Yazar "Marouf, Ahmed Al" seçeneğine göre listele

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    An efficient approach to predict eye diseases from symptoms using machine learning and ranker-based feature selection methods
    (MDPI, 2023) Marouf, Ahmed Al; Mottalib, Md Mozaharul; Alhajj, Reda; Rokne, Jon; Jafarullah, Omar
    The eye is generally considered to be the most important sensory organ of humans. Diseases and other degenerative conditions of the eye are therefore of great concern as they affect the function of this vital organ. With proper early diagnosis by experts and with optimal use of medicines and surgical techniques, these diseases or conditions can in many cases be either cured or greatly mitigated. Experts that perform the diagnosis are in high demand and their services are expensive, hence the appropriate identification of the cause of vision problems is either postponed or not done at all such that corrective measures are either not done or done too late. An efficient model to predict eye diseases using machine learning (ML) and ranker-based feature selection (r-FS) methods is therefore proposed which will aid in obtaining a correct diagnosis. The aim of this model is to automatically predict one or more of five common eye diseases namely, Cataracts (CT), Acute Angle-Closure Glaucoma (AACG), Primary Congenital Glaucoma (PCG), Exophthalmos or Bulging Eyes (BE) and Ocular Hypertension (OH). We have used efficient data collection methods, data annotations by professional ophthalmologists, applied five different feature selection methods, two types of data splitting techniques (train-test and stratified k-fold cross validation), and applied nine ML methods for the overall prediction approach. While applying ML methods, we have chosen suitable classic ML methods, such as Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), AdaBoost (AB), Logistic Regression (LR), k-Nearest Neighbour (k-NN), Bagging (Bg), Boosting (BS) and Support Vector Machine (SVM). We have performed a symptomatic analysis of the prominent symptoms of each of the five eye diseases. The results of the analysis and comparison between methods are shown separately. While comparing the methods, we have adopted traditional performance indices, such as accuracy, precision, sensitivity, F1-Score, etc. Finally, SVM outperformed other models obtaining the highest accuracy of 99.11% for 10-fold cross-validation and LR obtained 98.58% for the split ratio of 80:20.
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    Detecting and understanding sentiment trends and emotion patterns of twitter users-a study on the demise of a bollywood celebrity
    (MDPI, 2022) Marouf, Ahmed Al; Rokne, Jon G. G.; Alhajj, Reda
    Detecting societal sentiment trends and emotion patterns is of great interest. Due to the time-varying nature of these patterns and trends this detection can be a challenging task. In this paper, the emotion patterns and trends are detected among social media users in a certain case and it is noted that the detection of the trends and patterns is especially difficult in this medium because of the use of informal language. In particular, the role of social networks in the expression of emotions relating to the death of a well-known and loved Bollywood actor Sushant Singh Rajput (SSR) by their fans is explored. The data for the analysis of the emotional state and the sentiment levels of the fans has been acquired from Twitter posts. Different existing sentiment analysis algorithms were compared for the study and chosen for identifying the sentiment trend over a specific timeline of events. The same Twitter posts were also analyzed for emotional content by extracting linguistic features using the psycholinguistic package, Linguistic Inquiry and the Word Count package (LIWC), relating to emotions. Additionally, viral hashtags extracted from the Twitter posts have been segmented and analyzed in order to identify new viral hashtags expressed by the posts over time. The associations between the old and new viral hashtags and between sentiment trends and emotional shifts among the fan base of SSR have been determined and presented graphically.
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    Enhanced potato pest identification: a deep learning approach for identifying potato pests
    (2024) Sohel, Amir; Shakil, Md. Shahriar; Siddiquee, Shah Md. Tanvir; Marouf, Ahmed Al; Rokne, Jon G.; Alhajj, Reda
    Potato crops and their salability are influenced by potato pests in that both crop yield and quality are reduced. This in turn reduces the income for potato farmers due to lower prices for the crop, lower crop yield, trade restriction and reduced market access. Agricultural viability over the long run therefore depends on sustainable pest management. In order to efficiently detect potato pests, a dataset was constructed which contains eight prevalent potato species that were taken from several sources. Image pre-processing techniques were employed enhance image quality for compatibility with deep learning models. Among InceptionV3, VGG-16, and MobileNetV2 models, VGG-16 attained the highest accuracy of 94.44%, outperforming others. Inception-V3 achieved 58% accuracy, while MobileNetV2 reached 75%. Pre-processing has a major influence on improving result accuracy, which emphasizes its significance in enhancing model performance, according to an evaluation of its effects. These findings might lead to the development of pest management strategies for potato farming that are more effective. The efficient use of VGG-16 in potato pest identification systems is demonstrated by its excellent performance. Using deep learning models can therefore reduce financial losses and promote sustainable potato production. This study provides an approach for further investigation into the best ways to control pests in potato production, allowing farmers to overcome the obstacles and take advantage of valuable market prospects even in the face of pest threats.
