## 1 Introduction  
Zero-crossing point (ZCP) detection is very useful for frequency estimation of a sinusoidal signal under various disturbances, such as noise and harmonics. Zero-crossing point detection is an important mechanism that is useful in various power system and power electronics applications, such as synchronization of a power grid [1], the switching pulse generation in triggering circuits for power electronics devices, control of switch gear, equipment for load shedding or protection and signal processing, and it is also used in other fields such as radar and nuclear magnetics [2]. This technique's appeal stems largely from its ease of implementation and resilience in the presence of frequency fluctuation. The ZCP estimation tools have certain flaws such as the technique's accuracy in the presence of transients, harmonics and signal noise, which is still dubious. The most important requirement for properly determining the ZCP of any signal using digital control methods is that the signal should be free of any false ZCPs. Harmonics due to nonlinear loads will have an impact on zero-crossing points of voltage signals and also lead to power quality issues such as malfunction of protection devices, lower power factor, increasing losses and decreasing power system efficiency [3]. The existence of transients, harmonics or noise in the system enhances the probability of a false ZCP occurrence. This issue has not been effectively addressed since there has been relatively little study on the subject. Artificial intelligence (AI) is now used in a variety of sectors, especially in electrical for load forecasting [4] and image processing [5]. In this paper, an AI approach is used to predict the ZCP class in a distorted sinusoidal signal. AI analyzes the system and predicts outcomes based on previously known data. ZCP detection technique is developed to estimate the distorted sinusoidal signal in a power grid using least square optimization in [6]. However, this method is not suitable where fast zero-crossing detection is required. A neural network-based approach is developed in [7] to detect the ZCP for a sinusoidal signal with only frequency variation from 49 Hz to 51 Hz but not considered the distortion in the signal with noise. Opto-coupler based ZCP detection is developed in [8]. This approach results in phase distortion due to the diode's non-zero forward voltage. A novel ZCP tool is developed in [9] using a differentiation circuit and reset flip-flop that leads to less delay time for ZCP detection. This tool performance is examined up to 7.27% total harmonic distortion (THD) only. Support vector machine based ZCP detection tools are developed in [10] by considering the 100 microseconds sample-distorted sinusoidal signal. A new zero-crossing detection algorithm based on narrow-band filtering is developed in [11]. In this algorithm, normalized electrical quantity is passed through a narrow band filter. A ZCP detection algorithm is developed in [12] based on linear behavior of the sinusoidal signal at the zero-crossing point. In this methodology, the author has used multistage filtering and line fitting techniques. A zero-crossing detection based digital signal processing method is proposed in [13] for an ultrasonic gas flow meter, and this methodology is used for ultrasonic wave propagation time calculation. If the freewheeling angle exceeds 30, the zero crossing of back EMF will be undetectable. Detection of ZCP in the back EMF signal for brushless DC motors under sensorless safety operation, i.e., the maximum free-wheeling angle, is proposed in [14]. Multiple ZCP detections in ultrasonic flow meters to determine time of flight are proposed in [15]. In this paper, time taken to obtain ZCP is used as time of flight. Hardware support for measuring the periodic components of signals based on the number of ZCP is proposed in [15].