Proceedings of the Third International Conference on Innovations in Computing Research (ICR'24)

• Autore: Daimi Kevin
• Pubblicazione: Cham : , : Springer International Publishing AG, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (794 pages)
• Altri autori: Al SadoonAbeer
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Data Science -- Extracting Official Agencies' Communication Patterns During the COVID-19 Pandemic: A Text Mining Approach -- 1 Introduction -- 2 Related Work -- 3 Text Mining Methodology -- 3.1 Data Collection and Pre-processing -- 3.2 Text Mining Approaches -- 4 Results and Discussion -- 4.1 Word Analysis -- 4.2 Collocation Analysis -- 4.3 Topic Modeling -- 4.4 Sentiment and Correlation Analysis -- 5 Conclusion and Future Work -- References -- Towards Automated Policy Predictions via Structured Attribute-Based Access Control -- 1 Introduction -- 2 Algorithm Overview -- 3 Application -- 3.1 Data Set -- 3.2 Policy Prediction with Time Series -- 4 Related Work -- 5 Conclusions and Future Work -- References -- Exploratory Analysis of Gamblers' Financial Transactions to Mine Behavioral Pattern Data -- 1 Introduction -- 2 Previous Works -- 3 Data -- 4 Exploring Data Slices -- 5 80th and 99th Percentiles -- 6 99th Percentiles -- 7 Dynamic Time Frames -- 8 Quantitative Comparisons of Session Series -- 9 Limitations -- 10 Discussion -- References -- The Detection of Misstated Financial Reports Using XBRL Mining and Intelligible MLP -- 1 Introduction -- 2 The Conceptual Framework -- 2.1 AI-Based Financial Analysis -- 2.2 Cross-Section Characterisation of Reported Numbers -- 3 Methodology -- 3.1 Web-Mining of XBRL Financial Reports -- 3.2 Input Pre-selection, MLP Topology and Learning -- 4 Results -- 5 Conclusion -- References -- University Student Enrollment Prediction: A Machine Learning Framework -- 1 Introduction -- 2 Literature Review -- 3 Methodology and Framework -- 4 Results and Discussion -- 5 Conclusion -- References -- Early Prediction of Sepsis Utilizing Multi-branches Multi-tasks Hybrid Deep Learning Model -- 1 Introduction -- 2 Review of Previous Studies. ; 3 Multi-branches Multi-tasks Hybrid Deep Learning Model for Sepsis Predicting -- 3.1 Global Feature Extraction Module (Branch 1) -- 3.2 Local Feature Extraction Module (Branch 2) -- 3.3 Multi-tasks Learning -- 4 Experiments -- 5 Conclusion -- References -- Comprehensive Analysis of Iris Dataset Using K-Mean and Fuzzy K-Mean Clustering Algorithm -- 1 Introduction -- 2 Background -- 2.1 Clustering -- 2.2 Big Data -- 2.3 Data Stream -- 3 Material and Methods -- 3.1 Dataset Description -- 3.2 Preprocessing -- 3.3 K-Nearest Neighbor -- 3.4 Parameter Evaluation -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- An Efficient and Reliable scRNA-seq Data Imputation Method Using Variational Autoencoders -- 1 Introduction -- 2 Related Work -- 2.1 Classic Methods -- 2.2 Deep Learning Methods -- 3 Methods -- 3.1 A Brief on VAE -- 3.2 Our Data Imputation Approach Based VAE -- 4 Experiments -- 4.1 Datasets -- 4.2 Data Preprocessing -- 5 Results -- 5.1 Mean Square Error (MSE) -- 5.2 Clustering Results -- 5.3 Running Time -- 6 Conclusion -- References -- Prediction of Automotive Vehicles Engine Health Using MLP and LR -- 1 Introduction -- 2 Related Work -- 3 Data Preprocessing -- 4 Multi-Layer Perceptron -- 4.1 Working Flow -- 5 Logistic Regression -- 5.1 Model Implementation and Training -- 5.2 Model Evaluation -- 5.3 Hyperparameter Tuning with BayesSearch CV -- 6 Results -- 6.1 MLP Before Data Preprocessing -- 6.2 MLP After Data Preprocessing and Optimization -- 6.3 Comparing MLP Results -- 6.4 Logistic Regression -- 6.5 Comparing MLP with LR -- 7 Conclusion -- 8 Future Work -- References -- Medical Image Character Recognition Using Attention-Based Siamese Networks for Visually Similar Characters with Low Resolution -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Model Architecture -- 3.2 Model + Attention Mechanism. ; 4 Experiment, Result and Analysis -- 4.1 Dataset Description -- 4.2 Training Strategy -- 4.3 Result -- 4.4 Performance Analysis on AUC-ROC Curve -- 4.5 Performance Analysis Using Feature Map Visualisation -- 4.6 Quantitative Analysis with Related Works with Background Interference -- 5 Conclusion -- References -- Toward Smart Bicycle Safety: Leveraging Machine Learning Models and Optimal Lighting Solutions -- 1 Introduction -- 2 Using AI in Bikes Lights -- 3 Methodology -- 3.1 Data Collection -- 3.2 Pre-processing -- 3.3 Term Frequency-Inverse Document Frequency Features (TF-IDF) -- 3.4 Model Development -- 3.5 Model Evaluation -- 4 Results and Discussion -- 5 Conclusion and Future Work -- References -- vThrot: Fine-Grained, Virtual I/O Resource Redistribution Scheme -- 1 Introduction -- 2 vThrot -- 2.1 System Architecture -- 2.2 The Reclamation Process of vThrot -- 2.3 The Redistribution Process of vThrot -- 2.4 vThrot Priority-Based I/O