IECE - Institute of Emerging and Computer Engineers

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    Academic Editor
    Author
    Contributions by role
    Author 2
    Editor 3
    ORCID
    0000-0001-8769-4989
    Teerath Kumar
    School of Computing, National College of Ireland, Ireland
    Summary
    Edited Journals
    IECE Transactions on Emerging Topics in Artificial Intelligence
    IECE Contributions
    Articles
    2
    Edited
    3
    Free Access | Research Article | 07 December 2024
    Predictive Analysis for Road Safety Enhancement in Chicago County
    by
    Reshma Shaik
    ,
    Kislay Raj
    ,
    Aditya Singh
    ,
    Teerath Kumar
    IECE Transactions on Computer Science | Volume 2, Issue 1: 1-9, 2024 | DOI:10.62762/TCS.2024.766854
    Abstract
    With the increasing incidents of fatal road injuries, there is an urgent need for developing effective road safety management systems. The study aims to develop predictive models based on machine learning to forecast the likelihood of road collisions depending on factors like weather, road condition, time, and driver behaviour in Chicago, USA. A machine learning approach has been applied to the crash dataset to evaluate the factors affecting the prevalence of road accidents. Python programming and the Jupyter Notebook platform have been used for performing descriptive statistics, correlation and three classification algorithms (Random Forest, KNN, Decision Tree and MLP Classification). Obtai... More >
    Graphical Abstract
    Predictive Analysis for Road Safety Enhancement in Chicago County
    Free Access | Research Article | 21 November 2024
    Enhancing Energy Performance in Irish Dwellings: A Machine Learning Approach to Retrofit Interventions
    by
    Samiksha Tripath
    ,
    Teerath Kumar
    IECE Transactions on Social Statistics and Computing | Volume 1, Issue 4: 89-101, 2024 | DOI:10.62762/TSSC.2024.898106
    Abstract
    This research investigates the impact of retrofit interventions on the energy performance of domestic buildings in Ireland using predictive machine learning (ML) models. The study applies machine learning models to classify Building Energy Rating (BER) for dwellings in County Dublin Ireland. Keeping the focus on selecting features in a highly correlated dataset, the study predicts energy ratings with an accuracy of 69 percent. Light Gradient Boosting Machine Classifier is observed for best performance among twenty plus ML models applied for prediction. The study also performs retrofit experiments on dwelling features and evaluate their effectiveness towards improving the energy performance o... More >
    Graphical Abstract
    Enhancing Energy Performance in Irish Dwellings: A Machine Learning Approach to Retrofit Interventions
    Free Access | Research Article | Feature Paper | 21 May 2024 | Cited: 4
    Improved Object Detection Algorithm Based on Multi-scale and Variability Convolutional Neural Networks
    by
    Jiaxun Yang
    ,
    Yilihamujiang Gapar
    IECE Transactions on Emerging Topics in Artificial Intelligence | Volume 1, Issue 1: 31-43, 2024 | DOI:10.62762/TETAI.2024.115892
    Abstract
    This paper proposes an improved object detection algorithm based on a dynamically deformable convolutional network (D-DCN), aiming to solve the multi-scale and variability challenges in object detection tasks. First, we review traditional methods in the field of object detection and introduce the current research status of improved methods based on multi-scale and variability convolutional neural networks. Then, we introduce in detail our proposed improved algorithms, including an improved feature pyramid network and a dynamically deformable network. In the improved feature pyramid network, we introduce a multi-scale feature fusion mechanism to better capture target information at different... More >
    Graphical Abstract
    Improved Object Detection Algorithm Based on Multi-scale and Variability Convolutional Neural Networks
    Free Access | Research Article | Feature Paper | 20 April 2024 | Cited: 2
    Real-Time Object Detection Using a Lightweight Two-Stage Detection Network with Efficient Data Representation
    by
    Shaohuang Wang
    IECE Transactions on Emerging Topics in Artificial Intelligence | Volume 1, Issue 1: 17-30, 2024 | DOI:10.62762/TETAI.2024.320179
    Abstract
    In this paper, we introduce a novel fast object detection framework, designed to meet the needs of real-time applications such as autonomous driving and robot navigation. Traditional processing methods often trade-off between accuracy and processing speed. To address this issue, we propose a hybrid data representation method that combines the computational efficiency of voxelization with the detail capture capability of direct data processing to optimize overall performance. Our detection framework comprises two main components: a Rapid Region Proposal Network (RPN) and a Refinement Detection Network (RefinerNet). The RPN is used to generate high-quality candidate regions, while the RefinerN... More >
    Graphical Abstract
    Real-Time Object Detection Using a Lightweight Two-Stage Detection Network with Efficient Data Representation
    Free Access | Research Article | Feature Paper | 07 April 2024 | Cited: 10
    YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems
    by
    Ming Yang
    ,
    Xiangyu Fan
    IECE Transactions on Emerging Topics in Artificial Intelligence | Volume 1, Issue 1: 1-16, 2024 | DOI:10.62762/TETAI.2024.894227
    Abstract
    With the rapid development of autonomous driving technology, the demand for real-time and efficient object detection systems has been increasing to ensure vehicles can accurately perceive and respond to the surrounding environment. Traditional object detection models often suffer from issues such as large parameter sizes and high computational resource consumption, limiting their applicability on edge devices. To address this issue, we propose a lightweight object detection model called YOLOv8-Lite, based on the YOLOv8 framework, and improved through various enhancements including the adoption of the FastDet structure, TFPN pyramid structure, and CBAM attention mechanism. These improvement... More >
    Graphical Abstract
    YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems