Breast cancer disease classification using fuzzy-ID3 algorithm based on association function

Idris, Nur Farahaina; Ismail, Mohd Arfian; Mohamad, Mohd Saberi; Kasim, Shahreen; Zakaria, Zalmiyah; Sutikno, Tole

doi:10.11591/ijai.v11.i2.pp448-461

Cited by 10 publications

(12 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is necessary to analyse the application of the best algorithm for finding the feature vector that best suits the classification algorithm to be applied. CBIR research is also developing in the application of hybrid algorithms, such as in improving classification results by applying hybrid fuzzy system, decision tree (ID3), and assosition function algorithms [35].…”

Section: Review Of Content-based Image Retrivalmentioning

confidence: 99%

See 1 more Smart Citation

Review on integration of ontology and deep learning in cultural heritage image retrieval

Budiman,

Sugiarto,

Hendriyanto

2024

IJEECS

View full text Add to dashboard Cite

Image retrieval methods are currently developing towards big data processing. The literature review is focused on image big data extraction with cultural heritage domain as training and testing datasets. The development of image retrieval process starts from content-based using machine algorithms, deep learning to ontology-based. Image recognition research with cultural heritage domain is conducted because of the importance of preserving and appreciating cultural heritage, in this case, cultural heritage images such as Indonesian Batik are discussed. Batik motif images are Indonesian cultural heritage that has thousands of motifs that are grouped into many classes with a non-linear hyperplane. The problem is focused on processing big data that has many classes. Currently research is evolving into knowledge-based image retrieval using ontologies due to semantic gap constraints. The results of this literature study can be the basis for developing research on the application of appropriate deep learning algorithms so as to utilize the hierarchy of classes and subclasses of image ontologies with cultural heritage domains.

show abstract

Section: Review Of Content-based Image Retrivalmentioning

confidence: 99%

“…In handling very large data waves and many classes, CBIR has developed from  ISSN: 2502-4752 Indonesian J Elec Eng & Comp Sci, Vol. 35, No. 1, July 2024: 583-592 584 machine learning that applies shallow learning to the use of deep learning [5]- [7], [11].…”

Section: Introductionmentioning

confidence: 99%

Review on integration of ontology and deep learning in cultural heritage image retrieval

Budiman,

Sugiarto,

Hendriyanto

2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…It is used to represent decisions and decision-making explicitly [18]. As the name suggests, DT is a tree-based model characterized by its simplicity in understanding decisions and the ability to select the most preferential features [19]. In addition, it can classify data without vast calculations [20].…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Machine learning (ML) is one of the AI branches that focus on a statistical model and algorithm that can perform a proper task without outright comments or instructions [7]; ML and deep learning have been widely used in the medical imaging domain [8], [9]. This work used a convolutional neural network (CNN) to diagnose a brain tumor (BT) disease in a federated environment [10].…”

Section: Introductionmentioning

confidence: 99%

Distributed brain tumor diagnosis using a federated learning environment

Mahlool

Abed

2022

Bulletin EEI

View full text Add to dashboard Cite

In the last few years, a very huge development has occurred in medical techniques using artificial intelligence tools, especially in the diagnosis field. One of the essential things is brain tumor (BT) detection and diagnosis. This kind of disease needs an expert physician to decide on the treatment or surgical operation based on magnetic resonance imaging (MRI) images; therefore, the researchers focus on such kind of medical images analysis and understanding to help the specialist to make a decision. in this work, a new environment has been investigated based on the deep learning method and distributed federated learning (FL) algorithm. The proposed model has been evaluated based on cross-validation techniques using two different standard datasets, BT-small-2c, and BT-large-3c. The achieved classification accuracy was 0.82 and 0.96 consecutively. The proposed classification model provides an active and effective system for assessing BT classification with high reliability and accurate clinical findings.

show abstract

Breast cancer disease classification using fuzzy-ID3 algorithm based on association function

Cited by 10 publications

References 42 publications

Review on integration of ontology and deep learning in cultural heritage image retrieval

Review on integration of ontology and deep learning in cultural heritage image retrieval

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

Distributed brain tumor diagnosis using a federated learning environment

Contact Info

Product

Resources

About