Blar i Institutt for datateknologi og informatikk på tidsskrift "Mathematics"

Viser treff 1-9 av 9

    • COVID-19 Genome Sequence Analysis for New Variant Prediction and Generation 

      Ullah, Amin; Malik, Khalid Mahmood; Saudagar, Abdul Khader Jilani; Khan, Muhammad Badruddin; Hasanat, Mozaherul Hoque Abul; AlTameem, Abdullah; AlKhathami, Mohammed; Sajjad, Muhammad (Journal article; Peer reviewed, 2022)

    • EADN: An Efficient Deep Learning Model for Anomaly Detection in Videos 

      Ul Amin, Sareer; Ullah, Mohib; Sajjad, Muhammad; Alaya Cheikh, Faouzi; Hijji, Mohammad; Hijji, Abdulrahman; Khan, Muhammad (SKKU) (Peer reviewed; Journal article, 2022)
      Surveillance systems regularly create massive video data in the modern technological era, making their analysis challenging for security specialists. Finding anomalous activities manually in these enormous video recordings ...

    • FADS: An Intelligent Fatigue and Age Detection System 

      Hijji, Mohammad; Yar, Hikmat; Ullah, Fath U Min; Alwakeel, Mohammed M.; Harrabi, Rafika; Aradah, Fahad; Alaya Cheikh, Faouzi; Muhammad, Khan; Sajjad, Muhammad (Peer reviewed; Journal article, 2023)
      Nowadays, the use of public transportation is reducing and people prefer to use private transport because of its low cost, comfortable ride, and personal preferences. However, personal transport causes numerous real-world ...

    • Intelligent Image Super-Resolution for Vehicle License Plate in Surveillance Applications 

      Hijji, Mohammad; Khan, Abbas; M. Alwakeel, Mohammad; Harrabi, Rafika; Aradah, Fahad; Alaya Cheikh, Faouzi; Sajjad, Muhammad; Khan, Muhammad (SKKU) (Peer reviewed; Journal article, 2023)
      Vehicle license plate images are often low resolution and blurry because of the large distance and relative motion between the vision sensor and vehicle, making license plate identification arduous. The extensive use of ...

    • Mapping Tools for Open Source Intelligence with Cyber Kill Chain for Adversarial Aware Security 

      Yamin, Muhammad Mudassar; Ullah, Mohib; Ullah, Habib; Katt, Basel; Hijji, Mohammad; Muhammad, Khan (Peer reviewed; Journal article, 2022)
      Open-source intelligence (OSINT) tools are used for gathering information using different publicly available sources. With the rapid advancement in information technology and excessive use of social media in our daily ...

    • MoMo: Mouse-Based Motion Planning for Optimized Grasping to Declutter Objects Using a Mobile Robotic Manipulator 

      Jagatheesaperumal, Senthil Kumar; Rajamohan, Varun Prakash; Saudagar, Abdul Khader Jilani; AlTameem, Abdullah; Sajjad, Muhammad; Muhammad, Khan (Peer reviewed; Journal article, 2023)
      The aim of this study is to develop a cost-effective and efficient mobile robotic manipulator designed for decluttering objects in both domestic and industrial settings. To accomplish this objective, we implemented a deep ...

    • Optimized Deep-Learning-Based Method for Cattle Udder Traits Classification 

      Afridi, Hina; Ullah, Mohib; Nordbø, Øyvind; Alaya Cheikh, Faouzi; Larsgard, Anne Guro (Peer reviewed; Journal article, 2022)
      We propose optimized deep learning (DL) models for automatic analysis of udder conformation traits of cattle. One of the traits is represented by supernumerary teats that is in excess of the normal number of teats. ...

    • Second-Order Spatial-Temporal Correlation Filters for Visual Tracking 

      Yu, Yufeng; Chen, Long; He, Haoyang; Liu, Jianhui; Zhang, Weipeng; Xu, Guoxia (Peer reviewed; Journal article, 2022)
      Discriminative correlation filters (DCFs) have been widely used in visual object tracking, but often suffer from two problems: the boundary effect and temporal filtering degradation. To deal with these issues, many DCF-based ...

    • Variational Inference over Nonstationary Data Streams for Exponential Family Models 

      Masegosa, Andres; Ramos-López, Dario; Salmeron, Antonio; Langseth, Helge; Nielsen, Thomas D. (Peer reviewed; Journal article, 2020)
      In many modern data analysis problems, the available data is not static but, instead, comes in a streaming fashion. Performing Bayesian inference on a data stream is challenging for several reasons. First, it requires ...