Comparative Study of Supervised Machine Learning Algorithms on Thoracic Surgery Patients based on Ranker Feature Algorithms

Abdulhadi, Hezha M. Tareq; Talabani, Hardi Sabah

doi:10.21928/uhdjst.v5n2y2021.pp66-74

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…The mechanism of an intelligent transportation system based on IoT and cloud computing techniques was proposed by [17]. The main objective of the proposed research is to develop an intelligent transport system that focuses on technique is proposed by [19] to detect application traffic in software-defined networking (SDN).…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

A Review of Various Machine Learning Techniques and its Application on IoT and Cloud Computing

Talabani,

Jumaa

2024

Tikrit J. Pure Sci.

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The employ of Internet of Things has become an integral part of our daily life, especially in developed countries and societies, which in turn are considered to be one of the basic areas on which the mechanisms of their work depend to a large extent the applications of algorithms used in the field of machine learning are available due to the high accuracy that characterizes these applications and the margin of safety that these techniques offer. As a result, scientific research for these applications is increasing every day and leads to different results for these applications in different areas of the Internet of Things and its multiple uses. This study presents an analysis of machine learning techniques and the need for ML and its types. The article focuses on current research on the integration of IoT with cloud computing technology and the benefits of linking cloud computing techniques with IoT systems. An overview of different machine learning algorithms like SVM and neural network algorithms like ANN are discussed. Deep learning algorithms; CNN, RNN, and ensemble learning techniques are reviewed in terms of developed models, goals, applications, and the results achieved. The study offers a comparative analysis of the application of different ML and deep learning algorithms.

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Section: Support Vector Machines (Svm)mentioning

confidence: 99%

A Review of Various Machine Learning Techniques and its Application on IoT and Cloud Computing

Talabani,

Jumaa

2024

Tikrit J. Pure Sci.

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“…(2 * Precision * Recall) / (Precision + Recall) [42] Error Rate FP + FN / TP + TN + FP + FN [42] Accuracy 100% -Error Rate [42] The experimental results in Table 5 show that the Lazy IBK algorithm based on Manhattan Distance obtained a perfect outcome in classifying an Obfuscated Malware dataset classes (benign or Malware) with outstanding accuracy, precision, recall, and F-measure of 99.99%, 1.000, 1.000, 1.000 respectively. In our model, the precision and recall values are equal to 1.000. furthermore, the F-Measure metric depends on precision and recall values as the harmonic mean to be calculated.…”

Section: Metrics Equations Referencesmentioning

confidence: 99%

Obfuscated Malware Memory Detection Employing Lazy Instance Based Learner Algorithm Based On Manhattan Distance Function

Sabah Talabani,

Abdulhadi,

Ali

2024

PJBAS

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Malware is a severe threat to the network and host system security. It is frequently the primary cause of many events, such as Distributed Denial-of-Service attacks (DDoS), spam emails, etc. The detection and elimination of Malware are the subjects of intensive study. As a result, many antivirus programs have been created to help identify and remove Malware. The issue with this antivirus software is that it uses an obsolete method of detecting Malware, the signature-matching approach, which the primary forms of code obfuscation may deceive. Since then, this has resulted in the creation of a new generation of metamorphic and polymorphic Malware. In this paper, we investigated using the Instance-Based Learner (IBK) algorithm for detecting obfuscated Malware in a given dataset. Utilizing the Lazy IBK technique in malware detection is beneficial because the algorithm can accurately detect and classify the obfuscated Malware in the dataset using the Manhattan Distance function, one of the most well-known distance metric functions for measuring the distance between points. We analyzed an obfuscated malware dataset of 58,596 records selected from 3 malware categories. The algorithm was illustrated on the dataset utilizing 10-fold cross-validation. The results demonstrate that the proposed algorithm can quickly and accurately detect obfuscated Malware with an accuracy of 99.99%, a precision of 100%, and a recall of 100%, respectively.

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“…1) Supervised Learning: In supervised machine learning, the computer model is trained to find correlation between inputs and outputs by being fed with a dataset containing sample data that already exist. Basically, the goal is to improve performance by training and extracting knowledge from existing instances allowing it thus-to predict outcomes of unknown inputs [8] . A training set is formed, where known samples are included and each input (output) takes with past features.…”

Section: Machine Learningmentioning

confidence: 99%

The current landscape of digital forensics employing machine learning approaches: A Review

Talabani

2024

Int. J. Comput. Artif. Intell.

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This paper discusses and evaluates the present state of digital forensics, as well as how machine learning techniques are used in this field. The paper covers technological advances in forensics medicine and how we may gain from the performance of machine learning algorithms to compare their performances for improvement on data collection, analysis and investigation. The focus is on the benefits and challenges that may arise while adopting algorithms: Naive Bayes (NB), K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Principal Component Analysis (PCA) and Kmeans. Apart from analyzing the latest research and studies in this subject area. Furthermore, tracing new trends in the digital forensics' domain and outline ways that machine learning can be used for better performance.

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Comparative Study of Supervised Machine Learning Algorithms on Thoracic Surgery Patients based on Ranker Feature Algorithms

Cited by 7 publications

References 11 publications

A Review of Various Machine Learning Techniques and its Application on IoT and Cloud Computing

A Review of Various Machine Learning Techniques and its Application on IoT and Cloud Computing

Obfuscated Malware Memory Detection Employing Lazy Instance Based Learner Algorithm Based On Manhattan Distance Function

The current landscape of digital forensics employing machine learning approaches: A Review

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