Skip to main content

Research Repository

Advanced Search

All Outputs (10)

A New Contrastive Learning based Vision Transformer for Sentiment Analysis using Scene Text Images (2024)
Journal Article
Palaiahnakote, S., Kapri, D., Saleem, M. H., & Pal, U. (2024). A New Contrastive Learning based Vision Transformer for Sentiment Analysis using Scene Text Images. International Journal of Pattern Recognition and Artificial Intelligence, https://doi.org/10.1142/S0218001424520293

Sentiment analysis using scene text images is complex and challenging because it has an arbitrary
background, and the method should rely on only visual features. Unlike most existing methods that use
either text or images or both, this study uses o... Read More about A New Contrastive Learning based Vision Transformer for Sentiment Analysis using Scene Text Images.

A Performance-Optimized Deep Learning-Based Plant Disease Detection Approach for Horticultural Crops of New Zealand (2022)
Journal Article
Saleem, M. H., Potgieter, J., & Arif, K. M. (2022). A Performance-Optimized Deep Learning-Based Plant Disease Detection Approach for Horticultural Crops of New Zealand. IEEE Access, 10, 89798-89822. https://doi.org/10.1109/access.2022.3201104

Deep learning-based plant disease detection has gained significant attention from the scientific community. However, various aspects of real horticultural conditions have not yet been explored. For example, the disease should be considered not only o... Read More about A Performance-Optimized Deep Learning-Based Plant Disease Detection Approach for Horticultural Crops of New Zealand.

Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach (2022)
Journal Article
Saleem, M. H., Potgieter, J., & Arif, K. M. (in press). Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach. Agronomy, 12(7), 1580. https://doi.org/10.3390/agronomy12071580

To apply weed control treatments effectively, the weeds must be accurately detected. Deep learning (DL) has been quite successful in performing the weed identification task. However, various aspects of the DL have not been explored in previous studie... Read More about Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach.

Weed Identification by Single-Stage and Two-Stage Neural Networks: A Study on the Impact of Image Resizers and Weights Optimization Algorithms (2022)
Journal Article
Saleem, M. H., Velayudhan, K. K., Potgieter, J., & Arif, K. M. (in press). Weed Identification by Single-Stage and Two-Stage Neural Networks: A Study on the Impact of Image Resizers and Weights Optimization Algorithms. Frontiers in Plant Science, 13, https://doi.org/10.3389/fpls.2022.850666

The accurate identification of weeds is an essential step for a site-specific weed management system. In recent years, deep learning (DL) has got rapid advancements to perform complex agricultural tasks. The previous studies emphasized the evaluation... Read More about Weed Identification by Single-Stage and Two-Stage Neural Networks: A Study on the Impact of Image Resizers and Weights Optimization Algorithms.

Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments (2021)
Journal Article
Saleem, M. H., Potgieter, J., & Arif, K. M. (2021). Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments. Precision Agriculture, 22(6), 2053-2091. https://doi.org/10.1007/s11119-021-09806-x

Recently, agriculture has gained much attention regarding automation by artificial intelligence techniques and robotic systems. Particularly, with the advancements in machine learning (ML) concepts, significant improvements have been observed in agri... Read More about Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments.

Image-Based Plant Disease Identification by Deep Learning Meta-Architectures (2020)
Journal Article
Saleem, M. H., Khanchi, S., Potgieter, J., & Arif, K. M. (2020). Image-Based Plant Disease Identification by Deep Learning Meta-Architectures. Plants, 9(11), 1451. https://doi.org/10.3390/plants9111451

The identification of plant disease is an imperative part of crop monitoring systems. Computer vision and deep learning (DL) techniques have been proven to be state-of-the-art to address various agricultural problems. This research performed the comp... Read More about Image-Based Plant Disease Identification by Deep Learning Meta-Architectures.

Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers (2020)
Journal Article
Saleem, M. H., Potgieter, J., & Arif, K. M. (2020). Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers. Plants, 9(10), 1319. https://doi.org/10.3390/plants9101319

Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the class... Read More about Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers.

Plant Disease Detection and Classification by Deep Learning (2019)
Journal Article
Saleem, M. H., Potgieter, J., & Arif, K. M. (2019). Plant Disease Detection and Classification by Deep Learning. Plants, 8(11), 468. https://doi.org/10.3390/plants8110468

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advanceme... Read More about Plant Disease Detection and Classification by Deep Learning.

Parametric Sensitivity Analyses for Perceived Impedance in Haptic Teleoperation (2019)
Journal Article
Uddin, R., Saleem, M. H., & Ryu, J. (2019). Parametric Sensitivity Analyses for Perceived Impedance in Haptic Teleoperation. International Journal of Control, Automation and Systems, 17(8), 2083-2096. https://doi.org/10.1007/s12555-018-0614-8

In this paper, sensitivity analyses (SA) are performed in order to find the effect of variations of master/slave site dynamics parameters on the perceived impedance in haptic teleoperation in the absence/presence of communication time delays. These a... Read More about Parametric Sensitivity Analyses for Perceived Impedance in Haptic Teleoperation.