EAI Endorsed Transactions on AI and Robotics
EAI Endorsed Transactions on AI and Robotics (eISSN: 2790-7511) covers all aspects of robotics and knowledge-based AI systems along with interdisciplinary approaches to computer science, control systems, computer vision, machine learning, electrical engineering, intelligent machines, mathematics, and other disciplines. An important goal of this journal is to extend cutting-edge technologies in the control and learning of both symbolic and sensory robots with regard to smart systems. Our journal contains articles on the theoretical, mathematical, computational, and experimental aspects of robotics and intelligent systems. INDEXING: Scopus, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions
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Explainable Transformer Models for Early Prediction of Chronic Diseases Using Longitudinal Electronic Health Records (EHRs)
Early prediction of chronic diseases using longitudinal electronic health records (EHRs) is critical for enabling timely interventions and improving patient outcomes. However, existing deep learning approaches often function as black-box models, limiting their clinical adoption due to a lack of transparency and interpretability. This study proposes an explainable transformer-based framework for early chronic disease prediction that effectively models temporal dependencies in longitudinal EHR data while providing clinically meaningful explanations. The proposed approach integrates a time-aware transformer architecture with attention-based interpretability mechanisms to capture complex patient trajectories across heterogeneous clinical events, including diagnoses, laboratory results, medications, and demographic attributes. To enhance explainability, we incorporate feature-level and temporal attention visualization, enabling identification of influential clinical factors and critical time windows contributing to disease onset predictions. Extensive experiments conducted on large-scale longitudinal EHR datasets demonstrate that the proposed model consistently outperforms state-of-the-art machine learning and deep learning baselines in terms of predictive accuracy, recall, and early risk detection capability. Furthermore, qualitative evaluation with clinician-oriented explanation analyses confirms that the generated explanations align with established medical knowledge, enhancing trust and clinical usability. This work advances the integration of explainable artificial intelligence in healthcare by offering a robust and interpretable transformer-based solution for early chronic disease prediction, supporting data-driven decision-making in real-world clinical settings.
Copyright (c) 2025 Hewa Majeed Zangana, Maryam A. Sulaiman https://creativecommons.org/licenses/by-nc-sa/4.0
An Integrated Beetle Antennae Search–Enabled Navigation Framework for Omnidirectional AGVMobile Robots in Unknown Environments
The Beetle Antennae Search algorithm is a relatively one of the recent optimization approach inspired by the foraging behavior of long-horn beetles, that copy how they use their antennae to explore the environment. This algorithm due to its simple structure and derivative-free nature, is well suited for robotic applications with limited computational resources. Despite many BAS-based navigation studies still handle path planning and control independently, or apply BAS only as an offline trajectory optimization tool. As a result, less attention has been given to implement it in the real-time navigation framework. On the other hand, Mobile robots, in an unseen environment cannot work these problems separately rather they must continuously detect obstacles, update their environment model, keep re-planning safe routes, adjust control gains, and follow a predetermined reference trajectory. An integrated BAS-enabled navigation combining Simultaneous Localization and Mapping framework for an omnidirectional mobile robot in CoppeliaSim is presented in this research. A LiDAR-based occupancy grid is updated continually during motion, and obstacle detection via SLAM is used to improve safety. A modified A* algorithm is used to create collision-free avoidance pathways, which are then smoothed. At the same time, a BAS-driven adaptive PID controller is used to adjust the gains by using trajectory deviation as a feedback signal. To enable precise path following, a nine-sensor infrared array is used for line tracking. The integrated system can follow smoothed avoidance paths, calculate safe bypasses, identify impediments, and return to the original path. In the experiments, the system was tested in two scenarios - no obstacles and with three levels of obstacles. The results indicated higher performance when SLAM and BAS used together than conventional PID based navigation while software simulation of SLAM often hurting the performance as well.
Reinforcment Learning-Based Adaptive Control for Collaborative Robots in Dynamic Envirnoments
Copyright (c) 2025 Ata Jahangir Moshayedi, Atanu Shuvam Roy, Utsab Karan, Mingjun Zhang, David Bassir https://creativecommons.org/licenses/by-nc-sa/4.0
A Highly Efficient ACO-SA Algorithm for Robot Path Planning
Intelligent algorithms continue to develop, and efficient path planning for mobile robots in complex environments relies heavily on such algorithms. This article proposes an ACO-SA path planning method that combines ant colony and simulated annealing to solve the problems of slow iteration and long computation in classical ant colony algorithms. The training base map is gridded and modeled, and the path is initially calculated and parameterized using traditional ant colony algorithms. The simulated annealing cooling mechanism is introduced to optimize the pheromone strategy, and the robustness of the algorithm is tested using a multi-modal large model random map. Simulation shows that under the map of the training base, the path length of the ACO-SA algorithm remains unchanged, and the convergence speed is improved by 88.9% and 58.3% respectively, while the running time is shortened by 1.5% and 3.5% respectively; In the worst results of the random map, the shortest path is shortened by 36.57% and 35.95% respectively compared to the traditional ant colony algorithm. This algorithm has better optimization effect and path stability, providing a practical solution for intelligent detection robot path planning.
Copyright (c) 2025 Wei Li, Feng Yang, Zhibin Li, Juan Mou, Yanmei Ha, Yi Liu, Yanmei Qin, Peiyang Wei, Linlin Chen, Xun Deng, Tinghui Chen, Jia Liu, Jianhong Gan, ZhenZhen Hu, Yonghong Deng, Guodong Li, Qifeng Su https://creativecommons.org/licenses/by-nc-sa/4.0
FrequencyFormer: Oriented Object Detection with Frequency Transformer
Detecting objects with oriented bounding boxes have shown impressive generalizations in the challenging scenes with densely packed objects with arbitrary rotations. Existing oriented object detectors rely on customized operations like anchor pre-definition and NMS post-processing for accuracy improvement. However, those components usually bring extensive computational costs and complicate the pipeline, and thus limit the scalability of existing methods. In this paper, we propose a new paradigm, FrequencyFormer, for end-to-end oriented object detection. Upon the Transformer based encoder-decoder framework, two key ingredients are proposed to adapt it to detect oriented objects robustly. First, a frequency boosted query updatestrategy is designed to enhance the shape encoding of object queries by incorporating the frequency vectors of oriented objects. Second, a dynamic matching strategy is introduced to facilitate the training process, in which the matching weights are adjusted adaptively as the training progress. Experimental results on DOTA and HRSC2016 datasets demonstrate that our FrequencyFormer achieves competitive performance with stateof-the-art methods.
