International Journal of Advances in Intelligent Informatics (IJAIN)
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274 research outputs found
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Chemometric classification and authentication of four Aquilaria species from essential oil profiles using GC-MS/GC-FID and ANN
Full text linkAgarwood, derived from the Aquilaria species, is among the most valuable aromatic resources, yet frequent species misidentification hampers conservation efforts and compliance with trade regulations. This study applied a chemometric ANN framework to classify four Aquilaria species (A. malaccensis, A. beccariana, A. subintegra, and A. crassna) based on essential oil composition. A total of 720 samples (180 per species, each analyzed in triplicate) were extracted by hydrodistillation and profiled using GC–MS coupled to GC–FID. Six compounds were consistently detected, and three (δ-guaiene, 10-epi-γ-eudesmol, γ-eudesmol) were retained for classification based on ≥95% detection frequency and >0.2% relative abundance. Pearson correlation guided feature selection, and ANN models were trained using both a 70:15:15 train–validation–test split and stratified 5-fold cross-validation with 1000 bootstrap resamples. The optimized network achieved near-perfect performance, with a mean accuracy of ~99.8% (95% CI: 99.6–100.0), and precision, recall, and F1 scores all exceeding 99.5%. In comparison, bootstrapped confidence intervals were tightly bounded at 100%, confirming robustness against data leakage. These findings demonstrate that correlation-guided feature selection combined with ANN modeling enables reproducible and highly accurate species authentication, offering a practical framework for integration into agarwood quality control, conservation monitoring, and international trade compliance
Enhanced diabetes and hypertension prediction using bat-optimized k-means and comparative machine learning models
No full textThis research aims to develop an analytical approach to classification statistics. The proposed approach combines machine learning with optimization. Considering the urgency of research related to exploring the best methods to apply to sports data. This study proposes a novel framework that combines the k-means clustering results with the bat algorithm to optimize performance prediction for athletes in Indonesia. The proposed method aims to explore the data by comparing the classification performance of random forests, extremely randomized trees, and support vector machines. We conducted a case study using primary data from 200 respondents at Surabaya State University and the East Java National Sports Committee. The accuracy results in this study indicate that, based on the performance evaluation metric, the best approach is random forest clustering using k-means with bat algorithm optimization, achieving 81.25% accuracy, compared with other machine learning approaches. This research contributes to the field of classification statistics by introducing a novel hybrid framework that integrates machine learning, clustering, and optimization techniques to improve predictive accuracy, particularly in sports analytics. Beyond sports science, the proposed approach can be adapted to other domains that require robust performance prediction and decision support, such as health analytics, educational assessment, and human resource selection
Geometry-aware light field angular super-resolution using multiple representations
Full text linkLight Field Angular Super-Resolution (LFASR) is a critical task that enables applications such as depth estimation, refocusing, and 3D scene reconstruction. Acquiring LFASR from Plenoptic cameras has an inherent trade-off between the angular and spatial resolution due to sensor limitations. To address this challenge, many learning-based LFASR methods have been proposed; however, the reconstruction problem of LF with a wide baseline remains a significant challenge. In this study, we proposed an end-to-end learning-based geometry-aware network using multiple representations. A multi-scale residual network with varying receptive fields is employed to effectively extract spatial and angular features, enabling angular resolution enhancement without compromising spatial fidelity. Extensive experiments demonstrate that the proposed method effectively recovers fine details with high angular resolution while preserving the intricate parallax structure of the light field. Quantitative and qualitative evaluations on both synthetic and real-world datasets further confirm that the proposed approach outperforms existing state-of-the-art methods. This research improves the angular resolution of the light field without reducing spatial sharpness, supporting applications such as depth estimation and 3D reconstruction. The method is able to preserve parallax details and structure with better results than current methods
Community preserving sparsification based on K-core for enhanced community detection in attributed networks
