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共 769 条记录，以下是281-290 订阅

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IMU-based Human Activity Recognition using machine learning and Deep learning models

IMU-based Human Activity Recognition using Machine Learning ...

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international conference on signal processing and Information Security (ICSPIS)

作者： Saad AlKharji Aysha Alteneiji Kin Poon Computer Engineering Department Khalifa University Abu Dhabi United Arab Emirates EBTIC (Emirates ICT Innovation Center) Khalifa University Abu Dhabi United Arab Emirates

Research into Human Activity Recognition (HAR) with wearable sensors is attracting a lot of attention due to its wide range of applications. In this paper, we use a microcontroller with an integrated Inertial Measurement Unit (IMU) to design various machine learning (ML) and Deep learning (DL) models. Five different activities were captured: walking, walking up a staircase, walking down a staircase, jumping, and falling. Both ML and DL models were trained on a variety of IMU data. A comparison was performed among the models based on their accuracy scores and confusion matrices to determine the most effective one. the proposed LSTM model achieved an accuracy score of 99% when trained with accelerometer and gyroscope data from the IMU.

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Improving Imbalanced Data Classification through Integrated Data-Level and Algorithm-Level Techniques 6

Improving Imbalanced Data Classification Through Integrated ...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： El Hajjami, Salma Amelal, Hicham El-Hajjami, Abdelkarim Kaushik, Keshav Khan, Inam Ullah Ibn Zohr University Faculty of Science LabSIV Department of Computer Science Agadir Morocco INP-ENSEEIHT University of Toulouse Department of Computer Science and Telecommunications France School of Computer Sciences University of Petroleum and Energy Studies Uttarakhand Dehradun India Department of Electronic Engineering Isra University Pakistan

ISBN: (纸本)9798350304480

Imbalanced class distribution is common issue in machine learning and data mining. It affects various applications like fraud detection, medical diagnosis, and network intrusion detection. the mentioned problem occurs when the majority class greatly outnumbers the minority class, hindering learning systems designed for balanced distribution. Furthermore, presence of overlapping instances between two classes exacerbates problem. this paper introduces a novel method that integrates ensemble and cost-sensitive techniques at algorithm-level with OSBNR approach at data-level to address this challenge. Furtherly, suggested technique is compared with conventional resampling methods, in conjunction with three ensemble algorithms and three cost-sensitive algorithms, to determine optimal combination. Simulation presents OSBNR is having optimal results in terms of processing time and evaluation metrics like G-mean, AUC-ROC, TPR-Recall, precision, Fl-score and FPR. PyOSBNR is simulated as python index package which can easily handle class imbalance in the presence of behavioral noise. © 2023 IEEE.

关键词： Cost-sensitive methods Ensemble methods Fraud detection Imbalanced class distribution machine learning OSBNR PyOSBNR

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Chatbot for Banking Sector Using NLP and ML 1

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6th international conference on Soft Computing and signal processing, ICSCSP 2023

作者： Chaithanya, D. Deepa, G. Pavani, K. Bindu Manjusha, G. Kumar, V. Vishal Department of IT Vignan Institute of Technology and Science Hyderabad India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

this study focuses on chat bots, which are intelligent machines that can understand users’ questions in natural language and respond correctly in a dialog. When using it, users experience communication more like they are speaking with a real person, more like a virtual assistant (Omofowa et al., E-banking service quality and customer satisfaction: evidence from deposit money bank in South-South Nigeria. Webology vol 18, special issue on computing technology and information management, 2021). they are able to react to questions and communicate in our language. Inadequate agents typically require an extended period to handle a single request at banks, customer service centers, and inquiry desks, wasting time and degrading the quality of customer care. this chatbot’s main goal is to enable clients to converse by asking inquiries in plain English and then getting appropriate answers in response. the suggested solution would assist in emulating the customer care experience while still attending to and resolving the customer’s inquiries (Aparaj et al., ICAICTE 2013 international conference on advanced information communication technology in engineering, 2013), with the difference that the consumer would be chatting with a bot instead of a human. It may ease daily living by providing solutions for help desks, telephone answering machines, and customer service centers (Aruna et al., Int J Res Appl Sci Eng Technol, 2022). the architecture, methodology, and dataset that we produced from the FAQ sections of bank websites are all described in this article (Kulkarni et al., IRJET Int Res J Eng Technol 04:611, 2017). © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： machine learning

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three-Dimensional Hexagonal Braiding machine Chassis Parametric Modeling 5th

Three-Dimensional Hexagonal Braiding Machine Chassis Paramet...

