Nur Hamid

Swarm unmanned surface vehicles (USVs) have become significantly prominent for maritime defense and security operations. Cooperative encirclement task, as part of maritime operations, is a critical capability of autonomous swarm USVs that requires motion stability, robust coordination, and energy efficiency under dynamic conditions. The key challenges in encirclement tasks are that most approaches focus solely on translational and positional motion while neglecting the integration of rotational motion around the target. In addition, convergence speed remains a persistent issue in reinforcement learning-based methods. To address these challenges, this work proposes the integration of Tesla-valve optimization into multi-agent reinforcement learning (TVO-MARL) to improve the encirclement task performance. The novel TVO approach is applied into two baselines of MARL including multi-agent proximal policy optimization (MAPPO), and MAPPO-LSTM (long short-term memory). The proposed methods are assessed in the 3D simulation platform using multiple performance metrics, including cumulative reward, angular coverage, rotation number, coverage variance, trajectory smoothness, and energy consumption. Experimental results show that proposed methods outperform the baseline for the convergence speed in training stage. Moreover, the top-performing trained models demonstrate that MAPPO-TVO significantly improves rotational stability, while MAPPO-LSTM-TVO achieves the highest cumulative reward and angular coverage, along with smoother trajectories and substantially reduced energy consumption. Statistical analysis confirms that these improvements are significant compared to baseline MAPPO and MAPPO-LSTM methods. The results reveal clear trade-offs across encirclement objectives and highlight the complementary benefits of nature-inspired optimization via TVO and temporal policy representation, providing a robust solution for swarm USV encirclement task.

Swarm unmanned surface vehicles (USVs) have been increasingly explored for maritime defense and security operations, particularly in scenarios requiring the rapid detection and interception of multiple attackers. The target detection reliability and defender–target assignment stability are significantly crucial to ensure quick responses and prevent mission failure. A key challenge in such missions lies in the assignment of targets among multiple defenders, where frequent reassignment can cause instability and inefficiency. This paper proposes a novel ETA-hysteresis-guided reinforcement learning (RL) framework for continuous multi-target hunting with swarm USVs. The approach integrates estimated time of arrival (ETA)-based task allocation with a dual-threshold hysteresis mechanism to balance responsiveness and stability in multi-target assignments. The ETA module provides an efficient criterion for selecting the most suitable defender–target pair, while hysteresis prevents oscillatory reassignments triggered by marginal changes in ETA values. The framework is trained and evaluated in a 3D-simulated water environment with multiple continuous targets under static and dynamic water environments. Experimental results demonstrate that the proposed method achieves substantial measurable improvements compared to basic MAPPO and MAPPO-LSTM, including faster convergence speed (+20–30%), higher interception rates (improvement of +9.5% to +20.9%), and reduced mean time-to-capture (by 9.4–19.0%), while maintaining competitive path smoothness and energy efficiency. The findings highlight the potential of integrating time-aware assignment strategies with reinforcement learning to enable robust, scalable, and stable swarm USV operations for maritime security applications.

Brain tumors are among the deadliest diseases worldwide and require early and accurate diagnosis via Magnetic Resonance Image (MRI). Deep learning techniques, particularly convolutional neural networks (CNNs), have been widely adopted for analyzing brain MRI images in tumor classification tasks. However, individual models of conventional CNNs often struggle with tumor diversity due to their restricted receptive fields and homogeneous feature extraction, leading to suboptimal diagnostic precision.We propose a hybrid transfer learning framework based on CNN architectures for enhanced multiclass brain tumor classification. A publicly accessible brain MRI dataset from Kaggle is employed in this study. The dataset, constructed from Figshare, SARTAJ, and Br35H, contains 7023 original images categorized into four classes: glioma, meningioma, pituitary, and no tumor.We introduce a preprocessing pipeline involving MRI cropping and affine-based augmentation to balance a strategically modified version of our dataset, which includes intentional class imbalance. Leveraging transfer learning, we fine-tune lightweight pre-trained models such as ResNet50V2, MobileNetV2, DenseNet121, EfficientNetV2S, and NASNetMobile and adopt a hybrid feature extraction strategy that integrates multilevel feature maps. Experimental evaluations reveal that our hybrid model (ResNet50V2 + MobileNetV2 + DenseNet121) achieves superior performance, attaining 98.75% accuracy, 98.76% precision, 98.75% recall, and 98.75% F1-score, outperforming individual models and state-of-the-art methods. The results highlight that integrating diverse features from multiple CNNs enhances classification robustness by capturing complementary tumor characteristics. In conclusion, our study advances medical imaging diagnostics by demonstrating the efficacy of hybrid transfer learning in overcoming data imbalance and model limitations, offering a reliable tool for clinical decision-making.