Full text linkCommunity detection is an important aspect of complex network analysis, especially in attribute networks where topological structure and attribute information both play a role in community formation. Traditional structure-based methods tend to result in topologically dense but semantically inconsistent communities, while attribute-based approaches can improve semantic coherence but face scalability constraints and high computational costs. On the other hand, graph sparsification techniques have been used to reduce the size of the network, but most focus on structural aspects alone and rarely consider attributes, so the quality of the resulting community is often degraded. This study proposes CPSK (Community Preserving Sparsification based on K-core), a sparsification framework that combines k-core decomposition with attribute-based side weighting. This approach is designed specifically for attribute networks, with the aim of maintaining a balance between structural reduction and community semantic consistency, while improving the efficiency of the detection process. Evaluation of the six datasets showed that CPSK consistently generates more stable and meaningful communities than existing attribute-based community detection methods, while maintaining an edge in computing efficiency on large-scale and heterogeneous networks
Performance analysis on convergence of particle swarm optimization and incremental conductance MPPT method for NTR 5E3E PV module
Full text linkParticle swarm optimization (PSO), a technique in Artificial Intelligence, is one of the MPPT methods used to optimize the output of a Photovoltaic (PV) system. The PSO is well known for its convergence in Maximum Power Point Tracking (MPPT). However, no comprehensive study has been conducted on the performance of the PSO and incremental-conductance (INC) MPPT combination for the NTR 5E3E PV module. This study aims to provide a detailed performance analysis of the convergence of PSO and INC combination compared to PSO MPPT during maximum power (MP) tracking on NTR 5E3E PV module. This research work studies the relationships among PV parameters and other parameters affected during the implementation of PSO-INC MPPT. The study found that, in terms of efficient power and time consumption during the Maximum Power (MP) tracking process, the PSO-INC MPPT combination provides the highest average peak power at the shortest time compared to standalone PSO. The efficiency of PSO-INC Average Power is near 98.9% to 99.93%, compared to PSO MPPT, which is between 95.7% and 99.3%. The PSO and INC MPPT were tested on a boost converter without altering the specific electrical component characteristics to ensure accurate output during testing. Furthermore, a boost converter is sufficient to meet the overall requirements for the research work and simulation testing. The characteristics of the PSO and INC MPPT are observed using MATLAB/Simulink. This research assesses the robustness of the PSO-INC combination, advancing hybrid MPPT technology by demonstrating its performance
Gender classification performance optimization based on facial images using LBG-VQ and MB-LBP
Full text linkIn the computer vision and machine learning field, especially for gender classification based on facial images, feature extraction is one of the inseparable parts. Various features can be extracted from images, including texture features. Several prior studies show that the Linde Buzo gray vector quantization (LBG-VQ) and Multi-block local binary pattern (MB-LBP) methods can extract texture features from images. The LBG-VQ produces less optimal performance in gender classification on the FEI facial images dataset. On the other hand, the MB-LBP produces more optimal performance when applied to the FERET facial images dataset. Therefore, this study was conducted to discover the gender classification performance when the LBG-VQ and MB-LBP methods are implemented independently or in combination on the FEI facial images dataset. Three preprocessing stages are involved before extracting images' features: noise removal, illumination adjustment, and image conversion from RGB to grayscale. The extracted features are then used as training material for several classification methods, namely Naïve Bayes, SVM, KNN, Random Forest, and Logistic Regression. Then, the K-Fold Cross Validation method is used to evaluate the trained models. This study discovered that the implementation of MB-LBP tends to show a performance improvement compared to the LBG-VQ. Furthermore, the most optimal classification model, with a performance of 91.928%, was formed by implementing Logistic Regression with MB-LBP on LBG-VQ quantized images. In conclusion, this study successfully formed an optimized gender classification model based on the FEI facial images dataset
Enhanced mixup for improved time series analysis
Full text linkTime series data analysis is crucial for real-world applications. While deep learning has advanced in this field, it still faces challenges, such as limited or poor-quality data. In areas like computer vision, data augmentation has been widely used and highly effective in addressing similar issues. However, these techniques are not as commonly explored or applied in the time series domain. This paper addresses the gap by evaluating basic data augmentation techniques using MLP, CNN, and Transformer architectures, prioritized for their alignment with state-of-the-art trends in time series analysis rather than traditional RNN-based methods. The goal is to expand the use of data augmentation in time series analysis. The paper proposed EMixup, which adapts the Mixup method from image processing to time series data. This adaptation involves mixing samples while aiming to maintain the data's temporal structure and integrating target contributions into the loss function. Empirical studies show that EMixup improves the performance of time series models across various architectures (improving 23/24 forecasting cases and 12/24 classification cases). It demonstrates broad applicability and strong results in tasks like forecasting and classification, highlighting its potential utility across diverse time series applications
