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共 71201 条记录，以下是321-330 订阅

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Secure Messaging in Post Quantum Cryptography using NTRUEncryption 15

Secure Messaging in Post Quantum Cryptography using NTRUEncr...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kaveri, S.P. Radhakrishnan, Indu Honnavalli, Prasad B. Pes University Dept. Computer Science Bangalore India

ISBN: (纸本)9798350370249

QuantumChat is a real-time messaging application designed to address the emerging threat of quantum computing to traditional encryption. By utilizing post-quantum encryption methods, the software guarantees confidential and safe communication. Hybrid cryptography facilitates a smooth transition for consumers by enabling backward compatibility with traditional encryption techniques. Combining state-of-the-art security with intuitive features, QuantumChat provides a next-generation solution for safe communication in a rapidly changing digital environment. The hybrid cryptography approach strikes a compromise between compatibility and security by combining both classical and post-quantum encryption. This makes it possible for users to use QuantumChat without compromising their ability to safely interact with others using conventional cryptographic techniques. With regard to secure communications, the QuantumChat software is a major advancement that tackles the impending risks associated with quantum computing. In the rapidly changing world of digital security, QuantumChat seeks to provide consumers a private and secure communication platform by fusing cutting-edge encryption technology with an intuitive user interface. © 2024 IEEE.

关键词： Quantum computers

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Analysis of Scanline and Minimum Entropy Selection Heuristics in Model Synthesis and Wave Function Collapse 5

Analysis of Scanline and Minimum Entropy Selection Heuristic...

引用

5th International Conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2024

作者： Harvey, Lucas Srivastava, Gautam Brandon University Dept. of Math and Computer Science Canada

ISBN: (纸本)9798350354690

In this paper, we analyze two versions of a texture synthesis algorithm, study the cases in which they fail to produce a successful result and present modifications that could be made to lessen their rates of failure. This algorithm, Model Synthesis, and its variation Wave Function Collapse are designed to take in a small sample input image, or set of image constraints, and produce a larger pseudorandom output image in which every region of the output image is locally similar to an element of the input image. Both versions of the algorithm accomplish this task by considering their output image as a grid of cells with each cell initially in a superposition of all possibilities for itself and resolving cells one by one until all cells have been resolved from their superposition to a fixed value. One of the key differences between the two versions of the algorithm is the order in which cells are selected to be resolved, one simply selects in a scanline order, while the other resolves first those cells that have the minimum entropy, and thus which we can be most certain of their eventual state. For many inputs, the minimum entropy model reaches a state in which its output is not consistent with the input and thus fails, while the scanline model does not. This paper looks at the cases in which this occurs and concludes that this is often caused by the minimum entropy model creating regions of elevated constraints in its solution. Finally, it presents a possible alteration to the algorithm which allows a minimum entropy model to avoid this manner of failure among a subset of test cases. © 2024 IEEE.

关键词： Image segmentation

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Intelligent Multimodal Human Behavior Recognition using Inertial and Video Sensors

Intelligent Multimodal Human Behavior Recognition using Iner...

引用

2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Abro, Iqra Aijaz Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331510503

Tracking human actions, especially in the field of medicine, has encouraged interest in computer vision, specifically for detecting gait anomalies to aid in rehabilitating patients with irregular patterns. In this paper, the proposed system employs vision-based and inertial-based modalities that enable the recording of human actions and encourage human-machine collaboration. The inertial sensors were preprocessed using a Butterworth filter, as its magnitude response is often monotonic and maximally flat in the passband, providing smoothness. Multiple key features, such as Mel Frequency Cepstral Coefficients (MFCC) and auto-regression coefficients have been extracted. On the other hand, human detection has been performed for vision-based data, and main features such as angles and velocities have been calculated. The final results have been fused using multimodal survey fusion. The fused data was then forwarded for optimization using yeo-johnson power optimizer. Artificial Neural Networks (ANN) have been trained to perform classification. Evaluation using the confusion matrix on the Heriot-Watt University of Sao Paulo (HWU-USP) dataset proficient of identifying nine complex actions showed an accuracy rate of 83.33%. © 2024 IEEE.

