EAI Endorsed Transactions on AI and Robotics
type: channel
description: 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 (CiteScore: 7.6), CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions
publisher: EAI
language: en
publication name: EAI Endorsed Transactions on AI and Robotics
issn: 2790-7511
copyright: This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
items: An Efficient Hybrid Model With Harris Hawks Optimization Algorithm for Predicting Oat Water
items: Explainable Transformer Models for Early Prediction of Chronic Diseases Using Longitudinal Electronic Health Records (EHRs)
items: An Integrated Beetle Antennae Search–Enabled Navigation Framework for Omnidirectional AGVMobile Robots in Unknown Environments
items: A Highly Efficient ACO-SA Algorithm for Robot Path Planning
items: Student Behavior Detection in Classroom Environments Using Deep Learning Models
items: FrequencyFormer: Oriented Object Detection with Frequency Transformer
items: FDD-YOLO: A Lightweight Multi-scale Prohibited Items Detection Model
items: A Stacking Based Ensemble Learning Approach for Accurate Identification of Tumor Homing Peptides in Precision Cancer Therapeutics
items: Quantum AI for Dark Web Narcotics Detection: A Hybrid Cybersecurity Framework
items: Explainable AI for Customer Churn Prediction in the Energy Sector Using Ensemble Machine Learning Models
items: Generative AI's Sociotechnical Evolution: Scaling Limits, Governance Gaps, and Sustainable Pathways
items: Hardware-Aware INT8 Quantization and FPGA Deployment of MobileNetV2 for Real-Time Facial Landmark Detection
items: Optimizing Machine Learning Architectures for Time Series Forecasting: A Hybrid rvGA-eNM Approach
items: AI-Driven Predictive Maintenance in Infrastructure and Facilities Management
items: A Novel Method for Enhancing Warehouse Operations Using Heterogeneous Robotic Systems for Autonomous Pick-and-Deliver Tasks
items: Localization of the Work Coordinate System Using Computer Vision and Geometric Transformations on Three-Axis CNC Milling Machines
items: Enhancing the Prediction of IL-4 Inducing Peptides Using Stacking Ensemble Model
items: An Explainable AI Based Deep Ensemble Transformer Framework for Gastrointestinal Disease Prediction from Endoscopic Images
items: Advancing Public Safety with Real-Time Life Jacket Detection and Demographic Profiling Using YOLOv8 and Age Classification
items: Comparative Analysis of Transformer and LSTM Architectures for Cybersecurity Threat Detection Using Machine Learning
items: Leveraging Synthetic Mammograms to Enhance Deep-Learning Performance for Breast Cancer Classification Using EfficientNetV2L Architecture
items: Facial Emotion Recognition by CNN Combined Ensemble Model
items: CAD-guided 6D pose estimation with deep learning in digital twin for industrial collaborative robot manipulation
items: An adaptive traditional Chinese herbal medicine image recognition model via ED-HLOA-optimized DenseNet201
items: AI-Powered Predictive Analytics for Financial Risk Management in U.S. Markets
items: MSCSO: A Hybrid Nature-Inspired Algorithm for High-Dimensional Traffic Optimization in Urban Environments
items: P2PLLMEdge: Peer-to-Peer Framework for Localized Large Language Models using CPU only Resource-Constrained Edge
items: Explainable AI Based Deep Ensemble Convolutional Learning for Multi-Categorical Ocular Disease Prediction
items: Reimagining Asteroid Risk Assessment: A Comparative Review of Advanced Machine Learning Techniques
items: Robust Robotic Arm Calibration combining Multi-Distance Optimization Approach with Lagrange Starfish Optimization Algorithm

