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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

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Wheat Leaf Disease Identification Based on a Fusion Model Integrating Attention Mechanisms and Channel-Spatial Reconstruction Convolution 5

Wheat Leaf Disease Identification Based on a Fusion Model In...

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5th international conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Xie, Haifan Zou, Rongxin Yang, Zicong Jin, Aitong Wang, Junxi Li, Yu Zhuhai College of Science and Technology Department of Computer Zhuhai China Zhuhai College of Science and Technology Department of Big Data Zhuhai China Institute of Intelligent Manufacturing and Aviation Zhuhai College of Science and Technology ZhuHai China Zhuhai College of Science and Technology Alibaba Cloud Big Data Application College Zhuhai China

ISBN: (纸本)9798331506612

With the continuous growth in food demand and the escalating importance of crop health, the challenges posed by the high costs and low efficiency of traditional wheat leaf disease detection methods have become increasingly acute. In light of the intricacies and low discernibility of the morphological and color characteristics of wheat leaf diseases and pests in natural environments, this study introduces a novel and efficient recognition and classification model, SCC2f-CSPDarknet53-CBAM, offering a precise and rapid solution for identifying diseases and pests, thereby supporting early prevention and scientific management. By incorporating the SCC-C2f module into the base of C2f-CSPDarknet53, this model enhances the efficiency of network feature fusion and the capability to extract critical features. Furthermore, the integration of the ResBlock-CBAM module at the terminal end bolsters the generalization and recognition abilities for targets. the study employs the Wheat Leaf Disease dataset, which is constructed through a blend of online and offline methods, with meticulous data processing, for training purposes. Experimental results reveal that the SCC2f-CSPDarknet53-CBAM model achieves an average recognition accuracy of 99.54% in the task of wheat leaf disease detection and classification, surpassing mainstream models in recent years, showcasing remarkable accuracy and adaptability. © 2024 IEEE.

关键词： Convolutional neural networks

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Few-Shot Learning Using Data Augmentation and Time-Frequency Transformation for Time Series Classification 5

Few-Shot Learning Using Data Augmentation and Time-Frequency...

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5th international conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Zhang, Hao Pang, Zhendong Wang, Jiangpeng Li, Teng School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9798350357950

Deep neural networks (DNNs) that tackle the time series classification (TSC) task have provided a promising framework in signal processing. In real-world applications, as a data-driven model, DNN s are suffered from insufficient data. Few-shot learning has been studied to deal with this limitation. In this paper, we propose a novel few-shot learning framework through data augmentation, which involves transformation through the time-frequency domain and the generation of synthetic images through random erasing. Additionally, we develop a sequence-spectrogram neural network (SSNN). this neural network model composes of two sub-networks: one utilizing 1D residual blocks to extract features from the input sequence while the other one employing 2D residual blocks to extract features from the spectrogram representation. In the experiments, comparison studies of different existing DNN models with/without data augmentation are conducted on an amyotrophic lateral sclerosis (ALS) dataset and a wind turbine fault (WTF) dataset. the experimental results manifest that our proposed method achieves 93.75% F1 score and 93.33% accuracy on the ALS datasets while 95.48% F1 score and 95.59% accuracy on the WTF datasets. Our methodology demonstrates its applicability of addressing the few-shot problems for time series classification. © 2023 IEEE.

关键词： data augmentation deep neural networks few-shot learning time series classification

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English Speech Recognition using Convolution Neural networks and IoT 5

English Speech Recognition using Convolution Neural Networks...

