New research into human-computer interaction seeks to consider the consumer's emotional status to provide a seamless human-computer interface. This would make it possible for people to survive and be used in widespread fields, including education and medicine. Multiple techniques can be defined through human feelings, including expressions, facial images, physiological signs, and neuroimaging strategies. This paper presents a review of emotional recognition of multimodal signals using deep learning and comparing their applications based on current studies. Multimodal affective computing systems are studied alongside unimodal solutions as they offer higher accuracy of classification. Accuracy varies according to the number of emotions observed, features extracted, classification system and database consistency. Numerous theories on the methodology of emotional detection and recent emotional science address the following topics. This would encourage studies to understand better physiological signals of the current state of the science and its emotional awareness problems.

The Internet of Things (IoT) is one of today's most rapidly growing technologies. It is a technology that allows billions of smart devices or objects known as "Things" to collect different types of data about themselves and their surroundings using various sensors. They may then share it with the authorized parties for various purposes, including controlling and monitoring industrial services or increasing business services or functions. However, the Internet of Things currently faces more security threats than ever before. Machine Learning (ML) has observed a critical technological breakthrough, which has opened several new research avenues to solve current and future IoT challenges. However, Machine Learning is a powerful technology to identify threats and suspected activities in intelligent devices and networks. In this paper, various ML algorithms have been compared in terms of attack detection and anomaly detection, following a thorough literature review on Machine Learning methods and the significance of IoT security in the context of various types of potential attacks. Furthermore, possible ML-based IoT protection technologies have been introduced.

Water is a basic human need in all economic operations. Farmland, renewable energy, the industrial industry, and mining are all critical economic areas. Water supplies are under severe strain. With the population increase, the requirement for water from competing economic sectors is increased. So, there is not enough water left to meet human needs and maintain environmental flows that maintain the integrity of our ecosystems. Underground water is becoming depleted in many sectors, making now and future generations near the point of being deprived of protection from the increasing climate variability. Therefore, the critical role that information technology methods and internet communication technologies (ICT) play in water resources managing to limit the excessive waste of fresh water and to control and monitor water pollution. In this paper, we have to review research that uses the internet of things (IoT) as a communication technology that controls the preservation of the available amount of water and not wastes it by homeowners and farmers. In contrast, they use water, and we have also reviewed some researches that preserve water quality and reduce its pollution.

The Internet has caused the advent of a digital society; wherein almost everything is connected and available from any place. Thus, regardless of their extensive adoption, traditional IP networks are yet complicated and arduous to operate. Therefore, there is difficulty in configuring the network in line with the predefined procedures and responding to the load modifications and faults through network reconfiguring. The current networks are likewise vertically incorporated to make matters far more complicated: the control and data planes are bundled collectively. Software-Defined Networking (SDN) is an emerging concept which aims to change this situation by breaking vertical incorporation, promoting the logical centralization of the network control, separating the network control logic from the basic switches and routers, and enabling the network programming. The segregation of concerns identified between the policies concept of network, their implementation in hardware switching and data forwarding is essential to the flexibility required: SDN makes it less complicated and facilitates to make and introduce new concepts in networking through breaking the issue of the network control into tractable parts, simplifies the network management and facilitate the development of the network. In this paper, the SDN is reviewed; it introduces SDN, explaining its core concepts, how it varies from traditional networking, and its architecture principles. Furthermore, we presented the crucial advantages and challenges of SDN, focusing on scalability, security, flexibility, and performance. Finally, a brief conclusion of SDN is revised.

Automation frees workers from excessive human involvement to promote ease of use while still reducing their input of labor. There are about 2 billion people on Earth who live in cities, which means about half of the human population lives in an urban environment. This number is rising which places great problems for a greater number of people, increased traffic, increased noise, increased energy consumption, increased water use, and land pollution, and waste. Thus, the issue of security, coupled with sustainability, is expected to be addressed in cities that use their brain. One of the most often used methodologies for creating a smart city is the Internet of Things (IoT). IoT connectivity is understood to be the very heart of the city of what makes a smart city. such as sensor networks, wearables, mobile apps, and smart grids that have been developed to harness the city's most innovative connectivity technology to provide services and better control its citizens The focus of this research is to clarify and showcase ways in which IoT technology can be used in infrastructure projects for enhancing both productivity and responsiveness.