# Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization
## 4 Artificial Intelligence Application in MEMS System: Current Trends
### AI Application in MEMS-Based Sensors
[49] proposed a fusion algorithm consisting of least squares support vector machine (LSSVM) [53] and chaotic particle swarm optimization (CPSO). It proved to have better performance than the backpropagation artificial neural network (BP-ANN) for reducing the random drift of the MEMS gyroscope. The authors treated the problem as a chaotic time series issue and performed signal de-noising. Using phase space reconstruction (PSR) along with the C-C method helped with dimension and complexity reduction. [PERSON] et al. [50] proposed a method that comprised a genetic algorithm (GA) and a very well-known and intensively used ML model, support vector machine (SVM), to solve the temperature drift in MEMS gyroscope. Interestingly, [PERSON] et al. [51] also used GA with an optimized backpropagation neural network (BPNN) for temperature drift compensation. GA helped the BPNN to avoid local minimums by search optimization. The achieved result was 173 times better than the traditional polynomial fitting. [PERSON] et al. [52] targeted a similar issue of temperature drift by proposing a parallel approach in their article. The presented method was more complete than the earlier approaches discussed. The analysis included the signal value analysis along with temperature compensation. They decomposed the signal using Immune-Based Particle Swarm Optimization (IPSO) for optimal Variational Modal Decomposition (VMD). After signal decomposition, the obtained intrinsic mode functions were further classified into the noise term (this was removed), the mixed term (consisting of both the noise and useful information), and the feature term. The feature term contained the temperature drift, which was further compensated by using Backpropagation (BP)-Adaboost prediction, which was then mixed with the SG-filtered mixed term to obtain the de-noised, temperature-compensated signal. The papers discussed above demonstrate that combining different disciplines, such as GA and PSO, with AI can improve data analysis and understand and improve model performance. Effective ground vibration monitoring is crucial to avoid geological disasters, such as earthquakes. [PERSON] et al. [54] proposed a CNN-based monitoring scheme on MEMS sensed data. The challenges were related to the collection and cleaning of the data, as the data contained noise, bias, offset errors, and different structures. Nonetheless, the authors reached an overall accuracy of 98.82% with synthetic data and an accuracy of 81.64% on a real dataset. As mentioned, data scarcity is an issue regarding seismological research using low-cost MEMS sensors. Moreover, the data quality is degraded due to inherent noise in accelerorumeters. There have been many attempts [55; 56; 57; 58] to capture the seismic sequence using AI models. Still, these supervised approaches suffered from the incomplete reconstruction of the seismic waveform, over-fitting, and smaller dataset, unable to capture the diversity of the waves. To overcome these challenges, [PERSON] et al. [59] proposed a model Earthquake Generative Adversarial Network (EQGAN), an unsupervised technique to automatically capture and generate stable seismic waveform using the frameworks of GAN, LSTM, and NN. MEMS sensors have numerous applications in consumer electronics, such as pedestrian navigation systems, and AI algorithms are intensively used for real-time path tracking, activity recognition, and posture recognition. Models such as tree-based algorithms (decision tree, random forest, extreme gradient boosting), SVM, CNN, NN, and LSTM have found quite popular usages in such cases [60; 61; 62; 63; 64]. [PERSON] et al. [65] presented a multi-scale Convolutional Neural Network (CNN) with adaptive learning for fault detection in MEMS inertial sensors in UAVs. The authors claimed that the proposed method could handle the temperature drift of inertial sensors and achieved high fault detection accuracy compared to the strategies presented in papers [66; 67; 68; 69; 70; 71]. [PERSON] et al.