Processing -- 3 Performance Evaluation -- 4 Conclusion -- References -- Bayesian Optimization-Based CNN Model for Blood Glucose Estimation Using Photoplethysmography Signals -- 1 Introduction -- 2 Literature Review -- 3 Methodology Overview -- 3.1 Dataset and Pre-processing -- 3.2 Basic Architecture of the CNN Model -- 3.3 Bayesian Optimization -- 4 Results and Discussion -- 5 Conclusion -- References -- Comparing Convolutional Neural Networks and Transformers in a Points-of-Interest Experiment -- 1 Introduction -- 2 Overview of Deep Learning Architectures -- 2.1 Convolutional Neural Network (CNN) -- 2.2 Transformers -- 3 Construction of Mini-Places Dataset -- 3.1 Dataset Selection -- 3.2 Dataset Optimization -- 4 Training of Deep Learning Models -- 4.1 Image Classification Models -- 4.2 Comparison of the Models -- 5 Assessment of Experimental Findings -- 6 Conclusion -- References. ; Gender and Age Extraction from Audio Signal Using Convolutional Neural Network, MFCC and Spectrogram -- 1 Introduction -- 1.1 Speaker Gender Recognition -- 2 CORPUS -- 3 Methodology -- 3.1 Proposed Model Architecture -- 4 Results -- 4.1 Gender Recognition -- 4.2 Age Extraction -- 5 Conclusion -- References -- The Hybrid Model Combination of Deep Learning Techniques, CNN-LSTM, BERT, Feature Selection, and Stop Words to Prevent Fake News -- 1 Introduction -- 2 Literature Review -- 2.1 Materials and Methods -- 2.2 Counterfeit News Prevention Challenges -- 2.3 Previous Fake News Prevention Attempts -- 2.4 Accuracy Results and Discussion -- 3 Methodology for Hybrid Model of CNN-LSTM + BERT + Feature Selection + Stop Words -- 3.1 LSTM + CNN -- 3.2 Stop Words -- 3.3 BERT -- 3.4 TFIDF and Feature Selection -- 4 Critical Analysis -- 5 Conclusion -- References -- Comparative Analysis of Decision Tree Algorithms Using Gini and Entropy Criteria on the Forest Covertypes Dataset -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Dataset Description -- 3.2 Decision Tree Classifier -- 3.3 Evaluation Metrics -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- A Comparative Analysis of Random Forest and Support Vector Machine Techniques on the UNSW-NB15 Dataset -- 1 Introduction -- 2 Related Works -- 3 Proposed Intrusion Detection Systems -- 3.1 Data Extraction -- 3.2 Data Balancing -- 3.3 Data Transformation -- 3.4 Model Parameter Selection -- 3.5 Model Fitting -- 4 Evaluation of the Models -- 4.1 Confirmation of the Candidate Model Using SMOTE -- 5 Conclusion and Future Directions -- References -- A Comparative Study of Speed Measurement Using Radar Guns and Pneumatic Counter -- 1 Introduction -- 2 Materials and Methods -- 2.1 Study Area -- 2.2 Equipment and Tools -- 2.3 Methodology -- 3 Results and Discussion -- 3.1 Cosine Effect Correction. ; 3.2 Statistical Description of Data Obtained -- 3.3 Dispersion and Regression Statistical Analysis -- 3.4 Suggested Fitting Equation -- 3.5 Discussions -- 4 Conclusions -- References -- Comparative Analysis of Preprocessing Techniques for KNN Classification on the Diabetes Dataset -- 1 Introduction -- 2 Related Work -- 3 Material and Methods -- 3.1 Dataset Description -- 3.2 Preprocessing -- 3.3 K-Nearest Neighbor -- 3.4 Parameter Evaluation -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Computer Science and Computer Engineering Education -- Code Smells for Assessing and Improving Students' Coding Skills and Practices -- 1 Introduction -- 2 Background -- 2.1 Code Smells -- 2.2 Teaching Object-Oriented Programming -- 3 Motivation -- 4 Our Approach -- 4.1 Implementation and Experiment -- 5 Results -- 5.1 Size Complexities -- 5.2 Distribution of Code Smells -- 5.3 Code Smells Density -- 5.4 Code Smells and Earned Grades -- 5.5 Code Smells Trends -- 5.6 Summary -- 6 Conclusion -- References -- An Investigation on Assessment Strategies, Student Engagement, and Retention for Large Cohorts Affected by COVID Learning Disruptions -- 1 Introduction -- 2 Background and Pedagogy -- 2.1 Assessment Strategies -- 2.2 Study Design and Procedure -- 3 Results and Discussions -- 3.1 Results -- 3.2 Student Engagement -- 3.3 Student Retention -- 4 Conclusion -- References -- Self-organization as a Key Principle of Adaptive Intelligence -- 1 Introduction -- 2 From Single Synapse to Neural Network -- 2.1 Neural Timing -- 2.2 Basic Adaptive Response -- 3 Self-organized Network Learning -- 3.1 Winner-Take-All -- 3.2 Reinforcement -- 4 Modular Functional Connectivity -- 4.1 Functional Specificity -- 4.2 Functional Plasticity -- 5 The Receptive Field Concept -- 5.1 Sensitivity and Selectivity to Input Dimensions. ; 5.2 From-Simple-to-Complex Functional Organization.
• Titolo autorizzato: Proceedings of the Third International Conference on Innovations in Computing Research (ICR'24)
• ISBN: 3-031-65522-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910878985003321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Networks and Systems Series