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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 763 条记录，以下是171-180 订阅

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GNP ATTACK: TRANSFERABLE ADVERSARIAL EXAMPLES VIA GRADIENT NORM PENALTY 30

GNP ATTACK: TRANSFERABLE ADVERSARIAL EXAMPLES VIA GRADIENT N...

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30th IEEE international conference on Image processing (ICIP)

作者： Wu, Tao Luo, Tie Wunsch, Donald C. Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO USA

ISBN: (纸本)9781728198354

Adversarial examples (AE) with good transferability enable practical black-box attacks on diverse target models, where insider knowledge about the target models is not required. Previous methods often generate AE with no or very limited transferability;that is, they easily overfit to the particular architecture and feature representation of the source, white-box model and the generated AE barely work for target, blackbox models. In this paper, we propose a novel approach to enhance AE transferability using Gradient Norm Penalty (GNP). It drives the loss function optimization procedure to converge to a flat region of local optima in the loss landscape. By attacking 11 state-of-the-art (SOTA) deep learning models and 6 advanced defense methods, we empirically show that GNP is very effective in generating AE with high transferability. We also demonstrate that it is very flexible in that it can be easily integrated with other gradient based methods for stronger transfer-based attacks.

关键词： Adversarial machine learning Transferability Deep neural networks Input gradient regularization

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Building a Multi-Objective Recommender System Using machine learning Based on Light Gradient-Boosting machine 23

Building a Multi-Objective Recommender System Using Machine ...

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6th international conference on Big Data Technologies, ICBDT 2023

作者： Li, Jiaxiang Rutgers School of Communication and Information Rutgers University-New Brunswick NJ08901 United States

ISBN: (纸本)9798400707667

In the scientific field of big data, creating a recommender system that effectively balances diverse user preferences represents a significant challenge. As a response to this challenge, this paper presents a comprehensive methodology to build a multi-objective recommender system using the power of machine learning, particularly focusing on the Light Gradient-Boosting machine (LightGBM). Using the OTTO product dataset from a Kaggle competition, we delve into the process of data preprocessing, feature engineering, and model selection, in an effort to build an optimal, multi-objective recommender system. Our single-model approach, grounded in LightGBM, allows us to adeptly handle the vast and complex dataset. this method, while conceived for the OTTO competition, contributes a broader insight into the development of recommender systems that cater to multiple objectives. through rigorous evaluation, our work demonstrates that this approach substantially enhances performance in terms of accuracy and processing speed, providing not only an effective solution for the competition's problem statement but also a significant advancement in the field of recommender system technology. © 2023 ACM.

关键词： Recommender systems

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An Improved Positioning System for 6G Cellular Network 26

An Improved Positioning System for 6G Cellular Network

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26th international conference on Computer and Information Technology, ICCIT 2023

作者： Islam Sifat, Md. Zahidul Mostafa Amir Faisal, Muhammad Hossain, Md. Mujahid Islam, Md. Aminul International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh

ISBN: (纸本)9798350359015

Information about the whereabouts of oneself or our cell phones has always been crucial to humans. Technological advancements have supported us across various domains, spanning from interior environments to outdoor global positioning systems. However, achieving real-time indoor positioning has remained a formidable task. the requirement for enhanced positioning has gained prominence in the context of the emergence of 6G cellular networks. the introduction of novel radio technologies, characterized by reduced end-to-end latency, specialized control protocols, and increased processing capacity at the network edge, has opened doors to harnessing the 6G cellular network's full potential for precise localization in indoor and outdoor settings. Within the 6G cellular network context, this study successfully implemented the classic signal fingerprinting approach using the Received signal Strength Indicator in combination with machine learning algorithms. Consequently, it proposed an improved positioning strategy designed for indoor localization scenarios in the next 6G cellular network environment. Using this method, with two datasets, the mean error distance of 0.08025523, meaning an accuracy of 99.92% and 0.10692412 was achieved, which is 99.89% accuracy. © 2023 IEEE.

