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检索条件"任意字段=2017 International Conference on Machine Learning and Soft Computing, ICMLSC 2017"

共 2746 条记录，以下是71-80 订阅

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Serverless computing Approach for Deploying machine learning Applications in Edge Layer 36

Serverless Computing Approach for Deploying Machine Learning...

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36th international conference on Information Networking (ICOIN)

作者： Bac, Ta Phuong Tran, Minh Ngoc Kim, YoungHan Soongsil Univ Sch Elect Engn Seoul South Korea

ISBN: (纸本)9781665413329

Serverless computing- a stateless cloud computing model, is an emerging solution that has shown significant benefits to efficiency and cost for event-driven applications in the cloud environment, including artificial intelligence (AI), machine learning applications. With serverless computing, the machine learning system's complexity is minimized, flexible and straightforward in management. However, operating and managing serverless machine learning services on clouds faces many limitations such as latency and data privacy. Local distributed edge computing nodes which are closed to users can address these challenges of cloud-serverless AI applications. Based on this motivation, in this paper, we propose an architecture for deploying machine learning workload as serverless functions in the edge environment. We illustrate our proposed approach and evaluate its performance and effectiveness by exploiting a holistic end-to-end image classifier, a famous machine learning use case in the MNIST dataset. Our proof of concept provides comprehensive assessments that prove its effectiveness in latency reduction and distributed machine learning deployment.

关键词： Serverless computing machine learning Edge-cloud computing architecture

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Comparing Performance of Different machine learning Methods for Predicting Severity of Construction Work Zone Crashes

Comparing Performance of Different Machine Learning Methods ...

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ASCE international conference on computing in Civil Engineering (I3CE)

作者： Mashhadi, Ali Hassandokht Rashidi, Abbas Medina, Juan Markovic, Nikola Univ Utah Dept Civil & Environm Engn Salt Lake City UT 84112 USA

ISBN: (数字)9780784485248

ISBN: (纸本)9780784485248

In 2020, more than 102,000 work zone crashes occurred in the United States, resulting in over 45,000 injuries and more than 850 fatalities. These numbers are higher than 2019 records, despite lower traffic volumes due to the COVID-19 pandemic. Also, population growth and the increased load on infrastructure are expected to lead to more construction work zones and, consequently, more crashes and fatalities in the future. As such, understanding the factors contributing to work zone crashes and their severity can assist the Department of Transportation (DOT) in safety management and planning. Accordingly, this study investigated the effect of freeway construction work zones on the severity of crashes by employing machine learning algorithms. Moreover, the performance of three of the most common machine learning models, decision tree, random forest (RF), and XGBoost, were evaluated based on accuracy, precision, recall, and F1-score. The model was developed using crash data and work zone information from the state of Utah between 2017 and 2021, considering an extensive set of work zone attributes, such as road factors, environmental conditions, driver attributes, and work zone features. The study results showed that RF has the best performance in severity classification, achieving an accuracy of 88.6%. Moreover, feature importance analysis reveals that roadway surface conditions, crash type, motorcycle involvement, weather conditions, and roadway junction type are significant contributors to work zone crash severity. The findings of this study provide valuable insights for the analysis of construction work zone crashes and DOT's safety management plans.

关键词： Work zone Safety Safety Management machine learning Ensemble learning Construction Safety

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STAD: A Traffic Anomaly Detection Technology Based on Siamese Neural Network

STAD: A Traffic Anomaly Detection Technology Based on Siames...