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    Exploring gene expression and clinical data for identifying prostate cancer severity levels using machine learning methods
    (Institute of Electrical and Electronics Engineers Inc., 2023) Marouf, Ahmed Al; Alhajj, Reda; Rokne, Jon G.; Ghose, Sunita; Bismar, Tarek A.
    Prostate cancer (PCa) is the most common type of cancer in men worldwide. It is a cancer that starts in the small walnut-shaped male gland called the prostate. From the prostate, it can form a metastasis into other organs. If detected and diagnosed early the survival rate may increase to 95%. Therefore, early detection and diagnosis are important tasks performed by a pathologist. The pathologist identifies the severity levels using a scale called the Gleason grading group (GGG). The GGG is found by pathologists by looking at a biopsy sample and assigning a grade of low, intermediate, or high to the sample. The pathologist then assesses a second sample in the same manner. The GGG is found by adding these two scores provides the total Gleason score. In this paper, we have explored tissue microarray (TMA) and clinical data collected by pathologists of Alberta Precision Laboratory, for predicting the severity level of prostate cancer using various machine learning methods. Traditional classifiers, such as Naïve Bayes, Decision Tree, Support Vector Machine with Radial basis function (RBF), Logistic Regression, and ensemble classifiers, such as Random Forest, and Bagging with k-nearest neighbors have been applied through the machine learning pipeline containing imputation and sampling techniques. An integrated SMOTE-Tomek Links method is adopted for handling the class imbalance problem. The highest accuracy obtained is 99.64% from the Random Forest method.
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    Genomic biomarker discovery in disease progression and therapy response in bladder cancer utilizing machine learning
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Liosis, Konstantinos Christos; Marouf, Ahmed Al; Rokne, Jon G.; Ghosh, Sunita; Bismar, Tarek A.; Alhajj, Reda
    Cancer in all its forms of expression is a major cause of death. To identify the genomic reason behind cancer, discovery of biomarkers is needed. In this paper, genomic data of bladder cancer are examined for the purpose of biomarker discovery. Genomic biomarkers are indicators stemming from the study of the genome, either at a very low level based on the genome sequence itself, or more abstractly such as measuring the level of gene expression for different disease groups. The latter method is pivotal for this work, since the available datasets consist of RNA sequencing data, transformed to gene expression levels, as well as data on a multitude of clinical indicators. Based on this, various methods are utilized such as statistical modeling via logistic regression and regularization techniques (elastic-net), clustering, survival analysis through Kaplan–Meier curves, and heatmaps for the experiments leading to biomarker discovery. The experiments have led to the discovery of two gene signatures capable of predicting therapy response and disease progression with considerable accuracy for bladder cancer patients which correlates well with clinical indicators such as Therapy Response and T-Stage at surgery with Disease Progression in a time-to-event manner.
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    MCNN-LSTM: Combining CNN and LSTM to classify multi-class text in imbalanced news data
    (Institute of Electrical and Electronics Engineers Inc., 2023) Hasib, Khan Md; Azam, Sami; Karim, Asif; Marouf, Ahmed Al; Shamrat, F. M. Javed Mehedi; Montaha, Sidratul; Yeo, Kheng Cher; Jonkman, Mirjam; Alhajj, Reda; Rokne, Jon G.
    Searching, retrieving, and arranging text in ever-larger document collections necessitate more efficient information processing algorithms. Document categorization is a crucial component of various information processing systems for supervised learning. As the quantity of documents grows, the performance of classic supervised classifiers has deteriorated because of the number of document categories. Assigning documents to a predetermined set of classes is called text classification. It is utilized extensively in a wide range of data-intensive applications. However, the fact that real-world implementations of these models are plagued with shortcomings begs for more investigation. Imbalanced datasets hinder the most prevalent high-performance algorithms. In this paper, we propose an approach name multi-class Convolutional Neural Network (MCNN)-Long Short-Time Memory (LSTM), which combines two deep learning techniques, Convolutional Neural Network (CNN) and Long Short-Time Memory, for text classification in news data. CNN's are used as feature extractors for the LSTMs on text input data and have the spatial structure of words in a sentence, paragraph, or document. The dataset is also imbalanced, and we use the Tomek-Link algorithm to balance the dataset and then apply our model, which shows better performance in terms of F1-score (98%) and Accuracy (99.71%) than the existing works. The combination of deep learning techniques used in our approach is ideal for the classification of imbalanced datasets with underrepresented categories. Hence, our method outperformed other machine learning algorithms in text classification by a large margin. We also compare our results with traditional machine learning algorithms in terms of imbalanced and balanced datasets.
