Regression Algorithm Research Articles

A flexible and efficient algorithm for high dimensional support vector regression

In high dimensional statistical learning, variable selection and handling highly correlated phenomena are two crucial topics. Elastic-net regularization can automatically perform variable selection and tends to either simultaneously select or remove highly correlated variables. Consequently, it has been widely applied in machine learning. In this paper, we incorporate elastic-net regularization into the support vector regression model, introducing the Elastic-net Support Vector Regression (En-SVR) model. Due to the inclusion of elastic-net regularization, the En-SVR model possesses the capability of variable selection, addressing high dimensional and highly correlated statistical learning problems. However, the optimization problem for the En-SVR model is rather complex, and common methods for solving the En-SVR model are challenging. Nevertheless, we observe that the optimization problem for the En-SVR model can be reformulated as a convex optimization problem where the objective function is separable into multiple blocks and connected by an inequality constraint. Therefore, we employ a novel and efficient Alternating Direction Method of Multipliers (ADMM) algorithm to solve the En-SVR model, and provide a complexity analysis as well as convergence analysis for the algorithm. Furthermore, extensive numerical experiments validate the outstanding performance of the En-SVR model in high dimensional statistical learning and the efficiency of this novel ADMM algorithm.

Optimal iterative learning PI controller for SISO and MIMO processes with machine learning validation for performance prediction

The multivariable process plays a significant role in industrial applications, and designing a controller for the Multi-Input Multi-Output process is challenging due to dynamic process changes and interactions between system variables. Traditionally, the PI family of controllers has been used for its simple design, easy tuning, and quick deployment. However, these processes require complex control actions due to multiple loops in process plants. Thus, this paper proposes an Iterative Learning Controller Dead-time compensating PI, which utilizes the newly developed hybrid Simulated Annealing-Ant Lion Optimization algorithm for Single-Input Single-Output process simulation and real-time experimentation on the Quadruple Tank System. To validate the effectiveness of the developed controller, Machine Learning techniques such as regression and ensemble trees are used to accurately predict the actual system response using error values from respective processes. The simulation and experimental results demonstrate that the proposed controller achieved better performance. The regression and ensemble tree algorithm models effectively predicted the actual response. The obtained data shows that the proposed controller improved system stability and robustness by minimizing nearly half of the overshoot and improving settling time, with an average of 29.9596% faster than the other controller in the SISO process and 14.6116% in the MIMO process.

Leveraging machine learning to enhance appointment adherence at a novel post-discharge care transition clinic.

This study applies predictive analytics to identify patients at risk of missing appointments at a novel post-discharge clinic (PDC) in a large academic health system. Recognizing the critical role of appointment adherence in the success of new clinical ventures, this research aims to inform future targeted interventions to increase appointment adherence. We analyzed electronic health records (EHRs) capturing a wide array of demographic, socio-economic, and clinical variables from 2168 patients with scheduled appointments at the PDC from September 2022 to August 2023. Logistic regression, decision trees, and eXtreme Gradient Boosting (XGBoost) algorithms were employed to construct predictive models for appointment adherence. The XGBoost machine learning model outperformed logistic regression and decision trees with an area under the curve (AUC) of 72% vs 65% and 67%, respectively, in predicting missed appointments, despite limited availability of historical data. Key predictors included patient age, number of days between appointment scheduling and occurrence, insurance status, marital status, and mental health and cardiac disease conditions. Findings underscore the potential of machine learning predictive analytics to significantly enhance patient engagement and operational efficiency in emerging healthcare settings. Optimizing predictive models can help balance the early identification of patients at risk of non-adherence with the efficient allocation of resources. The study highlights the potential value of employing machine learning techniques to inform interventions aimed at improving appointment adherence in a post-discharge transition clinic environment.

PERBANDINGAN AKURASI ALGORITMA K-NEAREST NEIGHBOR DAN LOGISTIC REGRESSION UNTUK PREDIKSI CUSTOMER CHURN

In this highly developed era, many transaction activities have shifted from offline to online, which is commonly called E-commerce. This study provides an overview of customer churn prediction using two algorithms that are often used, namely K-Nearest Neighbor and Logistic Regression. In this study, a comparison was made of the two algorithms in determining significant accuracy in predicting customer churn or unsubscribing customers. This research was conducted with three tests to compare the performance of the two algorithms from the distribution of test data by 30 percent, 25 percent, and 20 percent. The algorithms have a good performance on the distribution of test data by 20 percent, and Logistic Regression algorithm has a better performance at 82.89% than K-Nearest Neighbor algorithm which is only 81.84%.

