Fuzzy Unordered Rule Using Greedy Hill Climbing Feature Selection Method: An Application to Diabetes Classification

Al-Behadili, Hayder Naser Khraibet; Ku-Mahamud, Ku Ruhana

doi:10.32890/jict2021.20.3.5

Cited by 6 publications

(2 citation statements)

References 40 publications

(47 reference statements)

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“…The 10-fold cross-validation method is the professional standard for determining a learning scheme's error rate on a given dataset. Ten times ten-fold cross-validation was performed for reliable results [72], [75]. Furthermore, the 10-fold cross-validation separates the dataset into ten parts (folds).…”

Section: Resultsmentioning

confidence: 99%

Trojan Horse Infection Detection in Cloud Based Environment Using Machine Learning

Kanaker¹,

Karim

Awwad

et al. 2022

Int. J. Interact. Mob. Technol.

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Cloud computing technology is known as a distributed computing network, which consists of a large number of servers connected via the internet. This technology involves many worthwhile resources, such as applications, services, and large database storage. Users have the ability to access cloud services and resources through web services. Cloud computing provides a considerable number of benefits, such as effective virtualized resources, cost efficiency, self-service access, flexibility, and scalability. However, many security issues are present in cloud computing environment. One of the most common security challenges in the cloud computing environment is the trojan horses. Trojan horses can disrupt cloud computing services and damage the resources, applications, or virtual machines in the cloud structure. Trojan horse attacks are dangerous, complicated and very difficult to be detected. In this research, eight machine learning classifiers for trojan horse detection in a cloud-based environment have been investigated. The accuracy of the cloud trojan horses detection rate has been investigated using dynamic analysis, Cukoo sandbox, and the Weka data mining tool. Based on the conducted experiments, the SMO and Multilayer Perceptron have been found to be the best classifiers for trojan horse detection in a cloud-based environment. Although SMO and Multilayer Perceptron have achieved the highest accuracy rate of 95.86%, Multilayer Perceptron has outperformed SMO in term of Receiver Operating Characteristic (ROC) area.

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Section: Resultsmentioning

confidence: 99%

Trojan Horse Infection Detection in Cloud Based Environment Using Machine Learning

Kanaker¹,

Karim

Awwad

et al. 2022

Int. J. Interact. Mob. Technol.

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“…The wrapper approach is widely utilized due to its efficiency in handling more massive and complex datasets than the filter and embedded approaches [10]. However, different approaches have been suggested for solving FS issues, such as metaheuristic, heuristic, and evolutionary [11].…”

Section: Introductionmentioning

confidence: 99%

Heuristic Initialization Using Grey Wolf Optimizer Algorithm for Feature Selection in Intrusion Detection

2023

IJIES

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Anomaly detection deals with identification of items that do not conform to an expected pattern or items present in a dataset. The performance of the various mechanisms that are employed to execute anomaly detection is strongly dependent on the set of features that are utilized. Thus, not every feature in the dataset may be employed in the classification operation since certain characteristics may result in poor solution quality. Feature selection (FS) may reduce the size of high-dimensional datasets by eliminating unimportant features. Modified binary grey wolf optimizer (MBGWO) is a successful metaheuristic that has been used for FS in anomaly detection. Nonetheless, the MBGWO is a randomization population-based algorithm that has an issue in finding a good quality solution during the initial population procedure. Thus, this study proposes a heuristic modified binary grey wolf optimizer (heuristic MBGWO) algorithm for FS in intrusion detection to enhance the initial population of the MBGWO using a heuristic-based ant colony optimization algorithm (ACO). The heuristic MBGWO algorithm was evaluated on NSL-KDD benchmark dataset from the University California Irvine (UCI) repository against five (5) benchmark metaheuristic algorithms. experimental results of the heuristic MBGWO algorithm on the NSL-KDD dataset in terms of the number of chosen attributes and classification accuracy are superior to other benchmark optimization algorithms, where it obtained the best features with 99.85% classification accuracy. The proposed heuristic MBGWO algorithm can be used for FS in anomaly detection tasks that involve any dataset size from various application domains.

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Türk imalat sistemlerinde iş kazalarındaki örüntülerin çok durumlu logit model’e dayalı bir yaklaşımla belirlenmesi

MUTLU,

SELİM,

ALTUNTAŞ

2023

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Türk imalat sanayi sektörler arasında iş kazası sıklığı açısından ilk üç içinde yer almaktadır. Bu nedenle imalat sanayinde iş güvenliğinin artırılması ve iş kazalarına neden olan risklerin en aza indirilmesi için kaza neden-sonuç ilişkilerinin belirlenmesine ihtiyaç vardır. Bu çalışmada Türk imalat sistemlerindeki iş kazaları arasındaki örüntüleri bulmak için entegre bir veri odaklı yaklaşım önerilmiştir. Önerilen yaklaşım, C5.0, Sınıflandırma ve regresyon ağaçları (C&RT), Kuaterniyon tahmini (QUEST), Ki-kare otomatik etkileşim dedektörü (CHAID) ve Rastgele ağaçlar (Random Forest) olmak üzere karar ağacı algoritmalarını ve çok terimli logit modeli kullanmaktadır. Bu çalışmada 2013-2019 yılları arasında Türk imalat sanayinde meydana gelen 307.590 iş kazası kullanılmıştır. Yaralanma, ölüm ve uzuv kaybı olan tüm kazalar için sektör bölümü, kazanın yaşandığı coğrafi bölge, yıl, sapma, saat gün, cinsiyet ve yaş arasında iş göremezlik durumuna göre istatistiksel olarak anlamlı bir ilişki olduğu bulunmuştur. Ek olarak, sektör bölümü, kazanın yaşandığı coğrafi bölge ve yıl, karar ağacı algoritmalarına dayalı ilk beş tahmin edici arasında bulunmuştur.

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Fuzzy Unordered Rule Using Greedy Hill Climbing Feature Selection Method: An Application to Diabetes Classification

Cited by 6 publications

References 40 publications

Trojan Horse Infection Detection in Cloud Based Environment Using Machine Learning

Trojan Horse Infection Detection in Cloud Based Environment Using Machine Learning

Heuristic Initialization Using Grey Wolf Optimizer Algorithm for Feature Selection in Intrusion Detection

Türk imalat sistemlerinde iş kazalarındaki örüntülerin çok durumlu logit model’e dayalı bir yaklaşımla belirlenmesi

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