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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是181-190 订阅

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Contrastive learning-Based Sequential Recommendation Model 13th

Contrastive Learning-Based Sequential Recommendation Model

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13th international conference on Natural Language Processing and Chinese computing

作者： Zhang, Yuan Nuo, Minghua Jia, Xiaoyu Wang, Yao Inner Mongolia Univ Coll Comp Sci Hohhot 010021 Inner Mongolia Peoples R China Natl & Local Joint Engn Res Ctr Intelligent Infor Hohhot 010021 Inner Mongolia Peoples R China Inner Mongolia Key Lab Multilingual Artificial In Hohhot 010021 Inner Mongolia Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Contrastive learning has demonstrated remarkable performance in sequential recommendation tasks. The core principle of it is to maximize consistency between the original and augmented data while increasing the distance between the original data and other instances, thereby facilitating the learning of more discriminative features. Nonetheless, such an approach has limitations and may inadvertently augment the distance between samples that belong to the same category in the feature space. To tackle the above problems, we propose a novel contrastive learning paradigm for sequential recommendation, termed CLSRec (Contrastive learning-Based Sequential Recommendation Model). Our framework integrates cross-domain and intra-domain features through a meticulously designed contrastive loss function and introduces gray-scale positive sampling, aiming to address the inadequacy of focus on similar items in sequential recommendation. The proposed contrastive learning framework effectively captures intra-sequence item transition patterns and inter-sequence dependencies among items. Empirical evaluations on real-world datasets show that our model significantly outperforms advanced baseline models, validating its effectiveness in sequential recommendation scenarios.

关键词： Sequential Recommendation Contrastive learning Graph Neural Networks Collaborative Filtering

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HybridFL: Hybrid Approach Toward Privacy-Preserving Federated learning 6th

HybridFL: Hybrid Approach Toward Privacy-Preserving Federate...

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

作者： Ali, Sheraz Mamoon, Saqib Usman, Areeba Abidin, Zain ul Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan250022 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China University of Sargodha Sargodha Pakistan Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Software Engineering University of Jinan Jinan250022 China

ISBN: (纸本)9783031736988

In this study, we introduce a novel Hybrid Federated learning (HybridFL) approach aimed at enhancing privacy and accuracy in collaborative machine learning scenarios. Our methodology integrates Differential Privacy (DP) and secret sharing techniques to address inference risks during training and protect against information leakage in the output model. Drawing inspiration from recent advances, we present a HybridFL framework that combines the strengths of Homomorphic Encryption (HE) and Multi-Party Computation (MPC) to achieve secure computation without the computational overhead of pure HE methods. Our contributions include a privacy-preserving design for Federated learning (FL) that ensures local data privacy through secret sharing while leveraging DP mechanisms for noise addition. The system offers resilience against unreliable participants and is evaluated using various machine learning models, including Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), and linear regression. Furthermore, we address potential external threats by deploying predictive model outputs as robust services against inference attacks. Experimental results demonstrate improved accuracy and convergence speed, establishing the viability of HybridFL as an effective solution for collaborative machine learning with enhanced privacy guarantees. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Federated learning

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Evaluation of Document Deduplication Algorithms for Large Text Corpora 10th

Evaluation of Document Deduplication Algorithms for Large ...

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10th international conference on machine learning, Optimization, and data science, LOD 2024

作者： Leveling, Johannes Helmer, Lennard Stein, Benny Joerg Wegener, Dennis Sheikh, Zoha Fernandes, Elanton Abdelwahab, Hammam Sankt Augustin Germany Lamarr Institute for Machine Learning and Artificial Intelligence Sankt Augustin Germany

ISBN: (纸本)9783031824807

Performance of large language models (LLMs) is correlated with diverse and high-quality training data. One aspect of data quality is the presence of duplicate documents in the training data. In this paper, we evaluate five algorithms for deduplicating large data sets, namely MinHash/LSH, Exact Hashes, SimHash, Scalable Bloom filter, and Suffix Array. We report on their precision, recall, memory requirements and runtime on deduplicating OpenSubtitles and Oscar data for five languages (EN, DE, ES, FR, IT). We find that the best overall performance is achieved by using a MinHash/LSH, but other options such as Scalable Bloom filter can be more suitable in resource-critical situations. While precision varies between 0.833 and 0.985 across algorithms, recall varies between 0.247 and 0.989, indicating different levels of aggressiveness. We conclude that MinHash/LSH is the most suitable algorithm to deduplicate pretraining data for LLMs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data assimilation

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Quantum-Enabled (QeSVM) Facial Expression Identification of Autistic Children 4th

Quantum-Enabled (QeSVM) Facial Expression Identification of ...

