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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Apriori Analysis of Deep learning Models on a Multi-class Data Set 4th

Apriori Analysis of Deep Learning Models on a Multi-class Da...

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4th international conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Kosuru, Sai Sathwik Padmanabha Reddy, Y.C.A. Akanksha, Perala Venkata Chaitanya, Kotagiri Parsi, Snehaja Chunduri, Madhu Babu Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Medak Narsapur502313 India Department of Artificial Intelligence & Machine Learning Chaitanya Bharathi Institute of Technology Ganidept Hyderabad500075 India

ISBN: (纸本)9789819751990

Deep learning techniques have large scopes in terms of applications and resource requirements, hence calling for a deep analysis of their behavior. Many studies focused on the application part of these models and there is a relatively lesser discussion about the unit-wise behavior. A multi-class dataset was taken and three different models were trained where their performances of training processes are described in terms of runtime characteristics like learning rate, times, etc. An apriori analysis was conducted where the runtime characteristics are investigated for their similarity with pre-defined knowledge of their architectures, as opposed to the posterior analysis which particularly focuses on observations. the work also serves as a framework for defining the logistics of the model training processes and fine-tunes the parameters devised in future for the maximum accuracy of the model. the results were further converted into graphical representations for enhanced delivery of insights. the study is also novel where, as opposed to previous works, in which performances are only tested, the model is evaluated in a design-specific manner dealing with only architectures and the parameters defining them. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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7th international conference on Wireless Communications, Networking and applications, WCNA 2023

7th International Conference on Wireless Communications, Net...

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7th international conference on Wireless Communications, Networking and applications, WCNA 2023

ISBN: (纸本)9789819624089

the proceedings contain 38 papers. the special focus in this conference is on Wireless Communications, Networking and applications. the topics include: EffiNet: An Efficient, Low-Complexity Neural Network for Monocular Depth Estimation on Embedded Devices;adaptive Communication Spectrum Sensing Algorithm Based on Energy Detection;a Cache Scheduling Method Based on Adaptive Expiration for Data Process System;Research on Airport Communication and Navigation Equipment Inspection Assistance System Based on AR Technology;application of Quality Function Deployment in Assessing Unmanned Assault Capabilities for Mountain Counter-Terrorism Operations;research on the application of Internet of things Technology in Power Grid Limited Space Security Monitoring;research on the application of computer Vision Based on Deep learning in Autonomous Driving Technology;research on Cybersecurity System of Scientific Research Institutions Based on National Cybersecurity Classified Protection 2.0: Take the Chinese Academy of Agricultural Sciences as an Example;Research on DOA Estimation Method of Underwater Acoustic Signal Based on machine learning;Design and Implementation of Smart Home Control System Based on STM32;communication Anti-jamming System Based on Deep Reinforcement learning;fault Diagnosis of Infrared Sensor Based on Convolutional Neural Network;graph Neural Network Knowledge Graph Recommendation Model Integrating Deep Domain Information and Important Domain Information;an Approach to Microenvironment-Based Particle Swarm Optimization Algorithm;a Verifiable Ciphertext Retrieval Scheme for Smart Grids;panoptic Semantic Mapping Method for Tomato Growing Environment Based on K-Net and OctoMap;An Adversarial Attack Method for Multivariate Time Series Classification Based on AdvGAN;recognition and Calculation of Fish Rafts in Mariculture on the Basis of Artificial Intelligence;digital Twin for Power Load Forecasting.

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Elderly Health Care Data Integration Framework: Design and Implementation 4th

Elderly Health Care Data Integration Framework: Design and I...

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4th international conference on Data and Information in Online Environments

作者： Zhong, Zhean Liu, Chenyu Zhao, Jing Xiao, Jianmao Gao, Qinghang Gao, Chao Ouyang, Chuying Jiangxi Normal Univ Sch Software Nanchang 330022 Jiangxi Peoples R China Jiangxi Prov Engn Res Ctr Blockchain Data Secur & Nanchang 330022 Jiangxi Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300072 Peoples R China Jiangxi Normal Univ Dept Phys Nanchang 330022 Jiangxi Peoples R China

ISBN: (纸本)9783031807121;9783031807138

the application of Internet of things (IoT) detection devices in the field of elderly healthy generates a large amount of detection data, which faces problems such as diversified data sources and non-uniform parsing methods. the fragmentation and confusion of the process from data subscription to data analysis have become important challenges affecting the development of the healthy aging field. In this paper, we designed and proposed an elderly health care framework with a model-as-a-service orientation. the framework automates the integration of health data for the elderly, including subscription services. It handles Model Training Analysis through a workflow-based system. Users can configure workflows using a low-code method provided by the platform. First, we abstracted and formalized the integration problem and workflow and defined a workflow description language. then, we built an healthy aging unified modeling framework based on the problem model and provided a general introduction to the framework architecture. the module for analyzing is responsible for model training, persistence, and data analysis, enabling users to customize the selection of machine learning models and corresponding parametric. Ultimately, we built a model library and an external RESTful interface based on the automation option of Automated machine learning (AutoML). the experiments demonstrate this paper's proposed framework's feasibility and effectiveness.

