Face Emotion Recognition Based on Machine Learning: A Review

Computers can now detect, understand, and evaluate emotions thanks to recent developments in machine learning and information fusion. Researchers across various sectors are increasingly intrigued by emotion identification, utilizing facial expressions, words, body language, and posture as means of discerning an individual's emotions. Nevertheless, the effectiveness of the first three methods may be limited, as individuals can consciously or unconsciously suppress their true feelings. This article explores various feature extraction techniques, encompassing the development of machine learning classifiers like k-nearest neighbour, naive Bayesian, support vector machine, and random forest, in accordance with the established standard for emotion recognition. The paper has three primary objectives: firstly, to offer a comprehensive overview of effective computing by outlining essential theoretical concepts; secondly, to describe in detail the state-of-the-art in emotion recognition at the moment; and thirdly, to highlight important findings and conclusions from the literature, with an emphasis on important obstacles and possible future paths, especially in the creation of state-of-the-art machine learning algorithms for the identification of emotions

State of Art Survey for IoT Effects on Smart City Technology: Challenges, Opportunities, and Solutions

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

A State of Art Survey for Intelligent Energy Monitoring Systems

In this study, the significance and necessities of surveillance systems have been investigated in several areas - both in the use of neural networks, street lighting systems, factories, and laboratories - for the monitoring systems, especially concerning the design of artificial intelligence programs. The importance of these initiatives and how they can affect any sector and industry reach an essential point from here. Here we reach an important point. An algorithm and an extraordinary approach have been used in every field to develop an intelligent programmer. Something has been mentioned here: the ability to access these intelligent programs in all areas of life. We concentrate on a variety of fields of use and design of monitoring systems in this review article

A State of Art Survey of Nano Technology: Implementation, Challenges, and Future Trends

Nanotechnology is a field of study that aims to make our lives easier, safer, and more environmentally friendly. With current upgrades and alterations to available networking and communication paradigms, incorporating Wireless Nano Sensor Networks (WNSN) with various products, sensors, and devices would introduce new network paradigms. The Internet of Nano Things is a term for this concept (IoNT), To achieve seamless interconnection between Nano networks and existing communication networks and the Internet, many topologies and communication paradigms must be developed while technological hurdles are addressed. The amount of data accessible limits how much research and decision-making can be done. This research visualizes a wide range of nanotechnology applications. The goal of this project was to show in more than way this research method may be used to information recommendation services. The routing protocol is critical in WNSN and IoNT because of the many nanoscale constraints. While ensuring the flow of data and information, this routing protocol must take into account the specific features of nanoscale communication.This research aims to provide insight into the WNSN (Wireless Nano Sensor Networks) and IoNT (The Internet of Nano Things) paradigms, as well as a detailed assessment of a large number of current routing protocols that are tailored to the characteristics and features of nano communication. Big data applications with their features and characteristics in general also use cloud computing. This paper explains different hands based on neural networks and implemented on FPGAs (which is Field-Programmable Gate Array) and other genetic algorithms and neural networks. Many more approaches and models compare

A Comprehensive Survey of 5G mm-Wave Technology Design Challenges

Physical layer protection, which protects data confidentiality using information-theoretic methods, has recently attracted a lot of research attention. Using the inherent randomness of the transmission channel to ensure protection in the physical layer is the core concept behind physical layer security. In 5G wireless communication, new challenges have arisen in terms of physical layer security. This paper introduces the most recent survey on various 5G technologies, including millimeter-Wave, massive multi-input multiple outputs, microcells, beamforming, full-duplex technology, etc. The mentioned technologies have been used to solve this technology, such as attenuation, millimeter-Wave penetration, antenna array architecture, security, coverage, scalability, etc. Besides, the author has used descriptions of the techniques/algorithms, goals, problems, and meaningful outcomes, and the results obtained related to this approach were demonstrated

A Comprehensive Study of Kernel (Issues and Concepts) in Different Operating Systems

Various operating systems (OS) with numerous functions and features have appeared over time. As a result, they know how each OS has been implemented guides users' decisions on configuring the OS on their machines. Consequently, a comparative study of different operating systems is needed to provide specifics on the same and variance in novel types of OS to address their flaws. This paper's center of attention is the visual operating system based on the OS features and their limitations and strengths by contrasting iOS, Android, Mac, Windows, and Linux operating systems. Linux, Android, and Windows 10 are more stable, more compatible, and more reliable operating systems. Linux, Android, and Windows are popular enough to become user-friendly, unlike other OSs, and make more application programs. The firewalls in Mac OS X and Windows 10 are built-in. The most popular platforms are Android and Windows, specifically the novelist versions. It is because they are low-cost, dependable, compatible, safe, and easy to use. Furthermore, modern developments in issues resulting from the advent of emerging technology and the growth of the cell phone introduced many features such as high-speed processors, massive memory, multitasking, high-resolution displays, functional telecommunication hardware, and so on

Deep Learning Approaches for Intrusion Detection

Recently, computer networks faced a big challenge, which is that various malicious attacks are growing daily. Intrusion detection is one of the leading research problems in network and computer security. This paper investigates and presents Deep Learning (DL) techniques for improving the Intrusion Detection System (IDS). Moreover, it provides a detailed comparison with evaluating performance, deep learning algorithms for detecting attacks, feature learning, and datasets used to identify the advantages of employing in enhancing network intrusion detection

Comparison of Software Defined Networking with Traditional Networking

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