检索结果-内蒙古大学图书馆

Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： Muhammad Ahmad Nawaz Ul Ghani Kun She Muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

来源：评论

学校读者我要写书评

暂无评论

Incremental Weighted Ensemble for Data Streams With Concept Drift

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第1期5卷 92-103页

作者： Jiao, Botao Guo, Yinan Yang, Cuie Pu, Jiayang Zheng, Zhiji Gong, Dunwei School of Electromechanical and Information Engineering Beijing100083 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Victoria University of Wellington School of Engineering and Computer Wellington6140 New Zealand Electromechanical and Information Engineering Beijing100083 China Qingdao University of Science and Technology School of Information Science and Technology Qingdao266061 China

As a popular strategy to tackle concept drift, chunk-based ensemble method adapts a new concept by adjusting the weights of historical classifiers. However, most previous approaches normally evaluate the historical classifier based on an entire chunk newly arrived, which may cause delayed adaptation. To address the issue, two novel ensemble models, named incremental weighted ensemble (IWE) and incremental weighted ensemble for multi-classification (IWE-M), are proposed. At each time step, all base classifiers are incrementally updated on a newly arrived instance. Following that, the instance is collected into a cache array. Once a data chunk is formed, a new base classifier is created. More specially, a forgetting mechanism based on variable-size window is designed to adjust the weight of each base classifier in IWE in terms of its classification accuracy on the latest instances in an online manner. IWE-M, an extension of IWE, aims to solve multiclass problems with local concept drifts. In IWE-M, the weight of a base classifier is expanded to a weight vector. In this way, this ensemble model can retain specific historical information about nondrift regions from a local drift. Experimental results show that the proposed ensemble frameworks outperform six competitive approaches on accuracy and G-mean. © 2020 IEEE.

关键词： Classifiers

来源：评论

学校读者我要写书评

暂无评论

Energy Efficient Unequal Fault Tolerance Clustering Approach

引用

computer Systems science & engineering 2023年第5期45卷 1971-1983页

作者： Sowjanya Ramisetty Divya Anand Kavita Sahil Verma NZ Jhanjhi Mehedi Masud Mohammed Baz Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadIndia School of Computer Science and Engineering Lovely Professional UniversityPhagwaraIndia Department of Computer Science and Engineering Chandigarh UniversityMohaliIndia Department of Computer Science and Engineering Tailor’s UniversityMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia

For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network *** saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor *** of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to *** increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor *** Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster *** data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the ***,the MCH overhead *** the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.

关键词： Energy-efficiency unequal fault tolerant clustering approach particle swarm optimization master data aggregator energy efficient time division multiple access optimal nodes

来源：评论

学校读者我要写书评

暂无评论

A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第2期5卷 530-540页

作者： Huang, Liang Liang, Senjie Ye, Feiyang Gao, Nan College of Computer Science and Technology Zhejiang University of Technology Hangzhou310058 China College of Information Engineering Zhejiang University of Technology Hangzhou310058 China

Intent detection and slot filling are two main tasks in natural language understanding and play an essential role in task-oriented dialogue systems. The joint learning of both tasks can improve inference accuracy and is popular in recent works. However, most joint models ignore the inference latency and cannot meet the need to deploy dialogue systems at the edge. In this article, we propose a fast attention network (FAN) for joint intent detection and slot filling tasks, guaranteeing both accuracy and latency. Specifically, we introduce a clean and parameter-refined attention module to enhance the information exchange between intent and slot, improving semantic accuracy by more than 2%. The FAN can be implemented on different encoders and delivers more accurate models at every speed level. Our experiments on the Jetson Nano platform show that the FAN inferences 15 utterances per second with a small accuracy drop, showing its effectiveness and efficiency on edge devices. © 2023 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

PQTD-TM: Integrating information Retrieval with Machine Learning for effective Placement Topic Discovery 2024

PQTD-TM: Integrating Information Retrieval with Machine Lear...

