IJACI : International Journal of Advanced Computing and Informatics
Journal Title: International Journal of Advanced Computing and Informatics Initials: IJACI Abbreviation: Int.J.Adv.Comp.Inf. Frequency: Twice (January and July) Online ISSN : 3089-7483 Editor in Chief: Dr Dhani Ariatmanto DOI Prefix: 10.71129/ijaci Publisher: Cendekia Publisher IJACI: International Journal of Advanced Computing and Informatics is an international peer-reviewed open-access journal for the dissemination of original research and high-quality research in the fields of advanced computing, computer science, and computer engineering. The journal publishes state-of-the-art research in fundamental theory, applied research, experiments, and simulation as well as applications and systematic literature reviews. IJACI is an open-access journal that allows research articles can be freely downloaded, and distributed without any subscription. Submitted manuscripts should be written in English for initial review by the Editor. Furthermore, the manuscripts will be reviewed by a minimum of two reviewers (blind review). The journal publishes papers in areas including, but not limited to: 1. Computer Sciences: a. Artificial Intelligence, b. Machine Learning, c. Image Processing, d. Data Science, e. Big Data, f. Network and Communication 2. Informatics: a. Internet of Things, b. Cyber Security c. Control Systems, d. Robotics, e. Industrial Electronics and Industrial Applications
A Comparative Machine Learning and Deep Learning Models with ElasticNet Regularization for Predicting Student Outcomes: LGBM, CatBoost, ANN, DNN, and WDNN
Predicting student academic outcomes is critical for enhancing personalized learning and enabling timely interventions for at-risk students. This study presents a comprehensive comparative evaluation of machine learning and deep learning models—specifically LightGBM, CatBoost, ANN, DNN, and WDNN—enhanced with ElasticNet and Lasso regularization to address challenges of high-dimensional educational data. Using the Math and xAPI datasets, thirteen AI models were evaluated through holdout and k-fold cross-validation across 100 iterations to assess predictive accuracy, generalizability, and interpretability. Results show that CNN with ElasticNet consistently achieves the highest accuracy (up to 93.67%), while ANN performs optimally with Lasso, demonstrating the effectiveness of regularization in improving model stability and reducing overfitting. The findings also highlight the practical utility of ensemble and deep learning models for early detection of at-risk students and support the development of explainable AI frameworks for educational analytics. By addressing prior research limitations, including narrow dataset scope and the absence of advanced hybrid models, this study advances scalable, interpretable, and reliable predictive systems, aiding institutions in data-driven educational decision-making.
Copyright (c) 2026 Vina Eriyandi, Abdulhafiz Nuhu Ahmad (Author) https://creativecommons.org/licenses/by/4.0
Handling Multiclass Imbalance in Diabetes, Cancer, and Pneumonia Classification Using NR-Clustering SMOTE
The problem of imbalanced data in multiclass health classification often results in biased model predictions, particularly underrepresenting critical disease classes such as cancer and pneumonia. Traditional oversampling techniques like SMOTE often suffer from issues such as noise generation and class overlap, limiting their effectiveness in such complex domains.This research aims to address the challenge of multiclass imbalance in the classification of diabetes, cancer, and pneumonia by proposing an improved oversampling technique, NR-Clustering SMOTE, which integrates K-Means clustering and Euclidean distance.The proposed method starts by filtering noisy data using k-NN, clusters the minority class data with K-Means (optimized via Silhouette Score), and applies SMOTE within each cluster using Euclidean distance. This ensures localized sample generation, minimizes noise, and reduces class overlapping. The balanced dataset is then evaluated using ten machine learning algorithms, including Extra Trees, Random Forest, and Stacked Ensemble.Experimental results show significant improvements in classification metrics, especially for minority classes. For instance, after oversampling, Extra Trees achieved 89% accuracy and an AUC of 0.97—compared to only 48% and 0.50 on the original dataset.This demonstrates that NR-Clustering SMOTE effectively improves classifier sensitivity toward minority classes without compromising the majority class performance. The improvement is consistent across various models, proving the robustness of the proposed method. In conclusion, NR-Clustering SMOTE with Euclidean distance combined with ensemble classifiers like Extra Trees is a promising solution for handling multiclass imbalanced health data, particularly in domains requiring accurate detection of minority diseases.
