Engineering Commons^™

Face Mask Detection Based On Deep Learning: A Review, Shahad Fadhil Abbas, Shaimaa Hameed Shaker, Firas. A. Abdullatif

Journal of Soft Computing and Computer Applications

The coronavirus disease 2019 outbreak caused widespread disruption. The World Health Organization has recommended wearing face masks, along with other public health measures, such as social distancing, following medical guidelines, and thermal scanning, to reduce transmission, reduce the burden on healthcare systems, and protect population groups. However, wearing a mask, which acts as a barrier or shield to reduce transmission of infection from infected individuals, hides most facial features, such as the nose, mouth, and chin, on which face detection systems depend, which leads to the weakness of these systems. This paper aims to provide essential insights for researchers and …

Strangeness Detection From Crowded Video Scenes By Hand-Crafted And Deep Learning Features, Ali A. Hussan, Shaimaa H. Shaker, Akbas Ezaldeen Ali

Journal of Soft Computing and Computer Applications

Video anomaly detection is one of the trickiest issues in intelligent video surveillance because of the complexity of real data and the hazy definition of anomalies. Since abnormal occurrences typically seem different from normal events and move differently. The global optical flow was determined with the maximum accuracy and speed using the Farneback approach for calculating the magnitudes. Two approaches have been used in this study to detect strangeness in the video. These approaches are Deep Learning (DL) and manuality. The first method uses the activity map's development of entropy to detect the oddity in the video using a particular …

A Comprehensive Analysis Of Deep Learning And Swarm Intelligence Techniques To Enhance Vehicular Ad-Hoc Network Performance, Hussein K. Abdul Atheem, Israa T. Ali, Faiz A. Al Alawy

Journal of Soft Computing and Computer Applications

The primary elements of Intelligent Transportation Systems (ITSs) have become Vehicular Ad-hoc NETworks (VANETs), allowing communication between the infrastructure environment and vehicles. The large amount of data gathered by connected vehicles has simplified how Deep Learning (DL) techniques are applied in VANETs. DL is a subfield of artificial intelligence that provides improved learning algorithms able to analyzing and process complex and heterogeneous data. This study explains the power of DL in VANETs, considering applications like decision-making, vehicle localization, anomaly detection, traffic prediction and intelligent routing, various types of DL, including Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs) are …

Next-Generation Crop Monitoring Technologies: Case Studies About Edge Image Processing For Crop Monitoring And Soil Water Property Modeling Via Above-Ground Sensors, Nipuna Chamara

Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023–

Artificial Intelligence (AI) has advanced rapidly in the past two decades. Internet of Things (IoT) technology has advanced rapidly during the last decade. Merging these two technologies has immense potential in several industries, including agriculture.

We have identified several research gaps in utilizing IoT technology in agriculture. One problem was the digital divide between rural, unconnected, or limited connected areas and urban areas for utilizing images for decision-making, which has advanced with the growth of AI. Another area for improvement was the farmers' demotivation to use in-situ soil moisture sensors for irrigation decision-making due to inherited installation difficulties. As Nebraska …

Anti-Phishing Approach For Iot System In Fog Networks Based On Machine Learning Algorithms, Mahmoud Gad Awwad, Mohamed M. Ashour, El Said A. Marzouk, Eman Abdelhalim

Mansoura Engineering Journal

As the Internet of Things (IoT) continues to expand, ensuring the security and privacyِ of IoT systems becomes increasingly critical. Phishing attacks pose a significant threat to IoT devices and can lead to unauthorized access, data breaches, and compromised functionality. In this paper, we propose an anti-phishing approach for IoT systems in fog networks that leverages machine learning algorithms, including a .fusion with deep learning techniques We explore the effectiveness of eleven traditional machine learning algorithms combined with deep learning in detecting and preventing phishing attacks in IoT systems. By utilizing a diverse range of algorithms, we aim to enhance …

Deep Learning Based Classification Of Focal Liver Lesions With 3 And 4 Phase Contrast-Enhanced Ct Protocols, Ahmed El-Emam, Hossam El-Din Moustafa, Mohamed Moawad, Mohamed Aouf

Mansoura Engineering Journal

It had been noticed that 3-phase and 4-phase computed tomography protocols with contrast serve as standard examinations for diagnosing liver tumors. Additionally, many patients require periodic follow-up, which entails significant radiation exposure for them. Advancements in image processing facilitate automated liver lesion segmentation. However, the challenge remains in classifying these small lesions by doctors, especially when the liver has different types of lesions with very little intensity difference. Therefore, deep learning can be utilized for the classification of liver lesions. The present work introduces a CNN-based module for the classification of liver lesions. The module consists of four stages: data …