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Plant Stress Classification with Deep Learning Models Using STFT-Based 2D Representation of 6-Channel 500 kHz Plant Sound 4th

Plant Stress Classification with Deep Learning Models Using ...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Baek, Eu-Tteum Yoon, Hye-Seong AI&Bigdata Honam University Gwangju Korea Republic of

ISBN: (纸本)9789819787043

Plant stress is a physiological response triggered by various environmental factors, such as extreme weather conditions, diseases, or mechanical disturbances. Monitoring and classifying plant stress are crucial for assessing the overall health of vegetation and implementing timely interventions to ensure sustainable agricultural practices. However, estimating the magnitude of stress proves challenging due to a lack of comprehensive datasets and limited technological capabilities. therefore, in this study, we present a plant classification method leveraging a 2D representation based on Short-Time Fourier Transform of 6-channel 500 kHz sounds, recorded in scenarios such as tree cutting, drought, and other common environmental conditions. the experimental results demonstrate that our proposed method effectively predicts stress factors;however, due to a lack of a comprehensive dataset and limitations in data quality, the accuracy is slightly lower. these endeavors are expected to offer significant assistance in the analysis and judgment of plant stress. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： deep learning plant stress STFT

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Efficient Detection of Fake Twitter Profiles on Social Media through Hybrid Deep Learning Model 6th

Efficient Detection of Fake Twitter Profiles on Social Media...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Bibi, Haleema Ashraf, Humaira Jhanjhi, N.Z. Department of Computer Science and Information Technology International Islamic University Islamabad Pakistan School of Computer Science SCS Taylor’s University Subang Jaya Malaysia

ISBN: (纸本)9789819780891

Social media is an interactive and reliable tool used for communication among people around the world. It provides a facility for people to make accounts of it and to share their personal ideas, photos, interests, and other expressions. through social media, people can easily interact with each other. Everyone is in contact with their loved ones. In social media mostly used applications are Facebook, Instagram, WhatsApp, Twitter, LinkedIn, and YouTube. Due to the ever-increasing popularity of social media, it has become an ideal platform for the creation of fake accounts by spammers for undesirable activities. Spammers make fake accounts on social media to create panic among people. In this research, we aimed to detect fake profiles in one of the most used content-sharing applications Twitter. We have used the MIB Twitter dataset. Based on user-based attributes we have implemented deep learning techniques LSTM and GRU, and we proposed a hybrid LSTM-GRU. through experimental results, we gain better accuracies and small losses with deep learning techniques than machine learning algorithms for efficient detection of fake Twitter profiles. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tweets

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6th Asian conference on pattern recognition, ACPR 2021

6th Asian Conference on Pattern Recognition, ACPR 2021

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6th Asian conference on pattern recognition, ACPR 2021

ISBN: (纸本)9783031024436

the proceedings contain 85 papers presendted at a virtual meeting. the special focus in this conference is on pattern recognition. the topics include: Hierarchical Attentive Upsampling on Input Signals for Remote Heart Rate Estimation;CASIA-onDo: A New Database for Online Handwritten Document Analysis;UnDeepLIO: Unsupervised Deep Lidar-Inertial Odometry;exploiting Multi-scale Fusion, Spatial Attention and Patch Interaction Techniques for Text-Independent Writer Identification;DCT-DWT-FFT Based Method for Text Detection in Underwater Images;joint Dermatological Lesion Classification and Confidence Modeling with Uncertainty Estimation;Resting-State Electroencephalography (EEG)-Based Diagnosis System for Drug-Naive Female Major Depressive Disorder Patients;paperswithtopic: Topic Identification from Paper Title Only;spatial Spectral Joint Correction Network for Hyperspectral and Multispectral Image Fusion;Subject-Independent Motor Imagery EEG Classification Based on Graph Convolutional Network;multimodal Co-training for Fake News Identification Using Attention-aware Fusion;gaussian Distribution Prior Based Multi-view Self-supervised Learning for Serous Retinal Detachment Segmentation;learning Symbol Relation Tree for Online Handwritten Mathematical Expression recognition;Subject Adaptive EEG-Based Visual recognition;Spectro-Spatio-Temporal EEG Representation Learning for Imagined Speech recognition;Is Notable EEG Feature Extracted Over Time-Dependent Cognitive Load Variation During Intelligence Tests?;a Self-attention Based Model for Offline Handwritten Text recognition;Coarse-to-Fine Learning Framework for Semi-supervised Multimodal MRI Synthesis;intelligent recognition of Point Source Target Image Control Points with Simulation Datasets;q-Softplus Function: Extensions of Activation Function and Loss Function by Using q-Space;image Registration Between Real Image and Virtual Image Based on Self-supervised Keypoint Learning;fast Deep Asymmetric Hashing for Image Retriev

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6th Asian conference on pattern recognition, ACPR 2021

6th Asian Conference on Pattern Recognition, ACPR 2021

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6th Asian conference on pattern recognition, ACPR 2021

ISBN: (纸本)9783031023743

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6-DOF Motion Blur Synthesis and Performance Evaluation of Object Detection 27th

6-DOF Motion Blur Synthesis and Performance Evaluation of Ob...

