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共 777 条记录，以下是341-350 订阅

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Unsupervised Feature Selection via Adaptive Feature Clustering for High-dimensional Data 5

Unsupervised Feature Selection via Adaptive Feature Clusteri...

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5th international conference on Data Science and Information Technology, DSIT 2022

作者： Jia, Hong Weng, Yubin Shenzhen Institute of Artificial Intelligence and Robotics for Society College of Electronics and Information Engineering Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

the purpose of feature selection is to obtain a feature subset from the original data to realize dimension reduction, and at the same time, improve the performance of following machine learning task. Due to the lack of label information, unsupervised feature selection is more challenging. In this paper, the idea of sample clustering is applied to feature space. Clustering the features makes the similarity between features in the same cluster high and that from different clusters low. Subsequently, representative features are selected from each cluster to form the final feature subset. Along this way, an unsupervised feature selection algorithm which considers both the similarity and relevance of different features is proposed. Experiments in comparison with both traditional and novel algorithms on benchmark data sets are performed. the results show the superiority of the proposed method. © 2022 IEEE.

关键词： Feature Selection

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Event Prediction for Network Edge Devices using Log Analysis 5

Event Prediction for Network Edge Devices using Log Analysis

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5th international conference on Computer, Communication, and signal processing, ICCCSP 2021

作者： Rm, Pitchappan P Suraj Subramanian, A. Sabari Nathan, M. Harishkumar, R. Sadasivam, G. Sudha thomas, Manoj PSG College of Technology Dept. of Computer Science and Engineering Coimbatore India Samsung Research Institute Bangalore Bangalore India

ISBN: (纸本)9781665432771

Devices that control data flow at the boundary between two networks are known as Network Edge Devices and they are typically used as network entry or exit points. It is not possible to check and monitor these devices manually. Further monitoring these devices using a separate application creates unnecessary overhead. An effective approach is to automate monitoring of these devices by building intelligence into these devices to perform such tasks using machine learning. this paper proposes the usage of Elasticstack to ship all logs to a central server where they are analyzed to predict failures of the network edge devices. the research yielded good results while using application logs to predict failure of small cells. © 2021 IEEE.

关键词： machine learning

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Detection of toxicity in social media based on Natural Language processing methods 5

Detection of toxicity in social media based on Natural Langu...

引用

5th international conference on Intelligent Systems and Computer Vision, ISCV 2022

作者： Taleb, Mohammed Hamza, Alami Zouitni, Mohamed Burmani, Nabil Lafkiar, Said En-Nahnahi, Noureddine Sidi Mohamed Ben Abdellah University LISAC Laboratory Faculty of Sciences Dhar El Mahraz Fez Morocco High Technology School Innov-Tech Laboratory Departement of Engineering Rabat Morocco

ISBN: (纸本)9781665495585

Comments on important websites, such as popular news portals or social media platforms, are among the main ways of virtual interaction. Unfortunately, the behavior of users on these websites often becomes rude or disrespectful, by spreading toxic comments which can muddle the proper functioning of these sites. the aim of this research is to detect these toxic comments, and to find parts, toxic spans, of these comments to which toxicity can be attributed. thus, we explored and compared various classifiers belonging to three categories 'machine learning, Ensemble learning and Deep learning' and using different text representations. For detecting toxic spans in the comments, we applied an unsupervised method, we apply the Local Interpretable Model-Agnostic Explanations (LIME).the measures we used to evaluate our methods are accuracy, recall, and Fl-score. Our experiments showed that deep learning models performed unquestionably in the task of detecting toxic comments. the LSTM models with the Globe representation and LSTM with FastText were able to produce a higher F1 and accuracy compared to the other models used. For Toxic spans detction, the higher scores were obtained when combining LIME with classifier LSTM(GloVe) with an accuracy of 98% to identify the toxic spans. © 2022 IEEE.

关键词： Natural language processing systems

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Glucose Content Analysis using Image processing and machine learning Techniques 5

Glucose Content Analysis using Image Processing and Machine ...

