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Advancements in smart computing and information security : first international conference, ASCIS 2022, Rajkot, India, November 24-26, 2022, revised selected papers, part I / / edited by Sridaran Rajagopal, Parvez Faruki, Kalpesh Popat
| Advancements in smart computing and information security : first international conference, ASCIS 2022, Rajkot, India, November 24-26, 2022, revised selected papers, part I / / edited by Sridaran Rajagopal, Parvez Faruki, Kalpesh Popat |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (482 pages) |
| Disciplina | 943.005 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Electronic data processing
Punched card systems |
| ISBN | 3-031-23092-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Abstracts of Keynotes -- Post-pandemic Applications of AI and Machine Learning -- Smart and Soft Computing Methods for Prioritizing Software Requirements in Large-Scale Software Projects -- Your Readiness for Industry 4.0 -- Securing NexGen Automotives - Threats and Trends -- Cyber Attacks Classification and Attack Handling Methods Using Machine Learning Methods -- The Internet of Things (IoT) Ecosystem Revolution in the World of Global Sports -- Orchestration of Containers: Role of Artificial Intelligence -- Enterprise Cybersecurity Strategies in the Cloud -- Contents - Part I -- Contents - Part II -- Artificial Intelligence -- Galaxy Classification Using Deep Learning -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Dataset Collection -- 3.2 Proposed Deep Galaxies CNN Model -- 3.3 Overview of Algorithms -- 4 Comparative Results -- 4.1 Model Accuracy -- 5 Conclusion and Future Scope -- References -- Word Sense Disambiguation for Hindi Language Using Neural Network -- 1 Introduction -- 2 Related Work -- 2.1 Background -- 2.2 Variants of Word Sense Disambiguation Work -- 2.3 Existing Approaches for Disambiguation -- 3 Proposed Approach for WSD -- 3.1 Architecture of the Proposed WSD Model -- 3.2 Implementation Details -- 4 Result Discussion -- 5 Conclusion and Future Directions -- References -- Social Media Addiction: Analysis on Impact of Self-esteem and Recommending Methods to Reduce Addiction -- 1 Introduction -- 2 Related Work -- 3 Measures -- 3.1 Bergen Social Media Addiction Scale (BSMAS) [5] -- 3.2 Rosenberg Self-esteem Scale (RSES) [5] -- 3.3 Recommendation Methods [14, 15] -- 3.4 Dataset Collection 1 -- 3.5 Dataset Collection 2 -- 4 Proposed Methodology -- 4.1 Statistical Analysis -- 4.2 Recommendation System -- 5 Results and Discussion -- 5.1 Statistical Analysis.
5.2 Recommendation System -- 6 Conclusion -- References -- A Combined Method for Document Image Enhancement Using Image Smoothing, Gray-Level Reduction and Thresholding -- 1 Introduction -- 2 Types of Noises -- 2.1 Speckle Noise -- 2.2 Gaussian Noise -- 2.3 Salt and Pepper Noise -- 3 Proposed Work for Document Image Enhancement -- 3.1 Edge Preserving Image Smoothing -- 3.2 Gray Level Reduction -- 3.3 Image Thresholding Using Otsu's Method -- 4 Experimentation and Results -- 5 Conclusions and Future Work -- References -- A Comparative Assessment of Deep Learning Approaches for Opinion Mining -- 1 Introduction -- 2 Literature Review -- 3 Tools for Opinion Mining -- 4 Deep Learning Techniques -- 4.1 Convolutional Neural Network (CNN) -- 4.2 Recurrent Neural Network (RNN) -- 4.3 Long Short Term Memory (LSTM) -- 4.4 Deep Neural Networks (DNN) -- 4.5 Deep Belief Networks (DBN) -- 4.6 Recursive Neural Network (RECNN) -- 4.7 Hybrid Neural Network -- 5 System Architecture -- 6 Advantages of Deep Learning -- 7 When to Use Deep Learning -- 8 Disadvantages of Deep Learning -- 9 Conclusion -- References -- Performance Enhancement in WSN Through Fuzzy C-Means Based Hybrid Clustering (FCMHC) -- 1 Introduction -- 2 Related Work -- 3 Network Model -- 3.1 Radio Model -- 3.2 Assumptions -- 4 Proposed Algorithm -- 4.1 Cluster Formation Phase -- 4.2 Cluster Head Selection Phase -- 4.3 Communication Phase -- 5 Analytical