comparative Analysis of Real-Clinical MRI and BraTS Datasets for Brain Tumor Segmentation 26

Comparative Analysis of Real-Clinical MRI and BraTS Datasets...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Ranjbarzadeh, Ramin Keles, Ayse Crane, Martin Bendechache, Malika Department of Artificial Intelligence Engineering Faculty of Engineering and Natural Sciences Ankara Medipol University Ankara Turkey School of Computing Faculty of Engineering and Computing Dublin City University Ireland ADAPT Research Centre School of Computer Science University of Galway Ireland

ISBN: (纸本)9781837242672

This study compares the BraTS 2020 dataset with a Real-Clinical dataset from Ankara Bilkent City Hospital for brain tumor segmentation. We analyzed histogram attributes and image dimensions, revealing that the Real-Clinical dataset has a more diverse and skewed intensity distribution compared to the uniformity of the BraTS dataset. This inconsistency suggests potential challenges for algorithms trained on BraTS data when applied in clinical settings, which exhibit greater image variation. Additionally, the higher resolution and inclusion of the entire skull in the clinical dataset complicate processing and segmentation, necessitating more robust algorithms. Our research underscores the importance of developing advanced machine-learning tools that can handle the complexity and variability of clinical MRI scans, enhancing diagnostic accuracy and clinical applicability. This study lays the groundwork for improving medical imaging algorithms to ensure their effectiveness in real-world clinical environments. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Image segmentation

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Predictive Modeling and Analysis of Campus Placements Using Machine Learning Techniques 5

Predictive Modeling and Analysis of Campus Placements Using ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Gaurkhede, Sanskruti Burle, Rushikesh Gudadhe, Amit Chopra, PKP Gaitry Datta Meghe Institute of Higher Education and Research Computer science And Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798331530013

A prediction method for campus placements is designed to determine the likelihood that a student will be hired by a firm after completing a campus placement. Numerous parameters are taken into account by the model, which may be used to estimate the student's ability level. Some information is gathered from examinations administered through the placement management system, while other information is gathered from college-level sources such as academic performance, CGPA, pointers, projects, internships, HSC and SSC marks, etc. This placement prediction technique aims to assist students in enhancing their academic performance and outcomes while also helping them to acquire a soft skill that would increase their placement chances. The reputation and annual admissions of the institute are always contingent upon the student placements made by the institute. Thus, in an effort to enhance their organisation as a whole, the majority of schools diligently work to grow their placement department. The goal of this study is to evaluate student placement data from the previous year and use it to calculate the likelihood that current students will be placed on campus. Four distinct machine learning algorithms Logistic Regression, Decision Tree, Support Vector Machine, K Neighbour Classifier, and Random Forest have been tested for this purpose. © 2024 IEEE.

关键词： Self-supervised learning

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Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

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computers, Materials & Continua 2023年第3期74卷 5757-5776页

作者： Nebras M.Sobahi Ahteshamul Haque V S Bharath Kurukuru Md.Mottahir Alam Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)*** light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated *** achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the *** localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the *** develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation ***,the developed algorithm is combined with the PMCU implemented with the experimental setup of *** results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

关键词： Condition monitoring anomaly detection performance evaluation fault classification optimization

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Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign Recognition and Voice Alerts 5

Smart Drive Safe: Harnessing CNN for Enhanced Traffic Sign R...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Priya, D. Divya Chinnasamy, P. Ayyasamy, Ramesh Kumar Kiran, Ajmeera Jalil, Norazira Binti A. Sangodiah, Anbuselvan Mlr Institue of Technology Department of Computer Science Engineering Hyderabad India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Srivilliputtur India Faculty of Information and Communication Technology Perak Kampar Malaysia Quest International University Faculty of Computing and Engineering Perak Ipoh Malaysia

ISBN: (纸本)9798350379945

Traffic signs secure their safety on the roads and the protection of others around them;everyone must be aware of traffic signs and other aspects of road safety. Traffic sign detection is a highly relevant computer vision problem that forms the basis of numerous applications in sectors like the automotive industry. Between classes, traffic signs differ widely in terms of colour, shape, and the use of pictograms or text. The project's classification of traffic signs in an image into various categories is based on a deep neural network model. IoT devices that capture traffic signs and notify users of them are used to build a model. © 2024 IEEE.