Soft voting ensemble model to improve Parkinson’s disease prediction with SMOTE
Full text linkParkinson's disease is one of the major neurodegenerative diseases that affect the central nervous system, often leading to motor and cognitive impairments in affected individuals. A precise diagnosis is currently unreliable, plus there are no specific tests such as electroencephalography or blood tests to diagnose the disease. Several studies have focused on the voice-based classification of Parkinson's disease. These studies attempt to enhance the accuracy of classification models. However, a major issue in predictive analysis is the imbalance in data distribution and the low performance of classification algorithms. This research aims to improve the accuracy of speech-based Parkinson's disease prediction by addressing class imbalance in the data and building an appropriate model. The proposed new model is to perform class balancing using SMOTE and build an ensemble voting model. The research process is systematically structured into multiple phases: data preprocessing, sampling, model development utilizing a voting ensemble approach, and performance evaluation. The model was tested using voice recording data from 31 people, where the data was taken from OpenML. The evaluation results were carried out using stratified cross-validation and showed good model performance. From the measurements taken, this study obtained an accuracy of 97.44%, with a precision of 97.95%, recall of 97.44%, and F1-Score of 97.56%. This study demonstrates that implementing the soft-voting ensemble-SMOTE method can enhance the model's predictive accuracy
LC Map: a robust chaotic function for enhancing cryptographic security through key sensitivity and randomness analysis
Full text linkThe security of digital image data has become increasingly critical in modern communication systems. While chaos-based cryptography offers a promising solution, many existing algorithms lack rigorous security validation. This paper introduces the Logistic-Circle Map (LC Map), a novel one-dimensional compound chaotic system designed to provide a robust and efficient foundation for image encryption. By composing the Logistic Map and the Circle Map, the LC Map exhibits a broader chaotic range and higher dynamical complexity. The performance and security of an LC Map-based encryption scheme are extensively validated using a comprehensive dataset of 24 digital images. Security analysis demonstrates that the algorithm is highly resistant to brute-force, statistical, and differential attacks. It provides a vast key space and demonstrates very strong key sensitivity, both confirmed through experimental evaluation. Test results show near-ideal performance on standard security metrics, with a Number of Pixels Change Rate (NPCR) approaching 99.6%, a Unified Average Changing Intensity (UACI) approaching 33.4%, and an information entropy value nearing the theoretical maximum of 8. Further quantitative comparative analysis demonstrates the superiority of the LC Map in balancing security and computational efficiency. Thus, the LC Map is presented as a rigorously validated component for the development of future image cryptosystems
A two-layered collaborative approach for network intrusion detection system using blended shallow learning gaussian naïve bayes and support vector machine models
Full text linkThe majority of network intrusion detection systems use a signature matching technique. To detect abnormalities and unfamiliar attacks using machine learning methods is a more reliable approach. However, due to significant variations in attack trends, applying a single classifier is impractical for the effective detection of all types and forms of attacks, particularly rare attacks such as User2Root (U2R) and Remote2Local (R2L). Consequently, a hybrid strategy is expected to provide more promising performance. The proposed Two-Layered Collaborative Approach (TLCA) particularly addresses the problem as mentioned earlier. Principal Component Analysis optimizes variables to handle the variation resulting from every kind of attack. The proposed method investigates several types of attacks and discovered that the behaviors of U2R and R2L attacks are similar to those of regular users’ characteristics. To identify DoS and Probe attacks, TLCA uses a Shallow Learning classifier, such as Gaussian Naïve Bayes, as Layer 1. Similarly, the Support Vector Machine at Layer 2 discriminates between U2R and R2L and typical occurrences. We have divided KDDTrain+ into Set 1 and Set 2 by observing that it involves two 2-dimensional PCA analyses. Cross-sectional Correlated Feature Selection (CCFS) is employed to choose key attributes. PCA and KPCA are applied to datasets to reduce dimensionality. The results obtained using the proposed method on the NSL-KDD dataset are compared with those of available benchmark methods. According to the experimental data, TLCA outperforms all single machine learning classifiers and surpasses many current cutting-edge IDS approaches. The proposed method achieves detection rates of 92.4%, 92.3%, 95.6%, and 100% for DoS, Probe, R2L, and U2R, respectively. The proposed TLCA also demonstrates a better ability to identify unusual attacks. It also yields improved detection rate results for known attacks, at 94.1%, and for unknown attacks, at 91.1%, when using the KDDTest+ dataset for testing