EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
type: channel
description: EAI Endorsed Transactions on Industrial Networks and Intelligent Systems is open access, a peer-reviewed scholarly journal focused on ubiquitous computing, cloud computing, and cyber-physical system, all kinds of networks in large-scale factories, including a lot of traditional and new industries. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency (four issues per year). This journal is co-organized, and managed by Duy Tan University, Vietnam in collaboration with Passage to ASEAN (P2A), an ASEAN Entity. INDEXING: Scopus (CiteScore: 3.1), Compendex, DOAJ, ProQuest, EBSCO, DBLP
publisher: European Alliance for Innovation (EAI)
language: en
publication name: EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
issn: 2410-0218
copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
items: Scalable object-relational modeling for synthesizing multi-format visual analytics of STIX-based cyber threat intelligence data
items: EDIL-SegRayDP: Training-Free Iris Segmentation via Segmentation-First Ray-Wise Dynamic Programming
items: Constellation Design for Blind Detection of Orthogonal Space-Time Block Codes in Wireless MIMO Systems
items: A Novel Hybrid Transformer for RUL Prediction in Predictive Maintenance for Smart Manufacturing
items: Jamming for Security in Cognitive NOMA with Full-Duplex Energy Scavenging Unlicensed Transmitter
items: SCM-Net: A Lightweight AI-Based Sea Ice Classification for Climate Change
items: Artificial Intelligence-Driven Early Prediction of Student Dropout and Academic Outcomes in Higher Education: A Comparative Study of Advanced Machine Learning Approaches
items: IoT-Enabled Wireless Sensor Networks in Precision Agriculture: A Survey on Cross-Layer Resilience and Fault Management
items: HyperDyG: Hypergraph-Driven Dynamic Fusion for Semi-Supervised Multimodal Emotion Recognition
items: Deep Reinforcement Learning Approaches Against Jammers with Unequal Sweeping Probability Attacks
items: Few-Shot Classification Of Brain Cancer Images Using Meta-Learning Algorithms
items: The Efficiency Cost-Sensitive Loss of Transformer based on Mamba Mechanism for Aircraft Detection in Satellite Imagery
items: Optimizing Energy Harvesting Efficiency for IRS-aided TS-SWIPT System with Continuous and Discrete Phase Shifts
items: Optimizing UAV Trajectories in Optical IRS-Aided Hybrid FSO/RF Aerial Access Networks Using DRL Technique
items: A Multimodal Swarm Learning Approach for DDoS Detection in Internet of Things Infrastructure
items: FedNDA: Enhancing Federated Learning with Noisy Client Detection and Robust Aggregation
items: Return Loss Optimization in Rectangular Microstrip Patch Antennas Using Response Surface Methodology (RSM) for 5G Applications
items: COSMN: Clustering-Based Optimization for 360-Degree Live Streaming over Mobile Networks
items: A novel approach for graph-based real-time anomaly detection from dynamic network data listened by Wireshark
items: QoE-Energy Consumption Optimization for End-User Devices in Adaptive Bitrate Video Streaming Using the Lagrange Multiplier Method
items: https://publications.eai.eu/index.php/inis/article/view/8604
items: Secure UAV-assisted Mobile Edge Computing for IoT with Backscatter Communication in the Presence of a Moving Eavesdropper
items: Integrated Cloud-Twin Synchronization for Supply Chain 5.0
items: Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Detection
items: Enhancing AI-Inspired Analog Circuit Design: Optimizing Component Sizes with the Firefly Algorithm and Binary Firefly Algorithm
items: Coverage Probability of EH-enabled LoRa networks - A Deep Learning Approach
items: Joint Adaptive Modulation and Power Control Scheme for Energy Efficient FSO-based Non-Terrestrial Networks
items: Predicting the Severity of COVID-19 Pneumonia from Chest X-Ray Images: A Convolutional Neural Network Approach
items: An Efficient Method for BLE Indoor Localization Using Signal Fingerprint
items: Drug classification system based on drug composition and usage instructions

Scalable object-relational modeling for synthesizing multi-format visual analytics of STIX-based cyber threat intelligence data
type: item
description: The observed increase in cyber threat intelligence data, the diversity of sources, and relational complexity make it difficult for analysts to directly assess and interpret threat profiles from raw Structured Threat Information Expression (STIX) packages. This study utilises an object-relational model to transform all object and relationship types into a single unified representation and reconstruct the same graph model across three different visualisation software packages (plotly, matplotlib, pyvis). The proposed analytical framework generates extended threat models by combining multiple STIX datasets through entity extraction, creating observable entities from each dataset, and completing relationships among them; thus mapping relationship types to the operational meanings of attacks and enabling more comprehensive risk prioritisation. The case study is based on a public OASIS STIX example package. The enriched graph is dominated by relationship types such as uses, indicates, pattern-refers-to, and attributed-to, and the highest risk scores are assigned to the nodes labelled “Privilege Escalation” and “Ugly Gorilla”. Scalability tests performed on 1000-node synthetic directed graphs revealed a processing time of 8.08 seconds, memory consumption of 124.3 MB, and a frame rate of 215.05 fps during interactive preview. These findings demonstrate that the proposed framework offers a unified and viable foundation for visual analysis, risk prioritisation, and scalable analytical reporting of STIX-based cyber threat intelligence.
subject: Research articles
  topic: section
subject: STIX
  topic: keywords
subject: Cyber Threat Intelligence
  topic: keywords
subject: Graph Visualisation
  topic: keywords
subject: Threat Graph
  topic: keywords
subject: Scalability
  topic: keywords
creator: Muhammed Onur Kaya
creator: Resul Das
rights: Copyright (c) 2026 Muhammed Onur Kaya, Resul Das https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-18
publication date: 2026-06-18
doi: 10.4108/eetinis.132.12774

EDIL-SegRayDP: Training-Free Iris Segmentation via Segmentation-First Ray-Wise Dynamic Programming
type: item
description: Iris segmentation remains a critical yet challenging stage in biometric recognition, especially under off- axis capture, eyelid and eyelash occlusion, specular reflections, and illumination variations that violate the circular and unobstructed assumptions of classical pipelines. We present EDIL-SegRayDP, a training-free and explainable iris segmentation framework that departs from the conventional localization-first paradigm by treating annulus recovery as the primary optimization objective. Rather than committing early to a global center/radius hypothesis and refining it afterward, the proposed method performs segmentation-first boundary recovery with segmentation-aware center rescue and fail-safe outer-boundary control. Occlusion is handled explicitly through geometry-normalized masking and validity-aware annulus construction, while all key parameters are defined in scale-normalized form for cross-dataset portability. Experiments under a fixed-configuration protocol on IITD and CASIA-IrisV4-Interval show strong non-CNN performance with CPU-only inference, achieving an iris-mask mean Dice of 0.9106 on IITD and 0.9377 on CASIA-IrisV4- Interval, with corresponding pupil Dice of 0.9763 and 0.9755. Additional full-benchmark evaluations on CASIA-IrisV4-Lamp and CASIA-IrisV4-Thousand further confirm the portability of the proposed framework across more challenging and larger-scale subsets. Under the evaluation protocol adopted in this study, these results compare favorably with a recent training-free reference, supporting EDIL-SegRayDP as a competitive and interpretable training-free alternative for iris segmentation under non-ideal imaging conditions.
subject: Research articles
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: Deep Learning for IoT Big Data and Streaming Analytics
  topic: category
subject: Iris segmentation
  topic: keywords
subject: Training-free
  topic: keywords
subject: Dynamic programming
  topic: keywords
subject: Occlusion modeling
  topic: keywords
subject: Explainable biometrics
  topic: keywords
creator: Trong-Thua Huynh
creator: De-Thu Huynh
creator: Cong-Sang Duong
rights: Copyright (c) 2026 Trong-Thua Huynh, De-Thu Huynh, Cong-Sang Duong https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-19
publication date: 2026-05-19
doi: 10.4108/eetinis.132.12656

