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3rd international conference on Language Processing and Knowledge Management, LPKM 2024

3rd International Conference on Language Processing and Know...

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3rd international conference on Language Processing and Knowledge Management, LPKM 2024

ISBN: (纸本)9783031850660

The proceedings contain 29 papers. The special focus in this conference is on Language Processing and Knowledge Management. The topics include: Machine Learning Serving the School Orientation Process;improving Arabic Fake News Detection Across Context-Aware Attention Deep Model Based on Natural Language Processing;towards a Maude-Based Approach for Formal Modeling Deep Neural Networks;Anti-pattern Based IoRT-Aware Business Process Structure Verification Approach;opinion Analysis Based on a Sentiment Lexical Ontology and Deep Learning Models: Tunisian Dialect Case;disfluent-to-Fluent Tunisian Dialect Speech Translation with Fine-Tuning Pre-trained Language Models;BERT-Based Model for Sarcasm Detection in Arabic Texts;typology of Event Data Imperfections;towards Sentiment Analysis for Libyan Dialect;text Categorization Can Enhance Domain-Agnostic Stopword Extraction;normalized Orthography for Tunisian Arabic;deep Learning Approach for Early Prediction of Depression on Social Network;adapting Large Language Models to Biomedical Domain: A Survey of Techniques and Approaches;tunisian Arabic Understanding: Resources Analysis and Evaluation;from Data to Decisions: An Ontology-Driven Method for Opinion Mining;LLMs for Cyberbullying Detection in Political Social Media;Assessing BERT Models for Arabic Named Entity recognition in a Multi-dialectal Context;tunisian Normalized Pronunciation;traffScOnto: Ontology for Traffic Management in the Context of Smart City Domain;SERTUS Dataset Collection from Spontaneous Environments;an Agricultural Sentiment Dataset for Pest Control and Crop Diseases;deep Learning Approach to Identify and Classify Arabic Verbal Multi-word Expressions;a Design pattern-Based Approach for Analyzing MapReduce Applications;analyzing the Impact of Big Data in Mental Health;The Impact of AI on Knowledge Management;a Rule-Based System for Translating Libyan Dialect Dual Forms to Modern Standard Arabic.

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Unsupervised Hierarchical Disentanglement for Video Prediction 26

Unsupervised Hierarchical Disentanglement for Video Predicti...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - Theory and Applications (IMTA)

作者： Motallebi, Mohammad Reza Westfechtel, Thomas Li, Yang Harada, Tatsuya Univ Tokyo Tokyo Japan RIKEN Tokyo Japan

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Video prediction is a complicated task as countless possible future frames exist that are equally plausible. While recent work have made progress in the prediction and generation of future video frames, these work have not attempted to disentangle different features of videos such as an object's structure and its dynamics. Such a disentanglement would allow one to control these aspects to some extent in the prediction phase, while at the same time maintain the object's intrinsic properties that are learned as the model's internal representation. In this work, we propose Ladder Variational Recurrent Neural Networks (LVRNN). We employ a type of ladder autoencoder shown to be effective for feature disentanglement on images and apply it to the Variational Recurrent Neural Network (VRNN) architecture, which has been used for video prediction. We rely on extracted keypoints in each frame to separate the structure from the visual features. We then show how different levels of the ladder network learn to disentangle features and demonstrate that each of these levels can be used for controlling different aspects of future frames such as structure and dynamics. We evaluate our method on the Human3.6M and BAIR robot datasets. We show that our method is able to perform hierarchical disentanglement, yet provide reasonable results compared to similar methods.

关键词： Visualization Recurrent neural networks Predictive models Feature extraction Graph neural networks pattern recognition Object tracking

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3rd international Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

3rd International Workshop on Applications of Medical Artifi...

