Kooperativer Bibliotheksverbund

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Format: XI, 672 p. 305 illus., 227 illus. in color. , online resource.

Edition: 1st ed. 2024.

ISBN: 9783031622779

Series Statement: Lecture Notes in Networks and Systems, 1017

Content: Explore the forefront of computing with the proceedings of the Computing Conference 2024. Featuring 165 carefully selected papers from a pool of 457 submissions, this collection encapsulates the cutting-edge research and innovation presented during the conference. Delve into a diverse range of topics, insights, and methodologies that shape the future of computing. Whether you're an academic, researcher, or enthusiast, this concise volume offers a snapshot of the dynamic and collaborative spirit defining the Computing Conference 2024.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783031622762

Additional Edition: Printed edition: ISBN 9783031622786

Additional Edition: Printed edition: ISBN 9783031622793

Language: English

DOI: 10.1007/978-3-031-62277-9

URL: https://doi.org/10.1007/978-3-031-62277-9

URL: Volltext  (URL des Erstveröffentlichers)

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Format: 1 online resource (683 pages)

Edition: 1st ed.

ISBN: 9783031622779

Series Statement: Lecture Notes in Networks and Systems Series ; v.1017

Note: Intro -- Preface -- Contents -- Optimised Round Robin with Virtual Runtime for CPU Scheduling -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Round Robin Scheduler -- 3.2 Completely Fair Scheduling (CFS) -- 3.3 Virtual Runtime -- 3.4 Niceness -- 4 Proposed Methodology -- 5 Observation -- 5.1 Example of Processes with Same Arrival Time -- 5.2 Example of Processes with Different Arrival Time -- 5.3 Comparison with Other Round Robin Variations -- 6 Conclusions -- 6.1 Benefits of `Riti' -- 6.2 Limitations of `Riti' -- 6.3 Future Scope -- References -- Faster Lock-Free Atomic Shared Pointers -- 1 Introduction -- 2 Related Work -- 3 Algorithm -- 3.1 Notation and Assumptions -- 3.2 Operation: Load -- 3.3 Operation: Store -- 3.4 Operation: Exchange -- 3.5 Operation: Compare-and-Swap -- 3.6 Helper Functions -- 3.7 Diverging Temporary Reference Counters -- 4 Correctness -- 5 Evaluation -- 5.1 Operation: Load -- 5.2 Operation: Store -- 5.3 Operation: Exchange -- 5.4 Operation: Weak and Strong CAS -- 5.5 Operation: Weak and Strong CAS-Loops -- 5.6 Discussion -- 6 Conclusion and Outlook -- References -- CICO2e: A Compute Carbon Footprint Estimation Tool Based on Time Series Data -- 1 Introduction -- 2 Related Work -- 2.1 Determination of the Energy Consumption -- 2.2 Determination of the Carbon Intensity -- 3 Carbon Footprint Estimation with Static vs. Time Series Carbon Intensity -- 3.1 Method -- 3.2 Dataset (Regions and Time Frame) -- 3.3 Results -- 3.4 Discussion -- 3.5 Threats to Validity -- 4 Compute Carbon Footprint Estimation Tool (CICO2e) -- 5 Future Work -- 6 Conclusion -- References -- Image Classification Method Based on Chaos Neural Network -- 1 Introduction -- 2 Related Research Works -- 3 Proposed Method -- 3.1 Chaotic Neuron Model -- 3.2 Chaotic Neuron Model -- 3.3 Chaos NN Parameter Learning. , 3.4 CHAOS NN Condition Setting Based on Simulation Data -- 4 Experiment -- 4.1 Results Using Simulation Data -- 4.2 Comparison of Classification Efficiency -- 4.3 Learning Speed Comparison -- 4.4 Classification Index and Classification Efficiency -- 4.5 Evaluation Using Real Satellite Images -- 4.6 Setting CHAOS NN Parameters -- 4.7 Classification Result -- 5 Conclusion -- 6 Future Research Works -- References -- Human-Created