Communications in Mathematical Biology and Neuroscience (CMBN) is a peer-reviewed open access international journal, which is aimed to provide a publication forum for important research in all aspects of mathematical biology and neuroscience. This journal will accept high quality articles containing original research results and survey articles of exceptional merit.
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Modeling HIV and AIDS data in Trenggalek and Ponorogo with a bivariate zero-inflated Poisson approach
The presence of excess zeros and overdispersion in count data often leads to biased parameter estimates when analyzed using the standard Poisson regression model. This study aims to model the number of HIV and AIDS cases in Trenggalek and Ponorogo Regencies using the Bivariate Zero-Inflated Poisson (BZIP) regression approach. The BZIP model accommodates correlated count responses as well as excess zeros, commonly found in epidemiological data. Two response variables, the number of HIV cases (Y₁) and the number of AIDS cases (Y₂), were analyzed against five explanatory variables: the percentage of the population aged 25–29 years (X₁), low education level (X₂), condom use among couples of reproductive ages (X₃), participation in health education programs (X₄), and community health insurance coverage (X₅). Parameter estimation was performed using the Expectation-Maximization (EM) algorithm. The results show that health education significantly increases the likelihood that an area has no HIV cases, while health insurance significantly reduces the number of AIDS cases. Moreover, individuals aged 25–29 years were identified as the group most at risk for AIDS. The model also confirmed strong overdispersion and zero inflation, supporting the use of BZIP as a more appropriate model than the standard bivariate Poisson regression. Additionally, the BZIP model achieved the best performance, indicated by the lowest AIC value compared to previous models.
Topic-based sentiment analysis of Jamsostek mobile application reviews using the BERTopic method
Rapid developments in digital technology have changed the way public services are delivered, shifting from conventional systems to mobile-based platforms. BPJS Ketenagakerjaan has adopted this transformation through the development of the Jamsostek Mobile (JMO) application. The application can be accessed on the Google Play Store, and users can provide ratings and reviews on the Google Play Store page. To improve the quality of the JMO application, it is important to pay attention to these reviews. Therefore, sentiment analysis is needed to identify and analyse positive and negative sentiments related to the use of the JMO application. The method used combines the IndoBERT and BERTopic models to understand user opinions in greater depth. This study shows that 75% of 2,846 comments contain negative sentiments, and the IndoBERT model achieves 100% accuracy in sentiment classification. In topic modelling, five clusters were formed, with the most discussed topics being requests for app updates and users' difficulties in accessing the JMO app due to constant errors and slowness.
Modeling asymmetric volatility with long memory effect using a FIGARCH ANN approach: evidence from ANTM stock
This study models the volatility of daily returns for PT Aneka Tambang Tbk (ANTM), which exhibits volatility clustering, fat tails, asymmetry, and long memory. The analysis proceeds in two stages: (i) conditional variance modeling using FIGARCH (1, d, 1) to represent long-memory dynamics; and (ii) design of a FIGARCH ANN hybrid (backpropagation) to absorb residual nonlinearity/asymmetry. Preprocessing tests confirm stationarity in log returns, followed by ARIMA baseline selection and confirmation of conditional heteroskedasticity in the residuals. Long-memory estimation via the GPH procedure confirms statistically significant long memory (past shocks have persistent effects). Compared to GARCH (1,1) and EGARCH, FIGARCH provides a better fit because it has the smallest AIC/BIC value. Residual diagnostics for FIGARCH are clean, indicating no autocorrelation and no remaining ARCH effects the model captures the main volatility structure. Ten steps ahead forecasts show the conditional variance stabilizing, implying that shocks decay slowly (persistence) toward a relatively stable level. The ANN component trained on residuals/logvariance reduces error metrics (MSE/RMSE/MAE) compared with standalone FIGARCH, evidencing the benefit of nonlinear correction for short horizon accuracy.
Hybrid MobileNetV3-LSTM model for detecting phasic and tonic receptor responses in facial images
Pain was a complex and subjective experience that involved sensory, emotional, and cognitive aspects simultaneously. The responses of phasic and tonic receptors to painful stimuli exhibited different patterns and could be observed through facial expressions as a form of nonverbal communication. This study aimed to implement a MobileNetV3-LSTM model to classify phasic, tonic, and normal receptor responses using human facial expression images. The objective was to obtain the most optimal model for classifying facial expressions exposed to pain stimuli targeting phasic or tonic receptors. The methods involved the development and evaluation of three models: MobileNetV3Large, MobileNetV3Small, and their respective hybrid versions combined with LSTM, to examine the effect of incorporating temporal information on classification performance. According to results 10, the hybrid MobileNetV3Large-LSTM model performed the best, with an F1-score of 94%, accuracy of 93%, precision of 96%, and recall of 93% on the 12 test data. Meanwhile, the MobileNetV3Small-LSTM model reached 74% accuracy, 80% precision, 74% recall, and a 74% F1-score. The standalone MobileNetV3Large model only obtained 68% accuracy and an F1-score of 0.59, while MobileNetV3Small without LSTM achieved 75% accuracy and an F1-score of 0.56. These results suggest that the inclusion of LSTM layers greatly enhanced the accuracy in the model. This research added to the development of facial expression classification methodologies to recognize pain and complemented the body of knowledge on hybrid model utilization in deep learning.
