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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151-160 订阅

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One-Dimensional Dilated Hypothesized learning Method for Intrusion Detection System Under Constraint Resource Environment 2

One-Dimensional Dilated Hypothesized Learning Method for Int...

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2nd international conference on Advancements in Electrical, Electronics, Communication, computing and Automation, ICAECA 2023

作者： Shuriya, B. Umamaheswari, S. Rajendran, A. Sivaprakash, P. Ppg Institute of Technology Dept of Computer Science and Engineering Coimbatore India Kumaraguru College of Technology Dept of Electronics and Communication Engineering Coimbatore India Rathinam College of Arts and Science Dept of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350306811

When a breach is discovered, intrusion detection systems can alert server administrators and researchers and channel packets for cyber security incidents. In complex systems, these reports are becoming uncontrollable. To create a flexible and effective system for detecting intrusions for unforeseen threats, a number of challenges must be overcome. Systems that detect intrusions have frequently used advanced machine learning approaches, but they frequently require large amounts of computational power and processing time. In this article, the classification model is an IDS based on a one-dimensional-dilated hypothesized learning algorithm (1-DDHL). In order to account for the greatest possible pooling layer's complexity and eliminate the data redundancies brought on by pooling as well as down-sampling, dilated hypothesized inversion with a dilatation rate that includes two is initiated. In order to gather morecontexts, the dilated hypothesized convolution can widen its convolution layers. To analyze the effectiveness of the suggested solution, experiments were conducted using the CIC-IDS2017( Dataset 1)& CSE-CIC-IDS2018 (Dataset 2) sets of data, 2 well-known datasets that are publicly accessible. The 1-DDHL derived method is more accurate than some other deep learning methods currently in use, according to simulation results. With a problematic occurrence and avoidance rate of approximately 99.8% over dataset 1 and 99.97% for dataset 2, the suggested method was remarkably accurate. © 2023 IEEE.

关键词： learning algorithms

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Training and Serving machine learning Models at Scale 20th

Training and Serving Machine Learning Models at Scale

引用

20th international conference on Service-Oriented computing (ICSOC)

作者： Baresi, Luciano Quattrocchi, Giovanni Politecn Milan Informaz Bioingn Dipartimento Elettron Milan Italy

ISBN: (纸本)9783031209833;9783031209840

In recent years, Web services are becoming more and more intelligent (e.g., in understanding user preferences) thanks to the integration of components that rely on machine learning (ML). Before users can interact (inference phase) with an ML-based service (ML-Service), the underlying ML model must learn (training phase) from existing data, a process that requires long-lasting batch computations. The management of these two, diverse phases is complex and meeting time and quality requirements can hardly be done with manual approaches. This paper highlights some of the major issues in managing ML-services in both training and inference modes and presents some initial solutions that are able to meet set requirements with minimum user inputs. A preliminary evaluation demonstrates that our solutions allow these systems to become more efficient and predictable with respect to their response time and accuracy.

关键词： machine learning Runtime management Service orchestration

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A Hybrid Deep learning Network for Skin Lesion Extraction 1

引用

14th international conference on soft computing and Pattern Recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2022

作者： Rout, Ranjita Parida, Priyadarsan Dash, Sonali Department of Electronics and Communication Engineering School of Engineering and Technology GIET University Odisha Gunupur India Department of Electronics and Communication Engineering Chandigarh University Punjab Chandigarh India

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

Melanoma is one of the dangerous skin cancer worldwide. Manual detection generally takes more time and difficult to obtain accurate results due to various factors such as change in shape, size and color. In the recent years, convolutional neural network (CNN) plays a vital role for automatic detection of melanoma. In the proposed model, a hybrid deep learning method is developed for extraction of lesions from dermoscopic images by combining DeepLabV3 + with different base CNN networks. The proposed model is evaluated on the three dermoscopic datasets such ISIC 2016, 2017 and 2018. To validate the proposed model, different performance measurement metrics such as Accuracy, Jaccard Index (JI) and Dice Coefficient (DC) are calculated for the different datasets. The proposed model achieves 93% Accuracy, JI of 89% and DC of 90%. This demonstrate the superiority of the proposed model over state-of-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Extraction

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machine learning-based Inversion of Wireless Signals for Real-Time Gesture Recognition 12

Machine Learning-based Inversion of Wireless Signals for Rea...