Copyright (c) 2026 Chalvina Izumi Amalia, Nidya Rahmawati (Author) https://creativecommons.org/licenses/by/4.0
A Comprehensive Review of ConvNeXt Architecture in Image Classification: Performance, Applications, and Prospects
The convergence of ConvNeXt architecture and classification tasks highlights an auspicious direction in modern computer vision. This review systematically analyzes how ConvNeXt transforms these challenges by examining its implementation from 2020 to 2025. This paper investigates four primary domains where ConvNeXt has demonstrated remarkable potential: medical imaging and diagnosis, general computer vision, fine-level image classification, and defect inspection. Analysis reveals significant performance improvements resulting from these ConvNeXt applications: Data augmentation methods consistently improve Bleu-1 detection metrics and F1-score on scarce datasets. SE (Squeeze-and-Excitation), CA (Channel Attention), and CBAM (Convolutional Block Attention Module) in the ConvNeXt model result in improved performance. ConvNeXt has produced outstanding performance, achieving 99.5% accuracy in medical imaging and brain tumor diagnosis classification. Collectively, these findings demonstrate how ConvNeXt, integrated with modern classification capabilities, continues to advance rapidly. Despite this progress, the ongoing challenge of training stability remains. By critically analyzing these advances and limitations, this paper provides important insights into the current state and potential future development of ConvNext-based classification systems.
Copyright (c) 2026 wanda ilham, Abubakar Ahmad (Author) https://creativecommons.org/licenses/by/4.0
Fast Region-based Convolutional Neural Network in Object Detection: A Review
The evolution of Faster R-CNN within the field of among the most important tasks is object detection impactful developments in modern computer vision, offering high accuracy and architectural flexibility across a range of visual recognition tasks. This review presents a systematic analysis of how Faster R-CNN has been adapted and optimized between 2020 and 2025, examining its applications across diverse domains. The paper investigates three primary challenges where Faster R-CNN has shown considerable advancement: handling occluded objects, improving small object detection, and adapting to real-time constraints through lightweight and context-aware architectures. The analysis reveals notable performance improvements resulting from these enhancements: feature fusion and attention modules have improved detection in occluded scenes, small object detection has benefited from multi-scale representation and loss function refinement, and lightweight adaptations have expanded its usability in constrained environments. Collectively, these developments demonstrate how Faster R-CNN continues to evolve as a robust backbone for detection tasks. Nevertheless, challenges remain, including computational complexity, inference latency, and data dependency. By critically assessing recent advancements and ongoing limitations, this review offers comprehensive insights into the current state and future directions of Faster R-CNN-based object detection systems.
Copyright (c) 2026 Ricki Sastra Ricki, Dicky Hariyanto Dicky, Bahalwan Apriyansyah (Author) https://creativecommons.org/licenses/by/4.0
YOLOv8 for Object Detection: A Comprehensive Review of Advances,Techniques, and Applications
This paper presents a review focusing on the most current advancements in object detection techniques using YOLOv8 and their applications across a range of fields, such as surveillance systems, autonomous driving, smart agriculture, industrial quality control, and medical image analysis. YOLOv8, launched by Ultralytics in early 2023, represents the newest evolution of the You Only Look Once framework., YOLOv8 demonstrates notable improvements in both detection accuracy and processing speed compared to earlier versions. This review explores key updates in YOLOv8’s architecture, including enhanced backbone designs, efficient training processes, and optimized loss functions, alongside the incorporation of attention mechanisms and compact model structures. These findings from 40+ selected studies confirm YOLOv8’s potential as a reliable and efficient solution for object detection tasks in both academic research and real-world applications. This study serves as a comprehensive reference for researchers and practitioners seeking to optimize aiming to enhance YOLOv8-based models across diverse practical applications.