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4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Abirami, S. P. Chandar, B. Misha Coimbatore Inst Technol CIT Dept Comp Sci Coimbatore Tamil Nadu India VIT AP Univ Sch Comp Sci & Engn SCOPE Amaravati Andhra Pradesh India

ISBN: (纸本)9783031837920;9783031837937

Autism Spectrum Disorder (ASD) is a special neurodevelopmental condition identified in children with challenges in social communication, restricted interest, and repetitive behaviour traits. Early detection and identification of ASD is vital for effective intervention, yet traditional diagnostic methods often result in delayed diagnoses due to the subtlety and variability of early symptoms. Facial expression analysis is recently been looked upon as a significant approach for early detection, given the atypical facial expression patterns frequently observed in children with ASD. Recent advancements in machine learning (ML) and deep learning (DL) have improved the accuracy of facial expression recognition, enabling automated systems to detect these subtle differences more reliably. This research explores the possible incorporation of quantum computing principles, particularly through quantum support vector machines (QeSVMs), into the analysis of facial expressions in autistic children. Quantum computing offers unique advantages in processing high-dimensional data, allowing for more efficient and accurate pattern recognition in comparison to conventional classical methods. By leveraging quantum-enhanced machine learning techniques, this study aims to improve the early identification of ASD through facial expression analysis, providing a novel and potentially transformative approach to ASD diagnosis. The findings suggest that QeSVMs, when applied to facial expression data, can enhance classification accuracy and computational efficiency with huge scope for effective early detection strategies for ASD. This paper is one such effort that claims to be a growing field of knowledge. This novel research contributes to the growing body of knowledge of effectively integrating quantum computing and autism diagnosis, offering insights that could lead to better outcomes for children with ASD through earlier and more reliable detection methods.

关键词： Autism Spectrum Disorder Quantum computing Landmark Identification Geometric Features Early Intervention

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HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks 7th

HyperBrain: Anomaly Detection for Temporal Hypergraph Brain...

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7th international Workshop on machine learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Sadeghian, Sadaf Li, Xiaoxiao Seltzer, Margo Department of Computer Science University of British Columbia Vancouver Canada Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada Vector Institute Toronto Canada

ISBN: (纸本)9783031787607

Identifying unusual brain activity is a crucial task in neuroscience research, as it aids in the early detection of brain disorders. It is common to represent brain networks as graphs, and researchers have developed various graph-based machine learning methods for analyzing them. However, the majority of existing graph learning tools for the brain face a combination of the following three key limitations. First, they focus only on pairwise correlations between regions of the brain, limiting their ability to capture synchronized activity among larger groups of regions. Second, they model the brain network as a static network, overlooking the temporal changes in the brain. Third, most are designed only for classifying brain networks as healthy or disordered, lacking the ability to identify abnormal brain activity patterns linked to biomarkers associated with disorders. To address these issues, we present HyperBrain, an unsupervised anomaly detection framework for temporal hypergraph brain networks. HyperBrain models fMRI time series data as temporal hypergraphs capturing dynamic higher-order interactions. It then uses a novel customized temporal walk (BrainWalk) and neural encodings to detect abnormal co-activations among brain regions. We evaluate the performance of HyperBrain in both synthetic and real-world settings for Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder (ADHD). HyperBrain outperforms all other baselines on detecting abnormal co-activations in brain networks. Furthermore, results obtained from HyperBrain are consistent with clinical research on these brain disorders. Our findings suggest that learning temporal and higher-order connections in the brain provides a promising approach to uncover intricate connectivity patterns in brain networks, offering improved diagnosis. Our code is available at: https://***/ubc-systopia/HyperBrain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Anomaly detection

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Evaluating the Efficiency of FSL Models in the Diagnosis of Kyasanur Forest Disease 4th

Evaluating the Efficiency of FSL Models in the Diagnosis of ...

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4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Thangapandian, Vani Swamynathan, A. N. Devi, G. Gayathri Rajeswari Vedachalam Govt Arts Coll PG Dept Comp Sci Chengalpattu 603001 Tamil Nadu India Shrimathi Devkunvar Nanalal Bhatt Vaishnav Coll W Dept Comp Sci Chennai Tamil Nadu India

ISBN: (纸本)9783031837920;9783031837937

Background: Kyasanur Forest Disease (KFD) is a haemorrhagic viral fever endemic to the forest regions of Karnataka. Many machine learning models have been designed and tested for the accurate prediction of the onset of disease. The dataset must be large enough to accurately predict the disease which impacts the early prevention and aid in the treatment strategies. However, the dataset of KFD is scarcely available and machine learning models fail to predict accurately. Few Shot learning(FSL) is a unique kind of machine learning in which the model is trained to accomplish a task with a limited amount of training data. Objective: The key idea of this research work is to evaluate the efficiency and accuracy of various FSL methods in the prediction of the onset of KFD using the minimal dataset. Methodology: Many FSL methods from various FSL models have been trained and tested using the KFD dataset to compare the efficiency. Then the meta-accuracy and meta-loss of these methods are evaluated. Results: Among all, two methods Variational Encoders and Siamese networks show high meta-accuracy, 96% and 95% respectively. And their meta loss value is 0.08 which is very minimum. Compared with other methods, Variational Encoders and Siamese networks have shown better performance in predictive accuracy.

关键词： Disease detection KFD Few Shot learning Efficiency accuracy healthcare

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A Framework for Adopting machine learning in the Clinical Domain 2

A Framework for Adopting Machine Learning in the Clinical Do...

