Issah Zabsonre Alhassan^1,2,*, Gaddafi Abdul-Salaam¹, Michael Asante¹, Yaw Marfo Missah¹, Alimatu Sadia Shirazu¹

Journal of Cyber Security, Vol.8, pp. 577-607, 2026, DOI:10.32604/jcs.2026.085303 - 21 August 2026

Abstract A hybrid approach to pseudorandom number generation that couples ensemble learning with the Residue Number System (RNS) is presented in this paper. Unlike conventional deterministic generators that depend solely on direct algorithmic transformation, the proposed method first maps a seed-driven integer sequence into its RNS representation under the coprime moduli set {3, 5, 7, 11}, whose dynamic range is M = 1155, thereby introducing modular non-linearity through a static, stateless feature transformation. A soft-voting ensemble of Logistic Regression, Random Forest, and Support Vector Machine then serves as a decision layer that classifies and re-maps the… More >

Sayfudin Sayfudin^1,2, Deris Stiawan^3,*, Ferdiansyah Ferdiansyah⁴, Rahmat Budiarto⁵

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084177 - 13 August 2026

Abstract Tightening global regulation of digital toxicity demands hate-speech detection that is accurate, explainable, traceable, and forensically usable. The challenge intensifies in multilingual and code-mixed settings such as Indonesian social media, where linguistic variation and informal expressions cause feature sparsity and reduce machine learning (ML) effectiveness. Most prior work emphasizes text classification while neglecting actor profiling and the network structures through which hate speech propagates. We propose Dynamic Lexicon-Driven Network (DyLex-Net), an integrated framework for profiling actors who disseminate hate speech, combining dataset-driven dynamic-lexicon analysis, classical ML ensemble validation, and ego-network analysis under a forensic-readiness orientation.… More >

David Hason Rudd^1,*, Cesar Sanin², Md Rafiqul Islam³, Xianzhi Wang¹, Huan Huo¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084015 - 13 August 2026

Abstract The accurate classification of human emotions from speech remains a formidable challenge due to the dynamic, non-stationary properties of audio signals and pervasive background noise. Traditional single-domain extraction methods frequently fail to capture overlapping acoustic phenomena, resulting in high misclassification rates among acoustically similar emotions. To overcome this, we propose the Dual-Decomposition Ensemble (DDE-SER), an architecture that synergizes 1D adaptive frequency filtering with 2D spatial spectrogram separation. The framework operates through two distinct pipelines: an adaptive time-domain branch that leverages VGG-optiVMD to autonomously extract Intrinsic Mode Functions (IMFs), and a structural spectrogram branch that applies… More >

Sultan Shutyan Albalawi¹, Mohd Yamani Idna Idris^1,2,*, Ainuddin Wahid Bin Abdul Wahab¹

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.083860 - 13 August 2026

Abstract As Distributed Denial-of-Service (DDoS) attacks grow in size and complexity, standard intrusion detection systems are hitting a wall. Most struggle to generalize well, require too much computational power, or lack model diversity. To tackle this, we developed TS-SCHO-TSE, a unified hybrid framework for DDoS detection. Unlike traditional methods that treat feature selection and ensemble building as isolated, step-by-step tasks, our approach combines everything. We map feature masks, classifier states, voting weights, and hyperparameters into a single mixed discrete-continuous search space to optimize them all at once. We tested our system against classical functions (F1–F23), the More >

Sugeng Rifqi Mubaroq^1,*, Rolly Maulana Awangga², Tegar Ditya Pragama¹, Sidiq Fathummubin³, Ali Yusuf Abdulhaq¹

Intelligent Automation & Soft Computing, Vol.41, pp. 27-46, 2026, DOI:10.32604/iasc.2026.088039 - 11 August 2026

Abstract Decoding cognitive workload from electroencephalography (EEG) underpins passive brain–computer interfaces and adaptive learning technology, yet practical decoders share two weaknesses: they rely on a single family of features, and their accuracy collapses on recordings from an unfamiliar device, montage, or task. We address both. We first build a multi-specialist stacking decoder that fuses complementary spectral, Riemannian, and spatial views through a meta-learner. Evaluated across two public corpora under the leave-one-subject-out MOABB benchmarking protocol, it outperforms the best single specialist on the binary workload contrasts, and the strongest of these effects survives family-wide false-discovery-rate correction. The… More >