Noninvasive assessment of single kidney glomerular filtration rate using multiple diffusion weighted imaging models.

This study aimed to assess single kidney glomerular filtration rate (GFR) using various diffusion weighted imaging (DWI) models. We reviewed adult patients with kidney diseases who underwent magnetic resonance imaging (MRI) examination from February 2021 to December 2023. DWI with 13 b-values was performed using 3.0-T scanners. Diffusion parameters were calculated with multi-slice ROIs positioned in renal parenchyma using four DWI models, including monoexponential model (MEM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and intravoxel incoherent motion (IVIM). The split GFRs were measured by 99mTc-DTPA scintigraphy using Gates' method. Four different regression algorithms including the linear regression, regression tree, Gaussian regression and support vector machine (SVM) regression were employed to predict the GFR value based on different diffusion parameters. The leave-one-out cross validation was used to evaluate prediction ability of different models, and the performance of each model was quantified using the root mean square error (RMSE) and correlation coefficient. Fifteen (male/female, 10/5; age, 41.60±10.83 years) patients were included in this study. Among the four DWI models, the IVIM parameters with SVM regression model achieved the best performance with 0.184 RMSE and 0.789 correlation coefficient ( ). The parameters combining the four DWI models with SVM regression algorithm achieved the best performance in this study, with 0.171 RMSE and 0.815 correlation coefficient ( ). The DWI characteristics are able to serve as imaging biomarkers for assessing the function of single kidney. The integration of DWI into clinical practice could contribute to the advancement of non-invasive diagnostic methodologies.

Improving Alzheimer's Disease Prediction with Different Machine Learning Approaches and Feature Selection Techniques.

Machine learning (ML) has increasingly been utilized in healthcare to facilitate disease diagnosis and prediction. This study focuses on predicting Alzheimer's disease (AD) through the development and comparison of ML models using Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) algorithms. Additionally, feature selection techniques including Minimum Redundancy Maximum Relevance (mRMR) and Mutual Information (MI) were employed to enhance the model performance. The research methodology involved training and testing these models on the OASIS-2 dataset, evaluating their predictive accuracies. Notably, LR combined with mRMR achieved the highest accuracy of 99.08% in predicting AD. These findings underscore the efficacy of ML algorithms in AD prediction and highlight the utility of the feature selection methods in improving the model performance. This study contributes to the ongoing efforts to leverage ML for more accurate disease prognosis and underscores the potential of these techniques in advancing clinical decision-making.

Pass-by noise regulation - Uncertainties reduction thanks to tire sound emission modelling

The UN regulation R51-03 on vehicle sound emissions enters its third phase in July 2024, with the challenging level of 68 dB(A) to achieve for most of passenger cars and light commercial vehicles. The Lurban indicator, as calculated in the regulation, is a weighted average of acceleration and constant rolling speed tests. Thus, the tire sound emissions represent by far the first contributor to the vehicle compliance. Therefore, it is crucial to understand how tire sound changes under the effects of speed, acceleration, or temperature. In this paper, a study on tire sound behavior is presented. It is based on repeated measurements of pass-by sound emissions of a set of tires, under various conditions of speed, acceleration, and ambient temperature. Among a huge variety of tire sound emission models, a regression algorithm is used to identify the model that fits the best to the measured tire sound emissions data. This tire sound emission model is then used to foresee the impacts of Regulation R51-03 updates, aiming at reducing the uncertainties induced by temperature and test track discrepancies, by correcting the tire sound contribution. The effects of this correction on the uncertainties can be anticipated by the model.

MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer

BackgroundThe study aimed to identify the optimal model for predicting rectal cancer liver metastasis (RCLM). This involved constructing various prediction models to aid clinicians in early diagnosis and precise decision-making.MethodsA retrospective analysis was conducted on 193 patients diagnosed with rectal adenocarcinoma were randomly divided into training set (n = 136) and validation set (n = 57) at a ratio of 7:3. The predictive performance of three models was internally validated by 10-fold cross-validation in the training set. Delineation of the tumor region of interest (ROI) was performed, followed by the extraction of radiomics features from the ROI. The least absolute shrinkage and selection operator (LASSO) regression algorithm and multivariate Cox analysis were employed to reduce the dimensionality of radiomics features and identify significant features. Logistic regression was employed to construct three prediction models: clinical, radiomics, and combined models (radiomics + clinical). The predictive performance of each model was assessed and compared.ResultsKRAS mutation emerged as an independent predictor of liver metastasis, yielding an odds ratio (OR) of 8.296 (95%CI: 3.471–19.830; p < 0.001). 5 radiomics features will be used to construct radiomics model. The combined model was built by integrating radiomics model with clinical model. In both the training set (AUC:0.842, 95%CI: 0.778–0.907) and the validation set (AUC: 0.805; 95%CI: 0.692–0.918), the AUCs for the combined model surpassed those of the radiomics and clinical models.ConclusionsOur study reveals that KRAS mutation stands as an independent predictor of RCLM. The radiomics features based on MR play a crucial role in the evaluation of RCLM. The combined model exhibits superior performance in the prediction of liver metastasis.Clinical Trial NumberNot applicable.

CT-based Intra-thrombus and Peri-thrombus Radiomics for Prediction of Prognosis After Endovascular Thrombectomy: A Retrospective Study Across Two Centers.

Complications from endovascular thrombectomy (EVT) can negatively affect clinical outcomes, making the development of a more precise and objective prediction model essential. This research aimed to assess the effectiveness of radiomic features derived from pre-surgical CT scans in predicting the prognosis post- EVT in acute ischemic stroke patients. This investigation included 336 acute ischemic stroke patients from two medical centers, spanning from March 2018 to March 2024. The participants were split into a training cohort of 161 patients and a validation cohort of 175 patients. Patient outcomes were rated with the mRS: 0-2 for good, 3-6 for poor. A total of 428 radiomic features were derived from intra-thrombus and peri-thrombus regions in non-contrast CT and CT angiography images. Feature selection was conducted using a least absolute shrinkage and selection operator regression model. The efficacy of eight different supervised learning models was assessed using the area under the curve (AUC) of the receiver operating characteristic curve. Among all models tested in the validation cohort, the logistic regression algorithm for combined model achieved the highest AUC (0.87, with a 95% confidence interval of 0.81 to 0.92), outperforming other algorithms. The combined use of radiomic features from both the intra-thrombus and peri-thrombus regions significantly enhanced diagnostic accuracy over models using features from a single region (0.81 vs 0.70, 0.77), highlighting the benefit of integrating data from both regions for improved prediction. The findings suggest that a combined radiomics model based on CT imaging serves as a potent approach to assessing the prognosis following EVT. The logistic regression model, in particular, proved to be both effective and stable, offering critical insights for the management of stroke. AUC=area under the curve; EVT=endovascular thrombectomy; KNN=k-nearest neighbors; LASSO=least absolute shrinkage and selection operator; LightGBM=Light Gradient Boosting Machine; LR=logistic regression; MLP=multi-layer perceptron; RF=random forest; SVM=support vector machine; XGBoost=extreme gradient boosting.

Insights into nanoparticle toxicity against aquatic organisms using multivariate regression, read-across, and ML algorithms: Predictive models for Daphnia magna and Danio rerio

The production of nanoparticles (NPs) has recently become more prevalent owing to their numerous applications in the fast-growing nanotechnology industry. Although nanoparticles have growing applications, there is a significant concern over their environmental impact due to their inevitable release into the environment. With the increasing risk to aquatic organisms, D. magna and zebrafish (Danio rerio) have been preferred as important freshwater model organisms for risk assessment and ecotoxicological studies on metal oxide-based nanoparticles (MeOxNPs) in aquatic environments. It is unfeasible to assess the risks associated with every single NP through in vivo or in vitro experiments. As an alternative, in silico approaches are employed to evaluate the NP toxicity. To evaluate such performance, we have collected data from databases and literature reviews to develop models based on multivariate regression, read-across approach (RA), and machine learning (ML) algorithms following the principles of OECD (Organization for Economic Cooperation and Development) for QSAR modeling. This work has aimed to investigate which features are important drivers of nanotoxicity in D. magna and Danio rerio using simple periodic table-derived descriptors. Further, we have examined the effectiveness of read-across-derived similarity measures compared to traditional QSAR models. The results obtained from model 1 infers that nanoparticles' size, the number of metals, the core environment of the metal present in the metal oxide, and the oxidation number of the metal play a key role in the final expression of toxicity of nanoparticles to D. magna. On the other hand, the presence of higher molecular weight, the core of the metal, and the presence of oxygen influence the enzyme inhibition activity. The enzyme inhibition is correlated with the ability of zebrafish embryos to hatch, and therefore, the inhibition of ZHE1 seems to be the factor driving hatch delay. The study emphasized the importance of developing transferable, reproducible, and easily interpretable models for the early identification of nanoparticle features contributing to aquatic toxicity.