and AI-Generated Text: What's Left to Uncover? -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Collection -- 3.2 Participant -- 3.3 Survey/Questionnaire Design -- 4 Results -- 5 Conclusion -- References -- AraXLM: New XLM-RoBERTa Based Method for Plagiarism Detection in Arabic Text -- 1 Introduction -- 2 Arabic Language Characteristics -- 2.1 General Features of the Arabic Language -- 2.2 Summary -- 3 Related Work -- 4 Challenges and Limitations -- 4.1 Translation Quality -- 4.2 Fine-Tuning Model -- 4.3 Cross-Lingual Semantic Variability -- 5 The Proposed Solution -- 5.1 The AraXLM Framework -- 5.2 AraXLM Workflow -- 5.3 Summary -- 6 Discussion -- 7 Conclusion and Future Work -- References -- Boosting Customer Retention in Pharmaceutical Retail: A Predictive Approach Based on Machine Learning Models -- 1 Introduction -- 2 Background -- 2.1 Cross-Industry Standard Process for Data Mining (CRISP-DM) -- 2.2 Classification Models -- 2.3 Data Science Tools -- 3 Research Approach -- 3.1 Phase 1: Business Understanding -- 3.2 Phase 2: Data Understanding -- 3.3 Phase 3: Data Preparation -- 3.4 Phase 4: Modelling -- 3.5 Phase 5: Evaluation -- 3.6 Phase 6: Deployment -- 4 Discussion -- 5 Conclusions -- References -- Introducing Prediction Concept into Data Envelopment Analysis Using Classifier in Economic Forecast -- 1 Introduction -- 2 Proposed Methodology -- 2.1 DEA Model -- 2.2 DEA-Classifier Model. , 2.3 DEA-Classifier Model Validation -- 3 Experiments -- 3.1 Datasets -- 3.2 Implementation -- 4 Experimental Results and Discussions -- 5 Conclusion -- References -- Investigating Machine Learning Techniques Used for the Detection of Class Noise in Data: A Systematic Literature Review -- 1 Introduction -- 2 Background: Noise Types -- 2.1 Class Noise -- 2.2 Attribute Noise -- 2.3 Class Noise Detection and Class Noise Handling Techniques -- 3 Research Approach -- 3.1 Data Analysis -- 4 Discussion of Results -- 4.1 Classifier-Based Techniques -- 4.2 Distance-Based Techniques -- 4.3 Ensemble-Based Techniques -- 5 Conclusion -- References -- The Role of Chatbots in Data Analytics: An Evaluation of Functional Abilities -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Datasets -- 3.2 Prompt Engineering and Evaluation -- 4 Results -- 4.1 Dataset A: Stock Market Analysis -- 4.2 Dataset B: Heart Health Analysis -- 4.3 Dataset C: Synthetic Energy Performance Analysis -- 5 Discussion -- 5.1 Dataset A -- 5.2 Dataset B -- 5.3 Dataset C -- 6 Conclusion -- References -- An Analytical Investigation into the Impact of Product Color on the Price of Retail Products and Purchasing Decisions of Consumers -- 1 Introduction -- 2 Analytical Aspects -- 2.1 Requirements -- 2.2 Data Acquisition and Data Sources -- 2.3 Datasets Overview -- 2.4 Prepare Data -- 2.5 Feature Engineering -- 3 Analysis -- 3.1 Conduct Exploratory Analysis -- 3.2 Exploratory Visualizations -- 3.3 Exploratory Statistical Model -- 4 Data Visualizations -- 4.1 The Number of Products Based on Color -- 4.2 The Average Number of Likes Based on the Product's Color -- 4.3 Average Likes Count for the Products that Have More Than One Color -- 4.4 The Average Number of Likes for Products that Have More Than One Color Based on the Product's Category -- 4.5 Average Product's Price Based on Color. , 5 Challenges -- 6 Future Work -- 7 Conclusion -- References -- A Numerical Approach for the Fractional Laplacian via Deep Neural Networks -- 1 Introduction -- 1.1 Motivation -- 1.2 Recent Works on DL Techniques -- 2 Preliminaries -- 2.1 Setting -- 2.2 Approximation Theorem -- 3 Numerical Problem Modeling -- 3.1 Simulation of