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2016 2nd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE) / / Institute of Electrical and Electronics Engineers
| 2016 2nd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE) / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : IEEE, , 2016 |
| Descrizione fisica | 1 online resource : illustrations |
| Disciplina | 943.005 |
| Soggetto topico | Data mining |
| ISBN | 1-5090-5785-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | 2016 2nd International Conference on Electrical, Computer & Telecommunication Engineering |
| Record Nr. | UNINA-9910172646903321 |
| Piscataway, New Jersey : , : IEEE, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
2016 2nd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE) / / Institute of Electrical and Electronics Engineers
| 2016 2nd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE) / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : IEEE, , 2016 |
| Descrizione fisica | 1 online resource : illustrations |
| Disciplina | 943.005 |
| Soggetto topico | Data mining |
| ISBN | 1-5090-5785-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | 2016 2nd International Conference on Electrical, Computer & Telecommunication Engineering |
| Record Nr. | UNISA-996279841403316 |
| Piscataway, New Jersey : , : IEEE, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advanced Data Mining and Applications : 19th International Conference, ADMA 2023, Shenyang, China, August 21-23, 2023, Proceedings, Part I / / edited by Xiaochun Yang [and seven others]
| Advanced Data Mining and Applications : 19th International Conference, ADMA 2023, Shenyang, China, August 21-23, 2023, Proceedings, Part I / / edited by Xiaochun Yang [and seven others] |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (848 pages) |
| Disciplina | 943.005 |
| Collana | Lecture Notes in Computer Science Series |
| Soggetto topico | Data mining |
| ISBN | 3-031-46661-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Time Series -- An Adaptive Data-Driven Imputation Model for Incomplete Event Series -- From Time Series to Multi-Modality: Classifying Multivariate Time Series via Both 1D and 2D Representations -- Exploring the Effectiveness of Positional Embedding on Transformer-based Architectures for Multivariate Time Series Classification -- Modeling of Repeated Measures for Time-to-Event Prediction -- A Method for Identifying the Timeliness of Manufacturing Data Based on Weighted Timeliness Graph -- STAD: Multivariate Time Series Anomaly Detection Based on Spatio-temporal Relationship -- Recommendation I -- Refined Node Type Graph Convolutional Network for Recommendation -- Multi-level Noise Filtering and Preference Propagation Enhanced Knowledge Graph Recommendation -- Enhancing Knowledge-aware Recommendation with Contrastive Learning -- Knowledge-Rich Influence Propagation Recommendation Algorithm Based on Graph Attention Networks -- A Novel Variational Autoencoder with Multi-Position Latent Self-Attention and Actor-Critic for Recommendation -- Fair Re-ranking Recommendation Based on Debiased Multi-Graph Representations -- Information Extraction -- FastNER: Speeding Up Inferences for Named Entity Recognition Tasks -- CPMFA: A Character Pair-Based Method for Chinese Nested Named Entity Recognition -- STMC-GCN: A Span Tagging Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction -- Exploring the Design Space of Unsupervised Blocking with Pre-trained Language Models in Entity Resolution -- Joint Modeling of Local and Global Semantics for Contrastive Entity Disambiguation -- Fine-grained Review Analysis using BERT with Attention: A Categorical and Rating-based Approach -- Emotional Analysis -- Discovery of Emotion Implicit Causes in Products based on Commonsense Reasoning -- Multi-modal Multi-emotion Emotional Support Conversation -- Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations -- Generating Enlightened Suggestions based on Mental State Evolution for Emotional Support Conversation -- Deep One-Class Fine-Tuning for Imbalanced Short Text Classification in Transfer Learning -- EmoKnow: Emotion- and Knowledge-oriented Model for COVID-19 Fake News Detection -- Popular Songs: The Sentiment Surrounding the Conversation -- Market Sentiment Analysis based on Social Media and Trading Volume for Asset Price Movement Prediction -- Data Mining -- Efficient mining of high utility co-location patterns based on a query strategy -- Point-level Label-free Segmentation Framework for 3D Point Cloud Semantic Mining -- CD-BNN: Causal Discovery with Bayesian Neural Network -- A Preference-based Indicator Selection Hyper-heuristic for Optimization Problems -- An Elastic Scalable Grouping for Stateful Operators in Stream Computing Systems -- Incremental natural gradient boosting for probabilistic regression -- Discovering Skyline Periodic Itemset Patterns in Transaction Sequences -- Double-optimized CS-BP Anomaly Prediction for Control Operation Data -- Bridging the Interpretability Gap in Coupled Neural Dynamical Models -- Multidimensional Adaptative kNN Over Tracking Outliers (Makoto) -- Traffic -- MANet: An End-to-End Multiple Attention Network for Extracting Roads around EHV Transmission Lines from High-Resolution Remote Sensing Images -- Deep Reinforcement Learning for Solving the Trip Planning Query -- MDCN: Multi-Scale Dilated Convolutional Enhanced Residual Network for Traffic Sign Detection -- Identifying Critical Congested Roads based on Traffic Flow-Aware Road Network Embedding -- A Cross-Region-based Framework for Supporting Car-Sharing -- Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting -- Transformer Based Driving Behavior Safety Prediction For New Energy Vehicles -- Graph Convolution Recurrent Denoising Diffusion Model for Multivariate Probabilistic Temporal Forecasting -- A Bottom-Up Sampling Strategy for Reconstructing Geospatial Data from Ultra Sparse Inputs -- Recommendation II -- Feature Representation Enhancing by Context Sensitive Information in CTR Prediction -- ProtoMix: Learnable Data Augmentation on Few-shot Features with Vector Quantization in CTR Prediction -- When Alignment Makes a Difference: A Content-Based Variational Model for Cold-Start CTR Prediction -- Dual-Ganularity Contrastive Learning for Session-based Recommendation -- Efficient Graph Collaborative Filtering with Multi-layer Output-enhanced Contrastive Learning -- Influence Maximization with Tag Revisited: Exploiting the Bi-Submodularity of the Tag-Based Influence Function -- Multi-Interest Aware Graph Convolution Network for Social Recommendation -- Enhancing Multimedia Recommendation through Item-Item Semantic Denoising and Global Preference Awareness -- Resident-based Store Recommendation Model for Community Commercial Planning.c. |