Constellation Design for Blind Detection of Orthogonal Space-Time Block Codes in Wireless MIMO Systems
type: item
description: This paper investigates the problem of blind detection of orthogonal space–time block codes (OSTBCs) with quadrature amplitude modulation (QAM) in multiple-input multiple-output (MIMO) systems over quasistatic flat Rayleigh fading channels. To resolve the inherent rotational ambiguity in blind OSTBC detection, we propose a structurally constrained QAM constellation that enables unique symbol recovery without the use of pilot signals. Building on this design, we develop a low-complexity iterative detector, referred to as the iterative maximum-likelihood with averaged initial channel estimate (IML-AICE) detector, which jointly estimates the channel and transmitted symbols. The proposed detector incorporates a novel initialization strategy and an iterative refinement mechanism inspired by clairvoyant maximum-likelihood detection, leading to improved convergence and detection accuracy. The proposed framework enables reliable blind recovery of OSTBC symbols, thereby improving spectral efficiency by eliminating pilot overhead. Simulation results demonstrate that the proposed IML-AICE detector consistently outperforms existing trained and blind detection schemes across a range of signal-to-noise ratios and system configurations at low computational complexity.
subject: Research articles
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: Blind detection
  topic: keywords
subject: ML detector
  topic: keywords
subject: Least-Squares
  topic: keywords
subject: MIMO Systems
  topic: keywords
subject: Orthogonal Space-Time Block Code
  topic: keywords
creator: Minh-Tuan Le
creator: Trung-Hieu Nguyen
rights: Copyright (c) 2026 Minh-Tuan Le, Trung-Hieu Nguyen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-11
publication date: 2026-06-11
doi: 10.4108/eetinis.132.12472

A Novel Hybrid Transformer for RUL Prediction in Predictive Maintenance for Smart Manufacturing
type: item
description: Ensuring continuous operation and minimizing unexpected failures are critical priorities in industrial manufacturing. Due to this requirement, smart manufacturing has transformed maintenance strategies, moving from traditional scheduled approaches to predictive maintenance (PdM), which leverages actual machine health conditions. A powerful technique that enables accurate PdM is Remaining Useful Life (RUL) estimation. Accurate RUL prediction allows the assessment of machine health, thereby facilitating timely and appropriate maintenance decisions to sustain continuous operation and reduce repair costs. While several existing models have been developed for RUL estimation, they often struggle to capture long-term dependencies in time series data, limiting their predictive accuracy. In this study, we propose a novel architecture that combines a one-dimensional Convolutional Neural Network (1D-CNN) with two consecutive transformer encoders enhanced by Fourier transforms to address these challenges. The performance of the proposed model was evaluated based on two well-known benchmark datasets, C-MAPSS and its improved version, N-CMAPSS. The experiment results show that our approach outperforms current state-of-the-art methods in both prediction accuracy and computational efficiency, demonstrating its potential for practical applications.
subject: Research articles
  topic: section
subject: Deep Learning for IoT Big Data and Streaming Analytics
  topic: category
subject: Remaining Useful Lifetime
  topic: keywords
subject: Bayesian optimization,
  topic: keywords
subject: Transformer
  topic: keywords
subject: Fourier transform
  topic: keywords
subject: Convolutional Neural network
  topic: keywords
creator: Huu Du Nguyen
creator: Xuan Hoang Nguyen
creator: Bich Thuong Dao
creator: Cong Ngon Do
creator: Thu Huong Truong
rights: Copyright (c) 2026 Nguyen Huu Du, Nguyen Xuan Hoang, Dao Bich Thuong, Do Cong Ngon, Truong Thu Huong https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-20
publication date: 2026-05-20
doi: 10.4108/eetinis.132.12274

Jamming for Security in Cognitive NOMA with Full-Duplex Energy Scavenging Unlicensed Transmitter
type: item
description: The combination of cognitive radio (CR), full-duplex (FD) transmission, energy scavenging (ES), and non-orthogonal multiple access (NOMA) significantly improves spectral efficiency, spectrum utilization, and energy efficiency - key performance indicators in the development of future wireless communication infrastructures. However, the growing threat of eavesdropping necessitates robust physical-layer security mechanisms. The paper proposes NOMA CR networks with FD ES unlicensed transmitter (UT), referred to as NOEHwJ, to exploit primary interference as an energy source for UT's operation and a jamming source to secure UT's information. An in-depth evaluation of reliability and secrecy metrics for NOEHwJ is also presented. Through rigorous theoretical analysis and simulation, the study elucidates the impact of each enabling technology on secrecy capability. It is evident from the results that NOEHwJ delivers marked enhancements in security when compared to the benchmarked orthogonal multiple access (OMA) CR networks with FD ES UT (OEHwJ), thereby demonstrating the superior advantages of jointly employing CR, ES, NOMA, FD, and jamming techniques for the development of wireless communication architectures that are secure, energy-efficient, and adaptable to future demands.
subject: Research articles
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: NOMA
  topic: keywords
subject: energy scavenging
  topic: keywords
subject: cognitive radio
  topic: keywords
subject: full-duplex
  topic: keywords
subject: jamming
  topic: keywords
creator: Hoan Tran
creator: Khuong Ho-Van
creator: Pham Ngoc Son
rights: Copyright (c) 2026 Hoan Tran, Khuong Ho-Van, Pham Ngoc Son https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-21
publication date: 2026-04-21
doi: 10.4108/eetinis.131.12260

SCM-Net: A Lightweight AI-Based Sea Ice Classification for Climate Change
type: item
description: Sea ice is considered one of the most valuable sources of information for maintaining the balance of Earth’s climate system and preventing excessive warming. This study introduces a lightweight yet accurate model designed for real-time sea ice classification to provide an accessible and practical tool for operational use. We propose the SCM-Net, a deep learning model for sea ice classification applications, and compare its performance against other state-of-the-art models, including MobileNet, Residual Network (ResNet), Visual Geometry Group Network (VGGNet), Vision Transformers (ViT), and Shifted Window Transformers (SwinT). The Swin Transformer Convolutional Hybrid model (SCM-Net) is a lightweight model with around 45 times less parameters, enabling usage in real time applications. The results demonstrate that the proposed SCMNet model achieves a comparable and even better accuracy in comparison to other models. Moreover, the proposed model significantly reduces the number of parameters while improving inference efficiency. These results show that the proposed model is well suited for real time sea ice classification applications.
subject: Research articles
  topic: section
subject: Light-weight deep learning models
  topic: keywords
subject: real-time applications
  topic: keywords
subject: SCM-Net
  topic: keywords
subject: sea ice classification
  topic: keywords
subject: swin transformer
  topic: keywords
creator: Nazanin Baramaki
creator: Quang Le
creator: Brad McNiven
creator: Muhammad Fahim
rights: Copyright (c) 2026 Nazanin Baramaki, Quang Nhat Le, Brad D. E. McNiven, Muhammad Fahim https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-10
publication date: 2026-06-10
doi: 10.4108/eetinis.132.12246