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IEEE international Performance, computing, and Communications conference (IPCCC)

作者： Yin, Zinuo Ma, Hailong Hu, Tao Informat Engn Univ PLA Strateg Support Force Inst Informat Technol Zhengzhou Peoples R China

ISBN: (纸本)9798350302936

The current network traffic anomaly detection technology based on traditional machine learning is highly dependent on much fully marked data, making it difficult to effectively address the problem of imbalanced traffic training data. In this paper, we propose STAD, a traffic anomaly detection technology based on Siamese Neural Network to address the scarcity of attack samples in traffic training sets. First, we design a K-medoids-based few-shot sampling (KFS) method to extract few representative samples from each class of traffic, hoping to improve the detection reliability of our fewshot model. We then design a traffic anomaly detection method based on a Siamese Multi-Layer Perceptron (MLP). This method utilizes two identical MLP models to train paired traffic samples, capturing non- linear relationships across traffic features and learning the similarities and differences between data. Meanwhile, we also design a cost function containing two specific losses to improve the detection performance. The experimental results based on the CICIDS2017 dataset show that STAD is more suitable for few-shot traffic anomaly detection. Under the condition that only a small amount of malicious traffic is trained, STAD still maintains over 95% detection accuracy and detection rate for attacks.

关键词： traffic anomaly detection few-shot learning siamese neural network

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Exploring Explainable machine learning in Healthcare: Closing the Predictive Accuracy and Clinical Interpretability Gap 6th

Exploring Explainable Machine Learning in Healthcare: Closin...

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6th international conference on Recent Innovations in computing, ICRIC 2023

作者： Singh, Gautam Pal, Ashok Department of Mathematics Chandigarh University Punjab Mohali India

ISBN: (纸本)9789819728381

machine learning has emerged as a powerful tool for healthcare, aiding in patient outcome predictions and clinical decision-making (Bhardwaj et al. (2017 IEEE 41st annual computer software and applications conference (COMPSAC). IEEE, pp. 236–24 2017)). However, the opacity of many models has hindered their clinical adoption, where interpretability and transparency are vital. This study explores "Explainable machine learning for Healthcare," aiming to bridge the gap between clinical interpretability and prediction accuracy. It underscores the importance of interpretability in healthcare. In this study, we have developed two models (Naive Bayes and Logistic Regression) to accurately classify 24 diseases based on patient descriptions written in everyday English, with a 0.99 accuracy rate. A LIME explainer is attached to each model to explain the predictions they made. This study utilizes the "Symptom2Disease" dataset, sourced from Kaggle, containing 1200 data points, with 50 data points allocated to each disease. The study also suggests future research priorities, such as developing innovative algorithms and visualization methods that balance interpretability and prediction accuracy in healthcare. In conclusion, this research advocates for models that excel in both predictive accuracy and providing healthcare professionals with transparency for informed clinical decision-making. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Transparency

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Expand Object Detection Dataset Automatically via Deep learning Neural Networks 3

Expand Object Detection Dataset Automatically via Deep Learn...

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3rd international conference on computing and Information Technology, ICCIT 2023

作者： Liu, Bing Ganesan, Subramaniam Oakland University Department of Electrical and Computer Engineering RochesterMI United States

ISBN: (纸本)9798350321487

Object detection is an important research field in image processing. The state-of-the-art machine learning models are trained with huge number of samples to solve this problem. Manually labeling objects on images is labor intensive, expensive, and time consuming. It is desirable to create an automatic labeling system to reduce the cost and the time needed. A deep learning neural networks based automatic labeling system is proposed in this paper to expand Microsoft Common Objects in Context 2017 (COCO 2017) dataset from 80 classes to 81 classes by adding labels for the new class, human faces, automatically. The system contains an Auto-Labeling Model (ALM) which is trained using Web Image Dataset for Event Recognition FACE (WIDER FACE) dataset. ALM detects faces in COCO 2017 dataset and provides bounding boxes automatically with very high accuracy. The generated new labels by ALM are merged to the existing labels in COCO 2017 dataset automatically. 4,110 hours human effort is saved using this system. The expanded COCO 2017 dataset is evaluated quantitatively and visually to ensure the efficiency of the proposed auto-labeling system. © 2023 IEEE.

关键词： Object detection

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Characterizing the Performance of Emerging Deep learning, Graph, and High Performance computing Workloads Under Interference

Characterizing the Performance of Emerging Deep Learning, Gr...

