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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

共 1333 条记录，以下是251-260 订阅

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Pothole Detection System Using Object Detection through Dash Cam Video Feed 2

Pothole Detection System Using Object Detection through Dash...

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2nd international conference for Advancement in Technology, ICONAT 2023

作者： Sen, Shrinjoy Chakraborty, Deep Ghosh, Biswanil Roy, Bhabnashre Dutta Das, Krittika Anand, Jyoti Maiti, Prof. Aniket Techno International New Town Department of Mechanical Engineering Kolkata700156 India Techno International New Town Department of Computer Science and Engineering Kolkata700156 India

ISBN: (纸本)9781665475174

In the present work, the possibility of preventing damage to cars and increasing the safety of cars using Image processing has been explored. A method for modifying an image to produce a better image or to extract some relevant information from it is known as 'image processing' [1]. It is a kind of signal processing where the input is an image and the output can either be another image or traits or features related to that image. We have dived deep into machine learning and further explored Deep learning Techniques [2] using the custom trained CNN model. The following paper discusses the possibility of using YOLO Algorithm [3] to detect potholes and alert the driver to slow down thereby reducing possibilities of accidents. Initial Models tested used Yolov3 [4] with a small dataset of 600 images. The final model tested in this paper uses 3000 images of potholes hand clicked at angles ranging from 30 degrees to 90 degrees with respect to the ground. It further dives deep into object tracking along with the YOLOv4 algorithm [5] and implementing the deepSORT [6] algorithm. A little light is also shed on the probability of the use of ML Model to detect the most common roads used at most common timings to keep the driver well informed about all upcoming potholes and road disturbances seen previously. © 2023 IEEE.

关键词： Convolutional neural networks

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United Neurological study of disorders: Alzheimer's disease, Parkinson's disease detection, Anxiety detection, and Stress detection using various machine learning Algorithms 2

United Neurological study of disorders: Alzheimer's disease,...

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2nd international conference on signal and Information processing, IConSIP 2022

作者： Dixit, Sahaja Gaikwad, Akash Vyas, Vibha Shindikar, Mahesh Kamble, Ketaki College of Engineering Pune Department of Electronics and Telecommunication Maharashtra Pune411005 India College of Engineering Pune Department of Applied Science Maharashtra Pune411005 India

ISBN: (纸本)9781728168852

The biomedical study of large-scale multimodal neuroimaging data is popular nowadays due to advancements in deep learning and machine learning algorithms. This article has studied four different types of neurocircuitry disorders or diseases that help human beings prevent or detect them in the early stages for easily starting the treatment. a) Early detection and classification of Alzheimer's disease (AD) b) Parkinson's detection (a neurocircuitry disorder that had slowed the activity) c) Generalized anxiety disorder d) stress prediction for patients of stress) Here, this work briefly reviews the important literature on neurological diseases and explores how deep learning can help researchers to diagnose the disease at its primary stages © 2022 IEEE.

关键词： Neuroimaging

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Material Classification based on Non-contact Ultrasonic Echo signal Using Deep learning Approach 2

Material Classification based on Non-contact Ultrasonic Echo...

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2nd international conference on machine learning and Data Engineering, ICMLDE 2023

作者： Sahoo, Ajit Kumar Udgata, Siba K. WiSeCom Lab School of Computer and Information Sciences University of Hyderabad Hyderabad India

Non-contact ultrasonic sensors are generally used for range measurement and object detection. In addition to the shape and size of the target object, the identification of the material types plays a vital role in robotic navigation and autonomous vehicle applications. Ultrasonic echo signals contain a significant amount of information and can be used to recognize and categorize different materials. Echo signals can help robots detect the obstacle's material on the path and comply with its behavior accordingly. The emergence of deep neural networks has shown great promise, offering cutting-edge performance for a wide range of signal-processing tasks. This work uses the non-contact ultrasonic echo signal from a set of materials (glass, wood, metal plate, sponge, and cloth) for classification. The main idea is to classify the materials using the embedded information of reflected echo signals. Hilbert transform is used to get the envelope from the raw echo ultrasonic signal. In this paper, a novel architecture for one-dimensional convolutional neural network (1D-CNN) has been proposed to accomplish the classification task. The CNN model takes raw echo signals as input to detect and classify materials accurately. The performance of the classifier model is evaluated using accuracy, precision, recall, and F1-score. The proposed 1D-CNN deep learning-based multi-class classifier model can classify the different types of materials with an accuracy of 96%, precision of 95%, recall of 95%, and F1-score of 95%. © 2024 Elsevier B.V.. All rights reserved.