Jeevani Jayasinghe^1,2, Chee-Onn Chow², Lasini Wickramasinghe¹, Upaka Rathnayake^3,*

Energy Engineering, Vol.123, No.9, 2026, DOI:10.32604/ee.2026.085002 - 06 August 2026

Abstract Rooftop solar photovoltaic (PV) systems operate under weather conditions that differ significantly from Standard Test Conditions (STC), particularly in tropical regions. This study examines the impact of climatic factors on rooftop PV power generation in the Kurunegala district of Sri Lanka using measured power output and meteorological data. Three grid-connected PV systems with a capacity of 5 kW were monitored over six months, with hourly power output and inverter temperature recorded during the daytime. Corresponding weather data, including solar irradiance, ambient temperature, relative humidity, and cloud cover, were used in this research to identify their… More > Graphic Abstract

Yujie Shi, Guoxing Wu^*, Qingwei Wang, Xieli Fu, Wenfeng Yang

Energy Engineering, Vol.123, No.9, 2026, DOI:10.32604/ee.2026.074213 - 06 August 2026

Abstract This study proposes an optimized ensemble learning framework for energy-efficiency assessment in low-voltage distribution networks by integrating multiple data sources. The framework integrates heterogeneous data from smart meters, SCADA systems, meteorological stations, and network topology databases, employing advanced feature engineering to extract 89 essential predictors from 147 initial features. Three gradient boosting algorithms—Random Forest, XGBoost, and LightGBM—are combined through an elastic net stacking strategy with Bayesian hyperparameter optimization. The stacking ensemble achieved superior performance with an MAE of 118.4 kWh, an RMSE of 164.2 kWh, an MAPE of 3.98%, and an R² of 0.952, representing 16.8%… More >

Arvind Prasad^1,*, Ibrahim Aljubayri², Mohammad Zubair Khan^3,*, Abdulfattah Noorwali⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082021 - 30 June 2026

Abstract Intrusion detection in large-scale IoT deployments becomes particularly challenging during ongoing attack scenarios, where malicious traffic may temporarily dominate benign traffic. In such conditions, streaming network data exhibits severe class imbalance in favor of attack traffic, while device behavior remains heterogeneous, non-identically distributed (non-IID), and temporally evolving. Within federated learning environments, this imbalance can destabilize early aggregation rounds, dominant attack gradients bias the global model, distort decision boundaries, and degrade reliable discrimination of residual benign behavior. Since the server has no access to raw data, these effects can persist across communication rounds if not addressed… More >

Shafidah Shafian^1,*, Azlan Ismail^2,3

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080623 - 15 June 2026

Abstract Organic solar cells (OSCs) have progressed rapidly in recent years, driven by advances in donor polymers, non-fullerene acceptors, and increasingly complex binary and multicomponent blend architectures. Despite these achievements, device performance remains governed by strongly coupled molecular, morphological, and processing variables, making materials optimization inherently multidimensional and difficult to navigate using conventional trial-and-error approaches. The growing availability of experimental data and computational descriptors has therefore encouraged the integration of machine learning (ML) techniques into OSC research as a complementary strategy for accelerating materials discovery and device optimization. Among the available ML strategies, ensemble learning has… More >

Zafer Serin^1,*, Cihan Karakuzu², Uğur Yüzgeç²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079254 - 27 May 2026

Abstract This study proposes SWAGE-3D (Spectral Wasserstein Attention Generative Ensemble), an enhanced 3D-VAE-GAN framework for single-view 3D object reconstruction using voxel-based representations. The proposed model integrates RGB-D encoding, Wasserstein adversarial learning with hybrid Lipschitz regularization, and a self-attention–augmented generator to improve structural coherence and training stability. By combining variational latent modeling with stabilized Wasserstein optimization, the framework aims to address common challenges in 3D generative modeling, including mode collapse, unstable convergence, and insufficient global consistency. The encoder employs a depth-aware feature extraction strategy, while the discriminator utilizes a hybrid spectral normalization and gradient penalty mechanism to More > Graphic Abstract