| Record Nr. | UNISA-996565871103316 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advanced Data Mining and Applications : 19th International Conference, ADMA 2023, Shenyang, China, August 21-23, 2023, Proceedings, Part IV / / edited by Xiaochun Yang [and seven others]
| Advanced Data Mining and Applications : 19th International Conference, ADMA 2023, Shenyang, China, August 21-23, 2023, Proceedings, Part IV / / edited by Xiaochun Yang [and seven others] |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (717 pages) |
| Disciplina | 943.005 |
| Collana | Lecture Notes in Computer Science Series |
| Soggetto topico | Data mining |
| ISBN | 3-031-46674-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Deep Learning -- TeaE: an Efficient Method for Improving the Precision of Teaching Evaluation -- Graph Fusion Multimodal Named Entity Recognition Based on Auxiliary Relation Enhancement -- Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading Comprehension -- Multi-grained Logical Graph Network for Reasoning-based Machine Reading Comprehension -- Adaptive Prototype Learning with Common and Discriminative Features for Few-shot Relation Extraction -- Fine-grained Knowledge Enhancement for Empathetic Dialogue Generation -- Implicit Sentiment Extraction using Structure Generation with Sentiment Instructor Prompt Template -- SE-Prompt: Exploring Semantic Enhancement with Prompt Tuning for Relation Extraction -- Self-supervised Multi-view Clustering Framework with Graph Filtering and Contrast Fusion -- Semantic Selection and Multi-view Alignment for Image-Text Retrieval -- Voice Conversion with Denoising Diffusion Probabilistic GAN Models -- Symbolic & Acoustic: Multi-domain Music Emotion Modeling for Instrumental Music -- Document-level Relation Extraction with Relational Reasoning and Heterogeneous Graph Neural Networks -- A Chinese Named Entity Recognition Method based on Textual Information Perception Fusion -- Aspect-Based Sentiment Analysis via BERT and Multi-Scale CBAM -- A novel adaptive distribution distance-based feature selection method for video traffic identification -- SVIM: a Skeleton-based View-invariant Method for Online Gesture Recognition -- A Unified Information Diffusion Prediction Model based on Multi-task Learning -- Learning Knowledge Representation with Entity Concept Information -- Domain Adaptive Pre-trained Model for Mushroom Image Classification -- Training Noise Robust Deep Neural Networks with Self-supervised Learning -- Path integration enhanced graph attention network -- Graph Contrastive Learning with Hybrid Noise Augmentation for Recommendation -- User-Oriented Interest Representation on Knowledge Graph for Long-Tail Recommendation -- Multi-Self-Supervised Light Graph Convolution Network for Social Recommendation -- A Poisoning Attack Based on Variant Generative Adversarial Networks in Recommender Systems -- Label Correlation guided Feature Selection for Multi-label Learning -- Iterative Encode-and-Decode Graph Neural Network -- Community Detection in Temporal Biological Metabolic Networks based on Semi-NMF Method with Node Similarity Fusion -- UKGAT: Uncertain Knowledge Graph Embedding Enriched KGAT for Recommendation -- Knowledge Graph Link Prediction Model Based on Attention Graph Convolutional Network -- Knowledge Graph Embedding with Relation Rotation and Entity Adjustment by Quaternions -- Towards time-variant-aware Link Prediction in Dynamic Graph through Self-supervised Learning -- Adaptive Heterogeneous graph Contrastive clustering with Multi-Similarity -- Multi-Teacher Local Semantic Distillation from Graph Neural Networks -- AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining -- Rethinking the Evaluation of Deep Neural Network Robustness -- A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training -- TSCMR:Two-Stage Cross-Modal Retrieval -- Effi-Emp: An AI based approach towards positive empathic expressions -- Industry Track Papers -- Research on Image Segmentation Algorithm Based on Level Set. Ping Wu ((AVIC Shenyang Aircraft Design & Research Institute) -- Predicting learners’ performance using MOOC clickstream -- A Fine-grained Verification Method for Blockchain Data Based on Merkle Path Sharding -- A Privacy Preserving Method for Trajectory Data Publishing Based on Geo-indistinguishability -- HA-CMNet: A Driver CTR Model for Vehicle-Cargo Matching in O2O Platform -- A Hybrid Intelligent Model SFAHP-ANFIS-PSO for Technical Capability Evaluation of Manufacturing Enterprises -- A method for data exchange and management in the military industry field. Ping Wu ((AVIC Shenyang Aircraft Design & Research Institute) -- Multi-region Quality Assessment based on Spatial-Temporal Community Detection from Computed Tomography Images. |
| Record Nr. | UNISA-996565871503316 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advanced Data Mining and Applications : 19th International Conference, ADMA 2023, Shenyang, China, August 21-23, 2023, Proceedings, Part I / / edited by Xiaochun Yang [and seven others]
| Advanced Data Mining and Applications : 19th International Conference, ADMA 2023, Shenyang, China, August 21-23, 2023, Proceedings, Part I / / edited by Xiaochun Yang [and seven others] |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (848 pages) |