Artificial Intelligence-Driven Early Prediction of Student Dropout and Academic Outcomes in Higher Education: A Comparative Study of Advanced Machine Learning Approaches
type: item
description: Student dropout in higher education remains a critical challenge with significant academic, social, and economic implications. Early identification of students at risk of dropout enables institutions to design timely and targeted interventions that support academic success and improve retention rates. This study proposes a machine learning (ML)–driven framework for the early prediction of student dropout and academic outcomes in higher education using a comprehensive, real-world dataset collected from a higher education institution. The prediction task is formulated as a multiclass classification problem with three outcomes: dropout, enrolled, and graduate. To evaluate the effectiveness of different modeling approaches, we conduct a comparative analysis of widely used ML algorithms, including Logistic Regression, Naïve Bayes, k-Nearest Neighbors, Support Vector Machine, Decision Trees, Random Forest (RF), AdaBoost, XGBoost, LightGBM, and CatBoost. Results indicate that ensemble models achieve the best performance. RF attains the highest test accuracy (0.7797) and ROC-AUC (OvR) (0.8919), while LightGBM yields the best Macro-F1 (0.7082). Feature importance analysis shows that early academic progress indicators (approved units and semester grades) are the strongest predictors, followed by selected administrative/contextual factors such as tuition-fee status and course. Overall, this study provides empirical evidence supporting the use of ML techniques as effective decision-support tools for higher education institutions. The proposed framework offers actionable insights for administrators and policymakers seeking to develop data-driven strategies aimed at reducing dropout rates, improving academic success, and promoting equitable access to educational opportunities.
subject: Research articles
  topic: section
subject: Dropout prediction
  topic: keywords
subject: machine learning
  topic: keywords
subject: student performance prediction
  topic: keywords
creator: Nghia Trong Vo
creator: Quang Nhat Le
creator: Hang Le
rights: Copyright (c) 2026 Nghia Trong Vo, Quang Nhat Le, Hang Le https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-03
publication date: 2026-03-03
doi: 10.4108/eetinis.131.11758

IoT-Enabled Wireless Sensor Networks in Precision Agriculture: A Survey on Cross-Layer Resilience and Fault Management
type: item
description: Precision Agriculture (PA) utilizesWireless Sensor Networks (WSNs) to continuously monitor and collect dataon both plants and machinery. Thereafter, data-driven proactive decision-making is used for crop protection, yield optimization, and other agriculture management tasks. These WSNs rely on clustering protocols to facilitate sensor node communications. However, the adverse, spatio-temporal nature of agricultural environments and the need for real-time operation pose severe challenges that manifest as physical and protocol-level network faults. This survey adopts a novel perspective of Cross-Layer Resilience in Spatio- Temporal Agricultural IoT to evaluate how hardware-level physical faults cascade into routing failures. We survey a range of clustering protocols and fault management frameworks devised to address these faults using proactive and reactive techniques, ultimately enabling sensor networks to maintain survivability and continuous quality of service.
subject: Review article
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: Precision Agriculture
  topic: keywords
subject: Wireless Sensor Networks
  topic: keywords
subject: Clustering Protocols
  topic: keywords
subject: Fault Management Frameworks
  topic: keywords
creator: Nuwan Jayawardene
creator: Sulochana Sooriyaarachchi
creator: Chandana Gamage
rights: Copyright (c) 2026 Nuwan Jayawardene, Sulochana Sooriyaarachchi, Chandana Gamage https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-06-22
publication date: 2026-06-22
doi: 10.4108/eetinis.132.11509

HyperDyG: Hypergraph-Driven Dynamic Fusion for Semi-Supervised Multimodal Emotion Recognition
type: item
description: Speech emotion recognition (SER) is important in healthcare, education, human–computer interaction, and customer service. Multimodal emotion recognition (MER) integrates audio and textual modalities to achieve a comprehensive understanding of human affect, but still suffers from limited labeled data and complex cross-modal relations. To address these challenges, we propose HyperDyG, a dynamic hypergraph-driven MER framework. The HyperDyG leverages the strengths of dynamic hypergraph learning (DHL), cross-modal transformer (CMT), and an adaptive gated multimodal unit (GMU) for robust multimodal fusion. HyperDyG is further enhanced with a semi-supervised learning strategy that incorporates weak–strong augmentation, confidence-filtered pseudo-labeling, and consistency regularization to effectively exploit large-scale unlabeled data. The HyperDyG achieves state-of-the-art (SOTA) performance on the benchmark emotion dataset and maintains stable accuracy across varying unlabeled ratios. The findings of HyperDyG highlight the effectiveness and scalability of the proposed architecture in real-world low-label MER scenarios.
subject: Research articles
  topic: section
subject: Hypergraph learning
  topic: keywords
subject: Multimodal fusion
  topic: keywords
subject: Cross-modal transformer
  topic: keywords
subject: Dynamic gating
  topic: keywords
subject: Semi-supervised emotion recognition
  topic: keywords
creator: Nhut Minh Nguyen
creator: Thanh Trung Nguyen
creator: Thu Thuy Le
creator: Ngoc-Hanh Dang
creator: Luu Phuong Vo
creator: Thanh Hien Lam
creator: Duc Minh Ngoc Dang
rights: Copyright (c) 2026 Nhut Minh Nguyen, Thanh Trung Nguyen, Thu Thuy Le, Ngoc-Hanh Dang, Luu Phuong Vo, Thanh Hien Lam, Duc Minh Ngoc Dang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-19
publication date: 2026-02-19
doi: 10.4108/eetinis.131.10903

Deep Reinforcement Learning Approaches Against Jammers with Unequal Sweeping Probability Attacks
type: item
description: This paper investigates deep reinforcement learning (DRL) approaches designed to counter jammers that maximize disruption by employing unequal sweeping probabilities. We first propose a model and defense action based on a Markov Decision Process (MDP) under non-uniform attacks. A key drawback of the standard MDP model, however, is its assumption that the defending agent can acquire sufficient information about the jamming patterns to determine the transition probability matrix. In a dynamic environment, the attacker’s patterns and models are often unknown or difficult to obtain. To overcome this limitation, RL techniques such as Q-learning, deep Q-network (DQN), and double deep Q-network (DDQN) have been considered effective defense strategies that operate without an explicit jamming model. With Q-learning, defense strategies can still be computationally expensive and require long time to learn the optimal policy. This limitation arises because a large state space or a substantial number of actions causes the Q-table to grow exponentially. Leveraging the flexibility, adaptability, and scalability of RL, we first propose a DQN framework designed to handle large-scale action spaces across expanded channels and jammers. Furthermore, to overcome the inherent overestimation bias present in Q-learning and DQN algorithms, we investigate a DDQN framework. Assuming the estimation error of the action value in DQN follows a zero-mean Gaussian distribution, we then analytically derive the expected loss. Numerical examples are finally presented to characterize the performances of the proposed algorithms and the superiority of DDQN over DQN and Q-learning approaches.
subject: Research articles
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: Jamming Attacks
  topic: keywords
subject: Markov Decision Process
  topic: keywords
subject: Double Deep Q-Networks
  topic: keywords
subject: Data Rate Game
  topic: keywords
subject: Q-learning
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Deep Q-Networks
  topic: keywords
creator: Lan Nguyen
creator: Duy Nguyen
creator: Nghi Tran
creator: David Brunnenmeyer
rights: Copyright (c) 2025 Lan Nguyen, Duy Nguyen, Nghi Tran, David Brunnenmeyer https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-04
publication date: 2025-11-04
doi: 10.4108/eetinis.v12i4.10461

