• Uncertainty in Artificial Intelligence Environments:
• According to Russell & Norvig's foundational AI framework, an agent faces an uncertain environment if the environment is:

$$\text{Not fully observable (partially observable) } \mathbf{OR} \text{ Not deterministic (stochastic)}$$

• If the agent lacks complete state knowledge through its percepts, or if actions do not uniquely and deterministically determine the subsequent state, uncertainty inevitably arises.
• Conclusion: Not fully observable or not deterministic (Option 2).

Correct option: (2) Not fully observable or not deterministic

• Ensemble Modeling Techniques:
• Bagging (Bootstrap Aggregation): Trains multiple models independently on bootstrapped subsamples in parallel (e.g., Random Forest).
• Boosting: Trains models sequentially, where each successor model focuses on correcting residual errors of predecessor models (e.g., AdaBoost, XGBoost).
• Stacking (Stacked Generalization): Trains diverse base learners and uses a meta-model trained on cross-validated predictions to combine their outputs.
• Blending: A simplified heuristic variation of stacking that uses a single hold-out validation set rather than $k$-fold cross-validation. In standard academic machine learning curricula and competitive examinations, Blending is not classified as one of the fundamental theoretical ensemble architectures.
• Conclusion: Blending (Option 3).

Correct option: (3) Blending

• Artificial Neural Network Neuron Activation:
• Given neuron parameters:
• Weight vector: $W = [w_1, w_2] = [3, 2]$
• Input vector: $X = [x_1, x_2]^T = [-5, 7]^T$
• Bias: $b = 1$
• Calculating net input ($n$):

$$n = W X + b = (w_1 x_1 + w_2 x_2) + b$$ $$n = (3 \times (-5) + 2 \times 7) + 1 = (-15 + 14) + 1 = -1 + 1 = 0$$

• Evaluating transfer functions at net input $n = 0$:
• Log-Sigmoid (Logistic Function):

$$f(n) = \frac{1}{1 + e^{-n}} = \frac{1}{1 + e^0} = \frac{1}{1 + 1} = \frac{1}{2} = 0.5$$

• Hard limit: Returns 1 for $n \ge 0$, and 0 for $n \lt 0$.
• Linear: Returns $f(0) = 0$.
• Conclusion: Log-sigmoid transfer function produces an output of 0.5 (Option 4).

Correct option: (4) Log-sigmoid

• Bioinformatics and Machine Learning Tool Classification:
• FastQC $\to$ (III): Standard quality control diagnostic tool that analyzes high-throughput sequencing FASTQ files for per-base sequence quality, adapter content, and GC distribution.
• Support Vector Machine (SVM) $\to$ (I): Supervised machine learning classification algorithm that finds the optimal separating hyperplane with maximum margin.
• K-means clustering $\to$ (IV): Unsupervised clustering algorithm that partitions unlabeled data points into $K$ distinct clusters.
• DAVID (Database for Annotation, Visualization and Integrated Discovery) $\to$ (II): Online bioinformatics resource for gene ontology (GO) enrichment and biological pathway analysis.
• Matching Result: (A) - (III), (B) - (I), (C) - (IV), (D) - (II) (Option 3).

Correct option: (3) (A) - (III), (B) - (1), (C) - (IV), (D) - (HI)