Review on "Phase Segmentation of X-Ray Computer Tomography Rock Images using Machine Learning Techniques: An Accuracy and Performance Study"

Purpose Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones). Design/methodology/approach The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO), a population-based meta-heuristic algorithm. The authors use the SMOTE for oversampling and PSO for undersampling. Findings By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain data sets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective. Practical implications This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification. Originality/value Online credibility and performance data are applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world data sets with different imbalance ratios.

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Identifying malicious web domains using machine learning techniques with online credibility and performance data

2016 IEEE Congress on Evolutionary Computation (CEC) ◽

10.1109/cec.2016.7748347 ◽

2016 ◽

Cited By ~ 10

Author(s):

Zhongyi Hu ◽

Raymond Chiong ◽

Ilung Pranata ◽

Willy Susilo ◽

Yukun Bao

Keyword(s):

Machine Learning ◽

Performance Data ◽

Machine Learning Techniques ◽

Learning Techniques ◽

And Performance

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Identification of Pneumonia Disease Applying an Intelligent Computational Framework Based on Deep Learning and Machine Learning Techniques

Mobile Information Systems ◽

10.1155/2021/9989237 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Yar Muhammad ◽

Mohammad Dahman Alshehri ◽

Wael Mohammed Alenazy ◽

Truong Vinh Hoang ◽

Ryan Alturki

Keyword(s):

Machine Learning ◽

Medical Image Analysis ◽

Machine Learning Techniques ◽

World Health ◽

X Ray ◽

Expert Radiologist ◽

Learning Techniques ◽

Effective Manner ◽

The World ◽

Chest X Ray

Pneumonia is a very common and fatal disease, which needs to be identified at the initial stages in order to prevent a patient having this disease from more damage and help him/her in saving his/her life. Various techniques are used for the diagnosis of pneumonia including chest X-ray, CT scan, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Medical image analysis plays a vital role in the diagnosis of various diseases like MERS, COVID-19, pneumonia, etc. and is considered to be one of the auspicious research areas. To analyze chest X-ray images accurately, there is a need for an expert radiologist who possesses expertise and experience in the desired domain. According to the World Health Organization (WHO) report, about 2/3 people in the world still do not have access to the radiologist, in order to diagnose their disease. This study proposes a DL framework to diagnose pneumonia disease in an efficient and effective manner. Various Deep Convolutional Neural Network (DCNN) transfer learning techniques such as AlexNet, SqueezeNet, VGG16, VGG19, and Inception-V3 are utilized for extracting useful features from the chest X-ray images. In this study, several machine learning (ML) classifiers are utilized. The proposed system has been trained and tested on chest X-ray and CT images dataset. In order to examine the stability and effectiveness of the proposed system, different performance measures have been utilized. The proposed system is intended to be beneficial and supportive for medical doctors to accurately and efficiently diagnose pneumonia disease.

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Analysis of Crime Report by Data Analytics Using Python

Advances in Data Mining and Database Management - Challenges and Applications of Data Analytics in Social Perspectives ◽

10.4018/978-1-7998-2566-1.ch003 ◽

2021 ◽

pp. 54-79

Author(s):

G. Maria Jones ◽

S. Godfrey Winster

Keyword(s):

Machine Learning ◽

New Technologies ◽

Rapid Development ◽

Performance Measure ◽

Machine Learning Techniques ◽

Complex Data ◽

Time Data ◽

Related Data ◽

Learning Techniques ◽

And Performance

The ever-rapid development of technology in today's world tends to provide us with a dramatic explosion of data, leading to its accumulation and thus data computation has amplified in comparison to the recent past. To manage such complex data, emerging new technologies are enabled specially to identify crime patterns, as crime-related data is escalating. These digital technologies have the potential to manipulate and also alter the pattern. To combat this, machine learning techniques are introduced which have the ability to analyse such voluminous data. In this work, the authors intend to understand and implement machine learning techniques in real time data analysis by means of Python. The detailed explanation in preparing the dataset, understanding, visualizing the data using pandas, and performance measure of algorithm is evaluated.

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Impact Analysis of Machine Learning Techniques for COVID-19 Diagnosis: A Critical Review

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i2.863 ◽

2021 ◽

Vol 12 (2) ◽

pp. 510-516

Author(s):

Anshul, Et. al.

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Severe Acute Respiratory Syndrome ◽

Impact Analysis ◽

Machine Learning Techniques ◽

X Ray ◽

Learning Techniques ◽

The World ◽

Almost All

COVID-19 virus belongs to the severe acute respiratory syndrome (SARS) family raised a situation of health emergency in almost all the countries of the world. Numerous machine learning and deep learning based techniques are used to diagnose COVID positive patients using different image modalities like CT SCAN, X-RAY, or CBX, etc. This paper provides the works done in COVID-19 diagnosis, the role of ML and DL based methods to solve this problem, and presents limitations with respect to COVID-19 diagnosis.

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The Successful Delivery of Megaprojects: A Novel Research Method

Project Management Journal ◽

10.1177/875697281704800506 ◽

2017 ◽

Vol 48 (5) ◽

pp. 78-94 ◽

Cited By ~ 16

Author(s):

Giorgio Locatelli ◽

Miljan Mikic ◽

Milos Kovacevic ◽

Naomi Brookes ◽

Nenad Ivanisevic

Keyword(s):

Machine Learning ◽

Project Management ◽

Research Method ◽

Machine Learning Techniques ◽

Exact Test ◽

Delivery Performance ◽

Learning Techniques ◽

Benefits Realization ◽

And Performance ◽

Effective Design

Megaprojects are often associated with poor delivery performance and poor benefits realization. This article provides a method of identifying, in a quantitative and rigorous manner, the characteristics related to project management success in megaprojects. It provides an investigation of how stakeholders can use this knowledge to ensure more effective design and delivery for megaprojects. The research is grounded in 44 mega-projects and a systematic, empirically based methodology that employs the Fisher's exact test and machine learning techniques to identify the correlation between megaprojects’ characteristics and performance, paving the way to an understanding of their causation.

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