International Journal of Advances in Intelligent Informatics
Journal title International Journal of Advances in Intelligent Informatics Initials IJAIN Abbreviation Int. J. Adv. Intell. Informatics Frequency Four issues per year [2025] DOI prefix 10.26555 by Print ISSN 2442-6571 Online ISSN 2548-3161 Editor-in-chief Andri Pranolo Publisher Universitas Ahmad Dahlan Organizer UAD and ASCEE Computer Society Citation Analysis SCOPUS CiteScore Tracker 2026 | Web of Science | Google Scholar Cite IJAIN IJAIN_coll.bib | IJAIN_coll.ris | IJAIN_coll.xml International Journal of Advances in Intelligent Informatics is a peer-reviewed open-access journal. The journal invites scientists and engineers worldwide to exchange and disseminate theoretical and practice-oriented topics of advances in intelligent informatics within the whole spectrum of intelligent informatics. The scope includes, but is not limited to, Machine Learning & Soft Computing, Data Mining & Big Data Analytics, Computer Vision & Pattern Recognition, and Natural language processing. Submitted papers must be written in English for the minimum requirements of the initial review stage by editors, and a further review process by a minimum of three reviewers. Since October 2017, the journal has been ACCREDITATED with "A" or "1st" grade (the highest grade, SINTA 1) by the Ministry of Research, Technology and Higher Education (RistekDikti) of The Republic of Indonesia as an achievement for the peer-reviewed journal that has excellent quality in management and publication. The recognition published in Director Decree No. 48a/E/KPT/2017 October 30, 2017, & No. 51/E/KPT/2017 December 4, 2017, No. 30/E/KPT/2018 October 24, 2018, and has successfully maintained its national accreditation at SINTA 1, the highest level of journal accreditation in Indonesia based on recognition by the Ministry of Higher Education, Science, and Technology (Kemdiktisaintek) of the Republic of Indonesia through Decree No. 295/C/C3/KPT/2026, dated January 2, 2026 (Download Here). IJAIN has been ACCEPTED for SCOPUS indexing since June 5, 2018. Finally, accepted and published papers will be freely accessed in this website and the following abstracting & indexing databases: SCOPUS Directory of Open Access Journals (DOAJ) ASEAN Citation Index (ACI) Science and Technology Index (SINTA) by Kemdiktisaintek of The Republic of Indonesia EBSCO Host (Database: Applied Science & Technology Source Ultimate) ProQuest LLC (a license agreement signed on March 20, 2018) Microsoft Academic Search (MAS) Crossref Search GOOGLE Scholar Public Knowledge Project (PKP) Index Journal TOCs GARUDA: Garba Rujukan Digital by Ristekdikti - Indonesia Indonesia One Search BASE Bielefeld search engine OCLC WorldCat Indonesian Scientific Journal Database (ISJD) The journal has been listed in SHERPA/RoMEO policy LOCKSS Archiving system ULRICHSWEB ProQuest ROAD ISSN EZB Universitat Regensburg Open Science Directory by EBSCO information service OAI Address International Journal of Advances in Intelligent Informatics has OAI address: http://ijain.org/index.php/IJAIN/oai. Before submission, You have to make sure that your paper is prepared using the IJAIN paper TEMPLATE, has been carefully proofread and polished, and conformed to the author's guidelines. Online Submissions Already have a Username/Password for International Journal of Advances in Intelligent Informatics? GO TO LOGIN Need a Username/Password? GO TO REGISTRATION Registration and login are required to submit items online and to check the status of current submissions.
License and Copyright Agreement In submitting the manuscript to the journal, the authors certify that: They are authorized by their co-authors to enter into these arrangements. The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal. Please also carefully read IJAIN's Posting Your Article Policy at http://ijain.org/index.php/IJAIN/about/editorialPolicies#custom-5. The work is not under consideration for publication elsewhere. The work has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out. They secure the right to reproduce any material that has already been published or copyrighted elsewhere. They agree to the following license and copyright agreement. Copyright Authors who publish with International Journal of Advances in Intelligent Informatics agree to the following terms: Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Optimizing the compact convolution transformer for enhanced pneumonia detection
Pneumonia detection through medical imaging, especially using CT scans or X-rays, presents notable challenges due to the subtle and often unclear signs of the disease. This paper introduces a novel neural network model, the Compact Convolutional Transformer (CCT), designed to address these challenges by optimizing detection accuracy. The CCT model incorporates configuration dropout in its convolutional layers to enhance both robustness and precision.Experiments conducted on a dataset of 5,856 chest X-ray images from pediatric patients aged one to five years demonstrated the model's effectiveness, achieving a remarkable 97% accuracy, 97% recall, 98% precision, and an F1-score of 98%. When compared to state-of-the-art models like DarkNet-53 and VGG-19 + GradCAM, which achieved F1-scores of 97.3% and 95.61% respectively, the CCT model consistently matched or outperformed them, particularly when dealing with smaller and more complex datasets. Even models such as CNN + Bayesian Network, which used larger datasets, only reached an F1-score of 96.3%.These results underscore the superior efficiency and accuracy of the CCT model, highlighting its potential for broader applications in medical diagnostics and image analysis, especially in pneumonia detection.
