## 1 Introduction  
Soft computing (SC) refers to a group of methods that achieve tractable, robust, and low-cost solutions for problems that involve imprecise and uncertain conditions. Soft computing techniques are mainly inspired by intelligence in nature, such as the functioning principles of the biological brain, the reasoning mechanism of the human mind, or the natural selection principles of evolutionary processes. The term \"Soft Computing\" was proposed by [PERSON] [1, 2]. The results obtained in SC techniques are not mathematically exact; approximate optimum results are obtained, whereas, in Hard Computing (HC), certain and precise results are attained. However, this increases computational time and load. In addition, HC requires the ideal model of the system or analytical expressions describing the behavior of the system and is not suitable for complex real-world problems where ideal model is not available. SC is the aggregation of various computational techniques. Some of the SC techniques and their applications include fuzzy logic [1, 2], evolutionary algorithms [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], artificial neural networks (ANN) [19, 20, 21, 22, 23, 24, 25, 26, 27], and support vector machines (SVM) [19, 20, 28]. SVMs have been introduced by Vapnik [19, 28]. Briefly, SVMs set up a hyperplane that separates two different sample sets from each other with a maximum margin in classification [20]. SVMs have usually been used for classification, but they are also used in regression problems and are named Support Vector Regressors (SVR). SVRs have proven their capabilities in various studies of electromagnetics and microwaves due to their generalization properties [29; 30; 31; 32; 33; 34; 35; 36]. They are used to design single-band antennas [32; 33; 34; 35; 36]. Electromagnetic simulation programs are used for antenna analysis and synthesis [37; 38; 39]. As the number of design parameters increases, the processing load and the time spent in the analyses performed by these programs also increase. Antennas need to be optimized with the changes in geometric structure to obtain the desired response. This also increases the design time. Even if the designer's knowledge and experience are sufficient, repetitively running programs with/without optimization or trial and error method negatively affects the design time. Furthermore, successful results are not guaranteed after repetitive trials. Nowadays, there are many studies on the analysis and synthesis of microstrip antennas. Microstrip antennas have gained great interest since they were first introduced due to their features such as easy fabrication, low cost, compatibility with integrated circuits, and low weight. Moreover, by adding slots on the microstrip antenna patch and/or ground, antenna performance can be improved easily and efficiently. However, a general analytical formula for slots with various numbers and different types are not given in the literature so far. Additionally, the complexity of the design and time spent are increased with the number of slots. In this paper, in order to overcome these drawbacks, a soft computing-based method is utilized in the design of a microstrip patch antenna that operates in triple-band. The resonance frequency and gain that yield the desired performance are obtained by utilizing an SVR model. Multiple slots and arc truncation [40; 41] are implemented to patch the antenna to obtain an antenna that operates in triple-band with desired gains. The desired resonance frequencies of the triple-band are selected in 2.4 and 5 GHz bands of Wireless Local Area Network (WLAN) and the n80 (4.4-4.9 GHz) band [42] of 5G. There are two main novelties of the work presented, the first one is the novel structure of a triple-band antenna, and the second novelty is the implementation of a triple-band antenna designed using SVR for the first time. As far as the authors know, this work presents the first implementation of a triple-band antenna designed via SVR.