Decadal rainfall forecasting using CNN–BiLSTM: a case study in Indramayu, Indonesia
Failures in rice cultivation often result from extreme rainfall variability—ranging from droughts due to low precipitation to crop damage caused by excessive rainfall. This makes rice production highly sensitive to climate fluctuations, emphasizing the need for accurate rainfall forecasting to optimize planting and harvesting schedules. In Indramayu Regency, unpredictable rainfall patterns pose significant forecasting challenges. With the Indonesian Meteorological Agency’s (BMKG) average accuracy reaching only 63%, alternative approaches are urgently needed. This study employs a hybrid deep learning model—Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BiLSTM)—to address these limitations, as previous methods such as RNN and LSTM have struggled to capture seasonal patterns and the complexity of decadal rainfall data. The model was trained on rainfall data from January 2000 to July 2022 and tested on data from August 2022 to January 2025. The model architecture includes two convolutional layers (filters 16 and 32), max pooling, and three Bi-LSTM layers (64, 50, and 32 neurons), trained using the Adam optimizer (learning rate = 0.0001), a batch size of 64, and Mean Square Error (MSE) as the loss function. Evaluation results indicate a Mean Absolute Percentage Error (MAPE) of 17.49%, classified as “good” forecasting accuracy. This translates to an overall accuracy of 82.51% (MAPE-based classification). These findings demonstrate that the CNN-BiLSTM model effectively predicts decadal rainfall in Indramayu and has the potential to reduce crop loss by optimizing agricultural strategies such as drainage, fertilization, and harvesting aligned with rainfall projections for February to April 2025.
Residual-based unsupervised bearing fault detection using ICA-enhanced LSTM autoencoder
Early fault detection in rolling element bearings remains a challenging problem, particularly under unsupervised conditions where labeled fault data are unavailable. Incipient defects often generate weak impulsive vibration signatures that are easily masked by operational noise. This paper proposes a residual-based unsupervised fault detection method that integrates Independent Component Analysis (ICA) with a Long Short-Term Memory (LSTM) autoencoder. ICA is employed to decompose vibration signals into statistically independent components, enhancing fault-related impulsive features while suppressing redundant background vibration. An LSTM autoencoder is trained exclusively on healthy-condition data to learn normal temporal dynamics. Bearing anomalies are identified through reconstruction residuals, which quantify deviations from learned healthy behavior. Instead of fixed or heuristic thresholds, the decision boundary is determined via F1-score optimization, framing fault detection as a data-driven residual decision problem. The proposed approach is validated using the Case Western Reserve University (CWRU) bearing dataset under a 2 hp load condition, focusing on inner race faults. Experimental results demonstrate perfect fault recall and an ROC-AUC of 1.000, confirming the effectiveness of ICA-enhanced residual learning for early fault detection. The method is computationally efficient, interpretable, and suitable for practical predictive maintenance applications.
Plant and disease classification with explainable AI in web-based application
The application of deep learning for plant and disease recognition has become increasingly popular; however, most studies address these tasks separately due to limitations in dataset availability for model training and testing. This study aims to overcome such constraints by developing a multi-output classification framework that simultaneously predicts both plant species and associated diseases. Three state-of-the-art architectures were employed: NASNetMobile, a CNN-based model; a hybrid CNN-LSTM; and a CNN integrated with an Attention mechanism. A combined dataset of over 15.678 images was compiled from multiple public sources, covering 10 plant species and 27 disease classes with diverse real-world conditions. The training process was done using two different approaches: with and without data augmentation. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results show that NASNetMobile without data augmentation achieved the highest performance, with an F1-score of 99.79% for plant classification and 98.54% for disease classification, outperforming CNN-LSTM (98.86% and 95.2%) and CNN-Attention (98.65% and 93.3%). These findings demonstrate that lightweight yet robust architectures such as NASNetMobile can effectively bridge the gap between laboratory-trained models and field-ready agricultural applications, supporting the advancement of precision agriculture. To enhance interpretability, Local Interpretable Model-Agnostic Explanations (LIME) and Eigen-CAM were applied, providing intuitive visualizations that help users understand model predictions. The best-performing model was deployed in a web-based proof-of-concept application, developed using Streamlit. This work provides one of the first multi-output explainable frameworks for plant and disease classification deployable in a web-based system.
