Low Accuracy Models Research Articles

Synthetic Minority Oversampling Technique (SMOTE) for Boosting the Accuracy of C4.5 Algorithm Model

The low accuracy of the classification model may be caused by dataset imbalance. In reality, low-accuracy models are unacceptable. The purpose of this research is to address data imbalances in an employee performance dataset identified using the C4.5 method. SMOTE is the approach for addressing data imbalance. SMOTE is utilized to generate a large amount of data in the majority or minority class, which has an initial classification accuracy of just 17%. The C4.5 algorithm classifies the new dataset created by SMOTE, which consists of 11 attributes divided three times between training and testing data. The research found that with a 60:40 data split, the classification model had a 69% accuracy. Model accuracy climbed to 76% at 70:30 data splitting, and 86% at the final splitting, which was 80:20. The model's output matches the evaluation findings obtained using the confusion matrix. The research findings indicate that SMOTE may improve classification model accuracy by boosting data in imbalanced classes.

Multiple Disease Prediction System

Abstract: Artificial Intelligence and machine learning is having big impact on Today’s World in various field and we can find AI everywhere. One of the major industry health sectors generates large amount of patient data that can be processed in various form and help to improve patient health. So, with the help Artificial Intelligence and machine learning we have created prediction system that can detect disease at a time. There are many existing systems with less disease prediction, low accuracy and after prediction there is not solution for a person to improve their health status. Low accuracy model can create serious issue with patient minds which can create more problems. We have taken dataset of Parkinson, heart, Diabetes, Breast Cancer. The user will enter various parameter of the disease and the system will configure and tell patient’s health status. After knowing the result if patient is suffering from disease, he/she can talk to our chatbot for advice

Time Series Segmentation Based on Stationarity Analysis to Improve New Samples Prediction.

A wide range of applications based on sequential data, named time series, have become increasingly popular in recent years, mainly those based on the Internet of Things (IoT). Several different machine learning algorithms exploit the patterns extracted from sequential data to support multiple tasks. However, this data can suffer from unreliable readings that can lead to low accuracy models due to the low-quality training sets available. Detecting the change point between high representative segments is an important ally to find and thread biased subsequences. By constructing a framework based on the Augmented Dickey-Fuller (ADF) test for data stationarity, two proposals to automatically segment subsequences in a time series were developed. The former proposal, called Change Detector segmentation, relies on change detection methods of data stream mining. The latter, called ADF-based segmentation, is constructed on a new change detector derived from the ADF test only. Experiments over real-file IoT databases and benchmarks showed the improvement provided by our proposals for prediction tasks with traditional Autoregressive integrated moving average (ARIMA) and Deep Learning (Long short-term memory and Temporal Convolutional Networks) methods. Results obtained by the Long short-term memory predictive model reduced the relative prediction error from 1 to 0.67, compared to time series without segmentation.

Practical Accuracy Estimation for Efficient Deep Neural Network Testing

Deep neural network (DNN) has become increasingly popular and DNN testing is very critical to guarantee the correctness of DNN, i.e., the accuracy of DNN in this work. However, DNN testing suffers from a serious efficiency problem, i.e., it is costly to label each test input to know the DNN accuracy for the testing set, since labeling each test input involves multiple persons (even with domain-specific knowledge) in a manual way and the testing set is large-scale. To relieve this problem, we propose a novel and practical approach, called PACE (which is short for P ractical AC curacy E stimation), which selects a small set of test inputs that can precisely estimate the accuracy of the whole testing set. In this way, the labeling costs can be largely reduced by just labeling this small set of selected test inputs. Besides achieving a precise accuracy estimation, to make PACE more practical it is also required that it is interpretable, deterministic, and as efficient as possible. Therefore, PACE first incorporates clustering to interpretably divide test inputs with different testing capabilities (i.e., testing different functionalities of a DNN model) into different groups. Then, PACE utilizes the MMD-critic algorithm, a state-of-the-art example-based explanation algorithm, to select prototypes (i.e., the most representative test inputs) from each group, according to the group sizes, which can reduce the impact of noise due to clustering. Meanwhile, PACE also borrows the idea of adaptive random testing to select test inputs from the minority space (i.e., the test inputs that are not clustered into any group) to achieve great diversity under the required number of test inputs. The two parallel selection processes (i.e., selection from both groups and the minority space) compose the final small set of selected test inputs. We conducted an extensive study to evaluate the performance of PACE based on a comprehensive benchmark (i.e., 24 pairs of DNN models and testing sets) by considering different types of models (i.e., classification and regression models, high-accuracy and low-accuracy models, and CNN and RNN models) and different types of test inputs (i.e., original, mutated, and automatically generated test inputs). The results demonstrate that PACE is able to precisely estimate the accuracy of the whole testing set with only 1.181%∼2.302% deviations, on average, significantly outperforming the state-of-the-art approaches.