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5th international conference on 3D Imaging Technologies-Multidimensional signal processing and Deep learning, 3DIT-MSP and DL 2023

作者： Lu, Chenyu Gu, Xin College of Mechanical Engineering Donghua University Changning China

ISBN: (纸本)9789819751839

Aiming at the problem of how to reasonably select the structural parameters of the hexagonal three-dimensional braiding machine and the problem of not being able to obtain the movement of the chassis of the braiding machine in real time, a parametric model of the chassis of the hexagonal three-dimensional braiding machine based on the hexagonal geometric features is established and analyzed. Firstly, the structural parameters of the chassis of braiding machine are defined, and the relationship between the structural parameters is determined under the condition of no "derailment" of the carrier, so that a reasonable range of values of the structural parameters is obtained. Secondly, according to the analyzed parameters of the chassis, establish the coordinate matrix of the chassis of the braiding machine, and analyze the mapping relationship between the coordinates of the chassis and the actual position, so as to calculate the position information of each unit in the chassis in real time. Finally, a graphical interface is written in MATLAB to verify the correctness of the model, and the results show that there is consistency in the motion of the chassis between the actual working conditions and the established model, which proves the feasibility of the method. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Graphical user interfaces

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Efficiently Fusing Sparse Lidar for Enhanced Self-Supervised Monocular Depth Estimation 48

Efficiently Fusing Sparse Lidar for Enhanced Self-Supervised...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Wang, Yue Gong, Mingrong Xia, Lei Zhang, Qieshi Cheng, Jun Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen China University College London Department of Computer Science Gower Street WC1E 6BT United Kingdom

ISBN: (纸本)9781728163277

Monocular self-supervised depth estimation with a low-cost sensor is the mainstream solution to gathering dense depth maps for robots and autonomous driving. In this paper, based on the philosophy "less is more"(i.e., focusing only on valid pixels in sparse LiDAR), we propose a novel framework, Efficient Sparse Depth (EffisDepth), for predicting dense depth. the Sparse Feature Extractor (SFE) embedded in the proposed framework effectively handles sparse LiDAR by forming sparse tensors. the Slender Group Block (SGB) is the main building block in SFE, which extracts features from sparse tensors via a structure of two branches. Extensive experiments show that our method achieves state-of-the-art performance on the KITTI benchmark, demonstrating the effectiveness of each proposed component and the self-supervised learning framework. © 2023 IEEE.

关键词： Self-supervised depth estimation Sparse LiDAR Sparse tensor

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ICGSP 2022 - 2022 6th international conference on Graphics and signal processing

ICGSP 2022 - 2022 6th International Conference on Graphics a...

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6th international conference on Graphics and signal processing, ICGSP 2022

ISBN: (纸本)9781450396370

the proceedings contain 13 papers. the topics discussed include: face emotion recognition using dataset augmentation based on neural network;a fast image encryption scheme based on simultaneous permutation-diffusion;e-commerce website based on graphics and image processing technology (IPT);classification of vulnerable road users based on range-Doppler maps of 77 GHz MIMO radar using different machine learning approaches;an intelligent evaluation algorithm for the matching degree of music lyrics based on LabVIEW digital image;optimization of language models by word computing;analyses of Tibetan Yushu dialect nasals based on nasalance visualization system;research on spread spectrum signal acquisition;social media analysis in tour and travel industry;and review of highlight suppression methods for structured light 3D measurement.

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Conventional Neural Network for Maths Handwritten Digit Categorization 6

Conventional Neural Network for Maths Handwritten Digit Cate...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Garg, Shivya Kuchhal, Navya Barmola, Jigyasa Rawat, Vandana Singh, Neelam Graphic Era Deemed to Be University Department of Computer Application Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350304480

Handwritten digit recognition plays a crucial role in various sectors such as document processing, automatic form identification, and postal automation. this task involves accurately identifying and classifying handwritten digits into their respective numerical values. this presentation provides a summary of current developments in machine learning techniques for handwritten digit recognition. the primary objective of this research is to develop an advanced system for recognizing handwritten digits by harnessing state-of-the-art machine learning techniques. To train and test the suggested system, a sizable dataset of handwritten digits is used. We are utilizing the MNIST dataset here. To improve the quality of the input pictures and guarantee uniform feature representation, preprocessing techniques like image enhancement and normalization are used. Experimental results indicate that the CNN model surpasses all other investigated techniques in accuracy, boasting an impressive accuracy rate of over 98%. the capacity of the CNN model to automatically deduce pertinent characteristics from the input pictures, catching both local and global patterns, is credited with its better performance. Additionally, the model can successfully handle spatial relationships in the digit pictures because to CNNs'usage of convolutional layers. A number of practical applications, such as computerized form processing, digital document analysis, and intelligent character recognition, are possible with the suggested handwritten digit recognition system. the CNN model's high accuracy enables accurate digit categorization, enhancing efficiency and accuracy across a variety of applications. Future research can concentrate on investigating ensemble approaches, data augmentation methods, and fine-tuning techniques to improve the handwritten digit recognition system's performance even further. © 2023 IEEE.