Random Variables -- 3.2 Monte Carlo Training Set Generation -- 3.3 DNN Approximation -- 3.4 Method's Error Estimates -- 4 Numerical Examples -- 4.1 Example 1: Constant Source Term -- 4.2 Example 2: Non Constant Source Term -- 4.3 Example 3: Non Zero Boundary Term -- 4.4 Example 4: A Counter Example for the Algorithm -- 4.5 Discussion -- 5 Conclusions -- A Figures -- B Tables -- References -- RBF-SC: A Fast Community Detection Technique Using Radial Basis Functions -- 1 Introduction -- 2 Related Work -- 3 Radial Basis Function: Spectral Clustering -- 3.1 RBF Interpolation -- 3.2 Trade-Off Principle -- 3.3 Spectral Clustering with Optimized RBF -- 4 Experimental Results -- 4.1 Real Data Sets -- 4.2 The Role of the Shaping Parameters -- 5 Conclusions -- References -- Improving Medication Prescription Strategies for Discordant Chronic Comorbidities Through Medical Data Bench-Marking and Recommender Systems -- 1 Introduction -- 2 Related Works -- 3 Background on Machine Learning Algorithms -- 3.1 Nearest Neighbors -- 3.2 Random Forest -- 3.3 Support Vector Machine (SVM) -- 3.4 Artificial Neural Networks (ANNs) -- 4 Experiments and Results -- 4.1 Data Collection -- 4.2 Experimental Settings -- 5 Discussion -- 6 Conclusion and Future Work -- References -- Sentiment Analysis for Predicting the Variation Trend of Stocks: A Case Study of Vanke Co., Ltd. -- 1 Introduction -- 2 Background -- 2.1 News Sentiment Analysis for Stock Prediction -- 2.2 Natural Language Processing Approaches -- 3 System Design -- 3.1 Overview of the System. , 3.2 Named Entity Recognition -- 3.3 Database Structure -- 3.4 Translation -- 4 Implementation -- 4.1 Text Classification: Lexicon-Based Sentiment Analysis and TextCNN Model -- 4.2 TextCNN -- 4.3 Named Entity Recognition: Bi-LSTM and CRF Model -- 5 Evaluation -- 5.1 An Empirical Analysis of the Correlation Between Sentiment in News/Discussion and Stock Price -- 5.2 Non-linear Time Series Modeling -- 5.3 Evaluation of Text Classification Algorithm -- 5.4 Evaluation of Named Entity Recognition Algorithm -- 6 Conclusion and Future Work -- References -- Suicide Ideation Prediction Through Deep Learning: An Integration of CNN and Bidirectional LSTM with Word Embeddings -- 1 Introduction -- 2 Related Woks -- 3 Methodology -- 3.1 Dataset -- 3.2 Data Preprocessing -- 3.3 Word Embedding -- 3.4 CNN-BiLSTM Model -- 4 Results and Discussion -- 4.1 Result -- 4.2 Discussion -- 5 Conclusion -- References -- A Multi-clustering Unbiased Relative Prediction Recommendation Scheme for Data with Hidden Multiple Overlaps -- 1 Introduction -- 1.1 The Recruiting Process Hidden Multiple Overlaps Challenge -- 2 Previous Work -- 2.1 Recommendation Systems Methods -- 2.2 Coping with Overlapping Clusters -- 3 A Multi-clustering Relative Prediction Recommendation Scheme -- 3.1 Unified User-Item Modeling -- 3.2 Multi-clustering -- 3.3 Two-Sided Relative Prediction -- 4 TALENT.AI Recommender System -- 5 Evaluation -- 6 Conclusion -- References -- An Unsupervised Deep Learning Model for Aspect Retrieving Using Transformer Encoder -- 1 Introduction -- 2 Related Work -- 2.1 Methodologies of Aspect Extraction Task -- 2.2 Word Embedding Representations on Aspect Extraction Task -- 3 Proposed Architecture Description for RATE -- 3.1 Encoder -- 3.2 Attention-Based Sentence Embeddings -- 3.3 Aspect-Category Weight Representation -- 3.4 Reconstructed Sentence Embedding -- 3.5 Model Training. , 4 Results and Discussions.

Additional Edition: Print version: Arai, Kohei Intelligent Computing Cham : Springer International Publishing AG,c2024 ISBN 9783031622762

Language: English