Advancing Early Detection of Colorectal Adenomatous Polyps via Genetic Data Analysis: A Hybrid Machine Learning Approach

In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial early detector of colorectal cancer (CRC). The present study develops a classification ensemble model based on tuned hyperparameters. Surpassing accuracy percentages of early detection approaches used in previous studies, the current method exhibits exceptional performance in identifying ACRP and diagnosing CRC, overcoming limitations of CRC traditional methods that are based on error-prone manual examination. Particularly, the method demonstrates the following CRP identification accuracy data: 97.7 ± 1.1, precision: 94.3 ± 5, recall: 96.0 ± 3, F1-score: 95.7 ± 4, specificity: 97.3 ± 1.2, average AUC: 0.97.3 ± 0.02, and average p-value: 0.0425 ± 0.07. The findings underscore the potential of this method for early detection of ACRP as well as clinical use in the development of CRC treatment planning strategies. The advantages of this approach are highly expected to contribute to the prevention and reduction of CRC mortality.

Predicting the daily return direction of the stock market using hybrid machine learning algorithms

Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.

Compressive strength prediction of sprayed concrete lining in tunnel engineering using hybrid machine learning techniques

Sprayed concrete lining is a commonly employed support measure in tunnel engineering,which plays an important role in construction safety.Compressive strength is a key performance indicator of sprayed concrete lining,and the traditional measuring method is time-consuming and laborious.This paper proposes various hybrid machine learning algorithms to accomplish the advanced prediction of compressive strength of sprayed concrete lining based on the mixture design.Two hundred and five sets of experimental data were collected from a water conveyance tunnel in northwestern China for model construction,and each set of data was made up of six basic input variables(i.e.,water,cement,mineral powder,superplasticizer,coarse aggregate,and fine aggregate)and one output variable(i.e.,compressive strength).In order to eliminate the correlation between input variables,a new composite indicator(i.e.,the water-binder ratio)was introduced to achieve dimensionality reduction.After that,four hybrid models in total were built,namely BPNN-QPSO,SVR-QPSO,ELM-QPSO,and RF-QPSO,where the hyper-parameters of BPNN,SVR,ELM,and RF were auto-tuned by QPSO.Engineering application results indicated that RF-QPSO achieved the lowest mean absolute percentage error(MAPE)of 3.47% and root mean square error(RMSE)of 1.30 and the highest determination coefficient(R^(2))of 0.93 in the four hybrid models.Moreover,RFQPSO had the shortest running time of 0.15 s,followed by SVR-QPSO(0.18 s),ELM-QPSO(1.19 s),and BPNN-QPSO(1.58 s).Compared with BPNN-QPSO,SVR-QPSO,and ELM-QPSO,RF-QPSO performed the most superior performance in terms of both prediction accuracy and running speed.Finally,the importance of input variables on the model performance was quantitatively evaluated,further enhancing the interpretability of RF-QPSO.

Multi-objective optimisation with hybrid machine learning strategy for complex catalytic processes

Catalytic chemical processes such as hydrocracking,gasification and pyrolysis play a vital role in the renewable energy and net zero transition.Due to the complex and non-linear behaviours during operation,catalytic chemical processes require a powerful modelling tool for prediction and optimisation for smart operation,speedy green process routes discovery and rapid process design.However,challenges remain due to the lack of an effective modelling and optimisation toolbox,which requires not only a precise analysis but also a fast optimisation.Here,we propose a hybrid machine learning strategy by embedding the physics-based continuum lumping kinetic model into the data-driven artificial neural network framework.This hybrid model is adopted as the surrogate model in the multi-objective optimisation and demonstrated in the benchmarking of a hydrocracking process.The results show that the novel hybrid surrogate model exhibits the mean square error less than 0.01 by comparing with the physics-based simulation results.This well-trained hybrid model was then integrated with non-dominated-sort genetic algorithm(NSGA-II)as the surrogate model to evaluate and optimise the yield and selectivity of the hydrocracking process.The Pareto front from the multi-objective optimisation was able to identify the trade-off curve between the objective functions which is essential for the decision-making during process design.Our work indicates that adopting the hybrid machine learning strategy as the surrogate model in the multi-objective optimisation is a promising approach in various complex catalytic chemical processes to enable an accurate computation as well as a rapid optimisation.

Estimating SVCV waterborne transmission and predicting experimental epidemic development:A modeling study using a machine learning approach

Viral infectious diseases significantly threaten the sustainability of freshwater fish aquaculture.The lack of studies on epidemic transmission patterns and mechanisms inhibits the development of containment strategies from the viewpoint of veterinary public health.This study raises an epidemic mathematical model considering water transmission with the aim of analyzing the transmission process more accurately.The basic reproduction number R0 was derived by the model parameter including the water transmission coefficient and was used for the analysis of the virus transmission.Spring viremia of carp virus(SVCV)and zebrafish were used as model viruses and animals,respectively,to conduct the transmission experiment.Transmission through water was achieved by connecting two aquarium tanks with a water channel but blocking the fish movement between the tanks.With the collected experimental data,we determined the optimal hybrid machine learning algorithm to analyze the transmission process using an established mathematical model.In addition,future transmission was predicted and validated using the epidemic model and an optimal algorithm.Finally,the sensitivity of model parameters and the simulations of R0 variation were performed based on the modified complex epidemic model.This study is of significance in providing theoretical guidance for minimizing R0 by manipulating model parameters with containment measures.More importantly,since the modified model and algorithm demonstrated better performance in handling freshwater fish transmission problems,this study advances the future application of transmissible disease modeling with larger datasets in freshwater fish aquaculture.

Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.