1 Table of Contents: “... Sparse Counterfactual Explanations for Time Series Classification Problems. -- Human-in-the-loop...”

2 Table of Contents: “... about Fiber Optical Link Problems. -- Construction of artificial most representative trees by minimizing...”

3 Subjects: “...Artificial Intelligence...”

4 Subjects: “...Artificial Intelligence...”

5 Table of Contents: “... of Artificial Neural Networks in Geoscience and Petroleum Industry -- 5. On Support Vector Regression to Predict...”

6 Table of Contents: “...: Summarization -- Chapter 8: Program Management, Business Intelligence, and Reporting -- Chapter 9...”

7 Table of Contents: “... Optimization.-Chapter 14 Expensive Optimization -- Chapter 15 Real-world Applications...”

8 Subjects: “...Artificial intelligence / http://id.loc.gov/authorities/subjects/sh85008180...”

9 Table of Contents: “...Artificial Intelligence Background, Applications and Future -- Buckling Resistance Prediction...”

10 “... on artificial intelligence with Python...”

11 Subjects: “...Artificial intelligence / Medical applications / fast...”

12 Table of Contents: “... reinforcement learning for tackling smart grid pricing problem -- Teaching Learning Based Optimization Algorithm...”

13 Table of Contents: “... Penalty like Neurodynamic Approach to Convex Optimization Problems with Set Constraint -- Doubly...”

14 Subjects: “...Artificial intelligence / http://id.loc.gov/authorities/subjects/sh85008180...”

15 Table of Contents: “...Possible World Semantics for General Storage in Call-By-Value -- A Fully Abstract Relational Model...”

16 Table of Contents: “... Support System -- Intelligent Agents in Virtual Worlds -- Characterizing Web Service Substitutivity...”

17 Table of Contents: “... -- Chapter 3: Data Collection Problems -- Chapter 4: How to Keep Data Tidy -- Chapter 5: Thinking like a Data...”

18 “... and artificial intelligence can benefit their work...”

19 Subjects: “...Computational intelligence...”

20 Table of Contents: “... Problems and How to Solve Them -- H. Tanabe and M. Kimura, A trustworthiness scoring model for reviews...”