Autosomal dominant polycystic kidney disease (ADPKD) is the predominant hereditary factor leading to end-stage renal disease (ESRD) worldwide, affecting individuals across all races with a prevalence of 1 in 400 to 1 in 1000. The disease presents significant challenges in management, particularly with limited options for slowing cyst progression, as well as the use of tolvaptan being restricted to high-risk patients due to potential liver injury. However, determining high-risk status typically requires magnetic resonance imaging (MRI) to calculate total kidney volume (TKV), a time-consuming process demanding specialized expertise. Motivated by these challenges, this study proposes alternative methods for high-risk categorization that do not rely on TKV data. Utilizing historical patient data, we aim to predict rapid kidney enlargement in ADPKD patients to support clinical decision-making. We applied seven machine learning algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Tree, XGBoost, and Deep Neural Network (DNN)—to data from the Polycystic Kidney Disease Outcomes Consortium (PKDOC) database. The XGBoost model, combined with the Synthetic Minority Oversampling Technique (SMOTE), yielded the best performance. We also leveraged explainable artificial intelligence (XAI) techniques, specifically Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), to visualize and clarify the model’s predictions. Furthermore, we generated text summaries to enhance interpretability. To evaluate the effectiveness of our approach, we proposed new metrics to assess explainability and conducted a survey with 27 doctors to compare models with and without XAI techniques. The results indicated that incorporating XAI and textual summaries significantly improved expert explainability and increased confidence in the model’s ability to support treatment decisions for ADPKD patients.

Unmanned surface vehicles (USVs) are experiencing significant development across various fields due to extensive research, enabling these devices to offer substantial benefits. One kind of research that has been developed to produce better USVs is path planning. Despite numerous research efforts employing conventional algorithms, deep reinforcement learning, and evolutionary algorithms, USV path planning research consistently faces the challenge of effectively addressing issues within dynamic surface environments where USVs navigate. This study aims to solve USV dynamic environmental problems, as well as convergence problems in evolutionary algorithms. This research proposes a neuronal genetic algorithm that utilizes neural network input for processing with a genetic operator. The modifications in this research were implemented by incorporating a partially exponential-based fitness function into the neuronal genetic algorithm. We also implemented an inverse time variable to the fitness function. These two modifications produce faster convergence. Based on the experimental results, which were compared to those of the basic neural-network-based genetic algorithms, the proposed method can produce faster convergent solutions for USV path planning with competitive performance for total distance and time traveled in both static and dynamic environments.

This research explores women's pesantren as a forum for Islamic religious education for women to learn Islam and other soft skills that will be needed in the future. The location of this research is Nurul Huda Islamic Boarding School, Pandansari Batang Regency. Qualitative research methods, in-depth interview data collection techniques, and participatory observation of this research were conducted. The results of this research show that it is Pesantren Nurul Huda, a traditional Islamic boarding school or pesantren whose students live together under the guidance of a Pak Kyai and Bu Nyai. Pak Kyai and Bu Nyai guide their students to become good female leaders by adhering to the teachings of Islam because Islam does not prohibit women from being leaders in society. Women's boarding schools empower women to create gender equality between men and women, but that does not mean women have to be equal to men. Women's empowerment prioritises women's independence so that they are not too dependent on men.