关键词： Data curation Elderly health care Model-as-a-Service machine learning

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Miniaturisation of Binary Classifiers through Sparse Neural Networks 4th

Miniaturisation of Binary Classifiers Through Sparse Neural...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Cavallaro, Lucia Serafin, Tommaso Liotta, Antonio Free University of Bozen-Bolzano Piazza Domenicani 3 Bolzano39100 Italy Radboud University Toernooiveld 212 Nijmegen6525 EC Netherlands

ISBN: (纸本)9783031812408

the continuous advances of Artificial Intelligence (AI) techniques have created new application domains for smaller and more efficient machine learning (ML) models. In the context of embedded ML, network sparsification strategies became crucial steps to fit models with severe space constraints. Hence, the aim of this research is to evaluate Neural Network (NN) sparsification and compression on embedded systems. To do so, we investigate the problem of miniaturised binary classifiers (i.e., disease detection) in the computer vision domain. We applied a constant pruning technique during the training process of three architectures: a standard Convolutional Neural Network, CNN in short, (i.e., AlexNet), a residual network (i.e., ResNet), and a densely connected CNN (i.e., DenseNet). We varied: network sparsity (up to 95%), image resolution (from 8×8 up to 32×32), and quantisation. the results indicate that the use of sparse networks has a significant impact on the accuracy of miniaturised binary classifiers. With a 70% of sparsity, it was reached an accuracy improvement of 4% in low-resolution images (i.e., 8×8) compared to the standard dense approach. Our findings suggest that sparse NNs can significantly reduce both the size and computational demands of the models while increasing their accuracy on these edge cases. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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DC Optimization in Adversarial Sparse Support Vector machine 4th

DC Optimization in Adversarial Sparse Support Vector Machin...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Astorino, Annabella Di Francesco, Massimo Gaudioso, Manlio Gorgone, Enrico Manca, Benedetto Dipartimento di Ingegneria Informatica Modellistica Elettronica e Sistemistica Universitá della Calabria Rende87036 Italy Dipartimento di Matematica e Informatica Universitá degli Studi di Cagliari Cagliari09124 Italy

ISBN: (纸本)9783031812408

In supervised classification models, such as Support Vector machine, the main purpose is to predict the class membership of the incoming samples. In some real applications malicious inputs are inserted to mislead a vulnerable classifier, leading to a wrong prediction. In our work we focus first on the problem of introducing the smallest perturbation of a sample to induce incorrect classification and then on how to produce a significant downgrading of the classifier acting on a subset of the input samples. the novelty of the proposed approach is in the attempt of calculating sparse perturbations by minimizing the relative 0-pseudo-norm, which gives rise to a Difference of Convex (DC) optimization model. We present the results of some preliminary experiments. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Extended Optimal Control Problem for Practical application 4th

Extended Optimal Control Problem for Practical Application

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Diveev, Askhat Shmalko, Elizaveta Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783031812408

To apply a solution of the optimal control problem directly to the control object for which model this problem was solved, it is necessary to build a system of motion stabilization along the obtained optimal trajectory. However, the construction of such a stabilization system is not provided in the classical formulation of the optimal control problem. therefore, additional requirements for the properties of the optimal solution have been introduced into the optimal control problem. A formulation of the extended optimal control problem is introduced and the approaches to its numerical solution are discussed. One way to meet the introduced requirements is to build a stabilization motion system of an object along a program trajectory. As far as stabilization to the whole program trajectory, not from point to point on this trajectory, is rather challenging task, so for this, numerical methods of symbolic regression are proposed to be applied. the approach consists in application of machine learning by symbolic regression to the control synthesis problem. Symbolic regression allows to find a mathematical expression for control function as a function of the state space vector. Computational example of solving the extended optimal control problem for a robot group is presented. It is shown experimentally, that the solution of the extended optimal control problem is much less sensitive to disturbances, than a direct solution of the classical optimal control problem. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Stabilization

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

Workshop on Longitudinal Disease Tracking and Modeling with ...

