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IensNet: A novel and efficient approach for iris spoof detection via ensemble of deep models

Multimedia Tools and Applications 2025年 1-30页

作者： Sharma, Deepika Selwal, Arvind School of Computer Science Engineering and Technology Bennett University Greater Noida201310 India Department of Computer Science and Information Technology Central University of Jammu Samba India

Iris biometrics allow contactless authentication, which makes it widely deployed human recognition mechanisms since the couple of years. Susceptibility of iris identification systems remains a challenging task due to diversity in spoof or presentation attacks (PAs) that fails to assure consistency while adopting them in real life scenarios. Hence, iris PAs are the growing concerns that gained significant attention in recent past decade. To alleviate these attacks or recognize presentation attack instruments (PAIs), iris presentation attacks detection (IPAD) algorithms are designed to distinguish a real and fabricated iris trait. Aiming at the efficient iris spoof detection mechanism, in this research work we expound a novel ensemble learning-enabled model (IensNet) that learns three pre-trained and fined-tuned deep models (i.e. DenseNet161, ResNet and VGGNet) for better accuracy and generalized performance. The novel IensNet approach offers several merits (i.e. consolidated strengths of multiple models, improved generalization ability, etc.) as compared to a simple transfer learning strategy where the knowledge is drawn from single pre-trained model. Finally, our approach learns a novel fully-connected dual layer classifier via outcome of three fine-tuned models to yield a final classification result as bonafide or spoof iris trait. Our approach is evaluated on Notre Dame LivDet iris 2017 and Notre Dame contact lenses 2015 anti-spoofing datasets. The experimental analysis of IensNet offers outstanding performance with a lower ACER of 0.2% and 1.4% for Iris-LivDet-2017 and Notre Dame contact lenses 2015 dataset respectively. Besides, IensNet exhibit promising results in cross-dataset environment with an ACA of 91.46%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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A Novel Fuzzy Marine White Shark Optimization Based Efficient Routing and Enhancing Network Lifetime in MANET

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WIRELESS PERSONAL COMMUNICATIONS 2023年第4期132卷 2363-2385页

作者： Devi, K. Lalitha Bhat, C. Rohith Devi, K. Lalitha KSR College of Engineering Tiruchengode Namakkal India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu 602105 India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

A mobile ad hoc network (MANET) is an independent wireless temporary network established by employing a set of mobile nodes (i.e. laptops, smartphones, iPods, etc.) appropriate for the environment in which the network infrastructures are not fixed. The most common problems faced by MANET are energy efficiency, high energy consumption, low network lifetime as well as high traffic overhead which create an impact on overall network topology. Hence, it is necessary to provide an energy-effective CH election to take steps against such issues. Therefore, this paper proposes a novel model to enhance the network lifetime and energy efficiency by performing a routing strategy in MANET. In this paper, an optimal CH is selected by proposing a novel Fuzzy Marine White Shark optimization (FMWSO) algorithm which is obtained by integrating fuzzy operation with two optimization algorithms namely the marine predator algorithm and white shark optimizer. The proposed approach comprises three diverse stages namely Generation of data, Cluster Generation and CH selection. A novel FMWSO algorithm is proposed in such a way to determine the CH selection in MANET thereby enhancing the network topology, network lifetime and minimizing the overhead rate, and energy consumption. Finally, the performance of the proposed FMWSO approach is compared with various other existing techniques to determine the effectiveness of the system. The proposed FMWSO approach consumes minimum energy of 0.62 mJ which is lower than other approaches.

关键词： MANET Cluster head Fuzzy Marine White Shark Cluster generation Network lifetime Energy consumption

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Enhancing Ophthalmic Diagnostics: A New Horizon for OCT Retinal Analysis

Enhancing Ophthalmic Diagnostics: A New Horizon for OCT Reti...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Priyanka, S. Selvam, Prabu Vellore Institute of Technology School of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350361537

Ophthalmic diagnostics play a critical role in the early detection and management of various ocular diseases. Among the advanced imaging modalities employed in ophthalmology, Optical Coherence Tomography (OCT) has emerged as a cornerstone technology for non-invasive, high-resolution imaging of ocular structures. This paper analyzes the efficiency of three deep learning models such as VGG16, ResNet50, and InceptionV3, in Optical Coherence Tomography (OCT) images. This study compares the efficiency of these deep learning models in classifying four different pathological retinal conditions: Normal Retina (NR), Choroidal NeoVascularization (CNV), Diabetic Macular Edema (DME), and Early Age-Related Macular Degeneration (EAMD). In addition, we have performed two preprocessing operations, image enhancement and data augmentation, to improve the image quality and to over the data imbalance, respectively. We assessed the performance of these deep learning models using a benchmark dataset. After the experiments, we found that the VGG16 outperforms ResNet50 and InceptionV3 by accomplishing an accuracy of 98.7%. © 2024 IEEE.