Few-Shot Classification Of Brain Cancer Images Using Meta-Learning Algorithms
type: item
description: The primary objective of deep learning is to have good performance on a large dataset. However, when the model lacks sufficient data, it becomes a challenge to achieve high accuracy in predicting these unfamiliar classes. In fact, the real-world dataset often introduces new classes, and some types of data are difficult to collect or simulate, such as medical images. A subset of machine learning is meta learning, or "learning-to-learn", which can tackle these problems. In this paper, a few-shot classification model is proposed to classify three types of brain cancer: Glioma brain cancer, Meningioma brain cancer, and brain Tumor cancer. To achieve this, we employ an episodic meta-training paradigm that integrates the model-agnostic meta-learning (MAML) framework with a prototypical network (ProtoNet) to train the model. In detail, ProtoNet focuses on learning a metric space by computing distances to class prototypes of each class, while MAML concentrates on finding the optimal initialization parameters for the model to enable the model to learn quickly on a few labeled samples. In addition, we compute and report the average accuracy for the baseline and our methods to assess the quality of the prediction confidence. Simulation results indicate that our proposed approach substantially surpasses the performance of the baseline ResNet18 model, achieving an average accuracy improvement from 46.33% to 92.08% across different few-shot settings. These findings highlight the potential of combining metric-based and optimization-based meta-learning techniques to improve diagnostic support in healthcare applications.
subject: Research articles
  topic: section
subject: Brain cancer
  topic: keywords
subject: cancer classification
  topic: keywords
subject: few-shot learning
  topic: keywords
subject: meta-learning
  topic: keywords
creator: Tuyet-Nhi Thi Nguyen
creator: Muhammad Fahim
creator: Bradley D. E. McNiven
creator: Quang Nhat Le
creator: Nhan Duc Le
rights: Copyright (c) 2025 Tuyet-Nhi Thi Nguyen, Muhammad Fahim, Bradley D. E. McNiven, Quang Nhat Le, Nhan Duc Le https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-06
publication date: 2025-11-06
doi: 10.4108/eetinis.124.10405

The Efficiency Cost-Sensitive Loss of Transformer based on Mamba Mechanism for Aircraft Detection in Satellite Imagery
type: item
description: Detecting aircraft in satellite images poses considerable challenges due to complex backgrounds and variable conditions influenced by sensor geometry and atmospheric factors. Despite rapid advancements in deep learning algorithms, their main focus has been on ground-based imagery. This study offers a thorough evaluation and comparison of advanced object detection algorithms specifically designed for aircraft detection in satellite imagery. By leveraging the extensive HRPlanesV2 dataset and a rigorous validation process on the GDIT dataset, we trained a cutting-edge object detection model, YOLO-Mamba, published in June 2024. Additionally, we introduce YOLO-Mamba-TransGhost, which integrates a novel Transformer module SC3T and Ghost Convolution into the YOLO model’s backbone architecture. Furthermore, substituting the WIoU loss function with CIoU in YOLO-Mamba results in significant improvements in accuracy and small object detection. Experimental results on the GDIT dataset indicate that YOLO-Mamba-TransGhost improves mAP@.5 by approximately 2% compared to the original YOLO-Mamba. Similarly, tests on the HRPlanev2 data set reveal a notable reduction in model complexity and an impressive accuracy of 98.7% which is achieved by leveraging a cost-sensitive loss function that dynamically focuses training on higher quality samples, improving convergence and accuracy. Therefore, the proposed YOLO-Mamba-TransGhost model demonstrates superior accuracy and reduced complexity in aircraft detection from satellite imagery, highlighting its potential for practical applications in aerospace monitoring, disaster management, and surveillance systems domain.
subject: Research articles
  topic: section
subject: Deep Learning for IoT Big Data and Streaming Analytics
  topic: category
subject: YOLO
  topic: keywords
subject: Mamba
  topic: keywords
subject: Transformer
  topic: keywords
subject: Ghost Convolution
  topic: keywords
subject: Wiou loss function
  topic: keywords
creator: Manh-Tuan Do
creator: Manh-Hung Ha
creator: Minh-Huy Le
creator: Oscal Tzyh-Chiang
rights: Copyright (c) 2025 Manh-Tuan Do, Manh-Hung Ha, Minh-Huy Le, Oscal Tzyh-Chiang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-22
publication date: 2025-09-22
doi: 10.4108/eetinis.v12i4.8279

Optimizing Energy Harvesting Efficiency for IRS-aided TS-SWIPT System with Continuous and Discrete Phase Shifts
type: item
description: In recent years, intelligent reflecting surfaces (IRS) have emerged as a groundbreaking technology for enhancing spectral and energy efficiency in wireless communication, offering a cost-effective and energy-efficient solution. This study explores a simultaneous wireless information and power transfer (SWIPT) network employing time-switching (TS) receivers, where a base station (BS) transmits both data and energy signals to users with the assistance of an IRS. By appropriately tuning the phase shifts of IRS elements, transmission performance is optimized in terms of both energy harvesting and data efficiency. The primary objective is to maximize energy harvesting efficiency, defined as the ratio of total harvested energy at the users to the transmission power of the BS, while ensuring the required information rate, adhering to power constraints, and considering practical limitations on phase shifts. To tackle this challenge, an iterative algorithm incorporating non-convex approximations is developed to jointly optimize information beamformers, energy covariance matrix, and TS factors. Finally, numerical simulations validate the convergence and effectiveness of the proposed methodology.
subject: Research articles
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: energy harvesting efficiency
  topic: keywords
subject: intelligent reflecting surface (IRS)
  topic: keywords
subject: SWIPT
  topic: keywords
subject: continuous/discrete phase shifts
  topic: keywords
subject: time-switching (TS)
  topic: keywords
creator: Tuan Pham-Viet
creator: Van-Quang-Binh Ngo
creator: Quang Vinh Do
creator: Insoo Koo
rights: Copyright (c) 2025 Tuan Pham-Viet, Van-Quang-Binh Ngo, Quang Vinh Do, Insoo Koo https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-02
publication date: 2025-09-02
doi: 10.4108/eetinis.v12i3.9356

Optimizing UAV Trajectories in Optical IRS-Aided Hybrid FSO/RF Aerial Access Networks Using DRL Technique
type: item
description: This paper investigates hybrid free-space optics (FSO)/radio frequency aerial access networks (AANs) using a high-altitude platform (HAP) and multiple UAVs to dynamically serve terrestrial users under varying environmental conditions, such as atmospheric turbulence and cloud-induced attenuation. The optical intelligent reflecting surfaces (OIRS), mounted on the HAP, enhance the FSO signal distribution to multiple UAVs by enabling precise beam manipulation, improving link reliability, and increasing network scalability. A deep reinforcement learning (DRL)-based approach is developed to optimize UAV placement and user association in real time, maximizing end-to-end throughput while adhering to backhaul capacity constraints. The study takes into account FSO channel impairments, including path loss, turbulence-induced fading, and pointing misalignment, modeled using log-normal distributions. Numerical results demonstrate that the dynamic deployment of multi-UAV configuration, trained under realistic cloudy conditions, significantly outperforms single-UAV and static deployment strategies, achieving higher data rates and stable user connectivity. This work highlights the potential of deploying OIRS-assisted AANs supporting multiple UAVs to realize robust and high-performance 6G networks.
subject: Research articles
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: Unmanned aerial vehicles (UAVs)
  topic: keywords
subject: Intelligent reflecting surface (IRS)
  topic: keywords
subject: Free Space Optics (FSO)
  topic: keywords
subject: Deep Reinforcement Learning (DRL)
  topic: keywords
creator: Cuong NGUYEN
creator: Thang NGUYEN
creator: Hien PHAM
creator: Anh DO
creator: Ngoc Dang
rights: Copyright (c) 2025 Cuong NGUYEN, Thang NGUYEN, Hien PHAM, Anh DO, Ngoc Dang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-06
publication date: 2025-11-06
doi: 10.4108/eetinis.124.10134