| Disciplina | 943.005 |
| Collana | Lecture Notes in Computer Science Series |
| Soggetto topico | Data mining |
| ISBN | 3-031-46661-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Time Series -- An Adaptive Data-Driven Imputation Model for Incomplete Event Series -- From Time Series to Multi-Modality: Classifying Multivariate Time Series via Both 1D and 2D Representations -- Exploring the Effectiveness of Positional Embedding on Transformer-based Architectures for Multivariate Time Series Classification -- Modeling of Repeated Measures for Time-to-Event Prediction -- A Method for Identifying the Timeliness of Manufacturing Data Based on Weighted Timeliness Graph -- STAD: Multivariate Time Series Anomaly Detection Based on Spatio-temporal Relationship -- Recommendation I -- Refined Node Type Graph Convolutional Network for Recommendation -- Multi-level Noise Filtering and Preference Propagation Enhanced Knowledge Graph Recommendation -- Enhancing Knowledge-aware Recommendation with Contrastive Learning -- Knowledge-Rich Influence Propagation Recommendation Algorithm Based on Graph Attention Networks -- A Novel Variational Autoencoder with Multi-Position Latent Self-Attention and Actor-Critic for Recommendation -- Fair Re-ranking Recommendation Based on Debiased Multi-Graph Representations -- Information Extraction -- FastNER: Speeding Up Inferences for Named Entity Recognition Tasks -- CPMFA: A Character Pair-Based Method for Chinese Nested Named Entity Recognition -- STMC-GCN: A Span Tagging Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction -- Exploring the Design Space of Unsupervised Blocking with Pre-trained Language Models in Entity Resolution -- Joint Modeling of Local and Global Semantics for Contrastive Entity Disambiguation -- Fine-grained Review Analysis using BERT with Attention: A Categorical and Rating-based Approach -- Emotional Analysis -- Discovery of Emotion Implicit Causes in Products based on Commonsense Reasoning -- Multi-modal Multi-emotion Emotional Support Conversation -- Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations -- Generating Enlightened Suggestions based on Mental State Evolution for Emotional Support Conversation -- Deep One-Class Fine-Tuning for Imbalanced Short Text Classification in Transfer Learning -- EmoKnow: Emotion- and Knowledge-oriented Model for COVID-19 Fake News Detection -- Popular Songs: The Sentiment Surrounding the Conversation -- Market Sentiment Analysis based on Social Media and Trading Volume for Asset Price Movement Prediction -- Data Mining -- Efficient mining of high utility co-location patterns based on a query strategy -- Point-level Label-free Segmentation Framework for 3D Point Cloud Semantic Mining -- CD-BNN: Causal Discovery with Bayesian Neural Network -- A Preference-based Indicator Selection Hyper-heuristic for Optimization Problems -- An Elastic Scalable Grouping for Stateful Operators in Stream Computing Systems -- Incremental natural gradient boosting for probabilistic regression -- Discovering Skyline Periodic Itemset Patterns in Transaction Sequences -- Double-optimized CS-BP Anomaly Prediction for Control Operation Data -- Bridging the Interpretability Gap in Coupled Neural Dynamical Models -- Multidimensional Adaptative kNN Over Tracking Outliers (Makoto) -- Traffic -- MANet: An End-to-End Multiple Attention Network for Extracting Roads around EHV Transmission Lines from High-Resolution Remote Sensing Images -- Deep Reinforcement Learning for Solving the Trip Planning Query -- MDCN: Multi-Scale Dilated Convolutional Enhanced Residual Network for Traffic Sign Detection -- Identifying Critical Congested Roads based on Traffic Flow-Aware Road Network Embedding -- A Cross-Region-based Framework for Supporting Car-Sharing -- Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting -- Transformer Based Driving Behavior Safety Prediction For New Energy Vehicles -- Graph Convolution Recurrent Denoising Diffusion Model for Multivariate Probabilistic Temporal Forecasting -- A Bottom-Up Sampling Strategy for Reconstructing Geospatial Data from Ultra Sparse Inputs -- Recommendation II -- Feature Representation Enhancing by Context Sensitive Information in CTR Prediction -- ProtoMix: Learnable Data Augmentation on Few-shot Features with Vector Quantization in CTR Prediction -- When Alignment Makes a Difference: A Content-Based Variational Model for Cold-Start CTR Prediction -- Dual-Ganularity Contrastive Learning for Session-based Recommendation -- Efficient Graph Collaborative Filtering with Multi-layer Output-enhanced Contrastive Learning -- Influence Maximization with Tag Revisited: Exploiting the Bi-Submodularity of the Tag-Based Influence Function -- Multi-Interest Aware Graph Convolution Network for Social Recommendation -- Enhancing Multimedia Recommendation through Item-Item Semantic Denoising and Global Preference Awareness -- Resident-based Store Recommendation Model for Community Commercial Planning.c. |
| Record Nr. | UNINA-9910760274703321 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced Data Mining and Applications : 19th International Conference, ADMA 2023, Shenyang, China, August 21-23, 2023, Proceedings, Part IV / / edited by Xiaochun Yang [and seven others]
| Advanced Data Mining and Applications : 19th International Conference, ADMA 2023, Shenyang, China, August 21-23, 2023, Proceedings, Part IV / / edited by Xiaochun Yang [and seven others] |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (717 pages) |
| Disciplina | 943.005 |
| Collana | Lecture Notes in Computer Science Series |
| Soggetto topico | Data mining |
