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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Shete, Shweta G. Koshti, Priyanka Pujari, Vinayak I. Sanjay Ghodawat University Department of Artificial Intelligence and Machine Learning Kolhapur India Sanjay Ghodawat University Department of Computer Science Kolhapur India D.Y. Patil School of Engineering and Management Department of Computer Application Kolhapur India

ISBN: (纸本)9798350351378

This study investigates how learners' performance is affected by AI-driven personalization. This goal compares students' academic performance in AI-driven personalized learning environments to that of students in conventional learning environments. This study shows the rapid development and application of artificial intelligence (AI) in education has led to the emergence of personalized learning platforms that utilize AI to provide tailored learning experiences for individual students. The study shows a beneficial relationship between enhanced academic accomplishment, engagement, and satisfaction with individualized AI-based adaptive learning through quantitative and qualitative analysis. The results demonstrate how AI-driven customization can improve student achievement and change the way that education is delivered. © 2024 IEEE.

关键词： Students

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Feedback Delay-Tolerant Proactive Caching Scheme Based on Federated learning at the Wireless Edge

IEEE Networking Letters

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IEEE Networking Letters 2023年第1期5卷 26-30页

作者： Lin, Na Wang, Yamei Zhang, Enchao Yu, Keping Zhao, Liang Guizani, Mohsen Shenyang Aerospace University School of Computer Science Shenyang110136 China Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan Riken Riken Center for Advanced Intelligence Project Tokyo103-0027 Japan Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

Edge caching has emerged as a promising approach to meet explosive mobile data on 6G networks. One critical issue in edge caching is file popularity prediction. The federated learning (FL) based distributed algorithm is used to predict file popularity, which solves user privacy issues in centralized algorithms. However, due to the heterogeneity of user devices, the feedback time (i.e., the update and upload time of model parameters) is various for each user, which causes the feedback delay in the framework of FL-based edge caching. In this letter, we devise a feedback delay-tolerant proactive caching scheme (FLASH) based on FL in which each user utilizes hybrid filtering on stacked autoencoders to train the prediction model locally. Experimental results indicate that FLASH can effectively handle feedback delay and outperform counterpart algorithms in cache efficiency. © 2019 IEEE.

关键词： Forecasting

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Multi-Keywords Based Fully Homomorphic Encryption and Data Classification for Cloud Security and Privacy 2

Multi-Keywords Based Fully Homomorphic Encryption and Data C...

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2nd IEEE International Conference on Emerging Trends in Engineering and Medical sciences, ICETEMS 2024

作者： Sukeerthi, Kooragayala Kesavan, R. Kalaiselvan, S.A. Saveetha School of Engineering SIMATS Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331529352

When it comes to processing and storing massive amounts of data efficiently, cloud computing has become the dominant paradigm. Both consumers and suppliers of services in cloud settings are understandably worried about the safety of critical data. When it comes to protecting sensitive information during transmission in the cloud, homomorphic encryption has shown to be an effective method. This study presents a new homomorphic encryption method for efficient and safe computations on the cloud, determined by multistage partial homomorphic encryption. The learning using errors (LWE) issue, primarily an altered form of the BVG method, is the basis of the proposed completely homomorphic encryption technique. It is believed to be difficult to resolve the learning with mistakes issue, which is a challenge in machine learning. We were able to implement the technique by making use of MFHE, an encryption method that can process inputs encrypted under different keys. Our expansion of the technique to search for several keywords was based on this. To make data processing in the cloud safe and privacy protecting, the suggested system combines the strengths of multistage encryption with partial homomorphic encryption. © 2024 IEEE.

关键词： Fully Homomorphic Encrypted Scheme Multikey Search Token Searchable Encryption

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An Efficient Biometric Authentication Based on Face Image using Deep Generative Adversarial Network 2

An Efficient Biometric Authentication Based on Face Image us...

