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检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

共 783 条记录，以下是501-510 订阅

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Enhanced Deep learning for Massive MIMO Detection Using Approximate Matrix Inversion

Enhanced Deep Learning for Massive MIMO Detection Using Appr...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Ali J. Almasadeh Khawla A. Alnajjar Mahmoud A. Albreem Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781665482387

Massive multiple-input multiple-output (MIMO) is a crucial technology in fifth-generation (5G) and beyond 5G (B5G). However, the huge number of antennas used in massive MIMO systems causes a high computational complexity during signal detection. In this paper, we propose an efficient massive MIMO detection technique which is based on approximate matrix inversion methods and deep learning to enhance the system performance while keeping computational complexity low. three approximate methods which are Gauss–Seidel (GS), successive over-relaxation (SOR), and conjugate gradient (CG) are exploited for the initialization of a modified version of the MM network (MMNet) algorithm. the performance of the proposed technique is validated under both Gaussian and realistic channel scenarios, i.e., Quadriga channels models. Simulation results show that the proposed technique outperforms MMNet, minimum mean square estimation (MMSE), detection network (DetNet), and orthogonal approximate message passing deep net (OAMP-Net) in terms of symbol error rate (SER) during offline training. It also provides a significant SER improvement of up to 87% when compared to MMNet in the online training scenario.

关键词： Training Deep learning 5G mobile communication System performance signal processing algorithms Symbols Massive MIMO

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DSFL: Dynamic Sparsification for Federated learning

DSFL: Dynamic Sparsification for Federated Learning

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Mahdi Beitollahi Mingrui Liu Ning Lu Department of Electrical and Computer Engineering Queen's University Kingston ON Canada

ISBN: (纸本)9781665482387

Federated learning (FL) is considered the key, enabling approach for privacy-preserving, distributed machine learning (ML) systems. FL requires the periodic transmission of ML models from users to the server. therefore, communication via resource-constrained networks is currently a fundamental bottleneck in FL, which is restricting the ML model complexity and user participation. One of the notable trends to reduce the communication cost of FL systems is gradient compression, in which techniques in the form of sparsification are utilized. However, these methods utilize a single compression rate for all users and do not consider communication heterogeneity in a real-world FL system. therefore, these methods are bottlenecked by the worst communication capacity across users. Further, sparsification methods are non-adaptive and do not utilize the redundant, similar information across users' ML models for compression. In this paper, we introduce a novel Dynamic Sparsification for Federated learning (DSFL) approach that enables users to compress their local models based on their communication capacity at each iteration by using two novel sparsification methods: layer-wise similarity sparsification (LSS) and extended top- $K$ sparsification. LSS enables DSFL to utilize the global redundant information in users' models by using the Centralized Kernel Alignment (CKA) similarity for sparsification. the extended top- $K$ model sparsification method empowers DSFL to accommodate the heterogeneous communication capacity of user devices by allowing different values of sparsification rate $K$ for each user at each iteration. Our extensive experimental results 1 1 All code and experiments are publicly available at: https://***/mahdibeit/DSFL. on three datasets show that DSFL has a faster convergence rate than fixed sparsification, and as the communication heterogeneity increases, this gap increases. Further, our thorough experimental investigations uncover the similaritie

关键词： Performance evaluation Correlation Federated learning signal processing Market research Dynamic scheduling Servers

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Research on Motion Recognition Technology Based on Nanogenerator

Research on Motion Recognition Technology Based on Nanogener...

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Yuhang Zhang Haifeng Xu Jianjun Zhang Wei Wang Jianli Huang Renhai Feng School of Electrical Automation and Information Engineering Tianjin University Tianjin China TOEC technology Co. Ltd. Tianjin China Chengnan Power Supply Branch State Grid Tianjin Electric Power Company Tianjin China China Southern Power Grid Research Institute Co. Ltd. Guangzhou China

With the fast development of embedded system in the past twenty years, human-machine interaction has become more and more frequent. Wearable devices received extensive attention. In this paper, combined with digital signal processing, a set of nanogenerators are utilized for motion recognition. Difference between the reading electrical level sequence generated by nanogenerator and the standard sequence is calculated by ESP-32 using Dynamic Time Warping(DTW), so as to judge the action mode of wearer. Practical experiments show the feasibility of the proposed scheme.

关键词： Wavelet domain Microcontrollers Nanogenerators Wearable computers Noise reduction signal processing algorithms Voltage

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Analysis of Tourist Comments Based on Python Natural Language processing: Take Guilin Lijiang Waterfall Hotel as an example 22

Analysis of Tourist Comments Based on Python Natural Languag...