An Efficient Hybrid Model With Harris Hawks Optimization Algorithm for Predicting Oat Water
type: item
description: Oats are a cold-tolerant and high-yielding cultivated forage crop, which have relatively high requirements for water management. To improve the forecasting accuracy of irrigation requirements for oats, this paper proposes a novel hybrid neural network architecture, whose parameters are refined using the Harris Hawk optimization algorithm. It directly addresses two prevalent shortcomings in current predictive models: the typically imprecise manual adjustment of hyperparameters, and the inadequate modeling of both spatial and temporal dependencies with the data. By integrating these methodological improvements, the proposed method aims to achieve more precise and robust forecasts. Firstly, the hybrid model integrates a temporal convolutional network, a bidirectional long short-term memory network, and a multi-head attention mechanism. This method leverages multi-head attention to enrich feature representation, thereby facilitating a more comprehensive capture of the temporal dynamics inherent in alpine oat water demand. Secondly, the Harris Hawk optimization method is introduced to optimize the model’s hyperparameters, effectively avoiding local optimum. Experimental results on the oat water demand and environmental dataset from 2019 to 2023 indicate that the hybrid model achieved a mean absolute error and root mean square error of 0.3432 and 0.4863, respectively, thus representing reductions of approximately 52.73% and 46.39%compared to the traditional Long Short-Term Memory (LSTM) model. The coefficient of determination increased by about 15.31%. Ablation study results demonstrate that the complete hybrid model achieved Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 0.3432 and 0.4863, respectively, which represent reductions of approximately 52.89% and 43.75% compared to the baseline model, the coefficient of determination improving by about 22.16%. Compared with other methods, this method has a distinct advantage in forecasting the precision of oat …
subject: Research article
  topic: section
subject: Reinforcment Learning-Based Adaptive Control for Collaborative Robots in Dynamic Envirnoments
  topic: category
subject: Harris Hawk Optimization
  topic: keywords
subject: TCN-BiLSTM-MHA
  topic: keywords
subject: oat water demand
  topic: keywords
creator: He Jinbo
creator: Zirun Wang
creator: Wanzhen Huang
creator: Fanting Zhou
creator: Anqi Wang
creator: Xianjin Wu
creator: Pengzi Chen
creator: Tongli He
creator: Jianhong Gan
creator: Peiyang Wei
creator: Zhibin Li
creator: Chunjiang Wu
rights: Copyright (c) 2025 Jinbo He, Zirun Wang, Wanzhen Huang, Fanting Zhou, Anqi Wang, Xianjin Wu, Pengzi Chen, Tongli He, Jianhong Gan, Peiyang Wei, Zhibin Li, Chunjiang Wu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-04
publication date: 2026-03-04
doi: 10.4108/airo.12011

Explainable Transformer Models for Early Prediction of Chronic Diseases Using Longitudinal Electronic Health Records (EHRs)
type: item
description: 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.
subject: Research article
  topic: section
subject: Chronic Disease Prediction
  topic: keywords
subject: Electronic Health Records
  topic: keywords
subject: Explainable Artificial Intelligence
  topic: keywords
subject: Longitudinal Data
  topic: keywords
subject: Transformer Models
  topic: keywords
creator: Hewa Majeed Zangana
creator: Maryam A. Sulaiman
rights: Copyright (c) 2025 Hewa Majeed Zangana, Maryam A. Sulaiman https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-11
publication date: 2026-02-11
doi: 10.4108/airo.11517

An Integrated Beetle Antennae Search–Enabled Navigation Framework for Omnidirectional AGVMobile Robots in Unknown Environments
type: item
description: 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.
subject: Research article
  topic: section
subject: Reinforcment Learning-Based Adaptive Control for Collaborative Robots in Dynamic Envirnoments
  topic: category
subject: Beetle Antennae Search (BAS)
  topic: keywords
subject: Mobile Robot Navigation
  topic: keywords
subject: OMNI AGV
  topic: keywords
subject: Obstacle Avoidance
  topic: keywords
subject: Adaptive PID Control
  topic: keywords
subject: Occupancy Grid Mapping
  topic: keywords
subject: V-REP (CoppeliaSim)
  topic: keywords
creator: Ata Jahangir Moshayedi
creator: Atanu Shuvam Roy
creator: Utsab Karan
creator: Mingjun Zhang
creator: David Bassir
rights: Copyright (c) 2025 Ata Jahangir Moshayedi, Atanu Shuvam Roy, Utsab Karan, Mingjun Zhang, David Bassir https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-11
publication date: 2026-02-11
doi: 10.4108/airo.11438

A Highly Efficient ACO-SA Algorithm for Robot Path Planning
type: item
description: 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.
subject: Research article
  topic: section
subject: Reinforcment Learning-Based Adaptive Control for Collaborative Robots in Dynamic Envirnoments
  topic: category
subject: Path Planning
  topic: keywords
subject: Mobile Robot
  topic: keywords
subject: Ant Colony Algorithm
  topic: keywords
subject: Simulated Annealing Algorithm
  topic: keywords
subject: Grid Modeling
  topic: keywords
subject: ACO-SA
  topic: keywords
creator: Wei Li
creator: Feng Yang
creator: Zhibin Li
creator: Juan Mou
creator: Yanmei Ha
creator: Yi Liu
creator: Yanmei Qin
creator: Peiyang Wei
creator: Linlin Chen
creator: Xun Deng
creator: Tinghui Chen
creator: Jia Liu
creator: Jianhong Gan
creator: ZhenZhen Hu
creator: Yonghong Deng
creator: Guodong Li
creator: Qifeng Su
rights: 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
date: 2025-12-29
publication date: 2025-12-29
doi: 10.4108/airo.11177