A Multimodal Swarm Learning Approach for DDoS Detection in Internet of Things Infrastructure
type: item
description: The Internet of Things (IoT) has emerged as a foundational platform for driving intelligent solutions, playing a central role in the Fourth Industrial Revolution. Its potential lies in enabling seamless connectivity and real-time data exchange among diverse devices and systems, thereby powering advanced applications such as intelligent transportation, smart healthcare, precision agriculture, and automated manufacturing. These solutions promise to improve efficiency, optimize resource utilization, and enhance decision-making across various sectors. However, this potential is challenged by some issues, including security vulnerabilities, privacy concerns, and significant heterogeneity arising from the vast diversity of devices, communication protocols, and data formats. In this paper, we develop a multimodal deep learning solution to detect DDoS attacks on IoT infrastructure based on two data types: packet-based data and flow-based data. Firstly, the datasets containing packets labeled as benign or attack are processed into two branches: packet-based and flow-based features. Then, each branch is trained using two independent CNN models. Finally, the feature information extracted from both modalities is fused and fed into a concatenation-based classifier for DDoS attack detection. Experimental results on Edge-IIoTset and CiCIoMT2024 datasets indicate that the multimodal deep learning model within a decentralized machine learning architecture achieves performance comparable to centralized machine learning. In addition, our proposal is also robust to non-independent and identically distributed (non-IID) data in decentralized machine learning architecture.
subject: Research articles
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: DDoS
  topic: keywords
subject: Decentralized Machine Learning
  topic: keywords
subject: multimodal
  topic: keywords
subject: CNN
  topic: keywords
subject: Swam Learning
  topic: keywords
creator: Thuat Nguyen-Khanh
creator: Anh Pham-Nguyen-Hai
creator: Luan Van-Thien
creator: Quan Le-Trung
rights: Copyright (c) 2025 Thuat Nguyen-Khanh; Anh Pham-Nguyen-Hai, Luan Van-Thien, Quan Le-Trung https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-29
publication date: 2025-12-29
doi: 10.4108/eetinis.131.9961

FedNDA: Enhancing Federated Learning with Noisy Client Detection and Robust Aggregation
type: item
description: Federated Learning is a novel decentralized methodology that enables multiple clients to collaboratively train a global model while preserving the privacy of their local data. Although federated learning enhances data privacy, it faces challenges related to data quality and client behavior. A fundamental issue is the presence of noisy labels in certain clients, which damages the global model's performance. To address this problem, this paper introduces a Federated learning framework with Noisy client Detection and robust Aggregation, FedNDA. In the first stage, FedNDA detects noisy clients by analyzing the distribution of their local losses. A noisy client exhibits a loss distribution distinct from that of clean clients. To handle class imbalance issue in local data, we utilize per-class losses instead of the total loss. We then assign each client a noisiness score, calculated as the Earth Mover’s Distance between the per-class loss distribution of the client and the average distribution of all clean clients. This noisiness metric is more sensitive for detecting noisy clients compared to conventional metrics such as Euclidean distance or L1 norm. The noisiness score is subsequently transfered to and used in the server-side aggregation function to prioritize clean clients while reducing the influence of noisy clients. Experimental results demonstrate that FedNDA outperforms FedAvg and FedNoRo by 4.68% and 3.6% on the CIFAR-10 dataset, and by 10.65% and 0.48% on the ICH dataset, respectively, in a high noisy setting.
subject: Research articles
  topic: section
subject: Deep Learning for IoT Big Data and Streaming Analytics
  topic: category
subject: Federated learning
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Noisy clients
  topic: keywords
subject: Non-IID
  topic: keywords
subject: Class imbalance
  topic: keywords
creator: Tuan Dung Kieu
creator: Charles Fonbonne
creator: Trung-Kien Tran
creator: Thi-Lan Le
creator: Hai Vu
creator: Huu-Thanh Nguyen
creator: Thanh-Hai Tran
rights: Copyright (c) 2025 Tuan Dung Kieu, Charles Fonbonne, Trung-Kien Tran, Thi-Lan Le, Hai Vu, Huu-Thanh Nguyen, Thanh-Hai Tran https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-07-03
publication date: 2025-07-03
doi: 10.4108/eetinis.v12i3.8720

Return Loss Optimization in Rectangular Microstrip Patch Antennas Using Response Surface Methodology (RSM) for 5G Applications
type: item
description: In recent decades, wireless communication has advanced significantly. People increasingly rely on the Internet of Things, cloud computing, and big data analytics. These services require higher data rates, faster transmission and reception times, greater coverage, and increased throughput. 5G technology supports all of these features. Antennas, essential components of modern wireless devices, must be designed to meet the growing demand for fast and intelligent products. This study aims to optimize the dimensions and characteristics of a rectangular patch antenna. To examine the impact of independent variables (such as patch length, patch width, inset slot length, and inset slot width) on the response variables (return loss and resonant frequency), Response Surface Methodology (RSM) combined with Central Composite Design (CCD) was applied. The findings of the RSM analysis indicated that the experimental data were best represented by a quadratic polynomial model, with regression coefficients exceeding 0.970 for all responses. The optimized parameters identified are as follows: a patch length of 4.7 mm, a patch width of 4.7 mm, an inset slot length of 0.8 mm, and an inset slot width of 1.0 mm. The antenna designed using these optimized parameters achieved a target return loss of -45.865 dB at a frequency of 28.122 GHz. Finally, the results were validated using CST Studio Suite, which demonstrated good agreement with the experimental data.
subject: Research articles
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: 5G
  topic: keywords
subject: Central Composite Design
  topic: keywords
subject: CCD
  topic: keywords
subject: patch antenna
  topic: keywords
subject: return loss
  topic: keywords
subject: Response Surface Methodology
  topic: keywords
subject: RSM
  topic: keywords
creator: Thi Bich Ngoc Tran
creator: Van Su Dang
rights: Copyright (c) 2025 Thi Bich Ngoc Tran, Van Su Dang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-12
publication date: 2025-06-12
doi: 10.4108/eetinis.v12i3.8948