Copyright (c) 2025 Shuai Liu, Haiming Wang, Zhibin Li, Peiyang Wei https://creativecommons.org/licenses/by-nc-sa/4.0
FDD-YOLO: A Lightweight Multi-scale Prohibited Items Detection Model
X-ray security inspection faces challenges such as severe occlusion, scale variation, and complex background when detecting prohibited items, requiring real-time and accurate detection. Although the YOLO series of models has high inference efficiency, they suffer from problems such as feature redundancy, insufficient fine-grained feature extraction, and limited adaptability to overlapping objects. To overcome these limitations, we propose FDD-YOLO and design three novel modules: (1) The Frequency Domain Decomposition Network (FDDN) in the backbone network enhances the edges of metal objects and the contours of liquid containers by decomposing high-frequency and low-frequency features while reducing computational redundancy; (2) The Deformable Elastic Fusion Pyramid (DEFP) in the neck network adopts dynamic channel allocation and multi-scale deformable convolution to handle the geometric changes of folded and overlapping objects; (3) The lightweight Dual-channel Convolution (DualConv) improves multi-scale feature capture through grouping and point-by-point convolution, thereby reducing the number of parameters while improving the accuracy of small object detection. Tests on the SIXray, HIXray, and private GIX datasets show that FDD-YOLO achieves 2.6%, 3.2%, and 8.6% higher mAP than YOLOv11n, respectively, achieving accuracies of 94.8%, 84%, and 71.8%, respectively. This framework also reduces the number of parameters by 30.6% and the number of FLOPs by 26.9%, achieving an optimal balance between accuracy and efficiency, setting a new technical benchmark for real-time security inspections.
Frequency Domain Decomposition Network (FDDN)
Deformable Elastic Fusion Pyramid (DEFP)
Copyright (c) 2025 Zilong Xue, Bo Wang, Yuanwei Xie, Zhibin Li, Xiaozheng Fan, Chenyoukang Lin, Peiyang Wei, Linlin Chen, Xun Deng, Jianhong Gan https://creativecommons.org/licenses/by-nc-sa/4.0
A Stacking Based Ensemble Learning Approach for Accurate Identification of Tumor Homing Peptides in Precision Cancer Therapeutics
The identification of tumor-homing peptides (THPs) plays a pivotal role in the development of targeted cancer therapies and precision medicine. Current THP identification methods still suffer from limited feature representation, moderate predictive performance, and insufficient generalization, highlighting the need for more robust ensemble frameworks. In this study, we propose STHPP, an innovative stacking-based ensemble machine learning approach designed to improve the accuracy and reliability of THP discovery. Two benchmark datasets, referred to as the "main" and "small" datasets of Shoombuatong were collected, merged, and pre-processed in preparation to create a large dataset and then split for training and testing. The STHPP model applies a two-layer ensemble architecture: first layer that aggregates three heterogenous baseline classifiers, Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and then second layer applies CatBoost as a meta-classifier for post-processing predictive results of the base models. The two-layer architecture uses model diversity and concepts in ensemble learning to enhance generalization performance. The STHPP framework proposed got outstanding performance with accuracy 0.98, precision 0.97, sensitivity 0.99, specificity 0.97, and a Matthews Correlation Coefficient (MCC) of 0.98. These are better than the performances of current state-of-the-art approaches, which illustrates the effectiveness of using the stacking strategy in complicated peptide classification problems. The finding showcases the potential of STHPP as a strong and scalable computational platform for propelling peptide-based drug discovery research and targeted oncology.
Copyright (c) 2025 Jahid Hassan Akash, Rajib Mia, Abu Kowshir Bitto, Abdul Kadar Muhammad Masum, Jobaer Ahmed, Fokrul Islam Khan https://creativecommons.org/licenses/by-nc-sa/4.0
Quantum AI for Dark Web Narcotics Detection: A Hybrid Cybersecurity Framework
Through a six-month operational deployment with law enforcement agencies, this study introduces the Quantum Threat Detection Model (QTDM), a groundbreaking hybrid quantum-classical framework that exhibits quantifiable quantum advantage in counter-narcotics cybersecurity. The framework integrates NISQ-era quantum processors with dynamic workload partitioning and quantum kernel techniques to overcome significant constraints of conventional AI systems in the analysis of encrypted dark web transactions. Three groundbreaking contributions are shown via empirical validation: (1) 94.3% (±1.2%) classification accuracy for dark web drug transactions, which is 5.8 times faster than traditional GPU clusters in processing encrypted data; (2) finding a 10-qubit performance plateau and a 0.5% error rate threshold, which establishes ideal boundaries for resource allocation in NISQ-era implementations; and (3) the first GDPR/CCPA-aligned ethical governance protocol for quantum-powered surveillance, which includes algorithmic bias monitoring and quantum warrant procedures. Operational findings include 76% early detection rate for synthetic opioids, 92% adversarial resistance against GAN-generated obfuscation, and 42% improvement in trafficking network identification. The QTDM framework lowers the threat detection latency from 47 minutes to 8.2 minutes while processing 2.4 million transactions per day with 98.7% uptime. By offering a technological architecture and policy framework for the ethical implementation of quantum technology in international security applications, this study establishes quantum cybersecurity as an operational reality rather than a theoretical potential.