Student Behavior Detection in Classroom Environments Using Deep Learning Models
type: item
description: Observing student behavior in classroom environments provides valuable insights into attention and engagement. However, manual monitoring is labor-intensive and often inconsistent, particularly in large lecture settings. This study proposes a deep learning–based framework for automatic student behavior analysis in real classroom scenarios. A dedicated dataset was constructed, consisting of 3,373 images annotated with five attention-related behaviors—“Focused”, “Raising Hand”, “Distracted”, “Sleeping”, and “Using Phone”—totaling 9,659 labeled instances. The dataset captures diverse real-world conditions, including variations in classroom layout, camera viewpoints, and occlusion. We systematically evaluated stateof-the-art object detection models, including YOLOv8–YOLOv12 and the Real-Time Detection Transformer (RT-DETR). Experimental results show that YOLOv8m achieved the highest localization accuracy (mAP@0.5 = 0.920), YOLOv11s/m demonstrated the best overall performance (mAP@0.5:0.95 = 0.726), and RT-DETRX achieved the highest F1-score (0.886). Notably, larger model size does not necessarily translate into better performance. In addition to accuracy, inference speed was evaluated to assess real-time applicability. Lightweight models such as YOLOv11s achieved a favorable balance between performance and efficiency, enabling real-time processing on resource-constrained hardware. Furthermore, YOLOv11s—with high Precision (0.890) and only 9.4M parameters—was integrated with ByteTrackV2 to perform behavior tracking and temporal analysis in classroom environments. This enables the generation of behavior distribution charts that provide interpretable insights into student engagement over time. These findings demonstrate the potential of automated behavior recognition systems for classroom analytics and data-driven teaching improvement.
subject: Research article
  topic: section
subject: Computer Vision
  topic: keywords
subject: Student Behavior Detection
  topic: keywords
subject: YOLO
  topic: keywords
subject: ByteTrack
  topic: keywords
subject: RT-DETR
  topic: keywords
creator: Thi-Nguyen Nguyen
creator: Dinh-Thai Kim
creator: Anh-Phuong Pham
rights: Copyright (c) 2025 Thi-Nguyen Nguyen, Dinh-Thai Kim, Anh-Phuong Pham https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-07
publication date: 2026-04-07
doi: 10.4108/airo.11141

FrequencyFormer: Oriented Object Detection with Frequency Transformer
type: item
description: 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.
subject: Research article
  topic: section
subject: Transformer
  topic: keywords
subject: Oriented object
  topic: keywords
subject: Frequency
  topic: keywords
creator: Shuai Liu
creator: Haiming Wang
creator: Zhibin Li
creator: Peiyang Wei
rights: Copyright (c) 2025 Shuai Liu, Haiming Wang, Zhibin Li, Peiyang Wei https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-02
publication date: 2025-12-02
doi: 10.4108/airo.10701

FDD-YOLO: A Lightweight Multi-scale Prohibited Items Detection Model
type: item
description: 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.
subject: Research article
  topic: section
subject: Reinforcment Learning-Based Adaptive Control for Collaborative Robots in Dynamic Envirnoments
  topic: category
subject: Frequency Domain Decomposition Network (FDDN)
  topic: keywords
subject: Deformable Elastic Fusion Pyramid (DEFP)
  topic: keywords
subject: Dual-channel Convolution (DualConv)
  topic: keywords
subject: Prohibited Items
  topic: keywords
subject: X-ray Image
  topic: keywords
creator: Zilong Xue
creator: Bo Wang
creator: Yuanwei Xie
creator: Zhibin Li
creator: Xiaozheng Fan
creator: Chenyoukang Lin
creator: Peiyang Wei
creator: Linlin Chen
creator: Xun Deng
creator: Jianhong Gan
rights: 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
date: 2025-11-11
publication date: 2025-11-11
doi: 10.4108/airo.10277