Copyright (c) 2026 Vivi Afifah, Surni Erniwati (Author) https://creativecommons.org/licenses/by/4.0
Robust Digital Image Watermarking Using DWT, Hessenberg, and SVD for Copyright Protection
With the rapid advancement of digital technology, protecting visual content against misuse, unauthorized copying, and forgery has become increasingly important. This study proposes a robust and secure image watermarking method that synergistically integrates Discrete Wavelet Transform (DWT), Hessenberg Decomposition (HD), and Singular Value Decomposition (SVD) to enhance both imperceptibility and robustness against common image attacks. The method involves embedding the watermark into the low-frequency coefficients obtained through DWT, leveraging HD for structural stability, and using SVD to embed the watermark into the singular values, ensuring minimal perceptual distortion. The approach is designed to withstand a wide range of attacks, including filtering, noise addition, cropping, and lossy compression, while maintaining high fidelity of the watermarked images. The implementation was tested on multiple grayscale images, such as Lena, Baboon, and Cameraman. The robustness and transparency of the watermarking technique were evaluated quantitatively using metrics including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Correlation (NC), under various attack scenarios. Results indicate that the proposed method achieves high PSNR and SSIM values, demonstrating excellent imperceptibility, while maintaining a strong resistance to image distortions, as evidenced by the NC values remaining close to 1 after attacks. The experimental results confirm the method’s capability to preserve the embedded watermark reliably, with minimal perceptible differences in the visual quality of watermarked images.
Copyright (c) 2026 Muhammad Romadhona Kusuma, Supriadi Panggabean (Author) https://creativecommons.org/licenses/by/4.0
Mask Region-based Convolutional Neural Network in Object Detection: A Review
The integration of Mask R-CNN in object detection has emerged as a prominent advancement in computer vision, enabling accurate instance segmentation and object recognition in complex visual environments. This review systematically analyzes the progress of Mask R-CNN applications from 2020 to 2025, focusing on three key domains where this framework has demonstrated notable performance: data augmentation, occlusion handling, and small object detection. Extensive analysis reveals that data augmentation significantly enhances detection accuracy, particularly in scenarios with limited training data, by increasing mean Average Precision (mAP), F1-score, and recall. Techniques such as flipping, rotation, synthetic data generation, and noise perturbation consistently improved performance across various application contexts. In occluded environments, architectural enhancements including attention modules and feature pyramid networks enabled more robust detection of partially hidden objects, with improvements in precision, IoU, and segmentation quality. Furthermore, for small object detection, adaptations such as depth-based filtering, lightweight backbones, and enhanced feature extraction modules proved effective in capturing fine-grained details. Despite these advancements, challenges persist, including high computational cost, limited real-time capability, and sensitivity to data scarcity and environmental complexity. By critically evaluating these trends and limitations, this review offers valuable insights into the current state and future potential of Mask R-CNN-based object detection systems.
Copyright (c) 2025 Surni Erniwati, Vivi Afifah, Bahtiar Imran (Author) https://creativecommons.org/licenses/by/4.0
Single Shot Multibox Detector (SSD) in Object Detection: A Review
In computer vision, object detection is a core challenge, particularly in scenarios requiring real-time responses and limited computational resources. SSD is a single-pass object detection method that combines classification and localization, recognized for its rapid processing and efficiency. This systematic review analyzes the application and enhancement of SSD-based models across various domains between 2020 and 2025. The study focuses on three major challenges in object detection: real- time performance, small object detection, and deployment in limited-resource settings. It examines diverse strategies, including domain-specific data augmentation, architectural modifications such as deformable SSDs and DRMP modules, and the integration of lightweight backbones like MobileNet. The review highlights significant performance gains in accuracy and efficiency but also reveals persistent limitations, including difficulties in detecting small objects in complex scenes, vulnerability to occlusion, and challenges in model generalization. This study offers key insights into recent research trends and future directions for optimizing SSD-based frameworks to meet the demands of increasingly complex and dynamic detection scenarios.