Copyright (c) 2025 Gabriel Silva-Atencio https://creativecommons.org/licenses/by-nc-sa/4.0
Generative AI's Sociotechnical Evolution: Scaling Limits, Governance Gaps, and Sustainable Pathways
This study provides a comprehensive sociotechnical analysis of the development of generative artificial intelligence (GenAI) by analysing 50 systems (2014–2023) and interviewing 25 global experts in the area. Three separate architectural epochs are identified by the research, and each is distinguished by unique scale patterns. Additionally, it demonstrates that performance peaks at 200B parameters, when a 1% increase in Fréchet Inception Distance (FID) scores corresponds to an 8× increase in processing power. There are non-linear trade-offs between increasing skills and conserving energy, according to quantitative studies. According to qualitative study, there are significant disparities in the speed at which different industries adopt new technologies. Global South nations are more affected than others (88% lack frameworks), with implementation delays of 2.3 years and governance delays of 4.2 years. A validated optimization matrix showing that new building designs can make things 3.8 times more efficient but are hard to put into practice, (1) extended scaling laws that include energy and adoption metrics, and (3) sector-specific policy tools to close the 72% policy gaps in education and the 92% accuracy-adoption paradox in healthcare. The results indicate that institutional readiness, rather than mere technical expertise, affects real-world outcomes, challenging deterministic narratives of progress. They also provide us helpful ways to develop artificial intelligence (AI) that follow the rules of Green AI.
Copyright (c) 2025 Gabriel Silva Atencio https://creativecommons.org/licenses/by-nc-sa/4.0
Optimizing Machine Learning Architectures for Time Series Forecasting: A Hybrid rvGA-eNM Approach
This study introduces a hybrid rvGA-eNM (real-valued Genetic Algorithm with enhanced Nelder-Mead) optimization approach for time series forecasting, specifically designed to address data scarcity and computational efficiency challenges in operational environments. Unlike contemporary hybrid algorithms that prioritize accuracy through increased complexity, rvGA-eNM employs adaptive algorithm orchestration that explicitly separates global exploration and local exploitation phases through convergence-based transition mechanisms. Multi-domain validation across Indonesian crude oil prices (156 monthly observations), Gorontalo regional electricity consumption (26 annual observations), and Albania GDP (125 quarterly observations) demonstrates robust forecasting performance with MAPE values ranging from 3.37% to 6.33% and convergence times between 0.93 and 3.31 seconds. Comparative benchmarking against state-of-the-art hybrid algorithms reveals substantial computational advantages: rvGA-eNM achieves comparable accuracy with 72× faster computation than deep learning hybrids and converges within 100 iterations compared to 200-500 iterations for contemporary methods. The algorithm exhibits exceptional small-sample robustness, maintaining reliable forecasts with as few as 26 observations—a critical capability for emerging markets and data-constrained applications. Cross-domain consistency, evidenced by narrow MAPE variance (2.96 percentage points) across fundamentally different forecasting contexts, suggests genuine algorithmic generalizability without domain-specific customization requirements. This research contributes Algorithm Orchestration Theory, formalizing how complementary algorithm capabilities combine synergistically through adaptive phase transitions. The findings challenge conventional assumptions about minimum data requirements in forecasting and demonstrate that computational efficiency deserves elevation as a primary objective alongside accuracy. The hybrid rvGA-eNM offers practitioners a practical, efficient solution for diverse operational forecasting applications, particularly valuable in resource-constrained environments where sophisticated forecasting methods have traditionally been inaccessible.
Copyright (c) 2025 Wahab Musa, Muhammad Rifai Katili, Wrastawa Ridwan https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Driven Predictive Maintenance in Infrastructure and Facilities Management
This study addresses the critical challenge of transforming traditional reactive maintenance approaches within infrastructure and facilities management into proactive, data-driven strategies leveraging advancements in artificial intelligence. Conventional maintenance, often reliant on fixed schedules or post-failure interventions, falls short in mitigating unexpected downtimes and escalating costs. To overcome these limitations, this research deploys multiple machine learning algorithms, namely, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Random Forest Classifier (RFC), and Extreme Gradient Boosting (XGBoost), applied to a comprehensive synthetic predictive maintenance dataset. This dataset encapsulates key operational metrics including temperature, torque, rotational speed, and tool wear across diverse failure modes. The comparative analysis reveals that XGBoost substantially outperforms alternative models, achieving a remarkable accuracy of 98.9% in multiclass failure prediction, supported by an AUC nearing 1.0 and F1 scores above 0.98 in both validation and test sets. RFC and SVC closely follow, each delivering precision and recall rates exceeding 95%. Notably, KNN provides rapid inference, facilitating real-time applications despite slightly lower accuracy metrics (~96.6%). Advanced preprocessing techniques, such as feature scaling, label encoding, and synthetic minority oversampling (SMOTE), enhanced model robustness amid inherent class imbalances. Critical features, including torque and tool wear, exhibited pronounced predictive importance, aligning with known mechanical failure signatures. The findings underscore AI’s potential to revolutionize maintenance by offering granular failure diagnostics and enabling timely interventions, thus significantly reducing operational costs and preventing catastrophic infrastructure failures. This research advances the state-of-the-art by integrating interpretability, cross-model benchmarking, and practical scalability considerations, positioning AI-driven predictive maintenance as a cornerstone of modern infrastructure resilience and sustainability.
