Intelligent Prediction Model Research Articles

An Intelligent Model for Supercapacitors with a Graphene-Based Fractal Electrode to Investigate the Cyclic Voltammetry

The aim of this study was to investigate the performance measurement of supercapacitors using the electrochemical properties of cyclic voltammetry (CV). The use of CV is crucial in evaluating the electrochemical performance of supercapacitors and determining the surface area of the catalyst with regard to the fractal properties of the electrode. The study specifically focused on the CV behavior of a supercapacitor formed by a cobalt-doped ceria/reduced graphene oxide (Co-CeO2/rGO) fractal nanocomposite, and its assessment was conducted using a machine learning (ML) model with the enhanced XGBoost. The model was trained using an experimental open-source dataset. The results showed that the proposed XGBoost model had a superior ability to predict the CV behavior of the supercapacitor, with nearly perfect results for the MAE, RMSE, and R-squared metrics, which are effective at evaluating the performance of regression models. With the successful design of the proposed intelligent prediction model, the study is expected to provide valuable insights into forming novel nanocomposite forms with high accuracy and minimal need for experiments.

Enhancing the durability of concrete in severely cold regions: Mix proportion optimization based on machine learning

Compared with inland areas, the environment for concrete in severely cold regions is more harsh and includes ion erosion, changes in dry and wet conditions, low-temperature freeze–thaw cycles and other occurrences leading to surface damage to and the cracking of roads and bridges, which causes concrete structures to be unable to reach their service life due to insufficient durability. By optimizing the mix proportion (MP) of the materials used, the frost resistance and impermeability of concrete can be improved to enhance its durability. In this paper, we develop an intelligent prediction and optimization model of concrete durability (CD) based on a random forest (RF) model and NSGA-II, and a Pareto front of the optimal trade-off solutions can be obtained by using NSGA-II to perform the optimization. A final optimal solution, the one that is nearest to the ideal solution, is determined as the suggestion for decision-making. The research is verified by taking a key highway engineering project in the Plan for Revitalizing Northeast China as an example, and the results show that the following: (1) The key factors after screening are the water–binder ratio, cement content, coarse aggregate content, fine aggregate content, high-efficiency water-reducing agent and fly ash content. (2) In comparison with other machine learning algorithms, a filtered RF prediction model has high precision, the goodness of fit (R2) of the frost resistance and impermeability is higher than 0.95, and the root mean square error (RMSE) is less than 0.1. (3) After optimization, the chloride ion permeability coefficient of concrete is reduced by 47.9%, the relative dynamic elastic modulus (RDEM) is increased by 4.07%, and the cost is reduced by 2.4%. In summary, the proposed RF-NSGA-II intelligent hybrid optimization algorithm can improve the durability of concrete in severely cold regions while realizing the economic and environmental protection production of concrete to improve engineering safety performance and service life.

Intelligent Prediction of Photovoltaic Power Generation Considering Uncertainty Measurement

Background As a renewable energy, solar energy has the advantages of being non-polluting, clean, and renewable. With the variability of solar radiation, the complexity of meteorological factors, and other uncertain changes, how to measure its comprehensive uncertainty is very important. Objective The inherent regularity of the uncertainty behavior of photovoltaic power generation is revealed by the change process of photovoltaic power generation. Methods Using the empirical wavelet transform (EWT), the uncertainty measurement was studied from the perspective of social physics, and an optimized intelligent prediction model (PSOBOA-LSTM) was proposed based on the uncertainty. Results The intelligent prediction model (PSOBOA-LSTM) has a better prediction effect and higher accuracy than other models, and reveals the internal mechanism of the photovoltaic power generation system from the perspective of physics and sociology. Conclusion Using the PSOBOA-LSTM model can better facilitate the power dispatching department to reasonably arrange conventional power generation, coordinate operations and make maintenance arrangements based on the predicted photovoltaic power generation, and solve problems arising from the connection between grid dispatching and photovoltaic power generation forecasting.