A Stacking Based Ensemble Learning Approach for Accurate Identification of Tumor Homing Peptides in Precision Cancer Therapeutics
type: item
description: 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.
subject: Research article
  topic: section
subject: Tumor-Homing Peptides
  topic: keywords
subject: Stacking Ensemble Learning
  topic: keywords
subject: Feature Extraction
  topic: keywords
subject: Cancer Therapies
  topic: keywords
subject: Precision Medicine
  topic: keywords
creator: Jahid Hassan Akash
creator: Rajib Mia
creator: Abu Kowshir Bitto
creator: Abdul Kadar Muhammad Masum
creator: Md Jobaer Ahmed
creator: Md Fokrul Islam Khan
rights: 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
date: 2026-02-11
publication date: 2026-02-11
doi: 10.4108/airo.10265

Quantum AI for Dark Web Narcotics Detection: A Hybrid Cybersecurity Framework
type: item
description: 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.
subject: Research article
  topic: section
subject: Quantum Machine Learning
  topic: keywords
subject: Dark Web Analaytics
  topic: keywords
subject: Cybersecurity Framework
  topic: keywords
subject: Quantum-Classical Hybrid Systems
  topic: keywords
subject: Counter-Narcotics Intelligence
  topic: keywords
subject: Ethical AI Governance
  topic: keywords
creator: Gabriel Silva-Atencio
rights: Copyright (c) 2025 Gabriel Silva-Atencio https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-27
publication date: 2025-10-27
doi: 10.4108/airo.10248

Explainable AI for Customer Churn Prediction in the Energy Sector Using Ensemble Machine Learning Models
type: item
description: Customer churn prediction is crucial for energy providers to preserve revenue and market share in a competitive setting. This research explores implementing explainable AI (XAI) in customer churn prediction with machine learning algorithms as well as interpretability methods. This research utilizes a dataset formed by combining client and price information from Kaggle’s PowerCo dataset, making use of the ‘id’ column as a key. During the data preprocessing section, the data involves extensive preparation, such as dropping unnecessary columns, deleting duplicates, encoding category features, capping outlier values, and imputing missing values. The class imbalance problem was alleviated through making use of the Synthetic Minority Oversampling Technique (SMOTE) to enable robust training of models. The machine learning algorithms Random Forest, XGBoost, and LightGBM were built, implemented, and benchmarked with principal performance metrics like accuracy, precision, recall, F1-score, as well as ROC-AUC. Among the evaluated ensemble models, XGBoost demonstrated the most balanced trade-off between predictive accuracy and interpretability when combined with SHAP and LIME. SHAP (SHapley Additive exPlanations) as well as LIME (Local Interpretable Model-agnostic Explanations) are used for providing global as well as localinterpretability to identify key drivers of churn. This proposed approach exhibits potential to provide accurate predictions as well as salient insights, enabling energy providers to design targeted retention strategies.
subject: Research article
  topic: section
subject: Customer Churn Prediction
  topic: keywords
subject: Explainable AI (XAI)
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Random Forest
  topic: keywords
subject: XGBoost
  topic: keywords
subject: LightGBM
  topic: keywords
subject: SMOTE
  topic: keywords
subject: SHAP
  topic: keywords
subject: LIME
  topic: keywords
subject: Performance Metrics
  topic: keywords
subject: ROC-AUC
  topic: keywords
subject: PR-AUC
  topic: keywords
creator: Abdullah Al Mamun
creator: Md Fazla Saim Tanoor
creator: Abdul Kadar Muhammad Masum
creator: Touhid Bhuiyan
creator: Md. Maruf Hassan
rights: Copyright (c) 2025 Abdullah Al Mamun, Md Fazla Saim Tanoor, Abdul Kadar Muhammad Masum, Touhid Bhuiyan, Md. Maruf Hassan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-07
publication date: 2026-04-07
doi: 10.4108/airo.9813

Generative AI's Sociotechnical Evolution: Scaling Limits, Governance Gaps, and Sustainable Pathways
type: item
description: 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.
subject: Research article
  topic: section
subject: Reinforcment Learning-Based Adaptive Control for Collaborative Robots in Dynamic Envirnoments
  topic: category
subject: Artificial intelligence
  topic: keywords
subject: energy efficiency
  topic: keywords
subject: generative models
  topic: keywords
subject: governance latency
  topic: keywords
subject: scaling laws
  topic: keywords
subject: sociotechnical systems
  topic: keywords
creator: Gabriel Silva Atencio
rights: Copyright (c) 2025 Gabriel Silva Atencio https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-27
publication date: 2025-10-27
doi: 10.4108/airo.10075