关键词： Object detection

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CMiGiN 2022 - 2nd international conference on Conflict Management in Global Information Networks

CMiGiN 2022 - 2nd International Conference on Conflict Manag...

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2nd international conference on Conflict Management in Global Information Networks, CMiGiN 2022

The proceedings contain 27 papers. The topics discussed include: the concept of optimal behavior of a cyber object as a measure of its generalized uncertainty;intellectual model for classification of network cybersecurity events;automated tomato harvesting system using image processing methods;an approach to the formation of adaptive learning paths for students of cybersecurity in e-learning system;key generation method based on genitor genetic algorithm model;noise generator of interfering signals for suppression information leakage signal generated by liquid crystal monitor screen;mathematical spline processing method for filtering and compressing data;influence of information technologies on the human factors in aviation;and risk assessment of cyberattacks in conditions of hybrid war based on analysis of cybersecurity basic capacity in the civil security sector in Ukraine.

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2022 2nd IEEE international conference on Information Communication and Software Engineering, ICICSE 2022

2022 2nd IEEE International Conference on Information Commun...

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2nd IEEE international conference on Information Communication and Software Engineering, ICICSE 2022

ISBN: (纸本)9781665482202

The proceedings contain 44 papers. The topics discussed include: text analysis method based on multi-channel parallel classifier;a rudimentary proof on Goldbach conjectures;a combined algorithm for imbalanced classification based on dual distribution representation learning and classifier decoupling learning;transformer-based deep learning method for the prediction of ventilator pressure;multiple input single target streamflow forecast by neurowavelet networks;STDE: a single-senior-teacher knowledge distillation model for high-dimensional knowledge graph embeddings;a survey: complex knowledge base question answering;neural data-to-text generation guided by predicted plan;design and implementation of a perioperative medical data quality management platform;multi-view user preference learning with knowledge graph for recommendation;and representation learning of knowledge graph integrating entity description and language morphological structure information.

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COT2DNet: Contextual 2D Attention for Scene Text Recognition 2

COT2DNet: Contextual 2D Attention for Scene Text Recognition

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2nd international conference on Artificial Intelligence, Human-Computer Interaction and Robotics, AIHCIR 2023

作者： Qian, Yefei Ibrayim, Mayire Hamdulla, Askar Zhang, Chunhu Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China School of Computer Science and Technology Xinjiang University Xinjiang China

ISBN: (纸本)9798350360363

Although scene text recognition (STR) methods have made great progress, reading the text of irregularly shaped scenes remains a challenge. The current conversion of two-dimensional (2D) images to one-dimensional (1D) feature maps for sequence recognition may lose a large amount of spatial information and cannot recognize text with a large degree of curvature. In this paper, we propose an network infrastructure to recognize texts of arbitrary shapes, named COT2D Attention Text Recognition Network (COT2Net). Firstly, deformable convolution is added to the feature extraction module, which can perform feature extraction according to the shape of the character and expand the receptive field of feature extraction;Finally, in the Transformer encoding stage, the multi-head attention is changed to Contextual 2D Multi-head Attention (COT2D), which can make full use of the relationship between the input keys Q, K, and V to guide the learning of dynamic attention, thereby enhancing the visual expression ability of text images. The COT2DNet uses COT2D to learn the relevance of each character in a scene text image and achieves excellent results on public datasets. © 2023 IEEE.

关键词： Feature extraction

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Efficient Method for Computing Critical Path Delay using Longest Path Matrix Algorithm 2

Efficient Method for Computing Critical Path Delay using Lon...

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2nd international conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Prasad, Aparna Banuchitira, S.K. Srividhya, G. Mariammal, K. Department of Electronics Madras Institute of Technology Chennai India