Mohammad Morshed Uddin Al Mostam Sek Billah
Copyright (c) 2025 Seaam Bin Masud, Hasan Mahmud Sozib, Kamana Parvej Mishu, Rahima Binta Bellal, Mohammad Tahmid Ahmed, Anwarul Matin Jony, Syeda Tabassum, Mohammad Morshed Uddin Al Mostam Sek Billah https://creativecommons.org/licenses/by-nc-sa/4.0
A Novel Method for Enhancing Warehouse Operations Using Heterogeneous Robotic Systems for Autonomous Pick-and-Deliver Tasks
The rapid rise of warehouse automation has increased the need for reliable multi-robot coordination. Efficient task allocation and path planning are central challenges that affect picking speed, energy use, and system scalability. This paper proposes an integrated framework for warehouse-oriented multi-robot task allocation and route planning. The method combines the Hungarian algorithm for cost-minimized task distribution with an open-loop Traveling Salesman Problem (TSP) for path sequencing. Unlike approaches that apply these steps separately, our framework links them in a single design and adds two practical extensions: explicit handling of heterogeneous robot capacities and a reassignment phase that recovers tasks left unallocated after the first assignment. These additions improve coverage and efficiency while keeping computation lightweight. Simulations in MATLAB show good scaling with larger fleets and reductions in both travel distance and execution time. The proposed framework provides a heterogeneity-aware allocation mechanism, robust unassigned-task handling, and integrated path optimization, and can be extended to dynamic order insertion and obstacle-aware navigation in warehouse settings.
Copyright (c) 2025 Youssef MSALA, Hamed Oussama, Mohamed Talea, Mohamed Aboulfatah https://creativecommons.org/licenses/by-nc-sa/4.0
Localization of the Work Coordinate System Using Computer Vision and Geometric Transformations on Three-Axis CNC Milling Machines
This paper presents a novel methodology for the location of the workpiece coordinate system origin (WCS) on three-axis computer numerical control (CNC) milling machines. The proposed approach uses computer vision techniques to detect geometric features on the workpiece and calculates zero-point coordinates through pixel-based adjustment and coordinate transformation matrices. The method includes a calibration process to align the camera system with the machine’s coordinate reference frames and compensates for displacement between the vision sensor and the tool center point. Experimental validation was carried out on a HAAS VF1 CNC milling machine, comparing the proposed method with traditional probing techniques. The results demonstrate improved repeatability and accuracy in the location of the WCS, with deviations maintained within acceptable industrial tolerances. This approach facilitates faster setup times and improves process automation in intelligent manufacturing environments.
Copyright (c) 2025 Manuel Meraz Mendez, Jorge Duarte Loera, Claudia Lerma Hernández https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing the Prediction of IL-4 Inducing Peptides Using Stacking Ensemble Model
Interleukin-4 (IL-4) plays a critical role in immune regulation and inflammation suppression, and therefore precise prediction is important in immunotherapy and vaccine design. In this work, we present an innovative stacking ensemble-based predictive model for IL-4-inducing peptide discovery. The method combines the group of feature extraction techniques, i.e., Amino Acid Composition (AAC), Amphiphilic Pseudo Amino Acid Composition (APAAC), and their combinations, and their pruning using SHAP (SHapley Additive exPlanations) with only the most relevant features being retained. To solve the class imbalance problem inherent in the peptide data, the ADASYN (Adaptive Synthetic Sampling) algorithm was applied for synthetic oversampling. We applied eight machine learning classifiers: Logistic Regression, Random Forest, Support Vector Classifier, Decision Tree, K-Nearest Neighbors, XGBoost, LightGBM, and a stacking ensemble model, enabling the strong prediction on both imbalanced and balanced datasets. Our evaluation demonstrates the stacking model's better performance on the imbalanced and balanced dataset. Surprisingly, with combined characteristics, the stacking model over the independent test set yielded accuracy of 89.97% and Matthew's Correlation Coefficient (MCC) as 0.79. Accurate comparisons of performance over AAC and APAAC feature spaces indicate that the stacking model performs better than other classifiers in all instances, albeit more so under balanced scenarios, referring to data rebalancing requirements. This research not only highlights the precision of stacking ensembles in peptide classification tasks but also urges the integration of interpretable feature selection and data balancing in future immunoinformatic pipelines.
Copyright (c) 2025 Rajib Mia, Tawfiqul Hasan, Abu Kowshir Bitto, Mohammad Mahadi Hassan, Mohammed Shamsul Alam, Abdul Kadar Muhammad Masum https://creativecommons.org/licenses/by-nc-sa/4.0
An Explainable AI Based Deep Ensemble Transformer Framework for Gastrointestinal Disease Prediction from Endoscopic Images
Gastrointestinal diseases such as gastroesophageal reflux disease (GERD) and polyps remain prevalent and challenging to diagnose accurately due to overlapping visual features and inconsistent endoscopic image quality. In this study, we investigate the application of transformer-based deep learning models—Vision Transformer (ViT), Swin Transformer, and a novel Ensemble Transformer model—for classifying four categories: GERD, GERD Normal, Polyp, and Polyp Normal from endoscopic images. The dataset was curated and collected in collaboration with Zainul Haque Sikder Women's Medical College & Hospital, ensuring high-quality clinical annotations. All models were evaluated using precision, recall, F1 score, and overall classification accuracy. Our proposed Ensemble Transformer model, which fuses the outputs of ViT and Swin Transformer, achieved superior performance by delivering well-balanced F1 scores across all classes, reducing misclassification, and improving robustness with an overall accuracy of 87%. Furthermore, we incorporated explainable AI (XAI) techniques such as Grad-CAM and Grad-CAM++ to generate visual explanations of the model’s predictions, enhancing interpretability for clinical validation. This work demonstrates the potential of integrating global and local attention mechanisms along with XAI in building reliable, real-time, AI-assisted diagnostic support systems for gastrointestinal disorders, particularly in resource-limited healthcare settings.