A new grey intelligent prediction algorithm with multiobjective correction strategy

As a combination of the differential equation prediction model and intelligent optimization algorithm, the grey intelligent prediction algorithm has attracted increasing attention due to its outstanding performance in small-sample environments. However, most studies focus only on the improvement of algorithm performance, with little regard to the uniformity, ill-condition and overfitting of the algorithm. To promote the development of this field, we develop a new grey intelligent prediction algorithm with multiobjective correction strategy based on the new weakened accumulation grey optimization model and the multiobjective grey wolf optimizer. In this new prediction algorithm, the new weakened accumulation operation is utilized to enhance the predictive ability of the algorithm and mitigate the ill-condition of the system, the Bernoulli parameter and a discretization technique are used to activate the uniformity and unbiasedness of the algorithm, respectively, and the multiobjective grey wolf optimizer is employed to alleviate the overfitting of the system. Compared with the previous grey intelligent prediction models, the new prediction algorithm is more perfect and reasonable. Taking two energy datasets as research cases, the evaluation results of a system consisting of six evaluation metrics and the Diebold-Mariano test show that the proposed model outperforms the other six comparative models in terms of prediction performance and stability, which confirms the feasibility and validity of the algorithm.

Using Explainable Artificial Intelligence to Predict Potentially Preventable Hospitalizations: A Population-Based Cohort Study in Denmark.

The increasing aging population and limited health care resources have placed new demands on the healthcare sector. Reducing the number of hospitalizations has become a political priority in many countries, and special focus has been directed at potentially preventable hospitalizations. We aimed to develop an artificial intelligence (AI) prediction model for potentially preventable hospitalizations in the coming year, and to apply explainable AI to identify predictors of hospitalization and their interaction. We used the Danish CROSS-TRACKS cohort and included citizens in 2016-2017. We predicted potentially preventable hospitalizations within the following year using the citizens' sociodemographic characteristics, clinical characteristics, and health care utilization as predictors. Extreme gradient boosting was used to predict potentially preventable hospitalizations with Shapley additive explanations values serving to explain the impact of each predictor. We reported the area under the receiver operating characteristic curve, the area under the precision-recall curve, and 95% confidence intervals (CI) based on five-fold cross-validation. The best performing prediction model showed an area under the receiver operating characteristic curve of 0.789 (CI: 0.782-0.795) and an area under the precision-recall curve of 0.232 (CI: 0.219-0.246). The predictors with the highest impact on the prediction model were age, prescription drugs for obstructive airway diseases, antibiotics, and use of municipality services. We found an interaction between age and use of municipality services, suggesting that citizens aged 75+ years receiving municipality services had a lower risk of potentially preventable hospitalization. AI is suitable for predicting potentially preventable hospitalizations. The municipality-based health services seem to have a preventive effect on potentially preventable hospitalizations.

Mineral content estimation for salt lakes on the Tibetan plateau based on the genetic algorithm-based feature selection method using Sentinel-2 imagery: A case study of the Bieruoze Co and Guopu Co lakes

Salt lakes on the Tibetan Plateau (TP) are rich in lithium (Li), boron (B) and other mineral resources, and accurate assessment of the mineral content and spatial distribution of the brine in those salt lakes is important to guide the development and utilization of their mineral resources. There are few studies estimating the mineral content of salt lakes on the TP due to the lack of in situ investigation data. This study introduced an intelligent prediction model combining a feature selection algorithm with a machine learning algorithm using Sentinel-2 satellite data to estimate the Li, B, and TDS contents of Bieruoze Co and Guopu Co lakes on the TP. First, to enrich the spectral information, four mathematical transformations (reciprocal, logarithmic, reciprocal of logarithm, and first-order derivative) were applied to the original bands. Then, feature selection was performed using the genetic algorithm (GA) to select the optimal input variables for the model. Finally, prediction models were constructed by partial least squares regression (PLSR), multiple linear regression (MLR), and random forest (RF). The results showed that: 1) The spectral mathematical transformation provided rich spectral information for the mineral content estimation. 2) The performance of the estimation model constructed by the feature optimization method using GA was better than that of the estimation model constructed based on all spectral bands. Based on GA for feature optimization, the MAPE of GA-RF for estimating Li, B and TDS contents on the testing set was reduced by 77.52%, 28.54% and 36.79%, respectively. 3) Compared with the GA-MLR and GA-PLSR models, GA-RF estimated Li (R2=0.99, RMSE=1.15 mg L-1, MAPE=3.00%), B (R2=0.97, RMSE=10.65 mg L-1, MAPE=2.73%), and TDS (R2=0.93, RMSE=0.60 g L-1, MAPE=1.82%) all obtained the optimal performance. This study showed that the combination of the GA-based feature selection method and the RF model has excellent performance and applicability for monitoring the content of multiple minerals using Sentinel-2 imagery in salt lakes on the TP.