Copyright (c) 2026 Muhammad Munsarif, Norshuhani Zamin, Muhammad Sam’an https://creativecommons.org/licenses/by-sa/4.0
Blockchain-based 5G handoff authentication system using joint-graph-based delegated practical byzantine fault tolerance consensus approach
The 5G mobile networks offer extra benefits in terms of high data rates, lower latency, and more coverage in comparison to 4 G networks. However, the 5G network offers new levels of data transfer and processing speeds, ensuring users do not disconnect as they move from one cell to another. By considering these issues, this paper proposes a new blockchain-based, scalable, and reliable 5G handoff authentication system. The proposed approach is intended to ensure authentication using Exponential Elliptic Curve-Assisted Encryption (EEE) between the user and the base station. After the successful authentication, the user stores the details in the source base station. In a blockchain-based 5G handoff authentication system, when a user device enters the range of a new base station, it sends a handoff request using a pseudorandom frequency-hopping sequence. The serving base station sends a handover command to the mobile device, containing details about the target base station and the next frequency in the hopping sequence. This request is disseminated through an improved gossip algorithm that minimizes communication overhead and accelerates node authentication in the blockchain for consensus and validation. Similarly, in blockchain networks, the gossip protocol ensures that every node in the network receives information about the request for handoff while ensuring that the messages sent are not redundant. Therefore, the joint graph-based Delegated Practical Byzantine Fault Tolerance (JG-DPBFT) consensus process is utilized to verify the handoff.
Copyright (c) 2026 Ravindra Janardan Lawande, Sudhir Bapurao Lande, Shailendrakumar Mahadeo Mukane https://creativecommons.org/licenses/by-sa/4.0
Enhancing anomaly lane detection accuracy using feature cross attention in U-Net architecture
Lane departure detection is a crucial task in advanced driver assistance systems (ADAS) and autonomous driving, aimed at reducing accidents caused by unintentional road deviation. This study proposes a modified U-Net architecture enhanced with Feature Cross Attention (FCA) to improve lane departure anomaly detection. The objective is to enhance spatial sensitivity and context awareness in segmentation, especially under challenging driving conditions such as occlusions, poor lighting, and distorted lane geometry. The materials used include the publicly available Comma 2k19 LD dataset, comprising 2,000 manually annotated frames extracted from highway driving scenarios. Each frame includes synchronized video and driving telemetry, offering diverse visual conditions. Preprocessing steps include resizing, normalization, and annotation conversion to binary masks. An anomaly is defined as a spatial deviation threshold between predicted and ground-truth lane boundaries. The proposed method incorporates FCA at the bottleneck and decoder levels of the U-Net architecture. Evaluation was performed using Intersection over Union (IoU), Pixel Accuracy, and threshold-based anomaly criteria. The model achieved 99.19% Pixel Accuracy and 98.47% IoU, outperforming the baseline U-Net (97.56% and 97.46%, respectively). Visual results showed improved detection of subtle lane shifts. A confusion matrix generated over 210 validation images demonstrated perfect classification of normal and anomalous cases. These results confirm that FCA integration enhances segmentation precision and anomaly sensitivity. The approach is suitable for real-time deployment in autonomous systems. Future research may focus on temporal integration, lightweight optimization for embedded devices, and the extension of the framework to multi-lane or urban traffic environments.
Copyright (c) 2026 Zahir Zainuddin, Muhammad Abdillah Rahmat, Elly Warni, A. Ais Prayogi Alimuddin https://creativecommons.org/licenses/by-sa/4.0
Enhancing safety with IoT and machine learning: a novel smart safety net design
The Internet of Things (IoT) represents a complex network of embedded devices that exchange data through heterogeneous communication technologies, making them increasingly vulnerable to sophisticated cyber attacks. This paper presents a hybrid Intrusion Detection System (HIDS) that integrates Extra Trees (ExtraTreesClassifier) for feature selection with four ensemble classifiers: XGBoost, CatBoost, AdaBoost, and Gradient Boosting. Our approach performs supervised feature selection exclusively on training data to prevent information leakage, applies class balancing for imbalanced datasets, and evaluates each hybrid model using comprehensive metrics including ROC-AUC, PR-AUC, false positive/negative rates, and Matthews Correlation Coefficient. We validate our methodology on three benchmark datasets with contrasting characteristics: UNSW-NB15 (real-world network traffic, 175K samples), IoTNet24 (laboratory-controlled traffic, 23K samples), and BoTNeTIoT-L01 (large-scale laboratory traffic, 2.4M samples). On UNSW-NB15, our best model (EXT-GB) achieves 87.73% accuracy, 0.90 F1-score, 0.98 ROC-AUC, and 98.58% recall with 1.42% false negative rate, representing realistic performance for production IDS. On laboratory datasets after addressing class imbalance, models achieve near-perfect performance (IoTNet24: 99.96%, BoTNeTIoT: 99.99%). The 12-percentage-point performance gap between real-world and laboratory data highlights a critical finding: controlled laboratory datasets significantly overestimate real-world IDS capability, underscoring the importance of evaluation on realistic traffic captures for assessing production deployment readiness.