Copyright (c) 2025 Prof. Dr. Abdul kadar Muhammad Masum, Abu Kowshir Bitto, Shafiqul Islam Talukder, Md Fokrul Islam Khan, Mohammed Shamsul Alam, Khandaker Mohammad Mohi Uddin https://creativecommons.org/licenses/by-nc-sa/4.0
Advancing Public Safety with Real-Time Life Jacket Detection and Demographic Profiling Using YOLOv8 and Age Classification
This study introduces a robust life jacket identification system that incorporates YOLOv8, FaceNet, and AgeNet for real-time safety surveillance in settings such as beaches, swimming pools, and maritime activities. The YOLOv8 model is applied for detecting life jackets, while FaceNet and AgeNet do face recognition and age classification, respectively, dividing persons into age groupings like "Teenager" or "Adult." The technology proficiently recognizes life jackets, detects faces, and evaluates risk by analyzing demographic factors, such as age, to generate safety alerts. The model attained a remarkable precision of 0.9934, a recall of 0.9818, and mAP50 of 0.9948, therefore validating its efficacy in recognizing life jackets and identifying individuals at risk. In high-risk aquatic situations, real-time life jacket detection, age classification, and facial recognition make the system resilient and reliable, improving public safety and risk management.
Copyright (c) 2025 Md Abu Yusuf, Nur Mohammad Chowdhury, Ponchanon Datta Rone, Partha Pratim Saha, Md Iqbal Hossan, Debabrata Sarkar, Ranjan Paul, Md Rana Hossain, Madhusodan Chakraborty https://creativecommons.org/licenses/by-nc-sa/4.0
Comparative Analysis of Transformer and LSTM Architectures for Cybersecurity Threat Detection Using Machine Learning
The growing prevalence of advanced persistent threats (APTs), zero-day exploits, and the rapid proliferation of IoT devices have exposed limitations in traditional cybersecurity approaches. In response, this study presents a comparative analysis of deep learning models—specifically Long Short-Term Memory (LSTM) and Transformer-based architectures—for cybersecurity threat classification from textual data. Leveraging a standardized dataset and consistent preprocessing pipeline, both models are evaluated across key performance metrics, including accuracy, precision, recall, and F1-score. The results demonstrate that Transformer models significantly outperform LSTM-based approaches, exhibiting superior capacity to capture long-range dependencies, handle complex threat narratives, and generalize to previously unseen data. These findings offer valuable insights into the practical application of modern deep learning techniques in cybersecurity and provide a foundation for designing more robust and adaptive threat detection systems.
Copyright (c) 2025 Jobanpreet Kaur, Mani Prabha, Md Samiun, Syed Nazmul Hasan, Rakibul Hasan, Hammed Esa, Md Fakhrul Hasan Bhuiyan, Md Abdur Rob, Durga Shahi https://creativecommons.org/licenses/by-nc-sa/4.0
Leveraging Synthetic Mammograms to Enhance Deep-Learning Performance for Breast Cancer Classification Using EfficientNetV2L Architecture
INTRODUCTION: To improve survival rates for breast cancer, a leading cause of female mortality globally, early detection is essential. This study presents a deep learning framework for classifying mammogram images as normal or abnormal. OBJECTIVES: This research aims to enhance the performance of a deep learning model for breast cancer classification by augmenting a real mammogram dataset with synthetic images. The study evaluates the impact of progressively increasing the number of synthetic mammograms on the model's accuracy, precision, recall, and F1-score. METHODS: The approach utilizes the EfficientNetV2L model for classification. Data augmentation was performed by generating synthetic mammograms using Denoising Diffusion Probabilistic Models (DDPM). A baseline dataset of 410 real mammograms from the INbreast public dataset was augmented with an increasing number of synthetic images across four experimental scenarios. RESULTS: The model demonstrated substantial performance gains directly linked to the use of synthetic data. The best performance was achieved when 500 synthetic images were used, resulting in all evaluation metrics exceeding a score of 0.90. The results confirm that incorporating more synthetic images is a key factor in achieving both higher classification accuracy and more stable training convergence. CONCLUSION: These findings highlight the significant potential of synthetic image augmentation to address data scarcity, class imbalance, and model generalisation in medical image analysis. This method provides a scalable and privacy-preserving solution for breast cancer screening systems.
Denoising Diffusion Probabilistic Models (DDPM)
Copyright (c) 2025 Raymond Sutjiadi, Siti Sendari, Heru Wahyu Herwanto, Yosi Kristian https://creativecommons.org/licenses/by-nc-sa/4.0
Facial Emotion Recognition by CNN Combined Ensemble Model
Studying emotions can provide important information about a person's mental state. According to research, more than 50% of a person's current emotions can be identified from the human face. In this research, we propose an ensemble model for emotion recognition from facial images, which is obtained by combining the results obtained by retraining previously trained convolutional neural networks on a new and high-quality FaceEmocDS dataset. The methodological advantage of the ensemble model we propose is that the combination of VGG19, ResNet50, and DenseNet121 models allows us to take advantage of the strengths of each architecture: the ability to extract detailed features of VGG19, the stable learning process of ResNet50 through residual connections, and the efficiency of feature reuse of DenseNet121. This approach improves the results of individual models, increasing the accuracy to 85.66% . The FaceEmocDS dataset consists of 72,412 images and includes eight emotion classes, including a unique “contempt” class. The results show significant superiority when compared to other datasets (FER2013, AffectNet, CK+) and studies.
Copyright (c) 2025 Abdurahmon Kurbanov https://creativecommons.org/licenses/by-nc-sa/4.0
CAD-guided 6D pose estimation with deep learning in digital twin for industrial collaborative robot manipulation
6D pose estimation in the bin-picking task has attracted increasing attention from researchers. CAD model-based method have been proposed, demonstrating its effectiveness. However, most existing research relies on point cloud registration from the RGB-D camera, which is often not robust to noise and low-light conditions, leading to degraded point cloud quality and reduced accuracy. Thereby, the method accuracy is significantly affected. Moreover, detecting objects correctly plays a vital role in multiple objects. Supervised deep learning takes consideration into this task, but it typically requires a large amount of labeled data. In industrial environments, sample collection and model retraining are limited. To address these challenges, we introduce the potential approach that integrates the zero-shot learning YOLOE and DEFOM-Stereo model. The YOLOE detects and localizes the object without requiring object-specific training, while DEFOM-Stereo generates point clouds for the CAD model-based pose estimation. Extensive experiments demonstrate that the proposed approach achieves high accuracy in pose estimation, which is essential for grasp planning and manipulation tasks in robotics. Furthermore, the proposed approach is applied in a Unity3D-based digital twin, enabling enhanced virtual representation of a physical pickup target with an estimated pose. Hence, the research result supports more accurate and responsive digital twins for robotics toward the development of smart manufacturing systems.