Whale optimization with random contraction and Rosenbrock method for COVID-19 disease prediction

Coronavirus Disease 2019 (COVID-19), instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has hugely impacted global public health. To identify and intervene in critically ill patients early, this paper proposes an efficient, intelligent prediction model based on the machine learning approach, which combines the improved whale optimization algorithm (RRWOA) with the k-nearest neighbor (KNN) classifier. In order to improve the problem that WOA is prone to fall into local optimum, an improved version named RRWOA is proposed based on the random contraction strategy (RCS) and the Rosenbrock method. To verify the capability of the proposed algorithm, RRWOA is tested against nine classical metaheuristics, nine advanced metaheuristics, and seven well-known WOA variants based on 30 IEEE CEC2014 competition functions, respectively. The experimental results in mean, standard deviation, the Friedman test, and the Wilcoxon signed-rank test are considered, proving that RRWOA won first place on 18, 24, and 25 test functions, respectively. In addition, a binary version of the algorithm, called BRRWOA, is developed for feature selection problems. An efficient prediction model based on BRRWOA and KNN classifier is proposed and compared with seven existing binary metaheuristics based on 15 datasets of UCI repositories. The experimental results show that the proposed algorithm obtains the smallest fitness value in eleven datasets and can solve combinatorial optimization problems, indicating that it still performs well in discrete cases. More importantly, the model was compared with five other algorithms on the COVID-19 dataset. The experiment outcomes demonstrate that the model offers a scientific framework to support clinical diagnostic decision-making. Therefore, RRWOA is an effectively improved optimizer with efficient value.

A hybrid intelligent prediction model of autoencoder neural network and multivariate adaptive regression spline for uniaxial compressive strength of rocks

A hybrid intelligent prediction model of autoencoder neural network and multivariate adaptive regression spline for uniaxial compressive strength of rocks

Intelligent prediction of fatigue life of natural rubber considering strain ratio effect

AbstractAn intelligent prediction model of rubber fatigue life considering strain ratios based on the support vector regression (SVR) and an improved sparrow search algorithm (ISSA) is proposed. In order to solve the problem that traditional methods may lead to unsatisfactory prediction results, we establish a prediction model based on SVR. To further improve the prediction accuracy, we utilize the ISSA algorithm to optimize various hyper‐parameters in the model. Fatigue tests of filled natural rubber on dumbbell‐type specimens are performed and applied to verify the effectiveness of the proposed model. The results show that the ISSA optimized SVR (ISSA‐SVR) model is superior in terms of prediction accuracy, convergence speed, and stability compared with SVR models using the grid search or some standard optimization algorithms. Furthermore, comparisons of some published related models demonstrate that the ISSA‐SVR has the most accurate predictive results, which gather within 1.5 times the dispersion lines.

Variable Selection and Oil-Gas-Bearing Evaluation Using Multicomponent Seismic Data and AMPSO-ML Approaches

Summary Several challenges exist in the application of machine learning (ML) algorithms to reservoir prediction, such as the low accuracy of the reservoir prediction model, long training time, and complicated parameter adjustment. A good optimization algorithm is one of the keys to successfully training an ML model. Therefore, this study focuses on improving the ability of ML models to obtain rich reservoir characteristic information from multicomponent seismic data through an intelligent optimization algorithm and uses this information to predict the oil and gas distribution accurately. Adaptive mutation particle swarm optimization (AMPSO) was used to train the ML models [artificial neural network (ANN) and least-squares support vector machine (LSSVM)] and obtain intelligent prediction models (AMPSO-ANN and AMPSO-LSSVM) for multicomponent seismic reservoir prediction. First, a hyperparameter analysis of the ML models was conducted to determine their structure, and the parameters of the two models were searched globally by AMPSO to obtain an intelligent prediction model for reservoir distribution prediction. The two prediction models were then applied to synthetic reservoir prediction data to confirm their reliability and validity. Finally, they were applied to real data for multicomponent seismic gas reservoir distribution prediction to verify their accuracy. The predictive abilities of the two intelligent models on synthetic and real data sets were compared with those of the unoptimized ANN and support vector machine (SVM) models to verify their validity. The results showed that AMPSO improved the prediction ability of the two models and the prediction accuracy of oil/gas-bearing distribution on the synthetic data and gas-bearing distribution on the real data.