Evaluation of Performance -- 5.1 Performance Metrics -- 5.2 Simulation Parameters -- 5.3 Results and Discussion -- 6 Conclusion -- References -- A Review of Gait Analysis Based on Age and Gender Prediction -- 1 Introduction -- 2 Gait Analysis and Feature Extraction -- 2.1 Gait and Gait Cycle -- 2.2 Gait and Gait Cycle -- 2.3 Gait and Gait Cycle -- 2.4 Motivation and Application of GEI Motivation -- 3 Evolution Metric -- 4 Related Work. 5 Comparison and Summary of Related Research Work -- 6 Future Work -- 7 Limitations and Challenges -- 8 Conclusion -- References -- Handwritten Signature Verification Using Convolution Neural Network (CNN) -- 1 Introduction -- 1.1 About the Domain -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Converting Image to Binary -- 3.2 Noise Removal -- 3.3 Image Enlargement -- 4 Feature Extraction -- 5 Feature Selection -- 6 Classification -- 7 Conclusion and Future Work -- References -- Comparative Analysis of Energy Consumption in Text Processing Models -- 1 Introduction -- 2 Existing Approaches -- 3 Exploration of the Data-Set -- 3.1 Average Word Length -- 3.2 Average Character Length -- 3.3 Number of Comments -- 4 Modelling -- 4.1 Simple Machine Learning Model -- 4.2 DistilBERT Model -- 4.3 Conv1D Model -- 4.4 Gated Recurrence Unit - GRU Model -- 5 Results -- 6 Conclusion -- References -- Evolution Towards 6G Wireless Networks: A Resource Allocation Perspective with Deep Learning Approach - A Review -- 1 Introduction -- 1.1 6G Vision -- 1.2 Technical Objectives of 6G -- 2 Resource Allocation for 6G Wireless Networks -- 3 Summary of Deep Learning Algorithms Used for 6G Wireless Networks Resource Allocation -- 4 Conclusion and Future Scope -- Appendix -- References -- Automation of Rice Leaf Diseases Prediction Using Deep Learning Hybrid Model VVIR -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 4 Results -- 5 Discussion -- References -- A Review Based on Machine Learning for Feature Selection and Feature Extraction -- 1 Introduction -- 2 Preliminaries -- 2.1 Feature Selection -- 2.2 Reducing the Dimensionality -- 3 Related Works -- 3.1 Feature Selection Approaches -- 3.2 Feature Extraction Approaches -- 4 Discussion -- 5 Conclusion -- References -- Automating Scorecard and Commentary Based on Umpire Gesture Recognition -- 1 Introduction. 2 Literature Survey -- 3 Methodology -- 3.1 Umpire Gestures -- 3.2 Dataset -- 3.3 Feature Extraction -- 3.4 Classification of Umpire Gestures -- 3.5 Scorecard Updating Feature -- 4 Results and Discussion -- 5 Conclusion -- References -- Rating YouTube Videos: An Improvised and Effective Approach -- 1 Introduction -- 2 Previous Work -- 3 Implementation -- 3.1 Comment Collection and Preprocessing -- 3.2 Sentiment Measure -- 3.3 Word Cloud -- 3.4 Video Rating -- 4 Performance Review of Proposed Approach -- 4.1 Major Application: Detection of Clickbait Videos -- 5 Limitations and Loopholes -- 6 Result -- 7 Conclusion -- 8 Future Work -- References -- Classification of Tweet on Disaster Management Using Random Forest -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Preprocessing -- 3.2 Training, Validation and Testing -- 3.3 Feature Extraction -- 3.4 Random Forest Classification -- 3.5 Location Extraction -- 4 Results and Discussions -- 5 Datasets -- 6 Experiment -- 7 Validation -- 8 Conclusions -- References -- Numerical Investigation of Dynamic Stress Distribution in a Railway Embankment Reinforced by Geogrid Based Weak Soil Formation Using Hybrid RNN-EHO -- 1 Introduction -- 2 Proposed Methodology -- 2.1 Model Clay Barrier's Compositional Characteristics -- 2.2 Geogrid -- 2.3 Measuring Subgrade Stiffness -- 2.4 Multi Objective Function -- 2.5 Improving Settlement-Based Geogrid using Hybrid RNN-EHO Technique -- 2.6 The Procedure of the EHO in Realizing the Learning of RNN -- 3 Results and Discussion -- 3.1 Uncertainty Analysis -- 4 Conclusion -- References -- Efficient Intrusion Detection and Classification Using Enhanced MLP Deep Learning Model -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 4 Results and Discussion -- 5 Conclusion -- References. Classification of Medical Datasets Using Optimal Feature Selection Method with Multi-support Vector Machine -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 4 Results and Discussions -- 5 Conclusions -- References -- Predicting Students' Outcomes with Respect to Trust, Perception, and Usefulness of Their Instructors in Academic Help Seeking Using Fuzzy Logic Approach -- 1 Introduction -- 2 Literature Review -- 3 Proposed Work -- 4 Results and Discussions -- 5 Conclusion -- References -- Smart Computing -- Automatic Cotton Leaf Disease Classification and Detection by Convolutional Neural Network -- 1 Introduction -- 2 Literature Review -- 3 List of Cotton Diseases -- 4 Materials and Methods -- 4.1 Dataset and Data Augmentation -- 4.2 CNN Pre-trained Architectures -- 4.3 Classification by Proposed CNN -- 5 Results and Discussions of Research -- 5.1 Pre-trained Model -- 6 Conclusion -- References -- Analytical Review and Study on Emotion Recognition Strategies Using Multimodal Signals -- 1 Introduction -- 2 Literature Survey -- 2.1 Classification of Emotion Recognition Strategies -- 3 Research Gaps and Issues -- 4 Analysis and Discussion -- 4.1 Analysis with Respect to Publication years -- 4.2 Analysis on the Basis of Strategies -- 4.3 Analysis on the Basis of Implementation Tool -- 4.4 Analysis in Terms of Employed Datasets -- 4.5 Analysis on the Basis of Evaluation Measures -- 4.6 Analysis Using Evaluation Measures Values -- 5 Conclusion -- References -- An Image Performance Against Normal, Grayscale and Color Spaced Images -- 1 Introduction -- 2 Overview of Image Matching Techniques -- 2.1 SIFT -- 2.2 SURF -- 2.3 ORB -- 3 Experimental Results -- 3.1 L*A*B* Color Space -- 4 Conclusion -- References -- Study of X Ray Detection Using CNN in Machine Learning -- 1 Introduction -- 2 Literature Review -- 2.1 Methods. 3 Algorithm CNN Model Algorithm Model = Sequential(). |
| Record Nr. | UNISA-996508667303316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Code of fair competition for the punch board manufacturing industry as approved on March 2, 1934
| Code of fair competition for the punch board manufacturing industry as approved on March 2, 1934 |
| Pubbl/distr/stampa | Washington : , : United States Government Printing Office, , 1934 |
| Descrizione fisica | 1 online resource (pages 439-448) |
| Soggetto topico |
Punched card systems
Computer input-output equipment - United States |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Punch board manufacturing industry |
| Record Nr. | UNINA-9910705240203321 |
| Washington : , : United States Government Printing Office, , 1934 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data power / / guest editors, Robert Jaschke and Jo Bates
| Data power / / guest editors, Robert Jaschke and Jo Bates |
| Pubbl/distr/stampa | Bradford, West Yorkshire : , : Emerald Publishing, , 2019 |
| Descrizione fisica | 1 online resource (136 pages) |
| Disciplina | 410.18802855133 |
| Soggetto topico |
Electronic data processing
Punched card systems |
| ISBN | 1-83982-003-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910793951303321 |
| Bradford, West Yorkshire : , : Emerald Publishing, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data power / / guest editors, Robert Jaschke and Jo Bates
| Data power / / guest editors, Robert Jaschke and Jo Bates |
| Pubbl/distr/stampa | Bradford, West Yorkshire : , : Emerald Publishing, , 2019 |
| Descrizione fisica | 1 online resource (136 pages) |
| Disciplina | 410.18802855133 |
| Soggetto topico |
Electronic data processing
Punched card systems |
| ISBN | 1-83982-003-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910816921703321 |
| Bradford, West Yorkshire : , : Emerald Publishing, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Electronic participation : 14th IFIP WG 8. 5 International Conference, EPart 2022, Linköping, Sweden, September 6-8, 2022, proceedings / / edited by Robert Krimmer, [and six others]