| ISBN | 3-031-46674-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Deep Learning -- TeaE: an Efficient Method for Improving the Precision of Teaching Evaluation -- Graph Fusion Multimodal Named Entity Recognition Based on Auxiliary Relation Enhancement -- Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading Comprehension -- Multi-grained Logical Graph Network for Reasoning-based Machine Reading Comprehension -- Adaptive Prototype Learning with Common and Discriminative Features for Few-shot Relation Extraction -- Fine-grained Knowledge Enhancement for Empathetic Dialogue Generation -- Implicit Sentiment Extraction using Structure Generation with Sentiment Instructor Prompt Template -- SE-Prompt: Exploring Semantic Enhancement with Prompt Tuning for Relation Extraction -- Self-supervised Multi-view Clustering Framework with Graph Filtering and Contrast Fusion -- Semantic Selection and Multi-view Alignment for Image-Text Retrieval -- Voice Conversion with Denoising Diffusion Probabilistic GAN Models -- Symbolic & Acoustic: Multi-domain Music Emotion Modeling for Instrumental Music -- Document-level Relation Extraction with Relational Reasoning and Heterogeneous Graph Neural Networks -- A Chinese Named Entity Recognition Method based on Textual Information Perception Fusion -- Aspect-Based Sentiment Analysis via BERT and Multi-Scale CBAM -- A novel adaptive distribution distance-based feature selection method for video traffic identification -- SVIM: a Skeleton-based View-invariant Method for Online Gesture Recognition -- A Unified Information Diffusion Prediction Model based on Multi-task Learning -- Learning Knowledge Representation with Entity Concept Information -- Domain Adaptive Pre-trained Model for Mushroom Image Classification -- Training Noise Robust Deep Neural Networks with Self-supervised Learning -- Path integration enhanced graph attention network -- Graph Contrastive Learning with Hybrid Noise Augmentation for Recommendation -- User-Oriented Interest Representation on Knowledge Graph for Long-Tail Recommendation -- Multi-Self-Supervised Light Graph Convolution Network for Social Recommendation -- A Poisoning Attack Based on Variant Generative Adversarial Networks in Recommender Systems -- Label Correlation guided Feature Selection for Multi-label Learning -- Iterative Encode-and-Decode Graph Neural Network -- Community Detection in Temporal Biological Metabolic Networks based on Semi-NMF Method with Node Similarity Fusion -- UKGAT: Uncertain Knowledge Graph Embedding Enriched KGAT for Recommendation -- Knowledge Graph Link Prediction Model Based on Attention Graph Convolutional Network -- Knowledge Graph Embedding with Relation Rotation and Entity Adjustment by Quaternions -- Towards time-variant-aware Link Prediction in Dynamic Graph through Self-supervised Learning -- Adaptive Heterogeneous graph Contrastive clustering with Multi-Similarity -- Multi-Teacher Local Semantic Distillation from Graph Neural Networks -- AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining -- Rethinking the Evaluation of Deep Neural Network Robustness -- A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training -- TSCMR:Two-Stage Cross-Modal Retrieval -- Effi-Emp: An AI based approach towards positive empathic expressions -- Industry Track Papers -- Research on Image Segmentation Algorithm Based on Level Set. Ping Wu ((AVIC Shenyang Aircraft Design & Research Institute) -- Predicting learners’ performance using MOOC clickstream -- A Fine-grained Verification Method for Blockchain Data Based on Merkle Path Sharding -- A Privacy Preserving Method for Trajectory Data Publishing Based on Geo-indistinguishability -- HA-CMNet: A Driver CTR Model for Vehicle-Cargo Matching in O2O Platform -- A Hybrid Intelligent Model SFAHP-ANFIS-PSO for Technical Capability Evaluation of Manufacturing Enterprises -- A method for data exchange and management in the military industry field. Ping Wu ((AVIC Shenyang Aircraft Design & Research Institute) -- Multi-region Quality Assessment based on Spatial-Temporal Community Detection from Computed Tomography Images. |
| Record Nr. | UNINA-9910760287603321 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced data mining and applications : 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, proceedings / / edited by Weitong Chen, [and three others]
| Advanced data mining and applications : 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, proceedings / / edited by Weitong Chen, [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (552 pages) |
| Disciplina | 943.005 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Data mining |
| ISBN | 3-031-22064-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Finance and Healthcare -- Application of Supplemental Sampling and Interpretable AI in Credit Scoring for Canadian Fintechs: Methods and Case Studies -- 1 Introduction -- 2 Supplementary Sampling -- 2.1 Notations -- 2.2 Theories -- 2.3 Sampling Strategies -- 3 Techniques of Credit Scoring -- 4 Empirical Studies -- 4.1 Data Source and Sample Facts -- 4.2 Model Development and Comparisons -- 4.3 Model Evaluation -- 5 Conclusion -- References -- A Deep Convolutional Autoencoder-Based Approach for Parkinson's Disease Diagnosis Through Speech Signals -- 1 Introduction -- 2 Related Works -- 3 Proposed Approach -- 3.1 Dataset -- 3.2 Deep Convolutional AutoEncoder (DCAE) -- 3.3 MultiLayer Perceptron (MLP) -- 4 Experimental Results -- 5 Conclusion -- References -- Mining the Potential Relationships Between Cancer Cases and Industrial Pollution Based on High-Influence Ordered-Pair Patterns -- 1 Introduction -- 2 High-Influence Ordered-Pair Pattern -- 3 Basic Algorithm for Mining HIOPPs -- 3.1 Property Analysis of HIOPP -- 3.2 Description of Basic Algorithm -- 4 Optimizing Algorithm for Mining HIOPPs -- 4.1 Feasibility of Participation Instances -- 4.2 Obtaining Participating Instances -- 5 Experiments -- 5.1 Effectiveness of Mining Results -- 5.2 Performance Evaluation -- 6 Conclusion -- References -- Finding Hidden Relationships Between Medical Concepts by Leveraging Metamap and Text Mining Techniques -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Data Collection and Analysis -- 4.1 Data Extraction from the Source -- 4.2 MetaMap Module - Processing Phase -- 4.3 MetaMap Module - Preparation Phase -- 4.4 Title and Abstract Fetching Module -- 4.5 Closed Discovery Module -- 5 System Evaluation -- 6 Result Evaluation -- 7 Conclusion and Future Work -- References.