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Proceedings of the 2022 5th international conference on machine learning and Natural Language processing

作者： Wenqiang Sun College of Tourism & Landscape Architecture Guilin University of Technology China

ISBN: (纸本)9781450399067

Python is a computer language often used in tourism research, which can solve the problem of natural language processing well. In order to solve the problem of stagnation in the long-term tourism development of high star hotels that have been established for a long time, this study takes the Lijiang Waterfall Hotel in Guilin as a case study, crawls the tourists' comments on the Internet through technical means, and uses the natural language processing function based on Python language to process, analyze and visualize them. this study concluded that such hotels need to upgrade their hardware facilities, improve their staff training system and improving hotel dining experience.

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Human Finger Motion Analysis Based on machine learning 22

Human Finger Motion Analysis Based on Machine Learning

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Proceedings of the 2022 5th international conference on machine learning and Natural Language processing

作者： Jiaming Wang University of Chinese Academy of Sciences China

ISBN: (纸本)9781450399067

In modern medicine, doctors can use ultrasonic equipment to detect hand tendon slippage. However, due to the lack of obvious characteristics of the tendon on the ultrasound image, it is difficult to analyze the amount of tendon slippage when the hand is moving with eyes. In order to solve the shortage of medical image tracking, this paper proposes a real-time tracking algorithm based on target tracking by using control theory, image analysis and image processing technology. In this paper, ultrasonic equipment is used to collect the motion image data of the hand tendon, and the target tracking algorithm is used to realize the tracking of the hand tendon. three feature points of eight image sequences are tracked, and the tracking rate is 70%. However, some tracking frames disappear and cannot be tracked. through the image processing and improvement, the contrast is increased, the tracking effect is enhanced to a certain extent, the tracking rate remains above 75%, and the loss situation has been greatly improved.

关键词： image analysis TLD tracking tendon movement

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machine learning Based Prediction Models for Spontaneous Ureteral Stone Passage

Machine Learning Based Prediction Models for Spontaneous Ure...

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

作者： Nida Seraj Rashid Ali Department of Computer Engineering Zakir Husain College of Engineering and Technology Aligarh Muslim University Aligarh India

ISBN: (纸本)9781665476485

A prediction model was developed for spontaneous stone passage (SSP) using decision tree based machine learning (ML) methods which could serve as a promising tool for doctors to decide whether to administer active therapies and interventions to patients with ureteral stones, or wait for the SSP to happen. the work was performed on the medical records of 192 patients suffering from ureteral stones. A total of 20 features were taken into account including the target attribute, laboratory investigation data, radiographic results, stone characteristics, history of stones and interventions. On analysis, the dataset was found to be imbalanced with 65.1% cases of SSP occurred, and 34.9% SSP didn’t occur. the study included three data splitting approaches, namely hold-out method, 10-fold cross-validation, and stratified 10-fold cross-validation. Decision tree (DT), extreme gradient boosting trees (XGBoost), and random forest (RF) were employed as classification techniques in our work. the prediction accuracies on the test set using stratified 10-fold cross validation, 10-fold cross validation, and hold-out method respectively were 90.83%, 81.66%, and 75.00% for DT, 91.66%, 83.33%, and 83.33% for XGBoost, and 87.50%, 87.50%, and 83.33% for RF. the validation accuracies using stratified 10-fold cross validation, 10-fold cross validation, and hold-out method respectively were 85.55%, 81.66%, and 83.33% for DT, 88.89%, 83.33% and 83.33% for XGBoost, and 87.78%, 83.33%, and 77.78% for RF. It was observed that stratified k-fold cross-validation method gave the best results for our imbalanced dataset. the results are encouraging and suggest that the developed approach might be utilized to help urologists with counseling, prognosticating ureteral stone outcomes, and eventually selecting a suitable course of action or monitoring technique to deal with ureteral stones.

关键词： Radio frequency Image analysis Medical treatment Predictive models signal processing Decision trees Multimedia communication

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DeepSkinNet: A Deep learning Model for Skin Cancer Detection

DeepSkinNet: A Deep Learning Model for Skin Cancer Detection

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international conference on signal processing and Information Security (ICSPIS)

作者： A P Abhiram S M Anzar Alavikunhu Panthakkan Centre for Artificial Intelligence TKM College of Engineering Kollam India-691005 Department of Electronics and Communication TKM College of Engineering Kollam India-691005 College of Engineering and IT University of Dubai U.A.E.

ISBN: (纸本)9781665492669

In this article, an intelligent skin cancer detection system is proposed. It is common knowledge that skin cancer is one of the deadliest diseases in the world. therefore, in order to save human lives, it must be detected correctly at the early stage, for which a fully automatic system using Deep learning techniques can be used. In this work, the HAM10000 dataset is used for skin cancer image classification. A specific model for skin cancer classification, DeepSkinNet model, is proposed and tested. For skin lesion classification, the proposed DeepSkinNet model is compared to contemporary models such as AlexNet, VGG-16, and InceptionV3. the same dataset is trained with all the above models and the confusion matrix of the models is determined. Known quantitative measures such as accuracy, precision and recall are also evaluated. Finally, the experimental studies shows that the proposed model achieves better accuracy than the other models. the DeepSkinNet model has an accuracy, precision, recall and F1-score of 97.354%, 98%, 97% and 97% respectively.

