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Record Nr. |
UNISA996587859903316 |
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Autore |
Choi Bong Jun |
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Titolo |
Intelligent Human Computer Interaction : 15th International Conference, IHCI 2023, Daegu, South Korea, November 8-10, 2023, Revised Selected Papers, Part II |
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Pubbl/distr/stampa |
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Cham : , : Springer, , 2024 |
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©2024 |
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ISBN |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (355 pages) |
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Collana |
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Lecture Notes in Computer Science Series ; ; v.14532 |
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Altri autori (Persone) |
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SinghDhananjay |
TiwaryUma Shanker |
ChungWan-Young |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- AI and Big Data -- Automated Fashion Clothing Image Labeling System -- 1 Introduction -- 2 Related Research -- 3 Fashion Clothing Image Labeling System -- 3.1 Dataset Collection -- 3.2 Image Preprocessing -- 3.3 Yolo Training and Labeling Results -- 4 Conclusion -- References -- AI-Based Estimation from Images of Food Portion Size and Calories for Healthcare Systems -- 1 Introduction -- 2 Dataset of Uzbek Foods -- 3 Proposed Method -- 4 Experimental Results and Analysis -- 5 Limitation and Future Work -- 6 Conclusions -- References -- Blockchain Technology as a Defense Mechanism Against Data Tampering in Smart Vehicle Systems -- 1 Introduction -- 2 Background and Literature Review -- 3 Data Tampering in Smart Vehicle Systems -- 4 Blockchain Technology as a Solution in Smart Vehicle Systems -- 4.1 Technical Foundations of Blockchain in Smart Vehicle Systems -- 4.2 Practical Implementations and Prototypes -- 5 Conclusion -- References -- Classification of Weeds Using Neural Network Algorithms and Image Classifiers -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Pre-processing -- 3.3 Classification Models Architectures and Algorithms -- 4 Results -- 5 Future Scope and Conclusion -- |
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References -- Weed and Crop Detection in Rice Field Using R-CNN and Its Hybrid Models -- 1 Introduction -- 2 Related Work -- 3 Methods and Materials -- 3.1 Experimental Site and Image Acquisition -- 3.2 Data Pre-processing -- 4 Weed Detection Method -- 4.1 Regions with RCNN -- 4.2 Regions with RCNN-LSTM -- 4.3 Regions with RCNN-GRU -- 5 Results and Discussions -- 6 Conclusion -- References -- Deep Learning -- Spatial Attention Transformer Based Framework for Anomaly Classification in Image Sequences -- 1 Introduction -- 2 Related Study. |
3 Proposed Framework -- 3.1 Spatial Attention Module (SAM) -- 3.2 Shifted Window Transformer (SWIN) -- 3.3 SST for Anomaly Detection -- 4 Experimental Results and Discussions -- 4.1 Datasets -- 4.2 Results and Discussions -- 5 Conclusions -- References -- Development of LSTM-Based Sentence Generation Model to Improve Recognition Performance of OCR System -- 1 Introduction -- 2 Related Works -- 3 LSTM-Based Sentence Generation Model -- 3.1 Training Dataset -- 3.2 Implemented LSTM Model -- 4 Experiments and Results -- 5 Conclusion -- References -- Satellite Imagery Super Resolution Using Classical and Deep Learning Algorithms -- 1 Introduction -- 2 Background -- 2.1 Image Resolution -- 2.2 Interpolation for Image Enhancement -- 2.3 DWT and Noise Removal Techniques -- 3 Deep Learning Based Architectures for Image Super Resolution -- 3.1 High Level Architecture -- 3.2 Enhanced Deep Residual Networks for Single Image Super-Resolution(EDSR) -- 3.3 Wide Activation for Efficient and Accurate Image Super-Resolution(WDSR) -- 3.4 Deep Alternating Network(DAN) -- 4 Evaluation Metrics -- 5 Comparisons and Challenges -- 6 Comparisons and Challenges -- References -- Traffic Sign Recognition by Image Preprocessing and Deep Learning -- 1 Introduction -- 2 Related Works -- 2.1 Traditional Approach -- 2.2 Deep Learning Based Methods -- 3 Our Methodology -- 3.1 Dark Channel Prior Based Image Dehazing -- 3.2 Improved Detection Model -- 3.3 Data Augmentation -- 4 Experiments and Analysis -- 4.1 Results -- 4.2 Experimental Analysis and Discussion -- 5 Conclusion -- References -- Convolutional Autoencoder for Vision-Based Human Activity Recognition -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Convolutional Autoencoder (Conv-AE) -- 3.2 Convolutional Neural Network (CNN) -- 3.3 Pre-processing -- 3.4 Feature Representation -- 3.5 Classification. |
