# Recent Trends in AI-Based Intelligent Sensing
## 4 Practical Applications of Intelligent Sensing
### Lessons Learned  
In this section, we have identified a few ML-based foundational services in a broad range of intelligent sensing applications. We discussed how ML has been used to facilitate these services. The major contributions of this section are the coverage of the applications of intelligent sensing, which are gaining tremendous attention.  
ML is a revolutionary technology which attracts every other technology through its algorithms and impressive results. Agriculture represents one sector of the future of computing and communications. Intelligent sensing applied to this domain fulfills the needs of the farmers and the population by efficiently utilizing limited resources. Smart agriculture involves the incorporation of information technology into the traditional methods of farming. When dealing with smart farming or agriculture, factors like population movement, weather conditions, and demographics play a significant role. There are other parameters that are important in the field of agriculture. These may include surveillance in agriculture, supply control, environment sensing, length analysis of crop growth, disease identification, soil parameters, etc. Intruder detection and surveillance are very important and have attracted a great deal of attention. Nowadays, nearly every shop, home, or office needs a surveillance system for intrusion detection. Signature matching algorithms are the most effective method in intrusion detection systems for detecting malicious activities. Multilayer perceptron, Bayesian and J48 algorithms are common experimental techniques in anomaly detection. Image and video recognition plays a vital role in adapting IVSS (intelligent video surveillance system). The authors observed that sensors would greatly help in improving existing systems.  
Healthcare has become a high priority after the pandemic became rampant globally in 2020. AI and ML are widely used in smartizing healthcare systems. As mass spreading disease is not rare nowadays but instead has become normal, fast and reliable information helps to stop the infection to general public. Recently, CNN has been successfully adapted for healthcare and is used to classify X-ray images to diagnose heart diseases and ocular images, thereby helping in the diagnosis of cataract disease. Intelligent sensing is widely used to monitor the various parameters of the patients remotely, like pulse analysis, routine checkups, etc. Real-time PCR for diagnosing test results is also a CRISPR-based strategy and is effective for dealing with COVID-19. Hybrid approaches such as IS with ML, edge  
Figure 9: Intelligent Sensing for Visually Impaired.  
computing, and cloud services are also gaining attention of several corporations in this area. AI has been applied to road traffic control. However. intelligent sensing has been so improved in this domain that most of the work is done by sensors. For example, a vehicle may have an intelligent system installed inside it to help avoid accidents and recognize traffic. Some vehicles have installed cameras to monitor the surrounding. For the infrastructure part, there are speed monitoring systems with video surveillance installed at traffic signal poles or toll stations on highways to avoid road accidents. AI and ML combined proved to be successful in assistive technology. ML algorithms have improved speech intelligibility for both speech and hearing impaired people. Intelligent sensing also affects communication networks positively to make them smarter and more reliable. The omnipresent sensor network will play a very important role as one of the features of intelligence sensing. The sensor nodes will be easily traced remotely. Intelligent sensing with ML algorithms has been widely used to improve the intelligence of sensors.