Causality Discovery Based on Combined Causes and Multiple Causes in Drug-Drug Interaction -- 1 Introduction -- 2 Background -- 2.1 Combined Causes and Multiple Causes in DDI -- 2.2 Limitations of CBN -- 3 Proposed Method -- 4 Empirical Evaluation -- 5 Results and Discussions -- 6 Conclusion -- References -- An Integrated Medical Recommendation Mechanism Combining Promote Product Singular Value Decomposition and Knowledge Graph -- 1 Introduction -- 2 Proposed Methodology -- 2.1 Promote Product Singular Value Decomposition Algorithm -- 2.2 Knowledge Graph to Recommendation -- 3 Experiment -- 3.1 Environment and Dataset -- 3.2 Evaluation Metrics -- 3.3 Results and Analysis -- 4 Conclusion -- References -- Web and IoT Applications -- Joint Extraction of Entities and Relations in the News Domain -- 1 Introduction -- 2 Research Status -- 3 Methodology -- 3.1 Labeling Strategy for Central Entities -- 3.2 RoBERTa Presentation Layer -- 3.3 Improved BiLSTM* Layer -- 4 Experiment -- 4.1 Experimental Data and Experimental Environment -- 4.2 Evaluation Standard -- 4.3 Experimental Parameters -- 4.4 Experimental Design -- 4.5 Result Analysis -- 5 Conclusion -- References -- Event Detection from Web Data in Chinese Based on Bi-LSTM with Attention*-8pt -- 1 Introduction -- 2 Related Work -- 2.1 Pattern Matching Based Methods -- 2.2 Machine Learning Based Methods -- 3 ABiLSTM Model -- 3.1 Problem Formulation -- 3.2 Static Classification Model -- 3.3 Dynamic Model Maintenance -- 4 Experimental Evaluation -- 4.1 Dataset and Experimental Setup -- 4.2 Chinese Text Preprocessing -- 4.3 Sensitivity Analysis -- 4.4 Effectiveness Analysis -- 4.5 Dynamic Maintenance Comparison -- 5 Conclusion and Future Work -- References -- Sentiment Analysis of Tweets Using Deep Learning -- 1 Introduction -- 2 Related Work -- 2.1 Sentiment Analysis on Tweets. 2.2 Sentiment Analysis on Coronavirus Related Tweets -- 3 Data Collection and Pre-Processing -- 4 Methodology -- 4.1 Text Tokenization and Padding -- 4.2 Convolutional Neural Network Model (CNN) -- 4.3 Long Short-Term Memory (LSTM) -- 4.4 CNN-LSTM -- 4.5 Distiled Bidirectional Encoder Representation from Transformer (DistilBERT) -- 4.6 Stratified K-Fold Cross Validation -- 5 Experiments and Results -- 6 Conclusions -- References -- Cyber Attack Detection in IoT Networks with Small Samples: Implementation And Analysis -- 1 Introduction -- 2 Related Work -- 3 System Architecture -- 3.1 Network Topology -- 3.2 Attack Model -- 4 Threat Detection System -- 5 Modelling the Traffic Data and Evaluation -- 5.1 Results and Discussion -- 5.2 Supervised Methods -- 5.3 Unsupervised Methods -- 5.4 Comparison with Relatively Larger Dataset -- 6 Conclusion -- References -- SATB: A Testbed of IoT-Based Smart Agriculture Network for Dataset Generation -- 1 Introduction -- 2 Background and Related Work -- 2.1 Smart Agriculture -- 2.2 LoRaWAN -- 2.3 Related Work -- 3 SATB: A LoRaWAN SA Testbed -- 3.1 Components of SATB -- 3.2 Functionalities of SATB -- 4 A Case Study: Constructing an SA Dataset with SATB -- 4.1 Test Cases and Data Collection -- 4.2 Data Preprocessing -- 4.3 A Preliminary Study of the Dataset -- 5 Usage of the SATB Testbed -- 5.1 Development of Intrusion Detection Systems for SA -- 5.2 Preservation of Data Privacy and Integrity for SA -- 5.3 Development of Data-Driven Applications for SA -- 6 Conclusion -- References -- An Overview on Reducing Social Networks' Size -- 1 Introduction -- 2 Preliminaries -- 2.1 Problem Definition -- 2.2 Network Properties -- 3 Graph Sampling -- 3.1 Node Sampling -- 3.2 Edge Sampling -- 3.3 Traversal Based Sampling -- 4 Graph Coarsening -- 5 Recent Directions -- 6 Conclusion -- References. AuCM: Course Map Data Analytics for Australian IT Programs in Higher Education -- 1 Introduction -- 2 Related Work -- 3 The AuCM Dataset -- 3.1 Data Scraping -- 3.2 Data Processing -- 4 Statistical Analysis of AuCM -- 4.1 Analysis of the Number of Courses -- 4.2 Analysis of Curriculum Design -- 4.3 Analysis of Core Curriculum -- 4.4 Analysis of Prerequisites -- 5 Concept Semantics in AuCM -- 5.1 Semantic Feature Extraction and Analysis -- 5.2 Concept Map Learning -- 6 Conclusion -- References -- Profit Maximization Using Social Networks in Two-Phase Setting -- 1 Introduction -- 2 Background and Problem Definition -- 3 Mathematical Model and Solution Methodologies -- 4 Experimental