Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning

Aldelemy, Ahmad; Abd‐Alhameed, Raed A.

doi:10.51173/jt.v5i2.1226

Cited by 16 publications

(4 citation statements)

References 40 publications

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“…The application of machine learning algorithms allows for the detection of duplicate source code [25]. Ahmed Aldelemy and others [34] have used a k-folding (k=5) cross-validation classifier which resulted in an accuracy of over 92%.…”

Section: Machine Learning Phenomenamentioning

confidence: 99%

Detecting Source Code Plagiarism in Student Assignment Submissions Using Clustering Techniques

Raddam Sami Mehsen,

Majharoddin M. Kazi,

Hiren Joshi

2024

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In pragmatic courses, graduate students are required to submit programming assignments, which have been susceptible to various forms of plagiarism. Detecting counterfeited code in an academic setting is of paramount importance, given the prevalence of publications and papers. Plagiarism, defined as the unauthorized replication of written work without proper acknowledgment, has become a critical concern with the advent of information and communication technology (ICT) and the widespread availability of scholarly publications online. However, the extensive use of freeware text editors has posed challenges in detecting source code plagiarism. Numerous studies have investigated algorithms for revealing different types of plagiarism and detecting source code plagiarism. In this research, we propose an innovative strategy that combines TF-IDF (Term Frequency-Inverse Document Frequency) modifications with K-means clustering, achieving a remarkable precision rate of 99.2%. Additionally, we explore the hierarchical clustering method, which estimates an even higher precision rate of 99.5% compared to previous techniques. To implement our approach, we utilize the Python programming language along with relevant libraries, providing a robust and efficient system for source code plagiarism detection in student assignment submissions.

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Section: Machine Learning Phenomenamentioning

confidence: 99%

Detecting Source Code Plagiarism in Student Assignment Submissions Using Clustering Techniques

Raddam Sami Mehsen,

Majharoddin M. Kazi,

Hiren Joshi

2024

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show abstract

“…To offer real-time protection against mobile malware, the model can be included in currently installed antivirus or mobile security software. [19], [20] Investigate using more sensitive API calls to boost the precision and accuracy of the Android malware detection system. Examine whether adding other machine learning algorithms or ensemble techniques to the integrated learning model can improve its detection performance even more.…”

Section: Feature Extraction (Machine Learning Models)mentioning

confidence: 99%

Mobile based Malware Detection using Artificial Intelligence Techniques a review

Faez mahdi,

Saadoon Jasim

2024

J. Al-Qadisiyah Comp. Sci. Math.

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Malware attacks on mobile devices are becoming more common and more complicated every year. Malware writers see the open-source Android app as their main target because so many people use it. Artificial intelligence is used by most of the literature's mobile malware detection methods to find ransomware. Our research, on the other hand, makes it clear that most of the earlier studies used different metrics and models, as well as different datasets and classification features that came from static, dynamic, or hybrid analysis strategies. This makes comparing the different suggested detection methods more difficult and may also make the results less certain. The goal of this work is to solve the problem of AI-powered malware detection by sorting current methods and approaches into three groups: the type of dataset, the type of detection method used (machine learning models, deep learning models, and Behavioral Analysis model), and how well the method works. In this way, we suggest a convergent plan that can be used as a basis for future methods of finding malware on Android and as a solid standard for artificial intelligence work in this area.

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“…NB is effective enough to classify in many domains [22] and has been used to classify documents over the decades. The Naïve Bayesian algorithm gives better results in solving complex problems [23]. When NB employs the Bayes"rule to determine the probable class c* for a new document d, it calculates in Eq (2):…”

Section: Naive Bayes (Nb)mentioning

confidence: 99%

Supervised Machine Learning a Brief Survey of Approaches

2024

IJSER

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Machine learning has become popular across several disciplines right now. It enables machines to automatically learn from data and make predictions without the need for explicit programming or human intervention. Supervised machine learning is a popular approach to creating artificial intelligence. A computer algorithm is trained on input data that has been labeled for a certain output, making it one of two major areas of machine learning that has seen a lot of successful research. The model is trained until it can identify the underlying correlations and patterns between the input and output labels, enabling it to generate accurate labeling results when confronted with unexplored data. Supervised learning is good at solving classification and regression problems. The problem of regression occurs when the outputs are continuous, while the problem of classification occurs when the outputs are categorical. We will concentrate on the benefits and drawbacks of supervised learning algorithms in this review. Creating a precise model of the distribution of class labels in terms of predictor features is the aim of supervised learning. This work studied the most popular supervised machine learning methods, including Naive Bayes, Decision Trees, Support Vector Machines, Logistic Regression, K-Nearest Neighbors, and Deep Learning, which were discussed in this paper. We also emphasized the algorithms' advantages and disadvantages, and we ultimately talked about the difficulties in developing supervised machine learning algorithms.

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Binary Classification of Customer’s Online Purchasing Behavior Using Machine Learning

Cited by 16 publications

References 40 publications

Detecting Source Code Plagiarism in Student Assignment Submissions Using Clustering Techniques

Detecting Source Code Plagiarism in Student Assignment Submissions Using Clustering Techniques

Mobile based Malware Detection using Artificial Intelligence Techniques a review

Supervised Machine Learning a Brief Survey of Approaches

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