引用

16th International Conference on Contemporary Computing, IC3 2024

作者： Sardana, Neetu Kumar, Prabudh Goyal, Kritank Rishi Jain, Nehal Arora, Anuja Varshney, Deepika Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

Technical interviews, particularly coding rounds, are pivotal in shaping career trajectories, especially for roles in top-tier companies like Amazon, Google, and Microsoft. Understanding the patterns and nuances of coding problems and their associated tags is essential for effective interview preparation. To aid candidates in excelling in these assessments, we analyzed coding problems encountered in such interviews. We compile a comprehensive dataset of coding problems and their associated tags, sourced from real technical interviews, ensuring reliability and relevance. We propose a topic prediction approach for placement related questions, PQTD-TM (Placement question topic discovery using TF IDF and Machine Learning). The proposed technique integrates information retrieval technique (TF-IDF) with six machine learning classifiers for prediction. We evaluated the predictive performance of various machine learning models, including Logistic Regression, Decision Trees, Naive Bayes, KNN, One-vs-Rest Classifier, and Random Forest. Random forest attained the best prediction accuracy of 93%. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Click Fraud Detection Using Ensemble Classifier 1

引用

3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Purwar, Archana Jain, Art Chawla, Indu Gupta, Ishita Raj, Mayank Jain, Divansh Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (数字)9789819748600

ISBN: (纸本)9789819748594

Online advertising fraud is an emerging issue and has drawn researchers’ mind recently because it now poses a significant danger to an online advertising sector. A content publisher signs an agreement to place advertisements on various websites in exchange for commissions on the basis of the amount of traffic these advertisements generate. It may sometimes motivate fraudulent publishers to produce unauthorized clicks on their websites in order to maximize traffic to the advertisements, a practice known as click fraud. Although click fraud is identified as a huge issue, there are not many algorithms that have been able to effectively solve this problem. Our aim in this paper is to overcome the problem of click fraud and come up with an explicit model for the click fraud detection. In this paper, a model is proposed using recursive feature elimination (RFE) and ensemble classifier with Hellinger distance-based decision tree (HDDT). The proposed model is evaluated over the dataset hosted by a Chinese company TalkingData available freely from Kaggle and has resulted in an accuracy of 99.72% as compared to the traditional decision tree. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Marketing

来源：评论

学校读者我要写书评

暂无评论

Student Placement Prediction Using Machine Learning Algorithms 2nd

Student Placement Prediction Using Machine Learning Algorith...

引用

2nd International Conference on Emerging Trends in engineering and technology, EGTET 2022

作者： Sajwan, Samarth Bhardwaj, Rudraksh Mishra, Revaan Jaiswal, Shruti Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9789819943616

One of the most perplexing issues confronting higher education institutions today is how to increase student placement performance. Placement estimation becomes more complex as the number of educational institutions increases. Educational organizations explore more efficient technologies to assist them in improving their decision-making practices, as well as in developing creative methods. Providing new insight into instructional processes is a critical component in resolving quality issues. Machine learning methods are used to extract information from historical data contained in the libraries of educational organizations. Our model will generate a recommendation system that forecasts the placement level of a student. This model assists an organization’s selection cell in identifying prospective students, assessing their technical and interpersonal abilities, and assisting them in developing them. Students in their pre-final and final years of B. Tech programs may also use this work to determine their individual placement status and probability of achieving it. This enables them to exert additional effort in order to get placements in organizations with higher hierarchies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Virtual Fitness Trainer using Artificial Intelligence 2024

Virtual Fitness Trainer using Artificial Intelligence

引用

16th International Conference on Contemporary Computing, IC3 2024

作者： Gurbuxani, Shrey Gupta, Lakshay Madan, Kapil Department of Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

An AI wellness coach is a software program that guides users through physical health routines by leveraging MediaPipe, OpenCV, and Python features. The program uses OpenCV computer vision techniques to monitor user progress and provide feedback on procedure and frame. To prepare the video data and provide real-time assessments, MediaPipe is used. Additionally, the app makes use of machine learning algorithms to provide users with tailored wellness recommendations and promote adoption. When these advances are combined, clients have an incredibly intelligent and practical way to advance their physical fitness. © 2024 Copyright held by the owner/author(s).