Classification of essential and non-essential genes in human genome sequence data using ensemble machine learning
DNA (Deoxyribonucleic Acid), RNA (Ribonucleic Acid), and proteins are basic biochemical molecules essential for cellular organization. DNA serves as the primary store of genetic information encoded in genes, and humans are estimated to have 20,000 to 30,000 genes. This genetic information is represented in a chemical code. Essential genes are fundamental to various biological mechanisms, and when disrupted or removed, they may cause genetic defects, mutations, or, in extreme conditions, result in organismal fatality. Identifying these genes through experimental methods requires large resources and is often inefficient. Computational methods, especially those involving machine learning, offer a more efficient and effective solution to this challenge. This research explores the ability of machine learning techniques to build classification models for human gene sequence data. Two data sets, Cellular Essential Gene (CEG) and Organism Essential Gene (OEG), were analyzed, with genes categorized as essential or non-essential. The study was structured through multiple phases, such as data acquisition, cleaning, feature engineering, and dividing the dataset into subsets for model training and evaluation. Model construction followed this phase, where various ensemble learning techniques were applied. These included algorithms like Decision Tree, Support Vector Machine, Random Forest, Extreme Gradient Boosting, and Adaptive Boosting. The best overall results were achieved using the SVM model with 5-mers, reaching a sensitivity of 0.81 and ROC AUC of 0.81 on the CEG dataset, and a PR AUC of 0.46, sensitivity of 0.69, and ROC AUC of 0.80 on the OEG dataset. The consistently high accuracy results indicate that these models effectively distinguish essential and non-essential genes. These machine learning-based classification models can potentially be valuable tools in the healthcare field, contributing to a deeper understanding of normal gene function in organisms.
Outlier detection in multivariate time series: an application of hybrid DNN-DBSCAN technique
In spite of on-going advances and utilization of Deep Neural Networks (DNN) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) techniques to enhance outlier detection in multivariate time series (MTS) data, research is yet to explore an approach that integrates the capabilities of the two techniques for complex data representation. The paper therefore combines the two techniques to obtain the hybrid DNN-DBSCAN technique. It is demonstrated in simulated data that the resulting technique achieves improved precision and recall of outlier detection based on a number of performance metrics. In particular, the new procedure adequately captures the complexity involved with the underlying high-dimensionality of MTS data which poses problems for outlier detection to traditional methods.
Modeling the impact of public opinion and preventive practices on malaria transmission in central and northern Benin
Malaria remains a major public health challenge in Sub-Saharan Africa, especially in Northern Benin, where seasonal variations drive transmission. The success of prevention and treatment depends on community compliance, which is influenced by public risk perception and social attitudes. Despite the widespread implementation of preventive interventions, malaria transmission persists at alarming levels in many regions. Existing mathematical models have often overlooked the role of population opinions and behavioral responses in shaping the effectiveness of these interventions. To address this gap, we extended a mathematical model integrating malaria transmission dynamics with an opinion dynamics framework. The study was conducted across four Benin districts, using five years (2019–2023) of real-world malaria surveillance data from the National Malaria Control Program. Behavioral data were derived from the 2022 Malaria Behavior Survey. The model was calibrated using nonlinear least squares estimation techniques. Analytical results confirm the positivity and boundedness of the model, and a disease-free periodic solution was established. The control reproduction number (Rc) was computed using the monodromy matrix method. The numerical analysis revealed that increased the percentage of favorable adherence to prophylactic measures results in a slight but consistent decrease in malaria incidence. Specifically, in Bante, when partial adherence rose from 52% to 100%, the effective reproduction number decreased by 82.75%. Furthermore, we also note that a higher baseline influence rate (Ω0) contributed to a substantial reduction in effective reproduction number. In Sinende, increasing Ω0 from 0.05 to 50 reduced malaria incidence by 17.65%. The findings highlight that incorporating population behaviors and opinions into disease modeling enhances the effectiveness of public health strategies for sustainable malaria control in endemic areas.
Optimal control on model of COVID-19 with ineffective vaccination and quarantine
In this paper, we present an optimal control approach for an (SEIRV) epidemic model of COVID-19 disease by controlling quarantine measures on susceptible individuals and controlling the vaccination rate for susceptible individuals, exposed but not yet infectious individuals, and asymptomatic infectious individuals to reduce the disease burden and related costs. We have proven that an optimal control does exist, and we used Pontryagin’s maximum principle to characterize the optimal control. In numerical simulations, we solve the optimal control problem by the fourth-order Runge–Kutta method. Moreover, we discuss different cases for optimal control of quarantine measures and vaccination quantity presented through graphical representations.
Time-Delay-Induced Hopf Bifurcation in a Fractional-Order Ecological System
In this paper, we investigate a fractional-order delayed dynamical system. Taking the time delay τ as the bifurcation parameter, we establish that the equilibrium is asymptotically stable for all τ<τ0, and a Hopf bifurcation occurs as τ passes through the critical value τ0. We derive the characteristic equation and verify the transversality condition, which identifies the onset of oscillations and characterizes the stability of the emerging periodic solutions. Numerical experiments carried out in MATLAB with a predictor–corrector scheme support the analysis and illustrate the resulting oscillatory behavior.
Global dynamics of a multi-stage fractional HIV-infected model with general incidence rate and drug efficacy
For several decades researchers have been studying global dynamics of viral infection models to prevent wide outbreak of scattered virus both in population and vivo such as Dengue fever, SARS-COV2, HRSV, and immunodeficiency diseases. In this paper, our main aim is obtaining sufficient conditions for the global stability of equilibria of a Caputo fractional derivative order system with general incidence functional response by using Lyapunov’s method and LaSalle’s invariance principle. We prove the global stability of stationary points by the values of the basic reproduction number (R0) and we consider this threshold as a strong index for our sensitivity analysis. We confirm theoretical results through numerical simulations.