关键词： Deep learning Information security signal processing Skin Lesions Skin cancer Image classification

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Neural NILM learning Paradigms: From Centralised to Decentralised learning

Neural NILM Learning Paradigms: From Centralised to Decentra...

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international conference on signal processing and Information Security (ICSPIS)

作者： Hafsa Bousbiat Christoph Klemenjak Yassine Himeur Wilfried Elmenreich Abbes Amira Wathiq Mansoor Shadi Atalla Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria College of Engineering and IT University of Dubai Dubai UAE Department of Computer Science University of Sharjah UAE

ISBN: (纸本)9781665492669

Non-intrusive Load Monitoring (NILM) is becoming a paramount in both industrial and residential sectors to achieve efficient energy consumption. thus, research on this matter flourished in recent years, where deep neural networks gained the highest interest from the research community, commonly referred to as neural NILM. As a predominant practice, neural NILM models follow a centralised based learning scheme where the energy data is assumed to be available in a central node for training. However, this practice and the enormous amount of data required by these algorithms raise privacy and security concerns from the consumer’s side since energy data can reveal in-home activities and occupancy records if intercepted. Federated learning (FL), also referred to as collaborative learning, is seen as viable solution to address these issues. Nonetheless, its application in neural NILM is still in its infancy and many challenges are yet to be *** current paper presents an overview of neural NILM models following both a centralised and a federated learning paradigm. Furthermore, it identifies the main challenges with regard to both learning paradigms along with potential future research directions for more robust, secure and privacy-preserving models in the neural NILM industry.

关键词： Deep learning Training Industries Load monitoring Federated learning Neural networks signal processing algorithms

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machine learning-Based Electric Vehicle Charging Demand Prediction Using Origin-Destination Data: A UAE Case Study

Machine Learning-Based Electric Vehicle Charging Demand Pred...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Eiman ElGhanam Mohamed Hassan Ahmed Osman Department of Electrical Engineering American University of Sharjah Sharjah UAE

ISBN: (纸本)9781665482387

Optimal prediction and coordination of the energy demand of electric vehicles (EVs) is essential to address the energy availability and range anxiety concerns of current and potential EV users. As a result, different EV demand predictors are developed in the literature based on traffic simulators and/or locally-generated EV charging datasets, to provide the required inputs for EV demand management programs. these predictors, however, may not reliably scale to model the EV energy requirements in different regions, particularly with the scarcity of real-world data on EV driving patterns. this work proposes a data-driven, machine learning (ML)-based EV demand predictor based on vehicular traffic flow data between different origin-destination (OD) pairs. the proposed model incorporates the driving patterns in the regions under consideration to determine the corresponding EV energy consumption and hence, the minimum EV energy requirements per trip. the data used in this work is obtained from TomTom Move O/D Analysis portal for the cities of Dubai and Sharjah, UAE. Different ML models are trained on the dataset to develop the EV demand predictor, namely random forests (RF), extreme gradient boosting (XGBoost), multilayer perceptron (MLP) and linear regression models. Results reveal that the MLP offers a superior performance to all other models, with an $R^th\ >\ 0.8$ and a symmetric mean absolute percentage error of ≈ 20% on both the training and testing data subsets, and a significantly lower training time compared to RF and XGBoost. this makes it suitable for EV demand predictions to incorporate regular updates in vehicular traffic flow data for further model tuning.

关键词： Training Radio frequency Energy consumption Urban areas Predictive models Data models Electric vehicle charging

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AI driven Chatbot and its Evolution

AI driven Chatbot and its Evolution

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Advances in Science and Technology (ICAST), international conference on

作者： Atharvaa Rane Chinmay Ranade Hardik Bandekar Riva Jadhav Vidya Chitre Vidyalankar Institute of Technology Wadala Mumbai Maharashtra India

ISBN: (纸本)9781665492645

In today's fast-paced world, when humans want everything to be done swiftly and precisely, traditional systems cannot keep up it, and hence the necessity for chatbots arose. When compared to a manual system, a chatbot responds to frequently requested inquiries faster and in less time. A chatbot is a software that interacts with humans. Artificial Intelligence (AI) has enabled human-machine interaction, with Natural Language processing (NLP) playing a critical role in language learning. NLP facilitates the development of chatbots. It provides us with an answer via pattern matching. Over the years, several varieties of chatbots have been developed, including text-to-text, text-to-speech, and speech-to-speech.

关键词： learning (artificial intelligence) Manuals machine learning Chatbots Software Pattern matching

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