引用

Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031845246

the proceedings contain 21 papers. the special focus in this conference is on Longitudinal Disease Tracking and Modeling with Medical Images and Data. the topics include: Disease Progression Modelling and Stratification for Detecting Sub-trajectories in the Natural History of Pathologies: application to Parkinson’s Disease Trajectory Modelling;back to the Future: Challenges of Sparse and Irregular Medical Image Time Series;Individualized Multi-horizon MRI Trajectory Prediction for Alzheimer’s Disease;towards Longitudinal Characterization of Multiple Sclerosis Atrophy Employing SynthSeg Framework and Normative Modeling;segHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints;Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting;registration of Longitudinal Liver Examinations for Tumor Progress Assessment;Tracking Lesion Evolution Using a Boundary Enhanced Approach for MS Change Segmentation (BEAMS);a Radiological-Based Coordinate System for the Human Body: A Proof-of-Concept;language Models Meet Anomaly Detection for Better Interpretability and Generalizability;A Diffusion Model Embedded WCSAU-Net for 3D MRI Brain Tumor Segmentation;predicting Human Brain States with Transformer;Cross-Modality Image Quality Prediction for Time-Resolved CT from Breathing Signals;RATNUS: Rapid, Automatic thalamic Nuclei Segmentation Using Multimodal MRI Inputs;HyperMM: Robust Multimodal learning with Varying-Sized Inputs;HEMIT: H&E to Multiplex-Immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator;Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis;medPromptX: Grounded Multimodal Prompting for Chest X-Ray Diagnosis;predicting Stroke through Retinal Graphs and Multimodal Self-supervised learning;multimodality for Diagnosis of Asian Choroidal Vasculopathy: Results from a Novel Dataset and Deep-learning Experiments;multimodality Frequency Feature Customized learning for Pediatric Ventricu

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4th international Research conference on machine learning, Deep learning, and Blockchain, IRCICD 2023

4th International Research Conference on Machine Learning, D...

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4th international Research conference on machine learning, Deep learning, and Blockchain, IRCICD 2023

ISBN: (纸本)9783031882364

the proceedings contain 9 papers. the special focus in this conference is on machine learning, Deep learning, and Blockchain. the topics include: Development of an Intrusion Detection Model Using a Support Vector machine to Identify Cyber Attacks;vector Quest: A Faster and Better Open-Domain Question-Answering System;deep learning Approach to Indian Sign Language Recognition;false Data Injection Attack Prediction Using Federated Deep learning Approach;block Chain Ballot: A Secure and Transparent E-Voting System;combination of IoT and Block Chain for Safe Data Management;A Prototype for Secure and Efficient Real Estate Transactions Using IPFS and Block Chain Technology.

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Secure Storage Method of Students’ Mental Health Privacy Data Based on machine learning 4th

Secure Storage Method of Students’ Mental Health Privacy Da...

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4th international conference on IoT and Big Data Technologies for Health Care, IotCare 2023

作者： Wang, Lijing Wang, Lili Changchun University of Finance and Economics Changchun130122 China

ISBN: (纸本)9783031900839

When sharing data, the theft of students’ mental health privacy data may pose a threat to students themselves, medical institutions, and even society. How to realize the safe storage of mental health privacy data is a problem that needs to be studied in medical data sharing. this paper aims to study a secure storage method of students’ mental health privacy data based on machine learning. Combining the medium k-means algorithm of machine learning with the k-anonymity model, the k-anonymity clustering algorithm is constructed to realize the anonymization of students’ mental health privacy data and complete the primary safe storage. the key is generated by the neural network algorithm in machine learning, and the anonymous student mental health privacy data is encrypted to complete the deep security storage. the results show that the decryption speed of the research method is the fastest, and the implementation efficiency of the security storage algorithm proposed in this paper is high;After the safe storage process, the information loss of the research method is less, and the integrity of private data can be better guaranteed. After the research method is processed, the entropy value is larger, which indicates that the amount of information contained in the processed private data is smaller, and it is more difficult to be stolen. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Anonymity

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application of Deep learning for Wildfire Risk Management: Preliminary Results 4th

Application of Deep Learning for Wildfire Risk Management:...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： De Rango, Alessio D’Ambrosio, Donato Mendicino, Giuseppe Department of Environmental Engineering University of Calabria Ponte Pietro Bucci Rende87036 Italy Department of Mathematics and Computer Science University of Calabria Ponte Pietro Bucci Rende87036 Italy

ISBN: (纸本)9783031812439

Due to ongoing climate change and the related increased risk of drought, wildfires in the Mediterranean are expected to become more frequent and intense. this note introduces the use of Deep learning to produce susceptibility maps for wildfire risk assessment to tackle this scenario. Indeed, thanks to the improved computational capabilities of modern GPUs, recent literature has pointed out that it is possible to train Deep learning models on big datasets by outperforming standard approaches in several application domains, including the one object of this study. An Artificial Neural Network (ANN) model has been developed and applied to the Calabria region (southern Italy) study area. the network has been trained and validated over ten years of data by considering both meteorological and geomorphological data, and human factors. Preliminary results are promising, evidencing a higher network’s accuracy to the operational state-of-the-art Fire Weather Index (FWI) method, currently applied for forecasting and mitigation purposes in the study area. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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