A Machine Learning Model for Predicting the Propagation Rate Coefficient in Free-Radical Polymerization.

The propagation rate coefficient (kp) is one of the most crucial kinetic parameters in free-radical polymerization (FRP) as it directly governs the rate of polymerization and the resulting molecular weight distribution. The kp in FRP can typically be obtained through experimental measurements or quantum chemical calculations, both of which can be time consuming and resource intensive. Herein, we developed a machine learning model based solely on the structural features of monomers involved in FRP, utilizing molecular embedding and a Lasso regression algorithm to predict kp more efficiently and accurately. The result shows that the model achieves a mean absolute percentage error (MAPE) of only 5.49% in the predictions for four new monomers, which indicates that the model exhibits strong generalization capabilities and provides reliable and robust predictions. In addition, this model can accurately predict the influence of the ester side chain length of (meth)acrylates on kp, aligning well with established scientific knowledge. This approach offers a straightforward and practical model for other researchers to rapidly obtain accurate kp values by employing monomer structural information. The model is sufficiently general to apply to a wide range of (meth)acrylate and butadiene FRP monomers, thereby supporting kinetic modeling of polymerization reactions.

APPLICATIONS OF NON-TRADITIONAL EARNED VALUE MANAGEMENT MODELS IN PROJECT ANALYTICS

Effective management of financial resources in projects is crucial for project success. Often, difficulties with financial resources, such as budget overruns, lead to unfavorable consequences that directly impact the successful completion of the project, the quality of the outcome, and stakeholder satisfaction. Therefore, identifying and developing tools for the effective management of financial resources, is of paramount importance. The purpose of this study is to apply non-traditional earned value management (EVM) models based on machine learning to predict project costs. To achieve the research objectives, previous literature on the topic was analyzed, a dataset of past projects was prepared, and a machine learning model was applied. The study found that non-traditional models, such as the regression algorithm AdaBoost, produced results close to the actual costs. The research indicates that the developed model could become an indispensable tool for project management and business decision-making, as it demonstrates the ability to adapt to various conditions and make accurate forecasts.

Machine learning-assisted SCAPS device simulation for photovoltaic parameters prediction of [formula omitted] perovskite solar cells

Perovskite solar cells (PSCs) have garnered significant research attention because of their remarkable surge in power conversion efficiency (PCE) surpassing 26% within a short timeframe. However, the widespread adoption of these highly efficient PSCs is hampered by the inherent toxicity associated with lead (Pb)-based perovskites, which currently dominate the field. Researchers have explored alternative cations like Ge2+ and Sn2+ Owing to their identical oxidation states to Pb2+, paving the way for lead-free PSC development. By examining the critical factors influencing CsSnI3 thin film solar cells’ performance as well as their interdependencies, this work seeks to improve the efficiency of these devices. To do so, machine learning algorithms (ML) are employed to figure out the critical factors influencing the completion of CsSnI3 solar cells. A new dataset of 7870 data points is generated through simulations with SCAPS-1D to effectively depict the perovskite solar cell. Five ML regression algorithms are thereafter utilized to predict the photovoltaic parameters of solar cells, such as the fill factor (FF), open circuit voltage (VOC), short circuit current (JSC), and PCE. The study shows that random forest (RF) and decision tree (DT) are the best performing algorithms, among which DT produces high accuracy and low error for predicting the PCE with a coefficient of determination R2 of 0.99, Pearson’s coefficient (Rvalue) of 0.99, Spearman’s correlation coefficient (ρ) of 0.99 and root mean square error of 0.08 on the testing set. Finally, this study offers valuable guidance for further experiment optimization by shedding light on the optimal device dimensions and essential components.

