Author
Contributions by role
Author 2
Ikram Majeed Khan
Department of Computer Science, Coventry University, UK.
Summary
Edited Journals
IECE Contributions

Free Access | Research Article | 05 March 2025
Attention-Guided Wheat Disease Recognition Network through Multi-Scale Feature Optimization
IECE Transactions on Sensing, Communication, and Control | Volume 2, Issue 1: 11-24, 2025 | DOI:10.62762/TSCC.2025.435806
Abstract
Accurate and timely detection of wheat diseases remains crucial for sustainable agriculture, particularly in major wheat-producing regions. Wheat diseases pose a significant threat to global food security, need precise and timely detection to promote sustainable agriculture. Existing approaches consistently employ single-scale features with shallow-layered convolutional neural networks (CNNs). To bridge the research gaps, we introduce a novel Multi-Scale Wheat Disease Network (MSWDNet) with feature collaboration for wheat disease recognition supported by a comprehensive dataset collected from wheat fields. This study fills research gaps by introducing a novel technique to improve detection a... More >

Graphical Abstract
Attention-Guided Wheat Disease Recognition Network through Multi-Scale Feature Optimization

Free Access | Research Article | 09 November 2024
In-depth Urdu Sentiment Analysis Through Multilingual BERT and Supervised Learning Approaches
IECE Transactions on Intelligent Systematics | Volume 1, Issue 3: 161-175, 2024 | DOI:10.62762/TIS.2024.585616
Abstract
Sentiment analysis is the process of identifying and categorizing opinions expressed in a piece of text. It has been extensively studied for languages like English and Chinese but still needs to be explored for languages such as Urdu and Hindi. This paper presents an in-depth analysis of Urdu text using state-of-the-art supervised learning techniques and a transformer-based technique. We manually annotated and preprocessed the dataset from various Urdu blog websites to categorize the sentiments into positive, neutral, and negative classes. We utilize five machine learning classifiers: Support Vector Machine (SVM), K-nearest neighbor (KNN), Naive Bayes, Multinomial Logistic Regression (MLR),... More >

Graphical Abstract
In-depth Urdu Sentiment Analysis Through Multilingual BERT and Supervised Learning Approaches