关键词： Python

来源：评论

学校读者我要写书评

暂无评论

Analytical study of the encoder-decoder models for ultrasound image segmentation

Analytical study of the encoder-decoder models for ultrasoun...

引用

作者： Srivastava, Somya Vidyarthi, Ankit Jain, Shikha Department of Computer Science Engineering & Information Technology Jaypee Institute of Information Technology Noida India

Accurate diagnosis and treatment planning for medical conditions rely heavily on the results of medical image segmentation. Medical images are available in many modalities like CT scans, MRI, histopathological, and ultrasound images. Among all, the real-time analysis of the ultrasound is the most complex as the internal organ’s visualization requires experience from the radiologist. Diagnosing the medical conditions and unavailability of experienced radiologists during an emergency requires automated segmentation which heavily depends on computer-aided diagnostic systems. The new generation CAD systems are found to incorporate advanced deep learning algorithms to produce accurate segmentation results. While most of the segmentation models relate to the encoder-decoder model as the base architecture and thus evolve a variety of modifications in its pipeline architecture. This paper presents the analytical study of the various Encoder- Decoder based models like UNet, Residual UNet (Res-U-Net), Dense UNet (DenseUNet), Attention UNet, UNet + +, Double UNet, and U2Net (U-Squared-Net) on ultrasound image segmentation. Further, the paper presents the various trade-offs, application areas, open challenges, and performance analysis of these models on benchmark datasets, namely the HC18 Challenge dataset, CUM dataset, and B-mode Ultrasound Nerve Segmentation dataset. The performance analysis of these models is presented using the six state-of-the-art metrics like Dice coefficient, Jaccard index, sensitivity, specificity, Mean Absolute distance, and Housdorff Distance. Based on the above parameters U2-Net (U-Squared-Net) outperformed all other neural network models for all three datasets. In terms of all four criteria (Dice Coefficient: 0.92, 0.89, 0.9, Jaccard Index: 0.81, 0.79, 0.81, Sensitivity: 0.86, 0.84, 0.86, Specificity: 0.97, 0.95, 0.96), the U2-Net (U-Squared-Net) model performed the best. Over the HC18 Challenge dataset, the CUM dataset, and the B-Mode Ultrasound ne

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Framework for Superior Multi-Modal Medical Image Fusion: Merging Conventional and Deep Learning Methods 2

A Hybrid Framework for Superior Multi-Modal Medical Image Fu...

引用

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Pathak, Disha Mohini Tewari, Tribhuwan Kumar Jaypee Institute of Information Technology Department of Computer Science Engineering & Information Technology Noida India

ISBN: (纸本)9798350373110

Background: Medical imaging has developed into a vital diagnostic tool for radiologists and physicians over the past few decades. However, depending only on one imaging modality frequently yields incomplete data, making it difficult to correctly diagnose illnesses, lesions, and molecular activity. The field of medical image fusion research is expanding due to the increasing need for complete anatomical and physiological data. Method: In this paper, we offer an effective hybrid multi-modal medical image fusion framework. Our system uses a traditional fusion method to first break down the raw images. A pre-trained VGG-16 network processes the obtained coefficients to efficiently extract features. For low-level coefficients, these characteristics are fused using a maximum selection method, and for high-level coefficients, an attention fusion rule. A reverse transformation is then applied to rebuild the fused coefficients. Results: The results of this study show that the proposed strategy performs exceptionally well in terms of entropy and (SSIM) on comparing with different fusion methods. Its third Peak Signal-to-Noise Ratio (PSNR) and second Mutual information (MI) rankings demonstrate its durability and image fusion performance. Conclusion: This hybrid method offers a powerful tool for better diagnosis and analysis by greatly improving the quality and information content of fused medical images. © 2024 IEEE.

关键词： Image fusion

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：