关键词： Automotive industry

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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Leveraging ChatGPT for enhanced stock selection and portfolio optimization

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Neural Computing and Applications 2025年第8期37卷 6163-6179页

作者： Huang, Zhendai Liao, Bolin Hua, Cheng Cao, Xinwei Li, Shuai College of Computer Science and Engineering Jishou University Jishou416000 China School of Business Jiangnan University Wuxi214122 China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu90570 Finland VTT-Technology Research Center of Finland Kaitovayla 1 Oulu90570 Finland

The public release of ChatGPT represents a significant milestone in generative AI technology, enabling the autonomous generation of content based on pre-training. This breakthrough presents new opportunities for advancements in the field of portfolio selection. This paper aims to introduce a comprehensive portfolio selection method by applying ChatGPT for stock selection and combining it with optimization algorithms to jointly optimize portfolio selection. Compared to randomly selected stocks, the portfolios optimized using ChatGPT-selected stocks and solved with the egret swarm optimization algorithm (ESOA) demonstrate higher diversification and lower volatility, leading to superior portfolio optimization results. Additionally, to validate ESOA’s superiority, its performance is compared against genetic algorithm (GA) and particle swarm optimization (PSO) on five metrics: risk, expected return, Sharpe ratio, objective value, and penalty term. Under equivalent experimental setting, ESOA exhibits a better ability to balance the relationship between risk and return. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Optimization algorithms

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Myorcadial Infarction Classification with 15 Lead Electrocardiogram (ECG) Signal using Hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BILSTM) 8

Myorcadial Infarction Classification with 15 Lead Electrocar...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Mirza, Ahmad Haidar Nurmaini, Siti Partan, Radiyati Umi Herdiansyah, Muhammad Izman Universitas Sriwijaya Faculty of Computer Science Universitas Bina Darma Faculty of Engineering Palembang Indonesia University of Sriwijaya Intelligent System Research Group Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty of Medicine Palembang Indonesia Universitas Bina Darma Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798350342604

More than three branches of death due to cardiovascular disease. Myocardial infarction (MI) is one of the most dangerous forms of coronary heart disease, with the highest mortality rate among other types of coronary heart disease. MI is caused by reduced oxygen demand in heart muscle tissue when oxygen is pumped throughout the body. To detect MI, experts can use ECG signals. ECG is a tool that can be used to record the activity of biological signals formed due to the electrical activity of the heart. Various studies have been conducted using deep learning to help detect and predict MI. This research proposes a hybrid model of CNN-LSTM and CNN-BILSTM model techniques to predict MI. The MI dataset from the Physiobank (PTB) ECG database consisting of 52 normal subjects and 148 patients was used for training models. The proposed deep learning model uses automatic feature extraction. This study suggests a simple deep learning architecture with automatic feature extraction to learn from data. Evaluation of model performance resulted in an average accuracy of 98.80% for the CNN-LSTM model and 99.90% for the CNN-BILSTM model. © 2023 IEEE.

关键词： Long short-term memory

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PersianRAG: A Retrieval-Augmented Generation System For Persian Language 15

PersianRAG: A Retrieval-Augmented Generation System For Pers...

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15th International Conference on Information and Knowledge Technology, IKT 2024

作者： Hosseini, Hossein Zare, Mohammad Sobhan Mohammadi, Amir Hossein Kazemi, Arefeh Zojaji, Zahra Nematbakhsh, Mohammad Ali Department of Software Engineering University of Isfahan Isfahan Iran ADAPT Research Centre Dublin City University Dublin Ireland Department of Software Engineering Faculty of Computer Engineering University of Isfahan Isfahan Iran

ISBN: (纸本)9798331522254

Retrieval augmented generation (RAG) models, which integrate large-scale pre-trained generative models with external retrieval mechanisms, have shown significant success in various natural language processing (NLP) tasks. However, applying RAG models in Persian language as a low-resource language, poses distinct challenges. These challenges primarily involve the preprocessing, embedding, retrieval, prompt construction, language modeling, and response evaluation of the system. In this paper, we address the challenges towards implementing a real-world RAG system for Persian language called PersianRAG. We propose novel solutions to overcome these obstacles and evaluate our approach using several Persian benchmark datasets. Our experimental results demonstrate the capability of the PersianRAG framework to enhance question answering task in Persian. © 2024 IEEE.

关键词： Modeling languages

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