# Recent Trends in AI-Based Intelligent Sensing
## 2 AI Methods for Intelligent Sensing
### Deep Learning Algorithms/Models in Intelligent Sensing
\begin{table}
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**Reference Number** & **Machine Learning Algorithm/Model** & **Dataset Used** & **Description** & **Parameters Influencing the Performance** & **Advantages** & **Limitations** \\ \hline \multirow{4}{*}{[74]} & \multirow{4}{*}{[75]} & epsilon-SVR, & Linear regression (LR), Convolutional & & A Framework is proposed for stress recognition in real-time using peripheral physiological signals. & 1. Less Prediction error; 2. Converient for real-time social signals. & 1. Less Prediction error; 2. Converient for real-time social signals. & The model is limited to the slight movement of physiological signals. \\ \hline \multirow{4}{*}{[76]} & Linear regression (LR) and neural network (NN). & Chronic Kidney Disease (CKD) from patients. & A hybrid intelligent model is proposed to guess chronic kidney disease from a patient’s data on the cloud environments to improve services in healthcare in smart cities. & & The proposed model significantly improves accuracy compared to other models. & & The hybrid model is limited to a small amount of data of a patient’s record. \\ \hline \multirow{4}{*}{[77]} & R.A.LE & lung sound & PEAP Dataset [75]. & Performance of K-NN and SVM classifiers are compared using the pulmonary acoustic signal from RALE clients (MFCC). & Mel-frequency cepstral coefficients (MFCC). & Analysis of feature vectors is via ANOVA and separately fed into SVM and K-NN classifiers. & 1.The amount of data used to train and test the classifier is very small. 2. Collection of data was carried out in a controlled environment. \\ \hline \multirow{4}{*}{[77]} & Ranking SVM. & NUS-WIDE dataset [79]. & The interaction between social images and online users is analyzed. & Color, texture, and GIST features. & Powerful learning method and heterogenous acoustic sensor data improve performance. & External factors such as images based on cultural and geographic locations are not considered for prediction. \\ \hline \multirow{4}{*}{[80]} & K-NN, AdaBoost, SVM, RF Logistic regression (LR). & Non-contact sensor data. & The non-contact sensor data. & Age and BMI the signs of HR, RR/ HRV parameters from a patient’s models during a period of 23 weeks of HD sessions. & Age and BMI based predictive models of patients. & Using machine learning, based predictive models, high accuracy is obtained. & The main limitation is the prediction of clinical events in advance and the other parameters like BP and the patient’s medical history using a multi-class prediction model. \\ \hline \multirow{4}{*}{[81]} & Support Vector Machine (SVM). & CRCNS-ORIG and DIEM. & A model is proposed to detect mental weakness of older and younger people by allocation, and data while watching a video. & Pupil diamonds, eye blinking, gaze allocation, and cascade mean & Improves detection accuracy using an automated feature selection method. & 1. Limited no. of participants. 2. Eye-tracking data are collected in a controlled environment. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of Machine Learning Algorithms/Models in Intelligent Sensing. for estimating latitude and longitude, which results in overfitting. An RNN-based learning model is proposed in [88] to monitor underwater sensor networks in real time, which improves the delay and reduces the cost of packet transmission. * Generative Adversarial Network--GAN comprises two models; one is the generator, and the other is the discriminator. The two are trained in tandem via an adversarial process. These networks have been implemented for the security of IoT systems [89]. A conditional GAN-based DL method is presented for the reconstruction of CS-MRI that is compressed sensing magnetic resource imaging using compressed MR data [90]. In [91], the authors proposed a GAN-based method to generate X-ray prohibited images with different item poses. According to the paper, the quality of the images is good as compared to DC-GAN and WGAN-GP.