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

共 769 条记录，以下是161-170 订阅

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6th international conference on Security and Privacy in New Computing Environments, SPNCE 2023

6th International Conference on Security and Privacy in New ...

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6th international conference on Security and Privacy in New Computing Environments, SPNCE 2023

ISBN: (纸本)9783031736988

the proceedings contain 23 papers. the special focus in this conference is on Security and Privacy in New Computing Environments. the topics include: Consortium Blockchain Storage Optimization Based on Fountain Codes;an Incentive Mechanism and An Offline Trajectory Publishing Algorithm Considering Sensing Area Coverage Maximization and Participant Privacy Level;Research on Face Recognition System Based on RLWE Homomorphic Encryption;feedback Feed-Forward Iterative learning Control for Non-affine Nonlinear Discrete-Time Systems with Varying Trail Lengths;open-Closed-Loop Iterative learning Control Based on Differential Evolution Algorithm for Nonlinear System;overview of Vehicle Edge Computing and Its Security;conFlow: Contrast Network Flow Improving Class-Imbalanced learning in Network Intrusion Detection;anomaly Detection of Unstable Log Data Based on Contrastive learning;An Integration-Enhanced ZNN Approach for Chaotic Combination Synchronization with External Disturbances∗;a Lightweight Anomaly Detection Method for Industrial Processes Based on Event Correlation Behavior;a Novel Polar Code-Based Key Encapsulation Mechanism with Non-permutation Equivalent Public Key;two-Stage Multi-lingual Speech Emotion Recognition for Multi-lingual Emotional Speech Synthesis;EncoderMU: machine Unlearning in Contrastive learning;NoCrypto: A Web Mining Behavior Detection Method Based on RGB Images;image Copy-Move Forgery Detection in the Social Media Based on a Prior Density Clustering and the Point Density;detection of Speech Spoofing Based on Dense Convolutional Network;speech Emotion Recognition Based on Recurrent Neural Networks with Conformer for Emotional Speech Synthesis;Route Privacy-Preserving Authentication Scheme Based on PUF in VANETs;Stable NICE Model-Based Picture Generation for Generative Steganography;computer-Generated Image Forensics Based on Vision Transformer with Forensic Feature Pre-processing Module;VoIP Steganalysis Using Shallow Multiscale Convolution and

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Data, Information, Knowledge, Wisdom Pyramid Concept Revisited in the Context of Deep learning 15th

Data, Information, Knowledge, Wisdom Pyramid Concept Revisit...

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15th international KES conference on Intelligent Decision Technologies, KESIDT 2023

作者： Kostek, Bożena ETI Faculty Audio Acoustics Laboratory Gdańsk University of Technology Narutowicza 11/12 Gdańsk80-232 Poland

ISBN: (纸本)9789819929689

In this paper, the data, information, knowledge, and wisdom (DIKW) pyramid is revisited in the context of deep learning applied to machine learning-based audio signal processing. A discussion on the DIKW schema is carried out, resulting in a proposal that may supplement the original concept. Parallels between DIWK pertaining to audio processing are presented based on examples of the case studies performed by the author and her collaborators. the studies shown refer to the challenge concerning the notion that classification performed by machine learning (ML) is/or should be better than human-based expertise. Conclusions are also delivered. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Audio signal processing

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machine learning-Based Prediction of Cardiovascular Diseases Using Flask 6th

Machine Learning-Based Prediction of Cardiovascular Diseases...

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

作者： Sagar Reddy, V. Supraja, Boddula Vamshi Kumar, M. Krishna Chaitanya, Ch. Department of Electronics and Communication Engineering VNR VJIET Telangana Hyderabad India

ISBN: (纸本)9789819984503

Health care is an inescapable task in a human’s life. Cardiovascular diseases, also known as CVDs, are amongst the most prevalent causes of death globally, costing the lives of around 17.9 million people each year. CVDs include heart and blood vessel abnormalities that encompass problems such as coronary heart disease, brain disease, rheumatic heart disease, and more. the deaths can be reduced by early detection and treatment of cardiac problems. the present study compares the performance of various machine learning methodologies like SVM, KNN, and decision tree in terms of accuracy. For the prediction of cardiovascular diseases, we have taken few inputs like BP, cholesterol, glucose, current smoking, cigarettes count, BMI, and age. After implementing these methods, KNN had given the best accuracy in the detection of cardiovascular diseases (CVDs). KNN achieved about 80% accuracy in detection. After that we developed a user-friendly website, where users can be able to check whether they were facing any heart-related diseases by giving their parameters like glucose, cholesterol, BP, and so on. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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machine learning-Aided Piece-Wise Modeling Technique of Power Amplifier for Digital Predistortion 48

Machine Learning-Aided Piece-Wise Modeling Technique of Powe...

