java框架如何在人工智能应用中发挥作用？

import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.evaluation.Evaluation;
import weka.core.Instances;
import weka.core.converters.ConverterUtils;

// 加载数据集
Instances data = ConverterUtils.DataSource.read("spam.arff");

// 创建分类器
NaiveBayes classifier = new NaiveBayes();

// 训练分类器
classifier.buildClassifier(data);

// 评估分类器
Evaluation eval = new Evaluation(data);
eval.evaluateModel(classifier, data);

// 打印评估结果
System.out.println("正确率：" + eval.correct());

import edu.stanford.nlp.ling.CoreAnnotations;
import edu.stanford.nlp.ling.CoreLabel;
import edu.stanford.nlp.pipeline.Annotation;
import edu.stanford.nlp.pipeline.StanfordCoreNLP;

// 创建 NLP 管道
Properties props = new Properties();
props.setProperty("annotators", "tokenize, ssplit, pos, lemma, sentiment");
StanfordCoreNLP pipeline = new StanfordCoreNLP(props);

// 分解文本
String text = "这部电影让我太失望了。";
Annotation annotation = pipeline.process(text);

// 提取情感
for (CoreLabel token : annotation.get(CoreAnnotations.TokensAnnotation.class)) {
    System.out.println(token.get(CoreAnnotations.SentimentClass.class));
}

import org.tensorflow.keras.models.Sequential;
import org.tensorflow.keras.layers.Conv2D;
import org.tensorflow.keras.layers.MaxPooling2D;
import org.tensorflow.keras.layers.Flatten;
import org.tensorflow.keras.layers.Dense;

// 创建模型
Sequential model = new Sequential();
model.add(new Conv2D(32, (3, 3), activation="relu", inputShape=(28, 28, 1)));
model.add(new MaxPooling2D((2, 2)));
model.add(new Conv2D(64, (3, 3), activation="relu"));
model.add(new MaxPooling2D((2, 2)));
model.add(new Flatten());
model.add(new Dense(128, activation="relu"));
model.add(new Dense(10, activation="softmax"));

// 编译和训练模型
model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]);
model.fit(x_train, y_train, epochs=10);