Reactive Load Pre-measurement for Large Industrial Users Based on Improved GBDT Regression Algorithm

Abstract With the development of intelligence and marketization of power system, reactive load prediction is the key technology applied to the intelligent technical means of power grid. For large industrial users, the management of reactive power is one of the key factors to maintain the voltage stability. Therefore, the accurate prediction of the reactive load of large industrial users is of great significance to optimize the operation of the power system and reduce energy waste. To this end, this paper realizes the prediction set date based on the improved gradient lifting tree (Gradient Boosting Decision Tree, GBDT) regression algorithm, selects the whole data as the historical data for training, and compares the data with the traditional prediction method. The experiments show that the improved GBDT regression algorithm is 28.9% more accurate than the time series algorithm MA, which predicts only from historical data.

Generalized field inversion strategies for data-driven turbulence closure modeling

Most data-driven turbulence closures are based on the general structure of nonlinear eddy viscosity models. Although this structure can be embedded into the machine learning algorithm and the Reynolds stress tensor itself can be fit as a function of scalar- and tensor-valued inputs, there exists an alternative two-step approach. First, the spatial distributions of the optimal closure coefficients are computed by solving an inverse problem. Subsequently, these are expressed as functions of solely scalar-valued invariants of the flow field by virtue of an arbitrary regression algorithm. In this paper, we present two general inversion strategies that overcome the limitation of being applicable only when all closure tensors are linearly independent. We propose to either cast the inversion into a constrained and regularized optimization problem or project the anisotropy tensor onto a set of previously orthogonalized closure tensors. Using the two-step approach together with either of these strategies then enables us to quantify the model-form error associated with the closure structure independent of a particular regression algorithm. Eventually, this allows for the selection of the a priori optimal set of closure tensors for a given, arbitrary complex test case.

A DTI-based radiomics model for predicting epidermal growth factor receptor (EGFR) amplification in adult IDH1-wild glioblastomas.

Molecular alteration events are common in glioblastomas, the isocitrate dehydrogenase (IDH)-wild of which have had poor survival results so far. The progress of radiomics-based model provides novel sights for its preoperatively noninvasive prediction. To develop a radiomics-based model for predicting epidermal growth factor receptor (EGFR) amplification status in IDH1-wild glioblastomas of adults by pretreatment diffusion tensor imaging (DTI). A total of 124 patients with diagnosed glioblastomas were retrospectively collected. Six conventional magnetic resonance imaging (MRI) features of all the tumors were evaluated visually. Patients were divided into the training (n = 87) and the test set (n = 37) with a ratio of 7:3. Radiomics features were extracted from two regions of the glioblastomas, which were the total tumor (ROI_1) and the solid portion of tumor (ROI_2). The radiomics features extracted from the DTI and T1-contrast-enhanced (T1C) images were selected using the least absolute shrinkage and selection operator (LASSO) regression algorithm. Logistic regression analysis was conducted to develop models for EGFR amplification prediction in the training set. The radiomics model based on ROI_1 demonstrated favorable discrimination in both the training (area under the curve [AUC] = 0.86) and the test set (AUC = 0.82) (P < 0.05). Combining the radiomics features and the conventional feature tumor location, no significant improvement of AUCs was achieved (AUC = 0.86 and 0.81). The radiomics model derived from pretreatment DTI may have potential in differentiating the EGFR mutation status in glioblastomas.

Integrating Bioinformatics and Experimental Validation to Identify Mitochondrial Permeability Transition-Driven Necrosis-Related lncRNAs that can Serve as Prognostic Biomarkers and Therapeutic Targets in Endometrial Carcinoma.

Endometrial carcinoma (EC) is a common malignant tumor in women with high mortality and relapse rates. Mitochondrial permeability transition (MPT)-driven necrosis is a novel form of programmed cell death. The MPT-driven necrosis related lncRNAs (MRLs) involved in EC development remain unclear. We aimed to predict the outcomes of patients with EC by constructing a novel prognostic model based on MRLs and explore potential molecular functions. A risk prognostic model was developed utilizing multi-Cox regression in conjunction with the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm, which was based on MRLs. The predictive efficacy of the model was evaluated through receiver operating characteristic (ROC) curve analysis, as well as nomogram and concordance index (C-index) assessments. Patients were categorized into high- and low-risk groups based on their median risk scores. Notably, the high-risk group exhibited significantly poorer overall survival (OS) outcomes. Gene ontology (GO) and Gene set enrichment analysis (GSEA) demonstrated that Hedgehog and cell cycle pathways were enriched in the high-risk group. Tumor Immune Dysfunction and Exclusion (TIDE) displayed that patients in the high-risk group showed a high likelihood of immune evasion and less effective immunotherapy. A significant disparity in immune function was also observed between two groups. Based on the nine-MRLs, drug sensitivity analysis identified several anticancer drugs with potential efficacy in prognosis. Meanwhile, the results demonstrated that OGFRP1 plays a carcinogenic role by affecting mitochondrial membrane permeability in EC. Therefore, the risk model constructed by nine MRLs could be used to predict the clinical outcomes and therapeutic responses in patients with EC effectively.

