International Research Journal of Multidisciplinary Technovation

A Unified Model for Estimation of Reference Evapotranspiration Using an Assembly of Ensemble Learners Coupled with Swarm Intelligence Optimizers

2025-08-06T04:56:09+00:00

Several machine learning models and their ensembles have been suggested for reference evapotranspiration (ET₀) modeling at different climatic regions. Researchers reported that optimizing model hyperparameters using an intelligent algorithm significantly improves the performance of such models. However, ensemble models hybridized with hyperparameter optimizers have hardly been applied for the precise estimation of ET₀ worldwide. The current research is devoted to designing sixteen hybrid versions of four ensemble models, alternatively coupled with four popular swarm intelligence optimization algorithms and finding the best-fit model against different input combinations of available climatic parameters for the groundwater-stressed region of North Bengal, India. The performances of four ensemble models and their sixteen hybrid versions were compared in terms of four well-recognized statistical metrics: the coefficient of determination (R²), Nash-Sutcliffe efficiency (NSE), root mean squared error (RMSE), and mean absolute error (MAE). Experimental results depicted that in nearly 92% of cases, the hybrid versions outperformed the primary ensemble models, irrespective of the available climatic parameters. In most cases, the ensemble models hybridized with the whale optimization algorithm (WOA) produced the highest estimation accuracy, followed by the sailfish optimizer (SFO). Solar radiation was also found to be the most significant climatic parameter for estimating ET₀ in this region.

YOLO-Based Generalized Framework for Leukemia Cell Detection Using Unified Microscopic Image Datasets

2025-07-21T07:28:46+00:00

Acute lymphoblastic leukemia is a kind of blood cancer that attacks the lymphoblast, a subgroup of white blood cells. Leukemia is a potentially lethal hematological cancer that requires prompt diagnosis. A skilled manual blood smear examination is one of the laborious and prone to human error conventional diagnostic methods. Although current automated methods developed by researchers use either single-cell or multi-cell pictures to detect leukemia cells, they frequently lack model generalization that perform better on heterogeneous datasets. They are also insufficient for deployment in real time. This study aims to develop generalized real-time system for detecting ALL cells from single and multi-cell microscopic blood smear images. The system utilizes three YOLO based state-of-the-art models: YOLO11, YOLOv8 and YOLOv5. The core novelty of this study lies in the creation of a unified dataset that integrates both single-cell and multi-cell microscopic blood smear images, this enables the model to learn generalized representations from diverse image contexts. Three datasets are merged to create the unified dataset, ALL-IDB1: multi- cell images, ALL-IDB2 & C-NMC-19: single-cell images. Image annotation and preprocessing are performed using Roboflow platform, while Google Colab is used for training and testing. These models are trained separately on individual datasets and the unified dataset.The performance of generalized YOLO models is assessed and contrasted against dataset-specific models using mAP@50 and recall metrics on the same set of unseen images from all three datasets.The experimental results indicate that generalized YOLOv8 model achieved notably high recall and competitive map@50, demonstrating strong adaptability and accuracy. These results highlight YOLOv8 as a promising solution for developing generalized model for leukemia cell detection.

Detection and Classification of Genetic Acute Myeloid Leukemia Cells using Deep Learning Techniques

2025-08-04T08:41:11+00:00

Acute Myeloid Leukemia (AML) is a hematological disease that is defined by the fast growth of aberrant myeloid precursor cells in the blood and bone marrow, which disrupts normal hematopoiesis. Treatment and prognosis are influenced by the early detection of this deadly illness and its appropriate classification. Therefore, utilizing the Human Leukemia Cytomorphology Collection dataset, which comprises leukemic and normal single-cell images of Acute Myeloid Leukemia (AML) type, this research suggests a deep learning-based hybrid model for automated leukemia detection and classification. By taking morphological characteristics and genetic abnormalities into account, leukemic cells have been distinguished. The features in this study are extracted using MobileNetV2, ResNet-101, and VGG-16. Then, feature-level stacking is performed using the Support Vector Machine and Random Forest classifiers for final classification using Principal Component Analysis (PCA). Utilising image segmentation, normalisation, data augmentation, and data oversampling techniques, the pipeline improves data quality and corrects class imbalance. Additionally, t-distributed Stochastic Neighbour Embedding (t-SNE), which shows the extracted features used for the detection of leukemia subtypes, and Gradient-Weighted Class Activation Mapping (Grad-CAM) images help with interpretability by highlighting important decision areas. The suggested study achieved 98.35% accuracy, 95.87% precision, 95.84% sensitivity, 98.97% specificity, and 95.74% F1-Score. Along with the trial results, a comparison of the four separate frameworks, viz., MobileNetV2, ResNet-101, VGG-16, and Vision Transformer, has also been carried out. The comparison shows that the proposed model outperforms the other frameworks. The outcomes show that the suggested model has the capability to be used a reliable means for the prompt identification of AML and its subtypes.