A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

Abstract

Case-based reasoning has been a widely-used approach to assist humans in making decisions through four steps: retrieve, reuse, revise, and retain. Among these steps, case retrieval plays a significant role because the rest of processes cannot proceed without successfully identifying the most similar past case beforehand. Some popular methods such as angle-based and distance-based similarity measures have been well explored for case retrieval. However, these methods may match inaccurate cases under certain extreme circumstances. Thus, a triangular similarity measure is proposed to identify commonalities between cases, overcoming the drawbacks of angle-based and distance-based measures. For verifying the effectiveness and performance of the proposed measure, case-based reasoning was applied to an agricultural decision support system for pest management and 300 new cases were used for testing purposes. Once a new pest problem is reported, its attributes are compared with historical data by the proposed triangular similarity measure. Farmers can obtain quick decision support on managing pest problems by learning from the retrieved solution of the most similar past case. The experimental result shows that the proposed measure can retrieve the most similar case with an average accuracy of 91.99% and it outperforms the other measures in the aspects of accuracy and robustness.

A Low-Latency and Energy-Efficient Neighbor Discovery Algorithm for Wireless Sensor Networks

“…In recent years, wireless sensor networks have been extensively studied [1,[3][4][5][6][7][8][23][24][25][26][27][28]; as a cornerstone of constructing wireless sensor networks, increasingly sophisticated protocols for neighbor discovery have been proposed. These neighbor-discovery protocols mainly fall into two categories.…”

“…Wireless sensor networks have been widely adopted in a wide range of Internet-of-Things (IoT) applications such as smart cities [1], health-care systems [2,3], intelligent detection [4,5], and remote monitoring [6][7][8]. In general, a large number of sensor nodes would be dispatched or deployed in some monitoring area to collect or aggregate information for further intelligent decisions.…”

“…We will show the procedure of deriving the transmitting probability in the following parts. As described in the algorithm, node u i selects the Transmit state with probability p t i = 1 n (Line 4-5), selects the Listen state with probability θ i − p t i (Line 6-7), and selects the Sleep state with probability 1 − θ i (Line [8][9]. We analyze how to determine the transmission probability and show the efficiency of the algorithm.…”

An Efficient Case Retrieval Algorithm for Agricultural Case-Based Reasoning Systems, with Consideration of Case Base Maintenance

“…The similarity measurements between two associated cases are stored in the association table as well. The similarity and the dissimilarity measurements are both calculated according to Reference [32]. In Table 3, for filling in the similar association, the cases that achieve the top two highest measurements will be chosen.…”

“…The promising experimental results indicated that the proposed similarity measure outperformed classical methods. Zhai et al [32] proposed a novel triangular similarity measure, overcoming the shortcomings of cosine similarity and Euclidean distance similarity. The experimental result showed that their proposal had strong robustness and great accuracy.…”