A unified consensus-based parallel algorithm for high-dimensional regression with combined regularizations

A unified consensus-based parallel algorithm for high-dimensional regression with combined regularizations

Machine Learning based Lung Cancer Diagnostic System using Optimized Feature Subset Selection

Lung is a vital organ that plays a major role in respiration. Without breathing, one may not survive in this world. Hence lung is an important organ that acts as filter to absorb oxygen and supply it to heart where pumping takes place through blood vessel in the circulatory system .The pumped blood takes oxygen and other nutrients to every other parts of the body. Hence one must take care of lung. There are various diseases associated with lungs. Lung Cancer is a deadly disease that spread across the countries all over the world. An early detection of lung cancer has been proved to improve the survival rate of human life. There are various resources are available to detect the lung cancer disease. They are low dose CT-scans, X-rays, blood-based screening, pathology slide reading, biopsy’s test, survey data(clinical dataset) etc. helps to predict the disease well in advance. Our proposed work uses two clinical datasets that has various features to detect how likely the persons get affected from the lung disease. Dataset1 includes features such as age, gender, smoking, yellow fingers, anxiety, peer-pressure, chronic disease, fatigue, allergy, wheezing, alcohol, coughing, shortness of breath, swallowing difficulty, and chest pain. Also, the work has experimented with another dataset2 that represents causes of lung cancer due to exposure of pesticide. Our proposed diagnostic system consider all these features in total and perform feature selection to extract optimal feature subsets using cuckoo search algorithm then perform classification using machine learning algorithms such as Linear Support Vector Machine, Logistic Regression and Random Forest algorithm. It is observed that with the cuckoo search algorithm, dataset 1 achieves an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100% by LR Classifier. The Linear SVC classifier achieves an accuracy of 90%, a precision of 88%, a recall of 86%, and an F1-score of 87%.The Random forest Classifier achieves an accuracy of 91%, precision of 86%, recall of 93%, and F1-score of 90%. For dataset 2, both the LR classifier and Linear SVC classifier outperform with an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100%. Whereas Random Forest provides accuracy of 97%, precision of 97%, recall of 96%, and F1-score of 97%.

Maximizing chitin and chitosan recovery yields from Fusarium verticillioides using a many-factors-at-a-time approach

The extraction of chitin and chitosan presents challenges due to the complexity of the process and the influence of many variables. This study aimed to optimize chitin and chitosan extraction from Fusarium verticillioides by analyzing many additives and processing variables and modeling their yields using multiple linear regression (MLR) and evolutionary algorithms. FT-IR analysis confirmed the presence of characteristic bands in the extracted samples, and SEM analysis further revealed the microfibrillar appearance of the chitin and the dense, non-porous structure of the chitosan. The Ant Lion Optimizer (ALO) was employed to select significant factors and optimize model parameters. A transformation was applied to capture nonlinear relationships, and the fine-tuned models showed improved predictive power, with p-values of 0.00203 for chitin and 0.00884 for chitosan. Multi-objective optimization (MOO) using the Adaptive Geometry Estimation-based Multi-Objective Evolutionary Algorithm (AGE-MOEA) further identified significant factors for optimal yields, achieving 3 g of Arginine, 100 ml of culture medium volume, 7 to 11 days of incubation time, 0.2 to 1.76 ml of Oligochitin, 1.4 g of FeSO4, 1.5 g of K2HPO4, and 1 g of NaCl. Therefore, the integration of ALO and AGE-MOEA algorithms effectively modeled and optimized chitin and chitosan yields by maximizing biopolymer recovery, enabling significant industrial exploitation.