Hardware-Aware INT8 Quantization and FPGA Deployment of MobileNetV2 for Real-Time Facial Landmark Detection
type: item
description: Facial landmark detection is a key component of always-on edge vision systems, but practical deployment requires balancing localization accuracy, model size, throughput, and power consumption. This study proposes a two-stage, hardware-aware framework for MobileNetV2-based facial landmark detection. In Stage I, a lightweight detector is developed in PyTorch and evaluated in FP32 and INT8 using post-training quantization (PTQ) and quantization-aware training (QAT). In Stage II, the quantized model is realized on the AMD/Xilinx Kria KV260 FPGA and assessed in terms of real-time throughput, power consumption, and hardware resource utilization. INT8 quantization reduces the model size from 6.59 MB to 1.65 MB, and QAT retains accuracy more effectively than PTQ (91.74% vs. 90.94%) relative to the FP32 baseline (92.42%). The hardware implementation achieves approximately 30 FPS at approximately 3 W while using 14.8% of LUTs, 8.1% of FFs, 16.3% of BRAM, and 4.5% of DSPs. Among the evaluated platforms, the KV260 delivers the highest measured energy efficiency, whereas the RTX 4060 delivers the highest throughput. Within the evaluated setup, these results support the practicality of explicitly separating software-stage quantization analysis from hardware-stage realization for real-time, low-power facial landmark detection on FPGA-based edge platforms.
subject: Research article
  topic: section
subject: facial landmark detection
  topic: keywords
subject: MobileNetV2
  topic: keywords
subject: quantization-aware training
  topic: keywords
subject: post-training quantization
  topic: keywords
subject: edge computing
  topic: keywords
subject: AMD/Xilinx Kria KV260
  topic: keywords
creator: Van-Khoa Pham
creator: Manh-Dung Do
creator: Trung-Nghia Dang
creator: Long Tran
rights: Copyright (c) 2025 Van-Khoa Pham, Manh-Dung Do, Trung-Nghia Dang, Long Tran https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-25
publication date: 2026-03-25
doi: 10.4108/airo.10070

Optimizing Machine Learning Architectures for Time Series Forecasting: A Hybrid rvGA-eNM Approach
type: item
description: 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…
subject: Research article
  topic: section
subject: Reinforcment Learning-Based Adaptive Control for Collaborative Robots in Dynamic Envirnoments
  topic: category
subject: hybrid optimization
  topic: keywords
subject: genetic algorithm
  topic: keywords
subject: Nelder-Mead
  topic: keywords
subject: time series forecasting
  topic: keywords
subject: algorithm orchestration
  topic: keywords
subject: small-sample forecasting
  topic: keywords
subject: computational efficiency
  topic: keywords
creator: Wahab Musa
creator: Muhammad Rifai Katili
creator: Wrastawa Ridwan
rights: Copyright (c) 2025 Wahab Musa, Muhammad Rifai Katili, Wrastawa Ridwan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-31
publication date: 2025-10-31
doi: 10.4108/airo.9976

AI-Driven Predictive Maintenance in Infrastructure and Facilities Management
type: item
description: 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 …
subject: Research article
  topic: section
subject: Predictive Maintenance
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Infrastructure Management
  topic: keywords
subject: Failure Diagnosis
  topic: keywords
subject: Data-Driven Maintenance
  topic: keywords
subject: IoT
  topic: keywords
subject: Model Interpretability
  topic: keywords
creator: Seaam Bin Masud
creator: Hasan Mahmud Sozib
creator: Kamana Parvej Mishu
creator: Rahima Binta Bellal
creator: Mohammad Tahmid Ahmed
creator: Anwarul Matin Jony
creator: Syeda Tabassum
creator: Mohammad Morshed Uddin Al Mostam Sek Billah
rights: 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
date: 2025-12-08
publication date: 2025-12-08
doi: 10.4108/airo.9975