Brainprint authentication under varying environmental conditions: machine learning versus deep learning
This study explores the potential of using EEG signals for biometric authentication through the development of Convolutional Neural Network (CNN) model. In particular, the electroencephalogram (EEG) signals were recorded in different ambient noise settings: quiet environment, low-distraction environment, and high-distraction environment. Traditionally, EEG-based authentication requires a separate feature extraction step prior to the use of machine learning algorithm. Feature extraction process is usually cumbersome, which relies heavily on human experts, and prone to information loss. Thus, a CNN model based on EEGNet architecture is proposed to train EEG datasets collected from 45 volunteers who were instructed to look at images presented to them in all the three acoustic conditions. Using a variety of performance metrics, notably precision and recall, the model’s performance was compared across various classification thresholds to account for the imbalanced nature of the dataset. The performance was also compared across different environmental conditions, with the highest F1-score in quiet conditions. Additionally, the CNN’s performance was compared against a probability-based Incremental Fuzzy-Rough Nearest Neighbour (prob-IncFRNN) model, with former outperforming the latter in all metrics.
Mathematical model analysis of stages of cervical cancer with the side effect hematological toxicity in radio-chemotherapy
Cervical cancer is the fourth most common cancer in women worldwide. Cancer treatments are often used to kill or remove tumors or metastases, reduce the risk of recurrence, and slow the development of tumors or metastases. They are also used to prevent and manage symptoms and complications caused by the disease and its treatments, ensuring the best possible quality of life. However, the effects of cancer treatments on patients are not always positive. The treatment of cervical cancer using a combination of radiotherapy and chemotherapy affects red and white blood cells as well as plasma. These side effects, known as hematological toxicity, led us to construct a mathematical model based on a population of hospitalized cervical cancer patients. This population is divided into five sub-populations classified according to disease stages, and the model is built using a system of differential equations. We analyzed the equilibrium points and performed stability analysis to study the population’s dynamics over time. Finally, we conducted numerical simulations to verify the results of the analysis.
The stability and random attractors of the stochastic model describing the oncolytic virotherapy with tumor-virus interactions
The treatment of cancer tumors varies based on cancer type, stage, location, and patient health. This research focuses on viral therapy, which employs viruses that target and eliminate malignant cells while sparing healthy ones. A stochastic model investigates suicidal viral tumor therapy, emphasizing the interaction between tumorous and non-tumorous cells. The study analyzes the stochastic dynamics and stability of the system's equilibrium points through stochastic differential equations. Key model factors affecting viral treatment outcomes are identified through bifurcation parameters and sensitivity analysis, validated by numerical simulation.
The influence of hunting cooperation, fear and anti-predator on the dynamic of a prey-predator model with disease in prey
In this study, we examine an eco-epidemiological prey-predator model that incorporates hunting cooperation, fear, and anti-predator behavior including linear harvest. There are two subclasses of prey: susceptible and diseased. We investigate thorough mathematical analysis, including the presence and stability of equilibria, the boundedness of the model, and the existence and uniqueness of solutions. The conditions under which local bifurcation could occur near the equilibrium points were discovered. Numerical simulations were run to validate the model's long-term behavior and comprehend the impact of the model's main parameters. The purpose is to demonstrate the analytical findings numerically and study the impact of changing the parameters on the dynamical behavior of the system, and control settings are determined by numerical simulations using MATLAB, R2021a.
Transformation of pellet-based planting media as an organic matter and nutrient enhancer for improving soil quality
Soil degradation driven by excessive inorganic fertilizer use and conventional cultivation practices has reduced land productivity in tropical regions, including Indonesia. This study developed biological pellet-based planting media to improve soil physical, chemical, and biological properties while providing controlled nutrient release. The research employed a panel design and split-plot layout on sandy Entisol soils, evaluating physical–chemical characteristics, rhizosphere microbial colonization, and nutrient-release kinetics using the Gompertz model. The results show that pellets increased soil porosity up to 56%, extended water retention to 22 hours, and significantly enhanced organic carbon as well as N, P, K, Ca, and Mg levels compared with the control. Nutrient release followed a lag–exponential–plateau pattern, indicating an efficient gradual-release mechanism. Microbial and soil-fauna activity more than doubled, demonstrating restored biological functioning of the soil. Treatments with a balanced organic–inorganic ratio (50:50) exhibited the best overall performance across all parameters. Overall, pellet-based planting media prove effective as multifunctional soil conditioners that enhance fertility, nutrient efficiency, and agroecosystem sustainability in marginal tropical soils.
