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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151-160 订阅

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6th international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2023

6th International Conference on Recent Trends in Image Proce...

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6th international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2023

ISBN: (纸本)9783031530814

the proceedings contain 31 papers. the special focus in this conference is on Recent Trends in Image processing and Pattern Recognition. the topics include: UI/UX for Aerospace Qualification Business Processes;Enhancing Android Malware Detection: CFS Based Texture Feature Selection and Ensembled Classifier for Malware App Analysis;a Comprehensive Analysis of Blockchain Network Security: Attacks and their Countermeasures;Enhancing CT Image Visualization and Analysis through Rescaling Raw Pixel Values to Hounsfield Units;VAIDS: A Hybrid Deep learning Model to Detect Intrusions in MQTT Protocol Enabled Networks;leveraging Handwriting Impairment as a Biomarker for Early Parkinson’s Disease Diagnosis;blockchain for Patient Data Integrity: Decentralised Storage and Retrieval in Modern Healthcare Systems;a Residual learning Approach Towards the Diagnosis of Colorectal Disease Effectively;Large-Language-Models (LLM)-Based AI Chatbots: Architecture, In-Depth Analysis and their Performance Evaluation;cincoCrypto - A Cryptocurrency Price Forecasting Tool for Everyone;Realistic Skin Image Data Generation Leveraging Conditional GAN and Classification Using Deep CNN;Diabetic Retinopathy Blood Vessel Detection Using Deep-CNN-Based Feature Extraction and Classification;analyzing Pulmonary Abnormality with Superpixel Based Graph Neural Network in Chest X-Ray;a Stack Ensemble Approach for Early Alzheimer Classification Using machine learning Algorithms;federated Unlearning and Server Right to Forget: Handling Unreliable Client Contributions;optimal Resource Allocation for Energy Harvested Cognitive Radio Networks Based on Learn Heuristic Algorithm;Deep Neural Networks Based Security Solution for ATM Transactions;crypto Bank: Cryptocurrency Wallet Based on Blockchain;radiography and thermography Based Image processing Techniques for Assessment of Lower Back Pain;modified Snapshot Ensemble Algorithm for Skin Lesion Classification;COVID-19 Disease Prediction Using Generative Adversarial

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Comprehensive Survey of machine learning Techniques for Detecting and Preventing Network Layer DoS Attacks 6th

Comprehensive Survey of Machine Learning Techniques for De...

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6th IFIP international conference on Internet of things, IFIP IoT 2023

作者： Bhatta, Niraj Prasad Ghimire, Ashutosh Hossain, Al Amin Amsaad, Fathi Wright State University DaytonOH45435 United States

ISBN: (纸本)9783031458811

With the increasing reliance on computer networks in our daily lives, the threat of network layer DoS (Denial of Service) attacks has become more prevalent. Attackers use various techniques to disrupt network services and cause loss of data, revenue, and reputation. Recent development in machine learning approaches have shown promise in prevention and detection of such types of attacks by several orders of magnitude. In this paper a thorough overview of machine learning approaches for detecting and preventing network layer DoS attacks is presented. Firstly, the basics of network layer DoS attacks, their classification, and the impact of these attacks is discussed. then, different machine learning techniques and the ways in which they can be utilized for attack detection and prevention is explored. Additionally, analysis on the strengths and limitations of each approach, and provide a comparative study of the most relevant works in this field is done. Finally, some obstacles in research and potential avenues for future exploration is presented. in the field of machine learning-based defense mechanisms against network layer DoS attacks is discussed. In this paper a detailed summary of the most up-to-date advancements or developments in machine learning-based defense mechanisms against network layer DoS attacks is shown and serve as a reference for one and all who are involved in this field. © 2024, IFIP international Federation for Information processing.

关键词： Network layers

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6th international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2023

6th International Conference on Recent Trends in Image Proce...

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6th international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2023

ISBN: (纸本)9783031530845

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A Comparative Analysis of machine learning and Deep learning Algorithms for Image Classification 6

A Comparative Analysis of Machine Learning and Deep Learning...

