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共 606 条记录，以下是11-20 订阅

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machine learning Regression Model Development and Data Visualization of Road Accident in Urdaneta City, Pangasinan, Philippines 24

Machine Learning Regression Model Development and Data Visua...

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6th asia conference on machine learning and computing, acmlc 2024

作者： Dorado, Danilo Aviles, Joey Graduate School Angeles University Foundation Pampanga Angeles City Philippines

ISBN: (纸本)9798400710018

Road accidents contribute significantly to annual fatalities. Various agencies engaged in road safety management are consistently striving for quality improvement to predict and mitigate accidents through the aid of information technology. As machine learning emerged, its data analysis, visualization, and prediction capabilities have proven to help policymakers. the main objective of this study is to develop a machine-learning model for predicting road accidents in Urdaneta City, Philippines. the dataset used consists of three years (2021-2023) of road accident records from the Emergency Medical Services (EMS). Various preprocessing methods are employed, and a grid search technique is implemented to identify optimal features for the model. Random Forest, XGBoost, AdaBoost, Decision Tree, and LGBM algorithms are developed to predict and visualize road accident occurrences. Upon development, it was found that XGBoost performs well among other models, acquiring a total Rsquared of 0.9996 in training and 0.9898 during testing. To validate the findings of the study, the model's interpretability was assessed using a William Plot. the SHAP and LIME methodologies offer valuable insights into how various variables affect the model's forecast of the monthly number of road accidents. the Permutation Feature Importance approach provides insight into the specific impact of each feature on the model's predictions, emphasizing the significant influence of time on accident frequency, particularly during peak hours or late-night accidents. the Box Plot offers valuable insights, with the median (Q2) indicated by a line showing a value of around 10. Leveraging this model can significantly reduce the likelihood of accidents in real-time, resulting in a safer and more intelligent transportation ecosystem. © 2024 Copyright held by the owner/author(s).

关键词： Decision trees

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A Comparative Study of Transfer learning Approaches For Strengthening Face Antispoofing Security 24

A Comparative Study of Transfer Learning Approaches For Stre...

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6th asia conference on machine learning and computing, acmlc 2024

作者： Ale, Prabhat Ghimire, Sunil Mali, Drish Shakya, Aman Joshi, Basanta Panday, Sanjeeb Prasad Institute of Science and Technology Tribhuvan University Butwal Nepal FuseMachines Pvt. Ltd. Kathmandu Nepal The University of Edinburgh Edinburgh United Kingdom Institute of Engineering Tribhuvan University Kathmandu Nepal

ISBN: (纸本)9798400710018

Systems based on face recognition technology are vulnerable to spoofing attacks where fake faces can be used to bypass facial recognition systems. A comparative analysis of various CNN-architecture designs fused with a machine learning model is experimented within this study on a custom dataset. the pre-trained weights of these CNN architectures are used in forward propagation to serve as feature extractors, extracting useful features from a face image. the extracted features are then utilized to train a variety of machine-learning algorithms. VGG16, InceptionV3, Xception, ResNet50, and MobileNetV2 were the state-of-the-art pre-trained CNN architectures employed in this study for extracting facial features from face images. Additionally, various machine learning algorithms including Logistic Regression, KNN, Decision Tree, Support Vector machines, Random Forest, Gradient Boosting, XGBoost, and LightGBM were trained on these extracted features to classify between real and spoof faces. Each combination was thoroughly investigated to determine how well each architecture and algorithm performed together for feature extraction and classification. Afterward, a comparative analysis was conducted using custom datasets to determine the best-performing combination. the most effective combination was then selected to develop a face-liveness detector. Among the designs investigated, the combination of VGG16 and Linear SVM appears to be the most promising, with an F1-score of 98.44% on test datasets. Furthermore, custom datasets were collected to enhance the research, taking into account the scarcity of Nepalese-origin facial datasets. A custom dataset was curated by collecting images and videos from 75 Nepalese-origin participants. © 2024 Copyright held by the owner/author(s).

关键词： Random forests

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Exploring the Role of Interpretability in Adversarial Defense Mechanisms based Malware Classification 6

Exploring The Role of Interpretability in Adversarial Defens...

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6th International conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Alhawari, Assem Ebadinezhad, Sahar Department of Computer Information Systems Near East University Nicosia99138 Cyprus Near East University Nicosia99138 Cyprus

ISBN: (纸本)9798331522667

this systematic literature review (SLR) presents a comprehensive overview of the current state of interpretability techniques in malware classification based on adversarial defense mechanisms, challenges, and future directions. Several ML and DL approaches have been integrated into the malware detection and classification defense systems, where adversarial attacks represent a crucial security threat against these defense systems. the interpretability techniques have been found very important to be applied to enhance the performance of these security systems and develop more explainable models and decisions for users and security experts. the SLR was conducted by combining and reviewing scientific literature on adversarial defense mechanisms-based interpretability techniques in the field of malware classification published between 2013 and 2024. this SLR presents a new insight into the categorization of interpretability techniques' implications in adversarial defense systems. We evaluate the importance and effectiveness of interpretability techniques by investigating ML models, adversarial attacks, evaluation methods, datasets, user trust, and malware features. Furthermore, the study provides challenges and future research directions for further existing issues. © 2025 IEEE.