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3rd international Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031820069

The proceedings contain 24 papers. The special focus in this conference is on Applications of Medical Artificial Intelligence. The topics include: SP-NAS: Surgical Phase recognition-Based Navigation Adjustment System for Distal Gastrectomy;transforming Multimodal Models into Action Models for Radiotherapy;enhanced Interpretability in Histopathological Images via Combined Tissue and Cell-Level Graph Analysis;targeted Visual Prompting for Medical Visual Question Answering;deep Learning for Resolving 3D Microstructural Changes in the Fibrotic Liver;predicting Falls Through Muscle Weakness from a Single Whole Body Image: A Multimodal Contrastive Learning Framework;Optimizing ICU Readmission Prediction: A Comparative Evaluation of AI Tools;source Matters: Source Dataset Impact on Model Robustness in Medical Imaging;evaluating Perceived Workload, Usability and Usefulness of Artificial Intelligence Systems in Low-Resource Settings: Semi-automated Classification and Detection of Community Acquired Pneumonia;incremental Augmentation Strategies for Personalised Continual Learning in Digital Pathology Contexts;assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears;automated Feedback System for Surgical Skill Improvement in Endoscopic Sinus Surgery;quantifying Knee Cartilage Shape and Lesion: From Image to Metrics;RadImageGAN – A Multi-modal Dataset-Scale Generative AI for Medical Imaging;Ensemble-KAN: Leveraging Kolmogorov Arnold Networks to Discriminate Individuals with Psychiatric Disorders from Controls;SCIsegV2: A Universal Tool for Segmentation of Intramedullary Lesions in Spinal Cord Injury;EHRmonize: A Framework for Medical Concept Abstraction from Electronic Health Records using Large Language Models;evaluating the Impact of Pulse Oximetry Bias in Machine Learning Under Counterfactual Thinking;normative Modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s Disease Diagnosis and Biomarker Identification;one-Shot Medical

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Toward an Ontology of pattern Mining over Data Streams 3rd

Toward an Ontology of Pattern Mining over Data Streams

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Samb, Dame Slimani, Yahya Ndiaye, Samba Management Department UIDT Cite Malick SY N2 BP: A967 Thies Senegal ISAMM Tunis Tunisia Mathematics and Computer Science Department Cheikh Anta Diop University Dakar Senegal

ISBN: (纸本)9783031463341

pattern Mining over Data Stream (PMDS) is part of the most significant task in data mining. A major challenge is to define a representational framework that unifies PMDS algorithms dealing with different pattern types (frequent itemset, high-utility itemset, uncertain frequent itemset), using different methods (test-and-generate, pattern-growth, hybrid) and different window models (landmark, sliding, decay, tilted) in a uniform fashion. This will help standardize the process and create a better understanding of the algorithm design, provide a base for unification and research opportunities. It also facilitates the variability management and allows the derivation of tools for wide experimentation. In this publication, we propose a reference ontology to formalize the domain knowledge around PMDS. The design process of the ontology followed leading practices in ontology engineering. It is aligned to the most popular data mining and machine learning ontologies and thus, represents a major contribution toward PMDS domain ontologies. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge representation

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Cross-Guided Feature Fusion with Intra-Modality Reweighting for Multi-Spectral Pedestrian Detection 26

Cross-Guided Feature Fusion with Intra-Modality Reweighting ...

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26th international conference on pattern recognition / 8th international Workshop on Image Mining - Theory and Applications (IMTA)

作者： Deng, Jian Li, Mingyue Chen, Yongxin Shao, Zhenzhou Qu, Ying Guan, Yong Zhang, Jun Shi, Zhiping Capital Normal Univ Coll Informat Engn Beijing Peoples R China Beijing Key Lab Light Ind Robot & Safety Verifica Beijing Peoples R China Beijing Adv Innovat Ctr Imaging Technol Beijing Peoples R China Capital Normal Univ Sch Math Sci Beijing Peoples R China Univ Tennessee Engn Coll Knoxville TN USA Beijing Smartchip Microelect Technol Co Ltd Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Multi-spectral pedestrian detection has gained extensive attention over the past decade. To alleviate the problem of modality imbalance in the multi-spectral tasks, a novel cross-guided feature fusion network based on the auto-encoder framework is proposed using RGB-thermal image pairs as inputs. To obtain the complementary features, a cross-guided loss is designed, so that the output images are balanced with both modalities in an unsupervised manner. An intra-modality reweighting module is implemented to filter the redundant features before the fusion. Finally, YOLOv3 is chosen as the detector fed by the fused features. The proposed method is verified using the public KAIST and VOT-RGBT datasets. Experimental results demonstrate that the proposed method can outperform the state-of-the-art methods, the miss rate of pedestrian detection reaches 48.57% and 4.52% using KAIST and VOT-RGBT datasets, respectively.

