Taking Yanqing-Chongli highway for Beijing Winter Olympic Games as an example, we adopt a method of using photogrammetry and artificial intelligence rock structure parameter to identify working face. In this method, seven index parameters system are established. We use the KNN intelligent algorithm ...
In this research paper, the proposed method introduced an optimized cost-efficient localization mechanism based on the KNN algorithm specifically tailored for underwater node localization. The key objectives of our proposed algorithm are to tackle the multifaceted challenges associated with underwater locali...
Solved Jump to solution I am trying to use kdtree_knn_classification to do a 2d k-nearest neighbour search on in-memory data. However i am getting an unhandled exception in the algorithm.compute() function. Attached is my code snippet. Is something wrong with my usage?...
2. The KNN algorithm, consisting of the prediction and learning steps. Inside KNN predict, the set TxK represents the K-nearest neighbors of x in the dataset T , where distance is measured by Euclidean (or Manhattan) distance in the input vector space. F req(TxK ) is the most frequent ...
Since the LP algorithm converts more tags into single labels, the multiple tag problem can be solved by using a single label classification problem. However, this approach has limitations, including the need for a large amount of training data and the limitation of being able to mark tabbed ...
1instructs NMSLIB to use the similarity model IBM Model1+BM25 (a weighted combination of two similarity scores). The fileheader_avg_embed_word2vec_text_unlemminstructs NMSLIB to use the cosine similarity between averaged word embeddings. In this case, we use an indexing/search algorithm SW-...
Solved Jump to solution I am trying to use kdtree_knn_classification to do a 2d k-nearest neighbour search on in-memory data. However i am getting an unhandled exception in the algorithm.compute() function. Attached is my code snippet. Is something wrong with my usage?...
Occasionally, the SVM algorithm presents non-convergence problems. In [4,12], the KNN algorithm was used, but only the Euclidean distance is explored. Likewise, no study is carried out to determine the optimal value of the number of neighbors in order to improve the performance of the ...
To improve the performance of roller bearing fault diagnosis, this paper proposes an algorithm based on subtraction average-based optimizer (SABO), variational mode decomposition (VMD), and weighted Manhattan-K nearest neighbor (WMH–KNN). Initially, the SABO algorithm uses a composite objective funct...
Determine whether the algorithm reaches the maximum number of iterations; if so, the loop ends and outputs the optimal SABO location [𝑘,𝛼][k,α] and optimal fitness value; if not, return to Step 2. 3.2. SABO–VMD–WMH–KNN Model After obtaining the IMF components through the SABO–...