关键词： Adversarial machine learning

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Ensemble learning with Feature Fusion for Well-Overflow Detection 5th

Ensemble Learning with Feature Fusion for Well-Overflow Dete...

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5th International conference on Neural computing for Advanced Applications (NCAA)

作者： Cui, Ziliang Liu, Li Xiong, Yinzhou Liu, Yinguo Su, Yu Man, Zhimin Wang, Ye Chongqing Univ Posts & Telecommun Chongqing Peoples R China Chongqing Energy Big Data Ctr Co Ltd Chongqing Peoples R China Shandong Zibo Polytech Vocat Sch Chongqing Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Traditional methods for kick prediction rely on experience and basic physical models, but they struggle to maintain high accuracy under complex well conditions and varying geological environments. Moreover, conventional approaches often fail to fully leverage big data resources. therefore, this study aims to improve the accuracy of kick prediction through machine learning models, especially by adopting an innovative feature fusion strategy to optimize the prediction model. We evaluated the comprehensive performance of six different machine learning models: GBM, LightGBM, CatBoost, XGBoost, Random Forest, and AdaBoost. Based on these evaluations, we proposed a feature score weighted fusion method. this method weights the importance scores of features based on the accuracy of each model, selecting the features that have the most significant impact on the prediction results. Experimental results show that the model utilizing the feature fusion strategy performs better on the test set than any single base model. Specifically, the GBM model, optimized through feature fusion, achieved better values in accuracy, F1 score, and F2 score, proving the effectiveness of this feature fusion strategy in enhancing the accuracy of kick predictions during well control operations. Furthermore, this study compared the performance of different models, providing valuable insights for kick prediction in well control operations. this research not only demonstrates the potential of feature fusion strategies in improving the accuracy of kick prediction but also offers new insights for the future application of machine learning technologies in the field of oilfield data analysis.

关键词： machine learning feature fusion kill well overflow

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6th International conference on Computational Intelligence in Pattern Recognition, CIPR 2024

6th International Conference on Computational Intelligence i...

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6th International conference on Computational Intelligence in Pattern Recognition, CIPR 2024

ISBN: (纸本)9789819780891

the proceedings contain 45 papers. the special focus in this conference is on Computational Intelligence in Pattern Recognition. the topics include: Vietnamese Text Summarization Using Transformer Approach;DQN Trader: Reinforcement learning for Automated Trading;indirect Feature Extraction for Efficient Fingerprint Recognition;A Novel and Intelligent Neuroimage Analysis for Tumor Classification with thermo-Color Segmentation Using VGG-16;a Deep Feature Ensemble Methodology for 2D Biomedical Image Classification;detection of Hardware Trojan in Nanocrossbar-Based Cryptographic Circuit;convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture;performance Comparison of Different machine learning Classifiers for Diabetes Prediction;Intelligent Anti-tamper Systems: Safeguarding Critical Infrastructure in the Era of Proliferating Interconnected Devices and IoBT;contrasting Various Cryptographic Algorithms for Storage and Cloud computing Services;A New Technique of Cellular-Automata-Based PUF Design in Nanoscale Crossbar Circuits;CrimeScene2Graph: Generating Scene Graphs from Crime Scene Descriptions Using BERT NER;a Squeeze and Excitation Framework Utilizing ResNet-152 for Alzheimer’s Disease Dementia Classification;Identification of Real or Fake Currency Using Semi-supervised Generative Adversarial Networks (SSGANs);efficient Detection of Fake Twitter Profiles on Social Media through Hybrid Deep learning Model;religious Sentiment Analysis and Detection on Social Media;Visualizing NASDAQ Composite Index Historical Data Using Tableau;typeface Identification for English Using Convolutional Neural Networks;Adaptive Ensemble of ML Regressors and LSTM for Stock Price Prediction;PolyNMT: Neural machine Translation Model with Seq2Seq Encoder-Decoder System;a Rectangular Slotted Sierpinski Fractal Bowtie Antenna for Multiband Wireless Communications;enhancing Cardiac Health: machine learning in Coronary Artery Disease Predi

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Soft computing and machine learning Application on Water Quality Prognostication 6th

Soft Computing and Machine Learning Application on Water Qua...