A Novel Method for Enhancing Warehouse Operations Using Heterogeneous Robotic Systems for Autonomous Pick-and-Deliver Tasks
type: item
description: 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.
subject: Research article
  topic: section
subject: Smart Warehousing
  topic: keywords
subject: Multi-Robot Coordination
  topic: keywords
subject: Task Allocation
  topic: keywords
subject: Pick-and-Deliver Tasks
  topic: keywords
subject: Industrial Robotics
  topic: keywords
subject: Logistics Optimization
  topic: keywords
subject: Navigation and Path Planning
  topic: keywords
subject: Heterogeneous Robotic Systems
  topic: keywords
creator: Youssef MSALA
creator: Hamed Oussama
creator: Mohamed Talea
creator: Mohamed Aboulfatah
rights: Copyright (c) 2025 Youssef MSALA, Hamed Oussama, Mohamed Talea, Mohamed Aboulfatah https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-23
publication date: 2025-09-23
doi: 10.4108/airo.9913

Localization of the Work Coordinate System Using Computer Vision and Geometric Transformations on Three-Axis CNC Milling Machines
type: item
description: 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.
subject: Technical Article
  topic: section
subject: Reinforcment Learning-Based Adaptive Control for Collaborative Robots in Dynamic Envirnoments
  topic: category
subject: milling
  topic: keywords
subject: work coordinate system
  topic: keywords
subject: repeatability
  topic: keywords
subject: computer vision
  topic: keywords
subject: geometric trasnformations
  topic: keywords
subject: optimization
  topic: keywords
creator: Manuel Meraz Mendez
creator: Jorge Duarte Loera
creator: Claudia Lerma Hernández
rights: Copyright (c) 2025 Manuel Meraz Mendez, Jorge Duarte Loera, Claudia Lerma Hernández https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-19
publication date: 2025-09-19
doi: 10.4108/airo.9898

Enhancing the Prediction of IL-4 Inducing Peptides Using Stacking Ensemble Model
type: item
description: 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.
subject: Research article
  topic: section
subject: Immunoinformatic
  topic: keywords
subject: Peptides
  topic: keywords
subject: Interleukin-4
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Stacking Ensemble
  topic: keywords
creator: Rajib Mia
creator: Tawfiqul Hasan
creator: Abu Kowshir Bitto
creator: Mohammad Mahadi Hassan
creator: Mohammed Shamsul Alam
creator: Abdul Kadar Muhammad Masum
rights: 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
date: 2025-10-27
publication date: 2025-10-27
doi: 10.4108/airo.9867

An Explainable AI Based Deep Ensemble Transformer Framework for Gastrointestinal Disease Prediction from Endoscopic Images
type: item
description: 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.
subject: Research article
  topic: section
subject: Gastrointestinal Disease
  topic: keywords
subject: Medical Image Processing
  topic: keywords
subject: Transformer Models
  topic: keywords
subject: Ensemble Model
  topic: keywords
subject: Explainable AI
  topic: keywords
creator: Prof. Dr. Abdul kadar Muhammad Masum
creator: Abu Kowshir Bitto
creator: Shafiqul Islam Talukder
creator: Md Fokrul Islam Khan
creator: Mohammed Shamsul Alam
creator: Khandaker Mohammad Mohi Uddin
rights: 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
date: 2025-08-25
publication date: 2025-08-25
doi: 10.4108/airo.9795

Advancing Public Safety with Real-Time Life Jacket Detection and Demographic Profiling Using YOLOv8 and Age Classification
type: item
description: 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.
subject: Research article
  topic: section
subject: Life Jacket
  topic: keywords
subject: Object detection
  topic: keywords
subject: Age Classification
  topic: keywords
subject: Yolov8
  topic: keywords
subject: Safety Protocol
  topic: keywords
creator: Md Abu Yusuf
creator: Nur Mohammad Chowdhury
creator: Ponchanon Datta Rone
creator: Partha Pratim Saha
creator: Md Iqbal Hossan
creator: Debabrata Sarkar
creator: Ranjan Paul
creator: Md Rana Hossain
creator: Madhusodan Chakraborty
rights: 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
date: 2025-09-18
publication date: 2025-09-18
doi: 10.4108/airo.9785

Comparative Analysis of Transformer and LSTM Architectures for Cybersecurity Threat Detection Using Machine Learning
type: item
description: 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.
subject: Research article
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Machine learning
  topic: keywords
subject: LSTM
  topic: keywords
subject: transformer
  topic: keywords
subject: threat classification
  topic: keywords
subject: emerging threats
  topic: keywords
subject: predictive analytics
  topic: keywords
creator: Jobanpreet Kaur
creator: Mani Prabha
creator: Md Samiun
creator: Syed Nazmul Hasan
creator: Rakibul Hasan
creator: Hammed Esa
creator: Md Fakhrul Hasan Bhuiyan
creator: Md Abdur Rob
creator: Durga Shahi
rights: 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
date: 2025-09-22
publication date: 2025-09-22
doi: 10.4108/airo.9759