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1st international conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Xu, Hao Song, Shualig Mao, Ze Univ Texas Austin Austin TX 78712 USA Univ Southern Calif Los Angeles CA USA

ISBN: (纸本)9798350364613;9798350364606

Throughput-oriented computing via co-running multiple applications in the same machine has been widely adopted to achieve high hardware utilization and energy saving on modern supercomputers and data centers. However, efficiently co-running applications raises new design challenges, mainly because applications with diverse requirements can stress out shared hardware resources (IO, Network and Cache) at various levels. The disparities in resource usage can result in interference, which in turn can lead to unpredictable co-running behaviors. To better understand application interference, prior work provided detailed execution characterization. However, these characterization approaches either emphasize on traditional benchmarks or fall into a single application domain. To address this issue, we study 25 up-to-date applications and benchmarks from various application domains and form 625 consolidation pairs to thoroughly analyze the execution interference caused by application co-running. Moreover, we leverage mini-benchmarks and real applications to pinpoint the provenance of co-running interference in both hardware and software aspects.

关键词： Co-running Deep learning Graph High Performance computing

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Semi-supervised Few-shot Network Intrusion Detection based on Meta-learning 16

Semi-supervised Few-shot Network Intrusion Detection based o...

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international conferences on Internet of Things (iThings) / Green computing and Communications (GreenCom) / Cyber, Physical and Social computing (CPSCom) / Smart Data, SmartData and Congress on Cybermatics (Cybermatics)

作者： Liu, Yao Zhou, Le Liu, Qiao Lan, Tian Bai, Xiaoyu Zhou, Tinghao Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China

ISBN: (纸本)9798350309461

Due to the rapid development of the Internet, the scale of networks is growing exponentially, and new types of attacks continue to emerge. These new attacks are becoming more threatening, and the means of attack are changing with days. Most network intrusion detection models fail to achieve the same level of detection performance in real-world network environments as in laboratory environments. This is because different types of intrusion traffic evolve over time, and new intrusion attacks continue to emerge, rendering the sample data used for model training gradually obsolete. Real-time and reliable sample data is always scarce. How to achieve accurate and effective network intrusion detection in few-shot or zero-shot scenarios is a significant challenge for researchers. To address the problem of lacking real-time labeled data for new or variant attacks in real networks, this paper proposes a few-shot intrusion detection model called SPN based on semi-supervised prototype network. In this model, the original classification problem is transformed into a metric learning problem by using prototype network in meta-learning. By redefining the construction process of prototype points through the newly designed soft K-means and soft masking mechanisms, the SPN model can incorporate a large amount of unlabeled data in training based on a small amount of labeled data. This enables the SPN model to achieve outstanding intrusion detection performance in few-shot scenarios, while also having the ability to identify unknown intrusion attacks in zero-shot scenarios. A series of experimental results on datasets CICIDS2017 and UNSW-NB15 demonstrate that the proposed SPN model can accurately detect known attacks in few-shot scenarios, achieving an accuracy rate of 96%. Furthermore, it can effectively detect unknown intrusion attacks in zero-shot scenarios with a detection rate of over 93%.

关键词： Intrusion detection Few-shot Zero-shot Semi-supervision Prototype network

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SIRO: A Deep learning-Based Next-Generation Optimizer for Solving Global Optimization Problems 15th

SIRO: A Deep Learning-Based Next-Generation Optimizer for So...

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15th international conference of the Metaheuristics international conference (MIC)

作者： Oyelade, Olaide N. Ezugwu, Absalom E. Saha, Apu K. Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast Antrim North Ireland North West Univ Unit Data Sci & Comp 11 Hoffman St ZA-2520 Potchefstroom South Africa Natl Inst Technol Agartala Dept Math Agartala 799046 Tripura India

ISBN: (纸本)9783031629112;9783031629129

This paper introduces the SIR Optimizer (SIRO), a novel next-generation learned metaheuristic algorithm inspired by biological systems and deep learning techniques. The optimizer uses the susceptible-infected-removed (SIR) epidemiological model to predict the population's susceptibility, active infections, and recoveries. To enhance the search process, SIRO incorporates deep learning into its initialization and parameter tuning components, enabling intelligent and autonomous behaviour. By generating initial solutions based on neural models, the algorithm achieves efficient, effective, and robust search outcomes. To validate the effectiveness of SIRO, a set of numerical hybrid test functions from the CEC 2017 benchmark, each characterized by 30 dimensions were utilized. The experimental results were compared against various state-of-the-art algorithms, demonstrating that SIRO outperforms its competitors. Moreso, it delivers highquality solutions while utilizing fewer control parameters. The incorporation of a learning process in SIRO leads to superior precision and computational efficiency compared to other optimization approaches in the existing literature.