Copyright (c) 2025 Quang Huan Dong, The-Thinh Pham, Tuan-Khanh Nguyen, Chi-Cuong Tran, Hoang-Huy Tran, Tan Do-Duy, Khang Hoang Vinh Nguyen, Quang-Chien Nguyen https://creativecommons.org/licenses/by-nc-sa/4.0
An adaptive traditional Chinese herbal medicine image recognition model via ED-HLOA-optimized DenseNet201
Research shows that ginseng, fritillaria cirrhosa and other Chinese herbal medicines and their active components can fight tumors via immune regulation, apoptosis induction, and signaling pathway modulation. Thus, deep learning-based for authentic Chinese herbal medicine identification and classification is gaining more attention. Although convolutional neural network (CNN)-based image recognition models have made progress in CHB recognition, they often face limitations such as simple structures, fixed parameters, and a singular optimization approach, primarily relying on learning rate adjustments, which impede achieving the high accuracy required for image recognition of CHB. To address this issue, this study proposes an elite differential mutation-based horned lizard optimization algorithm (ED-HLOA) and applies it to optimize a DenseNet201-based recognition model for CHB. It enables adaptive adjustments of the learning rate and compression factor for DenseNet201. Empirical studies on the dataset collected from practical application demonstrates that the ED-HLOA-optimized DenseNet201 model achieves high accuracy in CHB image classification, verifying the effectiveness of the algorithm. Compared with several state-of-the-art optimization algorithms, ED-HLOA performs well on both the training and verification sets, effectively avoiding overfitting.
Copyright (c) 2025 Peiyang Wei, Rundong Zou, Guangdong Dong, Wen Qin, Jianhong Gan, Oifeng Su, Zhibin Li https://creativecommons.org/licenses/by-nc-sa/4.0
AI-Powered Predictive Analytics for Financial Risk Management in U.S. Markets
In the fast-changing environment of financial complexity, efficient risk management is vital for economic stability as well as for growth. In this study, we present a robust AI-powered predictive analytics framework to improve financial risk classification in U.S. markets. The framework utilizes advanced machine learning techniques, a hybrid CatBoost and SVM model that allows it to solve challenges like class imbalance in a high-dimensional dataset while maintaining interpretable models. To probe errors, we use techniques such as Principal Component Analysis (PCA) and Synthetic Minority Oversampling Technique (SMOTE) for data quality and fairness in classification. Comprehensive experiments on a financial risk dataset are conducted to evaluate the framework at which it achieves high accuracy (95.93%) and F1-score (0.95) when compared to traditional machine learning models such as Logistic Regression and Random Forest. Furthermore, a feature importance analysis identifies important predictors of financial risk such as Total Debt-to-Income Ratio, Loan Duration, and Interest Rate, providing actionable on decision-making. Additionally, the proposed approach is not only highly scalable but it is also interpretable and adaptable to the dynamic demands of financial institutions. This study serves as a benchmark for predicting analytics for dealing with risk-associated challenges, leading to informed decision-making to ensure economic stability by integrating AI and machine learning in financial systems.
Copyright (c) 2025 Md Zikar Hossan, Muslima Begom Riipa, Md Azhad Hossain, Sweety Rani Dhar, Al Modabbir Zaman, Mohammad Hossain, Arif Hossen, Hasan Mahmud Sozib https://creativecommons.org/licenses/by-nc-sa/4.0
MSCSO: A Hybrid Nature-Inspired Algorithm for High-Dimensional Traffic Optimization in Urban Environments
Metropolitan regions have experienced higher economical and environmental pressure due to the fasted urbanization leading to increased traffic jams that necessitate the use of higher optimization techniques. Traditional traffic models do not usually take large-dimensional and dynamicity of urban mobility into consideration and require extraordinary computational approaches. Modified Sand Cat Swarm Optimization (MSCSO) improves the Sand Cat Swarm Optimization (SCSO) algorithm that adds Levy flights to global exploration and roulette wheel selection to adaptive exploitation to solve problems that are complex and high-dimensional. When used in urban traffic management, MSCSO works with enormous volumes of traffic, speed, weather, and incident, all of which may decrease Travel Time Index by 15 percent during rush hours. Benchmark tests are used to prove that MSCSO is better, scoring 0.0 in Sphere, Ackley and Rastrigin functions, and 28.0753 in Rosenbrock, whereas higher scores belong to Particle Swarm Optimization, Genetic Algorithms, Ant Colony Optimization and SCSO (e.g., 46). It supports urban planning, since a Flask-based web interface has the possibility to input and visualize real time traffic data in a simple way. The success of MSCSO is reliant on high-quality data and hardware-friendly algorithms but can scale to use real-time data sources, such as from GPS, machine learning traffic projections, and cloud hosting, and is of potential use in logistics, energy delivery, and resource assignment.