Leveraging Synthetic Mammograms to Enhance Deep-Learning Performance for Breast Cancer Classification Using EfficientNetV2L Architecture
type: item
description: 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.
subject: Research article
  topic: section
subject: Breast Cancer Detection
  topic: keywords
subject: Synthetic Mammograms
  topic: keywords
subject: EfficientNetV2L
  topic: keywords
subject: Denoising Diffusion Probabilistic Models (DDPM)
  topic: keywords
subject: Deep Learning in Medical Imaging
  topic: keywords
creator: Raymond Sutjiadi
creator: Siti Sendari
creator: Heru Wahyu Herwanto
creator: Yosi Kristian
rights: Copyright (c) 2025 Raymond Sutjiadi, Siti Sendari, Heru Wahyu Herwanto, Yosi Kristian https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-01
publication date: 2025-09-01
doi: 10.4108/airo.9749

Facial Emotion Recognition by CNN Combined Ensemble Model
type: item
description: 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.
subject: Research article
  topic: section
subject: Reinforcment Learning-Based Adaptive Control for Collaborative Robots in Dynamic Envirnoments
  topic: category
subject: CNN
  topic: keywords
subject: ResNet
  topic: keywords
subject: VGGNet
  topic: keywords
subject: DenseNet
  topic: keywords
subject: transfer learning
  topic: keywords
subject: ensemble model
  topic: keywords
subject: ESPCN
  topic: keywords
subject: hash_compare
  topic: keywords
creator: Abdurahmon Kurbanov
rights: Copyright (c) 2025 Abdurahmon Kurbanov https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-12
publication date: 2025-09-12
doi: 10.4108/airo.9720

CAD-guided 6D pose estimation with deep learning in digital twin for industrial collaborative robot manipulation
type: item
description: 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.
subject: Research article
  topic: section
subject: pose estimation
  topic: keywords
subject: computer vision
  topic: keywords
subject: digital twin
  topic: keywords
subject: industrial robot
  topic: keywords
creator: Quang Huan Dong
creator: The Thinh Pham
creator: Khanh Nguyen
creator: Chi-Cuong Tran
creator: Hoang Huy Tran
creator: Duy Tan Do
creator: Khang Hoang Vinh Nguyen
creator: Quang-Chien Nguyen
rights: 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
date: 2025-10-02
publication date: 2025-10-02
doi: 10.4108/airo.9676

An adaptive traditional Chinese herbal medicine image recognition model via ED-HLOA-optimized DenseNet201
type: item
description: 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.
subject: Research article
  topic: section
subject: Chinese Herbal Medicine
  topic: keywords
subject: tumors
  topic: keywords
subject: Neural Network
  topic: keywords
subject: Self-Adaptive Model
  topic: keywords
subject: Image Recognition
  topic: keywords
subject: Horned Lizard Optimization Algorithm
  topic: keywords
creator: Peiyang Wei
creator: Rundong Zou
creator: Guangdong Dong
creator: Wen Qin
creator: Jianhong Gan
creator: Oifeng Su
creator: Zhibin Li
rights: 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
date: 2025-07-23
publication date: 2025-07-23
doi: 10.4108/airo.9563

AI-Powered Predictive Analytics for Financial Risk Management in U.S. Markets
type: item
description: 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.
subject: Research article
  topic: section
subject: Financial risk management
  topic: keywords
subject: AI-powered
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: CatBoost
  topic: keywords
subject: SVM
  topic: keywords
subject: decision-making
  topic: keywords
subject: U.S. markets
  topic: keywords
creator: Md Zikar Hossan
creator: Muslima Begom Riipa
creator: Md Azhad Hossain
creator: Sweety Rani Dhar
creator: Al Modabbir Zaman
creator: Mohammad Hossain
creator: Arif Hossen
creator: Hasan Mahmud Sozib
rights: 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
date: 2025-08-22
publication date: 2025-08-22
doi: 10.4108/airo.9532