Quality control of trimethylolpropane synthesis by intelligent retrieval of near-infrared spectra

<p indent="0mm">We developed an intelligent inversion method of near-infrared spectroscopy to establish a quantitative analysis model of the product and its by-products in the production process of trimethylolpropane (TMP), to realize the rapid detection of its product and its by-product content, to quantitatively monitor the process synthesis of TMP, and to build an online monitoring system of the TMP synthesis process. Firstly, gas chromatography was used to determine the content of the product and its by-products during the synthesis of TMP, and then the NIR spectra of a large number of samples were collected, and finally, the partial least squares regression (PLS) method of TQ Analyst software was invoked to establish four intelligent prediction models for the product TMP, the by-products bis-trimethylolpropane, 2-ethyl-2-hexenal and 2-ethylacrolein. The results showed that the corrected root mean square errors (RMSE(C) of the four quantitative analytical models developed for TMP, bis-trimethylolpropane, 2-ethyl-2-hexenal and 2-ethylacrylaldehyde were 0.744, 0.300, 1.50 and 1.28, respectively, and the correlation coefficients (<italic>R</italic>) were 0.9868, 0.9840, 0.9945 and 0.9113, respectively. The root mean square errors of prediction (RMSEP) were 1.29, 0.588, 1.44, and 1.18, respectively, and the <italic>R</italic> were 0.9797, 0.9209, 0.9960, and 0.9323. It was finally confirmed that the developed NIR intelligent inversion model of the products and their by-products in the synthesis process of TMP has good prediction accuracy and prediction stability, and can be used for online monitoring of the synthesis process of TMP.

Design and Conceptual Development of a Novel Hybrid Intelligent Decision Support System Applied towards the Prevention and Early Detection of Forest Fires

Forest fires have become a major problem that every year has devastating consequences at the environmental level, negatively impacting the social and economic spheres of the affected regions. Aiming to mitigate these terrible effects, intelligent prediction models focused on early fire detection are becoming common practice. Considering mainly a preventive approach, these models often use tools that indifferently apply statistical or symbolic inference techniques. However, exploring the potential for the hybrid use of both, as is already being done in other research areas, is a significant novelty with direct application to early fire detection. In this line, this work proposes the design, development, and proof of concept of a new intelligent hybrid system that aims to provide support to the decisions of the teams responsible for defining strategies for the prevention, detection, and extinction of forest fires. The system determines three risk levels: a general one called Objective Technical Fire Risk, based on machine learning algorithms, which determines the global danger of a fire in some area of the region under study, and two more specific others which indicate the risk over a limited area of the region. These last two risk levels, expressed in matrix form and called Technical Risk Matrix and Expert Risk Matrix, are calculated through a convolutional neural network and an expert system, respectively. After that, they are combined by means of another expert system to determine the Global Risk Matrix that quantifies the risk of fire in each of the study regions and generates a visual representation of these results through a color map of the region itself. The proof of concept of the system has been carried out on a set of historical data from fires that occurred in the Montesinho Natural Park (Portugal), demonstrating its potential utility as a tool for the prevention and early detection of forest fires. The intelligent hybrid system designed has demonstrated excellent predictive capabilities in such a complex environment as forest fires, which are conditioned by multiple factors. Future improvements associated with data integration and the formalization of knowledge bases will make it possible to obtain a standard tool that could be used and validated in real time in different forest areas.

Prediction of malignancy upgrade rate in high-risk breast lesions using an artificial intelligence model: a retrospective study

High-risk breast lesions (HRLs) are associated with future risk of breast cancer. Considering the pathological subtypes, malignancy upgrade rate differs according to each subtype and depends on various factors such as clinical and radiological features and biopsy method. Using artificial intelligence and machine learning models in breast imaging, evaluations can be made in terms of risk estimation in different research areas. This study aimed to develop a machine learning model to distinguish HRL cases requiring surgical excision from lesions with a low risk of accompanying malignancy. A total of 94 patients who were diagnosed with HRL by image-guided biopsy between January 2008 and March 2020 were included in the study. A structured database was created with clinical and radiological characteristics and histopathological results. A machine learning prediction model was created to make binary classifications of lesions as malignant or benign. Random forest, decision tree, K-nearest neighbors, logistic regression, support vector machine (SVM), and multilayer perceptron machine learning algorithms were used. Among these algorithms, SVM was the most successful. The estimations of malignancy for each case detected by artificial intelligence were combined and statistical analyses were performed. Considering all cases, the malignancy upgrade rate was 24.5%. A significant association was observed between malignancy upgrade rate and lesion size (P = 0.004), presence of mammography findings (P = 0.022), and breast imaging-reporting and data system category (P = 0.001). A statistically significant association was also found between the artificial intelligence prediction model and malignancy upgrade rate (P < 0.001). With the SVM model, an 84% accuracy and 0.786 area-underthe- curve score were obtained in classifying the data as benign or malignant. Our artificial intelligence model (SVM) can predict HRLs that can be followed up with a lower risk of accompanying malignancy. Unnecessary surgeries can be reduced, or second line vacuum excisions can be performed in HRLs, which are mostly benign, by evaluating on a case-by-case basis, in line with radiology-pathology compatibility and by using an artificial intelligence model.