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

作者： Madanan, Mukesh Gunasekaran, Saraswathy Shamini Mahmoud, Moamin A. College of Graduate Studies Universiti Tenaga Nasional-UNITEN Malaysia Universiti Tenaga Nasional-UNITEN Department of Informatics Malaysia Universiti Tenaga Nasional-UNITEN Department of Computing Malaysia

ISBN: (纸本)9798350304480

Image classification is a popular and important area of image processing research in today's society. For machine learning, SVM is a very good classification model. CNN is a type of convolution neural network that has an unpredictable development and uses convolution calculations. It is one of the most well-known deep learning algorithms. this review thinks about and inspects exemplary AI and profound learning picture classification procedures involving SVM and CNN as specific illustrations. Using a large sample mnist dataset, this study found that CNN has an accuracy of 0.97 and SVM has an accuracy of 0.89;SVM has an accuracy of 0.85 and CNN has an accuracy of 0.82 when working with a small sample ImageNet dataset. Tests in this review show that for little example informational collections, standard ML has an improved arrangement impact than deep learning structure does. © 2023 IEEE.

关键词： deep learning image classification machine learning

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Personality Prediction System for Twitter Stream Using machine learning Models 1

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

作者： Prabakeran, S. Pattanaik, Pratik Chandrasekhar Abinaya, M. Faculty of Engineering and Technology School of Computing Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

Users’ behaviour on social media platforms reflects many aspects of real life, including personality, and these platforms provide a rich amount of textual data. Individuals communicate their thoughts, feelings, and ideas regularly, resulting in a rich and informative collection of personal data that may be used to a wide range of situations. As the word subconscious suggests, this is commonly referred to as "subconscious crowdsourcing," which means that people are unintentionally and subconsciously sharing information with their network. these people are therefore ideal candidates for personality prediction due to the massive expansion of those social media sites over the past ten years. Tweets, for instance, are frequently quite brief and display an odd language usage, consisting of acronyms and unique forms compared to textual sources. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Application of reinforcement learning algorithm on robot motion control and navigation in webots simulator 3

Application of reinforcement learning algorithm on robot mot...

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3rd international conference on signal processing and machine learning, CONF-spml 2023

作者： Tao, Sizhe Hu, Yuanzhe Huang, Heen Department of Engineering King's College London Strand LondonWC2R 2LS United Kingdom Department of Electronic and Electrical Engineering University College London Gower St LondonWC1E 6BT United Kingdom School of Materials and Energy Guangdong University of Technology Guangdong Guangzhou China

Covid-19 has changed people's way of life since its outbreak in December 2019. the transmission of the virus mainly relies on droplets or aerosols produced by infected people while they breathe, talk, cough or sneeze. the infection can occur over a long distance, especially indoors. To reduce connectivity between the people indoors such as in restaurants, the application of robots for food delivery may be one of the solutions. In this research, a reinforcement learning algorithm is applied to the motion control and navigation of a robot which can be modified into a robot food runner to maintain social distances in restaurants. In this project, we test the algorithm's ability to explore the environment and find a suitable path to the target point through learning by comparing its trajectories to the shortest theoretical path. the results obtained through the simulation on Webots show that the algorithm works well to find a destination from a randomly selected starting point even though a random obstacle is presented. However, still it has great potential to be extended to a continuous environment. © Published under licence by IOP Publishing Ltd.

