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Cloud IoT and "IoT" are two very different technologies that have involved both parts of our lives. It is foreseen that in the close future, their use will become more prevalent and become one of the most important tools of the Internet. "IoT is an Internet-based model that combines numerous technologies and aims to create useful information that requires a complementary technology to maintain data, and process and analyze the data generated to compensate for its shortcomings. The prevalent use of the Internet has led to the connection of objects to the Internet. Derived from this, objects can have their own digital identity and be in an ecosystem that can interrelate with all people and permit computers to organize and manage them. The advancement of this technology requires using infrastructures including Cloud IoT, because "IoT" for storage and processing of data requires the use of storage systems and processing technology servers. The combination of the two has created a new term called "IoT cloud" and is expected in the future of these two technologies that as an important part of the Internet has been being extensively used. Integration of "IoT" and Cloud IoT is not an easy task and establishes new key topics. The purpose of this article is to fully understand both technologies and their challenges and then to recognize the challenges and problems in the integration of these two technologies and open issues in this technology. [ABSTRACT FROM AUTHOR]

Face recognition is essential in security systems, credit card control and criminal identification. Since years ago, the recognition of people's faces from a designated database has been considered by any automatic security system without the need for a user. Over time, the applications of these systems are being developed with increasing efforts to improve practical security systems. Meanwhile, different techniques for face recognition have been developed and many classification algorithms have bee n proposed for it. These techniques have different goals according to the type of facial recognition application. in this research, we have tried to provide a new method for face recognition. In the proposed method, in the first stage an optimal subset of facial features is extracted using Scale Invariant Features Transform (SIFT), Speed-Up Robust Features (SURF) and Histogram Oriented Gradient (HOG) Then they are combined with each other. Also the PCA algorithm is used as a feature reduction feature, in the second stage We using SVM and MLP Neural Networks algorithms for Classification. In this research, we used two databases ORL and Yale. The highest Test results in Face Recognition for database ORL was obtained by SVM algorithm and combined SIFT+SURF features with a precision detection of 99.5% and execution time 5.8 Sec. and the best results for database Yale were obtained by MLPNN algorithm and combined SIFT+SURF+HOG features with a precision detection of 98.3% nd execution time 4.9 Sec. The results show the success of the proposed method in face recognition so that the face is correctly recognized even in low light images or with the rotation of the neck. [ABSTRACT FROM AUTHOR]

The desire to establish human-computer speech communication, as well as the expansion of the use of the Internet and the possibility of receiving various information services and remote services, has caused the increasing importance of various speech processing techniques. In fact, people's speech plays an important role in analyzing their personality. You can tell the gender of people from their voices. Currently, the automatic detection of human gender by machines has received special attention in the field of artificial intelligence. Speech recognition is a subjective phenomenon and, in this field, many researches have been conducted in recent years and different methods have been presented. Features of Capstral coefficients based on MFCC Mel and Capstral coefficients based on LPCC and LPC linear prediction are three successful features in the field of speech classification, especially in detecting the speaker's gender. In this article, a new method has been presented by using the features of MFCC, LPCC, and LPC and combining them with KNN and MLPNN classification, which increase the classification accuracy. The useful coefficients in these three features are combined and used to recognize the gender of the speaker. Before using the categories and to reduce the dimensions of the feature vector, we used the principal components of PCA. A TIMIT dataset has been used for this purpose. The presented method provides a high accuracy of 97% in detecting the male gender and an accuracy of nearly 98% in detecting the female gender. [ABSTRACT FROM AUTHOR]

The media is one of the most significant and effective factors in education in any community. If the digital media of a community is given to the committed forces, they can provide the ground for the growth and prosperity of that community and manage it to prosperity and perfection, although if they are given to non-committed people, it will become a factor in the annihilation of a community's culture and beliefs. In today's world, the media has caused intellectual and behavioral changes by affecting culture and community. Information and communication systems are also becoming progressively more complex and pervasive. These complexities have caused the messages produced by digital media to leave their audiences around the world with a kind of confusion and hesitation in choosing messages. The present study tends to review the effect of digital media on family education and its purpose is to determine the interrelationship of digital media and family education. The study indicated that the media have a major impact on their audiences and families exposed to more negative impacts. The family can play the most significant role in this regard to solve it and reduce the negative impacts of the media. [ABSTRACT FROM AUTHOR]

In recent years, there has been a need to design highly efficient cooling devices that are used in the manufacture of nanomaterials and some medical treatments. The objective of this paper is to design the membership shape of a variable speed cycle variable speed fuzzy logic controller for a cooling engine. The fuzzy logic controller sets the sampling time for the SVPWM in the inverter system in the variable speed drive cooling cycle. The research focuses on the systematic design of membership ranges for key design factors such as control error and control error rate. The upper and lower limits of the membership ranges are prepared from the static characteristics data obtained by modelling MATLAB. Membership range was tested on control error and error rate control experiments. The effect of sampling time on control performance has been investigated and has been shown to have a significant effect on the refrigeration cycle control performance of a variable speed drive, the results of proposed device demonstrate settling time less than (15 sec). [ABSTRACT FROM AUTHOR]