Copyright (c) 2026 Shokhan M. Al-Barzinji, Zahraa.H.Ameen, Noor Abdul Khaleq Zghair, Abubakr S. Issa, Samer Raad Azzawi, Ali Abdulateef Abdulbari https://creativecommons.org/licenses/by-sa/4.0
Improvement of CNN model using integration of multimodal MRI sequences and multilevel fusion to enhance performance for brain tumor classification
The prevalence of brain tumors has been increasing annually, and headaches, a common initial symptom, represent the most common manifestation. However, there is a paucity of research on effective methods of assessing brain tumors. This study proposes a novel approach by introducing various modality fusion techniques based on their fusion levels, which are then categorized into four groups: single-modal, data-level fusion, feature-level fusion, and multilevel fusion. A total of 51 combinations are designed to evaluate the efficacy of these fusion techniques and modality configurations. The experiments used a BraTS2021, which comprises four magnetic resonance imaging (MRI) sequences (flair, t1, t1ce and t2). Initially, the image was pre-processed, encompassing data selection, conversion, and normalization. Subsequently, it was input into a 13-layer CNN architecture for feature extraction. Classification was facilitated by a soft voting method in ensemble learning, incorporating support vector machine (SVM), k-nearest neighbor (KNN), logistic regression, random forest, and decision tree algorithms. The predictive efficacy of the model was rigorously assessed through a comprehensive suite of metrics, prominently featuring accuracy, AUCROC, AUCPR, Cohen's Kappa, and MCC. The results indicate that multilevel fusion exhibits optimal performance, with an average accuracy of 95.84%, followed by feature-level fusion and data-level fusion, at 95.12% and 94.77%, respectively. The optimal fusion technique was identified as the combination with the FF configuration (1,2),3,4), producing an accuracy of 96.62%. The best-model combination proposed exhibited an accuracy difference of nearly 6% from the baseline model, underscoring the efficacy of the proposed approach. These empirical results establish a robust baseline for future investigations into sophisticated fusion architectures across hierarchical integration levels.
Copyright (c) 2026 Ade Umar Ramadhan, Shofwatul Uyun https://creativecommons.org/licenses/by-sa/4.0
MobileNet-driven detection of bacterial and viral pneumonia with Grad-CAM heatmap insights
Pneumonia is a major cause of childhood illness and death, and chest X-rays remain the most accessible diagnostic tool. Differentiating bacterial from viral pneumonia, however, is difficult because of overlapping radiographic patterns. This study explores MobileNet architectures combined with Grad-CAM visualization to provide efficient and interpretable pneumonia classification. The main contribution of this research is to demonstrate that MobileNet combined with Grad-CAM not only produces accurate predictions but also highlights radiologically meaningful regions of the lungs, thereby improving transparency and trust in automated diagnosis. A dataset of 5,842 pediatric chest X-rays from Guangzhou Women and Children’s Medical Center was used, including bacterial, viral, and normal cases. MobileNet and MobileNetV2 were trained with stochastic gradient descent, categorical cross-entropy, 20 epochs, and batch size of 32, and validated through 10-fold cross-validation. Grad-CAM was applied to generate heatmaps for model interpretability. Results indicated that MobileNet outperformed MobileNetV2. MobileNet achieved 79.32% accuracy, 81.02% precision, 78.15% recall, 77.82% F1-score, and 89.49% specificity. Its AUC-ROC reached 94.64% (macro) and 90.52% (micro). MobileNetV2 obtained 76.44% accuracy, 74.45% F1-score, and 93.61% macro AUC-ROC. Grad-CAM confirmed that both models attended to pneumonia-related lung regions, with MobileNet producing sharper localized activations and MobileNetV2 showing broader patterns. In conclusion, MobileNet with Grad-CAM provides an accurate, efficient, and interpretable framework for pneumonia detection, making it suitable for deployment in resource-limited clinical settings.