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27th international conference on pattern recognition, ICPR 2024

作者： Yang, Hanjin Li, Feng Zhang, Lei School of Computer Science and Technology Donghua University Shanghai201600 China National Innovation Center of Advanced Dyeing and Finishing Technology Shandong Tai’an271000 China

ISBN: (纸本)9783031784439

the current state-of-the-art deep learning vision networks commonly employs synthetic approaches for data augmentation when confronted with scenes requiring motion blur. However, existing blur synthesis methods often fall short in accurately simulating motion blur as observed in real-world scenarios, consequently hindering the generalization capability of trained deep visual networks to real-world applications, a phenomenon known as domain shift effects [1]. To address this problem, we propose a novel non-uniform motion blur synthesis method for data augmentation. First, we randomly generate the camera’s motion trajectory using a more general six-degree-of-freedom (6-DOF) camera motion model, and then map this trajectory to pixel-level blur kernels. To efficiently perform spatially varying convolution, we employ non-negative matrix factorization (NMF) to decompose those blur kernels into a set of kernel basis and their corresponding mixing coefficients. this enables parallel execution of spatial variation convolutions, thereby significantly improving the efficiency of blur synthesis. Our experiments demonstrate consistently superior results of the proposed method on publicly available real datasets RealBlur, as well as synthetic datasets GoPro and REDS. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Non-negative matrix factorization

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Doubly Regularized Least Squares Twin Extreme Learning Machine for pattern Classification 6

Doubly Regularized Least Squares Twin Extreme Learning Machi...

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6th international conference on Artificial Intelligence and pattern recognition, AIPR 2023

作者： Wu, Qing Yu, Ming Chen, Lu School of Automation Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9798400707674

Least squares twin extreme learning machine (LSTELM) possesses better generalization ability than the standard extreme learning machine (ELM) by finding two nonparallel hyperplanes in ELM random feature space. However, LSTELM is not sparse in nature and does not take into account the intrinsic geometric information of the data. To overcome the above, this paper proposes one classification method based on LSTELM, namely doubly regularized least squares twin extreme learning machine (DRLSTELM). DRLSTELM uses L2,1-norm regularization and manifold regularization methods to improve the classification performance. Furthermore, in its nonlinear case, a hybrid kernel function is introduced to improve the generalization and learning ability of the model. To evaluate the classification performance of the proposed algorithm, experiments were conducted on the NDC datasets and the benchmark datasets respectively. the results show that the classification effect of DRLSTELM is significantly better than other state-of-the-art methods. © 2023 ACM.

关键词： Classification (of information)

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Moiré pattern Detection: Stability and Efficiency with Evaluated Loss Function 27th

Moiré Pattern Detection: Stability and Efficiency with Eval...

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27th international conference on pattern recognition, ICPR 2024

作者： Li, Zhuocheng Shen, Xizhu Luan, Simin Guo, Shuwei Boukhers, Zeyd Sui, Wei Wang, Yuyi Yang, Cong Soochow University Suzhou China Fraunhofer Institute for Applied Information Technology Sankt Augustin Germany University Hospital of Cologne Cologne Germany CRRC Zhuzhou Institute Co. Ltd. Hunan Zhuzhou412001 China

ISBN: (纸本)9783031784439

Detecting moiré patterns in digital images is essential as it offers insights for assessing image integrity and undertaking demoiréing processes. MoireDet is a simple and efficient moiré pattern detection neural network designed explicitly for moiré edge map estimation [23]. However, the random feature mapping of the Performer significantly affects the prediction for continuous video frames. this paper introduces MoireDet+ based on existing work, which introduces Vision Transformer into moiré-related tasks. MoireDet+ utilizes a mixed-encoder as a backbone, integrating both high- and low-level vision encoders in an FPN-like method, along with a spatial encoder to extract the complex spatial features of moiré patterns. Furthermore, we produce a rapid approximation-based evaluation method to aid loss function design in image restoration and similar tasks. MoireDet+ reaches a state-of-the-art level on mainstream datasets, reducing time costs by 18% compared to MoireDet and other baselines with comparable performance levels. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Restoration

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External Prompt Features Enhanced Parameter-Efficient Fine-Tuning for Salient Object Detection 27th

External Prompt Features Enhanced Parameter-Efficient Fine-T...