Evaluation -- 5 Conclusion and Future Direction -- References -- On-Device Application -- SESA: Fast Trajectory Compression Method Using Sub-trajectories Segmented by Stay Areas -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Extraction of Stay Area -- 3.2 Segmentation -- 3.3 SQUISH-E() -- 3.4 Integration -- 4 Experiment -- 4.1 Experimental Conditions -- 4.2 Experiment Results -- 4.3 Discussion -- 5 Conclusion and Future Work -- References -- Android Malware Detection Based on Stacking and Multi-feature Fusion -- 1 Introduction -- 2 Related Work -- 2.1 Features of Android Malware Detection -- 2.2 Feature Selection -- 2.3 Stacking Technique -- 3 Framework and Implementation of Android Malware -- 3.1 Feature Extraction and Preprocessing -- 3.2 Two-Level Feature Selection -- 3.3 Malware Detection Based on Stacking Structure -- 4 Experiments and Evaluations -- 4.1 Data Set and Experimental Environment -- 4.2 Experimental Process and Results -- 5 Conclusion -- References -- Influential Billboard Slot Selection Using Pruned Submodularity Graph -- 1 Introduction -- 2 Preliminaries and Problem Definition -- 3 Proposed Solution Approach -- 4 Experimental Evaluation. 4.1 Datasets Used -- 4.2 Experimental Setup -- 4.3 Algorithms Compared -- 4.4 Goals of the Experiments -- 4.5 Observations with Explanation -- 5 Conclusion and Future Research Directions -- References -- Quantifying Association Between Street-Level Urban Features and Crime Distribution Around Manhattan Subway Entrances -- 1 Introduction -- 2 Literature Review -- 2.1 Key Dimensions in Assessing Crimes Around Subway Stations -- 2.2 SVI, CV, and ML for Street Measures -- 3 Data and Methods -- 3.1 Hotspots of Crime Around Subway Stations in Manhattan -- 3.2 Analytical Framework -- 3.3 Data for Constructing Variables -- 4 Findings and Discussion -- 4.1 Regression Results -- 4.2 Urban Design Quality Matters -- 5 Conclusion -- References -- The Coherence and Divergence Between the Objective and Subjective Measurement of Street Perceptions for Shanghai -- 1 Introduction -- 2 Literature Review -- 3 Methods and Process -- 3.1 Analytical Framework -- 3.2 Site Investigation and Data Preparation -- 3.3 Quantifying Objective and Subjective Perception Scores -- 3.4 Coherence and Divergence of the Subjective and Objective Perceptions -- 3.5 Features that Cause Differences Between Objective and Subjective Measures -- 4 Results and Discussion -- 4.1 Spatial Mismatch Between Subjective and Objective Perceptions -- 4.2 Key Urban Features for Variances Between Two Models -- 5 Conclusion -- Appendix -- References -- Other Application -- A Comparative Study of Question Answering over Knowledge Bases -- 1 Introduction -- 2 Methodology -- 2.1 Problem Setting -- 2.2 KBQA Approaches -- 2.3 Summary -- 3 Experimental Setup -- 4 Results -- 4.1 End-to-end Comparison -- 4.2 Running Time -- 4.3 Influence of Question Taxonomy -- 4.4 Effects of Quantity of Questions -- 5 Conclusion -- References. A Deep Learning Framework for Removing Bias from Single-Photon Emission Computerized Tomography. |
| Record Nr. | UNISA-996500062303316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advanced data mining and applications : 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, proceedings / / edited by Weitong Chen, [and three others]
| Advanced data mining and applications : 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28-30, 2022, proceedings / / edited by Weitong Chen, [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (552 pages) |
| Disciplina | 943.005 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Data mining |
| ISBN | 3-031-22064-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Finance and Healthcare -- Application of Supplemental Sampling and Interpretable AI in Credit Scoring for Canadian Fintechs: Methods and Case Studies -- 1 Introduction -- 2 Supplementary Sampling -- 2.1 Notations -- 2.2 Theories -- 2.3 Sampling Strategies -- 3 Techniques of Credit Scoring -- 4 Empirical Studies -- 4.1 Data Source and Sample Facts -- 4.2 Model Development and Comparisons -- 4.3 Model Evaluation -- 5 Conclusion -- References -- A Deep Convolutional Autoencoder-Based Approach for Parkinson's Disease Diagnosis Through Speech Signals -- 1 Introduction -- 2 Related Works -- 3 Proposed Approach -- 3.1 Dataset -- 3.2 Deep Convolutional AutoEncoder (DCAE) -- 3.3 MultiLayer Perceptron (MLP) -- 4 Experimental Results -- 5 Conclusion -- References -- Mining the Potential Relationships Between Cancer Cases and Industrial Pollution Based on High-Influence Ordered-Pair Patterns -- 1 Introduction -- 2 High-Influence Ordered-Pair Pattern -- 3 Basic Algorithm for Mining HIOPPs -- 3.1 Property Analysis of HIOPP -- 3.2 Description of Basic Algorithm -- 4 Optimizing Algorithm for Mining HIOPPs -- 4.1 Feasibility of Participation Instances -- 4.2 Obtaining Participating Instances -- 5 Experiments -- 5.1 Effectiveness of Mining Results -- 5.2 Performance Evaluation -- 6 Conclusion -- References -- Finding Hidden Relationships Between Medical Concepts by Leveraging Metamap and Text Mining Techniques -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Data Collection and Analysis -- 4.1 Data Extraction from the Source -- 4.2 MetaMap Module - Processing Phase -- 4.3 MetaMap Module - Preparation Phase -- 4.4 Title and Abstract Fetching Module -- 4.5 Closed Discovery Module -- 5 System Evaluation -- 6 Result Evaluation -- 7 Conclusion and Future Work -- References.
Causality Discovery Based on Combined Causes and Multiple Causes in Drug-Drug Interaction -- 1 Introduction -- 2 Background -- 2.1 Combined Causes and Multiple Causes in DDI -- 2.2 Limitations of CBN -- 3 Proposed Method -- 4 Empirical Evaluation -- 5 Results and Discussions -- 6 Conclusion -- References -- An Integrated Medical Recommendation Mechanism Combining Promote Product Singular Value Decomposition and Knowledge Graph -- 1 Introduction -- 2 Proposed Methodology -- 2.1 Promote Product Singular Value Decomposition Algorithm -- 2.2 Knowledge Graph to Recommendation -- 3 Experiment -- 3.1 Environment and Dataset -- 3.2 Evaluation Metrics -- 3.3 Results and Analysis -- 4 Conclusion -- References -- Web and IoT Applications -- Joint Extraction of Entities and Relations in the News Domain -- 1 Introduction -- 2 Research Status -- 3 Methodology -- 3.1 Labeling Strategy for Central Entities -- 3.2 RoBERTa Presentation Layer -- 3.3 Improved BiLSTM* Layer -- 4 Experiment -- 4.1 Experimental Data and Experimental Environment -- 4.2 Evaluation Standard -- 4.3 Experimental Parameters -- 4.4 Experimental Design -- 4.5 Result Analysis -- 5 Conclusion -- References -- Event Detection from Web Data in Chinese Based on Bi-LSTM with Attention*-8pt -- 1 Introduction -- 2 Related Work -- 2.1 Pattern Matching Based Methods -- 2.2 Machine Learning Based Methods -- 3 ABiLSTM Model -- 3.1 Problem Formulation -- 3.2 Static Classification Model -- 3.3 Dynamic Model Maintenance -- 4 Experimental Evaluation -- 4.1 Dataset and Experimental Setup -- 4.2 Chinese Text Preprocessing -- 4.3 Sensitivity Analysis -- 4.4 Effectiveness Analysis -- 4.5 Dynamic Maintenance Comparison -- 5 Conclusion and Future Work -- References -- Sentiment Analysis of Tweets Using Deep Learning -- 1 Introduction -- 2 Related Work -- 2.1 Sentiment Analysis on Tweets. 2.2 Sentiment Analysis on Coronavirus Related Tweets -- 3 Data Collection and Pre-Processing -- 4 Methodology -- 4.1 Text Tokenization and Padding -- 4.2 Convolutional Neural Network Model (CNN) -- 4.3 Long Short-Term Memory (LSTM) -- 4.4 CNN-LSTM -- 4.5 Distiled Bidirectional Encoder Representation from Transformer (DistilBERT) -- 4.6 Stratified K-Fold Cross Validation -- 5 Experiments and Results -- 6 Conclusions -- References -- Cyber Attack Detection in IoT Networks with Small Samples: Implementation And Analysis -- 1 Introduction -- 2 Related Work -- 3 System Architecture -- 3.1 Network Topology -- 3.2 Attack Model -- 4 Threat Detection System -- 5 Modelling the Traffic Data and Evaluation -- 5.1 Results and Discussion -- 5.2 Supervised Methods -- 5.3 Unsupervised Methods -- 5.4 Comparison with Relatively Larger Dataset -- 6 Conclusion -- References -- SATB: A Testbed of IoT-Based Smart Agriculture Network for Dataset Generation -- 1 Introduction -- 2 Background and Related Work -- 2.1 Smart Agriculture -- 2.2 LoRaWAN -- 2.3 Related Work -- 3 SATB: A LoRaWAN SA Testbed -- 3.1 Components of SATB -- 3.2 Functionalities of SATB -- 4 A Case Study: Constructing an SA Dataset with SATB -- 4.1 Test Cases and Data Collection -- 4.2 Data Preprocessing -- 4.3 A Preliminary Study of the Dataset -- 5 Usage of the SATB Testbed -- 5.1 Development of Intrusion Detection Systems for SA -- 5.2 Preservation of Data Privacy and Integrity for