| Electronic participation : 14th IFIP WG 8. 5 International Conference, EPart 2022, Linköping, Sweden, September 6-8, 2022, proceedings / / edited by Robert Krimmer, [and six others] |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (213 pages) |
| Disciplina | 943.005 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Electronic data processing
Punched card systems |
| ISBN | 3-031-23213-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Voter authentication in remote electronic voting governmental experiences: requirements and practices -- Using Open Government Data to Facilitate the Design of Voting Advice Applications -- Investigating Trust and Risk Perceptions in a Hybrid Citizen Journey -- Applications of Data-driven Policymaking in the Local Energy Transition: a Multiple-Case Study in the Netherlands -- The Great Divide: Empirical Evidence of a Decoupling of Digital Transformation and Sustainability -- Sharing, Cooperation or Collective Action? A Research Agenda for Online Interaction in Digital Global Governance -- Similarity-based Dataset Recommendation across Languages and Domains to Sentiment Analysis in the Electoral Domain -- Genres of Participation in Social Networking Systems: A Study of the 2021 Norwegian Parliamentary Election -- Digitising the Judicial Sector: A Case Study of the Dutch KEI Programme -- A Song of Digitization and Law: Design Requirements for a Digitization Check of the Legislative Process -- dministrative Burden in Digital Self-Service: An Empirical Study About Citizens in Need of Financial Assistance -- The human touch meets digitalization: on discretion in digitized services. |
| Record Nr. | UNISA-996508671903316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
The evolution of pervasive information systems / / edited by Manuele Kirsch Pinheiro, [and three others]
| The evolution of pervasive information systems / / edited by Manuele Kirsch Pinheiro, [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (195 pages) |
| Disciplina | 943.005 |
| Soggetto topico |
Electronic data processing
Punched card systems |
| ISBN | 3-031-18176-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- What Is a "Pervasive Information System" (PIS)? -- 1 Introduction -- 2 What Is an Information System (IS)? -- 3 Information System Evolution: Towards a Pervasive Information System -- 4 Defining Pervasive Information System -- 5 PIS Requirements and Characteristics -- 5.1 Minimal Requirements -- 5.2 Additional Characteristics -- 6 Final Remarks -- References -- Design and Modeling in Pervasive Information Systems -- 1 Introduction -- 2 Research Approach -- 3 Results of the Systematic Mapping Study -- 3.1 RQ1. What Is the Distribution Evolution of the Sources? -- 3.2 RQ2. How Is Addressed the Design and Modeling of Pervasive Information Systems in Research Proposals? -- 3.2.1 Paper Type -- 3.2.2 Nature of the Proposal -- 3.2.3 Added Value of the Proposal -- 3.2.4 Usage of the IoT Based System -- 3.2.5 Application Domain -- 3.2.6 Discussion -- 3.3 RQ3. How Are Met the PIS Requirements in These Design-Dedicated Research Proposals? -- 4 Conclusion and Open Issues -- References -- SMS References*12pt -- The Context Awareness Challenges for PIS -- 1 Introduction -- 2 Literature Review -- 3 Towards a Context Facility -- 4 Impact of a Context Facility Vision on Context Management -- 5 Discussion -- 6 Conclusion -- Bibliography -- Middleware Supporting PIS: Requirements, Solutions, and Challenges -- 1 Introduction -- 2 Requirements for PIS Middleware -- 2.1 Sensing and Actuation Support -- 2.2 Context-Awareness -- 2.3 Dynamic Adaptation Capabilities -- 2.4 Quality of Context Management -- 2.5 Application Development Support -- 2.6 Support for Multiple Interaction Patterns -- 2.7 Enabling Interoperability -- 2.8 Security and Privacy -- 2.9 Scalability -- 2.10 Energy Efficiency and Energy-Awareness -- 3 State of the Art on Middleware Supporting PIS Requirements -- 3.1 QoC Management.