关键词： Butterworth filters

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RGB-D Scene Classification: A Unified Framework with Vision Transformers and Contextual Models 3

RGB-D Scene Classification: A Unified Framework with Vision ...

引用

3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Ahmed, Muhammad Waqas Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331504861

Indoor scene classification poses a significant challenge, crucial for advancements in robotics, augmented reality, and smart environments. This paper presents an innovative model designed for classifying indoor scenes using RGB-D images. The model seamlessly integrates unsupervised segmentation, Vision Transformers (VT), convolutional neural networks, and conditional random fields. The workflow starts with segmenting the RGB-D data through the DBSCAN algorithm, followed by feeding the segmented images into a Vision Transformer for feature extraction. The VT features two branches: one dedicated to capturing fine-grained local patch representations and another focused on modeling long-range dependencies through self-attention mechanisms. The resulting features are then processed by a ResNet-18 architecture for multi-object classification, enhancing the understanding of the scene's content. Ultimately, the classified objects and contextual information from the VT's relation modeling branch are merged within a Conditional Random Field framework for comprehensive scene classification. Testing on the NYUDv2 and SUN RGB-D datasets reveals that our approach significantly outperforms existing techniques in accurately classifying diverse indoor environments. This methodology sets the stage for enhanced scene understanding, with promising applications in robotics, augmented reality, and smart environments. ©2024 IEEE.

关键词： Convolutional neural networks

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Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey

Recent Advancements in Microscopy Image Enhancement using De...

引用

2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Dutta, Debasish Sonowal, Neeharika Barauh, Risheraj Chetia, Deepjyoti Kalita, Sanjib Kr Gauhati University Dept. of Computer Science Assam India

ISBN: (纸本)9798350376876

Microscopy image enhancement plays a pivotal role in understanding the details of biological cells and materials at microscopic scales. In recent years, there has been a significant rise in the advancement of microscopy image enhancement, specifically with the help of deep learning methods. This survey paper aims to provide a snapshot of this rapidly growing state-of-the-art method, focusing on its evolution, applications, challenges, and future directions. The core discussions take place around the key domains of microscopy image enhancement of super-resolution, reconstruction, and denoising, with each domain explored in terms of its current trends and their practical utility of deep learning. © 2024 IEEE.

关键词： Image denoising

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A Robust Fraud Detection System using Deep Belief Network and Restricted Boltzmann Machines 7

A Robust Fraud Detection System using Deep Belief Network an...

引用

7th International Conference on Signal Processing and Information Security, ICSPIS 2024

作者： Javeed, Madiha Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350368673

Online security has become popular as it delivers enriched monitoring services for digital world without obstructing the usage of online services. Several techniques have been introduced to increase the quality of security systems for online faulty transactions detection. An effective model has been proposed in this research by using various domain data learning processes, deep belief networks, and restricted Boltzmann machines to provide improved transactions monitoring. Two datasets have been utilized in proposed methodology to ensure that online systems are safe and secure. The datasets used are credit card fraud detection and abstract data set for credit card fraud detection. Deep belief networks and restricted Boltzmann machines combination has helped us improve the results of proposed system. An innovative prospect to researchers has been provided by proposing a security mechanism for credit cards-based fraud detection. It has been proved that the deep belief network has been able to outperform by achieving 99.2% mean accuracy. © 2024 IEEE.

关键词： Economic and social effects

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Intervention and Conditioning in Causal Bayesian Networks 38

Intervention and Conditioning in Causal Bayesian Networks

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Galhotra, Sainyam Halpern, Joseph Y. Computer Science Dept. Cornell University United States

Causal models are crucial for understanding complex systems and identifying causal relationships among variables. Even though causal models are extremely popular, conditional probability calculation of formulas involving interventions pose significant challenges. In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy of mechanisms that determine interventions to calculate a range of probabilities. We show that by making simple yet often realistic independence assumptions, it is possible to uniquely estimate the probability of an interventional formula (including the well-studied notions of probability of sufficiency and necessity). We discuss when these assumptions are appropriate. Importantly, in many cases of interest, when the assumptions are appropriate, these probability estimates can be evaluated using observational data, which carries immense significance in scenarios where conducting experiments is impractical or unfeasible. © 2024 Neural information processing systems foundation. All rights reserved.

