Modeling a Novel Hyper-Parameter Tuned Deep Learning Enabled Malaria Parasite Detection and Classification

A theoretical methodology is suggested for finding the malaria parasites’presence with the help of an intelligent hyper-parameter tuned Deep Learning(DL)based malaria parasite detection and classification(HPTDL-MPDC)in the smear images of human peripheral blood.Some existing approaches fail to predict the malaria parasitic features and reduce the prediction accuracy.The trained model initiated in the proposed system for classifying peripheral blood smear images into the non-parasite or parasite classes using the available online dataset.The Adagrad optimizer is stacked with the suggested pre-trained Deep Neural Network(DNN)with the help of the contrastive divergence method to pre-train.The features are extracted from the images in the proposed system to train the DNN for initializing the visible variables.The smear images show the concatenated feature to be utilized as the feature vector in the proposed system.Lastly,hyper-parameters are used to fine-tune DNN to calculate the class labels’probability.The suggested system outperforms more modern methodologies with an accuracy of 91%,precision of 89%,recall of 93%and F1-score of 91%.The HPTDL-MPDC has the primary application in detecting the parasite of malaria in the smear images of human peripheral blood.

Hyper-parameter optimization of multiple machine learning algorithms for molecular property prediction using hyperopt library

Due to outstanding performance in cheminformatics,machine learning algorithms have been increasingly used to mine molecular properties and biomedical big data.The performance of machine learning models is known to critically depend on the selection of the hyper-parameter configuration.However,many studies either explored the optimal hyper-parameters per the grid searching method or employed arbitrarily selected hyper-parameters,which can easily lead to achieving a suboptimal hyper-parameter configuration.In this study,Hyperopt library embedding with the Bayesian optimization is employed to find optimal hyper-parameters for different machine learning algorithms.Six drug discovery datasets,including solubility,probe-likeness,h ERG,Chagas disease,tuberculosis,and malaria,are used to compare different machine learning algorithms with ECFP6 fingerprints.This contribution aims to evaluate whether the Bernoulli Na?ve Bayes,logistic linear regression,Ada Boost decision tree,random forest,support vector machine,and deep neural networks algorithms with optimized hyper-parameters can offer any improvement in testing as compared with the referenced models assessed by an array of metrics including AUC,F1-score,Cohen’s kappa,Matthews correlation coefficient,recall,precision,and accuracy.Based on the rank normalized score approach,the Hyperopt models achieve better or comparable performance on 33 out 36 models for different drug discovery datasets,showing significant improvement achieved by employing the Hyperopt library.The open-source code of all the 6 machine learning frameworks employed in the Hyperopt python package is provided to make this approach accessible to more scientists,who are not familiar with writing code.

An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ

In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random selection.To address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN model.In addition,different types and parameter ranges of existing genetic algorithms are used.Acomparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.

Optimized Decision Tree and Black Box Learners for Revealing Genetic Causes of Bladder Cancer

The number of studies in the literature that diagnose cancer with machine learning using genome data is quite limited.These studies focus on the prediction performance,and the extraction of genomic factors that cause disease is often overlooked.However,finding underlying genetic causes is very important in terms of early diagnosis,development of diagnostic kits,preventive medicine,etc.The motivation of our study was to diagnose bladder cancer(BCa)based on genetic data and to reveal underlying genetic factors by using machine-learning models.In addition,conducting hyper-parameter optimization to get the best performance from different models,which is overlooked in most studies,was another objective of the study.Within the framework of these motivations,C4.5,random forest(RF),artificial neural networks(ANN),and deep learning(DL)were used.In this way,the diagnostic performance of decision tree(DT)-based models and black box models on BCa was also compared.The most successful model,DL,yielded an area under the curve(AUC)of 0.985 and a mean square error(MSE)of 0.069.For each model,hyper-parameters were optimized by an evolutionary algorithm.On average,hyper-parameter optimization increased MSE,root mean square error(RMSE),LogLoss,and AUC by 30%,17.5%,13%,and 6.75%,respectively.The features causing BCa were extracted.For this purpose,entropy and Gini coefficients were used for DT-based methods,and the Gedeon variable importance was used for black box methods.The single nucleotide polymorphisms(SNPs)rs197412,rs2275928,rs12479919,rs798766 and rs2275928,whose BCa relations were proven in the literature,were found to be closely related to BCa.In addition,rs1994624 and rs2241766 susceptibility loci were proposed to be examined in future studies.