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6th International conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Mallika Singh, Nanhay Lathar, Pankaj Netaji Subhas University of Technology East Campus Delhi110031 India Delhi Skill and Entrepreneurship University Delhi110077 India

ISBN: (纸本)9789819783281

Water is important for survival. With increasing cases of water quality deterioration, it is need of the hour to use brimming machine learning technologies which can aid in water quality prediction and further classification of it into fit or unfit. Traditional WQI methods used are highly data-dependent. Using machine learning algorithm leverages such issues as ML algorithms can adapt according to data and learn from various sources. Various boosting algorithms such as regression, boosting algorithm, optimization algorithms can be used to improve accuracy and feature selection. Reliability also depends on data preprocessing methods, and novel H2O AutoML ensemble model using KNN imputation method has been proven to be more accurate. the accuracy achieved by H2O AutoML is 95.29% (balanced data) and 96.9% in imbalanced data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Anomaly detection using different types of machine learning models in the context of "smart maintenance technologies in the manufacturing industry" 6

Anomaly detection using different types of machine learning ...

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6th International conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Giliyana, San Mälardalen University Universitetsplan 1 Västeras Sweden Mälardalen Industrial Technology Center John Engellaus Gata 1 Eskilstuna633 61 Sweden

Industry 4.0 presents nine technologies, including the Industrial Internet of things (IIoT), Big Data and Analytics, Cloud computing, etc. the progress of Industry 4.0 places a new demand for maintenance. Some of the nine technologies of Industry 4.0, such as IIoT, Big Data and Analytics, Cloud computing and Augmented Reality (AR), as well as Cyber-Physical System (CPS) and machine learning, play an important in the development of smart maintenance technologies. In smart maintenance research, it is presented how IIoT can be used for machine connection and maintenance data collection, machine learning models for maintenance data analysis and failure prediction, AR for maintenance instructions, etc. Although previous smart maintenance research presents many technologies for smart maintenance, the manufacturing companies still face many implementation challenges when implementing and using to add benefits to maintenance organizations in line with companies main goals. Many manufacturing companies still utilize reactive maintenance and are experiencing too much downtime. In this paper, I have shown how two machine learning models, Isolation Forest and Regression Learner, and the statistical technique, Interquartile range (IQR), can be applied in order to detect anomalies in an unsupervised dataset, consisting of travel time for a linear guide, and temperature, in a drill station, which is part of a Cyber-Physical production system, located at a smart production laboratory in Sweden. © 2025 the Authors.

关键词： Smart manufacturing

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Privacy-Preserving three-Factors Authentication and Key Agreement for Federated learning 6th

Privacy-Preserving Three-Factors Authentication and Key Agr...

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6th International conference on machine learning for Cyber Security, ML4CS 2024

作者： Wang, Guojun Jiang, Guixin Zhao, Yushuai Ji, Yinglin Yancheng Polytechnic College Yancheng224005 China School of Electronics and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9789819645657

With the development of big data and distributed computing, federated learning has received widespread attention for protecting data privacy. Federated learning is a distributed machine learning framework that allows multiple participants to collaboratively train shared models with locally retained data. However, the issues of communication security and authentication between participants have emerged as key challenges affecting the security of federal learning systems. To address this challenge, we propose an authentication and key agreement protocol for federated learning environment. the proposed protocol enables authentication between edge devices and server, which establishes a secure session key to secure communication between participants. Additionally, we verify the security of the session key by the verification tool. Finally, experimental evaluation results show that the protocol performs well in terms of computational overhead and communication latency and is suitable for large-scale federated learning systems with resource-constrained devices. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Secure Resource Allocation via Constrained Deep Reinforcement learning 6th

Secure Resource Allocation via Constrained Deep Reinforceme...

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6th International conference on machine learning for Cyber Security, ML4CS 2024

作者： Sun, Jianfei Gao, Qiang Wu, Cong Li, Yuxian Wang, Jiacheng Niyato, Dusit School of Computing and Information Systems Singapore Management University Singapore188065 Singapore School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu611130 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9789819645657

the proliferation of Internet of things (IoT) devices and the advent of 6G technologies have introduced computationally intensive tasks that often surpass the processing capabilities of user devices. Efficient and secure resource allocation in serverless multi-cloud edge computing environments is essential for supporting these demands and advancing distributed computing. However, existing solutions frequently struggle with the complexity of multi-cloud infrastructures, robust security integration, and effective application of traditional deep reinforcement learning (DRL) techniques under system constraints. To address these challenges, we present SARMTO, a novel framework that integrates an action-constrained DRL model. SARMTO dynamically balances resource allocation, task offloading, security, and performance by utilizing a Markov decision process formulation, an adaptive security mechanism, and sophisticated optimization techniques. Extensive simulations across varying scenarios-including different task loads, data sizes, and MEC capacities-show that SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs and a 41.5% improvement in energy efficiency over state-of-the-art methods. these enhancements highlight SARMTO ’s potential to revolutionize resource management in intricate distributed computing environments, opening the door to more efficient and secure IoT and edge computing applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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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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