Copyright (c) 2025 Kuldeep Vayadande, Viomesh Kumar Singh, Amol Bhosle, Ranjana Gore, Yogesh Uttamrao Bodhe, Aditi Bhat, Zulfikar Charoliya, Aayush Chavan, Pranav Bachhav, Aditya Bhoyar https://creativecommons.org/licenses/by-nc-sa/4.0
P2PLLMEdge: Peer-to-Peer Framework for Localized Large Language Models using CPU only Resource-Constrained Edge
In this research, we present P2PLLMEdge, a pioneering peer-to-peer framework designed to enable localized Large Language Models (LLMs) to operate efficiently in resource-constrained edge environments, exemplified by devices such as the Raspberry Pi 4B and CPU-only laptops. The framework addresses critical challenges, including limited computational capacity, network overhead, and scalability, by leveraging lightweight RESTful communication protocols, model-specific quantization, and decentralized task distribution. Key results demonstrate that P2PLLMEdge achieves substantial performance improvements. On average, Peer 2 (CPU-only laptop) achieves a 44.7% reduction in total duration (tpeer2, total = 15.87 × 109 ns) compared to Peer 1 (Raspberry Pi 4B, tpeer1, total = 28.18 × 109 ns). The framework processes tokens at a rate of 21.77 tokens/second on advanced LLMs like Granite3.1-moe:1b, significantly outperforming the baseline. Peer 1, employing quantized LLMs such as smolm2:360m-instruct-q8_0, reduces prompt evaluation duration by 23.2% (tpeer1, prompt_eval = 0.76 × 109 ns) compared to larger models like qwen2.5:0.5binstruct (tpeer1, prompt_eval =0.99 × 109 ns). Peer 2 demonstrates superior summarization capabilities, with evaluation durations (tpeer2, eval) reduced by 72.8% (tpeer2, eval = 5.15 × 109 ns) for explanation-type prompts relative to Peer 1 (tpeer1, eval = 18.93 × 109 ns). The framework also achieves significant network efficiency, reducing inter-peer communication durations by up to 44.9% (tpeer2, network = 25.83 × 109 ns vs. tpeer1, network = 46.92 × 109 ns). Peer-to-peer synergy ensures seamless task execution, where Peer 1 generates text and offloads computationally intensive summarization tasks to Peer 2, achieving a balance between performance and resource utilization. The novelty of P2PLLMEdge lies in its ability to seamlessly integrate lightweight LLMs with decentralized edge devices, achieving advanced natural language processing functionalities entirely on edge devices traditionally deemed unsuitable for such tasks. This framework provides an adaptable, and cost-effective approach for deploying quantized LLM-driven applications. Future directions include scaling the framework to multi-peer environments, optimizing task scheduling algorithms, and exploring integration with heterogeneous LLM-enabled systems. The codes are available on https://github.com/ParthaPRay/peer_to_peer_local_llm_interaction.
Copyright (c) 2025 Partha Pratim Ray, Mohan Pratap Pradhan https://creativecommons.org/licenses/by-nc-sa/4.0
Explainable AI Based Deep Ensemble Convolutional Learning for Multi-Categorical Ocular Disease Prediction
Diseases of the eye such as diabetic retinopathy, glaucoma, and cataract remain among the leading causes of blindness and vision impairment worldwide. Diagnosis in its early stages followed by early treatment is crucial to preventing permanent loss of vision. Recent advances in Artificial Intelligence (AI), particularly Transfer Learning and Explainable AI (XAI), have proven highly promising in automating the identification of retinal pathologies from medical images. In this paper, we propose an ensemble deep learning approach that integrates four pre-trained convolutional neural networks, i.e., VGG16, MobileNet, DenseNet, and InceptionV3, to classify retinal images into four categories: diabetic retinopathy, glaucoma, cataracts, and normal. The ensemble method leverages the power of multiple models to improve classification accuracy. Additionally, Explainable AI techniques are applied to make the model more interpretable, with visual explanations and insights into AI system decision-making and thereby establishing clinical trust and reliability. The system is evaluated on a new benchmarked eye disease dataset used from Hugging Face, and the results in terms of accuracy and model transparency are encouraging. This research contributes towards developing reliable, explainable, and efficient AI-driven diagnostic systems to assist healthcare professionals in the early detection and management of eye diseases
Copyright (c) 2025 Abu Kowshir Bitto, Rezwana Karim, Mst Halema Begum, Md Fokrul Islam Khan Khan, Dr. Md. Maruf Hassan, Prof. Dr. Abdul kadar Muhammad Masum https://creativecommons.org/licenses/by-nc-sa/4.0
Reimagining Asteroid Risk Assessment: A Comparative Review of Advanced Machine Learning Techniques
The escalating discovery rate of Near-Earth Asteroids (NEAs) has intensified the need for advanced computational frameworks capable of evaluating their impact risks with high precision. Traditional machine learning models, while foundational for early NEA classification and trajectory prediction, increasingly falter when confronted with the intricate, high-dimensional dynamics of asteroid motion. This limitation underscores the necessity for sophisticated techniques that reconcile computational efficiency with predictive accuracy across large, multi-dimensional datasets. This review systematically evaluates state-of-the-art machine learning algorithms—including quantum-enhanced models, hybrid quantum-classical frameworks, and lightweight convolutional neural networks (CNNs)—for their efficacy in asteroid risk assessment. By analyzing outcomes from recent studies, we contrast performance metrics such as accuracy, computational cost, and scalability. For instance, Quantum K-Nearest Neighbors (QKNN) demonstrates a 15% accuracy improvement over classical counterparts in high-dimensional data classification, while XGBoost achieves 99.99% precision in asteroid diameter prediction. Lightweight CNNs, such as MobileNetV1, further enable real-time processing on resource-constrained platforms like CubeSats, reducing latency by 30%.