Detection of Alzheimer’s Disease Progression Using Integrated Deep Learning Approaches

Alzheimer’s disease(AD)is an intensifying disorder that causes brain cells to degenerate early and destruct.Mild cognitive impairment(MCI)is one of the early signs of AD that interferes with people’s regular functioning and daily activities.The proposed work includes a deep learning approach with a multimodal recurrent neural network(RNN)to predict whether MCI leads to Alzheimer’s or not.The gated recurrent unit(GRU)RNN classifier is trained using individual and correlated features.Feature vectors are concate-nated based on their correlation strength to improve prediction results.The feature vectors generated are given as the input to multiple different classifiers,whose decision function is used to predict the final output,which determines whether MCI progresses onto AD or not.Our findings demonstrated that,compared to individual modalities,which provided an average accuracy of 75%,our prediction model for MCI conversion to AD yielded an improve-ment in accuracy up to 96%when used with multiple concatenated modalities.Comparing the accuracy of different decision functions,such as Support Vec-tor Machine(SVM),Decision tree,Random Forest,and Ensemble techniques,it was found that that the Ensemble approach provided the highest accuracy(96%)and Decision tree provided the lowest accuracy(86%).

On convergence of covariance matrix of empirical Bayes hyper-parameter estimator

Regularized system identification has become the research frontier of system identification in the past decade.One related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to infinity.In this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been given.However,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is required.In general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance matrix.Thus,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter estimator.In this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting distribution.Moreover,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.

Machine learning models and over-fitting considerations

Machine learning models may outperform traditional statistical regression algorithms for predicting clinical outcomes.Proper validation of building such models and tuning their underlying algorithms is necessary to avoid over-fitting and poor generalizability,which smaller datasets can be more prone to.In an effort to educate readers interested in artificial intelligence and model-building based on machine-learning algorithms,we outline important details on crossvalidation techniques that can enhance the performance and generalizability of such models.

LP-SVR Model Selection Using an Inexact Globalized Quasi-Newton Strategy

In this paper we study the problem of model selection for a linear programming-based support vector machine for regression. We propose generalized method that is based on a quasi-Newton method that uses a globalization strategy and an inexact computation of first order information. We explore the case of two-class, multi-class, and regression problems. Simulation results among standard datasets suggest that the algorithm achieves insignificant variability when measuring residual statistical properties.

A novel stacking-based ensemble learning model for drilling efficiency prediction in earth-rock excavation

Accurate prediction of drilling efficiency is critical for developing the earth-rock excavation schedule.The single machine learning(ML)prediction models usually suffer from problems including parameter sensitivity and overfitting.In addition,the influence of environmental and operational factors is often ignored.In response,a novel stacking-based ensemble learning method taking into account the combined effects of those factors is proposed.Through multiple comparison tests,four models,e Xtreme gradient boosting(XGBoost),random forest(RF),back propagation neural network(BPNN)as the base learners,and support vector regression(SVR)as the meta-learner,are selected for stacking.Furthermore,an improved cuckoo search optimization(ICSO)algorithm is developed for hyper-parameter optimization of the ensemble model.The application to a real-world project demonstrates that the proposed method outperforms the popular single ML method XGBoost and the ensemble model optimized by particle swarm optimization(PSO),with 16.43%and 4.88%improvements of mean absolute percentage error(MAPE),respectively.