Copyright (c) 2026 Yuri Pamungkas, Adrian Jaleco Forca, Muhammad Nur Afnan Uda, Uda Hashim https://creativecommons.org/licenses/by-sa/4.0
Enhancing chest image classification using PCA and CNN model
Deep learning (DL) has a significant impact on X-ray images for diagnosing and categorizing a range of lung disorders. The proliferation of extensive annotated image datasets has resulted in the emergence of convolutional neural networks (CNNs) as a useful instrument for the tasks of image recognition and categorization. Despite this abundance, the primary challenge in medical diagnosis continues to be the classification of these images. This research aims to enhance image classifiers by using the CNN model; both training and testing datasets underwent analysis using the suggested CNN system. An analysis and comparison are conducted on the impact of feature extraction using the Principal Components Analysis (PCA) technique. The study attains maximal classification efficiency by preparing images through dimensionality reduction before classification, concurrently enhancing the efficacy of CNNs in feature extraction. The optimal tuning strategies for enhancing the performance of the proposed CNN were found to include boosting the number of epochs, changing the optimizer, and decreasing the learning rate, as well as improving algorithm gains by using pre-trained weights. The suggested system outperforms previously utilized approaches like VGG or DenseNet, with more than 99.80 percent accuracy, precision, recall, and F1-score values. The suggested methodology demonstrates considerable promise for enhancing the efficacy and precision of lung disease diagnoses derived from chest X-ray images, thereby offering clinicians beneficial decision support and accelerating the implementation of treatment strategies. Furthermore, the developed model facilitates the identification of pulmonary ailments, encompassing critical conditions like COVID-19, thereby facilitating timely and efficacious patient care.
Copyright (c) 2026 Thair A. Kadhim https://creativecommons.org/licenses/by-sa/4.0
Automatic coloring of Ulos motif using large language model and non-dominated sorting differential evolution algorithm
Ulos is a traditional Batak textile with high cultural significance, yet artisans lack tools to modernize designs while preserving heritage. Previous research primarily uses single-objective methods that fail to accommodate user preferences. This study introduces the first multi-objective framework for traditional textile coloring, differentiating itself by using a large language model (LLM) as a natural language interface for non-technical weavers. The LLM translates user design preferences into dynamic objective functions to guide a Non-dominated Sorting Differential Evolution (NSDE) algorithm. Performance is assessed using the epsilon indicator, aesthetic metrics (contrast and colorfulness), and user preference scores. The system achieved high convergence with an epsilon-indicator value and user preference scores reaching 0.66. Additionally, a paired t-test (p=0.07) demonstrated the algorithm's robustness across parameter variations. This approach enables artisans to create culturally authentic, aesthetically optimized designs through intuitive interaction.
Copyright (c) 2026 Arlinta Barus, Samuel Indra Gunawan Situmeang, Humasak Simanjuntak, Robert Aritonang, Nicholas Hutabarat, Grase Panjaitan https://creativecommons.org/licenses/by-sa/4.0
AI-driven stress monitoring in melon crops via graph neural networks
Melon cultivation is highly vulnerable to abiotic and biotic stress, and early detection remains difficult when monitoring relies on a single sensing modality. This study investigated a multimodal stress-classification framework that combined root-zone measurements and canopy reflectance descriptors for melon monitoring under greenhouse conditions. Soil pH, nitrogen, phosphorus, potassium, and temperature were acquired using an RS485 multi-parameter sensor, while canopy images were captured using a Raspberry Pi NoIR camera and converted into Normalized Difference Vegetation Index features. Each synchronized observation was represented as a graph with fixed variable nodes and correlation-based edges, enabling relation-aware learning through a Graph Convolutional Network. The proposed model was evaluated using cross-validation and compared against conventional machine learning and non-graph deep learning baselines. The graph-based model achieved the best overall classification performance, indicating that explicit modeling of soil-canopy dependencies improved discrimination between healthy and stressed plants. The results suggest that graph-structured multimodal fusion is a promising strategy for AI-assisted crop stress monitoring.
Copyright (c) 2026 Son Ali Akbar, Jihad Rahmawan, Etika Dyah Puspitasari, Anton Yudhana, Novi Febrianti https://creativecommons.org/licenses/by-sa/4.0
IndoBERTSkill: pretrained domain-specific language model for recognition Indonesian skill
The pretrained language model in Indonesian is already available for natural language processing tasks. However, this pre-trained model has been trained on Indonesian text, which has a different structure from the job description. Due to this, the pre-trained language model is less effective for skill recognition purposes. IndoBERTSkill is a novel pre-trained domain-specific language model that recognizes Indonesian language skills. It is built on the Bidirectional Encoder Representations from Transformers (BERT) architecture. IndoBERTSkill was trained on an extensive collection of Indonesian language texts from the Indonesian Wikipedia, the English Wikipedia, and Indonesian job descriptions from the job portal. IndoBERTSkill's performance was evaluated through two main approaches: (1) language modeling via Masked Language Model (MLM) prediction, and (2) fine-tuning on a custom annotated dataset (NERSkill) for Named Entity Recognition (NER) tasks. The fine-tuning process involved training a classification layer on top of the IndoBERTSkill model using BIO tagging to identify hard skills, soft skills, and technology entities. Similarly, the skill recognition model derived from IndoBERTSkill exhibits the highest F1-Score among various pre-trained language models, precisely at 87%, thus demonstrating robustness and strong generalizability for skill entity recognition in Indonesian job descriptions. IndoBERTSkill provides valuable resources for developing Indonesian natural language processing applications that require skills introduction. This could increase the accuracy and efficiency of skills recognition across various domains, including job matching, education, and training.