引用

5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Vijayakumari, R. Bannatti, Akshay Ashok Maranan, Ramya Kaur, Jaspreet Kamatchi, S. Department of Research and Innovation Saveetha School of Engineering Simats Krishna University Machilipatnam Chennai Kanchipuram India Simats India Gulzar Group of Institutions India Simats Krishna University Saveetha School of Engineering Machilipatnam Chennai TAMIL NADU Kanchipuram India

ISBN: (纸本)9798350383522

the middle era of speech popularity makes it viable for machines and those to communicate effectively and seamlessly. the demand for particular and dependable voice popularity structures is rising as Internet of things (IoT) applications are expanding quick. the use of convolutional neural networks (CNNs) to enhance speech recognition performance, especially for the linguistic English language, is presented in detail in this summary. the complex phonetics and phonology of the English language, in addition to its numerous accents, dialects, and regional editions, are all covered within the field of linguistics. For efficient human-pc interplay in a whole lot of IoT contexts, including smart houses, healthcare systems, and voice-controlled gadgets, accurate linguistic English speech reputation is crucial. For quite a few pc imaginative and prescient and audio processing obligations, convolutional neural networks (CNNs) have turn out to be effective deep mastering fashions. In this paper, we explore the use of CNNs in IoT contexts for linguistic English voice popularity. the collection of speech data, preprocessing, function extraction, model schooling, and reputation are the primary components of the recommended system. To ensure robustness and generality, we use huge-scale datasets of English speech to train the CNN fashions. Backpropagation and stochastic gradient descent are used to educate the version whilst optimizing the parameters to lessen the popularity blunders. this summary's end emphasizes the significance of unique English speech popularity in IoT programs. We can improve the effectiveness of speech popularity systems for the linguistic English language via using convolutional neural networks. By allowing greater intuitive and herbal interactions via speech commands and voice-managed devices, the suggested approach creates new opportunities for seamless human-gadget engagement in IoT environments. © 2024 IEEE.

关键词： Artificial Intelligence (AI) Audio analysis Convolutional Neural networks (CNNs) Deep Learning Internet of things (IoT) IoT applications IoT devices Language processing Linguistic English language Machine learning Natural Language processing (NLP) Neural network models Speech recognition Speech-to-text Voice recognition

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A Survey: Faults, Fault-tolerance & Fault Detection Techniques in WSN 5

A Survey: Faults, Fault-tolerance & Fault Detection Techniqu...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Shukla, Rupal Kumar, Ashwini Niranjan, Vandana Indira Gandhi Delhi Technical University for Women Electronics and Communication Engineering Delhi India

ISBN: (纸本)9798350398267

Present modern world become more automated, forecast-able and accurate by using wireless sensor network. To make a WSN more undependable and reliable, researchers have to make advance research on each and every domain of WSN, mainly on fault tolerant techniques. this paper thoroughly investigate the mechanism to achieve a fault tolerant network. the prime focus of this paper is to make understanding about faults, failures and its classification and detection in the network and explain the techniques to detection of fault, prevention of fault and recovery of the fault in the sensor network, these are the very crucial parameters of a fault tolerant network. © 2022 IEEE.

关键词： Wireless sensor networks

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MyDAS Corpus: Malay Social Media Texts for Detecting Depression, Anxiety, and Stress on Facebook 5

MyDAS Corpus: Malay Social Media Texts for Detecting Depress...

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5th international conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Ahmad, Zaaba Mohamed, Azlinah Conway, Mike Zakaria, Rozanizam Teo, Noor Hasimah Ibrahim Maskat, Ruhaila College of Computing Informatics & Mathematics Universiti Teknologi Mara Cawangan Perak Kampus Tapah Perak Tapah Malaysia Ysl Strategic Alliance Selangor Shah Alam Malaysia Centre for Digital Transformation of Health The University of Melbourne MelbourneVIC Australia Kulliyah of Medicine International Islamic University Malaysia Department of Psychiatry Pahang Kuantan Malaysia College of Computing Informatics & Mathematics Universiti Teknologi Mara Cawangan Melaka Kampus Jasin Melaka Merlimau Malaysia College of Computing Informatics & Mathematics Universiti Teknologi Mara Selangor Shah Alam Malaysia