关键词： SIR-model SIRO optimization algorithms bio-inspired computing deep learning machine learning

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Network Analysis of Multisource Packet Capture Files Using machine learning 5

Network Analysis of Multisource Packet Capture Files Using M...

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5th international conference on Circuits, Control, Communication and computing, I4C 2024

作者： Khanum, Farheen Lakshmi, P Sree Reddy, Harsha Vardhan School of Computer Science and Applications Reva University Karnataka Bengaluru India

ISBN: (纸本)9798331528539

The study uses machine learning methods to identify and detect Distributed Denial of Service (DDoS) attacks. The dataset used in this research was captured from the Packet capture (PCAP) files of the CTU-13, CIC-DDoS2019, and CIC-DoS2017 datasets, which contain different kinds of DDoS attacks such as ICMP, UDP, TCP, and HTTP flooding. Data features were extracted from attack packets classified using time windows, with the packet windows being of different lengths to correct the distribution of packets into the classes. Network analysis helps conceptualize and solve DDoS-related problems by analyzing traffic. To detect DDoS attacks, the proposed systems use machine learning classifiers: Logistic Regression, Decision Tree, Random Forest, K-NN (K5), XGBoost (XGB), and Light GBM (LGBM). The investigation of the 'PKT_COUNT' attribute led to a positive correlation and subsequent feature selection process, while only outliers were 'cleaned out' to yield higher gains in model accuracy. The target variable 'PKT_COUNT' was then transformed into a binary form to detect the analyzed malware easily. To create a robust model, significant efforts were made to clean the dataset with processing, such as detecting and removing the outliers, ensuring the model's reliability. The analysis of models was assessed concerning the area under the Receiver Operating Characteristic - Accuracy (ROC-AUC), mean Average Precision (AP), and Accuracy Scores. Therefore, the novel machine learning-based system presented in this paper introduces a robust method of DDoS attack detection that overcomes the drawbacks of existing steps to analyze network traffic. The study raises the possibility of developing more sophisticated classifiers to be incorporated into an environment to optimize DDoS attack detection and improve network security. © 2024 IEEE.

关键词： HTTP

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A Multimodal Named Entity Recognition Approach Based on Multi-Perspective Contrastive learning 7

A Multimodal Named Entity Recognition Approach Based on Mult...

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7th international conference on machine learning and Natural Language Processing, MLNLP 2024

作者： Liu, Huafu Wang, Yongli Liu, Dongmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350354973

Multimodal Named Entity Recognition (MNER) is a vision-language task aimed at detecting the scope of entities and determining their types in given text-image pairs. Although existing MNER models have made some progress, they often encounter issues like mismatched feature dimensions and inconsistent feature representations when interacting with text sequences. This makes it difficult to bridge the semantic gap between images and text. Therefore, this paper proposes a multimodal named entity recognition method based on multiperspective contrastive learning (MPCL-NER). Initially, the ALBERT pretrained language model and the ViT visual model are used to extract features from text and images, respectively. Based on this, image feature representations are enhanced through multi-perspective contrastive learning, which also promotes the integration of image and text information. Finally, the two types of features are merged through a multi-head adaptive attention mechanism and a gated fusion mechanism. This is followed by entity tagging with a CRF (Conditional Random Field) decoder. Experimental results show that this method outperforms other baseline methods on the public dataset Twitter-2017, achieving an F1 score of 86.11%, demonstrating the effectiveness of this approach. © 2024 IEEE.

关键词： Contrastive learning

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