Copyright (c) 2025 Juhartini, Dwinita Arwidiyarti, Desmiwati (Author) https://creativecommons.org/licenses/by/4.0
Lightweight Ensemble Models for Static Malware Detection: AddressingDeep Learning Trade-Offs with the Kaggle PE Dataset
Static malware detection plays a crucial role in cybersecurity by enabling the identification of malicious files without the need to execute them. This study explores the effectiveness of lightweight ensemble models as an efficient and interpretable alternative to deep learning approaches. Using the Kaggle PE Malware Dataset, eight classifiers including Random Forest, Extra Trees, Gradient Boosting, LightGBM, XGBoost, CatBoost, HistGradientBoosting, and Multilayer Perceptron were evaluated, with a one-dimensional Convolutional Neural Network serving as a performance benchmark. To address class imbalance, three oversampling methods were applied separately, namely Synthetic Minority Oversampling Technique, Adaptive Synthetic Sampling, and SMOTE combined with Edited Nearest Neighbors. The dataset was normalized, and SelectKBest was used to select the 20 most informative features. All models were tuned using the Optuna framework to ensure optimal performance. Experimental results showed that the Voting Classifier achieved perfect accuracy, precision, recall, and F1-score on the validation set when trained with SMOTE and the full feature set and retained nearly identical results using only the top 20 features. While the CNN baseline required more computational resources, it did not outperform the optimized ensemble models. Furthermore, SHAP analysis provided insights into feature importance and improved interpretability. These findings confirm that lightweight ensemble classifiers are a practical and effective solution for static malware detection, offering high accuracy, fast inference, and greater transparency, which makes them suitable for deployment in environments with limited computing capacity.
Copyright (c) 2026 Nidya Sari Rahmawati, Chalvina Izumi Amalia (Author) https://creativecommons.org/licenses/by/4.0
Robust Image Watermarking Based on Hybrid IWT-DCT-SVD
The digital technology development and the ease of sharing and modifying content, authentication of digital information and copyright protection have become crucial. Digital watermarking is one of the best promising solutions for this purpose, with the ability to insert hidden information (watermark) into digital media. This study presents a fine-tuning to hybrid image watermarking schemes based on a combination of Discrete Cosine Transform (DCT), Integer Wavelet Transform (IWT), and Singular Value Decomposition (SVD), the focus is for color watermark images and color host images. The main focus is on increasing the robustness of the watermarked imaged and significant imperceptibility. Fine tuning is done through a multi-block embedding strategy within the IWT-DCT-SVD domain. The proposed methods evaluated using image quality metrics Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), as well as endurance under a variety of common digital attacks including JPEG compression, Gaussian noise, Salt & Pepper noise, median filtering, rotation, and scaling. The results of the experiment showed that the multi-block fine-tuning approach significantly improved the robustness of the watermark while maintaining host image good visual quality.
Copyright (c) 2025 M. Iman Wahyudi, Izza Fauzi, Dwi Atmojo (Author) https://creativecommons.org/licenses/by/4.0
Robust Phishing Detection Using Random Forest with Isolation Forest Based Outlier Filtering
Phishing remains a prevalent cybersecurity threat that exploits deceptive websites to extract confidential user information, resulting in significant financial and reputational damages. Conventional detection methods, such as rule-based systems and signature matching, often struggle with noisy data and fail to adapt swiftly to evolving attack strategies, limiting their effectiveness in real-world scenarios. To address these challenges, this study introduces a robust phishing detection framework that integrates outlier removal through the Isolation Forest algorithm with classification using the Random Forest model. The proposed approach aims to improve data quality by eliminating anomalous instances that could negatively impact model training and generalization. The experiments utilized a comprehensive dataset comprising 11,430 instances and 87 features, which was meticulously pre-processed to enhance model performance. The results demonstrate that the proposed framework achieves high accuracy (96.78%), precision (96.09%), recall (97.20%), and an F1-score of 96.64%, outperforming many existing approaches. The results indicate its strong ability to detect phishing websites while maintaining interpretability. This approach offers an effective and interpretable method for phishing detection, emphasizing clarity and robustness in machine learning-based security solutions. The findings underscore the potential of combining anomaly detection with ensemble classification techniques to develop resilient, accurate, and transparent phishing detection systems.
