# Recent Trends in AI-Based Intelligent Sensing
## 4 Practical Applications of Intelligent Sensing
### Applications of Intelligent Sensing
To optimize the traffic light cycles, a technique based on genetic algorithm (GA) is used to improve the traffic light configuration [143]. Ref. [144] discusses the design of a traffic light controller that varies the cycle time according to the number of vehicles behind the red and green traffic lights. Another technique based on extension neural network (ENN) is used in outdoor environments to recognize the objects. A traffic light can be monitored by gathering data from the number of vehicles passing and then processing that data. Here, how intelligent sensing is used in traffic management is shown in Figure 5. With the emergence of smart sensors, various challenges faced by traffic management authorities such as traffic congestion, optimum route, travel cost, average waiting time, etc. can be solved. 4. Smart Healthcare--Unsupervised learning algorithms like clustering and principal component analysis (PCA) are used in [145]. In this technique, by maximizing and minimizing the resemblance of patients, the clustering algorithm outputs the labels within the clusters. PCA mainly focuses on reducing the dimension, especially when the features are achieved in a considerable number of dimensions. In [146], SVM is applied to classify imaging biomarkers of nervous and psychiatric diseases. Recently, CNN has been successfully implemented in the healthcare domain through knowledge from ocular images to assist in diagnosing congenital cataract disease [147]. Natural language processing (NLP) aims at better clinical decision making from the narrative text [148]. In [149], NLP is used to read chest X-ray reports to alert physicians to the possible requirement for anti-infective therapy. In healthcare organizations like insurance companies, the use of sensors is to provide accurate and reliable diagnostic results, which can be monitored remotely irrespective of whether the patient is at a clinic, hospital, or home, thereby improving healthcareefficiency. Healthcare management uses intelligent sensing for different purposes, as shown in Figure 6.  
Mass spreading diseases are not rare nowadays; in such cases, fast and reliable information helps to stop the infection to the general public. The mitigation is facilitated by early detection, identification of the cause, and finding a cure. In healthcare, DL and AI have been implemented to control such diseases. Intelligent sensing is also implemented for vaccine detection. In the case of COVID-19, WHO has recommended a swab-based SARS-CoV-2 test. From the swabed samples, the information related to E-gene from SARS-CoV-2 and gene from enzyme RNA-dependent polymerase, which is in charge of the copying of a DNA sequence into an RNA sequence during the transcription process, plays a key role in the identification of symptoms. Many researchers have observed that real-time PCR methods are also effective for diagnosing the test results [150; 151; 152]. In these approaches, the protein related to immunological defense is tagged to identify the potential targets using fluorescent tubes. A CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-Cas13-based strategy for viral inhibition has been found to be effective for dealing with SARS-CoV-2, which caused COVID-19, and emerging pan-coronavirus pandemic strains [153]. Intelligent sensing techniques can be employed to determine the diseases that cause the epidemic and pandemic. In [154], [PERSON] suggested active learning algorithms with cross-population datasets to test and train models that can compute the data with multiple mini-batches of information to help in detection and decision making. The work presented in [21] proposed an AI-based framework with the use of sensors already mounted on palmtop devices, like smartphones, cameras, inertial, microphone, temperature, and fingerprint sensors to collect information from patients. Deep Learning algorithms are implemented to do the multimodal analysis to detect the presence of COVID-19 symptoms.