COSMN: Clustering-Based Optimization for 360-Degree Live Streaming over Mobile Networks
type: item
description: The rapid growth of 360-degree video streaming has transformed how users experience immersive content, especially on mobile devices. However, delivering high-quality 360-degree video streams to mobile devices is challenging due to their constrained computational resources, limited bandwidth, and the need for real-time processing. The paper introduces COSMN (Clustering-Based Optimization for 360-Degree Video Streaming over Mobile Networks), an innovative framework to tackle these challenges. COSMN leverages a clustering-based optimization approach to dynamically adapt video streaming to the viewer’s region of interest (ROI), minimizing resource consumption while maintaining high-quality visuals for the most relevant portions of the video. The framework operates by dividing the 360-degree video into multiple tiles and clustering these tiles based on user viewing patterns. By predicting user behavior with clustering algorithms, COSMN efficiently prioritizes bandwidth and processing power for the tiles within the viewer’s ROI. The system also integrates adaptive bitrate streaming techniques to ensure seamless playback under varying network conditions. Experimental results demonstrate that COSMN significantly reduces bandwidth usage and computational load on mobile devices while providing a smooth and immersive viewing experience. Compared to traditional 360-degree online streaming methods, COSMN achieves superior performance in terms of latency, video quality, and resource efficiency. This work paves the way for scalable, 360-degree online streaming solutions on mobile platforms, making immersive video experiences more accessible and practical for everyday users.
subject: Research articles
  topic: section
subject: Deep Learning for IoT Big Data and Streaming Analytics
  topic: category
subject: Mobile Network
  topic: keywords
subject: Field Of View
  topic: keywords
subject: 360-Degree Video
  topic: keywords
subject: Clustering-Based Optimization
  topic: keywords
subject: Quality of Experience
  topic: keywords
subject: COSMN
  topic: keywords
creator: Hung Nguyen Viet
creator: Hoang Bui Huy
creator: Cong Tran Thanh
creator: Huong Truong Thu
rights: Copyright (c) 2025 Hung Nguyen Viet, Hoang Bui Huy, Cong Tran Thanh, Huong Truong Thu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-11
publication date: 2025-11-11
doi: 10.4108/eetinis.131.9499

A novel approach for graph-based real-time anomaly detection from dynamic network data listened by Wireshark
type: item
description: This paper presents a novel approach for real-time anomaly detection and visualization of dynamic network data using Wireshark, globally's most widely utilized network analysis tool. As the complexity and volume of network data continue to grow, effective anomaly detection has become essential for maintaining network performance and enhancing security. Our method leverages Wireshark’s robust data collection and analysis capabilities to identify anomalies swiftly and accurately. In addition to detection, we introduce innovative visualization techniques that facilitate the intuitive representation of detected anomalies, allowing network administrators to comprehend network conditions and make informed decisions quickly. The results of our study demonstrate significant improvements in both the efficacy of anomaly detection and the practical applicability of visualization tools in real-time scenarios. This research contributes valuable insights into network security and management, highlighting the importance of integrating advanced analytical methods with effective visualization strategies to enhance the overall management of dynamic networks.
subject: Research articles
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: Cyber Attacks
  topic: keywords
subject: Information Security
  topic: keywords
subject: Graph Visualization
  topic: keywords
subject: Temporal Dynamic Networks
  topic: keywords
subject: Wireshark
  topic: keywords
creator: Muhammed Onur Kaya
creator: Mehmet Ozdem
creator: Resul Das
rights: Copyright (c) 2024 Muhammet Onur Kaya, Mehmet Ozdem, Resul Das https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-01-07
publication date: 2025-01-07
doi: 10.4108/eetinis.v12i2.7616

QoE-Energy Consumption Optimization for End-User Devices in Adaptive Bitrate Video Streaming Using the Lagrange Multiplier Method
type: item
description: The reduction of greenhouse gas emissions in the Internet and ICT sectors has become a critical challenge. According to recent research, the key contributors to greenhouse gas emissions in Internet include high energy consumption factors such as data centers, transmission network devices, and end-user devices. Among Internet services, video streaming is one of the services having the highest traffic volume and number of users. Consequently, developing energy-efficient solutions for video streaming networks, particularly for end-user devices, is an urgent research priority. Reducing energy consumption in end-user devices in a video streaming system often requires compromises in parameters that impact the quality of user experience (QoE). Therefore, achieving an optimal trade-off between minimizing energy consumption and maintaining an acceptable QoE is a key objective. In this study, a cost function that integrates QoE and energy consumption is developed using the Lagrange multiplier method. Based on this function, an adaptive bitrate algorithm is proposed to select optimal video segments for video players, ensuring maximum QoE while minimizing energy consumption. The performance of the proposed method is evaluated using various types of video samples under varying network bandwidth conditions. Experimental results show that the proposed method reduces energy consumption of end-user devices by up to 6.7% and enhances QoE by 20% compared to previous methods.
subject: Research articles
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: Video streaming
  topic: keywords
subject: Adaptive Bitrate
  topic: keywords
subject: QoE
  topic: keywords
subject: Energy consumption
  topic: keywords
creator: Tien Vu Huu
creator: Thao Nguyen Thi Huong
rights: Copyright (c) 2025 Tien Vu Huu, Thao Nguyen Thi Huong https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-04-14
publication date: 2025-04-14
doi: 10.4108/eetinis.v12i3.8587

https://publications.eai.eu/index.php/inis/article/view/8604
type: item
title: Security-Reliability Analysis of NOMA-Assisted Hybrid Satellite-Terrestrial Relay Multi-Cast Transmission Networks Using Fountain Codes and Partial Relay Selection with Presence of Multiple Eavesdroppers
description: This article proposes a hybrid satellite-terrestrial relaying network (HSTRN) that integrates physical-layer security (PLS), Fountain codes (FCs), non-orthogonal multiple access (NOMA), and partial relay selection (PRS) to enhance system performance in terms of reliability, data rate, and security. In the proposed system, a satellite uses NOMA to simultaneously transmit Fountain packets to two clusters of terrestrial users. Data transmission is assisted by one of the terrestrial relay stations, selected by the PRS algorithm. We derive exact expressions for outage probability (OP) and system outage probability (SOP) at the legitimate users, as well as intercept probability (IP) and system intercept probability (SIP) at eavesdroppers. Monte Carlo simulations are realized to validate the accuracy of the analytical results, illustrate performance trends, and analyze the impact of key parameters on the considered performance.
subject: Research articles
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: Hybrid satellite-terrestrial relaying networks
  topic: keywords
subject: fountain codes
  topic: keywords
subject: non-orthogonal multiple access
  topic: keywords
subject: physical-layer security
  topic: keywords
subject: multicast transmission
  topic: keywords
creator: Toan Van Nguyen
creator: Trung Duy Tran
creator: Ngoc Son Pham
creator: Viet Tuan Pham
creator: Lam Thanh Tu
rights: Copyright (c) 2025 Toan Van Nguyen, Trung Duy Tran, Ngoc Son Pham, Viet Tuan Pham, Lam Thanh Tu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-04-11
publication date: 2025-04-11
doi: 10.4108/eetinis.v12i3.8604