Copyright (c) 2025 Amir Hamzah Dinnillah, Muhamad Widiarto, Adi Wijaya (Author) https://creativecommons.org/licenses/by/4.0
Wheat Fusarium Head Blight Severity Prediction in Southern Henan: K- Means-SMOTE and Ensemble Learning Comparison
Fusarium Head Blight (FHB) severely affects wheat yields and food safety, especially in Southern Henan, China. This study proposes a hybrid predictive framework combining K-means-SMOTE for class imbalance correction and ensemble learning algorithms LightGBM, CatBoost and Decision Tree —to enhance FHB severity classification. Using meteorological and disease data from 2016 to 2023, the model was trained on both original and oversampled datasets. Feature selection via Kendall’s tau identified key meteorological indicators critical for prediction. Experimental results show that LightGBM with SMOTE achieved the highest accuracy (0.9242) and F1 score (0.9242), followed closely by CatBoost with K-means-SMOTE. The study also finds that default hyperparameters often outperform tuned configurations, and ADASYN is less effective than SMOTE or K-means- SMOTE. Despite strong performance, the model’s regional specificity limits its generalisability, requiring adaptation for broader climatic and crop contexts. This work demonstrates the potential of integrating oversampling and ensemble techniques for early, accurate FHB severity prediction, supporting targeted disease management in agriculture.
Copyright (c) 2026 Fikri Maulana, Yavuz Selim Taspinar , Troy Agung Wibowo (Author) https://creativecommons.org/licenses/by/4.0
Stacked Ensemble of Gradient Boosting Machine, Categorical Boosting,and Extreme Gradient Boosting for Enhanced Tuberculosis Diagnosis
Tuberculosis (TB), a deadly infectious disease caused by Mycobacterium tuberculosis (MTB), remains one of the leading causes of death worldwide, particularly in low and middle-income countries. Despite over a century of eradication efforts, TB continues to pose a significant public health challenge. One of the main obstacles in controlling TB is the limitation of diagnostic facilities that can quickly and accurately detect the disease. This study aims to develop a predictive model for TB diagnosis using various machine learning (ML) algorithms, including Decision Tree, Neural Network, Gaussian Naive Bayes, Logistic Regression, AdaBoost, Categorical Boosting (CatBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). To prepare an optimal dataset, the research also included data preprocessing using encoding, scaling, and correlation analysis techniques. The dataset comprised 1,200 questionnaires collected from three hospitals in Sindh, with 950 respondents meeting the inclusion criteria. Experimental results showed that the GBM model achieved the highest accuracy of 95.61%, followed by CatBoost at 94.74% and XGBoost at 94.30%. To further enhance accuracy, stacked ensemble method was applied by combining GBM, CatBoost, and XGBoost as a meta-model, resulting in a remarkable accuracy of 96.49%. These findings indicate that the proposed stacked ensemble method can significantly improve TB diagnosis, offering faster and more efficient solutions compared to traditional methods. This research has the potential to enhance early detection and treatment of TB, especially in areas with limited healthcare resources.
Copyright (c) 2026 Amir Hamzah Dinnillah, Muhammad Fuad Abdullah (Author) https://creativecommons.org/licenses/by/4.0
Blaze Pose Graph Neural Networks and Long Short-Term Memory for Yoga Posture Recognition
Yoga posture recognition plays a vital role in guiding practitioners and preventing injuries. This research proposes a deep learning-based system that integrates Blaze Pose, Graph Neural Networks (GNN), and Long Short-Term Memory (LSTM) to improve the precision of yoga posture classification. The system begins by extracting 33 key points of the human body from input images using Blaze Pose, a lightweight and efficient pose estimation model optimized for real-time applications. These key points are then modeled into a graph structure and processed using GNN to capture spatial dependencies among joints. To handle the temporal dynamics of movement, an LSTM network is employed, enabling the system to recognize postures across sequential frames. The dataset used comprises 2,139 images categorized into eight yoga poses, including static and transitional movements. Preprocessing steps include image resizing, normalization, and key point extraction. Evaluation metrics, such as precision, recall, and F1-score, indicate strong performance across all classes, with particularly high scores for poses like "tree" and "triangle". Despite some misclassifications between similar poses like "cobra" and "warrior", the overall confusion matrix shows high predictive reliability. Experimental results demonstrate that the proposed GNN and LSTM model achieves a training accuracy of 99.70% and a test accuracy of 97.81%, outperforming models based solely on LSTM or GNN. The findings confirm that combining GNN and LSTM enhances the model's ability to learn both spatial and temporal patterns in human pose data. This system demonstrates potential as an intelligent yoga assistant, capable of delivering real-time feedback with high accuracy.
