Browsing by Subject "Deep Learning"

Now showing items 1-20 of 28

    • A Bilingual Scene-To-Speech Mobile Based Application 

      Karkar A.; Puthren M.; Al-Maadeed S. ( Institute of Electrical and Electronics Engineers Inc. , 2018 , Conference Paper)
      Scene-To-Speech (STS) is the process of recognizing visual objects in a picture or a video to say aloud a descriptive text that represents the scene. The recent advancement in convolution neural network (CNN), a deep ...

    • A survey on recent approaches in intrusion detection system in IoTs 

      Tabassum, Aliya; Erbad, Aiman; Guizani, Mohsen ( Institute of Electrical and Electronics Engineers Inc. , 2019 , Conference Paper)
      Internet of Things (IoTs) are Internet-connected devices that integrate physical objects and internet in diverse areas of life like industries, home automation, hospitals and environment monitoring. Although IoTs ease daily ...

    • ANFIS-Net for automatic detection of COVID-19 

      Al-ali A.; Elharrouss O.; Qidwai U.; Al-Maadeed, Somaya ( Nature Research , 2021 , Article)
      Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme ...

    • Applied Internet of Things IoT: Car monitoring system for Modeling of Road Safety and Traffic System in the State of Qatar 

      Jabbar, Rateb; Al-Khalifa, Khalifa; Kharbeche, Mohamed; Alhajyaseen, Wael; Jafari, Mohsen; ... more authors ( Hamad bin Khalifa University Press (HBKU Press) , 2018 , Conference Paper)
      One of the most interesting new approaches in the transportation research field is the Naturalistic Driver Behavior which is intended to provide insight into driver behavior during everyday trips by recording details about ...

    • AROMA: A recursive deep learning model for opinion mining in Arabic as a low resource language 

      Al-Sallab, Ahmad; Baly, Ramy; Hajj, Hazem; Shaban, Khaled Bashir; El-Hajj, Wassim; ... more authors ( Association for Computing Machinery , 2017 , Article Review)
      While research on English opinion mining has already achieved significant progress and success, work on Arabic opinion mining is still lagging. This is mainly due to the relative recency of research efforts in developing ...

    • BEMD-3DCNN-based method for COVID-19 detection 

      Riahi, A.; Elharrouss, O.; Al-Maadeed, Somaya ( Elsevier Ltd , 2022 , Article)
      The coronavirus outbreak continues to spread around the world and no one knows when it will stop. Therefore, from the first day of the identification of the virus in Wuhan, China, scientists have launched numerous research ...

    • Convolutional Sparse Support Estimator-Based COVID-19 Recognition from X-Ray Images 

      Yamac M.; Ahishali M.; Degerli A.; Kiranyaz, Mustafa Serkan; Chowdhury M.E.H.; ... more authors ( Institute of Electrical and Electronics Engineers Inc. , 2021 , Article)
      Coronavirus disease (COVID-19) has been the main agenda of the whole world ever since it came into sight. X-ray imaging is a common and easily accessible tool that has great potential for COVID-19 diagnosis and prognosis. ...

    • Deep learning and cultural heritage: The CEPROQHA project case study 

      Belhi, Abdelhak; Gasmi, Houssem; Al-Ali, Abdulaziz Khalid; Bouras, Abdelaziz; Foufou, Sebti; ... more authors ( Institute of Electrical and Electronics Engineers Inc. , 2019 , Conference Paper)
      Cultural heritage takes an important part of the history of humankind as it is one of the most powerful tools for the transfer and preservation of moral identity. As a result, these cultural assets are considered highly ...

    • Deep learning in classifying sleep stages 

      Al-Meer M.H.; Al Mamun M.D.A. ( Institute of Electrical and Electronics Engineers Inc. , 2018 , Conference Paper)
      This paper presents a deep feed-forward neural network classifier to automatically classify the stages of sleep using raw data taken from a single electropalatogram channel (Fpz-Cz). No features are extracted at all from ...

    • Deep Learning IoT Malware Detection Model for IoMT Edge Devices 

      KHARROUB, SULEIMAN KAYED (2021 , Master Thesis)
      Internet of Things (IoT) is defined as the massive collection of physical devices being connected to the Internet. IoT has a positive impact in multiple fields, such as health, agriculture, and power management sectors by ...

    • Detection of central serous retinopathy using deep learning through retinal images 

      Hassan, Syed Ale; Akbar, Shahzad; Khan, Habib Ullah ( Springer Nature , 2023 , Article)
      The human eye is responsible for the visual reorganization of objects in the environment. The eye is divided into different layers and front/back areas; however, the most important part is the retina, responsible for ...