Copyright (c) 2026 Meilany Nonsi Tentua, Suprapto Suprapto, Afiahayati Afiahayati https://creativecommons.org/licenses/by-sa/4.0
Automatic identification of herbal medicines using deep learning on leaf images
Indonesia has a high diversity of medicinal plants that are widely used in traditional healthcare practices. Identification of medicinal plants is commonly based on leaf morphology; however, similarities in leaf shape, texture, and color often cause misidentification, particularly among non-experts. This limitation highlights the need for an automated and reliable identification approach. The primary objective of this study is to develop and evaluate a deep learning–based system for the automatic identification of medicinal plants using leaf images, with a specific focus on comparing the performance and efficiency of MobileNetV2 and ResNet50V2 architectures. The research design adopts an experimental approach using an internally collected dataset of medicinal plant leaf images representing multiple plant classes. The dataset is divided into training and testing sets to evaluate model generalization. The methodology involves image preprocessing steps, including resizing, normalization, and data augmentation, followed by the application of transfer learning using MobileNetV2 and ResNet50V2 as feature extractors. Both models are trained under the same experimental settings and evaluated using standard classification metrics, including accuracy, precision, recall, F1-score, and confusion matrix analysis. The main outcomes and results indicate that both deep learning models achieve high classification performance. MobileNetV2 achieves an accuracy of 98.77%, precision of 98.84%, recall of 98.77%, and F1-score of 98.77%, while ResNet50V2 achieves an accuracy of 97.53%, precision of 97.87%, recall of 97.53%, and F1-score of 97.58%. The results demonstrate that MobileNetV2 provides slightly superior performance with lower computational complexity. In conclusion, lightweight deep learning architectures such as MobileNetV2 are effective and efficient for medicinal plant leaf identification and are suitable for implementation in mobile or resource-constrained environments.
Copyright (c) 2026 Anita Ahmad Kasim, Lukman Nadjamudiin, Muhammad Bakri, Chairunnisa Ar Lamasitudju, Harry Tanni Pagiu, Puguh Budi Prakoso, Anindita Septiarini, Bima Prihasto https://creativecommons.org/licenses/by-sa/4.0
Comparison of SVM kernels in brain tumor image classification using GLCM feature extraction
The human brain plays a vital role in regulating bodily functions, and abnormal cell growth may lead to life-threatening brain tumors. Automated computer-aided diagnosis systems are therefore essential to support early detection from MRI images. This study investigates brain tumor classification using Gray Level Co-occurrence Matrix (GLCM) feature extraction combined with Support Vector Machine (SVM) classification. Unlike prior works that typically employ a single kernel configuration, this study conducts a systematic comparison of four SVM kernels: linear, polynomial, radial basis function (RBF), and sigmoid under a consistent preprocessing pipeline and structured hyperparameter tuning framework. GLCM features, including energy, contrast, correlation, and homogeneity, were extracted at multiple distances and angles. Kernel performance was evaluated using controlled hyperparameter search procedures to ensure fair comparison across models. Experimental results on a binary MRI dataset consisting of 2,800 images demonstrate that the RBF kernel achieved the highest accuracy of 96% with C = 100 and gamma = 10, outperforming polynomial (74%), linear (72%), and sigmoid (71%) kernels. The findings highlight the importance of systematic kernel evaluation and parameter sensitivity analysis in texture-based medical image classification. The proposed GLCM–SVM framework provides a computationally efficient and interpretable approach that may support preliminary decision-aid systems for brain tumor screening.
Copyright (c) 2026 I Gede Susrama Mas Diyasa, Kraugusteeliana Kraugusteeliana, Hanif Nur Fadlilah, Yisti Vita Via, Anita Muliawati, Allan Ruhui Fatmah Sari, Erna Harfiani, Ni Made Ika Marini Mandenni, Deshinta Arrova Dewi https://creativecommons.org/licenses/by-sa/4.0
Iron welding spot segmentation using Nested UNet (UNet++) enhanced with diverse convolutional modules
Welding inspection plays an essential role in manufacturing industries to ensure the integrity and quality of weld joints. However, the prevalent manual inspection procedures are inherently subjective, prone to bias, and result in inconsistent quality assessments. Therefore, there is a strong need for an automated, intelligent system capable of objectively detecting welding spots. To address this, we propose an advanced segmentation model based on deep learning and computer vision techniques, specifically utilizing a Nested UNet (UNet++) architecture enhanced by extensive architectural modifications and comprehensive hyperparameter tuning. To further optimize segmentation performance, we systematically compare various convolutional blocks integrated into the bottleneck of the network architecture. Our experimental evaluation demonstrates that employing a VGG convolutional block at the bottleneck of Nested UNet achieves the highest performance, reaching an Intersection over Union (IoU) score of 76.18% and a validation loss of 0.1713 on our collected dataset. This research advances automated welding inspection by demonstrating that an enhanced Nested UNet with an optimized VGG bottleneck significantly improves weld spot segmentation accuracy. The proposed framework provides a robust benchmark for future studies on intelligent industrial visual inspection and deep learning-based quality assurance.