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5th international conference on Information and Communications Technology, ICOIACT 2022

作者： Yudhana, Anton Akbar, Son Ali Farezi, Andrio Ghazali, Kamarul Hawari Nuraisyah, Fatma Rosyady, Phisca Aditya Universitas Ahmad Faculty of Industrial Technology Department of Electrical Engineering Dahlan Indonesia Universiti Malaysia Faculty of Electrical and Electronics Engineering Technology Pahang Malaysia Universitas Ahmad Faculty of Public Health Department of Public Health Science Dahlan Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

Technology is constantly evolving to make it easier for people to work in biomedical research and technology daily. the glucose level checking system can use a urine analyzer detector as a color reader of the urine strip. this work aims to analyse glucose levels based on digital picture identification using the MATLAB application for patient glucose data processing. Injections are joint for diabetic people to control their blood sugar levels. Repeated injections might cause minor physical harm to the body that can hamper the immune system's ability to fight against pathogens. Numerous research has concentrated on non-invasive glucose-based testing, namely using urine. this study was created using image processing to examine the non-invasive glucose testing procedure. the noise is cleaned up using a Gaussian filter and histogram-based feature extraction for picture database extraction. Support vector machines classify data using a 70% training and 30% testing process. the SVM classification results had an accuracy of 85% and time processing of 0.5 seconds. In making medical decisions, it is possible to consider the effects of diabetes, pre-diabetes, and non-diabetes. © 2022 IEEE.

关键词： Support vector machines

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Advancements in Intrusion Detection Systems for Internet of things Using machine learning

Advancements in Intrusion Detection Systems for Internet of ...

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international conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： Shahid Ul Haq Ash Mohammad Abbas Department of Computer Engineering Zakir Hussain College of Engineering & Technology Aligarh Muslim University Aligarh India

ISBN: (纸本)9781665476485

Advancement in technology leads to connecting different types of devices or things to the Internet and enables the formation of a special kind of network called the Internet of things (IoT). Intrusion detection in an IoT is a challenging task due to its unique characteristics. machine learning schemes possess the potential to improve intrusion detection systems in case of an IoT. In this paper, we present a survey of advancements in research on the use of machine learning approaches for intrusion detection in an IoT. Our focus is on architectures, schemes, and the types of machine learning approaches used for intrusion detection. We compare different schemes based on their basis and features.

关键词： Intrusion detection machine learning signal processing Feature extraction Communications technology Behavioral sciences Internet of things

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6th IFIP TC 5 international conference on Computer, Communication, and signal processing, ICCSP 2022

6th IFIP TC 5 International Conference on Computer, Communic...

引用

6th IFIP TC 5 international conference on Computer, Communication, and signal processing, ICCSP 2022

ISBN: (纸本)9783031116322

the proceedings contain 23 papers. the special focus in this conference is on Computer, Communication, and signal processing. the topics include: Trust Aware Secure Routing Model in MANET: Self-improved Particle Swarm Optimization for Optimal Route Selection;Emulation and Analysis of Software-Defined Networks for the Detection of DDoS Attacks;Collision Elimination for Random Behavior Nodes in Ad Hoc Wireless Network Using Early Backoff Announcement (EBA);Smart License Approval Using AIOT;Automatic Identification of Heart Abnormalities Using PCG signals;predicting Students’ Satisfaction Towards Online Courses Using Aspect-Based Sentiment Analysis;A Survey on DL Based Frameworks for COVID-19 Radiological Diagnosis;impact of Chronic Lung Disease Using Deep learning: A Survey;converting Nebulous Ideas to Reality – A Deep learning Tool for Conditional Synthesis of Character Designs;intelligent System for Diagnosis of Herbs Disease Using Deep learning;performance Analysis of Adaptive Variable Exponent Based Total Variation Image Regularization Algorithm;early Prognosis of Preeclampsia Using machine learning;an Intelligent Intrusion Detection System Using Hybrid Deep learning Approaches in Cloud Environment;self Driving Car in a Constrained Environment;Detection and Classification of Paddy Leaf Diseases Using Deep learning (CNN);Long-Text-to-Video-GAN;preface;novel Hybrid Technique Enhancing Data Privacy and Security;SMADE - Smart Medical Assist Device for Elders;malayalam Language Textual Inference Identification through Attention over Embeddings;fashion Image Classification Using Deep Convolution Neural Network.

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NeuroBMI: A New Neuromorphic Implantable Wireless Brain machine Interface with A 0.48 μW Event-Driven Noise-Tolerant Spike Detector 5

NeuroBMI: A New Neuromorphic Implantable Wireless Brain Mach...

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5th IEEE international conference on Artificial Intelligence Circuits and Systems, AICAS 2023

作者： Chen, Jinbo Wu, Hui Liu, Xing Eskandari, Razieh Tian, Fengshi Zou, Wenjun Fang, Chaoming Yang, Jie Sawan, Mohamad Westlake University CenBRAIN Neurotech School of Engineering Hangzhou310024 China Hkust Department of Ece Hong Kong SAR Hong Kong