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    Rice leaf disease classification-a comparative approach using convolutional neural network (cnn), cascading autoencoder with attention residual u-net (caar-u-net), and mobilenet-v2 architectures
    (2024) Dutta, Monoronjon; Islam Sujan, Md Rashedul; Mojumdar, Mayen Uddin; Chakraborty, Narayan Ranjan; Marouf, Ahmed Al; Rokne, Jon George; Alhajj, Reda
    Classifying rice leaf diseases in agricultural technology helps to maintain crop health and to ensure a good yield. In this work, deep learning algorithms were, therefore, employed for the identification and classification of rice leaf diseases from images of crops in the field. The initial algorithmic phase involved image pre-processing of the crop images, using a bilateral filter to improve image quality. The effectiveness of this step was measured by using metrics like the Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). Following this, this work employed advanced neural network architectures for classification, including Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net), MobileNetV2, and Convolutional Neural Network (CNN). The proposed CNN model stood out, since it demonstrated exceptional performance in identifying rice leaf diseases, with test Accuracy of 98% and high Precision, Recall, and F1 scores. This result highlights that the proposed model is particularly well suited for rice leaf disease classification. The robustness of the proposed model was validated through k-fold cross-validation, confirming its generalizability and minimizing the risk of overfitting. This study not only focused on classifying rice leaf diseases but also has the potential to benefit farmers and the agricultural community greatly. This work highlights the advantages of custom CNN models for efficient and accurate rice leaf disease classification, paving the way for technology-driven advancements in farming practices.
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    Rice leaf disease classification—a comparative approach using convolutional neural network (cnn), cascading autoencoder with attention residual u-net (caar-u-net), and mobilenet-v2 architectures
    (2024) Dutta, Monoronjon; Islam Sujan, Md Rashedul; Mojumdar, Mayen Uddin; Chakraborty, Narayan Ranjan; Marouf, Ahmed Al; Rokne, Jon George; Alhajj, Reda
    Classifying rice leaf diseases in agricultural technology helps to maintain crop health and to ensure a good yield. In this work, deep learning algorithms were, therefore, employed for the identification and classification of rice leaf diseases from images of crops in the field. The initial algorithmic phase involved image pre-processing of the crop images, using a bilateral filter to improve image quality. The effectiveness of this step was measured by using metrics like the Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). Following this, this work employed advanced neural network architectures for classification, including Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net), MobileNetV2, and Convolutional Neural Network (CNN). The proposed CNN model stood out, since it demonstrated exceptional performance in identifying rice leaf diseases, with test Accuracy of 98% and high Precision, Recall, and F1 scores. This result highlights that the proposed model is particularly well suited for rice leaf disease classification. The robustness of the proposed model was validated through k-fold cross-validation, confirming its generalizability and minimizing the risk of overfitting. This study not only focused on classifying rice leaf diseases but also has the potential to benefit farmers and the agricultural community greatly. This work highlights the advantages of custom CNN models for efficient and accurate rice leaf disease classification, paving the way for technology-driven advancements in farming practices.
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    Xse-tomatonet: an explainable ai based tomato leaf disease classification method using efficientnetb0 with squeeze-and-excitation blocks and multi-scale feature fusion
    (2025) Assaduzzaman, Md; Bishshash, Prayma; Nirob, Asraful Sharker; Marouf, Ahmed Al; Rokne, Jon George; Alhajj, Reda
    Tomatoes are globally valued for their nutritional benefits and unique taste, playing a crucial role in agricultural productivity. Accurate diagnosis of tomato leaf diseases is vital to avoid ineffective treatments that can harm plants and ecosystems. While deep learning models excel in classifying these diseases, distinguishing subtle variations remains challenging. This study introduces XSE-TomatoNet, an enhanced version of EfficientNetB0, incorporating Squeeze-and-Excitation (SE) blocks and multi-scale feature fusion to boost classification performance. XSE-TomatoNet extracts multi-scale features, refines them with SE blocks, and merges them through Global Average Pooling, providing detailed and broad insights for precise disease classification. Our approach achieves an impressive accuracy of 99.11%, with 99% precision and recall, outperforming models like MobileNet and VGG19, especially when combined with data augmentation and ablation studies. The model achieved an average training accuracy of 99.41% and a validation accuracy of 98.88% in 10-fold cross-validation, showing strong generalization to unseen data. We also used LIME and SHAP for model interpretability, offering insights into the decision-making process, and employed Grad-CAM and Grad-CAM++ to visually highlight key areas in leaf images. Finally, the best model was integrated into a web-based system for practical use by tomato cultivators. • XSE-TomatoNet is an enhanced version of EfficientNetB0 which incorporates Squeeze-and-Excitation (SE) blocks and multi-scale feature fusion. • XSE-TomatoNet outperformed MobileNet (87.44%) and VGG-19 (95.50%), in terms of accuracy, achieving 99.41%. • Integration of interpretation using LIME and SHAP models gives higher level understanding of the diseases and employment of Grad-CAM and Grad-CAM++ shows visual representation of the diseased leaves.