关键词： CNN digit recognition epochs Handwritten recognition hidden layers machine learning neural network

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Building a Heroin Abuse Prediction Model: A Generalized machine learning Approach 6th

Building a Heroin Abuse Prediction Model: A Generalized Mach...

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6th international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2023

作者： Kumari, Divya Swetapadma, Aleena New Horizon College of Engineering Bangalore560103 India KIIT University Odisha Bhubaneswar751024 India

ISBN: (纸本)9783031530814

Aims: In the modern era, substance abuse is a global problem. CDC’s (Centre for Disease Control) National Center for Health Statistics reported in July 2021 that, more than 93000 drug overdose deaths occurred alone in the United States. Design: A cohort study. Setting: the drug consumption data is collected online in United Kingdom. Total 2000 records are present, out of which, 1885 records are considered for the work after initial data processing. the dataset has the drug consumption output outcome of 18 drugs, heroin is one of them. Measurements: In this work, an intelligent approach has been proposed for detecting abuse of one of the most illicit substances - heroin. A random forest-based machine learning model is proposed which can predict heroin abused individuals with very high accuracy. For the abuse prediction, various supervised machine learning methods are applied, and their performance is compared. these algorithms are applied to different sets of features. Among the applied five algorithms, feature importance score is calculated for logistic regression, ensemble learning (gradient boosting) and random forest. the feature importance score for each feature is calculated for all applied algorithms k-nearest neighbour and naïve bayes. the ranking of all features is done based on the obtained score. Findings: It is found in the study that the heroin-abuse dataset collected from UCI, two-classification based on random forest (RF) and gradient boosting (GB) achieved more than 90% accuracy as well as more than 90% sensitivity, specificity, precision, and f-score. the proposed model gave the best accuracy 94.697% for heroin-use detection. Conclusions: the assessment of the proposed framework on all possible feature sets shows that the framework works well for heroin abuse prediction as different give satisfactory results. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Demography

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Communication Interference signal Recognition Based on Complex Number Convolutional Neural Network 6

Communication Interference Signal Recognition Based on Compl...

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2022 6th international conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2022

作者： Chen, Yazhong Shan, Jingmiao Zhang, Chuanhao Department of Electrical Information Shandong University of Science and Technology Shandong Jinan250031 China

Modern wireless communication systems are facing an increasingly complex electromagnetic environment, which is affected by a variety of noise and interference signals. In the communication process, if the communication party can effectively identify the type of interference signal, it can take corresponding anti-interference measures to avoid or suppress interference to the maximum extent and reduce the damage of interference to communication quality. In recent years, with the continuous development of deep learning and its outstanding performance in image and speech processing, it has been verified that deep learning has extremely strong nonlinear mapping and data expression capabilities, inspiring researchers to apply deep learning to the field of communication anti-jamming, and improving the antijamming capability of the system. In this paper, an algorithm of interference signal recognition based on a complex convolution neural network is proposed. It also introduces the network architecture. Next, six typical interference signals at each level are identified, and the results are analyzed. the last chapter of the paper summarizes the full text. the experimental results show that the recognition ability of the interference signal recognition algorithm proposed in this paper is better than that of the traditional convolutional neural network. © Published under licence by IOP Publishing Ltd.

关键词： signal interference

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Text-Based Prediction of Heart Disease Doctor Chatbot Using machine learning 6th

Text-Based Prediction of Heart Disease Doctor Chatbot Using ...

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6th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2021

作者： Harlapur, Nehabanu H. Handur, Vidya School of Computer Science and Engineering KLE Technological University Hubballi580031 India

ISBN: (纸本)9789811935701

Health care is important to live a happy and healthy life [1]. So Chatbots can be helpful in monitoring current health status before visiting the doctors physically. Doctor Chatbot is a means to communicate with man and machines. A Doctor Chatbot allows a user to ask queries about a health issue in a manner that they would consult a doctor. Doctor Chatbot interprets human input and responds back using Artificial Intelligence [2] and Natural Language processing. therapeutic, Educational, Agricultural areas are important domains to consider. Chatbots can now be utilized everywhere and at any time. Every day, a large number of people, both young and elderly, suffer as a result of a heart attack. In the proposed study the main objective is to predict whether a person has heart related issues using Deep learning Techniques of machine learning. As a result accuracy of the prediction is measured. Comparative analysis of SVM, Naive Bayes, KNN algorithm is carried out and SVM outperforms other two algorithms. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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