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Gender Classification from Thermal Images using CNN

Gender Classification from Thermal Images using CNN

引用

2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Gurram, Harika Kethineni, Keerthi Kommineni, Deva Harshini Soumya, Kakani VRSEC Dept. Computer Science & Engineering Vijayawada India

ISBN: (纸本)9798350361537

In this study, two deep learning architectures - AlexNet and InceptionV3 - are used to evaluate gender categorization using thermal imagery. One of the most promising techniques for classifying gender is thermal imaging, which offers distinct insights into physiological traits. Divided into separate training and testing subsets, the dataset is made up of carefully preprocessed thermal pictures. InceptionV3, known for its complex design combining inception modules, and AlexNet, a pioneering convolutional neural network (CNN), are refined on the training data to adjust to the complexities of gender categorization. Then, in order to measure the performance of the trained models, they are rigorously evaluated on the testing set. Specifically, their accuracy is evaluated. Experimental findings show that InceptionV3 performs better than AlexNet, with an accuracy of 92.3% as opposed to 82.6% for AlexNet. This significant disparity highlights how much better InceptionV3 is at identifying subtle thermal patterns and characteristics, which improves gender categorization accuracy. The work makes a substantial contribution to the rapidly developing area of thermal imagingbased gender categorization by highlighting how crucial it is to use advanced deep learning architectures in order to improve performance and accuracy. Subsequent investigations might delve into novel methodologies, including multi-modal fusion or sophisticated methods, to enhance precision and resilience in tasks using thermal-based gender categorization. © 2024 IEEE.

关键词： AlexNet Convolutional Neural networks Deep learning Gender classification Inception V3 Thermal images

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GlobalLearn: An Efficient Multilingual Textual Summary for Education Videos 14

GlobalLearn: An Efficient Multilingual Textual Summary for E...

引用

14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Shetty, Pratham R. Prateek, M. Krishna, Anirudh Ranjive, R. Begum, M. Farida Dept. Computer Science and Technology PES University Bengaluru India

ISBN: (纸本)9798331531935

Educational videos have become a preferred medium for learning, offering advantages such as self-paced study, the ability to revisit concepts, and a distraction-free environment. However, the effective- ness of such videos can be amplified through concise, multilingual textual summaries. This paper introduces GlobalLearn, an AI powered educational video to text summarization application that integrates speech recognition, optical character recognition, and NLP-driven large language models(LLMs) to extract and summarize key concepts from educational videos. By integrating audio and visual transcripts, time-stamped summaries, and multilingual capabilities, GlobalLearn ensures acces- sibility for diverse global audiences. This user-centric approach not only supports efficient learning and revision, but also bridges linguistic barriers, fostering an inclusive educational ecosystem. The paper explores GlobalLearn’s design and technical architecture, and core functionalities to outline its potential impact on modern education. © 2025 IEEE.

关键词： Speech recognition

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Stock Price Prediction: A Systematic Review

Stock Price Prediction: A Systematic Review

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2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Emmanuel, Igbekele Oluwambo Tolulope, Olowe Omobolanle V, Taiwo Landmark University Dept. of Computer Science Omuaran Nigeria

ISBN: (纸本)9798350358155

Stock price is referred to as the price of a single share of a publicly traded company's saleable equity shares. It is not fixed and varies with market conditions. A share's price is the highest amount that someone is willing to pay for the stock or the lowest amount that it can be purchased for. Therefore, the prediction of stock prices is important to aid decision-making and to understand market trends. This review article aims to compare previous works that made use of various machine learning models in predicting Stock Prices. The models' accuracy, precision and as well their advantages and limitations are all discussed. The difficulties encountered in applying machine learning models for Stock Price prediction, as well as potential solutions, are also discussed. The article emphasizes the importance of ongoing research in this area as well as the advantages of using Support Vector Machine (SVM) and Recurrent Neural Network (RNN) models to improve the prediction of Stock Price. © 2024 IEEE.

关键词： Prediction models

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