关键词： 6G positioning cellular network localization algorithm machine learning signal fingerprint

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Deep learning for Spectrum Sensing of Sub-Nyquist Sampled signals 20

Deep Learning for Spectrum Sensing of Sub-Nyquist Sampled Si...

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20th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Li, Shiyao Ji, Hongbing Li, Lin Lu, Jian School of Electronic Engineering Xidian University Xi'an China College of Mathematics and Statistics Shenzhen University Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen China

ISBN: (纸本)9798350356328

Traditional Nyquist rate sampling faces significant challenges as communication technology progresses towards 6G. Multi-coset sampling emerges as a viable solution by reducing the sampling rate. However, this method necessitates signal restoration before processing. In response, an intelligent spectrum management system has been developed that utilizes deep learning to directly extract semantic information from multi-coset sampled signals, eliminating the need for complex data preprocessing. the system integrates multi-band spectrum sensing and modulation recognition, achieving an impressive 99% accuracy in both modulation recognition of single signals and sub-band positioning of multiple signals. © 2024 IEEE.

关键词： signal modulation

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the Uniqueness Problem of Physical Law learning 48

The Uniqueness Problem of Physical Law Learning

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Scholl, Philipp Bacho, Aras Boche, Holger Kutyniok, Gitta Department of Mathematics Ludwig-Maximilians-Universität München Germany Institute of Theoretical Information Technology Technical University of Munich Germany Bmbf Research Hub 6G-life Technical University of Munich Germany Department of Physics and Technology University of Tromsø Norway Germany

ISBN: (纸本)9781728163277

Physical law learning is the ambiguous attempt at automating the derivation of governing equations with the use of machine learning techniques. this paper shall serve as a first step to build a comprehensive theoretical framework for learning physical laws, aiming to provide reliability to according algorithms. One key problem consists in the fact that the governing equations might not be uniquely determined by the given data. We will study this problem in the common situation that a physical law is described by an ordinary or partial differential equation. For various different classes of differential equations, we provide both necessary and sufficient conditions for a function from a given function class to uniquely determine the differential equation which is governing the phenomenon. We then use our results to determine in extensive numerical experiments whether a function solves a differential equation uniquely. © 2023 IEEE.

关键词： learning differential equations machine learning this conference paper displays the most important results from the longer version [1] physical law learning the proofs can also be found in [1]

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Epileptic Seizure Detection using XGBC with SMOTE+ENN: Epileptic Seizure Detection through ML algorithms 6

Epileptic Seizure Detection using XGBC with SMOTE+ENN: Epile...

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6th international conference on Digital Medicine and Image processing, DMIP 2023

作者： Nagarajan, Sangeetha Rajangam, Vijayarajan Khan, Faiz Shukla, Mridul Singh, Manas School of Computer Science Engineering Vellore Institute of Technology Chennai India Centre for Healthcare Advancement Innovation and Research Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai Chennai India

ISBN: (纸本)9798400709425

Epilepsy is a disorder of the brain's nervous system in which irregular brain activity results in strange behavior, sensations, and at times loss of awareness. this paper investigates various machine learning (ML) models for the classification of Epileptic Seizure (ES) from electroencephalogram signals. Five classes in the ES dataset are converted into two prominent classes using synthetic minority oversampling with edited nearest neighbor technique and the ML algorithms are experimented on the two classes of the balanced dataset. the performance analysis with the standard metrics reveals that the extreme gradient boosting classifier performs better than other ML models with 98.65% accuracy. © 2023 ACM.

关键词： machine learning

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Detection of Stress over Social Interactions Using machine learning and Natural Language processing 6

Detection of Stress over Social Interactions Using Machine L...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Aishwarya, G. Kavitha, B. Mittalakod, Purnima S Sunitha, K. Ambore, Bhagyashree RNS Institute of Technology Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350304480

In the field of natural language processing, the investigation of stress is turning out to be increasingly critical. the advancement of recommender frameworks and computerized emotional well-being assessments can profit from the investigation of stress discovery for psychological wellness expectation, as per earlier examination. In this study, we enhance sentiment and emotion analysis to measure the stress levels of people by analyzing their social networking posts and comments. Using machine learning techniques and the deep learning sentiment analysis model BERT, we analyze sentiment using enormous datasets of tweets. We also used an unsupervised machine learning technique called Latent Dirichlet Allocation to scan a set of papers to find word and phrase patterns and create word groups and related phrases that most accurately represented the documents. this aids in predicting the subject that is connected to the textual information. We will be able to identify users'emotions online with the help of these models. Additionally, stress or depression can be studied using these feelings. In summary, the BERT model and ML models have very high detection rates. the benefits of this research can be shown in terms of mental wellness. © 2023 IEEE.

