Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing - Info and Reading Options
By Roger Y. Lee

"Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing" is published by Springer in Apr 20, 2016 - Cham and it has 185 pages.
“Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing” Metadata:
- Title: ➤ Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
- Author: Roger Y. Lee
- Number of Pages: 185
- Publisher: Springer
- Publish Date: Apr 20, 2016
- Publish Location: Cham
“Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing” Subjects and Themes:
- Subjects: ➤ Software engineering - Engineering - Artificial Intelligence (incl. Robotics) - Artificial intelligence - Computational intelligence - Parallel processing (electronic computers) - Computer networks - Electronic data processing, distributed processing
Edition Specifications:
- Format: hardcover
Edition Identifiers:
- The Open Library ID: OL28007170M - OL19902708W
- ISBN-13: 9783319338095 - 9783319338101
- ISBN-10: 3319338099
- All ISBNs: 3319338099 - 9783319338095 - 9783319338101
AI-generated Review of “Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing”:
"Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing" Description:
Open Data:
Intro -- Preface -- Contents -- Contributors -- 1 Dealing with Missing Values in Software Project Datasets: A Systematic Mapping Study -- Abstract -- 1 Introduction -- 2 Research Methodology -- 2.1 Mapping Questions -- 2.2 Search Strategy -- 2.3 Study Selection -- 2.4 Quality Assessment -- 2.5 Data Extraction -- 3 Results and Discussion -- 3.1 Publication Channels (MQ1) -- 3.2 Publication Trend (MQ2) -- 3.3 Research Types (MQ3) and Research Approaches (MQ4) -- 3.4 MV Techniques (MQ5) -- 3.4.1 MV Techniques Distribution -- 3.4.2 ML Imputation Techniques Distribution -- 3.4.3 Non-ML Imputation Techniques Distribution -- 3.5 Types of Data (MQ6) -- 3.6 Types of MV (MQ7) -- 3.7 Objectives of Investigating MV Techniques in SE Datasets (MQ8) -- 4 Implication for Research and Practice -- 5 Threats to Validity -- 6 Conclusion and Future Work -- References -- 2 Testing and Code Review Based Effort-Aware Bug Prediction Model -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Metrics and Datasets -- 4 Experiment and Results -- 4.1 Effort Based Evaluation -- 4.2 Percentage of Cost Effectiveness -- 4.3 Statistical Significance -- 4.4 Threats to Validity -- 5 Conclusion -- References -- 3 Improving the Efficiency of Schedulability Tests for Fixed Priority Preemptive Systems -- Abstract -- 1 Introduction -- 2 Background -- 2.1 System Model -- 2.2 Exact Schedulability Tests -- 3 Schedulability Tests -- 3.1 Discussions of Initial Values -- 3.2 Initial Values for Response Time Analysis -- 3.3 Initial Value for Processor Demand Analysis -- 3.4 Implementation -- 4 Evaluations -- 4.1 Stochastic Model -- 4.2 Performance Analysis -- 4.3 Discussion -- 5 Conclusion -- References -- 4 A GPU Accelerated Finite Differences Method of the Bioheat Transfer Equation for Ultrasound Thermal Ablation -- Abstract -- 1 Introduction -- 2 Biothermal Distribution of Focused Ultrasound
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