diff --git a/openvino_xai/methods/black_box/aise/classification.py b/openvino_xai/methods/black_box/aise/classification.py
index 513aed18..7c1947d1 100644
--- a/openvino_xai/methods/black_box/aise/classification.py
+++ b/openvino_xai/methods/black_box/aise/classification.py
@@ -57,6 +57,8 @@ def __init__(
             prepare_model=prepare_model,
         )
         self.bounds = Bounds([0.0, 0.0], [1.0, 1.0])
+        self.num_iterations_per_kernel: int | None = None
+        self.kernel_widths: List[float] | np.ndarray | None = None
 
     def generate_saliency_map(  # type: ignore
         self,
diff --git a/openvino_xai/methods/black_box/aise/detection.py b/openvino_xai/methods/black_box/aise/detection.py
index 1cd4b9a2..32c1f5ed 100644
--- a/openvino_xai/methods/black_box/aise/detection.py
+++ b/openvino_xai/methods/black_box/aise/detection.py
@@ -57,6 +57,8 @@ def __init__(
         )
         self.deletion = False
         self.predictions = {}
+        self.num_iterations_per_kernel: int | None = None
+        self.divisors: List[float] | np.ndarray | None = None
 
     def generate_saliency_map(  # type: ignore
         self,
diff --git a/openvino_xai/methods/black_box/rise.py b/openvino_xai/methods/black_box/rise.py
index e14c1f35..57b33bc2 100644
--- a/openvino_xai/methods/black_box/rise.py
+++ b/openvino_xai/methods/black_box/rise.py
@@ -45,6 +45,8 @@ def __init__(
         super().__init__(
             model=model, postprocess_fn=postprocess_fn, preprocess_fn=preprocess_fn, device_name=device_name
         )
+        self.num_masks: int | None = None
+        self.num_cells: int | None = None
 
         if prepare_model:
             self.prepare_model()
@@ -84,13 +86,11 @@ def generate_saliency_map(
         """
         data_preprocessed = self.preprocess_fn(data)
 
-        num_masks, num_cells = self._preset_parameters(preset, num_masks, num_cells)
+        self.num_masks, self.num_cells = self._preset_parameters(preset, num_masks, num_cells)
 
         saliency_maps = self._run_synchronous_explanation(
             data_preprocessed,
             target_indices,
-            num_masks,
-            num_cells,
             prob,
             seed,
         )
@@ -134,8 +134,6 @@ def _run_synchronous_explanation(
         self,
         data_preprocessed: np.ndarray,
         target_classes: List[int] | None,
-        num_masks: int,
-        num_cells: int,
         prob: float,
         seed: int,
     ) -> np.ndarray:
@@ -152,8 +150,8 @@ def _run_synchronous_explanation(
         rand_generator = np.random.default_rng(seed=seed)
 
         saliency_maps = np.zeros((num_targets, input_size[0], input_size[1]))
-        for _ in tqdm(range(0, num_masks), desc="Explaining in synchronous mode"):
-            mask = self._generate_mask(input_size, num_cells, prob, rand_generator)
+        for _ in tqdm(range(0, self.num_masks), desc="Explaining in synchronous mode"):
+            mask = self._generate_mask(input_size, self.num_cells, prob, rand_generator)
             # Add channel dimensions for masks
             if is_bhwc_layout(data_preprocessed):
                 masked = np.expand_dims(mask, 2) * data_preprocessed
diff --git a/tests/unit/methods/black_box/test_black_box_method.py b/tests/unit/methods/black_box/test_black_box_method.py
index cd42a539..0cee1c51 100644
--- a/tests/unit/methods/black_box/test_black_box_method.py
+++ b/tests/unit/methods/black_box/test_black_box_method.py
@@ -104,16 +104,22 @@ def test_preset(self, fxt_data_root: Path):
         self._generate_with_preset(method, Preset.SPEED)
         toc = time.time()
         time_speed = toc - tic
+        assert method.num_iterations_per_kernel == 20
+        assert np.all(method.kernel_widths == np.array([0.1, 0.175, 0.25]))
 
         tic = time.time()
         self._generate_with_preset(method, Preset.BALANCE)
         toc = time.time()
         time_balance = toc - tic
+        assert method.num_iterations_per_kernel == 50
+        assert np.all(method.kernel_widths == np.array([0.1, 0.175, 0.25]))
 
         tic = time.time()
         self._generate_with_preset(method, Preset.QUALITY)
         toc = time.time()
         time_quality = toc - tic
+        assert method.num_iterations_per_kernel == 50
+        np.testing.assert_allclose(method.kernel_widths, np.array([0.075, 0.11875, 0.1625, 0.20625, 0.25]))
 
         assert time_speed < time_balance < time_quality
 
@@ -171,16 +177,22 @@ def test_preset(self, fxt_data_root: Path):
         self._generate_with_preset(method, Preset.SPEED)
         toc = time.time()
         time_speed = toc - tic
+        assert method.num_iterations_per_kernel == 20
+        assert np.all(method.divisors == np.array([7.0, 4.0, 1.0]))
 
         tic = time.time()
         self._generate_with_preset(method, Preset.BALANCE)
         toc = time.time()
         time_balance = toc - tic
+        assert method.num_iterations_per_kernel == 50
+        assert np.all(method.divisors == np.array([7.0, 4.0, 1.0]))
 
         tic = time.time()
         self._generate_with_preset(method, Preset.QUALITY)
         toc = time.time()
         time_quality = toc - tic
+        assert method.num_iterations_per_kernel == 50
+        assert np.all(method.divisors == np.array([8.0, 6.25, 4.5, 2.75, 1.0]))
 
         assert time_speed < time_balance < time_quality
 
@@ -227,16 +239,22 @@ def test_preset(self, fxt_data_root: Path):
         self._generate_with_preset(method, Preset.SPEED)
         toc = time.time()
         time_speed = toc - tic
+        assert method.num_masks == 1000
+        assert method.num_cells == 4
 
         tic = time.time()
         self._generate_with_preset(method, Preset.BALANCE)
         toc = time.time()
         time_balance = toc - tic
+        assert method.num_masks == 5000
+        assert method.num_cells == 8
 
         tic = time.time()
         self._generate_with_preset(method, Preset.QUALITY)
         toc = time.time()
         time_quality = toc - tic
+        assert method.num_masks == 10_000
+        assert method.num_cells == 12
 
         assert time_speed < time_balance < time_quality