Analog Signal and Digital Signal Processing in Telecommunication System

The term digital signal is a term from a technology that converts an analog signal into digital data so that the signal can be processed more easily and quickly. The term digital itself is a system that only recognizes two conditions. The two conditions are usually represented by the numbers zero and one, on and off, or others. The smallest unit of digital signal is the bit

A Review: Internet of Things with Machine Learning to Develop Intelligent Systems

A fresh era of internet-connected sensing gadgets that bridge the gap between the real and virtual worlds has begun, thanks to the rapid advancements in hardware, software, and communication technologies. About twenty-five and fifty billion internet-enabled gadgets are predicted to be in use worldwide. The term "Internet of Things" is a term used to signify a system of electrical devices that communicate with one another. There is a wide variety of infrastructure, retail, transit, and individual healthcare services and applications made possible by the Internet of Things. IoT is a driving force behind the evolution of the Internet and other forms of modern communication technology. Smart computation and evaluation of massive data are crucial to the growth of Internet of Things applications. IoT applications may benefit from data science tools by discovering new patterns and insights in data. Industry applications of data science with the Internet of Things focus on volume, velocity, and pattern identification. With the help of machine learning's predictive analysis, programs can now anticipate both welcome and unwanted occurrences. Thus, machine learning systems not only identify out-of-the-ordinary conduct but also aid in deducing and predicting broader societal tendencies. Continuing modification and monitoring is necessary for efficacy and effectiveness in data analysis. There are two sections to this article: the first discusses the many uses of the Internet of Things where machine learning plays a role in creating an intelligent system, while the second looks ahead to the potential of IoT and machine learning in the advancement of communication devices. Questions such as "What is the classification of artificial intelligence that can be implemented in IoT?" and "How could machine intelligence be implemented in IoT applications?" will be answered in this article

Automatic modulation classification based deep learning with mixed feature

The automatic modulation classification (AMC) plays an important and necessary role in the truncated wireless signal, which is used in modern communications. The proposed convolution neural network (CNN) for AMC is based on a method of feature expansion by integrating I/Q (time form) with r/Ɵ (polar form) in order to take advantage of two things: first, feature expansion helps to increase features; the second is that converting to polar form helps to increase classification accuracy for higher order modulation due to diversity in polar form. CNN consists of six blocks. Each block contains symmetric and asymmetric filters, as well as max and average pooling filters. This paper uses DeepSig: RadioML which is a dataset of 24 modulation classes. The proposed network has outperformed many recent papers in terms of classification accuracy for 24 modulation types, with a classification accuracy of up to 96.06 at an SNR=20 dB

A Proposed Model for Predicting Employee Turnover of Information Technology Specialists Using Data Mining Techniques

This article proposes a data mining framework to predict the significant explanations of employee turn-over problems. Using Support vector machine, decision tree, deep learning, random forest, and other classification algorithms, the authors propose features prediction framework to determine the influencing factors of employee turn-over problem. The proposed framework categorizes a set of historical behavior such as years at company, over time, performance rating, years since last promotion, and total working years. The proposed framework also classifies demographics features such as Age, Monthly Income, and Distance from Home, Marital Status, Education, and Gender. It also uses attitudinal employee characteristics to determine the reasons for employee turnover in the information technology sector. It has been found that the monthly rate, overtime, and employee age are the most significant factors which cause employee turnover