SA -- 5.3 Development of Data-Driven Applications for SA -- 6 Conclusion -- References -- An Overview on Reducing Social Networks' Size -- 1 Introduction -- 2 Preliminaries -- 2.1 Problem Definition -- 2.2 Network Properties -- 3 Graph Sampling -- 3.1 Node Sampling -- 3.2 Edge Sampling -- 3.3 Traversal Based Sampling -- 4 Graph Coarsening -- 5 Recent Directions -- 6 Conclusion -- References. AuCM: Course Map Data Analytics for Australian IT Programs in Higher Education -- 1 Introduction -- 2 Related Work -- 3 The AuCM Dataset -- 3.1 Data Scraping -- 3.2 Data Processing -- 4 Statistical Analysis of AuCM -- 4.1 Analysis of the Number of Courses -- 4.2 Analysis of Curriculum Design -- 4.3 Analysis of Core Curriculum -- 4.4 Analysis of Prerequisites -- 5 Concept Semantics in AuCM -- 5.1 Semantic Feature Extraction and Analysis -- 5.2 Concept Map Learning -- 6 Conclusion -- References -- Profit Maximization Using Social Networks in Two-Phase Setting -- 1 Introduction -- 2 Background and Problem Definition -- 3 Mathematical Model and Solution Methodologies -- 4 Experimental Evaluation -- 5 Conclusion and Future Direction -- References -- On-Device Application -- SESA: Fast Trajectory Compression Method Using Sub-trajectories Segmented by Stay Areas -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Extraction of Stay Area -- 3.2 Segmentation -- 3.3 SQUISH-E() -- 3.4 Integration -- 4 Experiment -- 4.1 Experimental Conditions -- 4.2 Experiment Results -- 4.3 Discussion -- 5 Conclusion and Future Work -- References -- Android Malware Detection Based on Stacking and Multi-feature Fusion -- 1 Introduction -- 2 Related Work -- 2.1 Features of Android Malware Detection -- 2.2 Feature Selection -- 2.3 Stacking Technique -- 3 Framework and Implementation of Android Malware -- 3.1 Feature Extraction and Preprocessing -- 3.2 Two-Level Feature Selection -- 3.3 Malware Detection Based on Stacking Structure -- 4 Experiments and Evaluations -- 4.1 Data Set and Experimental Environment -- 4.2 Experimental Process and Results -- 5 Conclusion -- References -- Influential Billboard Slot Selection Using Pruned Submodularity Graph -- 1 Introduction -- 2 Preliminaries and Problem Definition -- 3 Proposed Solution Approach -- 4 Experimental Evaluation. 4.1 Datasets Used -- 4.2 Experimental Setup -- 4.3 Algorithms Compared -- 4.4 Goals of the Experiments -- 4.5 Observations with Explanation -- 5 Conclusion and Future Research Directions -- References -- Quantifying Association Between Street-Level Urban Features and Crime Distribution Around Manhattan Subway Entrances -- 1 Introduction -- 2 Literature Review -- 2.1 Key Dimensions in Assessing Crimes Around Subway Stations -- 2.2 SVI, CV, and ML for Street Measures -- 3 Data and Methods -- 3.1 Hotspots of Crime Around Subway Stations in Manhattan -- 3.2 Analytical Framework -- 3.3 Data for Constructing Variables -- 4 Findings and Discussion -- 4.1 Regression Results -- 4.2 Urban Design Quality Matters -- 5 Conclusion -- References -- The Coherence and Divergence Between the Objective and Subjective Measurement of Street Perceptions for Shanghai -- 1 Introduction -- 2 Literature Review -- 3 Methods and Process -- 3.1 Analytical Framework -- 3.2 Site Investigation and Data Preparation -- 3.3 Quantifying Objective and Subjective Perception Scores -- 3.4 Coherence and Divergence of the Subjective and Objective Perceptions -- 3.5 Features that Cause Differences Between Objective and Subjective Measures -- 4 Results and Discussion -- 4.1 Spatial Mismatch Between Subjective and Objective Perceptions -- 4.2 Key Urban Features for Variances Between Two Models -- 5 Conclusion -- Appendix -- References -- Other Application -- A Comparative Study of Question Answering over Knowledge Bases -- 1 Introduction -- 2 Methodology -- 2.1 Problem Setting -- 2.2 KBQA Approaches -- 2.3 Summary -- 3 Experimental Setup -- 4 Results -- 4.1 End-to-end Comparison -- 4.2 Running Time -- 4.3 Influence of Question Taxonomy -- 4.4 Effects of Quantity of Questions -- 5 Conclusion -- References. A Deep Learning Framework for Removing Bias from Single-Photon Emission Computerized Tomography. |
| Record Nr. | UNINA-9910632486403321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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. | UNINA-9910644261503321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 | ||
| ||