Secure UAV-assisted Mobile Edge Computing for IoT with Backscatter Communication in the Presence of a Moving Eavesdropper
type: item
description: The perception layer security (PLS) is crucial for ensuring that the data collected by Internet of Things (IoT) devices is accurate, reliable, and protected against various security threats. It helps maintain the overall integrity of the IoT ecosystem and builds trust in its applications. Our work explores the integration of network and PLS in a UAV-enabled mobile edge computing (MEC) system for IoT. This system supports multiple users with a combined non-orthogonal and time-division multiple access (NOTDMA) scheme and is based on backscatter communication (BC). In this system, the UAV-mounted server functions as a hybrid access point (HAP) and hovers over a cluster of energy-constrained IoT devices to transmit RF energy and assist them in performing tasks by employing BC. The IoT devices apply the combined NOTDMA scheme to offload their tasks to the HAP. A mobile passive eavesdropper attempts to intercept information from IoT devices without actively launching any attacks. A partial offloading scheme with various encryption algorithms is proposed to improve the system’s secrecy, which adapts to the users’ non-linear harvested energy levels. In addition, considering the network and physical security, we derive a approximation expression for the secrecy successful computation probability (SSCP). This expression incorporates factors such as harvested energy, local computing and encryption latency, edge offloading latency, processing, decryption, and the associated secrecy costs. The optimization problem for maximizing SSCP is formulated and solved using an Immune algorithm to find the optimal set of device parameters and UAV altitude. Key parameters affecting secrecy and latency performance are analyzed to better understand the system’s behavior. Numerical simulations are provided to validate the accuracy of our analysis.
subject: Research articles
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: physical layer security
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: mobile edge computing
  topic: keywords
subject: unmanned aerial vehicle
  topic: keywords
subject: RF energy harvesting
  topic: keywords
subject: secrecy successful computation probability
  topic: keywords
creator: Van Long Nguyen
creator: Duy-Hung Ha
creator: Van Truong Truong
creator: Thanh Truc Tran
creator: Dac Binh Ha
rights: Copyright (c) 2025 Van-Long Nguyen, Duy-Hung Ha, Van-Truong Truong, Tran Thanh Truc, Dac-Binh Ha https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-10-23
publication date: 2025-10-23
doi: 10.4108/eetinis.v12i3.8889

Integrated Cloud-Twin Synchronization for Supply Chain 5.0
type: item
description: The digital twin is thus emerging means of improving real-world performance from virtual spaces, especially relatedto Supply Chain 5.0 in Industry 5.0. This framework employs the integration of cloud computing and digital twin technologies to secure data storage, trusted tracking, and high reliability, is architectural for the integration of supply-chain sustainable enterprises. In this work, we introduce a high level architecture of cloud-based digital twin model for supply chain 5.0 , which was created to align the system of supply chain through real-time observation as well as real-timesupply chain 5.0 decision-making and control. This study introduces a cloud-based twin optimization model for Supply Chain 5.0, validated through genetic algorithm (GA) simulations. The model determines optimal weights to balance objectives, achieving an optimal objective function value that reflects trade-offs among operational efficiency, cost, and sustainability. A convergence plot illustrates the model’s iterative solution improvements, demonstrating its dynamic adaptability. Lastly, the proposed model defines and test a supply chain performance analysis through dynamic simulations.
subject: Research articles
  topic: section
subject: Integrated Cloud-Twin Synchronization
  topic: keywords
subject: ICTS
  topic: keywords
subject: Digital twin
  topic: keywords
subject: cloud computing
  topic: keywords
subject: industry 5.0
  topic: keywords
subject: supply chain 5.0
  topic: keywords
subject: optimization
  topic: keywords
subject: genetic algorithm
  topic: keywords
creator: Divya Sasi Latha
creator: Tartat Mokkhamakkul
rights: Copyright (c) 2024 Divya Sasi Latha, Tartat Mokkhamakkul https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-03-12
publication date: 2025-03-12
doi: 10.4108/eetinis.v12i2.8600

Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Detection
type: item
description: INTRODUCTION: A robust method is proposed in this paper to detect helmet usage in two-wheeler riders to enhance road safety. OBJECTIVES: This involves a custom made dataset that contains 1000 images captured under diverse real-world scenarios, including variations in helmet size, colour, and lighting conditions. This dataset has two classes namely with helmet and without helmet. METHODS: The proposed helmet classification approach utilizes the Multi-Scale Deep Convolutional Neural Network (CNN) framework cascaded with Long Short-Term Memory (LSTM) network. Initially the Multi-Scale Deep CNN extracts modes by applying Single-level Discrete 2D Wavelet Transform (dwt2) to decompose the original images. In particular, four different modes are used for segmenting a single image namely approximation, horizontal detail, vertical detail and diagonal detail. After feeding the segmented images into a Multi-Scale Deep CNN model, it is cascaded with an LSTM network. RESULTS: The proposed model achieved accuracies of 99.20% and 95.99% using both 5-Fold Cross-Validation (CV) and Hold-out CV methods, respectively. CONCLUSION: This result was better than the CNN-LSTM, dwt2-LSTM and a tailor made CNN model.
subject: Research articles
  topic: section
subject: Multi Scale CNN
  topic: keywords
subject: Long Short-Term Memory
  topic: keywords
subject: Discrete Wavelet Transform
  topic: keywords
subject: Helmet Detection
  topic: keywords
creator: Amrutha Annadurai
creator: Manas Ranjan Prusty
creator: Trilok Nath Pandey
creator: Subhra Rani Patra
rights: Copyright (c) 2024 Amrutha Annadurai, Manas Ranjan Prusty, Trilok Nath Pandey, Subhra Rani Patra https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-03-11
publication date: 2025-03-11
doi: 10.4108/eetinis.v12i2.7612

Enhancing AI-Inspired Analog Circuit Design: Optimizing Component Sizes with the Firefly Algorithm and Binary Firefly Algorithm
type: item
description: This paper explores the use of the Firefly Algorithm (FA) and its binary variant (BFA) in optimizing analog circuit component sizing, specifically as a case study for a two-stage operational amplifier (op-amp) designed with a 65nm CMOS process. Recognizing the limitations of traditional optimization approaches in handling complex analog design requirements, this study implements both FA and BFA to enhance convergence speed and accuracy within multi-dimensional search spaces. The Python-Spectre framework in this paper facilitates automatic, iterative simulation and data collection, driving the optimization process. Through extensive benchmarking, the BFA outperformed traditional FA, balancing exploration and exploitation while achieving superior design outcomes across key parameters such as voltage gain, phase margin, and unity-gain bandwidth. Comparative analysis with existing optimization methods, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), underscores the efficiency and accuracy of BFA in optimizing circuit metrics, particularly in power-constrained environments. This study demonstrates the potential of swarm intelligence in advancing automatic analog design and establishes a foundation for future enhancements in analog circuit automation.
subject: Research articles
  topic: section
subject: Deep Learning for IoT Big Data and Streaming Analytics
  topic: category
subject: Firefly Algorithm
  topic: keywords
subject: Binary Firefly Algorithm
  topic: keywords
subject: simulation-based optimization method
  topic: keywords
subject: two-stage op-amp
  topic: keywords
creator: Trang Hoang
rights: Copyright (c) 2024 Trang Hoang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-01-08
publication date: 2025-01-08
doi: 10.4108/eetinis.v12i2.7859

