Md. Ehsanul Haque

Md. Ehsanul Haque, Md. Saymon Hosen Polash, Rakib Hasan Ovi et al.

Grapes are among the most economically and culturally significant fruits on a global scale, and table grapes and wine are produced in significant quantities in Europe and Asia. The production and quality of grapes are significantly impacted by grape diseases such as Bacterial Rot, Downy Mildew, and Powdery Mildew. Consequently, the sustainable management of a vineyard necessitates the early and precise identification of these diseases. Current automated methods, particularly those that are based on the YOLO framework, are often computationally costly and lack interpretability that makes them unsuitable for real-world scenarios. This study proposes grape leaf disease classification using Optimized DenseNet 121. Domain-specific preprocessing and extensive connectivity reveal disease-relevant characteristics, including veins, edges, and lesions. An extensive comparison with baseline CNN models, including ResNet18, VGG16, AlexNet, and SqueezeNet, demonstrates that the proposed model exhibits superior performance. It achieves an accuracy of 99.27%, an F1 score of 99.28%, a specificity of 99.71%, and a Kappa of 98.86%, with an inference time of 9 seconds. The cross-validation findings show a mean accuracy of 99.12%, indicating strength and generalizability across all classes. We also employ Grad-CAM to highlight disease-related regions to guarantee the model is highlighting physiologically relevant aspects and increase transparency and confidence. Model optimization reduces processing requirements for real-time deployment, while transfer learning ensures consistency on smaller and unbalanced samples. An effective architecture, domain-specific preprocessing, and interpretable outputs make the proposed framework scalable, precise, and computationally inexpensive for detecting grape leaf diseases.

Md. Ehsanul Haque, S. M. Jahidul Islam, Jeba Maliha et al.

Chronic Kidney Disease (CKD) is a major global health issue which is affecting million people around the world and with increasing rate of mortality. Mitigation of progression of CKD and better patient outcomes requires early detection. Nevertheless, limitations lie in traditional diagnostic methods, especially in resource constrained settings. This study proposes an advanced machine learning approach to enhance CKD detection by evaluating four models: Random Forest (RF), Multi-Layer Perceptron (MLP), Logistic Regression (LR), and a fine-tuned CatBoost algorithm. Specifically, among these, the fine-tuned CatBoost model demonstrated the best overall performance having an accuracy of 98.75%, an AUC of 0.9993 and a Kappa score of 97.35% of the studies. The proposed CatBoost model has used a nature inspired algorithm such as Simulated Annealing to select the most important features, Cuckoo Search to adjust outliers and grid search to fine tune its settings in such a way to achieve improved prediction accuracy. Features significance is explained by SHAP-a well-known XAI technique-for gaining transparency in the decision-making process of proposed model and bring up trust in diagnostic systems. Using SHAP, the significant clinical features were identified as specific gravity, serum creatinine, albumin, hemoglobin, and diabetes mellitus. The potential of advanced machine learning techniques in CKD detection is shown in this research, particularly for low income and middle-income healthcare settings where prompt and correct diagnoses are vital. This study seeks to provide a highly accurate, interpretable, and efficient diagnostic tool to add to efforts for early intervention and improved healthcare outcomes for all CKD patients.

Md. Ehsanul Haque, Md. Saymon Hosen Polash, Md Al-Imran Sanjida Simla et al.

Due to the rapid growth in the number of Internet of Things (IoT) networks, the cyber risk has increased exponentially, and therefore, we have to develop effective IDS that can work well with highly imbalanced datasets. A high rate of missed threats can be the result, as traditional machine learning models tend to struggle in identifying attacks when normal data volume is much higher than the volume of attacks. For example, the dataset used in this study reveals a strong class imbalance with 94,659 instances of the majority class and only 28 instances of the minority class, making it quite challenging to determine rare attacks accurately. The challenges presented in this research are addressed by hybrid sampling techniques designed to improve data imbalance detection accuracy in IoT domains. After applying these techniques, we evaluate the performance of several machine learning models such as Random Forest, Soft Voting, Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), and Logistic Regression with respect to the classification of cyber-attacks. The obtained results indicate that the Random Forest model achieved the best performance with a Kappa score of 0.9903, test accuracy of 0.9961, and AUC of 0.9994. Strong performance is also shown by the Soft Voting model, with an accuracy of 0.9952 and AUC of 0.9997, indicating the benefits of combining model predictions. Overall, this work demonstrates the value of hybrid sampling combined with robust model and feature selection for significantly improving IoT security against cyber-attacks, especially in highly imbalanced data environments.