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27th international conference on pattern recognition, ICPR 2024

作者： Liang, Wen Ran, Peipei Bai, Mengchao Liu, Xiao Githinji, P. Bilha Zhao, Wei Qin, Peiwu Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Central Media Technology Institute Shenzhen Huawei China

Salient object detection (SOD) aims at finding the60;most salient objects in images and outputs pixel-level binary masks. Transformer-based methods achieve promising performance due to60;their global semantic un... 详细信息

ISBN: (纸本)9783031783463

Salient object detection (SOD) aims at finding the most salient objects in images and outputs pixel-level binary masks. Transformer-based methods achieve promising performance due to their global semantic understanding, crucial for identifying salient objects. However, these models tend to be large and require numerous training parameters. To better harness the potential of transformers for SOD, we propose a novel parameter-efficient fine-tuning method aimed at reducing the number of training parameters while enhancing the salient object detection capability. Our model, termed EXternal Prompt features Enhanced adapteRTuning (ExPert), features an encoder-decoder structure with adapters and injectors interspersed between the layers of a frozen transformer encoder. the adapter modules adapt the pre-trained backbone to SOD while the injector modules incorporate external prompt features to enhance the awareness of salient objects. Comprehensive experiments demonstrate the superiority of our method. Surpassing former state-of-the-art (SOTA) models across five SOD datasets, ExPert achieves 0.215 mean absolute error (MAE) in the ECSSD dataset with 80.2 M trained parameters, 21% better than SelfReformer [31] and 47% better than EGNet [33]. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Enhancing Bengali Text-to-Speech Synthesis through Transformer-Driven Text Normalization 27th

Enhancing Bengali Text-to-Speech Synthesis Through Transform...

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27th international conference on pattern recognition, ICPR 2024

作者： Ghosh, Krishnendu Patra, Munmun Dey, Eshita Indian Institute of Information Technology Dharwad Karnataka Dharwad India Techno International New Town West Bengal Kolkata India

this paper presents a transformer-driven approach60;for non-standard word (NSW) normalization in Bengali text-to-speech synthesis (TTS) systems. Our text normalization (TN) approach60;is realized over three modu... 详细信息

ISBN: (纸本)9783031784941

this paper presents a transformer-driven approach for non-standard word (NSW) normalization in Bengali text-to-speech synthesis (TTS) systems. Our text normalization (TN) approach is realized over three modules: pre-processing, NSW classification, and token-to-word expansion. the pre-processing module tokenizes the input sentences into words. the non-standard words are classified based on their format and other discriminative features in the next module. Apart from our proposed transformer-based approach, the present study explored four baseline TN approaches: rule-based, Conditional Random Field (CRF)-based, Long Short-Term Memory (LSTM)-based, and biLSTM-based. Based on the predicted types of the NSWs, standard words (SWs) are generated through token-to-word expansion using hand-crafted expansion rules. the performance of these five TN approaches is assessed on a corpus of around 6,000 NSWs, consisting of 6 initial and 14 final NSW types. the performances of these approaches are assessed using measures representing (i) direct accuracy and (ii) enhancements in text-to-speech synthesis systems. Based on direct accuracy, our proposed approach predicted correct NSW types for about 92% of cases. this performance is around 2% better than the performance of a rule-based approach. On the other hand, application-based measures guarantee that the generated utterances (i) sound natural and (ii) improve the quality of speech synthesis systems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Speech enhancement

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Edge Detection of MRI Brain Images Based on Segmentation and Classification Using Support Vector Machines and Neural Networks pattern recognition 6th

Edge Detection of MRI Brain Images Based on Segmentation and...

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6th international conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES 2022

作者： Iourzikene, Zouhir Benazzouz, Djamel Gougam, Fawzi Université M’Hamed BOUGARA de Boumerdes Boumerdes Algeria

ISBN: (纸本)9783031212154

Brain tumor (brain cancer) is a mass of abnormal cells that grow in the brain in an uncontrolled way. Brain CT and brain MRI are the most frequently performed examinations. the objective of this paper is to develop a method for the classification of brain MRI images of healthy cases and tumor cases. MRI brain database is obtained by preprocessing, segmentation, feature extraction. Feature extraction based on support vector machines (clustering) is used in this research. the objective of this method is to create several vectors and each vector contains a number of features of each image, so that we can make the classification by these features. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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