Intelligent Prediction Model on Soil Bamboo Fibre Mix for Road Construction

Manufacturing with natural materials has recently become popular as a reaction to global temperature challenges and the need for a more sustainable society. Concrete is used as a basic material in modern building projects. Steel is utilized as reinforcement to create tensile strength since concrete has a brittle tensile strength. When bamboo loses water, it shrinks far more than any other species of timber. Before being used for architectural purposes, bamboo should be suitably treated against insect or fungus assault. To overcome the above-mentioned drawbacks this research tends to introduce an intelligent prediction model for road construction. The input parameters, such as “mix type, soil, percentage of fiber, and fiber length”, are initially supplied as input to the optimal Neural Network (NN) model. A combination of +1 percent fiber, +2 percent fiber, +3 percent fiber, and +4 percent fiber is considered in the soil. The NN with Levy Updated Shark Smell Optimization (LU-SSO) based optimization provides the predicted output on “Maximum dry Density (MDD), California Bearing Ratio (CBR), and Optimum moisture content (OMC)”. Finally, error metrics are computed to analyze the performance of NN with the LU-SSO scheme. A less error of 0.1 is achieved in the proposed model when compared to the other existing approaches.

An Artificial Neural Network-Based Intelligent Prediction Model for Financial Credit Default Behaviors

With the rapid development of intelligent techniques, smart finance has become a hot topic in daily life. Currently, financial credit is facing increasing business volume, and it is expected that investigating the intelligent algorithms can help reduce human labors. In this area, the prediction of latent credit default behaviors can help deal with loan approval affairs, and it is the most important research topic. Machine learning-based methods have received much attention in this area, and they can achieve proper performance in some scenarios. However, machine learning-based models cannot have resilient objective function, which can cause failure in having stable performance in different problem scenarios. This work introduces deep learning that has the objective function with high freedom degree, and proposes an artificial neural network-based intelligent prediction model for financial credit default behaviors. The whole technical framework is composed of two stages: information encoding and backbone network. The former makes encoding toward initial features, and the latter builds a multi-layer perceptron to output prediction results. Finally, the experiments are conducted on a real-world dataset to evaluate the efficiency of the proposed approach.

Ensemble Tree Model for Long-Term Rockburst Prediction in Incomplete Datasets

The occurrence of rockburst can seriously impact the construction and production of deep underground engineering. To prevent rockburst, machine learning (ML) models have been widely employed to predict rockburst based on some related variables. However, due to the costs and complicated geological conditions, complete datasets to evaluate rockburst cannot always be obtained in rock engineering. To fill this limitation, this study proposed an ensemble tree model suitable for incomplete datasets, i.e., the histogram gradient boosting tree (HGBT), to build intelligent models for rockburst prediction. Three hundred fourteen rockburst cases were employed to develop the HGBT model. The hunger game search (HGS) algorithm was implemented to optimize the HGBT model. The established HGBT model had an excellent testing performance (accuracy of 88.9%). An incomplete database with missing values was applied to compare the performances of HGBT and other ML models (random forest, artificial neural network, and so on). HGBT received an accuracy of 78.8% in the incomplete database, and its capacity was better than that of other ML models. Additionally, the importance of input variables in the HGBT model was analyzed. Finally, the feasibility of the HGBT model was validated by rockburst cases from Sanshandao Gold Mine, China.