Copyright (c) 2025 Kuldeep Vayadande, Dnyaneshwar M. Bavkar, Ishwari Rohit Raskar, Umar Mubarak Mulani, Jyoti Kanjalkar, Rajashree Tukaram Gadhave, Preeti Bailke, Yogesh Bodhe, Ajit R. Patil https://creativecommons.org/licenses/by-nc-sa/4.0
Robust Robotic Arm Calibration combining Multi-Distance Optimization Approach with Lagrange Starfish Optimization Algorithm
In response to the limitations of existing robotic parameter calibration methods in terms of computational complexity, convergence speed, data requirements, and accuracy, this study proposes an innovative calibration scheme that combines an improved Lagrangian Starfish Optimization Algorithm (LSFA) with a Support Vector Machine (SVM) algorithm. By incorporating Lagrange interpolation and a multi-dimensional distance metric model (including Mahalanobis distance, Manhattan distance, Chebyshev distance, cosine distance, standardized Euclidean distance, and Euclidean distance), the enhanced starfish optimization algorithm significantly improves global search capabilities and local search accuracy. This effectively addresses issues such as initial value sensitivity, noise, and outliers, with the algorithm specifically designed for kinematic parameter calibration of robotic arms. Furthermore, the improved local search mechanism optimizes the position update strategy of starfish through a weighted system, preventing the algorithm from becoming trapped in local optima. To further enhance the accuracy of dynamic parameter calibration, this study integrates the SVM algorithm into the LSFA framework, proposing the LSFA-SVM method specifically for dynamic parameter calibration of robotic arms. Experiments demonstrate a 38.59% reduction in error compared to traditional SVM. The results indicate that LSFA excels in kinematic calibration of robotic arms, achieving a root mean square error (RMSE) of 0.29 mm, a 29.27% improvement over the traditional Starfish Optimization Algorithm (SFOA). This study provides an efficient and precise solution for robotic parameter calibration in complex environments.
Lagrangian Starfish Optimization Algorithm
Copyright (c) 2025 Yongtao Qu, Zhiqiang Li, Long Liao, Xun Deng, Yuanchang Lin, Tinghui Chen, Linlin Chen, Jia Liu, Peiyang Wei, Jianhong Gan, ZhenZhen Hu, Can Hu, Yonghong Deng, Wei Li, Zhibin Li https://creativecommons.org/licenses/by-nc-sa/4.0
A novel knowledge enhancement method for large-scale natural language training model
Knowledge enhancement-based large-scale natural language training model is an advanced language model that combines deep learning and knowledge enhancement. By learning from massive unlabeled data and combining with external knowledge such as knowledge graph, it breaks through the limitations of traditional models in interpretability and reasoning ability. Introducing knowledge into data-driven artificial intelligence model is an important way to realize human-machine hybrid intelligence. However, since most pre-trained models are trained on large-scale unstructured corpus data, the defects in certainty and explainability can be remedied to some extent by introducing external knowledge. To solve the above problems, we present a knowledge-enhanced large-scale natural language training model that integrates deep learning with external knowledge sources (e.g., knowledge graphs) to improve interpretability and reasoning ability. This approach addresses the limitations of traditional models trained on unstructured data by incorporating external knowledge to enhance certainty and explainability. We propose a new knowledge enhancement method and demonstrate its effectiveness through a long text representation model. This model processes structured, knowledge-rich long texts by extracting and integrating knowledge and semantic information at the sentence and document levels. It then fuses these representations to generate an enhanced long text representation. Experiments on legal case matching tasks show that our model significantly outperforms existing methods, highlighting its innovation and practical value.
large-scale natural language training model
Copyright (c) 2025 Qi Han, Gilja So https://creativecommons.org/licenses/by-nc-sa/4.0
Empowering Universal Robot Programming with Fine-Tuned Large Language Models
LLMs are transforming AI but face challenges in robotics due to domain-specific requirements. This paper explores LLM-generated URScript code for Universal Robots (UR), improving automation accessibility. A fine-tuning dataset of 20,000 synthetic samples, based on 514 validated human-created examples, enhances performance. Using the Unsloth framework, we fine-tune and evaluate the model in real-world scenarios. Results demonstrate LLMs’potential to simplify UR robot programming, highlighting their value in industrial automation. The video demo is available at the following link, and the codebase will be added soon: https://github.com/t1end4t/llm-robotics
Copyright (c) 2025 Tien Dat Le, Minhhuy Le https://creativecommons.org/licenses/by-nc-sa/4.0
Comparative Analysis of BAS and PSO in Image Transformation Optimization
This paper presents a comparative study between the Particle Swarm Optimization (PSO) algorithm and the Beetle Antennae Search (BAS) algorithm for optimizing image transformations, with a focus on their performance in handling noisy and non-noisy images. Our experiments reveal that BAS consistently achieves better results in terms of pixel change when compared to PSO. The algorithms were evaluated based on their ability to minimize the objective function, which measures the error between the transformed reference image and the target image. Our results demonstrate that both BAS and PSO can effectively optimize image transformations, but BAS consistently outperformed PSO in terms of convergence speed and final objective value. Additional experiments with varying objective functions further validated the robustness and efficiency of BAS in achieving accurate image alignment.
Copyright (c) 2025 Anik Dwivedi, Ameer Tamoor Khan, Shuai Li https://creativecommons.org/licenses/by-nc-sa/4.0
Cutting-Edge Techniques for Detecting Fake Reviews
The paper reviews various approaches for detecting fake reviews using different machine learning techniques, each with distinct strengths and limitations. It examines existing literature on supervised learning methods, unsupervised techniques, graph-based models, and hybrid approaches. Among these, unsupervised models rely on pattern recognition, while supervised methods, including SVM and transformer-based models like BERT, offer high accuracy but struggle with class imbalance and computational efficiency. Unsupervised and graph-based models serve as effective alternatives when labeled data is scarce or when complex relationships between reviews and users must be analyzed. Additionally, hybrid approaches that integrate multiple techniques are gaining traction, as they enhance feature selection and model performance. In this paper, we explore different methodologies for fake review classification, analyze their advantages and drawbacks, and highlight key challenges in the field.
Copyright (c) 2025 Kuldeep Vayadande, Amit Mishra, Gajanan R. Patil, Yogesh Bodhe, Pavitha Nooji, Ninad Kale, Anish Katariya, Amey Kharade, Parth Supekar, Lalit Patil https://creativecommons.org/licenses/by-nc-sa/4.0