# Recent Trends in AI-Based Intelligent Sensing
## 2 AI Methods for Intelligent Sensing
### Machine Learning Algorithms/Models in Intelligent Sensing  
In the last few years, the tremendous growth of ML-based approaches has expanded the research area of intelligent sensing. Generally, ML can be considered to be a subset of AI which handles complexities to solve a specific task. In this subsection, a brief overview of existing ML algorithms that improve the functioning of sensing systems is presented together with their advantages and disadvantages. Various scenarios portraying how machine learning methods are applied in intelligent sensing is depicted in Figure 2. ML algorithms are divided into supervised, semi-supervised, unsupervised, and reinforcement learning. 1. Supervised learning-based intelligent sensing--Supervised learning deals with the known and labeled data and is divided into two types: classification and regression. This approach has been successfully implemented for many years in the fields of image classification, fraud detection, medical diagnosis, weather forecasting, market forecasting, and life expectancy estimation. In [52], ECG data are collected via wearable sensors, which detect heartbeats automatically, and a supervised learning approach is used for arrhythmia classification. An artificial haptic neuron system is fabricated in [53]. The system comprises a Nafion-based memristor and a piezoelectric sensor. The sensory receptor converts external stimulus into an electric signal, and the memristor is used for further processing of the data collected from the sensor. A supervised learning method is implemented for the recognition of English letters by placing the sensor on the joint of a finger. A novel methodology proposed in [54] using supervised learning for resolving the collision of cash tags yields high classification accuracy of listed companies in London Stock Exchange. A hybrid model that  
Figure 2: Various Scenarios Portraying ML and DL-based Intelligent Sensing. combines ML and game theory is proposed in [55] to solve issues related to network selection in ultra-dense heterogeneous networks. * _K-Nearest Neighbors (K-NN)_ is an effective classification algorithm used for large datasets. Here, \(K\) represents the number of training samples that are near the test sample in the feature space [56]. In [57], a machine learning-based K-NN approach is used for load classification by collecting data from various smart plug sensors and other devices. * **Support Vector Machine (SVM)** is mainly used to categorize data attributes between classes by creating two-dimensional planes to minimize the classification error [58]. For example, Ref. [59] introduces a danger-pose detection system based on Wi-Fi devices that is used to monitor a bathroom while ensuring privacy. A machine learning-based detection approach usually requires large amount of data collected in target scenarios, which is challenging to detect danger situations. However, this work employed a machine learning-based anomaly-detection technique which requires a small amount of data in anomalous conditions. In this work, researchers first extracted the amplitude and phase shift from Wi-Fi Channel State Information (CSI) in order to detect low-frequency components associated with human activities. The static and dynamic features were then derived from the CSI changes over time. Finally, the static and dynamic characteristics are input into a one-class SVM which is employed as an anomaly-detection method to determine if a person is not in the bathtub, is bathing safely or in unsafe situations. * **Decision Tree (DT)** model consists of branches and nodes, wherein every node represents a test on every feature, and each branch has a value that the associated node can use to classify a sample [60]. A decision tree-based approach was presented in [61] for an intelligent transportation system (ITS). LIDAR sensors obtain point cloud data, which are then projected onto the XOY plane. After that, the images are classified into road and background grids for monitoring road traffic.