3.2 Protocols for Multiple Interaction Patterns -- 3.3 Enabling Interoperability -- 3.4 Security and Privacy -- 3.5 Scalability -- 3.6 Energy Efficiency and Energy-Awareness -- 4 PIS Middleware Proposals -- 4.1 QoC Management with QoCIM and Processing Functions -- 4.2 muDEBS -- 4.3 DeX Mediators -- 4.4 QoDisco -- 4.5 IoTVar -- 5 Open Challenges for Future PIS Middleware -- 5.1 Enabling End-to-End Interoperability -- 5.2 PIS Adaptive Middleware -- 5.3 Support to Develop PIS Relying on Middleware -- 5.4 Privacy and Security -- 5.5 Context Data Sampling and Filtering -- 5.6 PIS Sustainability -- 6 Conclusion -- References -- Edge Computing and Learning -- 1 Introduction -- 2 Edge Computing in Pervasive Computing -- 2.1 Principles and Examples -- 2.2 Terminology -- 3 Edge Pervasive Applications -- 3.1 Challenges -- 3.1.1 Application Design -- 3.1.2 Application Security -- 3.1.3 Application Data -- 3.1.4 Application Context -- 3.1.5 Application Placement -- 3.2 Pervasive Platforms -- 3.3 Conclusion -- 4 Machine Learning on the Edge -- 4.1 Principles -- 4.2 A Variety of Actors -- 4.3 A Specific Life-Cycle -- 4.4 Conclusion -- 5 Challenges -- 5.1 Model Development -- 5.2 Installation -- 5.3 Configuration -- 5.4 Data Collection -- 5.5 Model Execution -- 5.6 Model Monitoring -- 5.7 Model Update -- 6 Recent Trends -- 6.1 Microservice-Based Platform -- 6.2 Federated Learning -- 7 Conclusion -- References -- PIS: IoT & -- Industry 4.0 Challenges -- 1 Introduction -- 2 State of the Art -- 3 Existing Solutions -- 3.1 IIoT Protocols -- 3.2 Industry 4.0 Architectures -- 3.3 Standards -- 4 Discussions -- 5 Conclusions -- References -- PIS: Interoperability and Decision-Making Process-A Review -- 1 Introduction -- 2 Background and Related Reviews -- 3 Systematic Research Process -- 4 IT Artifacts for Interoperability and Their Implications in PIS/SoIS. 5 Ten Factors Influencing PIS/SoIS Interoperability -- 5.1 Technical Factors -- 5.2 Human Factors -- 5.3 Organizational Factors -- 5.4 Impact of Interoperability Factors in the Decision-making Processes -- 6 Key Findings and Reflections -- 7 Final Remarks and Future Directions -- References. |
| Record Nr. | UNISA-996547958803316 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Information integration and web intelligence : 24th international conference, IiWAS 2022, virtual event, November 28-30, 2022, proceedings / / edited by Eric Pardede, [and three others]
| Information integration and web intelligence : 24th international conference, IiWAS 2022, virtual event, November 28-30, 2022, proceedings / / edited by Eric Pardede, [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (594 pages) |
| Disciplina | 943.005 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Electronic data processing
Punched card systems |
| ISBN | 3-031-21047-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996500061003316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Kidney and kidney tumor segmentation : MICCAI 2021 Challenge, KiTS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, proceedings / / edited by Nicholas Heller, [and five others]
| Kidney and kidney tumor segmentation : MICCAI 2021 Challenge, KiTS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, proceedings / / edited by Nicholas Heller, [and five others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (173 pages) |
| Disciplina | 616.99461 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Electronic data processing
Punched card systems |
| ISBN | 3-030-98385-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Automated Kidney Tumor Segmentation with Convolution and Transformer Network -- 1 Introduction -- 2 Related Work -- 2.1 Medical Image Segmentation -- 2.2 Self-attention Mechanism -- 3 Methods -- 3.1 Network Architecture -- 3.2 Loss Function -- 3.3 Pre- and post- processing -- 3.4 Implementation Details -- 4 Results -- 4.1 Dataset -- 4.2 Metrics -- 4.3 Results on KITS21 Training Set -- 4.4 Results on KITS21 Test Set -- 5 Discussion and Conclusion -- References -- Extraction of Kidney Anatomy Based on a 3D U-ResNet with Overlap-Tile Strategy -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 2.4 Postprocessing -- 3 Results -- 4 Discussion and Conclusion -- References -- Modified nnU-Net for the MICCAI KiTS21 Challenge -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion and Conclusion -- References -- 2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion and Conclusion -- References -- Automated Machine Learning Algorithm for Kidney, Kidney Tumor, Kidney Cyst Segmentation in Computed Tomography Scans -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Network Architecture -- 2.4 Network Training -- 3 Results -- 4 Discussion and Conclusion -- References -- Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net -- 1 Introduction -- 2 Methods -- 2.1 Network Architecture -- 2.2 Segmentation from Low-Resolution CT -- 2.3 Fine Segmentation of Kidney -- 2.4 Segmentation of Tumor and Cysts -- 2.5 Training Protocols -- 3 Results.