Use of structurally-accurate 3D plant models for estimating light interception and photosynthesis of sweet pepper (Capsicum annuum) plants

Plant structure is a significant factor for influencing the light interception and photosynthesis of plants. The light interception on the plant surface can be analyzed by three-dimensional (3D) plant model and optical simulation, but its accuracy is directly affected by the structural accuracy of the 3D model. This study aims to analyze and compare the effect of the accuracy of 3D structural models on light interception and photosynthesis. 3D-scanned plant models with different structural accuracies were constructed, and the light interception and photosynthetic rate were analyzed at single leaf and whole-plant scales. When using a low accuracy model that lacked the fine structural details of the plant, it was overestimated in light interception and photosynthetic rate compared to the 3D-scanned model that has high structural accuracy. At the single leaf scale, the light interception was higher in the low-accuracy model than that in the 3D-scanned model due to self-shadings from higher curvature in the leaf surface. At the whole-plant scale, the light interception and the subsequent photosynthetic rate in the low-accuracy model were 18% and 45 to 58% higher than those in the 3D-scanned model at light intensities of 700–2000 μmol m−2 s−1 at the upper canopy. The 3D-scanned plant model could accurately estimate the light interception and photosynthetic rate of the plants through optical simulation. The presented methodology can contribute to accurate analyses of plant light environment, plant physiological response, and plant growth modelling.

Ground State Energy Functional with Hartree-Fock Efficiency and Chemical Accuracy.

We introduce the deep post Hartree-Fock (DeePHF) method, a machine learning-based scheme for constructing accurate and transferable models for the ground-state energy of electronic structure problems. DeePHF predicts the energy difference between results of highly accurate models such as the coupled cluster method and low accuracy models such as the Hartree-Fock (HF) method, using the ground-state electronic orbitals as the input. It preserves all the symmetries of the original high accuracy model. The added computational cost is less than that of the reference HF or DFT and scales linearly with respect to system size. We examine the performance of DeePHF on organic molecular systems using publicly available data sets and obtain the state-of-art performance, particularly on large data sets.

Optimisation in electromagnetics with the space‐mapping technique

PurposeOptimisation in electromagnetics, based on finite element models, is often very time‐consuming. In this paper, we present the space‐mapping (SM) technique which aims at speeding up such procedures by exploiting auxiliary models that are less accurate but much cheaper to compute.Design/methodology/approachThe key element in this technique is the SM function. Its purpose is to relate the two models. The SM function, combined with the low accuracy model, makes a surrogate model that can be optimised more efficiently.FindingsBy two examples we show that the SM technique is effective. Further we show how the choice of the low accuracy model can influence the acceleration process. On one hand, taking into account more essential features of the problem helps speeding up the whole procedure. On the other hand, extremely simple auxiliary models can already yield a significant acceleration.Research limitations/implicationsObtaining the low accuracy model is not always straightforward. Some research could be done in this direction. The SM technique can also be applied iteratively, i.e. the auxiliary model is optimised aided by a coarser one. Thus, the generation of hierarchies of models seems to be a promising venue for the SM technique.Originality/valueOptimisation in electromagnetics, based on finite element models, is often very time‐consuming. The results given show that the SM technique is effective for speeding up such procedures.