ISBN: (纸本)9798350340235

Iterative algorithms are a fundamental component of numerous computational tasks, from numerical simulations to optimization problems. To ensure the efficiency and reliability of these iterative processes, it is crucial to determine suitable iteration bounds that balance computational cost and accuracy. Recent techniques to compute critical path delay like machine learning-based approaches. Path S ensitization techniques heavily rely on historical data. Hence changes in the initial conditions and inaccuracies in data often lead to unreliable predictions and result in significant deviation. Canonicity is important in multipliers to reduce complexity and DS P filters like IIR and FIR filters, whose stability decides the performance of the system. In this research work, a novel approach utilizing the Longest Path Matrix Algorithm(LPM) is presented, commonly known as the Critical Path Method (CPM). It is the ability to identify the longest path in a Directed Acyclic Graph (DAG) involving delays between nodes for complex networks. Multi-rate DAGs can be used but it contains many redundant nodes and edges. It provides a deterministic approach and involves matrix-based operations to compute critical paths, thus achieving precise, efficient and predictable results. CPM involves constructing, manipulating matrices and computing iteration bounds, which can be efficiently handled in MATLAB. Thus, it offers a robust and versatile solution for a wide range of iterative problems thereby optimizing the convergence of iterative algorithms. Minimum cycle mean algorithm is a better method than LPM with a lower polynomial time complexity. The DAG is implemented in Xilinx Vivado software to obtain the resource and power utilization for the various matrices obtained. The DAG used for LPM is applied to various filters like pipelined FIR filter, clustered lookahead pipelined filter and all pole lattice filter and their resource utilization is tabulated. © 2023 IEEE.

关键词： critical path critical path delay(CPD) digital signal processing(DSP) Direct Acyclic Graph (DAG) Iteration Bound Longest Path Matrix (LPM) MATLAB

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2nd international conference on signal Image processing and Communication, ICSIPC 2022

2nd International Conference on Signal Image Processing and ...

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2nd international conference on signal Image processing and Communication, ICSIPC 2022

ISBN: (纸本)9781510657694

The proceedings contain 88 papers. The topics discussed include: analysis of underwater noise characteristics of ships in offshore waters;based on beidou short message of high-precision landslide early warning information release technology research;analysis method of regional soil pollutants based on distributed sensing technology;application of short-range wireless communication technology in electrophysiological signal metrology;a deep learning approach of heartbeat classification for the single-lead ECG signals and inter-patient paradigm;research on finite element analysis and signal denoising based on electromagnetic acoustic emission;integration and construction of gas monitoring and warning visualization platform;feature extraction and classification method of electrooculogram based on variational modal decomposition;research on interference avoidance technology for low earth orbit satellite based on spatial processing;and recognition and removal of EMG artifacts in single-channel EEG signals based on variational mode decomposition and second-order blind identification.

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TalTech Systems for the PROCESS signal processing Grand Challenge

TalTech Systems for the PROCESS Signal Processing Grand Chal...

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international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Erik Illaste Tanel Alumäe Tallinn University of Technology Estonia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The PROCESS Challenge aims to detect cognitive decline, including early stages like mild cognitive impairment, through spontaneous speech. This paper describes TalTech’s systems prepared for the challenge that applied machine learning models incorporating multimodal features to address both regression and classification tasks. For regression, the Lasso model achieved an RMSE of 2.54 on the test set, achieving 2nd place in the challenge. For classification, the XGBoost model achieved a macro F1 score of 0.61, placing 6th. These results demonstrate the potential of integrating diverse speech-based features and predictive modeling for scalable, early detection of cognitive decline.

关键词： Speech analysis machine learning signal processing Predictive models Feature extraction Acoustics Speech processing Dementia

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Baby Cry Decoder: A boon for the Hearing Impaired Caregiver 2

Baby Cry Decoder: A boon for the Hearing Impaired Caregiver

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2nd IEEE Mysore Sub Section international conference, MysuruCon 2022

作者： Pradhan, Bijin Nhuchhe Shorya, Kartikeya Kushwaha, Abhishek Shah, Gaurav Raj Venkatesh, K. Mamatha Bai, B.G. Ankalaki, Shilpa Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

The cry of a baby, the main means of communication between the baby and the caregiver carries wealth of information about the status and the need of the baby. A caregiver who is hearing impaired is placed at an extreme disadvantage because of his inability to perceive this vital channel of communication with the baby. The main aim of this work is to bridge this gap by building a decoder which captures the cry of the baby and classifies it into various categories which will help the caregiver in taking the appropriate action thus empowering the hearing challenged caregiver. This study introduces several pre-processing techniques as well as a variety of feature extraction schemes such as MFCC, spectrogram, and fundamental frequency, among others to characterize the cries of babies. machine learning classifiers may be trained using these characteristics. In this research activity, recently proposed neural network architectures like EfficientNet and ResNet are used along with established machine learning classifiers such as KNN and SVM for building the classifier. This study thoroughly examines prior research in all relevant areas of decoding cries of babies in order to gain insights into the most recent, cutting-edge works in infant cry signal processing and categorization. The main objective of this research project is to compare various models for the purpose of determining the optimal model for classifying cries of infants. © 2022 IEEE.

关键词： signal processing

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