# Recent Trends in AI-Based Intelligent Sensing
## 2 AI Methods for Intelligent Sensing
### Parameters Affecting the Performance of Intelligent Sensing  
This subsection presents a review of some of the parameters that affect the performance of intelligent sensing. Intelligent sensing methods have been promising with state-of-the-art results in several areas, such as healthcare, image segmentation, agriculture, soft sensors, etc. The use of sensor systems in industrial, scientific, and consumer equipment is extensive and is continuously increasing in domains like automation. Essentially, industrial information revolutions need more sensors of every kind. The focus of the sensor system is to provide reliable signals and evaluate information. The smart sensing units include a sensing element and proper signal processing function within the same package. Tables 4 give a list of parameters that affect the performance of intelligent sensing based on the results reported in literature. Key information includes the title and year of publication of each paper, and parameters that influence the performance of the various intelligent sensing approaches, such as temperature, accuracy, cost, time, occupancy, dependency, etc. One of the parameters is feature extraction in image recognition. Several techniques of pre-processing are used for enhancing certain features and removing unnecessary data. These techniques include digital spatial filtering, contrast enhancement, gray level distribution linearization, and image subtraction [94]. Measurement of redundancy in test samples is attempted to achieve test loss minimization, which can lead to a reduction of test maintenance costs and also ensure the integrity of test samples [95]. Evaluating ML algorithms is an important part of any project. Accuracy is one of the essential parameters to judge the performance of the trained model. Classification accuracy is defined as the fraction of correct predictions relative to the total number of input samples. The most crucial aspect of this matter is the collection of data from multiple sources. The data usually goes through several stages of pre-processing to make it in presentable form. Intelligent sensing approaches are in general associated with technological applications where they are applied. For example, in cognitive radio, the sensing approach will be different from applications in a smart grid. The work in [102] presented an artificial intelligence-based approach for high-speed data delivery with latency regulation. Compared to CogMAC (Cognitive Medium Access Control) and AHP (Analytic Hierarchy Process) protocols, the decentralized approach helps in creating opportunistic methods for spectrum access and better design of channel selection mechanisms. The work presented in [103] proposed a method for integrating intelligence close to the sensor, which will enable decision making in local nodes before transferring the information to cloud or server. The local intelligence will be helpful in producing smart data that can be used for analysis to produce effective outcomes. Techniques such as normalization, linearization, and data cleaning can be done at local nodes in a piconet. Such inclusion will be helpful in the elimination of unnecessary steps, which needs to be done very frequently before data is  
approach was proposed in [105]. The proposed method will help to collect data, extract useful information, and transfer it for further processing. In the case of more sensitive data sensing, such as clinical or eHealth, Ref. [106] presented the implementation of gateway and scoring mechanisms to reduce the latency and to analyze the performance of systems. Such implementations have shown good performance in fog computing environments, where restricted resources are available at local nodes. The work presented in [107] shows the importance and challenges of IoT-based healthcare information sensing. The work presents challenges related to information acquisition, sensing, storage, processing, analytics, and presentation.