Copyright (c) 2025 Happid Ridwan Ilmi, Emad Taha Khalaf (Author) https://creativecommons.org/licenses/by/4.0
Hybrid LSTM-MLP Framework for Enhanced Sales Forecasting
This study introduces a hybrid framework combining Long Short-Term Memory (LSTM) networks and Multi-Layer Perceptrons (MLP) to enhance sales forecasting through advanced feature processing. The LSTM component is employed to capture temporal dependencies in sequential sales data, while the MLP focuses on processing numerical and categorical features to complement the predictive accuracy. This integration leverages the strengths of both architectures, enabling more comprehensive and effective feature processing compared to traditional approaches. The methodology encompasses data preprocessing, model development, and evaluation across performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R2). By employing real-world sales datasets, the framework undergoes rigorous testing to compare its effectiveness against existing models. Experimental results show that the hybrid LSTM-MLP model achieves notable improvements, including reduced MAE, MSE, and RMSE, while delivering 99% R2 values, higher than standalone LSTM, MLP, and other traditional methods. These findings underscore the model’s potential to offer accurate and stable predictions, demonstrating its utility for forecasting tasks where tabular and sequential data interplay. The proposed framework not only contributes to advancing predictive methodologies in sales forecasting but also serves as a reference for adopting hybrid strategies in similar studies. Its effectiveness in handling feature complexity and producing reliable forecasts highlights its applicability in real-world forecasting scenarios and lays the foundation for further research in hybrid neural network designs.
Copyright (c) 2026 Onesinus Saut Parulian Tamba, Md. Munirul Hasan (Author) https://creativecommons.org/licenses/by/4.0
Hybrid GRU-KAN Model for Energy Consumption Prediction in Commercial Building Cooling
Energy management efficiency is crucial in reducing operational costs and the environmental impact of commercial buildings. Cooling systems, as one of the main components of energy consumption, become a challenge due to their complex and non-linear nature. This study proposes a hybrid model combining Gated Recurrent Units (GRU) and Kolmogorov-Arnold Networks (KAN) to enhance the prediction accuracy of electrical energy consumption in commercial building cooling systems. The dataset used is from a Heating, Ventilation, and Air Conditioning (HVAC) system, covering seven key features, such as outdoor temperature, humidity, and cooling load. The experimental results show that GRU-KAN provides the best performance with an RMSE value of 4.168, MAE of 2.242, and R² of 0.819, and outperforms the GRU, LSTM, and LSTM-KAN models for energy consumption prediction. This study contributes to optimizing energy consumption in building cooling systems through a deep learning approach based on the GRU-KAN model. The hybrid architecture effectively captures both temporal dependencies and nonlinear relationships, offering a robust and accurate approach for HVAC energy forecasting.
Copyright (c) 2025 Jumadi, Jalal Uddin Md Akbar (Author) https://creativecommons.org/licenses/by/4.0
Cervical Cancer Prediction with Stacked Tabnet, Random Forest, and LightGBM
Cervical cancer remains a significant global health concern, with a substantial burden on affected individuals and healthcare systems. Early detection and effective prediction of cervical cancer risk factors are essential for reducing its incidence and improving patient outcomes. This study focused on predicting indicators and diagnosing cervical cancer by leveraging a comprehensive dataset containing demographic data, lifestyle choices, and previous medical records. The study aimed to develop a predictive model that not only assists in the early diagnosis and intervention of cervical cancer but also identifies potential risk factors. The dataset comprises four targets of cervical cancer tests: Hinselmann, Schiller, Cytology, and Biopsy, each involving 858 participants and 30 features. The preprocessing phase resolved 22.14% of missing values in each dataset by employing the Mice iterative imputer, followed by data balancing through Adaptive Synthetic Sampling (ADASYN). Seven machine learning (ML) algorithms, namely Random Forest (RF), Linear Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Tabnet, and Light Gradient Boosting Machine (LGBM) were employed. The most effective classifier for accuracy using the full model is the Stacking Ensemble Tabnet, RF, LGBM methods, achieving the best accuracy of 100%. This study highlights the utilization of stacked ensemble methods to enhance the efficiency of cervical cancer prediction. Additionally, it extends beyond the potential of ML algorithms, particularly Random Forest (RF), in predicting cervical cancer, promoting resource-conscious healthcare practices.