    • Fetal ECG extraction from maternal ECG using deeply supervised LinkNet++ model 

      Arafat, Rahman; Mahmud, Sakib; Chowdhury, Muhammad E.H.; Yalcin, Huseyin Cagatay; Khandakar, Amith; ... more authors ( Elsevier , 2023 , Article)
      Fetal heart monitoring and early disease detection using non-invasive fetal electrocardiograms (fECG) can help substantially to reduce infant death through improved diagnosis of Coronary Heart Disease (CHD) in the fetus. ...

    • Fuzzy Identification-Based Encryption for healthcare user face authentication 

      Aggarwal, Mahima; Zubair, Mohammed; Unal, Devrim; Al-Ali, Abdulla; Reimann, Thomas; ... more authors ( Hamad bin Khalifa University Press (HBKU Press) , 2022 , Article)
      Background: Internet of Medical Things (IOMT) has the potential to monitor health continuously and in real-time. One of the main issues that arise in IOMT is how securely the data can be transmitted to the clinical team. ...

    • GearFaultNet: Novel Network for Automatic and Early Detection of Gearbox Faults 

      Dutta, Proma; Podder, Kanchon Kanti; Sumon, Md. Shaheenur Islam; Chowdhury, Muhammad E. H.; Khandakar, Amith; ... more authors ( IEEE , 2024 , Article)
      Electrical and mechanical equipment with rotating parts often face the challenge of early breakdown due to defects in the gears or rolling bearings. Automated industrial systems can be significantly impeded by this type ...

    • HYPER-VINES: A HYbrid Learning Fault and Performance Issues ERadicator for Virtual NEtwork Services over Multi-Cloud Systems 

      Gupta L.; Salman T.; Das R.; Erbad A.; Jain R.; ... more authors ( Institute of Electrical and Electronics Engineers Inc. , 2019 , Conference Paper)
      Fault and performance management systems, in the traditional carrier networks, are based on rule-based diagnostics that correlate alarms and other markers to detect and localize faults and performance issues. As carriers ...

    • Intelligent monitoring system for crowd monitoring and social distancing with mask control 

      Al Madeed, Somaya; elharrouss, Omar; Ottakath, Najmath ( Qatar University Press , 2020 , Poster)
      Due to the current COVID situation , there's a huge need for crowd control as well as efficient social distancing. Security cameras everywhere but personnel to monitor it, a few. In this project we use crowd counting and ...

    • Jamming Detection in IoT Wireless Networks: An Edge-AI Based Approach 

      Hussain, Ahmed; Abughanam, Nada; Qadir, Junaid; Mohamed, Amr ( ACM Digital Library , 2022 , Conference Paper)
      Wireless enabling technologies in critical infrastructures are increasing the efficiency of communications. In the era of 5G and beyond, more technologies will be allowed to connect to mobile networks, enabling the Internet ...

    • Low Rate DoS Attack Detection in IoT - SDN using Deep Learning 

      Ilango, Harun Surej; Ma, Maode; Su, Rong ( Institute of Electrical and Electronics Engineers Inc. , 2021 , Conference Paper)
      The lack of standardization and the heterogeneous nature of IoT, exacerbated the issue of security and privacy. In recent literature, to improve security at the network level, the possibility of using SDN for IoT networks ...

    • New Deep Learning-Based Approach for Wind Turbine Output Power Modeling and Forecasting 

      Daneshvar Dehnavi, Saeed; Shirani, Ardeshir; Mehrjerdi, H; Baziar, Mohammad; Chen, Liang ( Institute of Electrical and Electronics Engineers Inc. , 2020 , Article)
      An intelligent machine learning-based method is developed in this paper for modeling and prediction of the wind turbine (WT) output power. The developed technique makes use of the advanced machine learning models for ...

    • A novel deep learning technique for morphology preserved fetal ECG extraction from mother ECG using 1D-CycleGAN 

      Promit, Basak; Nazmus Sakib, A.H.M; Chowdhury, Muhammad E.H.; Al-Emadi, Nasser; Cagatay Yalcin, Huseyin; ... more authors ( Elsevier , 2024 , Article)
      The non-invasive fetal electrocardiogram (fECG) enables easy detection of developing heart abnormalities, leading to a significant reduction in infant mortality rate and post-natal complications. Due to the overlapping of ...