关键词： Reinforcement learning

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Cost Analysis and Optimization of Virtual machine Allocation in the Cloud Data Center 6

Cost Analysis and Optimization of Virtual Machine Allocation...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： De, Utpal Chandra Satapathy, Rabinarayan Patra, Sudhansu Shekhar School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India Faculty of Emerging Technologies Sri Sri University Cuttack India

ISBN: (纸本)9798350398496

IoT sensors and devices are being used in more and more applications every day. Cloud computing has been introduced in order to meet the storage and processing demands for time-sensitive applications on low-powered end devices that leverage IoT devices and sensors. processing and analysis of IoT workloads are carried out by virtual machines (VMs) housed inside of actual computers. It is an NP-complete issue to allocate VMs inside a physical machine (PM). If a VM is unsatisfied with the provided service, the VM may decide to re-join the line as a feedback customer. Whenever the system is empty, the physical machine may take a working vacation rather than remaining in the system idle. Working vacations and vacation service times are both independent random variables with exponential distributions. An M/M/1 feedback queue in steady-state with balking, reneging, and recurrent working vacations (WV) have been applied in the VM allocation to the PMs. the matrix approach is used to determine the model's steady-state system length distributions. A population based meta-heuristic approach teaching learning based optimization (TLBO) technique is provided for cost optimization and performance features of the VM allocation. Numerical findings presented as tables are used to perform sensitivity analysis. © 2023 IEEE.

关键词： Sensitivity analysis

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Caries Level Classification using Combination of Hu and Zernike Invariant Moments Features and machine learning 6

Caries Level Classification using Combination of Hu and Zern...

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6th international conference on Information and Communications Technology, ICOIACT 2023

作者： Jusman, Yessi Aini, Devie Nur Puspita, Sartika Chamim, Anna Nur Nazilah University of Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Yogyakarta55183 Indonesia University of Muhammadiyah Yogyakarta Dental School Department of Oral Biology Yogyakarta55183 Indonesia

ISBN: (纸本)9798350315639

One of the most important parts of the human body is the teeth. In the human body, teeth are used as a digestive process and a beauty in the human body. thus, it is necessary to prevent dental disease so that the teeth can function properly. Dental caries is the most common dental disease in everyday life. With advances in technology, artificial intelligence systems or image processing can be used in the process of diagnosing dental caries images. A total of 4 classes of dental caries imagery were used in this study, namely classes 1, 2, 3, and 4. the dental caries image consisted of 347 which were divided into 314 training images and 33 test images. the purpose of this study is to design a computer-based dental caries level classification system with image processing that uses a combination of the Hu and Zernike Moment Invariant Method used for feature extraction and the machine learning Method (KNN) used for dental caries classification in the Matlab 2020a application. the results of the classification that has been carried out will obtain the KNN accuracy value. the highest accuracy is the KNN method with an accuracy of 94.07% when using the combination of Hu and Zernike Moment Invariant feature extraction algorithms. © 2023 IEEE.

关键词： Feature extraction

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Research on Tibetan-Chinese Neural machine Translation Integrating Statistical Method 6

Research on Tibetan-Chinese Neural Machine Translation Integ...

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6th international conference on machine learning and Natural Language processing, MLNLP 2023

作者： Zhou, Maoxian The Computer College of Qinghai Normal University Qinghai Xining810016 China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Qinghai Xining810008 China Qinghai Engineering and Technology Research Center for Tibetan Information Processing Qinghai Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining810008 China

ISBN: (纸本)9798400709241

In recent years, with the emergence of deep learning methods, Neural machine Translation has become a new research direction of machine translation. Due to the scarcity of digital resources in Tibetan, there is only a small-scale Tibetan-Chinese bilingual parallel corpus to train translation models. In the case of insufficient resources, the performance of Neural machine Translation decreases significantly. In this regard, considering that the word alignment results in Tibetan-Chinese Statistical machine Translation are better, and the alignment information in Tibetan-Chinese Neural machine Translation model is significantly different from that in Tibetan-Chinese Statistical machine Translation, this paper proposes a Tibetan-Chinese Neural machine Translation method that integrates statistical methods. We using Statistical machine Translation method to generate word alignment information of Tibetan-Chinese parallel corpus to supervise the training process of Tibetan-Chinese Neural machine Translation model to make the model achieve more accurate translation and alignment effect. Experimental results show that this method can effectively improve translation quality in low resource environment. © 2023 ACM.

关键词： Neural machine translation

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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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