ISBN: (纸本)9798331528553

the application of Natural Language processing (NLP) in mental health monitoring has significantly expanded;however, the specific challenges of interpreting Depression, Anxiety, and Stress (DAS) in Malay language social media texts have not been adequately addressed. this gap underscores the need for NLP solutions that are sensitised to the linguistic and cultural specificities of Malay-speaking populations. this study develops and validates a specialised Malay language corpus from social media content, targeting DAS. Utilising a hybrid ground truth strategy that integrates self-reports with expert assessments, the research offers methodological refinements in the analysis of Malay linguistic patterns and the deployment of machine learning classifiers to efficiently identify mental health indicators. the paper reviews existing methodologies, outlines a novel corpus development strategy, and discusses classifier performance. the Decision Tree classifier achieved the highest F1 score of 0.75, followed by the Support Vector Machine (SVM) with an F1 score of 0.73, and Random Forest with 0.70. Multinomial Naive Bayes (MNB) and K-Nearest Neighbors (KNN) demonstrated lower performances with F1 scores of 0.55 and 0.52 respectively. Comprehensive analyses using bi-gram networks and t-SNE visualisations explore the nuanced linguistic indicators of mental health states, culminating in a discussion of the implications for future NLP applications in mental health monitoring. © 2024 IEEE.

关键词： Support vector machines

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CNN-RNN Based Hybrid Deep Learning Model for Predicting Fluctuations in the Stock Market 5

CNN-RNN Based Hybrid Deep Learning Model for Predicting Fluc...

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5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Singh, Gurpreet Rani, Lekha Sarangi, Pradeepta Kumar Sharma, Kapil Sahoo, Ashok Kumar Malhotra, Raghav Chitkara University Institute Of Engineering And Technology Chitkara University Punjab India Chitkara University School Of Engineering And Technology Chitkara University Himachal Pradesh India Graphic Era Hill University Dehradun India

ISBN: (纸本)9798350330861

To enhance predicting performance, particularly in temporal data processing, Convolutional Neural networks (CNNs) and Recurrent Neural networks (RNNs) play a significant role in learning while using ANNs. According to past research works, some time-series may exhibit similar properties over time. Consequently, the relationship between various temporal data must be considered in modeling to generate accurate predictions. Nevertheless, general RNN models have significant limitations in achieving the required target. this research study proposes a new kind of architecture known as HNNs (Hybrid Neural networks), which combines the hidden state transfer method of the RNN with the convolution layer of a CNN. Multiple RNN models' concurrency characteristics are extracted by running RNNs along time steps with CNN integration. In addition to possessing the size of the number of time-oriented data, this innovative architecture also possesses the extent in the time dimension in RNN. this paper examines several DNN (Deep Neural Network) models and the theory underlying the DNN model. For our experiment, we utilize National Stock Exchange data. the results indicate that the Mean Absolute Percentage Error (MAPE) calculated for the RNN model is 1.005831265 and the MAPE value for CNN-RNN model is 0.902297821. this shows that the hybrid CNN-RNN model performs slightly better than the RNN model. © 2023 IEEE.

关键词： Recurrent neural networks

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Time Series Prediction Application of Deep Learning in Multidimensional Signal processing 5th

Time Series Prediction Application of Deep Learning in Multi...

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5th international conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Ding, Jiahao Yang, Diansheng Tianjin Key Laboratory of Equipment Design and Manufacturing Technology Tianjin University Tianjin300354 China Guangzhou Saite Intelligence Technology Co. Ltd. Huangpu District Guangdong Guangzhou510700 China

ISBN: (纸本)9789819751808

this article explores the application of deep learning techniques for time series prediction in multidimensional signal processing, specifically utilizing long short-term memory networks (LSTM). Traditional time series forecasting methods face many challenges when dealing with high-dimensional and nonlinear data. To address these challenges, this study introduces the basic concepts of time series forecasting, the application of deep learning in this field, and the characteristics and challenges of multidimensional signal processing. We adopt the LSTM model to process multi-dimensional time series data and conduct experimental verification on multiple public datasets. Experimental results show that compared with traditional methods, our method performs well in both prediction accuracy and efficiency. this study not only proposes a new multi-dimensional time series forecasting framework but also experimentally proves the effectiveness and advantages of deep learning technology in this field, providing new insights and methodological guidance for future research in related fields. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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A Novel General Purpose processing ASIC for Inertial sensors 5