Copyright (c) 2026 Thomas Brian, Anggarjuna Puncak Pujiputra, Oskar Natan, Yohanes Yohanie Fridelin Panduman https://creativecommons.org/licenses/by-sa/4.0
Enhancing multi-document summarization through topic–pattern-based sentence selection
The growing volume of digital text content requires automated summarization systems as fundamental tools that enable users to access information at high speed. Automatic Multi-Document Summarization (MDS) requires systems to produce a summary that combines essential information from multiple documents. The extractive methods, which rely on lexical signals and sentence-based rules, yield only repetitive results because they cannot capture complex thematic relationships. This study developed an improved extractive MDS model that combines topic modeling with pattern-based semantic indicators and a method to choose diverse sentences. The model employs LDA to identify concealed thematic structures, retrieves typical word patterns to improve topic models, and selects topics via a greedy algorithm that reduces redundancy to achieve appropriate salience and coverage. The proposed system achieves better results than classical baselines on the DUC 2006 and DUC 2007 datasets, outperforming Lead, CLASSY04, KL-SUM, LexRank, TextRank, and PETMSUM. The system demonstrates superior performance over all baseline methods, achieving better results on the ROUGE-1, ROUGE-2, and ROUGE-SU4 evaluation metrics. The results show that extractive summarization tasks achieve their best performance when topic–pattern representations are combined with diversity-aware scoring methods.
Copyright (c) 2026 Shaufiah, Yuefeng Lia, Richi Nayaka, Yutong Wu https://creativecommons.org/licenses/by-sa/4.0
Enhancing minority class recognition in cattle monitoring: A robustness analysis of lightweight decision-level fusion
Accelerometer-based monitoring has become an important approach in precision livestock farming, but achieving both high sensitivity for rare, health-relevant behaviors and computational efficiency remains challenging. Deep learning methods can address class imbalance but are often unsuitable for edge deployment due to their computational cost. This study evaluates the robustness of a lightweight decision-level fusion framework for classifying imbalanced cattle behavior using triaxial accelerometer data. To ensure rigorous evaluation, the Synthetic Minority Over-sampling Technique (SMOTE) was applied only in the training feature space to prevent data leakage. Because the dataset is strongly imbalanced, with Salt Licking (SLT) as the minority yet health-relevant class, model performance was assessed using Macro-F1 for global robustness and Recall_SLT for rare-event sensitivity. Individual tree-based models achieved strong results, with the best single model, XGBoost, obtaining 93.70% accuracy and 0.9421 Macro-F1. The proposed soft-voting fusion of Extra Trees, XGBoost, and CatBoost further improved performance to 94.21% accuracy and 0.9447 Macro-F1, with a statistically significant gain over the best single model (Wilcoxon signed-rank test, (p=0.0326). The framework also maintained strong minority-class recognition, with SLT achieving precision = 0.9571, recall = 0.9853, and PR-AUC = 0.9990. These results show that lightweight decision-level fusion can improve robustness and rare-event sensitivity without temporal deep learning, making it suitable for resource-constrained edge monitoring in livestock systems.
Copyright (c) 2026 Putri Nayla Sabri, Nisrina Nurhafizhah, Amrul Faruq, Achmad Fauzan Hery Soegiharto, Muhammad Ilham Perdana https://creativecommons.org/licenses/by-sa/4.0
Enhanced tuberculosis diagnosis: microscopic automatic stitching of sputum samples utilizing the SURF feature detector
In conventional tuberculosis diagnosis, 100-300 fields of view (FOVs) must be observed, which can lead to observer fatigue. For both tasks, an automatic stitching framework was developed that extends conventional feature-based transformations by incorporating affine-geometry-based feature matching and RANSAC-based homography refinement, thereby accounting for the unique low-texture morphology and irregular patterns of Mycobacterium tuberculosis in ZN-stained sputum smears. The system was tested on a set of 10 overlapping image pairs with a fixed overlap of 30%. Among the evaluated image pairs, the proposed optimized method achieved a 100% success rate. Objective zero-pixel metric-based quantitative analysis also validated higher transparency quality compared to other methods. The proposed SURF implementation reached a minimum number of 345.263 zero-pixels, outperforming standard SURF (964.247) and SIFT (1.069.687). This improved robustness to rotation and illumination variations made the optimized SURF-affine framework a preferred choice for automatic TB diagnosis systems.