4 Discussion and Conclusion -- References -- Less is More: Contrast Attention Assisted U-Net for Kidney, Tumor and Cyst Segmentations -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Network Architecture -- 3 Results -- 4 Discussion and Conclusion -- References -- A Coarse-to-Fine Framework for the 2021 Kidney and Kidney Tumor Segmentation Challenge -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 3.1 Metric -- 3.2 Results and Discussions -- 4 Conclusion -- References -- Kidney and Kidney Tumor Segmentation Using a Two-Stage Cascade Framework -- 1 Introduction -- 2 Methods -- 2.1 Kidney-Net -- 2.2 Masses-Net -- 2.3 Loss Function -- 3 Experiment -- 3.1 Datasets -- 3.2 Pre-processing and Post-processing -- 3.3 Training and Implementation Details -- 3.4 Metrics -- 4 Results and Discussion -- 5 Conclusion -- References -- Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT Images -- 1 Introduction -- 2 Method -- 2.1 Architecture -- 2.2 Squeeze-and-Excitation Module -- 2.3 Deep Supervision -- 2.4 Loss Function -- 3 Experiments -- 3.1 Datasets -- 3.2 Metrics -- 3.3 Pre- and Post-processing -- 3.4 Implementation Details -- 4 Result -- 5 Discussion and Conclusion -- References -- A Two-Stage Cascaded Deep Neural Network with Multi-decoding Paths for Kidney Tumor Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Kidney Localization Network -- 2.2 Multi-decoding Segmentation Network -- 2.3 Global Context Fusion Block -- 2.4 Regional Constraint Loss Function -- 3 Experimental Results -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusion -- References -- Mixup Augmentation for Kidney and Kidney Tumor Segmentation -- 1 Introduction -- 2 Methods. 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion -- References -- Automatic Segmentation in Abdominal CT Imaging for the KiTS21 Challenge -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion and Conclusion -- References -- An Ensemble of 3D U-Net Based Models for Segmentation of Kidney and Masses in CT Scans -- 1 Introduction -- 2 nnU-Net Determined Details -- 2.1 3D U-Net Network Architecture -- 2.2 3D U-Net Cascade Network Architecture -- 2.3 Preprocessing -- 2.4 Training Details -- 3 Method -- 3.1 Training and Validation Data -- 3.2 Pretraining -- 3.3 Annotations -- 3.4 Regularized Loss -- 3.5 Postprocessing -- 3.6 Final Submission -- 4 Results -- 4.1 Single-Stage, High-Resolution 3D U-Net -- 4.2 3D U-Net Cascade -- 4.3 Model Ensemble -- 4.4 Postprocessing -- 4.5 Test Set Results -- 5 Discussion and Conclusions -- References -- Contrast-Enhanced CT Renal Tumor Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion and Conclusion -- References -- A Cascaded 3D Segmentation Model for Renal Enhanced CT Images -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion and Conclusion -- References -- Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT -- 1 Introduction -- 2 Materials and Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Baseline 3D U-Net -- 2.4 Cognizant Sampling Leveraging Clinical Characteristics -- 2.5 Statistical Evaluation -- 3 Results -- 4 Discussion and Conclusion -- References. A Coarse-to-Fine 3D U-Net Network for Semantic Segmentation of Kidney CT Scans -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Data Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion and Conclusion -- References -- 3D U-Net Based Semantic Segmentation of Kidneys and Renal Masses on Contrast-Enhanced CT -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Network Architecture -- 2.4 Loss Function -- 2.5 Optimization Strategy -- 2.6 Validation -- 2.7 Post-processing -- 3 Results -- 4 Discussion and Conclusion -- References -- Kidney and Kidney Tumor Segmentation Using Spatial and Channel Attention Enhanced U-Net -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Data Augmentations -- 2.4 Proposed Method -- 2.5 Residual U-Net for Comparison -- 2.6 Implementation and Training -- 2.7 Inference Procedure -- 3 Results -- 4 Conclusion -- References -- Transfer Learning for KiTS21 Challenge -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion and Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996464542303316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Learner corpora in language testing and assessment / / edited by Marcus Callies, University of Bremen, Sandra Götz, Justus Liebig University, Giessen