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

作者： Aswini, K.R.N. Al-Fatlawy, Ramy Riad Jagadish, Sripelli Ramaswamy, Palanivel Deepak Kumar, B.P. KNS Institute of Technology Department of Artificial Intelligence and Machine Learning Bangalore India The Islamic University Najaf Iraq SR University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of AI and DS Bengaluru India CMR Technical Campus Hyderabad India

ISBN: (纸本)9798350317558

face authentication is one of the technique to prevent the loss of personal data, however the process is challenging due to variations in the patterns of the mouth, nose, eyes, and angles. A face authentication system that leverages an enhanced Deep Generative Adversarial Network (DGAN) is being developed as a solution to the problem. The process begins with preprocessing Histogram Equalization method to enhance the image quality for generator input. GAN generator to create synthetic image using data augmentation method to identify the true face, which is helpful for identifying fake one. Then AlexNet called discriminator to extract the feature of input image and compare the real and fake images to authenticate the person. The implemented DGAN model demonstrates superior performance, achieving Precision of (97.12%), recall of (97.31%) and F1-Score of (97.01%). When compared to previous models like AutoRegressive Biometric Authentication-Oriented Face Detection (ARBAOFD), deep steganography with Celebes (DSC) and Convolutional Neural Network (CNN) © 2024 IEEE.

关键词： AutoRegressive Celebes Convolutional Neural Network Generative Adversarial Network Steganography

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An Efficient Lattice-Based Authentication Protocol for the Vehicular Ad Hoc Network 7th

An Efficient Lattice-Based Authentication Protocol for the...

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7th International Conference on Attacks and Defenses for Internet-of-Things, ADIoT 2024

作者： Chen, Xinyong Chen, Jiageng Luo, Jinquan Liu, Hongwei School of Computer Science and Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Hubei Wuhan430079 China School of Mathematics and Statistics Central China Normal University Hubei Wuhan430079 China

ISBN: (纸本)9783031855924

Vehicular Ad Hoc Networks (VANETs) are crucial for intelligent transportation systems (ITS), yet they face significant security and privacy challenges. The open wireless environment makes VANETs vulnerable to attacks, and existing authentication protocols often have limitations, such as susceptibility to quantum threats or high communication overhead. In this paper, we propose an effective privacy-preserving authentication protocol using lattice-based group signatures for VANETs. Our protocol ensures quantum security and provides robust security features. It not only ensures privacy protection for vehicles but also enables efficient tracing of malicious signers when necessary. In addition, we show that the proposed protocol features small and fixed public key and signature sizes, independent of the number of members, and incurs low communication overhead. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Authentication Protocol

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Life Expectancy Prediction using machine learning Technique - KNN 3

Life Expectancy Prediction using Machine Learning Technique ...

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3rd IEEE World Conference on Applied intelligence and Computing, AIC 2024

作者： Narooka, Preeti Vashistha, Aarushi Mitra, Priyanka Derashri, Gulisha School of Computer Science Engineering Manipal University Department of Artificial Intelligence and Machine Learning Rajasthan Jaipur303007 India Jaipur Engineering College and Research Centre Department of Computer Science and Engineering Jaipur303905 India Jaipur Engineering College and Research Centre Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350384598

In the modern world, life expectancy turns out to be the most valuable factor in determining population's health. Today, it is more crucial to predict the future because life is more precarious due to a variety of circumstances, including social, economic, environmental, educational, lifestyle, and health conditions. The machine learning algorithm KNN for life expectancy is presented in this work. The dataset gathered for this study is separated into a training set (70%) and a test set (30%), and the forecasts precision is examined. To avoid misclassification, KNN needs a larger sample size, and the best K are chosen more in accordance with cross-validation experience than with strong statistical theory. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparing machine learning Algorithms for Real-Time Forest Fire Detection using Color, Motion, and Shape Features 5

Comparing Machine Learning Algorithms for Real-Time Forest F...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Dixit, Rinku Sharma Choudhary, Shailee Lohmor Mahaveerakannan, R. Kaveya, E. New Delhi Institute of Management Department of Artificial Intelligence and Machine Learning Delhi110062 India Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798331518097

Forest fires are extremely dangerous to ecosystems, animals, and humans;thus, it is crucial to detect them early on to limit the harm they cause. Presenting a novel framework for the detection of forest fires is the objective of this study. To increase detection speed and accuracy, this framework mixes colour, motion, and form information with machine learning algorithms. To capture the dynamic nature of fire - its ever-changing hues, distorted forms, and unending motion - an iterative method is employed. Colour probabilities are determined during the segmentation stage before going to the verification step. The categorisation stage and subsequent picture processing both make use of colour histograms. These characteristics allow the system to distinguish between flames and other things more effectively. Evaluating the framework's efficacy involved using intersection over union (IoU) ratios and frame-based evaluation. While IoU is used to test the system's ability to localise the fire inside an image, frame-based assessment is used to test the accuracy of fire detection. Tests on the VisiFire dataset proved the framework's dependability with an 89.97% true-positive rate and a 10.03% false-negative rate. And with an average frame rate of 21.70 FPS, the system is fast enough for real-time processing workloads. Considering these results, it seems like the suggested approach could greatly enhance forest fire early warning capabilities. It can also maintain a high degree of accuracy while providing quicker response times. Protecting forest ecosystems, reducing human life danger, and improving disaster management are all possible outcomes of integrating this technology into early warning systems. This framework is a huge step forward in the world of modern fire detection systems. © 2024 IEEE.