ISBN: (纸本)9798350332674

the use of Brain-machine Interfaces (BMIs) in neuroscience research and neural prosthetics has seen widespread application. With the technology trend shifting from wearable to implantable wireless BMIs featuring increasing channel counts, the volume of data generated requires impractically high bandwidth and transmission power for the implants. In this paper, we present NeuroBMI, a novel neuromorphic implantable wireless BMI that leverages a unified neuromorphic strategy for neural signal sampling, processing, and transmission. the proposed NeuroBMI and neuromorphic strategy reduces transmitted data rate and overall power consumption. NeuroBMI takes into account the high sparsity of neural signals by employing an integrateand-fire sampling based analog-to-spike converter (ASC), which generates digital spike trains based on triggered events and avoids unnecessary data sampling. Additionally, an event-driven noise-tolerant spike detector and event-driven spike transmitter are also proposed, to further reduce the energy consumption and transmitted data rate. Simulation results demonstrate that the proposed NeuroBMI achieves a data compression ratio of 520, with the proposed spike detector consuming only 0.48 μW. © 2023 IEEE.

关键词： Transmitters

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Survey on Crop Disease Detection and Identification Based on Deep learning 5th

Survey on Crop Disease Detection and Identification Based on...

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5th EAI international conference on Innovations and Interdisciplinary Solutions for Underserved Areas, InterSol 2022

作者： Faye, Demba Diop, Idy UCAD Dakar Senegal

ISBN: (纸本)9783031231155

Plant diseases lead to a reduction in both quality and quantity of agricultural production. 50% of agricultural losses are due to these diseases. Due to poverty and lack of infrastructures in some countries, their identification remains difficult. Plant pathologists use several techniques to identify these diseases. But these techniques are time-consuming and relatively expensive for farmers. Nowadays, several models based on image processing (IP) techniques, machine learning (ML) algorithms and deep learning (DL) algorithms have been proposed for automatic detection and identification of plant diseases. In this study, we divided these models into two groups: models based on IP and classical ML algorithms, and those based on DL. DL coupled with the transfer learning (TL) technique has become the most widely used method because of its impressive performance. the critical analysis of these models has allowed us to identify potential challenges in the field of automatic plant disease diagnosis. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Image processing

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A machine learning-based Segmentation Approach for Measuring Similarity Between Sign Languages 10

A Machine Learning-based Segmentation Approach for Measuring...

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10th Workshop on the Representation and processing of Sign Languages: Multilingual Sign Language Resources, sign-lang 2022

作者： Jui, Tonni Das Bejarano, Gissella Rivas, Pablo Baylor University 1311 S 5th St WacoTX76706 United States

ISBN: (纸本)9791095546863

Due to the lack of more variate, native and continuous datasets, sign languages are low-resources languages that can benefit from multilingualism in machine translation. In order to analyze the benefits of approaches like multilingualism, finding the similarity between sign languages can guide better matches and contributions between languages. However, calculating the similarity between sign languages again implies a laborious work to measure how close or distant signs are and their respective contexts. For that reason, we propose to support the lexical similarity measurement between sign languages through a video-segmentation-based machine learning model that will quantify this match among signs of different sign languages. Using a machine learning approach, the similarity measurement process can run more smoothly than a more manual approach. We use a pre-trained temporal segmentation model for British Sign Language (or BSL). We test it on three datasets, an American Sign Language (ASL) dataset, an Indian Sign Language (ISL), and an Australian Sign Language (or Auslan) dataset. We hypothesize that the percentage of segmented and recognized signs by this machine learning model can represent the percentage of overlap or similarity between British and the other three sign languages. In our ongoing work, we evaluate three metrics considering Swadesh's and Woodward's list and their synonyms. We found that our intermediate-strict metric coincides with a more classical analysis of the similarity between British and American Sign Language, as well as with the classical low measurement between Indian and British sign languages. On the other hand, our similarity measurement between British and Australian Sign language holds for part of the Australian Sign Language and not the whole data sample. © European Language Resources Association (ELRA), licensed under CC-BY-NC 4.0.

关键词： machine learning

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Degradation Trajectory Tracking of MOSFETs Based on machine learning and CEEMDAN

Degradation Trajectory Tracking of MOSFETs Based on Machine ...

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), international conference on

作者： Zhaorui Li Dongqiang Shi Ying Zhang Kai Li Huazhan Gui Feng Yuan School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China Beijing Aerospace Automatic Control Institute Beijing China

MOSFET is one of the most popular silicon carbide power electronic devices, is widely popular in base station, energy storage, aerospace and railway scenarios. As one of the score electronic components in power electronic system, power MOSFETs degradation performance is closely related to the overall operation of power of power electronic system, which directly affects the stability and security of the system. In this work, taking the MOSFETs accelerated thermal aging datasets provided by NASA as the research project, we proposed a deep extreme learning based on CEEMDAN(complete ensemble empirical mode decomposition) with adaptive noise to simulate the degradation trend of MOSFETs. the experimental results show that the method can fit the degradation trend well on the high noise dataset.

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