Copyright (c) 2026 Nadhya Gita Anggana, Indrarini Dyah Irawati, Suci Aulia, Lestari https://creativecommons.org/licenses/by-sa/4.0
Toward effective Text-to-MongoDB query translation
Translating natural language questions into MongoDB queries is critical for flexible data access in current NoSQL systems. However, semantic ambiguity in user questions and MongoDB's dynamic schema make this work difficult. This study presents QMQL (Question to Mongo Query Language), a hybrid approach meant to address these challenges. QMQL combines a Graph Attention Network (GAT) for refining schema elements with a Retrieval-Augmented Generation (RAG) mechanism that employs BERT embeddings to retrieve relevant schema and resolve semantic ambiguity. A T5-base model is used to generate a MongoDB query corresponding to the user’s question. An experimental evaluation on an extended dataset encompassing various real-world domains demonstrates the effectiveness of the proposed approach. QMQL achieves excellent performance with an EMA of 0.89, an EM of 0.91, and a BLEU score of 0.95, exceeding previous approaches, particularly for semantically ambiguous questions and sophisticated queries across flexible MongoDB schemas,
Copyright (c) 2026 Aicha Aggoune https://creativecommons.org/licenses/by-sa/4.0
Enhancing clinical safety in automated breast ultrasound segmentation through a sensitivity-prioritized detection and statistical fail-safe mechanism
Accurate breast ultrasound (BUS) lesion segmentation is critical for early diagnosis but is challenged by image artifacts and the reliance of foundation models on manual prompting. Existing automated frameworks often lack robust fail-safe mechanisms, leading to missed diagnoses. To address this reliability gap, this study proposes a novel, fully automated hybrid segmentation framework that synergistically integrates three key components: (1) a recall-optimized YOLOv9 detector tailored to minimize clinical false negatives; (2) a MedSAM2 foundation model efficiently fine-tuned via Low-Rank Adaptation (LoRA) for ultrasound specifics; and (3) a statistical fallback mechanism that acts as a crucial safety net to recover spatial prompts during detection failures. Evaluated on the public BUSI dataset, the recall-dominant detection module achieved a Recall of 0.8238. Supported by this robust prompting and fallback strategy, the segmentation module achieved a Dice coefficient of 0.8818 and an IoU of 0.8113. By effectively integrating specialized detection with adaptive segmentation and a statistical fail-safe, the proposed pipeline offers a highly reliable automated approach for computer-aided screening systems.
Copyright (c) 2026 Daffa Muhamad Azhar, Nanik Suciati https://creativecommons.org/licenses/by-sa/4.0
Surgical-aware video masked autoencoders with phase-conditioned attention for laparoscopic action recognition
Fine-grained surgical action recognition in laparoscopic videos remains a challenge, even with recent advances in deep learning. While current VideoMAE approaches reach 89.11% accuracy on cholecystectomy tasks, they face specific limitations. Random masking strategies often miss surgical instruments that occupy only 10% to 15% of frames. Furthermore, context-independent models struggle with visually similar actions across different phases, and symmetric two-stream architectures tend to waste computational resources. To solve this, we developed SA-VideoMAE, a surgical-aware video masked autoencoder specifically designed for laparoscopic action recognition. Our method utilizes surgical-aware adaptive masking that integrates YOLOv7x object detection to prioritize instrument patches. This increased instrument visibility from 10% to 60% during training, ensuring the model focuses on action-relevant regions rather than static backgrounds. We also utilized phase-conditioned hierarchical attention to inject learnable phase embeddings into the attention mechanisms, enabling the model to disambiguate visually similar actions based on surgical context. For efficiency, our asymmetric dual-stream architecture processes RGB using ViT-Base (86M parameters) and optical flow using ViT-Tiny (5.7M parameters), achieving a 47% parameter reduction compared to symmetric designs. Our training process then balanced reconstruction, classification, temporal consistency, and phase prediction through a novel multi-objective optimization strategy. Results from Cholec80's Calot's Triangle Dissection phase show 93.5% accuracy, representing a 4.4 percentage-point improvement over the verified baseline. Notably, challenging action recall improved from 51% to 74% while maintaining real-time inference at 62ms per clip. These findings demonstrate that encoding surgical domain knowledge into video architectures significantly enhances action recognition performance.
Copyright (c) 2026 Hakim Nasaoui, Hassan Silkan, Insaf Bellamine https://creativecommons.org/licenses/by-sa/4.0
Multi-sensor weather data prediction-based artificial neural network algorithm
Accurate weather prediction is essential for supporting various human activities and mitigating the impacts of changing atmospheric conditions. Recent advances in artificial intelligence have enabled the development of data-driven forecasting models capable of capturing complex relationships among meteorological variables. This study proposes an Artificial Neural Network (ANN)-based weather prediction model using multi-sensor weather data, including temperature, humidity, precipitation, solar irradiance, and wind velocity. The proposed ANN architecture consists of an input layer, two hidden layers, and an output layer. Model performance was evaluated using three training–testing data splits (90/10, 80/20, and 70/30) with 100 and 150 training epochs. Prediction performance was assessed using accuracy and Root Mean Squared Error (RMSE). Experimental results demonstrate that the proposed model achieves the best performance with a 70/30 training–testing split and 150 training epochs, providing the highest prediction accuracy and the lowest RMSE among the evaluated configurations. These findings indicate that a relatively simple ANN architecture can effectively model multi-sensor weather data and provide reliable weather predictions.