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2nd international conference on data, Information and computing science, CDICS 2024

作者： Tan, Esther Meng-Yoke Liu, Fan Chia, Yuen Kwan National University of Singapore Singapore

ISBN: (纸本)9781643685762

The usage of machine learning in the clinical domain enables better decision-making skills and improves the quality of health care services rendered to the patients. This paper described a new MLOps framework which addresses the challenges faced when applying AI in the clinical domain. These include the handling of heterogenous, incomplete and evolving medical data sets, ensuring privacy of patients, controlling the versions of dataset and models adopted. The proposed framework supports a repository of evolving anonymized patient medical data, tracking of the feature selection and models adopted using versioning control system and records the accuracy of their outcomes. It also enables audit tracking and ensures transparency and accountability, thus helping to increase medical workers' confidence in machine learning and encourages the use of ML in the clinical work. For evaluation, the proposed framework is set up in the clinical environment using existing patient’s records. Evidences, such as records of model training using specific datasets traceable to the actual patients’ records, are collected. These evidences fulfill the audit requirements and form the basis to justify research funding in future AI-related medical work. Finally, the study also proposes future work and directions to extend the framework into other domains. © 2025 The Authors.

关键词： Adversarial machine learning

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Synthetic data Generation Using GANs for Improved machine learning Models 12th

Synthetic Data Generation Using GANs for Improved Machine Le...

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12th international conference on Recent Trends in computing, ICRTC 2024

作者： Vinoth Kumar, S. Kumar, Kavuri Santosh Kotteeswaran, C. Manoj, S. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819789450

Explores the utilization of Generative Adversarial Networks (GANs) for generating synthetic images to address challenges related to limited data availability and privacy concerns. GANs, trained on diverse datasets, are capable of producing high-fidelity images from random noise. For example, training a GAN on the MNIST dataset allows for the creation of digit images that closely resemble those in MNIST, which can then be used to train other neural networks. Our evaluations demonstrate that these synthetic images maintain high fidelity and are effective in various tasks, including classification and object detection. This highlights the potential of GANs for dataset augmentation and enhancement of model performance, providing a viable solution for expanding training datasets while preserving data privacy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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Accelerate K-Mode Algorithms Using The Triangle Inequality 16th

Accelerate K-Mode Algorithms Using The Triangle Inequality

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16th international conference on Scalable Uncertainty Management, SUM 2024

作者： Nguyen, Vu-Linh Nguyen-Mau, Toan Huynh, Van-Nam Heudiasyc Laboratory University of Technology of Compiègne Compiègne France Japan Advanced Institute of Science and Technology Nomi Japan

ISBN: (纸本)9783031762345

Clustering is an unsupervised machine learning task that aims to discover natural groups in the given dataset. K-mode algorithms, which are adaptions of K-mean algorithms for continuous data, are among the most popular algorithms for discovering clusters in categorical data. In this paper, we present some results on how to accelerate them using the triangle inequality, while still always computing exactly the same result as the original K-mode algorithms. We also provide some empirical evidence to illustrate the potential gains provided by leveraging the triangle inequality. Finally, we envision future work aimed at providing a comprehensive understanding of the use of triangle inequality in accelerating other clustering algorithms for categorical data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Implementation of machine learning Techniques for Predictive Security Analytics 12th

Implementation of Machine Learning Techniques for Predictive...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

作者： Moon, Pradnya S. Deshmukh, Anand B. Sarode, Harsha Jitendra Sharma, Shweta Birare, Komal Madhukar Anandpwar, Winit Nilkanth Department of Computer Technology Yashwantrao Chavan College of Engineering Maharashtra Nagpur India Department of Information Technology Sipna College of Engineering and Technology Maharashtra Amravati India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India Department of Artificial Intelligence and Data Science Arya College of Engineering Rajasthan Jaipur India Information Technology Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Computer Science Dr. Ambedkar College Deekshabhoomi Maharashtra Nagpur India

ISBN: (纸本)9789819601462

machine learning (ML)-based predictive security analytics has become an important part of modern defense. This study gives a brief overview of how machine learning can be used in predictive security analytics. Traditional security measures aren’t working as well as they used to because online risks are getting smarter and more complicated. We need to use proactive and predictive strategies instead. machine learning systems can look through huge amounts of data and find trends and outliers that could mean an attack or security breach. Using controlled, unstructured, and reinforcement learning, predictive security analytics can spot problems early on and stop them before they become a real problem. Supervised learning algorithms, like support vector machines and neural networks, let security events be put into groups based on data that has already been named. This makes it easier to find threats. Unsupervised learning methods, like grouping and anomaly detection, make it easier to spot strange behavior that could be a sign of a security breach even without any training data. Because they learn from how they interact with their surroundings, reinforcement learning models let security systems change and get better over time. By adding machine learning models to security data tools, companies can improve their cyber defenses, which lets them proactively reduce threats and control risks. Furthermore, the ability of ML algorithms to learn new things all the time makes it possible for security measures to be flexible enough to react to changing dangerous environments. However, problems like bad data, models that are hard to understand, and threats from other people mean that ML for predictive security analytics needs to be carefully thought out and implemented using strong methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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