Modeling and evaluating targeted interventions to curb avian influenza spread among humans and domestic birds
Avian influenza, caused by influenza A viruses, has drawn substantial attention globally due to increased deaths from time to time. This study examines control interventions for avian influenza transmission in humans and domestic birds using a deterministic mathematical model. The model incorporates three time-dependent interventions: vaccinating susceptible birds, maintaining proper hygiene practice, and culling infected birds. The next-generation matrix method is used to determine the effective reproduction number. Optimal control theory is applied by incorporating: vaccination, proper human hygiene practices, and culling of infected birds. To determine the necessary conditions for the existence of optimal controls, the Pontryagin’s Maximum Principle is employed. The optimal control problem is then solved using the forward-backward sweep method based on the fourth-order Runge-Kutta algorithm, implemented in MATLAB. Results from the optimal control analysis show that the strategy combining all interventions is the most effective approach for controlling the disease. Furthermore, the Incremental Cost-Effectiveness Ratio (ICER) is used to identify the most cost-effective strategy for disease control. The findings show that Strategy VII has a negative ICER, indicating that it is less costly and averts more infections. Therefore, to eliminate the disease, we recommend the implementation of all the aforementioned control measures.
Concrete crack segmentation using U-Net based models with various pre-trained backbones
The use of deep learning to detect and analyze structural damage in concrete is gaining increasing attention, but exploration of U-Net with various pre-trained backbone models has not been conducted. This study aims to overcome these limitations by developing a U-Net-based concrete crack segmentation framework that utilizes eight modern and lightweight backbones, namely EfficientNetB0, VGG16, VGG19, ResNet34, ResNet50, DenseNet121, MobileNetV2, InceptionV3, and Xception. A total of 458 labeled images from the Concrete Crack Segmentation dataset were used as training and testing data, representing the variety of concrete surface conditions in real environments. Evaluation was conducted using Dice and Intersection over Union (IoU) metrics. The test results showed that the Xception backbone provided the best performance, with a Dice value of 0.8461 and an IoU of 0.7384, surpassing EfficientNetB0 and VGG16/VGG19 which were previously considered stable in segmentation tasks. This finding confirms that the depthwise separable convolution mechanism in Xception is capable of extracting thin crack features more representatively. This study provides an important contribution in selecting the optimal backbone for concrete crack segmentation models, while opening up opportunities for implementing more accurate and efficient structural condition monitoring technology on an industrial and public infrastructure scale.
Bifurcation and persistence conditions of Sokol-Howell prey-predator model with toxins and additional food for predators
This work investigates the conditions for local bifurcation in a three-species food chain model, comprising prey, predator, and top predator, incorporating factors such as fear, a weak Allee effect, toxins, and supplementary food in the predator population, utilizing the Sokol-Howell functional response. This system possesses thirteen equilibrium points. Transcritical and pitchfork bifurcations occur near the extinction equilibrium point (Q0), the predator-top predator-free equilibrium point (Q1), the prey-top predator-free equilibrium point (Q3), while near and the top predator-free equilibrium point (Q4), while saddle node bifurcations are found near the prey-free equilibrium point (Q8), and the interior equilibrium point (Q10). Moreover, the persistence conditions have been derived to ensure the coexistence and stability of all species. Numerical simulation performed using matlab support the analytic finding and illustrate the complex dynamic behaviors near the equilibrium points. This study provides deeper insight into how Allee effect, toxin effects and additional food sources influence the bifurcation structure and persistence of the three-species ecological system.
Water potability classification using machine learning: a case study on handling incomplete data
Water is essential for the preservation of life on Earth, particularly for drinking purposes. Despite the abundance of water in the earth's ecosystem, the world is currently grappling with a significant global issue of contaminated water, a problem that extends beyond natural contamination and includes industrial wastewater. In this study, we aimed to investigate the potential of decision tree-based machine learning models, including decision trees, ensemble boosting, ensemble bagging, and random forests, in predicting water potability based on specific parameters. We used publicly available data from the "Water Quality and Potability" Kaggle dataset. Due to the high number of missing values for some parameters in the dataset, our research initially converted continuous, missing values into discrete or categorical values. We then filled these gaps with a general label, "unknown," instead of using mean or median values as other studies had done. Initially the result showed that the highest accuracy was random forest; our analysts showed that the sulfate parameters created confusion for the machine learning model due to the many missing values. So that, this research decided to exclude sulfate data from the dataset, and this research showed significant results where all decision tree-based machine learning models can achieve 100% accuracy, precision, recall, and f1-score on evaluation using the test dataset.
Data-driven approach for temperature and relative humidity forecasting in solar dryer dome facility
Accurate prediction of temperature and relative humidity is essential in Solar Dryer Dome (SDD) facilities to ensure strict quality of the agricultural products. Compared to traditional control models, ML-based approaches provide benefits in terms of adaptability, as they are capable of capturing complex and non-linear patterns in time series data. In this project, primary time series datasets collected from SDD facility in North Jakarta were used to forecast the next hour temperature and relative humidity values within the 5-minutes resolution. There were three sensors placed in three different locations: outside the facility near the front door, inside the facility near the front door, and near the fan inlet. The collected data showed daily seasonality patterns with temperature and relative humidity possess an inversely correlated relationship due to their inherent physical interaction. On the modeling part, this research compared three different predictive models such as XGBoost, Linear Regression, and Facebook’s Prophet based on the RMSE and R2 scores on the test set. XGBoost and Linear Regression provided on-par performance, with XGBoost showing its best at temperature prediction (RMSE: 1.47°C, R2: 0.96) and Linear Regression showing its best at relative humidity prediction (RMSE: 3.20%, R2: 0.96). Meanwhile, Prophet exhibits the lowest performance in both variables with RMSE: 3.56°C and R2: 0.72 for temperature, and RMSE: 8.42% and R2: 0.76 for relative humidity forecasting. Despite the low performance, the Prophet model was advantageous in terms of consistency across horizons, while the two best models show decreasing performance in higher horizon settings. These findings suggest the promising application of ML techniques in SDD facilities to support sustainability in agro-industrial business.
