A tuned feed-forward deep neural network algorithm for effort estimation

Öztürk, Muhammed Maruf

doi:10.1080/0952813x.2021.1871664

Cited by 8 publications

(7 citation statements)

References 41 publications

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“…With the rapid development of CT technology, the acquired CT images are clearer and have higher resolution. CT can play an increasingly important role in the early detection, localization, and lesion characterization of lung cancer and has become a clinical early detection of lung cancer and the most commonly used and most effective method[21][22][23]. CT images can clearly show the edge, shape, and density of lung nodules and the relationship with adjacent tissues.…”

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confidence: 99%

Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning

Liang¹,

Li²,

Fu³

2021

Journal of Healthcare Engineering

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Lung cancer is one of the most malignant tumors. If it can be detected early and treated actively, it can effectively improve a patient’s survival rate. Therefore, early diagnosis of lung cancer is very important. Early-stage lung cancer usually appears as a solitary lung nodule on medical imaging. It usually appears as a round or nearly round dense shadow in the chest radiograph. It is difficult to distinguish lung nodules and lung soft tissues with the naked eye. Therefore, this article proposes a deep learning-based artificial intelligence chest CT lung nodule detection performance evaluation study, aiming to evaluate the value of chest CT imaging technology in the detection of noncalcified nodules and provide help for the detection and treatment of lung cancer. In this article, the Lung Medical Imaging Database Consortium (LIDC) was selected to obtain 536 usable cases based on inclusion criteria; 80 cases were selected for examination, artificial intelligence software, radiologists, and thoracic imaging specialists. Using 80 pulmonary nodules detection in each case, the pathological type of pulmonary nodules, nonlime tuberculous test results, detection sensitivity, false negative rate, false positive rate, and CT findings were individually analyzed, and the detection efficiency software of artificial intelligence was evaluated. Experiments have proved that the sensitivity of artificial intelligence software to detect noncalcified nodules in the pleural, peripheral, central, and hilar areas is higher than that of radiologists, indicating that the method proposed in this article has achieved good detection results. It has a better nodule detection sensitivity than a radiologist, reducing the complexity of the detection process.

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confidence: 99%

Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning

Liang¹,

Li²,

Fu³

2021

Journal of Healthcare Engineering

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“…DNNs resulted in relatively higher prediction accuracy in a study by Mensah et al 17 against two benchmarks, namely, the ATLM and ordinary least squares regression 73 . DNN models have similarly yielded improved prediction accuracy in previous studies 62,74–79 . Specifically, we setup a DNN, which makes use of multiple hidden layers with their respective neurons and an output layer with two neurons (for the duplex variables ) to automatically learn from a set of projects and provide the resulting predictions for the targets.…”

Section: Methodsmentioning

confidence: 99%

“…73 DNN models have similarly yielded improved prediction accuracy in previous studies. 62,[74][75][76][77][78][79] Specifically, we setup a DNN, which makes use of multiple hidden layers with their respective neurons and an output layer with two neurons (for the duplex variables) to automatically learn from a set of projects and provide the resulting predictions for the targets. The DNN used in this study has two hidden layers with 5 and 2 neurons, respectively.…”

Section: Methodsmentioning

confidence: 99%

A classification scheme to improve conclusion instability using Bellwether moving windows

Mensah

Kudjo

2022

J Software Evolu Process

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Context The use of a subset of recently completed and exemplary data, namely, Bellwether moving window (BMW) has proven successful to result in improved accuracy in software effort estimation (SEE). These outcomes were achieved based on the theory that estimation outcome of a future event depends on previous events. Thus, the existence of a BMW yield improved prediction accuracy for new project estimation. However, the conclusion instability problem across learners still threatens the reliability of SEE for new projects. Such instability concerns are attributed to the data subset considered for the training and validation needs of learners. Objective To investigate whether the use of BMWs together with an effort classification scheme can minimize the conclusion instability problem across learners. Method We apply a Bellwether method comprising of three operators, namely, SORT+CLUSTER, GENERATE_TPM, and APPLY to sample the BMW from a pool of chronological projects from the Maxwell and International Software Benchmarking Standards Group (ISBSG) datasets. The sampled BMW is benchmarked against the entire collection of preprocessed projects, namely, growing portfolio to evaluate prediction and classification accuracy across a set of learners–ElasticNet regression, deep neural networks, and automatically transformed linear model. Results (1) BMW exists in the studied projects and (2) training the learners with a BMW of average window size 28.5%–75.5% of the growing portfolio (not older than 3 years) relatively minimizes the conclusion instability of prediction results. Conclusion When BMWs are available, we recommend their use for estimating the effort for a new project to minimize the conclusion instability problem.

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“…Instead, sophisticated parameter search techniques that was compatible with the structure of regression methods should be developed; 2) SEE performance was improved when the associated hyperparameter search technique was designed in accordance with the key principles of selected deep learning approach; and 3) Deep learning models outperform tree-based regression techniques like CART DE8 in terms of CPU time. The drawback of the study was that tuning time need to plan along with pruning of network [11].…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Software Test Effort Estimation using Genetic Algorithm and Neural Network

Chahar¹,

Bhatia²

2022

IJACSA

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In present scenario, the software companies are frequently involving software test effort estimation to allocate the resources efficiently during the software development process. Different machine learning models are developed to estimate the total effort that would be required before the software product could be delivered. These computational models are used to use the past data to estimate the efforts. In the current studies, test effort estimation for software is predicted using the Genetic algorithm and Neural Network. The attributes are selected using the Genetic algorithm and similarity measure between the attribute values has been computed using the Cosine Similarity measure. The simulation experiments were done using the PROMISE and Kaggle repository and implementation was done using the MATLAB software. The performance metrics namely, precision, recall, and accuracy are computed to evaluate against the existing techniques. The accuracy of the proposed model is 91.3% and results are improved by 8.9% in comparison to existing technique and comparison has been made for superiority to predict the test effort for software development.

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A tuned feed-forward deep neural network algorithm for effort estimation

Cited by 8 publications

References 41 publications

Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning

Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning

A classification scheme to improve conclusion instability using Bellwether moving windows

Performance Analysis of Software Test Effort Estimation using Genetic Algorithm and Neural Network

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