Copyright (c) 2025 Mahendra Mahendra, Abdulhafiz Nuhu Ahmad, Suleiman Hamyar Suleiman Al Mazrui (Author) https://creativecommons.org/licenses/by/4.0
Robust Color Image Watermarking Using Dual Embedding via Schur Decomposition
This research introduces a Schur-based watermarking technique that employs a 4´4 matrix block and a two-phase embedding process to enhance robustness performance. The watermark bits are scrambled with a key to obtain scrambled watermark. The two watermark bits are embedded into the c-th vector of Schur decomposition. In addition to achieving a remarkable trade-off between imperceptibility and robustness, this study investigates the relationship between c-th vector of Schur decomposition to achieve balancing between invisibility and robust against several attacks. Evaluation results demonstrate outstanding performance in preserving the integrity and resilience of the watermark across various colour images. The method achieves high PSNR values, indicating excellent watermark extraction quality with minimal distortion to the images. Furthermore, comparisons with earlier approaches highlight its superior resistance to attacks on test images. The experimental results show the proposed scheme has an ability to maintain image quality and withstand attacks, the proposed method shows significant potential for copyright protection with average SSIM value of 0.9939 and an average NC of 0.9118 under various attacks.
Copyright (c) 2025 Ridwan Ridwan, Muhammad Nomani Kabir (Author) https://creativecommons.org/licenses/by/4.0
Tamper Localization and Content Restoration in Fragile Image Watermarking: A Review
The widespread availability of image editing software makes editing and distributing images easy, complicating the ability to trust their content. Such images can be manipulated in forgery attacks to disseminate false propaganda, undermining their authenticity and originality. This paper presents a review focusing on the tamper localization and content recovery in fragile image watermarking. As a result, image authentication plays a crucial role in protecting the integrity of digital images. This review investigates the primary challenge encountered by the researcher in tamper localization and self-recovery such as limited space for embedding the recovery data. Another problem faced by researchers in image authentication and self-recovery is the presence of the tamper coincidence problem. This review also discusses the tamper coincidence problem in fragile watermarking. It occurs when the recovery data is missing because both the original block and another block for storing recovery data are tampered with. This study serves as a comprehensive reference for researchers, and the issue of tampering coincidence may significantly affect the recovered image quality. The existing methods are discussed for embedding authentication bits and recovery data to examine the overall watermarking performance. These findings have a potential solution for identifying the tampered region problems in fragile image watermarking.
Copyright (c) 2026 Agit Amrullah, Afrig Aminuddin (Author) https://creativecommons.org/licenses/by/4.0
Image Watermarking for Ensuring Image Integrity and Robust Copyright Protection Based on Discrete Wavelet Transform
Digital media faces growing challenges related to unauthorized access, manipulation, and copyright infringement, necessitating robust solutions for authentication and copyright protection. This research introduces a novel dual watermarking scheme based on Discrete Wavelet Transform (DWT) to address these issues. The primary goal is to offer robust copyright protection by embedding a watermark containing ownership and copyright information in the spatial domain using DWT, serving as a digital signature for identifying and tracing rightful ownership. Additionally, the frequency domain embedding improves the watermark's resilience to common attacks. The scheme also incorporates a fragile watermark for content authentication, embedded in the least significant bit to detect tampering. By comparing the fragile watermark, the content's integrity and authenticity are verified. Experimental results confirm the scheme's effectiveness, showing high imperceptibility and robust performance in protecting against unauthorized alterations while preserving visual quality. The proposed scheme achieved robustness performance with NC value of 0.98 under JPEG compression, the scheme also has ability to ensure the tamper detection with accuracy of 0.996 under tampering rate of 0.84.