Vector basis S-cordial labeling of graphs with applications in biological models
Let \(G\) be a \((p,q)\) graph. Let \(V\) be an inner product space with basis \(S\). We denote the inner product of the vectors \(x\) and \(y\) by \(<x,y>\). Let \(\phi: V(G) \rightarrow S\) be a function. For edge \(uv\) assign the label \(<\phi(u),\phi(v)>\). Then \(\phi\) is called a vector basis \(S\)-cordial labeling of \(G\) if \(|\phi_{x}-\phi_{y}|\leq 1\) and \(|\gamma_i-\gamma_j |\leq 1\) where \(\phi_{x}\) denotes the number of vertices labeled with the vector \(x\) and \(\gamma_i\) denotes the number of edges labeled with the scalar \(i\). A graph which admits a vector basis \(S\)-cordial labeling is called a vector basis \(S\)-cordial graph. In this paper, we examine the vector basis {(1,1,1,1),(1,1,1,0),(1,1,0,0),(1,0,0,0)}-cordial labeling behaviour of Mongolian tent and parachute graph such a labeling finds applications in biological modelling.
Robust spatial Durbin model for dengue fever cases in Indonesia
Spatial regression analysis is a method employed for data exhibiting spatial dependencies. The Spatial Durbin Model (SDM) is one such method that accounts for spatial effects in both the dependent and independent variables. However, the presence of spatial outliers can compromise the accuracy of SDM predictions. To address this issue, a robust method is needed, namely the Robust Spatial Durbin Model (RSDM). This study applies the RSDM to model the factors influencing the spread of dengue haemorrhagic fever cases across Indonesia and to identify the superior modeling approach. The results indicate that the RSDM outperforms the standard SDM, evidenced by a higher Adjusted R² and a lower Mean Squared Error (MSE). Key factors identified as significantly influencing dengue haemorrhagic fever cases are population density, the number of doctors in healthcare facilities, and the percentage of the population covered by health insurance.
A comparative analysis of kernel filtering techniques for convolutional neural networks in fingerprint recognition
Fingerprint recognition remains one of the most widely adopted biometric methods for individual verification due to its uniqueness and reliability. This study investigates the role of kernel-based filtering in the feature extraction stage and evaluates the performance of four Convolutional Neural Network (CNN) architectures: Siamese Network, LeNet, ResNet, and VGG-19. Experiments were conducted on the SOCOFing dataset, with preprocessing steps involving image resizing, normalization, and augmentation. To enhance the discriminative quality of fingerprint features, Sobel, Gabor, Laplacian, and Gaussian kernels were applied prior to model training. Model performance was assessed using accuracy, loss, and the area under the ROC curve (AUC), with AUC emphasized as the primary metric to address class imbalance. The results demonstrate that Gaussian and Sobel kernels consistently yield superior and stable performance across all models. Furthermore, statistical validation through Friedman and Nemenyi tests confirmed significant differences among kernel–model combinations, underscoring the benefit of multi-kernel preprocessing in biometric classification. Overall, the findings highlight the critical role of kernel selection in optimizing fingerprint recognition systems, with Gaussian filtering showing particular promise for enhancing adaptability, robustness, and classification accuracy.
A fractional-order spatial-temporal model for the adoption dynamics of remote healthcare services
This paper presents a fractional-order (FO) spatial-temporal model for the adoption dynamics of Remote Healthcare Services (RHS) in a two-dimensional domain. The model incorporates memory effects and non-local temporal interactions through the Caputo nabla fractional derivative (CNFD), providing a realistic framework for social contagion processes where past states influence current behavior. The spatial domain is discretized using the Method of Lines (MOL) with periodic boundary conditions, transforming the original partial differential equations (PDEs) into a system of fractional ordinary differential equations (ODEs). We derive the equilibrium points (EPs) of the system and establish sufficient conditions for global asymptotic stability (GAS) via a Lyapunov functional (LF) approach. Numerical simulations validate the theoretical stability results, demonstrating convergence to the trivial equilibrium under the chosen parameter set. The proposed model offers a robust framework for analyzing spatial-temporal adoption dynamics with potential applications in public health, technology diffusion, and social behavior modeling.