MSCSO: A Hybrid Nature-Inspired Algorithm for High-Dimensional Traffic Optimization in Urban Environments
type: item
description: 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.
subject: Research article
  topic: section
subject: Reinforcment Learning-Based Adaptive Control for Collaborative Robots in Dynamic Envirnoments
  topic: category
subject: Hybrid Optimization
  topic: keywords
subject: animal foraging
  topic: keywords
subject: sand cat swarm optimization
  topic: keywords
subject: metaheuristics
  topic: keywords
subject: traffic optimization
  topic: keywords
creator: Kuldeep Vayadande
creator: Viomesh Kumar Singh
creator: Amol Bhosle
creator: Ranjana Gore
creator: Yogesh Uttamrao Bodhe
creator: Aditi Bhat
creator: Zulfikar Charoliya
creator: Aayush Chavan
creator: Pranav Bachhav
creator: Aditya Bhoyar
rights: 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
date: 2025-07-11
publication date: 2025-07-11
doi: 10.4108/airo.9344

P2PLLMEdge: Peer-to-Peer Framework for Localized Large Language Models using CPU only Resource-Constrained Edge
type: item
description: 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 …
subject: Research article
  topic: section
subject: Peer-to-peer
  topic: keywords
subject: Edge computing
  topic: keywords
subject: Quantized LLMs
  topic: keywords
subject: Resource-constrained edge
  topic: keywords
subject: Decentralized generative AI
  topic: keywords
subject: Web frameworks
  topic: keywords
creator: Partha Pratim Ray
creator: Mohan Pratap Pradhan
rights: Copyright (c) 2025 Partha Pratim Ray, Mohan Pratap Pradhan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-07-08
publication date: 2025-07-08
doi: 10.4108/airo.9292

Explainable AI Based Deep Ensemble Convolutional Learning for Multi-Categorical Ocular Disease Prediction
type: item
description: 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
subject: Research article
  topic: section
subject: Eye Disease
  topic: keywords
subject: Deep Ensemble Learning
  topic: keywords
subject: Transfer Learning
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Ocular Disease
  topic: keywords
creator: Abu Kowshir Bitto
creator: Rezwana Karim
creator: Mst Halema Begum
creator: Md Fokrul Islam Khan Khan
creator: Dr. Md. Maruf Hassan
creator: Prof. Dr. Abdul kadar Muhammad Masum
rights: 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
date: 2025-07-28
publication date: 2025-07-28
doi: 10.4108/airo.9234

Reimagining Asteroid Risk Assessment: A Comparative Review of Advanced Machine Learning Techniques
type: item
description: 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%.
subject: Review article
  topic: section
subject: Reinforcment Learning-Based Adaptive Control for Collaborative Robots in Dynamic Envirnoments
  topic: category
subject: Asteroid Impact
  topic: keywords
subject: Celestial Dynamics
  topic: keywords
subject: High Dimensional Data
  topic: keywords
subject: Risk Assessment of Impact
  topic: keywords
subject: NEAs
  topic: keywords
subject: Planetary Defense
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Space Security
  topic: keywords
subject: Variational Quantum Classifier
  topic: keywords
subject: VQC
  topic: keywords
subject: Model Compression
  topic: keywords
creator: Kuldeep Vayadande
creator: Dnyaneshwar M. Bavkar
creator: Ishwari Rohit Raskar
creator: Umar Mubarak Mulani
creator: Jyoti Kanjalkar
creator: Rajashree Tukaram Gadhave
creator: Preeti Bailke
creator: Yogesh Bodhe
creator: Ajit R. Patil
rights: 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
date: 2025-06-02
publication date: 2025-06-02
doi: 10.4108/airo.9142

Robust Robotic Arm Calibration combining Multi-Distance Optimization Approach with Lagrange Starfish Optimization Algorithm
type: item
description: 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.
subject: Research article
  topic: section
subject: Lagrangian Starfish Optimization Algorithm
  topic: keywords
subject: LSFA
  topic: keywords
subject: Support Vector Machine
  topic: keywords
subject: SVM
  topic: keywords
subject: Multi-dimensional distance metrics
  topic: keywords
subject: Robotic arm calibration
  topic: keywords
subject: Dynamic parameter estimation
  topic: keywords
subject: Intelligent optimization algorithm
  topic: keywords
creator: Yongtao Qu
creator: Zhiqiang Li
creator: Long Liao
creator: Xun Deng
creator: Yuanchang Lin
creator: Tinghui Chen
creator: Linlin Chen
creator: Jia Liu
creator: Peiyang Wei
creator: Jianhong Gan
creator: ZhenZhen Hu
creator: Can Hu
creator: Yonghong Deng
creator: Wei Li
creator: Zhibin Li
rights: 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
date: 2025-06-26
publication date: 2025-06-26
doi: 10.4108/airo.9002