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

作者： Krishna Chaitanya Bulusu, S.S. Tervo, Nuutti Susarla, Praneeth Sillanpaa, Mikko J. Silven, Olli Juntti, Markku Parssinen, Aarno OuluFI-90014 Finland OuluFI-90014 Finland OuluFI-90014 Finland

ISBN: (纸本)9781728163277

We propose a new power amplifier (PA) behavioral modeling approach, to characterize and compensate for the signal quality degrading effects induced by a PA with a machine learning (ML) aided piece-wise (PW) modeling approach. Instead of using a single pruned Volterra model, we use multiple small-size pruned Volterra models by classifying the input data into different classes. For that purpose, an ML classifier model is trained by extracting some crucial features from both the input signal statistics and the PA operating point. the simulation results indicate that our approach contributes to an improved performance/complexity trade-off than a single generalized memory polynomial (GMP) model in terms of PA behavior modeling and linearization. © 2023 IEEE.

关键词： Power amplifiers

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Prediction of Air Pollutant Concentrations in Airport Areas using machine learning Architecture 12

Prediction of Air Pollutant Concentrations in Airport Areas ...

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12th international conference on Image processing theory, Tools and Applications, IPTA 2023

作者： Wang, Huaijin Djemal, Khalifa Feiz, Amir Ali Chaudhuri, Sheli Sinha Paris Saclay University Univ. Evry Ibisc Evry France Paris Saclay University Univ. Evry Lmee Evry France Jadavpur University Electronics and Telecommunication Engineering Department Kolkata India

ISBN: (纸本)9798350325416

the importance of analyzing and predicting the levels of atmospheric pollutants is emphasized in environmental risk studies. Existing prediction models based on machine learning have been proposed for this purpose. In the case of experimental campaigns conducted in airport areas, one such pollutant is Sulphur HexaFluoride (SF6), a gas that is odorless, colorless, and rare in the air. Due to the weak linear relationship between atmospheric variables and the concentration of SF6, linear models are not suitable for this prediction task. To address this challenge and accurately predict atmospheric pollutant concentrations in airport areas, two prediction models were chosen and developed: a random forest machine learning model and a CNN-LSTM architecture, which is a deep learning approach. A comparison of results obtained by the two models achieved on two campaigns measures (Capitoul & Indianapolis) is presented. the accuracy of the prediction results is evaluated using the coefficient of determination (R2) and the Mean Absolute Error (MAE) as metrics. © 2023 IEEE.

关键词： Forestry

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A Human-machine Collaborative Video Summarization Framework Using Pupillary Response signals 6

A Human-Machine Collaborative Video Summarization Framework ...

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6th international conference on Information Communication and signal processing, ICICSP 2023

作者： Ma, Haigang Yu, Wenyuan Wang, Yaoyao Zhou, Minwei Ding, Nai Zheng, Jing Zhejiang University Hangzhou China Zhejiang Lab Hangzhou China Zhejiang University Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350339994

this paper proposes a novel, human-machine collaborative, pupillary response-based video summarization framework. Considering that humans are the end-users and evaluators of video content, it is natural to establish the links between video features and real-time viewers' attentive response for designing a video summarization framework. In this paper, pupil size, a pupillary response, is introduced as a real-time indicator for assessment of viewers' engagement and attention. Firstly, we augmented the TVSum dataset by replacing manual annotations with attention scores that converted from pupillary size signals to generate the perceptually driven dataset. Secondly, we developed a video summarization framework which uses cues from pupillary size signal of humans to predict frame-level attention scores, and then extracts key shots from videos. On the perception-driven dataset, the average F-measure of the proposed summarization method is 69.71%, while the precision and recall are 69.67% and 69.77%, respectively, which is a significant improvement compared to random summarization. Experimental results initially demonstrated that our modeling approach can learn the dynamic attention mechanism of viewers and apply it to video summarization. In addition, the experimental results validate the effect. © 2023 IEEE.

关键词： Attention mechanism Encoder-decoder framework Gated Recurrent Unit (GRU) Pupil size Video summarization

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A Comparative Study of machine learning and Deep learning Algorithm for Handwritten Digit Recognition 6

A Comparative Study of Machine Learning and Deep Learning Al...

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

作者： Sohail, Mohammed Lal Saini, Madan Singh, Vedant Pratap Dhir, Sarthak Patel, Vishal Apex Institute of Technology Department of CSE Chandigarh University Punjab India

ISBN: (纸本)9798350304480

Handwritten digit recognition is a complex task in various real-world applications such as bank check processing, postal automation recognition etc. In recent time, different kind of learning algorithms are used to analyze and resolve this issue. this paper presents a comparative study on machine learning and deep learning algorithms such as Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN), Support Vector machine (SVM), Random Forest, LeNet-5, and YOLOv7. Publically available MNIST, DIDA, and MNIST MIX handwritten digit dataset were used in experimental work. the objective of this study is to find the best algorithm which can give an acceptable accuracy. For measuring the accuracy various parameters such as precision, recall, specificity, and F-measure were used. On the basic of experimental results, YOLOv7 has achieved detection accuracies above 98.3% and LeNet 5 has detection accuracy of 99.15%. It has been observed that deep learning algorithms have achieved higher accuracy than machine learning algorithms. © 2023 IEEE.