| Learner corpora in language testing and assessment / / edited by Marcus Callies, University of Bremen, Sandra Götz, Justus Liebig University, Giessen |
| Pubbl/distr/stampa | Amsterdam, [Netherlands] ; ; Philadelphia, [Pennsylvania] : , : John Benjamins Publishing Company, , 2015 |
| Descrizione fisica | 1 online resource (226 p.) |
| Disciplina | 418.0076 |
| Collana | Studies in Corpus Linguistics (SCL) |
| Soggetto topico |
Language and languages - Ability testing - Data processing
Language and languages - Study and teaching - Data processing Electronic data processing Information storage and retrieval systems Punched card systems |
| Soggetto genere / forma | Electronic books. |
| ISBN | 90-272-6870-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Learner Corpora in Language Testing and Assessment; Editorial page; Title page; LCC data; Table of contents; Learner corpora in language testing and assessment: Prospects and challenges; Acknowledgements; References; Section I. New corpus resources, tools and methods; The Marburg Corpus of Intermediate Learner English (MILE); 1. Introduction; 2. Learner corpora in the light of the CEFR; 2.1 The raw data; 2.2 The annotation; 3. MILE - design and compilation; 4. Conclusion; References; Avalingua: Natural language processing for automatic error detection; 1. Introduction
2. Automatic error detection and correction2.1 Previous research; 2.2 Applications; 3. Avalingua; 3.1 Target; 3.2 Motivations; 3.3 The system; 3.3.1 Lexical module; 3.3.2 Spelling module; 3.3.3 Syntactic module; 3.3.4 Language identification; 3.3.5 Student model; 4. System evaluation; 4.1 A specific implementation; 4.2 The learner corpora; 4.3 Evaluation protocol; 4.4 Results; 4.5 Error analysis and discussion; 5. Conclusions; References; Data commentary in science writing: Using a small, specialized corpus for formative assessment practices; 1. Background and aims 2. Approaching data commentary from a pedagogical perspective: The case for small, specialized corpora annotated for discourse movesin the ESP classroom3. A small, specialized corpus of data commentaries; 4. The discourse annotation model; 5. Self-assessment and the role of the corpus; 5.1 Towards corpus-informed formative self-assessment activities; 5.1.1 Teacher-designed activities on moves in data commentaries; 5.1.2 Teacher-designed peer-assessment activities of master's thesis corpus data; 5.1.3 Teacher- and student-initiated activities involving students' own writing 6. Final remarks and outlookAcknowledgement; References; First steps in assigning proficiency to texts in a learner corpus of computer-mediated communication; 1. Introduction; 2. The CMC Learner Corpus; 2.1 CMC in the classroom; 2.2 The CMC corpora; 3. Criteria for assigning proficiency; 3.1 Following established practice; 3.2 Practicality and ease of implementation; 3.3 Reference native-speaker norms; 4. Method; 4.1 Performance decision trees; 4.2 Sequence of PDTs; 4.3 PDT for accuracy; 4.4 PDT for fluency; 4.5 PDT for complexity; 5. Results; 5.1 Preliminary results of proficiency ratings 5.2 Descriptive statistics5.3 Vocabulary level; 6. Discussion; 6.1 Validity of the proficiency measurement tool; 6.2 PDT proficiency levels and institutional status; 6.3 PDT proficiency levels and individual variation; 6.4 Limitations of the proposed measurement tool; 7. Conclusion; References; Appendix; Section II. Data-driven approaches to the assessment of proficiency; The English Vocabulary Profile as a benchmark for assigning levels to learner corpus data ; 1. Introduction; 2. Developmental indices and language proficiency; 3. The CEFR and reference level descriptions 4. The English Profile and criterial features |
| Record Nr. | UNINA-9910459726703321 |