引用

12th international conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Polo, Alessandro Capra, Federico Lusa, Samantha Rocca, Paolo Salas-Sanchez, Aaron A. Salucci, Marco ELEDIA@UniTN - University of Trento DICAM ELEDIA Research Center Trento Italy ELEDIA@XIDIAN Xidian University ELEDIA Research Center Xi'an China

ISBN: (纸本)9798350321074

This work presents the latest results on the development and experimental validation of a human-machine interaction systems based on the use of Wi-Fi commodity devices and the processing of the channel state information (CSI) for gesture and hand-pose recognition. The inversion method is based on a customized machine learning strategy that allows guaranteeing the real-time operation as well as high percentages of estimation accuracy and robustness. The reported experimental validation, carried out within a real house environment, verifies that the system can recognize four different arm/hand poses with accuracy higher than 95%. © 2023 IEEE.

关键词： Inverse problems

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Evaluation of Vietnamese Speech Recognition Platforms 21

Evaluation of Vietnamese Speech Recognition Platforms

引用

5th international conference on machine learning and soft computing, icmlsc 2021

作者： Diep, Hai Thanh Nguyen, Thi-My-Thanh Le, Bich Ngoc Dao, Quy Xuan Eastern International University Viet Nam

ISBN: (纸本)9781450387613

The purpose of this paper is to evaluate the performance of Vietnamese speech recognition systems provided by top Vietnamese companies such as Vais, Vtcc, Fpt, and Google. This paper presents the results in applying Vietnamese automatic speech recognition systems in news, interview, and music domains. We use recorded audios as inputs to compare the performance of Vietnamese automatic speech recognition systems by calculating Word Error Rate. Vais and Viettel obtain good results in news and interview domains while Google has good results in the music domain. The results demonstrated that all the providers Vais, Viettel, Google, and Fpt achieve good results but Vais is more dominant. © 2021 Association for computing machinery. All rights reserved.

关键词： Speech recognition

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Leveraging Fine-Tuned LightGBM for Advanced AI-Driven Android Malware Detection

Leveraging Fine-Tuned LightGBM for Advanced AI-Driven Androi...

引用

Advancements in Electrical, Electronics, Communication, computing and Automation (ICAECA), international conference on

作者： Rajasekar Kanniappan V. Jamuna Anna University Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Jerusalem College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Increasing instances of cyber threats, specifically malware, require faster creation of proper detection systems that utilize higher learning algorithms. This paper examines the effectiveness of an AI-malware detection technique whereby a LightGBM model finetuned on the CICAndMal2017 dataset is employed. As it will be shown, the CICAndMal2017 dataset is suitable to evaluate the proposed algorithms for detecting malware since this dataset offers sample of different types of malware and benign software. After carefully extracting the features, and selecting the best parameters that allow for the highest potential LightGBM achieved 97.56% accuracy. The results show that the model achieves low false positive rates while accurately identifying the presence or absence of malware in an application. This research helps to advance the cybersecurity field as a whole by presenting a fast, highly efficient, and accurate solution for real-time malware detection with the use of machine learning methods as a proof of concept for eradicating modern cyber threats.

关键词： Accuracy machine learning algorithms Scalability software algorithms machine learning Feature extraction Malware Real-time systems Telecommunication computing Computer security

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Differential Residual learning for Facial Expression Recognition 21

Differential Residual Learning for Facial Expression Recogni...

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5th international conference on machine learning and soft computing, icmlsc 2021

作者： Pu, Lipeng Zhu, Lingyun Chongqing University of Technology Liangjiang International College China

ISBN: (纸本)9781450387613

The facial expression recognition algorithm based on convolution neural network (Convolutional Neural Network) still has the problem of imperfect feature point extraction. Therefore, a facial expression recognition algorithm based on the combination of the detail feature extraction network and pre-training model is proposed. According to the emotional face and neutral face of the residual of the images, learn the residual to extract features, and make the feature information accurate. Then, the face feature markers generated by the feature extraction network are loaded into the pre-training model respectively for classification and recognition. By grouping and crossing experiments on datasets CK+ and FER2013, the average accuracy of face recognition is 95.74% and 73.11%, respectively. Compared with the state-of-the-art recognition model, this method is effective in facial expression recognition to some extent. © 2021 ACM.