Copyright (c) 2025 Lim Chen Gen, Siti Nur Avivah, Abu Jafar Md Muzahid (Author) https://creativecommons.org/licenses/by/4.0
Secure and Effective Image Integrity and Copyright Protection Using Two-Layer Authentication with Integer Wavelet Transform
The pervasive adoption of internet and digital communication technologies renders multimedia data, particularly images, vulnerable to piracy due to the ease with which unauthorized users can manipulate them. This research advocates for the application of IWT and SVD grounded in human visual characteristics to embed watermarks into host images, thereby fortifying their resilience against attacks while minimizing distortions in image quality. Authentication bits extracted from the content of 3×3-pixel blocks within the image are subsequently incorporated into the watermarked image, positioned within the LSB of each pixel to facilitate the authentication of RGB images against tampering attacks. The efficacy of the proposed method is evaluated through various geometric, compression, image processing, and tampering attacks, alongside comparison to alternative dual watermarking systems. Experimental findings reveal the precise identification of tampering in most instances, even when alterations impact only a small number of blocks. Moreover, the method demonstrates substantial robustness, even when subjected to extensive image processing attacks, with a PSNR value of around 48 dB and an authentication accuracy of 0.98 for tampering attacks, thus indicating potential for robust copyright protection.
Copyright (c) 2025 Ng Ching Onn, Siti Nur Avivah, Mohammad Minhazul Alam, Abubakar Ahmad (Author) https://creativecommons.org/licenses/by/4.0
Prediction of Alzheimer's Dementia Using Soft Voting Ensemble Learning with Machine Learning
Alzheimer's dementia (AD) is a degenerative brain disease characterized by a decline in cognitive function and memory. Predicting AD is crucial for preventing the disease from becoming more severe. Machine learning algorithms can aid in the early prediction of AD. The aim of this study is to develop a predictive model with improved accuracy using ensemble learning methods and machine learning algorithms. The experiment used the Oasis Longitudinal dataset from Oasis Brains, which includes details of patients with and without AD. This study proposed a binary classification using an ensemble learning classifier with the soft voting technique, incorporating four ensemble learning algorithms: Extreme Gradient Boosting (XGB), Random Forest (RF), AdaBoost (ADC), and Gradient Boosting (GBC). The proposed method is evaluated based on accuracy, precision, recall, and F1-score of the classification. The experimental results show that the combination of the Random Over Sampling (ROS) resampling method and grid search hyperparameter tuning for the Gradient Boosting algorithm achieved the best results, with an accuracy of 97%.
Copyright (c) 2025 Edwin Rudini, Ferda Ernawan (Author) https://creativecommons.org/licenses/by/4.0
Dual Watermarking Based on Human Visual Characteristics with IWT-SVD
The security and protection of digital content have become crucial for protecting ownership. Watermarking is an essential technique for protecting copyright and preserving the integrity of digital images. This study suggested a robust and secure image watermarking scheme that employs a combination of IWT-SVD to embed dual watermarks. The watermark logo is embedded by examining the orthogonal U with a set of conditions. This study also introduces threshold values in the red and blue channels for two watermarks embedded. Threshold values of 0.06 and 0.13 are proposed for red and blue channels, respectively, as the strength of watermark embedding. The threshold is revealed by incrementing a threshold one at a time against JPEG compression. The results indicate method exhibits a smaller reconstruction error of about 1.2415 and an SSIM value of 0.9158. The proposed watermarking method achieved an NC value of 0.9246 and a BER value of 0.0742. This indicates that the method exhibits better robustness than the existing method against various attacks. The suggested method shows good robustness against Gaussian noise, median filter, salt and pepper noise, and JPEG compression.
Copyright (c) 2025 Chairul Fikri, Fajar Agung Nugroho, Bahalwan Apriyansyah, Mohammed Fakhreldin (Author) https://creativecommons.org/licenses/by/4.0