检索结果-内蒙古大学图书馆

Enhancing Malicious QR Code Link Detection with Swarm-Intelligent Squirrel-Search Optimized ANN

Enhancing Malicious QR Code Link Detection with Swarm-Intell...

2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Marappan, Sibi Marappan, Harshini Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600119 India SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur603203 India

ISBN: (纸本)9798350319200

Recently, hackers have made plans to replicate harmful links by tricking people in a variety of ways. By acquiring access to sensitive information they use online, they attempt to remotely manipulate the victims' machines or obtain their data. QR codes are two-dimensional barcodes that can encode different types of data and are viewable by digital devices like smart phones. However, there is no recognized procedure for creating QR codes;therefore they could be vulnerable to a number of dubious assaults. The goal of this study is to use the Swarm-Intelligent Squirrel-Search Optimized Artificial Neural Network (SISSO-ANN) to identify harmful URLs concealed behind 2D QR codes. On the basis of barcode forgery, a cyber attack that may be carried out online has been proposed. Through a number of sites, a dataset of harmful and helpful Uniform Resource Locators (URLs) was produced, and their lexical properties were gathered. The experimental finding demonstrates that SISSO-ANN works better than other current techniques and achieves detection precision value of 93.80%,and accuracy of 93.50% which is much greater than the present secure QR code scanners. Additionally, the application with the least privilege authorization, SISSO-ANN, is the most privacy-friendly. © 2023 IEEE.

关键词： Malicious Links Quick Response (QR) Swarm-Intelligent Squirrel-Search Optimized Artificial Neural Network (SISSO-ANN) Uniform Resource Locators (URLs)

来源：评论

学校读者我要写书评

暂无评论

Real Time Object Detection & Recognition: A ComparativeStudy of YOLOv3 and YOLOv7 in OpenCV 15

Real Time Object Detection & Recognition: A ComparativeStudy...

引用

15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Geethanjali, T.M. Prithviraj, B. Prajwal, K.M. Gowda, Prajwal C.M. Priyanka Sujithra, M. Chaba, Yogesh PES College of Engineering Department of ISE Mandya India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Real-time object detection is a fundamental task in computer vision, finding applications in various domains such as autonomous vehicles, surveillance systems, robotics, and *** proposed work presents the design and implementation of a real-time object detection system using OpenCV (Open-Source computer Vision Library).The system aims to accurately and efficiently detect and localize objects in video streams or captured *** proposed work begins with dataset collection and annotation, acquiring a diverse dataset of images with annotated bounding boxes representing objects of *** annotated dataset is used for model training and *** deep learning algorithms are considered for object detection, including Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO), and Faster R-CNN, and their performance is compared to identify the most suitable *** techniques like resizing, normalization, and noise reduction are applied to enhance the quality of the input *** extraction is performed using deep learning models VGG16, which is fine-tuned on the annotated *** selected deep learning model is integrated into the real-time system using OpenCV's *** system is evaluated using standard metrics like precision, f1 score, recall, and mean average precision (mAP) to assess its detection *** evaluation is carried out on benchmark datasets and real-world scenarios to gauge the system's robustness and generalization capabilities using two different YOLO models i.e., YOLOv3 and YOLO v7. © Grenze Scientific Society, 2024.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Intelligent Deep Convolutional Neural Network Based Object DetectionModel for Visually Challenged People

引用

computer Systems science & engineering 2023年第9期46卷 3191-3207页

作者： S.Kiruthika Devi Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Mohamed K.Nour Ahmed Ashour Anwer Mustafa Hilal Department of Computing Technology SRM Institute of Science and TechnologyIndia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Engineering Mathematics and Physics Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial Intelligence(AI)and computer Vision(CV)advancements have led to many useful methodologies in recent years,particularly to help visually-challenged *** detection includes a variety of challenges,for example,handlingmultiple class images,images that get augmented when captured by a camera and so *** test images include all these variants as *** detection models alert them about their surroundings when they want to walk *** study compares four CNN-based pre-trainedmodels:ResidualNetwork(ResNet-50),Inception v3,DenseConvolutional Network(DenseNet-121),and SqueezeNet,predominantly used in image recognition *** on the analysis performed on these test images,the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and *** further improve the performance of the Inception v3 model,the thermal exchange optimization(TEO)algorithm is applied to tune the hyperparameters(number of epochs,batch size,and learning rate)showing the novelty of the *** accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer,hyperparameter optimizer,and factorization ***,Inception V3 can handle images of different *** makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things(IoT)-based devices.