Coverage Probability of EH-enabled LoRa networks - A Deep Learning Approach
type: item
description: The performance of energy harvesting (EH)-enabled long-range (LoRa) networks is analyzed in this work. Specifically, we employ deep learning (DL) to estimate the coverage probability (Pcov) of the considered networks. Our study incorporates a general fading distribution, specifically the Nakagami-m distribution, and utilizes tools from stochastic geometry (SG) to model the spatial distributions of all nodes and end-devices (EDs) with EH capability. The DL approach is employed to overcome the limitations of model-based methods that can only evaluate the Pcov under simplified network conditions. Therefore, we propose a deep neural network (DNN) that estimates the Pcov with high accuracy compared to the ground truth values. Additionally, we demonstrate that DL significantly outperforms the Monte Carlo simulation approach in terms of resource consumption, including time and memory.
subject: Research articles
  topic: section
subject: Coverage probability
  topic: keywords
subject: deep learning
  topic: keywords
subject: energy harvesting
  topic: keywords
subject: long range
  topic: keywords
subject: power beacon
  topic: keywords
creator: Thi-Tuyet-Hai Nguyen
creator: Tran Cong-Hung
creator: Nguyen Hong-Son
creator: Tan Hanh
creator: Tran Trung Duy
creator: Lam-Thanh Tu
rights: Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems https://creativecommons.org/licenses/by/3.0/
date: 2024-12-05
publication date: 2024-12-05
doi: 10.4108/eetinis.v12i2.6780

Joint Adaptive Modulation and Power Control Scheme for Energy Efficient FSO-based Non-Terrestrial Networks
type: item
description: Free-space optics (FSO)-based non-terrestrial networks (NTN) have garnered significant attention as a potential technology for forthcoming 6G wireless communications due to their exceptional data rate and extensive global coverage capability. Nevertheless, atmospheric attenuation, cloud attenuation, geometric loss, and atmospheric turbulence present numerous difficulties in developing these networks. To cope with these difficulties, we propose to apply a joint adaptive modulation and power control (JAMPC) scheme to FSO-based NTN. Our proposed JAMPC algorithm aims to enhance energy efficiency while guaranteeing the targeted outage probability, bit-error rate, and the required data rate. We develop mathematical models and derive closed-form expressions to implement the proposed algorithm and solve the optimization problem. The numerical results confirm that the JAMPC scheme helps NTN provide better energy efficiency and the ability to adapt to various channel conditions.
subject: Research articles
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: Satellite communications
  topic: keywords
subject: Free-space optics communications
  topic: keywords
subject: Cloud attenuation
  topic: keywords
subject: Atmospheric turbulence
  topic: keywords
subject: Adaptive modulation
  topic: keywords
subject: Power control
  topic: keywords
creator: Thang V. Nguyen
creator: Hien T. T. Pham
creator: Ngoc T. Dang
rights: Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems https://creativecommons.org/licenses/by/3.0/
date: 2024-12-03
publication date: 2024-12-03
doi: 10.4108/eetinis.v12i1.7317

Predicting the Severity of COVID-19 Pneumonia from Chest X-Ray Images: A Convolutional Neural Network Approach
type: item
description: This study addresses significant limitations of previous works based on the Brixia and COVIDGR datasets, which primarily provided qualitative lung injury scores and focused mainly on detecting mild and moderate cases. To bridge these critical gaps, we developed a unified and comprehensive analytical framework that accurately assesses COVID-19-induced lung injuries across four levels: Normal, Mild, Moderate, and Severe. This approach’s core is a meticulously curated, balanced dataset comprising 9,294 high-quality chest X-ray images. Notably, this dataset has been made widely available to the research community, fostering collaborative efforts and enhancing the precision of lung injury classification at all severity levels. To validate the framework’s effectiveness, we conducted an in-depth evaluation using advanced deep learning models, including VGG16, RegNet, DenseNet, MobileNet, EfficientNet, and Vision Transformer (ViT), on this dataset. The top-performing model was further enhanced by optimizing additional fully connected layers and adjusting weights, achieving an outstanding sensitivity of 94.38%. These results affirm the accuracy and reliability of the proposed solution and demonstrate its potential for broad application in clinical practice. Our study represents a significant step forward in developing AI-powered diagnostic tools, contributing to the timely and precise diagnosis of COVID-19 cases. Furthermore, our dataset and methodological framework hold the potential to serve as a foundation for future research, paving the way for advancements in the detection and classification of respiratory diseases with higher accuracy and efficiency.
subject: Research articles
  topic: section
subject: Deep Learning for IoT Big Data and Streaming Analytics
  topic: category
subject: Pneumonia Severity
  topic: keywords
subject: COVID-19
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
subject: Classification
  topic: keywords
creator: Thien B. Nguyen-Tat
creator: Viet-Trinh Tran-Thi
creator: Vuong M. Ngo
rights: Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems https://creativecommons.org/licenses/by/3.0/
date: 2024-11-25
publication date: 2024-11-25
doi: 10.4108/eetinis.v12i1.6240

An Efficient Method for BLE Indoor Localization Using Signal Fingerprint
type: item
description: The rise of Bluetooth Low Energy (BLE) technology has opened new possibilities for indoor localization systems. However, extracting fingerprint features from the Received Signal Strength Indicator (RSSI) of BLE signals often encounters challenges due to significant errors and fluctuations. This research proposes an approach that integrates signal filtering and deep learning techniques to improve accuracy and stability. A Kalman filter is employed to smooth the RSSI values, while Autoencoder and Convolutional Autoencoder models are utilized to extract distinctive fingerprint features. The system compares random test points with a reference database using normalized cross-correlation. Performance is assessed based on metrics such as the number of reference points with the highest cross-correlation (), average localization error, and other statistical indicators. Experimental results show that the combination of the Kalman filter with the Convolutional Autoencoder model achieves an average error of 0.98 meters with . These findings indicate that this approach effectively reduces signal noise and enhances localization accuracy in indoor environments.
subject: Research articles
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: Deep Learning for IoT Big Data and Streaming Analytics
  topic: category
subject: Indoor Localization
  topic: keywords
subject: Fingerprint
  topic: keywords
subject: Bluetooth Low Energy
  topic: keywords
subject: Autoencoder
  topic: keywords
creator: Trong-Thanh Han
creator: Phuc Nguyen Dinh
creator: Toan Nguyen Duc
creator: Vu Nguyen Long
creator: Hung Dinh Tan
rights: Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems https://creativecommons.org/licenses/by/3.0/
date: 2024-11-22
publication date: 2024-11-22
doi: 10.4108/eetinis.v12i1.6571

Drug classification system based on drug composition and usage instructions
type: item
description: This study presents a natural language processing (NLP) approach to classify drugs based on compositional and usage descriptions. NLP techniques including text preprocessing, word embedding, and deep learning models were applied to a Vietnamese drug dataset. Traditional machine learning models like Support Vector Machines (SVM) and deep models including Bidirectional Long Short-Term Memory (BiLSTM) and PhoBERT were evaluated. Besides, since there is a limitation in the information of our own collected data, some data augmentation techniques were applied to increase the variation of the dataset. Results show PhoBERT achieving 95% accuracy, highlighting the benefits of transferring knowledge from large language models. Errors primarily occurred between similar drug categories, suggesting taxonomy refinement could improve performance. In summary, an automated drug classification framework was developed leveraging state-of- the-art NLP, validating the feasibility of analyzing drug data at scale and aiding therapeutic understanding. This supports NLP’s potential in pharmacovigilance applications.
subject: Research articles
  topic: section
subject: Intelligent Sensing and Communication for Industrial IoT Networks
  topic: category
subject: Deep Learning for IoT Big Data and Streaming Analytics
  topic: category
subject: Drug classification
  topic: keywords
subject: NLP
  topic: keywords
subject: Drug Composition
  topic: keywords
creator: Hoang-Dieu Vu
creator: Vu-Hien Pham
creator: Quang-Dung Le
rights: Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems https://creativecommons.org/licenses/by/3.0/
date: 2024-11-07
publication date: 2024-11-07
doi: 10.4108/eetinis.v12i1.5995