One of the new challenges of biometric systems based on face analysis is kinship verification. Little efforts have been done in spite of the importance and functionality of this subject. Most of existing methods have been trying to exploit and represent techniques based on metric learning to increase verification rate, paying no attention to the effect of the features extracted from the faces. Despite the previous methods exploiting simple local features, we have focused on the combination and selection of effective features in this paper. To this end, local and global features were combined to describe the face images in a better way. The effective and discriminative features were selected using the kinship genetic algorithm and then fulfilled kinship verification. The proposed method is tested and analysed on the standard and big datasets KinFaceW-I and KinFaceW-II, and verification rates of 81.3% and 86.15% were obtained respectively.

Internet of Things (IoT) refers to the practice of designing and modeling objects connected to the Internet through computer networks. In the past few years, IoT-based health care programs have provided multidimensional features and services in real time. These programs provide hospitalization for millions of people to receive regular health updates for a healthier life. Induction of IoT devices in the healthcare environment have revitalized multiple features of these applications. In this paper, a disease diagnosis system is designed based on the Internet of Things. In this system, first, the patient's courtesy signals are recorded by wearable sensors. These signals are then transmitted to a server in the network environment. This article also presents a new hybrid decision making approach for diagnosis. In this method, a feature set of patient signals is initially created. Then these features go unnoticed on the basis of a learning model. A diagnosis is then performed using a neural fuzzy model. In order to evaluate this system, a specific diagnosis of a specific disease, such as a diagnosis of a patient's normal and unnatural pulse, or the diagnosis of diabetic problems, will be simulated.