关键词： Redundancy Detectors Feature extraction pattern recognition Task analysis

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3rd international conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

3rd International Conference on Emergent Converging Technolo...

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3rd international conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

ISBN: (纸本)9789819986453

The proceedings contain 56 papers. The special focus in this conference is on Emergent Converging Technologies and Biomedical Systems. The topics include: Smart Health Monitoring System for Elderly People;impact of Covid-19 and Subsequent Usage of IoT;design of Battery Monitoring System for Converted Electric Cycles;image Denoising Framework Employing Auto Encoders for Image Reconstruction;server Access pattern Analysis Based on Weblogs Classification Methods;multilingual Emotion recognition from Continuous Speech Using Transfer Learning;Violence Detection Using DenseNet and LSTM;financial Technology and Competitive Landscape in the Banking Industry of Bangladesh: An Exploratory Focus;on Parameterized Picture Fuzzy Discriminant Information Measure in Medical Diagnosis Problem;review on Deep Learning-Based Classification Techniques for Cocoa Quality Testing;a Curated Study on Machine Learning Based Algorithms and Sensors for Drone Technology in Various Application;automatic Detection of Coagulation of Blood in Brain Using Deep Learning Approach;deepPose: A 2D Image Based Automated Framework for Human Pose Detection and a Trainer App Using Deep Learning;Phylogenetic Study of Surface Glycoprotein (S1 Spike Protein) Sequence of SARS-CoV-2 Virus;pervasive and Wearable Computing and Networks;power of Image-Based Digit recognition with Machine Learning;open-Source Gesture-Powered Augmented Reality-Based Remote Assistance Tool for Industrial Application: Challenges and Improvisation;enhancing Biometric Performance Through Mitigation of Sleep-Related Breaches;Neural Network Based CAD System for the Classification of Textures in Liver Ultrasound Images;fuzzy Vendor–Buyer Trade Credit Inventory Model-Pentagonal Numbers in Permissible Limits Delay in Account Settlement with Supervised Learning;a Comparative Survey on Histogram Equalization Techniques for Image Contrast Enhancement.

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Local Neighborhood Average pattern: A Handcrafted Feature Descriptor for Hand Gesture recognition 3

Local Neighborhood Average Pattern: A Handcrafted Feature De...

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3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Bahuguna, Arti Namchyo, Shalom Ben Tenzing Chaudhary, Deepak Kumar Bhaumik, Gopa Govil, Mahesh Chandra National Institute of Technology Sikkim Computer Science and Engineering Sikkim India National Institute of Technology Jamshedpur Computer Science and Engineering Jharkhand India

ISBN: (纸本)9798350300710

This paper proposes a handcrafted feature-based descriptor namely Local neighborhood average pattern (LNAP) for static hand gesture recognition. The fact, that the local descriptors are important in numerous computer vision applications and show great performance cannot be overstated. This is accentuated further in difficult environments. This is the major driving force behind continued research in this field. We developed a feature descriptor LNAP in this paper, with the aim of extracting complete microstructural features achieved by evaluating excitation in different ways and directional relevant data based on connections between pixels taken at various spatial configurations inside each 3 × 3 neighborhood. An LNAP descriptor represents the structure of hand textures in a simple and compact coding manner, resulting in its unique code in less time and memory than existing approaches. The proposed LNAP descriptor extracts the dominant features from the hand region which are further classified using SVM. The proposed LNAP's performance is assessed using three benchmark datasets: Massey University Dataset (MUGD), ASL Digit Datasets, and Ouhands Dataset in respect of accuracy and F1-score and found to achieve an accuracy of 93% (MUGD Set1), 96% (MUGD Set2), 92 % (MUGD Set 3), 89% (MUGD Set4), 71% (MUGD Set5), 99%, and 47% respectively. The experimental findings show that the suggested LNAP produces better outcomes than existing techniques. © 2023 IEEE.

