# Recent Trends in AI-Based Intelligent Sensing
## 2 AI Methods for Intelligent Sensing
### Machine Learning Algorithms/Models in Intelligent Sensing
* **Ensemble Learning (EL)** is a method based on combining the outputs of basic classification algorithms to boost the performance of classification. It is robust to data overfitting problem and is better than a single classifier [62]. This method is proposed in [63], where soft sensors are used to collect data to predict the composition, flow rate, and other features of the product, e.g., fatty acid methyl esters (FAME), in the procedure of production of biodiesel from vegetable oil. * **Random Forest (RF)** is made of a combination of several DTs and constructed randomly to form a model for improving the overall results [64]. A random forest-based classifier is proposed in [65] for estimating the content of bulky metals in agricultural soil using hyperspectral sensor data and is shown to reduce computational cost and time. 2. Unsupervised learning-based intelligent sensing--Due to the large amount of unlabeled data in our everyday life, researchers have emphasized the unsupervised learning-based algorithms for intelligent sensing applications. This method consists of dimensionality reduction, generative networks, and clustering. Unsupervised learning-based intelligent sensing is proposed in [66], which is applied for real-time environment sensing to detect rare event instances intelligently. An unsupervised clustering-based method is introduced in [67] to describe an individual's behavioral pattern by analyzing 100 days of unlabeled sensor data of 17 older adults from their homes and extract information of their day-to-day activities at different times. To detect the change in Landsat images, unsupervised learning is used in [68] with mean-shift clustering and hybrid wavelet transform under the Multi-Objective Particle Swarm optimization (MO-PSO) framework. 3. Semi-Supervised intelligent sensing--This method deals with the combination of labeled and unlabeled data. To reduce the complexity of labeling all data for large datasets, semi-supervised methods are used. A robust model based on a semi-supervised approach is proposed in [69] to warn about the aircraft fault during the flight of a UAV by sensing real-time data such as angular velocity and pitch angle from flight sensors, and dramatically reduces the manual work. To detect faults in Additive Manufacturing (AM) products, a semi-supervised method with a few labeled data and a large number of unlabeled data is explored in [70]. 4. Reinforcement learning-based intelligent sensing--In the context of AI, reinforcement learning learns to make a sequence of decisions by interacting with its environment. One of the successful applications of this approach is to control autonomous cars by training the model. A deep reinforcement learning-based multi-sensor tracking fusion is proposed in [71] for vehicle tracking by learning on fused data from different sensors (camera and LIDAR). An intelligent sensing-based approach is introduced in [47] to autonomously monitor bridge conditions by collecting data from sensor nodes and make decisions using the reinforcement learning method. A novel approach based on YOLO V3 is proposed in [72] for multi-object tracking based on multi-agent deep reinforcement learning. This approach performs better in terms of precision, accuracy, and robustness. A routing protocol built on reinforcement learning is developed in [73] to find an optimal routing path for data transmission in a wireless network. Table 3 shows the comparison of several ML and DL algorithms used in different areas of intelligent sensing. ML is a branch of AI that advocates the idea of acquiring the right data so that a machine can learn how to solve a particular problem by itself. The rise of ML is due to the availability of large datasets, and the adoption of ML algorithms in the field of intelligent sensing is to create smart devices that can take actions based on what they sense from the environment. With the implementation of ML in sensors, the efficiency and robustness of the system will reach the next level in smart sensing applications.