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    Author 2
    ORCID
    0000-0003-1085-8759
    Fida Hussain Dahri
    School of Computer Science and Engineering, Southeast University, Nanjing
    Summary
    Research Assistant at Data and Intelligence Laboratory (D&Intel Lab), School of Computer Science and Engineering, Southeast University, China 🇨🇳.
    Edited Journals
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    Free Access | Research Article | 27 December 2024
    Advancing Robotic Automation with Custom Sequential Deep CNN-Based Indoor Scene Recognition
    by
    Fida Hussain Dahri
    ,
    Ghulam E Mustafa Abro
    ,
    Nisar Ahmed Dahri
    ,
    Asif Ali Laghari
    ,
    Zain Anwar Ali
    IECE Transactions on Intelligent Systematics | Volume 2, Issue 1: 14-26, 2024 | DOI:10.62762/TIS.2025.613103
    Abstract
    Indoor scene recognition poses considerable hurdles, especially in cluttered and visually analogous settings. Although several current recognition systems perform well in outside settings, there is a distinct necessity for enhanced precision in inside scene detection, particularly for robotics and automation applications. This research presents a revolutionary deep Convolutional Neural Network (CNN) model tailored with bespoke parameters to improve indoor picture comprehension. Our proprietary dataset consists of seven unique interior scene types, and our deep CNN model is trained to attain excellent accuracy in classification tasks. The model exhibited exceptional performance, achieving a t... More >
    Graphical Abstract
    Advancing Robotic Automation with Custom Sequential Deep CNN-Based Indoor Scene Recognition
    Free Access | Research Article | 20 November 2024
    Automated Early Diabetic Retinopathy Detection Using a Deep Hybrid Model
    by
    Asima Shazia
    ,
    Fida Hussain Dahri
    ,
    Asfand Ali
    ,
    Muhammad Adnan
    ,
    Asif Ali Laghari
    ,
    Tehniyat Nawaz
    IECE Transactions on Emerging Topics in Artificial Intelligence | Volume 1, Issue 1: 71-83, 2024 | DOI:10.62762/TETAI.2024.305743
    Abstract
    Recently, the primary reason for blindness in adults has been diabetic retinopathy (DR) disease. Therefore, there is an increasing demand for a real-time efficient classification and detection system for diabetic retinopathy (DR) to overcome fast-growing disease (DR). We introduced a novel deep hybrid model for auto-mated diabetic retinopathy (DR) disease recognition and classification. Our model leverages the power of CNN architectures: Inception V3 and VGG16 models by combining their strengths to cater to exact requirements. VGG16 model efficiently captures fine features and wide-ranging features such as textures and edges, crucial for classifying initial signs of DR. Similarly, Inception... More >
    Graphical Abstract
    Automated Early Diabetic Retinopathy Detection Using a Deep Hybrid Model