Comparative analysis of Machine learning and Deep learning algorithms for Software Effort Estimation

Varshini, A G Priya; K, Anitha Kumari; Janani, D.; Soundariya, S.

doi:10.1088/1742-6596/1767/1/012019

Cited by 21 publications

(17 citation statements)

References 15 publications

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“…The comparison of the LSTM neural network and SVM models represents a novel contribution to the literature, as previous research has primarily focused on the performance of individual models without direct comparison [26], [27], [28]. By conducting a head-to-head comparison of these two models, this research seeks to provide valuable insights into their relative strengths and weaknesses, thus offering a more informed basis for model selection in currency exchange rate prediction.…”

Section: Methodsmentioning

confidence: 99%

“…These metrics are standard in assessing the accuracy and goodness-of-fit of predictive models [27]. The choice of these metrics was informed by their relevance to regression tasks and their widespread use in evaluating machine learning models [28], [34].…”

Section: Evaluation Of Methods Performancementioning

confidence: 99%

“…MAE (Mean Absolute Error) provides information about the average of the absolute differences between predictions and actual values. The lower the MAE value, the better the model performance [38], [39], [40].…”

Section: Evaluation Of Methods Performancementioning

confidence: 99%

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Comparative Analysis of LSTM Neural Network and SVM for USD Exchange Rate Prediction: A Study on Different Training Data Scenarios

Rosita,

Moonlight

2024

SJI

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Purpose: This paper aims to investigate and compare the performance of LSTM Neural Network and Support Vector Machines (SVM) in predicting the USD exchange rate using three different training data scenarios: 45%, 55%, and 75%. The study employs a dataset from the Indonesian Central Bureau of Statistics (BPS) for the period of January 1 to June 30, 2021, encompassing attributes USD Selling Rate.Methods: The methods involve implementing LSTM and SVM algorithms within the Python programming language using Google Colaboratory. Three distinct training data scenarios are explored to evaluate the models' robustness. Performance metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared, are employed for evaluation.Result: Results reveal that LSTM demonstrates superior prediction accuracy compared to SVM across all scenarios, even though it incurs a longer training time. Notably, in the 75% training data scenario, LSTM achieves an MAE of 49.52, RMSE of 63.08, and R-squared of 0.37906, outperforming SVM with MAE of 138.33, RMSE of 161.58, and R-squared of 0.34277.Novelty: This study innovatively compares LSTM Neural Network and Support Vector Machines (SVM) for USD exchange rate prediction across different training scenarios (45%, 55%, and 75%). Unlike previous research focusing on individual models, this study systematically evaluates both methods, highlighting the nuanced balance between prediction accuracy and training time. The findings offer novel insights into LSTM and SVM applicability in currency forecasting, providing valuable guidance for researchers and practitioners in model selection based on specific predictive task requirements.

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Section: Methodsmentioning

confidence: 99%

Section: Evaluation Of Methods Performancementioning

confidence: 99%

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Comparative Analysis of LSTM Neural Network and SVM for USD Exchange Rate Prediction: A Study on Different Training Data Scenarios

Rosita,

Moonlight

2024

SJI

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“…From the obtained 𝑑1, 𝑑2, 𝑑3, … dn values, then the Root Mean Square Error (RMSE) value is calculated using equation (9). This RMSE value is used to determine whether or not a spermatozoa pathway is normal [86][87] [88]. each spermatozoa detected in the video which in its implementation is depicted with different colours, each path traversed is given a description in the form of writing a normal or abnormal path.…”

Section: Determination Of Spermatozoa Motility Abnormalitymentioning

confidence: 99%

Abnormality Determination of Spermatozoa Motility Using Gaussian Mixture Model and Matching-based Algorithm

Mas Diyasa,

Saputra,

Gunawan

et al. 2024

Journal of Robotics and Control

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Sperm analysis is an initial step in the examination conducted to identify infertility cases in humans. One aspect of sperm analysis involves observing the movement of spermatozoa and determining whether it is normal or abnormal. Normal spermatozoa movement is characterized by progressive motion at an average speed of 20 µm/second, while abnormal movement includes slow or non-motile spermatozoa. Traditional methods can be employed to assess the normality or abnormality of sperm movement, but they have drawbacks such as time-consuming procedures and diverse results depending on the expertise of the examiner. On the other hand, utilizing Computer-Assisted Sperm Analysis (CASA) equipment provides consistent results, albeit at a relatively high cost. Therefore, this research proposes an alternative method for determining sperm movement abnormalities using the Gaussian Mixture Model (GMM) for background subtraction and a matching-based algorithm to track and analyze the formed trajectories, distinguishing between normal and abnormal sperm movement. Human spermatozoa in real-time are used, and their movements are recorded in video format using a bright field microscope. The testing results for determining sperm movement abnormalities based on the GMM method and matching-based algorithm were successful, particularly in videos recorded at 50 fps recording speed, 20 minutes of liquefaction time, and 40x microscope lens magnification. This condition exhibited the highest average accuracy, with a tracking accuracy of 77.3% and an average accuracy for determining sperm motility abnormalities of 87.7%. Therefore, the combined tracking of sperm movement based on the GMM method and matching-based algorithm can be utilized to identify abnormalities in the movement of human spermatozoa.

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“…Besides, CART with tree pruning based on the error rates in cross-validation [17] (hereafter denoted as CART + Tree pruning) is shown to be the best method in the original CIL study [29]. Furthermore, Random Forest modeling is shown to be a promising method in recent effort estimation studies [1], [2], [34]. Therefore, we employ all these models and select the model with the smallest estimation error to evaluate and compare CIL and SCIL.…”

Section: B Target Variables and Estimation Modelsmentioning

confidence: 99%

Improvement and Evaluation of Data Consistency Metric CIL for Software Engineering Data Sets

2022

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Software data sets derived from actual software products and their development processes are widely used for project planning, management, quality assurance and process improvement, etc. Although it is demonstrated that certain data sets are not fit for these purposes, the data quality of data sets is often not assessed before using them. The principal reason for this is that there are not many metrics quantifying fitness of software development data. In that respect, this study makes an effort to fill in the void in literature by devising a new and efficient assessment method of data quality. To that end, we start as a reference from Case Inconsistency Level (CIL), which counts the number of inconsistent project pairs in a data set to evaluate its consistency. Based on a follow-up evaluation with a large sample set, we depict that CIL is not effective in evaluating the quality of certain data sets. By studying the problems associated with CIL and eliminating them, we propose an improved metric called Similar Case Inconsistency Level (SCIL). Our empirical evaluation with 54 data samples derived from six large project data sets show that SCIL can distinguish between consistent and inconsistent data sets, and that prediction models for software development effort and productivity built from consistent data sets achieve indeed a relatively higher accuracy.INDEX TERMS Data quality metric, data inconsistency, software project data analysis, software effort estimation, software productivity estimation.

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Comparative analysis of Machine learning and Deep learning algorithms for Software Effort Estimation

Cited by 21 publications

References 15 publications

Comparative Analysis of LSTM Neural Network and SVM for USD Exchange Rate Prediction: A Study on Different Training Data Scenarios

Comparative Analysis of LSTM Neural Network and SVM for USD Exchange Rate Prediction: A Study on Different Training Data Scenarios

Abnormality Determination of Spermatozoa Motility Using Gaussian Mixture Model and Matching-based Algorithm

Improvement and Evaluation of Data Consistency Metric CIL for Software Engineering Data Sets

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