E-ticketing systems (ETS) generate mixed information from categorical ticket attributes and free-text descriptions, yet many classification studies still treat these sources separately, which can limit routing accuracy. This study aims (i) to develop an effective preprocessing pipeline for hybrid data, (ii) to develop a two-stage feature selection (2-FS) pipeline for hybrid data, (iii) to design an ensemble classification framework that improves hybrid data performance, and (iv) to benchmark all implemented and proposed models using real-world ETS data. The methodology builds a unified preprocessing workflow by combining Natural Language Processing for textual feature with categorical encoding for categorical features, followed by feature concatenation and vector-space integration to form hybrid representations. Five baseline classifiers (LR, SVM, MNB, RF, and KNN) are evaluated and extended with majority-vote ensembles that pair LR with other classifiers (LR-S, LR-M, LR-R, LR-K, and LR-ALL). Model performance is assessed using accuracy, precision, recall, and F1-score, and the Friedman test is applied to examine statistical consistency across datasets. Results show that hybrid data consistently outperforms single-type datasets, while categorical-only data yields the lowest scores. The best hybrid ensemble of LR-K achieves up to 93% classification accuracy, with strong overall performance also observed for RF on hybrid data. The Friedman test indicates minimal rank differences across models, suggesting that several classifiers are competitive under this ETS setting. Limitations include possible noise and overfitting effects that reduce separation between dataset types. Future work will explore feature ranking-based selection, data-driven segmentation during preprocessing, multi-level classification, imbalance handling, and deeper ensemble strategies to strengthen robustness and generalization.
Global equity markets, including the Nikkei 225 Index, often experience high volatility due to economic and geopolitical factors. Sudden price changes may cause significant losses, making hedging essential for managing extreme risks and preserving portfolio value. This study evaluates the effectiveness of two option strategies, Iron Condor and short Strangle, as hedging tools for the Nikkei 225. The dataset consists of daily closing prices of the Nikkei 225 index, obtained from the Investing.com website (https://id.investing.com). The methodology combines Monte Carlo simulation-based and Antithetic Monte Carlo on the Geometric Brownian Motion model to generate future price paths, and the Black-Scholes-Merton model to value option premiums. The Iron Condor, a hedged strategy, limits both profit and loss, offering stability for conservative investors. The short Strangle, while potentially more profitable, exposes investors to unlimited downside risk. Simulation results show that the short Strangle yields higher average returns but with much greater volatility. In contrast, the Iron Condor provides more stable and controlled outcomes with lower extreme risk. Based on quantitative metrics such as mean profit, standard deviation, Value-at-Risk, and loss probability, the Iron Condor is concluded to be more effective for hedging in low-to-moderate volatility markets. This study offers practical insights for investors and risk managers when choosing risk management strategies based on individual risk tolerance.
Hemodialysis is a critical treatment for patients with end-stage chronic kidney disease (CKD). Although it is associated with a higher risk of mortality and complications compared to other renal treatment choices, many patients prefer hemodialysis as their renal replacement therapy. Traditional parametric survival models often struggle to capture the complexity of the distribution due to multimodal survival data in heterogeneous populations, such as hemodialysis patients. To overcome this, mixture models provide greater flexibility by combining several distributions to better reflect latent survival patterns. This study investigates mortality risk factors by applying a Bayesian Weibull mixture proportional hazards model to 151 hemodialysis patients from one hospital in Surabaya, Indonesia, in 2024, with data extracted from medical hospital records. The Expectation-Maximization and No-U-Turn Sampler (EM-NUTS) approach was used to estimate model parameters and latent class memberships. The Expectation-Maximization (EM) approach, originally developed for control charts and time series, was adapted for survival analysis to address latent heterogeneity. Exploratory analysis reveals multimodal survival distributions, suggesting distinct risk groups. Model selection using the Bayesian Information Criterion and the Akaike Information Criterion identified two latent groups with distinct risk profiles. In the first group, male gender and diabetes significantly increased mortality risk, while hypertension had a smaller effect. In the second group, hypertension was the dominant risk factor, with less influence from gender and diabetes. These findings emphasize the importance of accounting for population heterogeneity in the survival analysis of hemodialysis patients. The use of advanced Bayesian mixture models with EM-NUTS estimation provides robust tools for uncovering hidden subgroups and improving risk stratification, allowing for more personalized treatment strategies in CKD care.
Inspired by the hyperbolic secant distribution and a specific hyperbolic trigonometric identity, we propose a new continuous distribution with one parameter, called the Generic Krison distribution. We derive its probability density function, survival function, cumulative distribution function, hazard function, quantile function, and mean excess loss function. Closed‑form expressions for the moment generating function are also provided. Statistical measures including the raw moments, mean, variance, standard deviation, median, mode, skewness, kurtosis, coefficient of variation, and index of dispersion are obtained. The distribution exhibits constant moderate positive skewness and a constant coefficient of variation, making it a natural candidate for modeling positively skewed data. The parameter is estimated by the method of maximum likelihood; because the score equation is transcendental, the estimate is obtained numerically. We compare the Generic Krison distribution with the one‑parameter Rayleigh, Lindley, and Bilal distributions across three datasets (income, survival time, mortality rate). The results demonstrate that the Generic Krison distribution performs well on all three datasets, offering a promising new tool for statistical modeling.
Paddy production supply chain is a complicated multi-stakeholder system whereby any disruption in any one of the nodes can have a cascading effect on the entire system. There has been more recent interest in the supply chain resilience, although the use of weighted graph-theoretic models with agricultural networks in developing countries has been under-explored, particularly at the local and community level. This study has filled three gaps: (1) the fact that no weighted network models have been used to measure the strength of the interactions between actors in the paddy supply chain; (2) the fact that no node-level vulnerability analysis has been used based on centrality of nodes in the graph in the context of Malaysian agriculture; and (3) the fact that no node-level vulnerability analysis has been used based on the centrality of the nodes in the graph in the context of Malaysian agriculture. This work uses an artificial dataset based upon statistically grounded distributions parameterized against recorded Malaysian paddy supply chain parameters to model the paddy supply chain as a directed weighted graph G = (V, E, W). It has three weighted centrality measures: Weighted Degree Centrality (WDC), Weighted Betweenness Centrality (WBC), and Weighted Closeness Centrality (WCC). Structural statistics of networks, like density, clustering coefficient, and average path length, are also computed. The sensitivity analysis entails modeling the desired deletion of the most central node, Processing-1, to ascertain the vulnerability of the system. Results confirm that Processing-1 is the most common bottleneck in each of the three dimensions of centrality. Its removal is observed to decrease Farm-1 WDC by 66.7, making the betweenness centrality of Farm-1 decrease to 0, and reducing the average network closeness by approximately 34. These findings will offer quantitative and practical information to interested policymakers and supply chain planners who may be interested in having more resilient agricultural food systems.
