Predicting Incident Duration Based on Machine Learning Methods

doi:10.33103/uot.ijccce.21.1.1

Cited by 7 publications

(4 citation statements)

References 20 publications

(20 reference statements)

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“…In order to ensure unbiased testing results, we have increased the size of our testing set to 30%. Based on literature [42] and [43], this percentage is optimal for obtaining the best model performance. When applying this percentage to dataset1, we found that the results were almost identical to those achieved with a 25% testing set size.…”

Section: Resultsmentioning

confidence: 99%

Comparative Study of Anemia Classification Algorithms for International and Newly CBC Datasets

2023

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Data generated from modern applications and the internet in healthcare is extensive and rapidly expanding. Therefore, one of the significant success factors for any application is understanding and extracting meaningful information using digital analytics tools. These tools will positively impact the application's performance and handle the challenges that can be faced to create highly consistent, logical, and information-rich summaries. This paper contains three main objectives: First, it provides several analytics methodologies that help to analyze datasets and extract useful information from them as preprocessing steps in any classification model to determine the dataset characteristics. Also, this paper provides a comparative study of several classification algorithms by testing 12 different classifiers using two international datasets to provide an accurate indicator of their efficiency and the future possibility of combining efficient algorithms to achieve better results. Finally, building several CBC datasets for the first time in Iraq helps to detect blood diseases from different hospitals. The outcome of the analysis step is used to help researchers to select the best system structure according to the characteristics of each dataset for more organized and thorough results. Also, according to the test results, four algorithms achieved the best accuracy (Logitboost, Random Forest, XGBoost, Multilayer Perceptron). Then use the Logitboost algorithm that achieved the best accuracy to classify these new datasets. In addition, as future directions, this paper helps to investigate the possibility of combining the algorithms to utilize benefits and overcome their disadvantages.

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Section: Resultsmentioning

confidence: 99%

Comparative Study of Anemia Classification Algorithms for International and Newly CBC Datasets

2023

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“…It depends on ensemble learning that unites multiple classifiers to solve complicated problems and enhance the model performance. One of RF's strengths is its efficiency in handling massive training datasets [21].…”

Section: Learning Stagementioning

confidence: 99%

Feature-based real-time distributed denial of service detection in SDN using machine learning and Spark

Samaan

Jeiad

2023

Bulletin EEI

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Recently, software defined networking (SDN) has been deployed extensively in diverse practical domains, providing a new direction in network management by separating the control plane from the data plane. Nevertheless, SDN is vulnerable to distributed denial of service (DDoS) attacks resulting from its centralized controller. Several studies have been suggested to address the DDoS attacks in SDN utilizing machine learning approaches. However, these approaches are resource-intensive and cause performance degradation since they cannot perform effectively in large-scale SDN networks that generate vast traffic statistics. To handle all these challenges, we build a DDoS attack detection model in SDN using Spark as a big data tool to overcome the limitations of conventional data processing methods. Four machine learning algorithms are employed. The decision tree (DT) is elected to be used for real-time deployment based on the performance results, which indicates that it has the best accuracy of 0.936. The model performance is compared with state-of-the-art and shows an overall better performance.

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“…Because the number of features increases exponentially, the space of feature subsets expands exponentially, to direct the search toward an optimal subset, heuristic search methods such as forward search and backward elimination are used [43] [44]. There are three different ways to select features: unsupervised, supervised, and semi-supervised [45] [46]. When evaluating the significance of features without labels, unsupervised feature selection algorithms may use data variance or data distribution, supervised feature selection methods, on the other hand, examine the importance of features by evaluating their correlation with the classification method [47] [48].…”

Section: Techniques For Feature Selectionmentioning

confidence: 99%

Predicting Alzheimer's Disease Using Filter Feature Selection Method

2022

IJCCCE

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Alzheimer’s disease (AD) is caused by multiple variables. Alzheimer's disease development and progression are influenced by genetic variants. The molecular pathways causing Alzheimer's disease are still poorly understood. In Alzheimer's disease research, determining an effective and reliable diagnosis remains a major difficulty, particularly in the early stages (i.e., Moderate Cognitive Impairment (MCI)). Researchers and technologists working in the fields of machine learning and data mining can help improve the situation, early AD diagnosis but face a hurdle when it comes to high-dimensional data processing. By reducing irrelevant and redundant data from microarray gene expression data, the technique of feature selection can save computing time, improve learning accuracy, and encourage a deeper effect on the learning system or data. The feature selection strategy described in this article reduces data noise well. In particular, Pearson's correlation coefficient is used to assess data redundancy. The efficacy of these features is assessed using the Support Vector Machine (SVM) classification approach. The proposed approach has an accuracy of up to 91.1 %. As a result, newly established approaches for early diagnosis of Alzheimer's disease(AD) are being improved. Index Terms— Alzheimer’s Disease, Support vector machine, machine learning, feature selection, Pearson’s correlation coefficient.

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Predicting Incident Duration Based on Machine Learning Methods

Cited by 7 publications

References 20 publications

Comparative Study of Anemia Classification Algorithms for International and Newly CBC Datasets

Comparative Study of Anemia Classification Algorithms for International and Newly CBC Datasets

Feature-based real-time distributed denial of service detection in SDN using machine learning and Spark

Predicting Alzheimer's Disease Using Filter Feature Selection Method

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