4 Experimental Results -- 4.1 Datasets -- 4.2 Results and Discussions -- 5 Conclusions and Future Scope -- References -- Deep Learning Approach for Enhanced Object Recognition and Assembly Guidance with Augmented Reality -- 1 Introduction -- 2 Methodology -- 2.1 Dataset Collection and Image Pre-processing -- 2.2 Step Detection -- 3 Results and Discussion -- 3.1 Prototype -- 3.2 SSD Performance -- 3.3 YOLOv7 Performance -- 3.4 User Testing -- 4 Conclusion -- References -- 3D Facial Reconstruction from a Single Image Using a Hybrid Model Based on 3DMM and Deep Learning -- 1 Introduction -- 2 Related Work -- 2.1 3DMM-Based Methods -- 2.2 Image-Based Methods -- 3 Methodology -- 3.1 3D Morphable Model -- 3.2 Camera Model -- 3.3 Illumination Model -- 3.4 Model Fitting -- 4 Results Analysis -- 5 Conclusion -- References -- Human Activity Recognition with a Time Distributed Deep Neural Network -- 1 Introduction -- 2 Related Works -- 3 The Proposed Method -- 3.1 Input Dataset -- 3.2 Data Pre-processing -- 3.3 Time Distributed Frame Conversion -- 3.4 Time Distributed CNN Layers -- 3.5 LSTM Layers -- 3.6 Training and Testing -- 3.7 Evaluation -- 4 Experimental Results and Discussion -- 4.1 UCI Sensor Dataset [2] Results -- 4.2 OPPORTUNITY Sensor Dataset Results -- 5 Conclusions -- References -- Intelligent Systems |
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-- Artificial Neural Network to Estimate Deterministic Indices in Control Loop Performance Monitoring -- 1 Introduction -- 2 Background -- 2.1 Control Performance Monitoring -- 2.2 CPM Performance Indices -- 2.3 Machine Learning -- 2.4 Control-Loop Performance Assessment Whit Machine Learning -- 3 Methodology -- 4 Results -- 4.1 The Model with Machine Learning -- 5 Conclusions -- References -- Interference Mitigation in Multi-radar Environment Using LSTM-Based Recurrent Neural Network -- 1 Introduction. |
2 Signal Model and Interference Effect Analysis -- 3 LSTM-RNN Architecture -- 4 Methodology, Results and Discussions -- 5 Conclusions -- References -- Centrifugal Pump Health Condition Identification Based on Novel Multi-filter Processed Scalograms and CNN -- 1 Introduction -- 2 Experimental Setup -- 3 Proposed Framework -- 4 Results and Performance Evaluation -- 5 Conclusion -- References -- A Closer Look at Attacks on Lightweight Cryptosystems: Threats and Countermeasures -- 1 Introduction -- 1.1 Lightweight Cryptography -- 2 Related Work -- 2.1 Side-Channel Analysis and Countermeasures: -- 2.2 Light Lightweight Cryptographic Algorithm Design: -- 3 Types of Attacks on Cryptosystems -- 3.1 Passive Attacks -- 3.2 Active Attacks -- 4 Cryptographic Attacks -- 4.1 Ciphertext Only Attacks (COA) -- 4.2 Known Plaintext Attack (KPA) -- 4.3 Dictionary Attack -- 4.4 Brute Force Attack (BFA) -- 4.5 Man in Middle Attack (MIM) -- 5 Countermeasures for Lightweight Encryption -- 5.1 Fault Injection Attacks and Protections: -- 5.2 Post-quantum Lightweight Cryptography: -- 5.3 Energy-Efficient Cryptography: -- 5.4 Machine Learning and Lightweight Cryptography: -- 6 Conclusion -- References -- A Prototype of IoT Medication Management System for Improved Adherence -- 1 Introduction -- 2 The Design Methodology of an Innovative Pharmaceutical IoT Medication Product -- 2.1 External Technology of the Product -- 2.2 Internal Technology of the Product -- 2.3 Mobile Application to Control the Product -- 3 Prototype Development of an Innovative Pharmaceutical -- 4 Final Discussion -- References -- Mobile Computing and Ubiquitous Interactions -- Navigating the Complexities of 60 GHz 5G Wireless Communication Systems: Challenges and Strategies -- 1 Introduction -- 2 Weaknesses of the 60 GHz Massive MIMO System -- 3 Proposed Algorithm -- 3.1 Channel Model of Sparse Multipath. |
3.2 SAMP Algorithm -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- A Survey on Channel Estimation Technique Classifications and Various Algorithms -- 1 Introduction -- 2 Channel Estimation -- 2.1 Channel Estimation Classification -- 2.2 Channel Estimation Algorithms -- 3 Discussions and Future Research -- References -- Wearable-Based SLAM with Sensor Fusion in Firefighting Operations -- 1 Introduction -- 2 Method -- 2.1 System Overview -- 2.2 Map Points Calculation (MPC) -- 3 Experimental Setup -- 4 Results and Discussion -- 5 Conclusion -- References -- The Novel Electrocardiograph Sensor and Algorithm for Arrhythmia Computer Aided Detection -- 1 Introduction -- 2 Material and Methods -- 2.1 Proposed ECG Sensor -- 2.2 Pan-Tomkins Algorithm -- 2.3 P-QRS-T Detection -- 3 Experimental Results -- 4 Conclusion -- References -- Optimizing Sensor Subset Selection with Quantum Annealing: A Large-Scale Indoor Temperature Regulation Application -- 1 Introduction -- 2 SSSO Problem in Relation to Temperature Regulation -- 3 Quantum Motivation -- 4 Materials and Methods -- 4.1 Large-Scale Indoor Temperature Sensor Dataset -- 4.2 Our Implementation of the SSSO Problem -- 4.3 Experimental Setup -- 4.4 Hybrid Quantum Implementation Using D-Wave Quantum Annealer -- 5 Experimental |
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Results -- 6 Conclusion -- References -- Smart IoT-Based Wearable Lower-Limb Rehabilitation Assistance System -- 1 Introduction -- 2 System Design -- 2.1 Hardware Design -- 2.2 Data Flow and Application Design -- 2.3 Lower-Limb Rehabilitation Activity State Classification Algorithm -- 3 Conclusion -- References -- Using Machine Learning of Sensor Data to Estimate the Production of Cutter Suction Dredgers -- 1 Introduction -- 2 Literature Review -- 2.1 Dredger Productivity Estimation -- 2.2 Dredging Productivity Estimation in Similar Areas -- 2.3 Soil Classification. |
3 Research Setting and Methodology. |
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