Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control

Evaluating CLAHE and Temporal Smoothing Impact on Deep Learning-Based Young Crescent Moon Video Detection

Bayu Krisna Murti — 2026-10-05

Detecting the young crescent moon from video data poses significant challenges due to low contrast against the twilight sky, sensor noise, and atmospheric interference. Frame-wise contrast enhancement, such as Contrast Limited Adaptive Histogram Equalization (CLAHE), can introduce inter-frame intensity fluctuations that degrade both visual quality and the consistency of automated detection. This study evaluates the impact of CLAHE and Temporally Aware CLAHE (TA-CLAHE) preprocessing on YOLOv8n-based young crescent moon detection across three video sequences representing varying observational conditions. Temporal stability was assessed using Flicker Index (FI), Percent Flicker (PF), Temporal Standard Deviation (T-STD), and Frame Difference Mean (FDM). At the same time, detection performance was measured using Detection Consistency Rate (DCR) and centroid stability relative to a fixed ground truth. CLAHE substantially improves detection consistency, raising DCR by up to 16 percentage points over the Raw baseline (Video 1: 60.8% to 76.8%). TA-CLAHE further elevates detection to near-complete coverage — 99.9% on Video 1 and 99.2% on Video 3 — surpassing standard CLAHE by 23.1 and 9.5 percentage points, respectively. Temporal stability also improves: TA-CLAHE reduces FI by 19–32% and T-STD by 20–26%; the reduction in PF is modest (at most 27%) and is retained only as a secondary indicator given its known measurement limitations. These gains involve trade-offs: enhancement slightly loosens per-box localization, and under cloud occlusion, TA-CLAHE raises the invalid detection rate to 17%. Based on these findings, CLAHE is recommended as a sound, low-cost default preprocessing, while TA-CLAHE is preferred for applications requiring an uninterrupted, low-jitter detection track, provided it is paired with an explicit occlusion gate.

Enhancing Model Trust in Load Forecasting using Counterfactual Explanations

Muhammad Syarif Hidayatullah — 2026-09-02

This study investigates the effectiveness of Counterfactual Explanations (CE) in improving the interpretability of short-term electricity load forecasting models. The forecasting model was developed using Random Forest Regression (RFR) due to its ability to capture nonlinear multivariate patterns in electricity load data and to provide global, descriptive interpretability through feature-importance analysis. However, feature importance provides static and potentially biased explanations, making it insufficient for instance-level interpretability. To address this limitation, the Diverse Counterfactual Explanations (DiCE) framework was integrated to generate multiple “what-if” scenarios that illustrate model-consistent input adjustments capable of shifting forecast outcomes. The study uses hourly electricity load data from Panama collected between 2015 and 2020, with data from 2020 excluded during preprocessing due to atypical demand patterns caused by COVID-19. The model was trained using time-series cross-validation and optimized through grid search. Forecasting performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Counterfactual Explanations were assessed using validity, proximity, and compactness metrics, indicating that most generated scenarios are goal-aligned and remain moderately close to the original instances, while the compactness results reflect a feasibility trade-off when large prediction shifts are required. Overall, combining RFR with counterfactual analysis enhances model transparency by bridging global and local interpretability perspectives and provides decision-relevant, scenario-based insights to support transparent and data-driven planning in the energy sector.

Deep Learning–Based ASD Detection from EEG Signals: A Comparison of InceptionTime and XceptionTime Architectures

Rachmawati Rachmawati — 2026-09-01

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by persistent social-communication impairments and restricted or repetitive behaviors. While clinical assessment remains the diagnostic gold standard, the demand for objective and scalable screening tools has motivated EEG-based automated classification. However, under controlled experimental settings, the relative contributions of preprocessing choices and deep learning architecture selection to ASD detection performance remain insufficiently quantified. This study systematically benchmarks artifact-aware preprocessing and model architecture by comparing InceptionTime and XceptionTime across four end-to-end processing schemes that isolate the effects of independent component analysis (ICA) and classifier design under identical segmentation and evaluation protocols. A public King Abdulaziz University EEG dataset comprising 16 subjects (8 ASD, 8 controls) was used. Signals were bandpass-filtered using a fourth-order Butterworth filter (0.5–45 Hz), optionally denoised via ICA, and segmented into 4-s windows with 50% overlap. Models were evaluated using 8-fold subject-wise cross-validation. Performance was assessed using accuracy, precision, sensitivity, specificity, and F1-score, and statistical significance was tested with the Wilcoxon signed-rank test. The Butterworth+ICA+InceptionTime pipeline achieved the best results, with a mean accuracy of 0.9886 ± 0.0046 and an F1-score of 0.9879 ± 0.0049. ICA inclusion and architecture choice yielded significant improvements in accuracy ( for both, ). These findings indicate that structured artifact suppression and multi-scale temporal modeling jointly enhance EEG-based ASD classification, supporting their use in robust clinically oriented screening systems.