A Novel General Purpose Processing ASIC for Inertial Sensors

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5th international conference on Control, Communication and Computing, ICCC 2023

作者： Sruthimol, T. Raghunath, K.P. Daniel, Sanil K Ruma, S.R. College of Engineering Dept. of Electronics and Communication Kerala Trivandrum India ISRO Inertial Systems Unit Kerala India Engineering College of Engineering Dept. of Electronics and Communication Kerala Trivandrum India

ISBN: (纸本)9798350334128

High-performance inertial sensors are widely used for autonomous navigation, guidance and control applications. this requires its electronics to be co-located to the sensor with the best C-SWaP(Cost, Size, Weight, and Power) configuration. Analog circuit-based designs were implemented earlier for such requirements. However, such implementations require manual adjustment of circuit parameters based on sensors and are complex. the digital implementation provides reconfigurability and high performance. However, different kinds of sensors require different processing logic. the typical sensor processing requirements are accomplished by data converters and digital processing logic. the digital processing logic requirement may vary with respect to different kinds of sensors. However, all these requirements can be met by implementing different combinations of the following modules, like IIR, FIR, PID controller and oscillators. Hence, a novel CISC processor with the support of all these logic modules is implemented. the logic modules are fully configured by microcode-based programs and execute in different sequential orders based on sensor requirements. the above implementation has been completely implemented in FPGA and successfully interfaced with various MEMS Gyros and Accelerometers. Further ASIC realization was also carried out on the SCL-180nm Technology node with an area of 2 mm × 2 mm and a CQFP-64 pin package. © 2023 IEEE.

关键词： three term control systems

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2022 5th international conference on Signal processing and Information Security, ICSPIS 2022

2022 5th International Conference on Signal Processing and I...

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5th international conference on Signal processing and Information Security, ICSPIS 2022

ISBN: (纸本)9781665492652

the proceedings contain 36 papers. the topics discussed include: federated learning through goal programming: a computational study in cancer detection;design of an automated blockchain-enabled vehicle data management system;smart speed camera based on automatic number plate recognition for residential compounds and institutions inside Qatar;classification of cumin, fennel and carom using transfer learning;identification of robust features for classifying spam and ham images using transfer learning;deep neural networks for transmission impairment mitigation in long-reach 5G access networks;a study on seizure detection performance in an automated process by extracting entropy features;detection of compressed deepfake video drawbacks and technical developments;employing information theoretic metrics with data-driven occupancy detection approaches: a comparative analysis;enhanced extra trees classifier for epileptic seizure prediction;and performance comparison of credit card fraud detection system using machine learning.

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Audio Super-resolution Using Feed-Forward Neural networks 23

Audio Super-resolution Using Feed-Forward Neural Networks

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5th international conference on Image, Video and Signal processing, IVSP 2023

作者： True, Sean Yang, Cungang Department of Electrical and Computer Engineering Toronto Metropolitan University Toronto Canada

ISBN: (纸本)9781450398381

In this paper, a system for reducing the file size of an audio signal, and then performing super-resolution on the resultant signal to estimate the original, is proposed and designed. this design takes influence from the principles of audio sampling, as well as super-resolution systems designed for visual media, and is split into an encoder and a decoder. the encoder successfully reduces the file size of the audio file by a significant amount. the super-resolution-based decoder can also successfully generate a matching high-frequency audio track that can be combined with the encoded lossy audio in order to estimate the original audio with a reasonable degree of audible accuracy. While a number of improvements to the system can be made in the future, it shows great promise, as it accomplishes the goals it was designed to meet. © 2023 ACM.

关键词： Signal encoding

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