关键词： Feature extraction

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ADVANCING INTRUSION DETECTION WITH ML AND DEEP learning: A COMPARATIVE APPROACH

ADVANCING INTRUSION DETECTION WITH ML AND DEEP LEARNING: A C...

引用

2025 international conference on Energy, Power, Environment, Control and computing, ICEPECC 2025

作者： Shahid, Mashaal Arafat, Syed Yasser Tariq, Fahad Department of CSIT Mirpur University of Science and Technology AJ&K Pakistan Department of Training Fauji Foundation Head office Rawalpindi Pakistan

ISBN: (纸本)9781837243235

In network security, Intrusion Detection Systems (IDS) is increasingly important due to rise in cyber-attacks. To ensure accurate detection and prevention, IDS leverages machine learning (ML) and Deep learning (DL) algorithms on benchmark datasets;CICIDS2017 and NSL-KDD. These datasets, with diverse attack types, are ideal for training and testing. Traditional IDS struggle to detect zero-day attacks due to lack of predefined signatures, leading to higher false positive rates and vulnerabilities. Our proposed methodology focuses on a DL-based NIDS. We pre-process and normalize data from CICIDS2017, NSL-KDD, and a custom home network dataset. Supervised ML models, including Random Forest, Decision Tree, and Gaussian Naive Bayes, were applied to CICIDS 2017, achieving high accuracy. For DL, BiLSTM and Transformer models were trained on NSL-KDD for 30–60 epochs, with accuracy rates of 87%–99%. We utilized dual approach of normalization technique using Log Transformation and enhanced the Log Transformation by adding weightage to the features and retrained the model LSTM-CNN on NSL-KDD. Additionally, an LSTM-CNN model was applied to the Home Network dataset, the accuracy result of H-N Version1 was 100%. © The Institution of Engineering & Technology 2025.

关键词： BIDIRECTIONAL LONG-SHORT TERM MEMORY CONVOLUTIONAL NEURAL NETWORK GAUSSIAN NAIVE BAYES LONG-SHORT TERM MEMORY NETWORK INTRUSION DETECTION SYSTEM

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Toward Unpaired Multi-modal Medical Image Segmentation via learning Structured Semantic Consistency 6

Toward Unpaired Multi-modal Medical Image Segmentation via L...

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6th international conference on Medical Imaging with Deep learning (MIDL)

作者： Yang, Jie Zhu, Ye Wang, Chaoqun Li, Zhen Zhang, Ruimao Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen Peoples R China

Integrating multi-modal data to promote medical image analysis has recently gained great attention. This paper presents a novel scheme to learn the mutual benefits of different modalities to achieve better segmentation results for unpaired multi-modal medical images. Our approach tackles two critical issues of this task from a practical perspective: (1) how to effectively learn the semantic consistencies of various modalities (e.g., CT and MRI), and (2) how to leverage the above consistencies to regularize the network learning while preserving its simplicity. To address (1), we leverage a carefully designed External Attention Module (EAM) to align semantic class representations and their correlations of different modalities. To solve (2), the proposed EAM is designed as an external plug-and-play one, which can be discarded once the model is optimized. We have demonstrated the effectiveness of the proposed method on two medical image segmentation scenarios: (1) cardiac structure segmentation, and (2) abdominal multi-organ segmentation. Extensive results show that the proposed method outperforms its counterparts by a wide margin.

关键词： Unpaired multi-modal learning Structured semantic consistency learning Medical image segmentation

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A Sublinear-Regret Reinforcement learning Algorithm on Constrained Markov Decision Processes with reset action 2020

A Sublinear-Regret Reinforcement Learning Algorithm on Const...

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4th international conference on machine learning and soft computing (icmlsc)

作者： Watanabe, Takashi Sakuragawa, Takashi Kyoto Univ Grad Sch Human & Environm Studies Kyoto Japan

ISBN: (纸本)9781450376310

In this paper, we study model-based reinforcement learning in an unknown constrained Markov Decision Processes (CMDPs) with reset action. We propose an algorithm, Constrained-UCRL, which uses confidence interval like UCRL2, and solves linear programming problem to compute policy at the start of each episode. We show that Constrained-UCRL achieves sublinear regret bounds (O) over tilde (SA(1/2)T(3/4)) up to logarithmic factors with high probability for both the gain and the constraint violations.

关键词： reinforcement learning constrained Markov decision processes regret analysis expected average-reward criterion

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