关键词： Optical coherence tomography

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Performance improvement of the hybrid switch reluctance motor by notching method

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Scientia Iranica 2024年第10期31卷 790-797页

作者： Hasanzadeh, Saeed Rezaei, Hossein Taheri, Hadis Department of Electrical and Computer Engineering Qom University of Technology Qom Iran Department of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran Mazandaran Regional Electric Company Sari Iran

Hybrid switch reluctance motors are the family of switch reluctance motors (SRMs) that attenuate the magnetic saturation and increase the air gap magnetic flux by exploiting permanent magnets. The permanent magnet auxiliary air gap flux can affect the average torque and ripple. Commonly, the torque ripples reduction comes with the average torque drop. In this paper, the torque ripple reduction and average torque improvement are achieved for a 6/10 pole hybrid switch reluctance motor by inserting two symmetrical notches on its rotor. Also, the lengths of magnet and rectangular notches are optimized by the finite element method and sensitivity analysis. The comparison of the optimized design with the initial one carried out by finite element proves the efficiency of the proposed model. © 2024 Sharif University of technology. All rights reserved.

关键词： Sensitivity analysis

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Elastic Stack: A Comprehensive Overview 6

Elastic Stack: A Comprehensive Overview

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Shaji, Ayda Sara George, Mithu Mary Toc H Institute of Science and Technology Department of Computer Science and Engineering Kerala India Muthoot Institute of Technology and Science Department of Computer Science and Engineering Kerala India

ISBN: (纸本)9798350381689

This document thoroughly explores the Elastic Stack, comprising Elasticsearch, Logstash, Kibana, and Beats, emphasizing its effectiveness in real-time data analysis, log handling, and visualization. It examines the essential components, architecture, and practical uses in different fields, offering in-depth technical insights into each element, integration methods, and practical examples. Readers will gain a comprehensive grasp of the Elastic Stack's capabilities and its application potential in various scenarios by the conclusion of this paper. © 2024 IEEE.

关键词： Data handling

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A Review on Amalgamation of Textile-based Components for Standardization of Smart Fabrics 5

A Review on Amalgamation of Textile-based Components for Sta...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, Sandeep Haripriya Taneja, Neeti Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Computer Science Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

This article provides an overview of the fundamental ideas and methodologies utilized in the construction of fabric sensors, along with the frequently employed materials and procedures in the field of electronic textiles. It illustrates that depending on the level of integration within the fabric platform, sensing capabilities may be accomplished by either intrinsic or external modifications to textile substrates. The present study showcases the adaptability of fabric sensors in quantifying force, pressure, chemicals, humidity, and temperature fluctuations. The materials, connections, fabric circuits, interconnect, encapsulation, and manufacturing processes used in fabric technologies are very adaptable and diverse. However, they are not as durable as the components used in traditional electronics. The survey results indicate that it is feasible to create a comprehensive smart fabric system by combining various types of functional elements based on textiles. This effort aims to initiate the standardization of smart fabric sensing techniques and e-textile production procedures. © 2024 IEEE.

关键词： e-textiles Fabric sensors textile sensors

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Customer Feedback and Sentiment Analysis for Hotel Services 2

Customer Feedback and Sentiment Analysis for Hotel Services

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Abhyudhay, S.R. Aditya, G.M. Upadya, Achyuta K. Naik, Archana Ushashree, P. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India

ISBN: (纸本)9798350382952

In the era of digital transformation, the hospitality industry faces unique challenges and opportunities. With travellers increasingly relying on online reviews to make informed decisions, the role of sentiment analysis in the hotel sector has never been more critical. This comprehensive research aims to provide a profound understanding of sentiment analysis in the context of hotel reviews and delves into preprocessing and the application of state-of-the-art sentiment analysis techniques. The sentiment analysis model, based on SVM, achieved promising results in categorizing hotel reviews into positive, negative sentiments. The evaluation metrics indicate an accuracy of 82%. Visualizations such as the confusion matrix and ROC curve further demonstrate the model's effectiveness in distinguishing between positive and negative sentiments. The findings of this research shed light on the distribution of sentiments in hotel reviews, providing actionable insights for the hospitality industry. © 2024 IEEE.