关键词： Pre-trained models object detection visually challenged people deep learning Inception V3 DenseNet-121

来源：评论

学校读者我要写书评

暂无评论

Improving Biometric Security Using PulsePrint: Real-Time Defense Against Fingerprint Spoofing 2

Improving Biometric Security Using PulsePrint: Real-Time Def...

引用

2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Vigneshwaran, P. Maranco, M. Ivaturi, Kashyapa Abhiram Takur, Purab Acharya University Department of Computer Science and Engineering Karakul Bukhara Uzbekistan SRM Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Networking and Communications Chengalpattu Kattankulathur603203 India

ISBN: (纸本)9798350365269

Biometric authentication systems have become increasingly popular due to their convenience and perceived security benefits. However, traditional biometric modalities such as fingerprint recognition are vulnerable to spoofing attacks, where fake fingerprints can be used to deceive the system. In this paper, we propose PulsePrint, a novel biometric authentication solution that enhances security by incorporating real-time pulse detection alongside fingerprint recognition. By leveraging the unique pulsatile nature of blood flow in the fingertip, PulsePrint adds an additional layer of authentication, making it significantly more difficult for attackers to spoof fingerprints. We present the design and implementation of the PulsePrint system, detailing the integration of pulse detection algorithms with existing fingerprint recognition technologies. Furthermore, we evaluate the performance of PulsePrint through extensive experimentation, including testing against various spoofing techniques and benchmarking against conventional fingerprint recognition systems. Our results demonstrate that PulsePrint offers robust protection against fingerprint spoofing attacks while maintaining user convenience and real-time authentication speed. Overall, PulsePrint represents a promising advancement in biometric security, offering enhanced protection for applications requiring reliable and secure authentication. © 2024 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Task Scheduling based on Whale Optimization Algorithm Cyber-Physical Systems 2

Energy-Efficient Task Scheduling based on Whale Optimization...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Al-Jawahry, Hassan M. Shankar, Smitha Sharath Chythanya, Kanegonda Ravi Maharajan, K. Swathi, Akuthota The Islamic University Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Sr University School of Computer Science and Artificial Intelligence Warangal India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Krishnankoil India Cvr College of Engineering Department of CSE Telangana India

ISBN: (纸本)9798350317558

The extensive application of Cyber-Physical Systems (CPS) requires efficient optimization of computational units and physical plants. Task scheduling (TS) is critical to improving resource utilization and system efficacy within CPS. Standard task schedulers in embedded real-time techniques fall short of achieving CPS performance criteria due to task diversity and system heterogeneity. This research proposes a novel task scheduling technique called Whale Optimization Algorithm based Energy-Efficient TS (WOA-EETS) that is specifically designed for the CPS context. WOA-EETSS involves assigning number of autonomous tasks to various resources during the TS process. The objective function focuses on reducing task completion time and optimizing resource use. A series of simulations were run to demonstrate WOA-EETSS' improved performance. The comparison analysis demonstrated that the WOA-EETSS system performed better across a variety of parameters. The proposed approach attains the better performance in Task Execution (ms) of 347ms, Convergence Rate of 511ms and Average Load Balancing Ratio (%) of 69.91% from 100 iterations. © 2024 IEEE.

关键词： Cyber-Physical Systems Energy Efficient Task Diversity Task Scheduling Whale Optimization Algorithm

来源：评论

学校读者我要写书评

暂无评论

FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring

引用

Journal of computer science & Technology 2023年第5期38卷 970-984页

作者：胡春雨忽丽莎袁林陆佃杰吕蕾陈益强 Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 25000China Institute of Information Technology Hebei University of Economics and BusinessShijiazhuang 050061China School of Information Science and Engineering Shandong Normal UniversityJinan 250358China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power ***,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy *** first challenge addressed in this paper is communicating in a privacy-preserving manner among different *** second technical challenge is handling the dynamic expansion of the federation without model *** address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model *** on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical *** introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model *** experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the *** opens up opportunities for cooperation between different organizations in wearable health monitoring.

关键词： federated learning incremental learning random forest wearable health monitoring

来源：评论

学校读者我要写书评

暂无评论

Multi-Level Analysis for Pain Recognition from Physiological Signals 2

Multi-Level Analysis for Pain Recognition from Physiological...