Spectral Quality-Based Weighted Multi-ROI Fusion for Robust Imaging Photoplethysmography

Salsabila Aurellia — 2026-10-05

Imaging photoplethysmography (iPPG) enables non-contact heart rate (HR) estimation from facial videos, yet its reliability is strongly affected by spatial and temporal signal variability. While many studies focus on improving signal extraction algorithms, the impact of explicit facial region selection and quality-aware fusion remains underexplored. This study systematically proposed a spectral quality-based weighted multi-ROI fusion method and investigates the role of facial region-of-interest (ROI) selection, and temporal quality gating within a controlled and interpretable iPPG framework. A classical pipeline is employed to isolate spatial and quality-related effects. Experiments were conducted on 41 subjects from the UBFC-rPPG dataset. The results demonstrate that quality-weighted multi-ROI fusion provides statistically significant and more consistent performance compared to single-ROI and simple averaging strategies. With moderate temporal gating (τ = 0.20), the proposed method improves estimation accuracy while maintaining high coverage (92.31%) and full subject inclusion. A stricter threshold (τ = 0.25) further reduces the mean absolute error to 2.89 bpm and increases correlation to 0.86, albeit with reduced window-level coverage (41%). These findings highlight a clear trade-off between numerical accuracy and data availability and emphasize the importance of jointly considering spatial reliability and temporal quality control for robust and interpretable iPPG-based heart rate monitoring in realistic real-world deployment scenarios.

A Nonlinear Stochastic Differential Model of EEG Dynamics with Bayesian Change-Point Detection for Early Identification of Brain Abnormalities

Tito Waluyo Purboyo — 2026-10-05

Electroencephalography (EEG) signals exhibit complex nonlinear and stochastic dynamics that fundamentally reflect underlying neural processes. Despite decades of clinical application, conventional EEG analysis remains constrained by linear assumptions or opaque black-box architectures that obscure mechanistic understanding. This study proposes a mathematically grounded nonlinear stochastic differential equation (SDE) framework integrated with Bayesian change-point detection for early, interpretable identification of abnormal brain states. The proposed methodology explicitly characterises nonlinear neural interactions and time-varying stochastic fluctuations through estimation of biophysically meaningful parameters. As a proof-of-concept, validation is performed on data from a single patient (Patient 1, CHB-MIT Scalp EEG Seizure Database) and a single EEG channel (Fp1-F7). Within-patient five-fold cross-validation demonstrates high temporal consistency, with mean sensitivity of 96.8 +/- 0.3% and specificity of 94.2 +/- 0.2%, substantially outperforming conventional linear autoregressive (87.3%, 1.82 s latency) and spectral power density methods (81.5%, 1.35 s). An ablation study confirms that the combined SDE-Bayesian framework yields the highest performance, demonstrating a synergistic gain of +12.4 percentage points in sensitivity over SDE-alone and +8.5 over Bayesian change-point alone. Detection latency of 0.47 s falls within the clinically critical 1-2 second intervention window. Parameter estimation accuracy, validated on simulated data, achieves errors below 2% for all model parameters. Unlike existing approaches, the framework provides physiologically interpretable parameters-neural damping, nonlinearity strength, and stochastic noise level-suitable for mechanistic understanding of pathological brain dynamics. These proof-of-concept results represent a meaningful first step toward clinical deployment; robust generalisation conclusions await future multi-patient and multi-channel validation.

The Coordinated Energy Management Improvement Microgrid with Hybrid Storage Using Fuzzy Logic

Sri Sukamta — 2026-10-05

The increasing need for electrical energy in residential is caused by the increasing number of houses in rural areas. Currently, the distribution of electrical energy has not reached rural areas, especially in remote areas. Microgrid is a solution in providing electrical energy services for housing. However, the problems of voltage stability, energy management efficiency, energy storage usage period, load fluctuations are the main challenges in the microgrid system. The purpose of this study is to improve the provision of electrical power with an energy management strategy in an energy storage system using fuzzy logic. The method used is to identify load demand, weather data and take a systematic approach by modeling the microgrid system. Furthermore, the proposed energy management system with a coordinated approach, which compares droop control and fuzzy logic controllers. To evaluate the proposed strategy, this study was simulated using MATLAB, based on weather data and load demand. The results of the study showed that the voltage drop and maximum voltage overshot were 2.8 V and 1.6 V respectively in 0.07 S and 0.04 S. Therefore, the proposed strategy shows that there is an increase in efficiency, stability, and reliability when compared to the previous method.

Robust Video Encryption using Multi-Chaotic Map Cascade to Mitigate Statistical and Differential Attacks

Heru Lestiawan — 2026-10-05

The rapid proliferation of digital video transmission has necessitated robust security mechanisms to safeguard sensitive visual content against unauthorized access and interception. Conventional cryptographic algorithms, however, often struggle with the efficiency demands of high-volume video data. This study established and validated a lightweight yet highly secure video encryption scheme utilizing a Multi-Chaotic Map Cascade architecture. The proposed approach integrated the SHA-256 hash function for dynamic key generation with a hybrid hyperchaotic system comprising Henon, Lorenz, and Tent maps. This configuration facilitated a rigorous encryption process involving pixel coordinate permutation and bidirectional value diffusion. Comprehensive experimental evaluations were conducted on standard video sequences ranging from QCIF to Full HD resolutions to assess consistency and scalability. The results demonstrated that the algorithm achieved near-perfect randomness, with an average information entropy of 7.9988 bits and a Number of Pixels Change Rate of 100 percent, effectively satisfying the strict avalanche criterion. Furthermore, histogram analysis confirmed a statistically uniform distribution validated by Chi-Square tests. The scheme also exhibited strong resistance against visual reconstruction, maintaining Peak Signal-to-Noise Ratio values below 10 dB for encrypted content. Additionally, Structural Similarity Index (SSIM) analysis confirmed near-zero structural correlation (averaging 0.018) in encrypted frames while achieving perfect reconstruction (SSIM = 1.0) during decryption. Adjacent pixel correlation analysis demonstrated exceptional randomness with correlation coefficients approaching zero (|r| < 0.006) across horizontal, vertical, and diagonal directions, effectively eliminating spatial dependencies.