关键词： Sentiment analysis

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Integrated Crowd Counting System Utilizing IoT Sensors, OpenCV and YOLO Models for Accurate People Density Estimation in Real-Time Environments 1

Integrated Crowd Counting System Utilizing IoT Sensors, Open...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Jayasingh, Suvendra Kumar Naik, Pintu Swain, Satyaprakash Patra, Kumar Janardan Kabat, Manas Ranjan Institute of Management and Information Technology BPUT Department of Computer Science and Engineering Odisha Cuttack India Indian Institute of Technology Department of Computer Science and Engineering Jammu India

ISBN: (纸本)9798350361780

Real-time crowd monitoring plays a pivotal role in effectively managing public spaces and ensuring safety. This study investigates the fusion of IoT devices and the YOLO object detection model to accurately count crowds. IoT devices facilitate the instantaneous collection of data from cameras, while the YOLO model adeptly identifies individuals within recorded video frames. The study rigorously assesses the performance of three YOLO variants: YOLO V5, YOLO V8 and YOLO V8 NAS. Findings reveal that YOLO V8 NAS surpasses YOLO V5 and YOLO V8 in mean average precision (mAP), achieving an exceptional mAP of 95.1%. This heightened precision is attributed to the integration of Neural Architecture Search (NAS) into the YOLO V8 NAS model, fine-tuning its architecture specifically for crowd counting tasks. It analyzes various networking models proposed in earlier studies for analyzing crowded scenes in public spaces. It emphasizes the potential of a hybrid model involving an IP camera module and a Deep Neural Network for effective crowd sensing. In this setup, the IP camera captures video footage, while the DNN detects individuals and assesses crowd density based on the count of people recognized. This approach presents an encouraging solution for real-time crowd monitoring and management in public environments. © 2024 IEEE.

关键词： Face recognition

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Impact of Variable Sample Size on the Efficiency of Support Vector Machines in Cardiovascular Disease Detection 2

Impact of Variable Sample Size on the Efficiency of Support ...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Sarraju, Vijayalakshmi Pal, Jaya Kamilya, Supreeti Computer Science and Engineering Department Birla Institute of Technology Lalpur Ranchi Mesra India Computer Science and Engineering Department Birla Institute of Technology Ranchi Mesra India

ISBN: (纸本)9798350383867

Exploring the relationship between sample size and the performance of machine learning models in diagnosing cardiovascular diseases (CVD) aids in understanding the optimal data volume required to achieve reliable and accurate diagnostic predictions. This research focuses on examining how the size of the sample influences the efficacy of a particular machine learning algorithm, Support Vector Machine (SVM) in detecting cardiovascular diseases. The efficacy of the SVM models was assessed by employing datasets with different sample sizes. These include one original dataset and two statistically representative samples with sample sizes of 170 and 208 respectively. We evaluate multiple metrics, including F1-score, accuracy, specificity, sensitivity, precision, AUC and ROC curve. Based on the findings, it is evident that a larger sample size generally leads to improved precision and specificity. However, it is important to note that a decrease in sensitivity often accompanies this improvement. In addition, the study highlights the importance of using multiple performance metrics to comprehensively assess the efficacy of SVM models. ©2024 IEEE.

关键词： Support vector machines

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Deep Learning based Air Quality Forecasting using Satellite Data 6

Deep Learning based Air Quality Forecasting using Satellite ...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sree, K. Navya Reddy, Mancharla Rakesh Saraswathi, S. Department of Computer Science and Engineering Specialization with AI & ML Sathyabama Institute of Science and Technology Chennai India Department Computer science and engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Air pollution is a significant threat to human health and the environment. Accurate air quality forecasting is essential for effective mitigation strategies, including public health advisories, emission control measures, and policy interventions. This research focuses on the development of an advanced air quality forecasting system using machine learning techniques. The system uses historical data on pollutants such as PM2.5, NO2, and O3, along with meteorological parameters, to predict future air quality levels. The model is enhanced with the inclusion of sophisticated machine learning algorithms to better capture the intricate spatiotemporal patterns and enhance the accuracy of forecasting. The system may potentially provide real-time air quality forecasts that enable proactive measures for protecting public health and inform decisions for environmental policy. This research contributes to sustainable and resilient urban environments by providing valuable insights into air quality dynamics and supporting effective air pollution mitigation strategies. © 2025 IEEE.

关键词： air pollution Air quality forecasts cardiovascular diseases data-driven strategies environmental policy global climate international collaboration machine learning NO2 O3 PM2.5 policy-making pollution mitigation proactive alerts public health real-time monitoring respiratory diseases spatial variability temporal variability transboundary pollution

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