Trends of malnutrition and contributing factors among children under five years in Cameroon: lessons from four rounds of DHS data
Introduction: Malnutrition among children under five remains a pressing public health issue in Cameroon, contributing to high rates of morbidity and mortality. Understanding the factors influencing malnutrition over time is essential for designing effective, targeted interventions. Methods: This study uses data from four rounds of the Cameroon Demographic and Health Survey (CDHS) conducted in 2004, 2011, 2018, and 2022. Anthropometric indicators were used to assess nutritional outcomes, specifically stunting, wasting, and underweight. Bivariate analyses (Chi-square tests) and multivariate logistic regression models were employed to identify significant socioeconomic, demographic, and environmental predictors of malnutrition. Results: The prevalence of stunting declined from 35% in 2004 to 23% in 2022, reflecting gradual improvements in child nutrition. Despite this positive trend, malnutrition remains a significant concern. Key determinants of poor nutritional outcomes include low household wealth, limited maternal education, short birth intervals, lack of antenatal care, and rural residence. Conclusion: While there has been notable progress in reducing child malnutrition, it continues to pose a serious public health challenge in Cameroon. The study highlights the need for integrated, multisectoral strategies that address both direct child-related factors and broader household and maternal conditions. Policies aiming to improve maternal education, access to healthcare, and living standards are critical for further reducing malnutrition rates and enhancing child well-being.
Evaluation of the performance of LASSO, group LASSO, and sparse group LASSO in identifying factors that have a significant influence on dengue hemorrhagic fever in Indonesia
Dengue Hemorrhagic Fever (DHF) remains a major public health challenge in Indonesia, driven by complex interactions among climatic, demographic, socio-economic, health capacity, and environmental factors. Identifying significant determinants in high-dimensional data with grouped explanatory variables requires appropriate regularization techniques. This study evaluates and compares the performance of LASSO, Group LASSO, and Sparse Group LASSO (SGL) in identifying factors influencing DHF incidence across Indonesian provinces. Three penalized regression models were applied to data comprising 29 predictors organized into five thematic groups. Model performance was assessed using the coefficient of determination (R²) for data testing, with additional analysis of variable selection patterns to evaluate parsimony and epidemiological interpretability. LASSO was employed to identify dominant individual predictors, Group LASSO to assess the contribution of thematic variable groups, and SGL to simultaneously perform selection at both group and individual levels. The results indicate that SGL provides the best predictive performance, achieving an R² of 94.94%, followed by LASSO (88.91%) and Group LASSO (73.94%). LASSO produced the most parsimonious model, selecting only four socio-economic and demographic variables, but overlooked meaningful group structures. Group LASSO retained three complete groups (Demographic, Socio-Economic, and Sanitation/Residential Environment) but exhibited reduced accuracy due to the inclusion of weakly contributing variables. In contrast, SGL selected 23 relevant variables while preserving group structures, revealing that DHF incidence is primarily associated with socio-economic factors (unemployment, labor force size, poverty), demographic pressure, sanitation and housing conditions, and rainfall. In conclusion, Sparse Group LASSO emerges as the most effective method for identifying significant DHF determinants, offering an optimal balance between predictive accuracy, model parsimony, and epidemiological interpretability. The findings advocate for the adoption of integrated, data-driven regularization approaches in developing targeted DHF prevention and control strategies in Indonesia.
Analyzing comorbidity index for cancer patients during management using semi Markov chain process
Globally, cancer is a high health burden, as it’s the fifth leading cause of death, in the year 2022, a study by the WHO showed there were an estimated 20 million new cases, and the number of deaths totaled 9.7 million. In a group of 5 people, 1 person develops cancer in their lifetime, approximately 1:12 women and 1:9 men die from cancer. The management of cancer patients is complicated by the presence of comorbidities, which can significantly affect transition between time of diagnosis and treatment initiation. The data used in the study was sourced from Cancer regional registry. It entailed the following details of the patients: age, gender, cancer staging, pre-existing and post-existing health condition(s), and cancer subtype. Patients were grouped according to the stage in which they were diagnosed. Stage I and II are the early stages, while Stage III and IV are the late stages. The most prevalent cancer type in males is prostate cancer, while in females it is breast cancer, with hypertension as the commonest comorbid condition. Most of the cancer diagnoses were in late stage, that’s stage III and stage IV. This distribution showed that a significant proportion of patients were diagnosed at advanced stages (III and IV), which could affect treatment options and prognosis. Cardiovascular conditions were associated with cancer progression, treatment method, and overall survival. Patients diagnosed at later stages (III-IV) received systemic therapies as their treatment type. There is need to continue the study in the future by prospective researchers in terms of size and variability of the participants investigated at several centers in order to enhance inclusivity. This would be efficient by the use of longitudinal designs so that it monitors disease progression, its response and also model survival rates too.