Intelligent Model for Customer Churn Prediction using Deep Learning Optimization Algorithms

Business intelligence (BI) mentions to the technical and procedural structure which gathers, supplies, and examines the data formed by company action. BI is a wide term that includes descriptive analytics, procedure analysis, data mining, and performance benchmarking. Customer churn is a general problem across businesses from several sectors. Companies are working always for improving their supposed quality by way of providing timely and quality service to its customer. Customer churn is developed most initial challenges which several firms were facing currently. Many churn prediction techniques and methods were presented before in literature for predicting customer churn from the domains like telecom, finance, banking, and so on. Researchers are also working on customer churn prediction (CCP) from e-commerce utilizing data mining and machine learning (ML) approaches. This manuscript focuses on the development of Stacked Deep Learning with Wind Driven Optimization based Business Intelligence for Customer Churn Prediction model. The proposed model is considered an intelligent system that applies golden sine algorithm (GSA) based feature selection approach to derive a set of features. In addition, the stacked gated recurrent unit (SGRU) model is applied for the prediction of customer churns.

Seizure Prediction Analysis of Infantile Spasms.

Infantile spasms (IS) is a typical childhood epileptic disorder with generalized seizures. The sudden, frequent and complex characteristics of infantile spasms are the main causes of sudden death, severe comorbidities and other adverse consequences. Effective prediction is highly critical to infantile spasms subjects, but few related studies have been done in the past. To address this, this study proposes a seizure prediction framework for infantile spasms by combining the statistical analysis and deep learning model. The analysis is conducted on dividing the continuous scalp electroencephalograms (sEEG) into 5 phases: Interictal, Preictal, Seizure Prediction Horizon (SPH), Seizure, and Postictal. The brain network of Phase-Locking Value (PLV) of 5 typical brain rhythms is constructed, and the mechanism of epileptic changes is analyzed by statistical methods. It is found that 1) the connections between the prefrontal, occipital, and central regions show a large variability at each stage of seizure transition, and 2) 4 sub-bands of brain rhythms (θ, α, β, γ) are predominant. Group and individual variabilities are validated by using the Resnet18 deep model on data from 25 patients with infantile spasms, where the consistent results to statistical analyses can be observed. The optimized model achieves an average of 79.78 %, 94.46%, 75.46% accuracy, specificity, and recall rate, respectively. The method accomplishes the analysis of the synergy between infantile spasms mechanism, model, data and algorithm, providing a guideline to build an intelligent and systematic model for comprehensive IS seizure prediction.

A method for yarn quality fluctuation prediction based on multi-correlation parameter feature subspace mechanism in spinning process

Yarn quality is a key indicator in spinning production process, which directly determines the processing quality of the finished fabric. Considering the problems that the yarn quality is difficult to predict due to many influencing factors and widely monitoring dimensions existed. This paper proposes a multi-correlation parameters-based feature subspace model for yarn quality fluctuation prediction method. Firstly, Kernel Principal Component Analysis (KPCA) algorithm is used to analyze the relationship between the relevant parameters affecting yarn quality, and the characteristic subspace of yarn quality fluctuation is reconstructed. Then, a feature subspace-oriented deep learning prediction structure based on Deep Belief Network (DBN) is constructed, and the intelligent prediction model of yarn quality is established by using Particle Swarm Optimization (PSO) algorithm to optimize the modeling parameters. Finally, a case for spinning spun yarn process data set is used to validate the effectiveness of the proposed method, result shows that the proposed method can effectively analyze the multi-correlation parameters fluctuation law, which shows good effect for predicting yarn quality accurately.

Deep intelligent predictive model for the identification of diabetes

&lt;abstract&gt; &lt;p&gt;Diabetes mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Many complications arise if diabetes remains untreated and unidentified. Early prediction of diabetes is the most high-quality way to forestall and manipulate diabetes and its complications. With the rising incidence of diabetes, machine learning and deep learning algorithms have been increasingly used to predict diabetes and its complications due to their capacity to care for massive and complicated facts sets. This research aims to develop an intelligent computational model that can accurately predict the probability of diabetes in patients at an early stage. The proposed predictor employs hybrid pseudo-K-tuple nucleotide composition (PseKNC) for sequence formulation, an unsupervised principal component analysis (PCA) algorithm for discriminant feature selection, and a deep neural network (DNN) as a classifier. The experimental results show that the proposed technique can perform better on benchmark datasets. Furthermore, overall assessment performance compared to existing predictors indicated that our predictor outperformed the cutting-edge predictors using 10-fold cross validation. It is anticipated that the proposed model could be a beneficial tool for diabetes diagnosis and precision medicine.&lt;/p&gt; &lt;/abstract&gt;