引用

2nd International Conference on computer, Communication and Control, IC4 2024

作者： Behera, Chandan Kumar Padhy, Dushmant Vit Bhopal University School of Computing Science & Engineering Madhya Pradesh Sehore India Roland Institute of Technology Department of Mca and Computer Science Odisha Berhampur India

ISBN: (纸本)9798350387933

This paper addresses the need for automatic pain recognition in healthcare without relying on expert feature extraction from physiological signals. Instead, it introduces a deep learning approach that combines classification and feature extraction methods by incorporating multi-level information for accurate pain discrimination. Experimental results are based on BioVid Heat Pain database and Emopain-2021 dataset, which demonstrate its superiority in high accuracy over conventional methods. Notably, in the classification of Pain-0 and Pain-4, the approach achieves average of 84.8% and 87.8% accuracies in cross-validation tests. This research harnesses the power of deep learning to improve pain recognition using physiological signals, eliminating the need for extensive medical expertise. © 2024 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach for Electricity Load Forecasting using Hybrid Deep Learning Models 2

A Novel Approach for Electricity Load Forecasting using Hybr...

引用

2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Shyam Prasath, J. Natarajan, B. Venkatraman, K. Nallusamy, M. Revathi, M. Muruganantham, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Tamilnadu Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India University College of Engineering Bharathidasan Institute of Technology Campus Anna University Department of Computer Science and Engineering Tamilnadu Tiruchirappalli India K. Ramakrishnan College of Engineering Dept. of Ece Tamil Nadu Tiruchirappalli621112 India

ISBN: (纸本)9798350383287

The importance of electricity in both daily lives of people and the economy of the country has made it a vital commodity. Accurate forecasting of electricity load is crucial to regulate production and meet demands at different times. This research work uses the electricity load data of Spain which had samples for every hour in a day for four years and opted for the multivariate forecasting approach instead of treating the features as multiple univariate sequences. The data was broken down and windowed with features such as price of electricity, hour of the day, day of the week and so on. Numerous research studies have shown that traditional statistical methods fail to capture the complex relations, however neural networks are able to learn features through extraction of spatial relationships in the time series data. In order to model the dependencies, Recurrent Neural Networks (RNN) which are the base of Long Short-Term Memory Networks (LSTM) are commonly used. The proposed research uses multiple hybrid models such as stacked LSTMs with CNN (Convolutional Neural Networks) with and without skip connections. As shown by previous works this model can support long input sequences as it can be read as sub-sequences by the CNN and pieced together by the RNN thus capturing details and interconnections between multivariate factors. This research work analyzes and compare the performances of these models on complex multivariate time series data by making them predict energy loads for different time periods and calculating errors. This approach also deeply examined the strength and weakness of each of the models and their performance patterns and yields outstanding performance. © 2024 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Recognition and Classification of Apple and Sugarcane Plant Leaf Diseases using SVM with DAE Models 2

Recognition and Classification of Apple and Sugarcane Plant ...

引用

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

作者： Sunil Kumar, V. Jyothi, A.P. Sumanth, S. Nagamani, H.S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Peenya Bengaluru India Department of Computer Science and Computer Applications Kolar India Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350382952

For this leaf disease study, apple and sugarcane plants are identified and classified using a mix of character features and in-depth automated coding. Farm sugar cane leaves were copied for the online apple Plant Village Dataset. Pre-process images to reduce size and improve HLG filters. Image segmentation using DWT and GLCM feature extraction follows. Like the Low-Low (LL) band, DWT images produce 4-orthogonal subbands. Deep Auto Encoder and SVM algorithms retrieved subgroup functions and identified diseases from training data. If classification findings are ignored, the division stage receives data with HL, LH, and HH bands to explain approach consistency. Deep Auto encoder classifies illness using training data after extracting sub-band features. The Auto Encoder and SVM classifier demonstrated that the proposed model outperformed alternatives in LL band. Other subbands performed worse than the LL band. This study found that LL sub band data provided fine image data for better sickness categorization than HH, HL, and LH sub band data. © 2024 IEEE.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

引用

computers, Materials & Continua 2023年第3期74卷 6335-6349页

作者： Ibrar Amin Saima Hassan Samir Brahim Belhaouari Muhammad Hamza Azam Institute of Computing Kohat University of Science and TechnologyKohat26000K.P.KPakistan College of Science and Engineering Hamad bin Khalifa UniversityDohaQatar Centre of Research in Data Science Computer and Information Sciences DepartmentUniversiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia

Malaria is a lethal disease responsible for thousands of deaths worldwide every *** methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and *** automated diagnosis of diseases is progressively becoming *** deep learning models show high performance in the medical field,it demands a large volume of data for training which is hard to acquire for medical ***,labeling of medical images can be done with the help of medical experts *** recent studies have utilized deep learning models to develop efficient malaria diagnostic system,which showed promising ***,the most common problem with these models is that they need a large amount of data for *** paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer *** proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning *** of the proposed model is evaluated on a publicly available dataset of blood smear images(with malariainfected and normal class)and achieved a classification accuracy of 96.6%.

关键词： Generative adversarial network transfer learning semi-supervised malaria VGG16

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：