关键词： deep learning NLP social media stress

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Deep learning and Patch processing Based Lung Cancer Detection on CT Images 6th

Deep Learning and Patch Processing Based Lung Cancer Detecti...

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6th international conference on Communications and Cyber-Physical Engineering, ICCCE 2023

作者： Venkatesh, C. Sivayamini, L. Sarthika, P. Hema, M. Hemalatha, A. Lakshmi, G. Department of Electronics and Communication Engineering Annamacharya Institute of Technology and Sciences Rajampet India

ISBN: (纸本)9789819971367

Lung cancer is the most typical cancer in both men and women. Lung cancer symptoms typically appear in the human body when it is in its final stage, but with the aid of sophisticated technology and computer-aided systems, it is possible to predict at initial phase. Presently, a variety of conventional and machine learning techniques are available but they do not provide accurate detection and also takes a long time to process lung cancer detection. As a result, a novel method for detecting lung cancer in CT images was proposed which employs a patch processing and deep learning techniques for accurate detection and less computation time. the image quality of CT scans are improved by patch processing and deep learning technique classifies the detected tumor as malignant or benign. Finally, the statistical parameters such as Accuracy, MSE, Sensitivity, PSNR, Sensitivity, and Specificity were computed and compared with the existing system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computerized tomography

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Development of Automated Vending machine for the Application of Dispensing of Medicines 6th

Development of Automated Vending Machine for the Application...

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6th international conference on Soft Computing and signal processing, ICSCSP 2023

作者： Deepak, P. Rohith, S. Niharika, D. Harshith Kumar, K. Bhupal, Ram Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Karnataka Bengaluru India

ISBN: (纸本)9789819984503

this paper focuses on development of automated vending machine for the application of dispensing medicines. this enables minimal human intervention in accessing of the medicines. the machine offers a range of medical supplies including prescription medications, first-aid kits, and hygiene products, among others. this prototype uses Arduino microcontrollers, IR sensors, servomotors, GPS system that can securely dispense medications to patients effectively and efficiently. the use of an automatic medical vending machine offers a cost-effective and efficient approach to medicine delivery, reducing the burden on healthcare providers and enhancing patient experience and satisfaction. the prototype is tested for different test cases. It is observed that method is a quick and cost-effective solution for disposal of medicine. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Microcontrollers

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Ensemble of CNN and DenseNet121 Models for Pneumonia Prediction Using X-Ray Images 6

Ensemble of CNN and DenseNet121 Models for Pneumonia Predict...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Saxena, Suryansh Maji, Srabanti Agarwal, Aashray Goel, Anshul School of Computing DIT University Uttarakhand Dehradun India

ISBN: (纸本)9798350304480

When left untreated, pneumonia, a respiratory infection, can result in serious sickness or even death. In recent years, promising findings have been obtained in the accurate identification of pneumonia utilising X-ray image processing methods based on machine learning. In this article, we provide a deep learning-based approach for predicting pneumonia from X-ray images. We applied the averaging technique to combine CNN and DenseNet121, two well-known deep learning models, using a dataset from Kaggle. According to our tests, the CNN model had a test accuracy of 91.2% whereas DenseNet121 had an accuracy of 89.1%. the ensembled model's accuracy of 90.2 % shows how assembling can improve the accuracy of individual models. Our findings imply that deep learning models may prove to be a valuable tool for the precise and early detection of pneumonia, which may lead to better patient outcomes. © 2023 IEEE.

关键词： Artificial intelligence CNN formatting insert Lung disease detection machine learning neural networks Pneumonia detection using machine learning style styling

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