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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Jahan, Israt
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Computer Science & IT Electrical & Electronics Engineering

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Cucumber leaf disease identification in real-time via deep learning based algorithms Rahman, Md Mizanur; Nadim, Mahimul Islam; Akther, Mahinur; Ullah, Ahad; Ahmed, Jakaria; Ahmed, Muhammad Jalal Uddin; Jahan, Israt
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3127-3138

Abstract

Cucumber is a cash crop in Bangladesh as it is a side dish grown commercially in cultivable lands year-round. The early prediction of disease-prone crops could save grooming time and minimize losses. The conventional method of examining leaves just through observation of the human eye could only detect the diseases at an advanced stage without a concrete decision of which disease it might be and regular inspection is labour intensive, inaccurate and often unreliable. This study evaluates machine learning-based image analysis for classifying healthy and diseased cucumber leaves by training deep learning models to detect and identify observable traits. Total 1,629 images use as primary dataset and all the data collected from the cucumber field of Bangladesh. To fulfill this purpose, convolutional neural network (CNN), InceptionV3, and EfficientNetB4 are the models implemented in this paper to improve the classification of objects. The dataset was optimized by pre-processing techniques and the leaves are classified into four categories, namely angular leaf spot, downy mildew, powdery mildew, and good leaf. The EfficienNetB4 model achieved the highest train and test accuracy respectively 95% and 87%. A comparative examination of the available models was conducted in this paper to reach a solid decision.
Efficient brain cancer identification using ResNet50 and ResNet50 V2: a comparative study with a primary MRI dataset Mizanur Rahman, Md; Jahan, Israt; Das, Rana; Tasmia Alvi, Syada; Abida Anjum Era, Chowdhury; Asif Khan Akash, Atik; Sohel, Amir; Zaman, Zahura
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9142

Abstract

Primary malignant brain tumors along with central nervous system cause a significant amount of deaths every year, making brain cancer a major worldwide health problem. In South Asian countries, the number of patients suffering from brain cancer is steadily rising. Treatment effectiveness and improved patient outcomes depend on early detection. Using a dataset consisting of 6056 original raw MRI scans, this study evaluates how well convolutional neural networks (CNNs) diagnose brain cancer. We present ResNet50 and ResNet50V2 models assessed for their effectiveness in identifying brain cancers. Transfer learning and fine-tuning were employed to enhance model performance. The models demonstrated strong performance, with 87-99% accuracy rate. ResNet50V2 achieved the highest 99% accuracy. To detect tumor early, this work emphasizes how well the CNN-based machine learning methods help as timely intercession and patient care is necessary. Early prediction with 100% confidence and reliable precision is a critical issue in the modern world. Our goal is to use advanced algorithms to forecast images affected by cancer. Lastly, we will deploy an automated system that will enable us to confidently identify images affected by cancer. Our suggested methodology and its application could significantly impact the field of medical science by combining computer vision and health informatics.
Predicting the mental health of rural Bangladeshi children in coronavirus disease 2019 Moon, Nazmun Nessa; Hossain, Refath Ara; Jahan, Israt; Shakil, Shahriar; Uddin, Shihab; Hassan, Mahmudul; Nur, Fernaz Narin
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5501-5510

Abstract

The novel coronavirus disease 2019 (COVID-19) current pandemic is a worldwide health emergency like no other. It is not the only COVID-19 infection in infants, children, and adolescents that is causing concern among their families and professionals; there are also other serious issues that must be carefully detected and addressed. Major things are identified due to COVID-19, some elements are affecting children’s healthcare in direct or indirect ways, affecting them not just from a medical standpoint but also from social, psychological, economic, and educational perspectives. All these factors may have affected children’s mental development, particularly in rural settings. As Bangladesh faces a major challenge such as a lack of public mental health facilities, especially in rural areas. So, we discovered a method to predict the mental development condition of rural children that they are facing at this time of COVID-19 using machine learning technology. This research work can predict whether a rural child is mentally developed or mentally hampered in Bangladesh and this prediction gives nice feedback.
Co-Authors Abida Anjum Era, Chowdhury Ahmed, Jakaria Ahmed, Muhammad Jalal Uddin Akther, Mahinur Asif Khan Akash, Atik Das, Rana Hassan, Mahmudul Hossain, Refath Ara Mizanur Rahman, Md Moon, Nazmun Nessa Nadim, Mahimul Islam Nur, Fernaz Narin Rahman, Md Mizanur Shakil, Shahriar Sohel, Amir Tasmia Alvi, Syada Uddin, Shihab Ullah, Ahad Zaman, Zahura