Pattern Recognition and Machine Intelligence : 9th International Conference, PReMI 2021, Kolkata, India, December 15-18, 2021, Proceedings

• Autore: Ghosh Ashish
• Pubblicazione: Cham : , : Springer International Publishing AG, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (639 pages)
• Altri autori: KingIrwin BhattacharyyaMalay Sankar RayShubhra K. PalSankar
• Nota di contenuto: Intro -- Preface -- Organization -- Keynote Talks -- Recent Advances in the Deep CNN Neocognitron -- A Synergy of Evolution and Learning -- Invited Talks -- Explorations in Simulating Future Activity on Social Networks -- Recent Research Topics in Evolutionary Multiobjective Optimization: A Personal Perspective -- Tutorial Talks -- Spatial Data Science and Applications -- Document Image Analysis and Its Recent Trends -- Bone Microarchitectural Imaging in Osteoporosis - Recent Developments and Translational Studies -- Special Talk -- Early Gastric Cancer Detection by Deep Learning -- Industrial Talks -- AI Startups - An Indian Perspective -- Machine Reasoning -- Contents -- Image Processing -- Image Quality Assessment Using Combination of Deep Convolutional Neural Networks -- 1 Introduction -- 2 Deep CNN for IQA -- 2.1 Deep CNN Based Conventional NR-IQA -- 2.2 Proposed Combination of Deep CNNs for NR-IQA -- 3 Experiments -- 4 Conclusion -- References -- Blind Image Forgery Detection Using LBP and Statistical Moments -- 1 Introduction -- 2 Related Works -- 3 Proposed Work -- 3.1 Pre-processing -- 3.2 Local Binary Pattern Based Features from DWT Domain -- 3.3 Statistical Feature Extraction from DWT Domain -- 3.4 Ensemble Classifier -- 4 Experimental Results -- 4.1 Results and Discussion -- 4.2 Comparative Analysis -- 5 Conclusion -- References -- Edge Detection in Gray Scale Images Using Partial Sum of Second Order Taylor Series Expansion -- 1 Introduction -- 2 TSE Based Edge Detection -- 3 Experimental Analysis -- 4 Conclusion -- References -- Image Re-attentionizing Using Particle Swarm Optimization -- 1 Introduction -- 2 Image Re-attentionizing as an Optimization Problem -- 3 Proposed Method of Enhancing Saliency -- 3.1 Identifying the Known Variable Values -- 3.2 Finding Modified Feature Values of the Target Using PSO. ; 3.3 Reviving the Appearance for Intended Segment -- 4 Experimental Validation -- 4.1 Validation Through Saliency Map -- 4.2 Validation Through Eye-Tracking -- 5 Conclusion -- References -- Auto-Encoder Guided Attention Based Network for Hyperspectral Recovery from Real RGB Images -- 1 Introduction -- 2 Methodology -- 3 Experimental Results -- 3.1 Dataset Details -- 3.2 Result Analysis -- 4 Conclusions -- References -- Text, Voice and Video Processing -- Raga Classification in Carnatic Music Using Audio Thumbnailing -- 1 Introduction -- 2 Survey of Literature -- 3 Proposed Methodology -- 3.1 Compute SSM -- 3.2 Enhance SSM -- 3.3 Fitness Score -- 4 Implementation and Results -- 4.1 Dataset -- 5 Conclusion and Future Work -- References -- Wavelet Residual Learning for Efficient Future Frame Prediction from Natural Video Sequences -- 1 Introduction -- 2 Proposed Method -- 2.1 Brief Basics of Discrete Wavelet Transform -- 2.2 Network Architecture -- 2.3 Loss Function -- 3 Experiments -- 3.1 Datasets and Implementation Details -- 3.2 Quantitative and Qualitative Evaluation -- 3.3 Ablation Study -- 4 Conclusion -- References -- Voice Privacy Through Time-Scale and Pitch Modification -- 1 Introduction -- 2 Anonymization Using Time-Scale Modification -- 2.1 Speed Perturbation -- 2.2 Tempo Perturbation -- 2.3 Pitch (F0) Perturbation -- 2.4 Spectrum and Pitch Contour Analysis -- 3 Experimental Setup -- 3.1 Dataset -- 3.2 Baseline System -- 3.3 Objective Evaluation -- 4 Experimental Results -- 5 Summary and Conclusions -- References -- Abusive Speech Detection and Politeness Transfer -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Aggregation -- 3.2 Data Preprocessing -- 3.3 Vectorization -- 3.4 Word Embeddings -- 3.5 Baseline Models -- 3.6 State of the Art Models -- 3.7 Hybrid Model for Abusive Speech Classification. ; 3.8 Politeness Transfer Using BERT -- 4 Results and Analysis -- 5 Conclusion and Future Work -- References -- Voice Liveness Detection Using Bump Wavelet with CNN -- 1 Introduction -- 2 Proposed Approach -- 3 Experimental Setup -- 3.1 Database Used -- 3.2 Classifier Used: Convolutional Neural Network (CNN) -- 4 Experimental Results -- 5 Summary and Conclusions -- References -- Bioinformatics -- Classification of Task Evoked fMRI Signals Using Temporal Characteristics of Brain Regions -- 1 Introduction -- 2 Methodology -- 2.1 fMRI Dataset -- 2.2 Pre-processing and Parcellation -- 2.3 Feature Generation -- 2.4 Temporal Consistency in Activation Level - TCAL -- 2.5 Classification -- 3 Experimental Results and Discussion -- 3.1 Parameter Selection for TCAL -- 3.2 Performance of Features -- 3.3 Analysis of TCAL -- 4 Conclusion -- References -- Ensemble Models for Multi-class Classification of Diabetic Retinopathy -- 1 Introduction -- 1.1 Different Classes of Diabetic Retinopathy (DR) -- 2 Literature Review -- 3 Proposed Method -- 3.1 Data Preprocessing -- 3.2 Data Augmentation -- 3.3 Transfer Learning-Pretrained Models -- 4 Experimental Results -- 4.1 Dataset -- 4.2 Results -- 4.3 Conclusions -- References -- Identification of Potential Biomarkers Using Integrative Approach: An Application of ESCC -- 1 Introduction -- 2 Related Work -- 3 Proposed DE Framework -- 4 Performance Evaluation -- 4.1 Results on RNA-Seq -- 4.2 Results on Microarray -- 4.3 Validation -- 5 Conclusion -- References -- Autoencoder Assisted Cancer Subtyping by Integrating Multi-omics Data -- 1 Introduction -- 2 Experimental Details -- 2.1 Datasets -- 2.2 Proposed Autoencoder Architecture -- 2.3 Proposed Autoencoder Assisted Cancer Subtyping -- 3 Results -- 3.1 Cluster Evaluation -- 3.2 Biological Evaluation -- 4 Conclusion -- References. ; ADHDNet: A DNN Based Framework for Efficient ADHD Detection from fMRI Dataset -- 1 Introduction -- 2 Related Works and Concepts -- 3 Proposed Framework -- 4 Experiments and Results -- 5 Conclusion and Future Work -- References -- Deep Learning -- Multi-stage Transfer Learning Based Yoga Pose Recognition Using CNN -- 1 Introduction -- 2 Proposed Method -- 3 Dataset and Training Methodology -- 4 Results and Discussions -- 5 Conclusion -- References -- Autoencoder and Extreme Learning Machine Based Deep Multi-label Classifier -- 1 Introduction -- 2 Related Works -- 3 Proposed Work -- 3.1 Feature Reduction by DAE -- 3.2 Sequential Classification by S-MLELM -- 3.3 Architecture -- 4 Results and Discussion -- 4.1 Experimental Analysis -- 5 Conclusion -- References -- Neural Pooling for Graph Neural Networks -- 1 Introduction -- 2 Related Work -- 2.1 Graph Neural Networks -- 2.2 Graph Pooling -- 3 Methodology -- 3.1 Properties of Graph Pooling -- 3.2 Neural Pooling Method 1 -- 3.3 Neural Pooling Method 2 -- 4 Experimental Setup -- 4.1 Datasets -- 4.2 Training and Evaluation -- 5 Results and Discussion -- 6 Complexity -- 7 Conclusion -- References -- Pedestrian Detection and Movement Direction Recognition with Convolutional Neural Network -- 1 Introduction -- 2 Related Work -- 3 Dataset Description -- 4 Material and Methods -- 4.1 Pedestrian Detection -- 4.2 Movement Direction Recognition -- 5 Results and Discussion -- 6 Conclusion -- References -- Deep Learning Based Tree Detection and Counting for Remotely Sensed Images -- 1 Introduction -- 1.1 Remote Sensing -- 2 Concepts Used -- 2.1 Convolutional Neural Network (CNN/ConvNet) ch20cnnone,ch20cnntwo -- 2.2 Chlorophyll and Near Infrared Radiation -- 2.3 Sliding Window Technique -- 3 Study Area and Datasets -- 4 Methodology -- 5 Results -- 5.1 Tree Detection and Counting -- 6 Limitations -- 7 Conclusions. ; 8 Direction for Future Work -- References -- CateReR: A Graph Neural Network-Based Model for Category-Wise Reliability-Aware Recommendation -- 1 Introduction -- 1.1 Motivation -- 1.2 Related Work -- 1.3 Contribution -- 2 Proposed Framework: CateReR -- 2.1 Problem Statement -- 2.2 An Overview of CateReR -- 2.3 Category-Specific User Reliability Modeling -- 2.4 Missing Rating Prediction -- 3 Experiments -- 3.1 Datasets -- 3.2 Baselines and Experimental Settings -- 3.3 Results and Discussion -- 4 Conclusion and Future Work -- References -- Medical Image Processing -- Attention Assisted Patch-Wise CNN for the Segmentation of Fluids from the Retinal Optical Coherence Tomography Images -- 1 Introduction -- 2 Methodology -- 2.1 Dataset and Data Preprocessing -- 2.2 Network Architecture -- 3 Results and Discussion -- 4 Conclusion -- References -- A Reinforcement Learning Framework for Lung Segmentation of COVID-19 and Pneumonia Affected Chest X-Ray Image -- 1 Introduction -- 2 Related Works -- 3 Lung Segmentation -- 3.1 Image Pre-processing -- 3.2 Policy Gradient Method -- 3.3 Feature Extraction -- 3.4 The U-Net Architecture -- 4 Experimental Results -- 4.1 Dataset -- 4.2 Lung Segmentation Task -- 5 Conclusions -- References -- Deep and Bio-Inspired Spiking Neural Networks Based Optimized Multi-modal Neurological Image Fusion Model -- 1 Introduction -- 2 Methodology -- 2.1 VGG-16 Network -- 2.2 Coupled Neural P System -- 2.3 Whale Optimization Algorithm -- 3 Proposed Fusion Method -- 4 Results and Discussion -- 4.1 Dataset and Experimental Settings -- 4.2 Subjective and Parametric Result Analysis -- 5 Conclusion -- References -- Model Compression Techniques for Atrial Fibrillation Detection on Mobile Devices -- 1 Introduction -- 2 Methodology -- 3 Experimental Setup and Results -- 4 Conclusion and Future Work -- References. ; Speckle De-noising with Local Oriented Structure for Edge Preservation in Ultrasound Images.
• Titolo autorizzato: Pattern Recognition and Machine Intelligence
• ISBN: 9783031127007
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910874684103321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Computer Science Series