diff --git a/monai/metrics/fid.py b/monai/metrics/fid.py
index d655ac1bee..596f9aef7c 100644
--- a/monai/metrics/fid.py
+++ b/monai/metrics/fid.py
@@ -82,7 +82,7 @@ def _cov(input_data: torch.Tensor, rowvar: bool = True) -> torch.Tensor:
 
 def _sqrtm(input_data: torch.Tensor) -> torch.Tensor:
     """Compute the square root of a matrix."""
-    scipy_res, _ = scipy.linalg.sqrtm(input_data.detach().cpu().numpy().astype(np.float_), disp=False)
+    scipy_res, _ = scipy.linalg.sqrtm(input_data.detach().cpu().numpy().astype(np.float64), disp=False)
     return torch.from_numpy(scipy_res)
 
 
diff --git a/monai/transforms/utils_pytorch_numpy_unification.py b/monai/transforms/utils_pytorch_numpy_unification.py
index 98b75cff76..365bd1eab5 100644
--- a/monai/transforms/utils_pytorch_numpy_unification.py
+++ b/monai/transforms/utils_pytorch_numpy_unification.py
@@ -88,7 +88,7 @@ def moveaxis(x: NdarrayOrTensor, src: int | Sequence[int], dst: int | Sequence[i
 def in1d(x, y):
     """`np.in1d` with equivalent implementation for torch."""
     if isinstance(x, np.ndarray):
-        return np.in1d(x, y)
+        return np.isin(x, y)
     return (x[..., None] == torch.tensor(y, device=x.device)).any(-1).view(-1)
 
 
diff --git a/pyproject.toml b/pyproject.toml
index c2ab92a43d..9dc9cf619b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,7 +39,18 @@ exclude = "monai/bundle/__main__.py"
 
 [tool.ruff]
 line-length = 133
-lint.ignore = ["F401", "E741"]
+target-version = "py39"
+
+[tool.ruff.lint]
+select = [
+  "E", "F", "W", # flake8
+  "NPY",         # NumPy specific rules
+]
+extend-ignore = [
+  "E741", # ambiguous variable name
+  "F401", # unused import
+  "NPY002", # numpy-legacy-random
+]
 
 [tool.pytype]
 # Space-separated list of files or directories to exclude.
diff --git a/tests/test_compute_f_beta.py b/tests/test_compute_f_beta.py
index 43ebb6a6d5..be2a7fc176 100644
--- a/tests/test_compute_f_beta.py
+++ b/tests/test_compute_f_beta.py
@@ -59,7 +59,7 @@ def test_with_nan_values(self):
         metric = FBetaScore(get_not_nans=True)
         metric(
             y_pred=torch.Tensor([[1, 1, 1], [1, 1, 1], [1, 1, 1]]),
-            y=torch.Tensor([[1, 0, 1], [np.NaN, np.NaN, np.NaN], [1, 0, 1]]),
+            y=torch.Tensor([[1, 0, 1], [np.nan, np.nan, np.nan], [1, 0, 1]]),
         )
         assert_allclose(metric.aggregate()[0][0], torch.Tensor([0.727273]), atol=1e-6, rtol=1e-6)