关键词： Fire detectors

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Graph Convolutional Network Combined with Semantic Feature Guidance for Deep Clustering

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Tsinghua science and Technology 2022年第5期27卷 855-868页

作者： Junfen Chen Jie Han Xiangjie Meng Yan Li Haifeng Li Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province the College of Mathematics and Information ScienceHebei UniversityBaoding 071002China School of Applied Mathematics Beijing Normal University ZhuhaiZhuhai 519087China Department of Computer Teaching Hebei UniversityBaoding 071002China

The performances of semisupervised clustering for unlabeled data are often superior to those of unsupervised learning,which indicates that semantic information attached to clusters can significantly improve feature representation *** a graph convolutional network(GCN),each node contains information about itself and its neighbors that is beneficial to common and unique features among *** these findings,we propose a deep clustering method based on GCN and semantic feature guidance(GFDC) in which a deep convolutional network is used as a feature generator,and a GCN with a softmax layer performs clustering ***,the diversity and amount of input information are enhanced to generate highly useful representations for downstream ***,the topological graph is constructed to express the spatial relationship of *** a pair of datasets,feature correspondence constraints are used to regularize clustering loss,and clustering outputs are iteratively *** external evaluation indicators,i.e.,clustering accuracy,normalized mutual information,and the adjusted Rand index,and an internal indicator,i.e., the Davidson-Bouldin index(DBI),are employed to evaluate clustering *** results on eight public datasets show that the GFDC algorithm is significantly better than the majority of competitive clustering methods,i.e.,its clustering accuracy is20% higher than the best clustering method on the United States Postal Service *** GFDC algorithm also has the highest accuracy on the smaller Amazon and Caltech ***,DBI indicates the dispersion of cluster distribution and compactness within the cluster.

关键词： self-supervised clustering graph convolutional network feature correspondence semantic feature guidance confusion matrix evaluation indicator

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Point Cloud Registration of non-rigid objects in sparse 3D Scans with applications in Mixed Reality

arXiv

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arXiv 2022年

作者： Jha, Manorama Artificial Intelligence and Machine Learning School of Computer Science and Mathematics

Point Cloud Registration is the problem of aligning the corresponding points of two 3D point clouds referring to the same object. The challenges include dealing with noise and partial match of real-world 3D scans. For non-rigid objects, there is an additional challenge of accounting for deformations in the object shape that happen to the object in between the two 3D scans. In this project, we study the problem of non-rigid point cloud registration for use cases in the Augmented/Mixed Reality domain. We focus on a special class of non-rigid deformations that happen in rigid objects with parts that move relative to one another in joints, for example, robots with hands and machines with hinges. We propose an efficient and robust point-cloud registration workflow for such objects and evaluate it on real-world data collected using the Microsoft Hololens 2, a leading Mixed Reality device. © 2022, CC BY.

关键词： Mixed reality

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Visual Computing Unified Application Using Deep learning and computer Vision Techniques

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第1期18卷 59-74页

作者： Sowmya, B.J. Meeradevi Seema, S. Dayananda, P. Supreeth, S. Shruthi, G. Rohith, S. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Karnataka Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Karnataka Bengaluru India

Vision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinct class of machine learning algorithms that utilize multiple layers to gradually extract more advanced features from raw input. This is beneficial when using a matrix as input for pixels in a photo or frames in a video. computer vision is a field of artificial intelligence that teaches computers to interpret and comprehend the visual domain. The main functions implemented include deepfake creation, digital ageing (de-ageing), image animation, and deepfake detection. Deepfake creation allows users to utilize deep learning methods, particularly autoencoders, to overlay source images onto a target video. This creates a video of the source person imitating or saying things that the target person does. Digital aging utilizes generative adversarial networks (GANs) to digitally simulate the aging process of an individual. Image animation utilizes first-order motion models to create highly realistic animations from a source image and driving video. Deepfake detection is achieved by using advanced and highly efficient convolutional neural networks (CNNs), primarily employing the EfficientNet family of models. © 2024 by the authors of this article. Published under CC-BY.

关键词： Animation

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