Voting-Based Deep Convolutional Neural Networks (VB-DCNNs) for M-QAM and M-PSK Signals Classification

Talha, Muhammad; Sarfraz, Mubashar; Rahman, Atta; Ghauri, Sajjad A.; Mohammad, Rami Mustafa A.; Krishnasamy, Gomathi; Alkharraa, Mariam

doi:10.3390/electronics12081913

Cited by 7 publications

(5 citation statements)

References 31 publications

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“…This section contains the graphical representation of various aspects of the data. Visual representation plays a pivotal role in the data mining and knowledge discovery process [38][39][40][41][42]. Data visualization techniques are used to demonstrate various aspects.…”

Section: Graphical Representation Of Datamentioning

confidence: 99%

Predicting Global Energy Consumption Through Data Mining Techniques

Rahman,

Abahussin,

Alghamdi

et al. 2024

IJDNE

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With the explosion of the global population and technological progress, the electricity demand has skyrocketed. To ensure a consistent flow of power, it's essential to accurately predict energy usage ahead of time. Failure to do so could lead to potential outages and disrupt our daily lives. This research reviews previous research in the field of using data mining techniques to analyze electricity consumption data, optimize energy performance of buildings, and predict energy consumption in various industries. The study also aims to uncover patterns, correlations, and rules in electricity consumption worldwide using data mining techniques. The analysis is performed using various data mining techniques, such as simple K-Means and Expectation Maximization (EM). This selection is based on their prominent applications for similar problems in literature. The simple K-Means and EM algorithms showed successful outcomes on the dataset, which is evident in the clustering plots. Further, the performance of the Hierarchical Clustering algorithm was not up to the desired standard. This is probably due to the nature of the available dataset. These outcomes of the analysis will provide a valuable resource for decision-makers and stakeholders in the energy sector, as it will provide a deeper understanding of electricity consumption patterns and trends. This could lead to a sustainable future.

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Section: Graphical Representation Of Datamentioning

confidence: 99%

Predicting Global Energy Consumption Through Data Mining Techniques

Rahman,

Abahussin,

Alghamdi

et al. 2024

IJDNE

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show abstract

“…Furthermore, the loss rate stands for the total errors or the variance between the predicted and the actual values. Using a variety of metrics will significantly help in defining the performance of the proposed study [51][52][53][54][55][56]. The Equations (1)-( 4) of the previously mentioned measures are seen below.…”

Section: Performance Measurementioning

confidence: 99%

Deep Learning Approach to Recyclable Products Classification: Towards Sustainable Waste Management

Ahmed,

Alotaibi,

Al-Qahtani

et al. 2023

Sustainability

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Effective waste management and recycling are essential for sustainable development and environmental conservation. It is a global issue around the globe and emerging in Saudi Arabia. The traditional approach to waste sorting relies on manual labor, which is both time-consuming, inefficient, and prone to errors. Nonetheless, the rapid advancement of computer vision techniques has paved the way for automating garbage classification, resulting in enhanced efficiency, feasibility, and management. In this regard, in this study, a comprehensive investigation of garbage classification using a state-of-the-art computer vision algorithm, such as Convolutional Neural Network (CNN), as well as pre-trained models such as DenseNet169, MobileNetV2, and ResNet50V2 has been presented. As an outcome of the study, the CNN model achieved an accuracy of 88.52%, while the pre-trained models DenseNet169, MobileNetV2, and ResNet50V2, achieved 94.40%, 97.60%, and 98.95% accuracies, respectively. That is considerable in contrast to the state-of-the-art studies in the literature. The proposed study is a potential contribution to automating garbage classification and to facilitating an effective waste management system as well as to a more sustainable and greener future. Consequently, it may alleviate the burden on manual labor, reduce human error, and encourage more effective recycling practices, ultimately promoting a greener and more sustainable future.

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“…To assess the performance of the proposed model on the given datasets, four measures are used: accuracy, F-score, recall, and precision. Further, intelligent methods are used in many health informatics [46][47][48][49][50], data visualization [51][52][53][54][55], and other related areas [56][57][58].…”

Section: Evaluation Metricsmentioning

confidence: 99%

A Deep-Learning Approach to Driver Drowsiness Detection

Ahmed,

Alabdulkarem,

Alomair

et al. 2023

Safety

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Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety. To address the issue of road safety, the proposed model offers a method for evaluating the level of driver fatigue based on changes in a driver’s eyeball movement using a convolutional neural network (CNN). Further, with the help of CNN and VGG16 models, facial sleepiness expressions were detected and classified into four categories (open, closed, yawning, and no yawning). Subsequently, a dataset of 2900 images of eye conditions associated with driver sleepiness was used to test the models, which include a different range of features such as gender, age, head position, and illumination. The results of the devolved models show a high degree of accountability, whereas the CNN model achieved an accuracy rate of 97%, a precision of 99%, and recall and F-score values of 99%. The VGG16 model reached an accuracy rate of 74%. This is a considerable contrast between the state-of-the-art methods in the literature for similar problems.

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Voting-Based Deep Convolutional Neural Networks (VB-DCNNs) for M-QAM and M-PSK Signals Classification

Cited by 7 publications

References 31 publications

Predicting Global Energy Consumption Through Data Mining Techniques

Predicting Global Energy Consumption Through Data Mining Techniques

Deep Learning Approach to Recyclable Products Classification: Towards Sustainable Waste Management

A Deep-Learning Approach to Driver Drowsiness Detection

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