| Amsterdam, [Netherlands] ; ; Philadelphia, [Pennsylvania] : , : John Benjamins Publishing Company, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Learner corpora in language testing and assessment / / edited by Marcus Callies, University of Bremen, Sandra Götz, Justus Liebig University, Giessen
| Learner corpora in language testing and assessment / / edited by Marcus Callies, University of Bremen, Sandra Götz, Justus Liebig University, Giessen |
| Pubbl/distr/stampa | Amsterdam, [Netherlands] ; ; Philadelphia, [Pennsylvania] : , : John Benjamins Publishing Company, , 2015 |
| Descrizione fisica | 1 online resource (226 p.) |
| Disciplina | 418.0076 |
| Collana | Studies in Corpus Linguistics (SCL) |
| Soggetto topico |
Language and languages - Ability testing - Data processing
Language and languages - Study and teaching - Data processing Electronic data processing Information storage and retrieval systems Punched card systems |
| ISBN | 90-272-6870-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Learner Corpora in Language Testing and Assessment; Editorial page; Title page; LCC data; Table of contents; Learner corpora in language testing and assessment: Prospects and challenges; Acknowledgements; References; Section I. New corpus resources, tools and methods; The Marburg Corpus of Intermediate Learner English (MILE); 1. Introduction; 2. Learner corpora in the light of the CEFR; 2.1 The raw data; 2.2 The annotation; 3. MILE - design and compilation; 4. Conclusion; References; Avalingua: Natural language processing for automatic error detection; 1. Introduction
2. Automatic error detection and correction2.1 Previous research; 2.2 Applications; 3. Avalingua; 3.1 Target; 3.2 Motivations; 3.3 The system; 3.3.1 Lexical module; 3.3.2 Spelling module; 3.3.3 Syntactic module; 3.3.4 Language identification; 3.3.5 Student model; 4. System evaluation; 4.1 A specific implementation; 4.2 The learner corpora; 4.3 Evaluation protocol; 4.4 Results; 4.5 Error analysis and discussion; 5. Conclusions; References; Data commentary in science writing: Using a small, specialized corpus for formative assessment practices; 1. Background and aims 2. Approaching data commentary from a pedagogical perspective: The case for small, specialized corpora annotated for discourse movesin the ESP classroom3. A small, specialized corpus of data commentaries; 4. The discourse annotation model; 5. Self-assessment and the role of the corpus; 5.1 Towards corpus-informed formative self-assessment activities; 5.1.1 Teacher-designed activities on moves in data commentaries; 5.1.2 Teacher-designed peer-assessment activities of master's thesis corpus data; 5.1.3 Teacher- and student-initiated activities involving students' own writing 6. Final remarks and outlookAcknowledgement; References; First steps in assigning proficiency to texts in a learner corpus of computer-mediated communication; 1. Introduction; 2. The CMC Learner Corpus; 2.1 CMC in the classroom; 2.2 The CMC corpora; 3. Criteria for assigning proficiency; 3.1 Following established practice; 3.2 Practicality and ease of implementation; 3.3 Reference native-speaker norms; 4. Method; 4.1 Performance decision trees; 4.2 Sequence of PDTs; 4.3 PDT for accuracy; 4.4 PDT for fluency; 4.5 PDT for complexity; 5. Results; 5.1 Preliminary results of proficiency ratings 5.2 Descriptive statistics5.3 Vocabulary level; 6. Discussion; 6.1 Validity of the proficiency measurement tool; 6.2 PDT proficiency levels and institutional status; 6.3 PDT proficiency levels and individual variation; 6.4 Limitations of the proposed measurement tool; 7. Conclusion; References; Appendix; Section II. Data-driven approaches to the assessment of proficiency; The English Vocabulary Profile as a benchmark for assigning levels to learner corpus data ; 1. Introduction; 2. Developmental indices and language proficiency; 3. The CEFR and reference level descriptions 4. The English Profile and criterial features |
| Record Nr. | UNINA-9910797195003321 |
| Amsterdam, [Netherlands] ; ; Philadelphia, [Pennsylvania] : , : John Benjamins Publishing Company, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