关键词： Textures

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Advancements in Alzheimer's Disease Diagnosis: The MRI-CNN Synergy for Early Detection 3rd

Advancements in Alzheimer's Disease Diagnosis: The MRI-CNN S...

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3rd international conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Ahmad, Siftain Gnyani, Kasula Rajhans, Abhishek Sinha, Bam Bahadur Indian Inst Informat Technol Ranchi Ranchi 834010 Jharkhand India

ISBN: (纸本)9783031640698;9783031640704

Alzheimer's disease is a neurodegenerative condition with profound implications for both physical and mental well-being. Its clinical manifestations encompass a range of high-risk symptoms, including impaired self-care abilities, emotional and behavioral disturbances, cognitive deficits, memory impairment, declining motor skills and coordination, and reduced appetite. If left unaddressed, Alzheimer's disease can lead to dire outcomes, including fatality. Timely diagnosis is paramount to mitigating the irreversible neurological damage associated with the condition. Raising awareness among at-risk individuals can empower them to proactively adopt preventive measures. Recent research has demonstrated the potential of computational approaches in Alzheimer's diagnosis. Early detection plays a pivotal role in enhancing treatment efficacy and pharmaceutical interventions. Our study is focused on the early identification of Alzheimer's disease, with a specific emphasis on the utilization of MRI as a diagnostic input. Employing a 17-layer Convolutional Neural Network (CNN), renowned for its pattern recognition capabilities, we have achieved an impressive accuracy of 99.8%.

关键词： Convolutional Neural Network pattern recognition Decision Support System Deep Learning Classification

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Partial Discharge pattern recognition of 10kV XLPE Cable Defects Based on VMD and GA-BP 3

Partial Discharge Pattern Recognition of 10kV XLPE Cable Def...

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3rd international Symposium on Electrical, Electronics and Information Engineering, ISEEIE 2023

作者： Qin, Chunxu Zhu, Pengfei Liu, Liqiang Xue, Yunpeng Shang, Jiafeng Huang, Yongxiang Engineering Research Center of Large Energy Storage Technology Ministry of Education Inner Mongol University of Technology Hohhot010051 China

The study of cable partial discharge pattern recognition is of great importance for the stability of power cables. In the high voltage laboratory, a platform was established to test partial discharge signals of 10kV XLPE cable, such as metal impurities, water infiltration of main insulation, and scratches of main insulation, utilizing the high frequency current method. The entropy value of each modal component were studied by using variation modal decomposition for the collected PD *** quantities such as the power spectral entropy mean, fuzzy entropy mean, sample entropy mean of each modal component, and IMF1's fuzzy entropy value were chosen. To optimize the back propagation neural network algorithm for partial discharge pattern recognition, a genetic algorithm was employed. © The Institution of Engineering & Technology 2023.

关键词： Partial discharges

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3rd international conference on Applied Mathematics, Modeling and Computer Simulation, AMMCS 2023

作者： Liu, Zhe School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia

ISBN: (纸本)9781643684581

Pythagorean fuzzy sets (PFSs) are a versatile tool for handling uncertain problems and have proven effective in practical applications. However, many existing Pythagorean fuzzy similarity measures have counter-intuitive situations, making it challenging to measure the similarity or difference between PFSs accurately. To address this issue, we propose two similarity measures for PFSs inspired by the Tanimoto similarity measure. We also explore the properties of the proposed measures and provide several numerical examples to illustrate their superior performance in processing fuzzy information from PFSs. Finally, we demonstrate the applicability of the proposed measures in solving pattern recognition problem. © 2023 The Authors.

关键词： Fuzzy sets

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