java框架中有哪些用于AI集成的常见库？

import org.tensorflow.TensorFlow;
import org.tensorflow.DataType;
import org.tensorflow.Operation;
import org.tensorflow.Session;
import org.tensorflow.Tensor;

public class ImageClassifier {

    public static void main(String[] args) {
        // 训练和评估图像分类模型...

        // 预测新图像的类别
        Tensor image = ...;
        try (TensorFlow tf = TensorFlow.newInstance()) {
            Session session = tf.newSession();

            // 获取模型的输入和输出操作
            Operation inputOperation = ...;
            Operation outputOperation = ...;

            // 运行预测
            Tensor prediction = session.runner().feed(inputOperation, image).fetch(outputOperation).run().get(0);

            // 解析预测结果...
        }
    }
}

import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.feature.Tokenizer
import org.apache.spark.ml.pipeline.Pipeline
import org.apache.spark.sql.DataFrame

public class TextClassifier {

    public static void main(String[] args) {
        // 加载文本数据...

        // 将文本数据转换为特征向量
        Tokenizer tokenizer = new Tokenizer().setInputCol("text").setOutputCol("words");

        // 训练逻辑回归模型
        LogisticRegression lr = new LogisticRegression().setMaxIter(10).setRegParam(0.01);

        // 构建管道
        Pipeline pipeline = new Pipeline().setStages(new PipelineStage[] { tokenizer, lr });

        // 训练管道模型
        PipelineModel model = pipeline.fit(data);

        // 使用模型对新文本进行预测
        DataFrame predictions = model.transform(newData);

        // 解析预测结果...
    }
}

import weka.classifiers.trees.J48;
import weka.core.Instances;
import weka.core.converters.ArffLoader;

public class DecisionTreeClassifier {

    public static void main(String[] args) {
        // 加载数据
        ArffLoader loader = new ArffLoader();
        loader.setFile(new File("data.arff"));
        Instances data = loader.getDataSet();

        // 训练决策树分类器
        J48 tree = new J48();
        tree.buildClassifier(data);

        // 预测新实例的类别
        Instance instance = ...;
        double prediction = tree.classifyInstance(instance);

        // 解析预测结果...
    }
}