关键词： CNN KNN LeNet-5 MNIST Random Forest SVM YOLOv7

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Enhancing Brain Tumour Aid Diagnosis with Augmented Reality rendering-based 6 DoF Object Pose Estimation 6

Enhancing Brain Tumour Aid Diagnosis with Augmented Reality ...

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6th international conference on signal processing and Information Security, ICSPIS 2023

作者： Amara, Kahina Kerdjidj, Oussama Atalla, Shadi Guerroudji, Mohamed Amine Mansoor, Wathiq Zenati, Nadia Centre for Development of Advanced Technologies Algiers Algeria University of Dubai College of Engineering and It Dubai United Arab Emirates

ISBN: (纸本)9798350329599

Accurately estimating the position and orientation of objects in augmented reality (AR) is a crucial task known as 6 Degrees of Freedom (6 DoF) object pose estimation. this capability enables machines to understand their surroundings in three dimensions, enabling them to perform various tasks like object recognition, tracking, interaction, and manipulation. In this paper, we propose an enhanced approach for estimating the pose of objects in AR using 6 DoF, specifically focusing on augmenting and visualising brain tumours in 3D to assist with diagnosis. Recently, there has been significant progress in developing generalisable 6 DoF object pose estimators that demonstrate competitive performance. these estimators can accurately determine the pose of an unseen object without requiring specific training or fine-tuning for each new object being tested. We tackle the challenge of estimating 6 DoF poses using only a single RGB image by leveraging a state-of-the-art object pose estimator called Gen6D. By utilising RGB data images and applying Gen6D to estimate the 6 DoF pose of a phantom head texture-less object, we can achieve augmented visualisations of brain tumours. the adoption of deep learning techniques for 6 DoF pose estimation holds immense potential and practical value in the field of medicine, particularly for medical practice and training purposes. © 2023 IEEE.

关键词： Augmented reality

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Exploiting machine learning Technique for Attack Detection in Intrusion Detection System (IDS) Based on Protocol 6th

Exploiting Machine Learning Technique for Attack Detection i...

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6th international conference on Information and Knowledge Systems (ICIKS)

作者： Aladesote, Olomi Isaiah Fakoya, Johnson Tunde Agbelusi, Olutola Fed Polytech Dept Comp Sci Ile Oluji Ondo Nigeria Fed Univ Technol Akure Dept Software Engn Akure Ondo Nigeria

ISBN: (纸本)9783031516634;9783031516641

An intrusion detection system (IDS) can be either software or hardware that computerizes the process of keeping track of and evaluating network or computer system activity for indications of security issues. IDS is a crucial component of the security infrastructure of many organizations due to an increase in the frequency and intensity of attackers over the past decades. the study proposes machine learning techniques for the classification and detection of normal and attack traffics using protocol types records of the NSL-KDD dataset. three sets of datasets were extracted from NSL-KDD datasets based on ICMP, UDP, and TCP. the experiment was conducted on WEKA 3.8.5 using KNN, KStar, LWL, BayesNet, Naive Bayes, and PART algorithms. the results indicated that the PART algorithm has the highest performance rating while NaiveBayes has the lowest performance rating utilizing the Correlation-based feature selection (CFS) using the Ranking Filter approach. It is concluded that the PART algorithm performs well across the dataset while NaiveBayes does not perform well across the dataset.

关键词： Correlation-based feature selection Intrusion Detection System NSL-KDD dataset

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Network Information Security Risk Assessment Method Based on machine learning Algorithm 6th

Network Information Security Risk Assessment Method Based on...

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6th European-Alliance-for-Innovation (EAI) international conference on Advanced Hybrid Information processing (ADHIP)

作者： Jiang, Ruirong Wan, Liyong Jiangxi Univ Software Profess Technol Nanchang 330041 Jiangxi Peoples R China

ISBN: (纸本)9783031288661;9783031288678

the current computer network information security risk assessment methods have the problems of low assessment accuracy, which seriously restricts the assessment effect. In order to solve this problem and improve the effect of network information security risk assessment and the level of network information security, this paper designs a network information security risk assessment method based on network learning algorithm. Describe the risk calculation form, extract the performance characteristics of network information, identify the network risk factors, draw conclusions according to logical reasoning, adopt computer network risk control and defense measures, use machine learning algorithm to build a security system model, and optimize the security risk assessment mode. the experimental results prove that the highest accuracy rate of the network information security risk assessment method is 95.612%, indicating that the network information security risk assessmentmethod is more practical after combining the machine learning algorithm.

关键词： machine learning Network information Security risk Risk assessment Security defense Security risks

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