Management of Wireless Communication Systems Using Artificial Intelligence-Based Software Defined Radio

Bargarai, Faiq A. Mohammed; Abdulazeez, Adnan Mohsin; Tiryaki, Volkan Mujdat; Zeebaree, Diyar Qader

doi:10.3991/ijim.v14i13.14211

Cited by 33 publications

(25 citation statements)

References 29 publications

(40 reference statements)

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“…• After many overlaps and pooling layers, a complete layer of connection is in the previous strata. Both neurons are linked to each neuron in the current strata, and spatial information is not retained in the complete connection layer [51], [56]. The fully connected layer will incorporate local knowledge in conjunction layer or pooling layer, with category discrimination, in synthesizing the features derived from the front.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Abdullah

Abdulazeez

2021

JSCDM

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Facial emotional processing is one of the most important activities in effective calculations, engagement with people and computers, machine vision, video game testing, and consumer research. Facial expressions are a form of nonverbal communication, as they reveal a person's inner feelings and emotions. Extensive attention to Facial Expression Recognition (FER) has recently been received as facial expressions are considered. As the fastest communication medium of any kind of information. Facial expression recognition gives a better understanding of a person's thoughts or views and analyzes them with the currently trending deep learning methods. Accuracy rate sharply compared to traditional state-of-the-art systems. This article provides a brief overview of the different FER fields of application and publicly accessible databases used in FER and studies the latest and current reviews in FER using Convolution Neural Network (CNN) algorithms. Finally, it is observed that everyone reached good results, especially in terms of accuracy, with different rates, and using different data sets, which impacts the results.

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Abdullah

Abdulazeez

2021

JSCDM

Self Cite

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“…The critical point behind the classification is an evaluation metric used to understand the performance and efficiency of an algorithm [44]. Building an intelligent detection system capable of detecting various types of network intrusions, one must evaluate the performance of the model via using different evaluation metrics, then compare the results to find the best fit model [45], [46]. During the classification process, the Cross-validation method as a statistical approach is used to enhance the accuracy.…”

Section: Evaluation Performance Appropriate Metricsmentioning

confidence: 99%

Evaluation of Classification Algorithms for Intrusion Detection System: A Review

Salih

Abdulazeez

2021

JSCDM

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Intrusion detection is one of the most critical network security problems in the technology world. Machine learning techniques are being implemented to improve the Intrusion Detection System (IDS). In order to enhance the performance of IDS, different classification algorithms are applied to detect various types of attacks. Choosing a suitable classification algorithm for building IDS is not an easy task. The best method is to test the performance of the different classification algorithms. This paper aims to present the result of evaluating different classification algorithms to build an IDS model in terms of confusion matrix, accuracy, recall, precision, f-score, specificity and sensitivity. Nevertheless, most researchers have focused on the confusion matrix and accuracy metric as measurements of classification performance. It also provides a detailed comparison with the dataset, data preprocessing, number of features selected, feature selection technique, classification algorithms, and evaluation performance of algorithms described in the intrusion detection system.

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“…While [69] utilized Python to build a model using the 'Raspberry Pi 3 model b+'; or even some techniques related to stability like gain scheduling technique [70]. It's worth to mention that this technique (Machine Learning technique) can be used for different applications that may be merged with some robots to perform important tasks [71][72][73][74][75].…”

Section: Figure 4 Planning Procedures 4 Related Work For Controlling Mmentioning

confidence: 99%

Vision-Based Mobile Robot Controllers: A Scientific Review

Abdulazeez¹

2021

TURCOMAT

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Today, there are different types of self-controlled robots. Some of them had critical effects on our lives like industrial and medical robots. Others are for military usages such as drones and the pets robots just for entertainment. The crucial differences between this kind of robot and the controlled ones are their ability to move on their own and make decisions based on their observations of the world around them. Mobile robots must have a data source used as an input dataset and processed to change their behavior; for instance, moving, stopping, rotating, or doing any required action based on the information gathered from the surrounding environment. Different types of sensors were used to feed robots controllers with data. Such data source could be ultrasonic sensor, laser sensor, torque sensor, or vision sensor. Robots integrated with cameras were becoming an essential field of study. They recently attracted significant attention from researchers it has been commonly used in many sectors of healthcare, manufacturing, and many other services. The robot needs a controller with a powerful mechanism of realization to deal with such incoming data. The world of mobile robot controllers is discussed in this paper, and the latest trends were reviewed. This review aims to provide a general understanding of robot controllers and navigation methods developed over the last few years.

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Management of Wireless Communication Systems Using Artificial Intelligence-Based Software Defined Radio

Cited by 33 publications

References 29 publications

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Evaluation of Classification Algorithms for Intrusion Detection System: A Review

Vision-Based Mobile Robot Controllers: A Scientific Review

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