Mathematical modeling of filariasis and HIV/AIDS co-infection in the context of vector dynamics
This study develops a unique fractional-order model comprising nine compartments to explore the dynamical behavior of the progression of filariasis and the immunosuppressive retrovirus (HIV) comorbidity under explicit vector influence. The study utilizes fractional order stochastic to examine the dynamics of randomness in the epidemiological model. Using fractional stability theory, we determined the reproduction number, the pathogen-absent state equilibrium, and the requirements for dual-level stability analytically. A center manifold approach is used to investigate bifurcation behavior at the critical threshold, demonstrating the forward bifurcation which the disease transmission can be early controlled by reducing R0 less than zero. A fractional optimal control framework is developed; utilizing Pontryagin’s Maximum Principle in the context of fractional order to provide the best strategy to manage the spread of the co-infection. We then did the numerical simulations to verify the theoretical findings and demonstrated how fractional stochastic models provide better understanding of the fluctuations in epdermiological models, and how fractional order can affect disease persistence and the effectiveness of combination therapies. According to our findings, in areas where HIV/AIDS and filariasis are endemic, memory effects play a crucial role in co-infection dynamics and provide guidance for developing integrated, economically sound public health programs. We also found out that, the strategic implementation of intense initial interventions is crucial in managing filariasis and HIV/AIDS co-infection. Thus, by prioritizing early intervention measures, the overall burden of the diseases would be significantly minimized. Therefore, the insights from our study will inform targeted interventions, optimize resource allocation, and improve the overall efficacy of public health strategies in regions burdened by multiple endemic diseases.
Skeletal keypoint-based pipeline as a computer vision-based approaches
Classroom engagement analysis plays an important role in understanding students’ learning behaviors in a non-intrusive manner. However, many existing computer vision-based approaches rely on complex deep learning architectures and time-consuming dataset construction, which limit their applicability in practical classroom settings. This study proposes a skeletal keypoint-based pipeline for classroom engagement analysis that combines person detection and single-person pose estimation. The extracted keypoints are represented in a low-dimensional form and used as input features for lightweight engagement classification models. Two classifiers, namely Ridge Classifier and Gradient Boosting, are evaluated to assess the effectiveness of the proposed pipeline. Experimental results on a test set of 432 samples show that the Ridge Classifier achieves an accuracy of 0.90 with a macro-average recall of 0.94. In contrast, Gradient Boosting achieves an accuracy of 0.96 and a macro-average precision of 0.98. In addition, the proposed pipeline significantly improves data collection efficiency, achieving approximately a sixteen-fold reduction in pose data acquisition time compared to conventional approaches. These results demonstrate that the proposed pipeline provides a practical solution for individual-level classroom engagement analysis under controlled classroom.
A study of energies and thermodynamic aspects of polycyclic aromatic compounds in quantitative structure-property relationship
Polycyclic refers to a molecule that contains more than one cyclic component, usually two or more fused rings. Naphthalene, anthracene, pyrene, and benzo(a)pyrene are examples of polycyclic organic compounds. A number of polycyclic hydrocarbons are hazardous to human health, including the carcinogen benzo(a)pyrene found in automobile exhaust and tobacco smoke. The natural world has a wide range of polycyclic aromatic compounds, including pigments from plants and animals, that are notably beautiful and have unusually high chemical stability. In this study, polycyclic aromatic compounds are modeled as molecular graphs using their molecular structures, and various energies are computed for them. There is also statistical modeling for the physicochemical properties of energies. For these substances, the QSPR analysis is performed, and conclusions are based on the results.
Mathematical analysis of malware propagation in computer networks using an SPIQR epidemic model
In modern times, malware poses a significant threat to information security. Consequently, numerous mathematical models have been developed to describe the dissemination of malware. This study presents the SPIQR (susceptible-protected-infectious-quarantined-recovered) model to analyze how malware spreads through computer networks. We initiate our analysis by formulating a nonlinear dynamic equation that delineates the dissemination of malware. Subsequently, we compute the basic reproduction number (BRN), denoted by R0 for the proposed model. The network's stability is contingent upon the magnitude of R0. Particularly, if R0 < 1, the system remains free from malware, while if R0 >1, the network enters an endemic state. We determine the system's equilibrium points and perform numerical simulations to investigate malware spread within the network, aiming to confirm our theoretical results. In addition, we examine the nodes' communication range to provide more comprehensive data. We also examine the networks' susceptible, protected, infected, quarantined, and recovered node dynamics.
The influence of non-linear harvesting on a web of two prey with one predator, combined with fear and refuge strategies concerning prey populations
Under the influence of various functional responses: Beddington–DeAngelis and Holling type IV via predator interaction with two different kinds of prey populations in a web containing all these creatures, tends for discussing two distinct types of investigations, analytical and numerical, the proposed model joining two defense strategies concerning the prey populations, fear effects and refuge respectively, with presentence of the force of nonlinear harvesting act as a pressure on both prey populations, the system’s positivity and boundedness is the analytical already start with, seven acceptable equilibrium points find the existence, then the Jacobian and Lyapunov methods concerning these equilibria utilize to understand local and global stability conditions. Confirming analytical investigations via numerical analysis simulations, where the analytical results assert that fear and refuge stabilize population dynamics and reinforce coexistence. Still, while acting, the nonlinear harvesting can play a crucial role in determining the system's coexistence, extinction, or biodiversity loss. Sustaining predator–prey ecosystems confirms the Importance of Indirect strategies, as fear and refuge, and harvesting.