Md. Ehsanul Haque, Abrar Fahim, Shamik Dey et al.

Early and accurate detection of the bone fracture is paramount to initiating treatment as early as possible and avoiding any delay in patient treatment and outcomes. Interpretation of X-ray image is a time consuming and error prone task, especially when resources for such interpretation are limited by lack of radiology expertise. Additionally, deep learning approaches used currently, typically suffer from misclassifications and lack interpretable explanations to clinical use. In order to overcome these challenges, we propose an automated framework of bone fracture detection using a VGG-19 model modified to our needs. It incorporates sophisticated preprocessing techniques that include Contrast Limited Adaptive Histogram Equalization (CLAHE), Otsu's thresholding, and Canny edge detection, among others, to enhance image clarity as well as to facilitate the feature extraction. Therefore, we use Grad-CAM, an Explainable AI method that can generate visual heatmaps of the model's decision making process, as a type of model interpretability, for clinicians to understand the model's decision making process. It encourages trust and helps in further clinical validation. It is deployed in a real time web application, where healthcare professionals can upload X-ray images and get the diagnostic feedback within 0.5 seconds. The performance of our modified VGG-19 model attains 99.78\% classification accuracy and AUC score of 1.00, making it exceptionally good. The framework provides a reliable, fast, and interpretable solution for bone fracture detection that reasons more efficiently for diagnoses and better patient care.

Shudipta Banik, Muna Das, Trapa Banik et al.

The detection of brain tumor in MRI is an important aspect of ensuring timely diagnostics and treatment; however, manual analysis is commonly long and error-prone. Current approaches are not universal because they have limited generalization to heterogeneous tumors, are computationally inefficient, are not interpretable, and lack transparency, thus limiting trustworthiness. To overcome these issues, we introduce MobileDenseAttn, a fusion model of dual streams of MobileNetV2 and DenseNet201 that can help gradually improve the feature representation scale, computing efficiency, and visual explanations via GradCAM. Our model uses feature level fusion and is trained on an augmented dataset of 6,020 MRI scans representing glioma, meningioma, pituitary tumors, and normal samples. Measured under strict 5-fold cross-validation protocols, MobileDenseAttn provides a training accuracy of 99.75%, a testing accuracy of 98.35%, and a stable F1 score of 0.9835 (95% CI: 0.9743 to 0.9920). The extensive validation shows the stability of the model, and the comparative analysis proves that it is a great advancement over the baseline models (VGG19, DenseNet201, MobileNetV2) with a +3.67% accuracy increase and a 39.3% decrease in training time compared to VGG19. The GradCAM heatmaps clearly show tumor-affected areas, offering clinically significant localization and improving interpretability. These findings position MobileDenseAttn as an efficient, high performance, interpretable model with a high probability of becoming a clinically practical tool in identifying brain tumors in the real world.

Md. Ehsanul Haque, S. M. Jahidul Islam, Shakil Mia et al.

Liver diseases are a serious health concern in the world, which requires precise and timely diagnosis to enhance the survival chances of patients. The current literature implemented numerous machine learning and deep learning models to classify liver diseases, but most of them had some issues like high misclassification error, poor interpretability, prohibitive computational expense, and lack of good preprocessing strategies. In order to address these drawbacks, we introduced StackLiverNet in this study; an interpretable stacked ensemble model tailored to the liver disease detection task. The framework uses advanced data preprocessing and feature selection technique to increase model robustness and predictive ability. Random undersampling is performed to deal with class imbalance and make the training balanced. StackLiverNet is an ensemble of several hyperparameter-optimized base classifiers, whose complementary advantages are used through a LightGBM meta-model. The provided model demonstrates excellent performance, with the testing accuracy of 99.89%, Cohen Kappa of 0.9974, and AUC of 0.9993, having only 5 misclassifications, and efficient training and inference speeds that are amenable to clinical practice (training time 4.2783 seconds, inference time 0.1106 seconds). Besides, Local Interpretable Model-Agnostic Explanations (LIME) are applied to generate transparent explanations of individual predictions, revealing high concentrations of Alkaline Phosphatase and moderate SGOT as important observations of liver disease. Also, SHAP was used to rank features by their global contribution to predictions, while the Morris method confirmed the most influential features through sensitivity analysis.