Copyright (c) 2026 Indra Riyanto, Erica Reva Akilah, Akhmad Musafa, Eka Purwa Laksana, Nazori Agani https://creativecommons.org/licenses/by-sa/4.0
Machine learning model for classifying the severity level of cybersecurity attacks
Cyberattacks are becoming increasingly sophisticated, necessitating defense mechanisms that go beyond simple detection to include severity assessment for prioritizing mitigation. This study proposes a comprehensive machine learning framework to classify cyberattack severity levels (Low, Medium, High) using a modern, high-dimensional dataset. Addressing the critical challenge of class imbalance, the research integrates the Synthetic Minority Oversampling Technique (SMOTE) with a rigorous feature selection process involving SelectKBest. Four algorithms Naive Bayes, K-Nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM) were evaluated using 10-fold cross-validation. The results demonstrate that the SVM model with an RBF kernel achieves superior performance with an accuracy of 97.30% and a False Negative Rate (FNR) of only 3.1% for high-severity threats. This research contributes a robust, data-driven approach to severity classification that effectively handles feature non-linearity and class imbalance, offering actionable insights for real-time security operations.
Copyright (c) 2026 Imam Riadi, Sri Winiarti, Herman Yuliansyah, Muhammad ‘Arif Bin Mohamad https://creativecommons.org/licenses/by-sa/4.0
Distance-based wrapper model for human activity recognition
In this paper, we developed an effective wrapper-based model to optimize the recognition of physical human daily life activities recorded by smartphone built-in sensors. The proposed model employs Sequential Forward Selection (SFS) method in combination with K-Nearest Neighbor (KNN) classifier based on a group of five commonly used distance measures: (Manhattan, Euclidean, Chebyshev, Canberra, and Correlation) each having a specific geometric neighborhood interpretation. The proposed SFS-KNN multi-distance approach enables each distance metric to guide the feature selection process. It examines how the resulting feature subsets, determined by each distance, impact the overall recognition performance. The goal is to identify the best feature subset that achieves the highest accuracy with the lowest dimensionality. . Our proposed distance-based wrapper model was validated on two publicly available WISDM and UCI-HAR datasets under 10-fold cross-validation. The experimental results obtained on both datasets showed that the performance of the proposed model is significantly affected by the distance measure used due to generating different feature subsets for each distance. For the WISDM dataset, our model achieves an overall accuracy of 93.39% based on Euclidean distance, with a reduction ratio of 71.42%. It also offers a substantial reduction of 95.9% in the feature dimensions of the UCI-HAR dataset using Correlation distance, with a recognition rate of 99.33%. These outcomes confirm the superiority of our wrapper model over other feature selection-based approaches proposed for the same datasets.
Copyright (c) 2026 Rana Alauldeen Abdalrahman, Laith AL-Frady, Ruaa Ali Khamees https://creativecommons.org/licenses/by-sa/4.0
HAD-CDA: a hybrid approach for anomaly detection in streaming data with concept drift adaptation
Streaming anomaly detection is a difficult task because the data distribution is changing, and concept drift degrades the performance of traditional detection methods. To solve the issue, this article suggests the HAD-CDA (Hybrid Anomaly Detection with Concept Drift Adaptation), which is a combined system that can detect local anomalies in the time series with LSTM Autoencoders (LSTM-AE) and global anomalies in the distribution with Quant Tree-EWMA (QT-EWMA). The proposed framework accomplishes three objectives: (i) the use of the dynamic weighting mechanism, which automatically changes the contribution of each component (lambda between 0.2 and 0.8) according to their effectiveness, (ii) two concept drift detectors are proposed, i.e., the Kolmogorov-Smirnov and Page-Hinkley tests, to allow detecting concept drift; and (iii) the use of Elastic Weight Consolidation (EWC) to reduce catastrophic forgetting during update of the model. Experiments on four real-world streaming datasets, HTTP, SMTP, ForestCover, and Shuttle, indicate that HAD-CDA has AUCs of 0.95-0.97, an 8-9% improvement over state-of-the-art methods, and F1-scores of 0.81-0.95. The recall measure obtained by the LSTM-AE element is 0.8094, compared to 0.9399, and the specificity of QT-EWMA is very high at 0.9399. The framework is highly adaptable to different types of drifts, regaining around 9092 performance levels before drift, and 1525 windows would be needed by a baseline method, with a low processing latency of 12.430.9 ms per window. Having a per-sequence complexity of O(1), memory cost of O(n), low DIS (approximately 0.08), and stability indices of 0.02-0.04, the suggested HAD-CDA framework is an accurate, efficient, and robust solution to real-world streaming anomaly detection in changing data
Copyright (c) 2026 Sree Ram Murthy, Venkata Narayana https://creativecommons.org/licenses/by-sa/4.0