16 research outputs found
An ensemble-based decision tree approach for educational data mining
Nowadays, data mining and machine learning techniques are applied to a variety of different topics (e. g., healthcare and disease, security, decision support, sentiment analysis, education, etc.). Educational data mining investigates the performance of students and gives solutions to enhance the quality of education. The aim of this study is to use different data mining and machine learning algorithms on actual data sets related to students. To this end, we apply two decision tree methods. The methods can create several simple and understandable rules . Moreover, the performance of a decision tree is optimized by using an ensemble technique named Rotation Forest algorithm. Our findings indicate that the Rotation Forest algorithm can enhance the performance of decision trees in terms of different metrics. In addition, we found that the size of tree generated by decision trees ensemble were bigger than simple ones. This means that the proposed methodology can reveal more information concerning simple rules
RECOMED: A Comprehensive Pharmaceutical Recommendation System
A comprehensive pharmaceutical recommendation system was designed based on
the patients and drugs features extracted from Drugs.com and Druglib.com.
First, data from these databases were combined, and a dataset of patients and
drug information was built. Secondly, the patients and drugs were clustered,
and then the recommendation was performed using different ratings provided by
patients, and importantly by the knowledge obtained from patients and drug
specifications, and considering drug interactions. To the best of our
knowledge, we are the first group to consider patients conditions and history
in the proposed approach for selecting a specific medicine appropriate for that
particular user. Our approach applies artificial intelligence (AI) models for
the implementation. Sentiment analysis using natural language processing
approaches is employed in pre-processing along with neural network-based
methods and recommender system algorithms for modeling the system. In our work,
patients conditions and drugs features are used for making two models based on
matrix factorization. Then we used drug interaction to filter drugs with severe
or mild interactions with other drugs. We developed a deep learning model for
recommending drugs by using data from 2304 patients as a training set, and then
we used data from 660 patients as our validation set. After that, we used
knowledge from critical information about drugs and combined the outcome of the
model into a knowledge-based system with the rules obtained from constraints on
taking medicine.Comment: 39 pages, 14 figures, 13 table
Reordering and Partitioning of Distributed Quantum Circuits
A new approach to reduce the teleportation cost and execution time in Distributed Quantum Circuits (DQCs) was proposed in the present paper. DQCs, a well-known solution, have been applied to solve the problem of maintaining a large number of qubits next to each other. In the distributed quantum system, the qubits are transferred to another subsystem by a quantum protocol like teleportation. Hence, a novel method was proposed to optimize the number of teleportation and to reduce the execution time for generating DQC. To this end, first, the quantum circuit was reordered according to the qubits placement to improve the computational execution time, and then the quantum circuit was modeled as a graph. Finally, we combined the genetic algorithm (GA) and the modified tabu search algorithm (MTS) to partition the graph model in order to obtain a distributed quantum circuit aimed at reducing the number of teleportation costs. A significant reduction in teleportation cost (TC) and execution time (ET) was obtained in benchmark circuits. In particular, we performed a more accurate optimization than the previous approaches, and the proposed approach yielded the best results for several benchmark circuits
A new nested ensemble technique for automated diagnosis of breast cancer
Nowadays, breast cancer is reported as one of most common cancers amongst women. Early detection of this cancer is an essential to aid in informing subsequent treatments. This study investigates automated breast cancer prediction using machine learning and data mining techniques. We proposed the nested ensemble approach which used the Stacking and Vote (Voting) as the classifiers combination techniques in our ensemble methods for detecting the benign breast tumors from malignant cancers. Each nested ensemble classifier contains 'Classifiers' and 'MetaClassifiers'. MetaClassifiers can have more than two different classification algorithms. In this research, we developed the two-layer nested ensemble classifiers. In our two-layer nested ensemble classifiers the MetaClassifiers have two or three different classification algorithms. We conducted the experiments on Wisconsin Diagnostic Breast Cancer (WDBC) dataset and K-fold Cross Validation technique are used for the model evaluation. We compared the proposed two-layer nested ensemble classifiers with single classifiers (i.e., BayesNet and Naive Bayes) in terms of the classification accuracy, precision, recall, F1 measure, ROC and computational times of training single and nested ensemble classifiers. We also compared our best model with previous works reported in the literatures in terms of accuracy. The results demonstrate that the proposed two-layer nested ensemble models outperformance the single classifiers and most of the previous works. Both SV-BayesNet-3-MetaClassifier and SV-Naive Bayes-3-MetaClassifier
achieved accuracy 98.07% (K = 10). However, SV-Naive Bayes-3-MetaClassifier is more efficiency as it needs less time to build the model