Search Results - (( fraud detection learning algorithm ) OR ( java application testing algorithm ))

Detection of Fraud and Non-Fraud Transaction using Machine Learning Algorithm by Sujang Karing, Darryl

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Fraud detection in shipping industry based on location using machine learning comparison techniques by Ganesan Subramaniam, Mr.

“…Results from this study indicates the usage of k-NN is proven to be most effective algorithm in terms of performance and accuracy required by the shipping industry fraud detection.…”

Credit Card Fraud Detection Using AdaBoost and Majority Voting by Randhawa, Kuldeep, Loo, Chu Kiong, Seera, Manjeevan, Lim, Chee Peng, Nandi, Asoke K.

“…In this paper, machine learning algorithms are used to detect credit card fraud. …”
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Credit Card Fraud Detection Using New Preprocessing And Hybrid Machine Learning Techniques by Gasim, Esraa Faisal Malik

“…The second contribution to this research is to develop multiple hybrid machine learning models in order to enhance the detection of fraudulent activities in the credit card fraud detection domain.…”
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A Location-Based Fraud Detection in Shipping Industry Using Machine Learning Comparison Techniques by Subramaniam G.A.L., Mahmoud M.A., Abdulwahid S.N., Gunasekaran S.S.

Abnormalities and fraud electric meter detection using hybrid support vector machine & genetic algorithm by Yap K.S., Abidin I.Z., Ahmad A.R., Hussien Z.F., Pok H.L., Ismail F.I., Mohamad A.M.

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A voting-based hybrid machine learning approach for fraudulent financial data classification / Kuldeep Kaur Ragbir Singh by Kuldeep Kaur , Ragbir Singh

“…Standard base machine learning algorithms, which include a total of twelve individual methods as well as the AdaBoost and Bagging methods, are firstly used. …”
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Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo by Terence, Yong Koon Beh, Swee, Chuan Tan, Hwee, Theng Yeo

“…This paper reports our experience in applying data balancing techniques to develop a classifier for an imbalanced real-world fraud detection data set. We evaluated the models generated from seven classification algorithms with two simple data balancing techniques. …”
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Development of an intelligent information system for financial analysis depend on supervised machine learning algorithms by Lei, X., Mohamad, U.H., Sarlan, A., Shutaywi, M., Daradkeh, Y.I., Mohammed, H.O.

“…In the financial sector, machine learning algorithms are used to detect fraud, automate trading, and provide financial advice to investors. …”
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RSA Encryption & Decryption using JAVA by Ramli, Marliyana

“…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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A comparative analysis of anti-phishing website techniques: identifying optimal approaches to enhance cybersecurity by Yau, Jia Xin

“…Consequently, it is critical to identify effective detection techniques for fraudulent websites. The research consists of analysing the characteristics of phishing websites, extracting their essential features using the wrapper method, and classifying websites as phishing or legitimate using supervised and unsupervised learning algorithms. …”
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Comparison of Search Algorithms in Javanese-Indonesian Dictionary Application by Yana Aditia, Gerhana, Nur, Lukman, Arief Fatchul, Huda, Cecep Nurul, Alam, Undang, Syaripudin, Devi, Novitasari

“…Performance Testing is used to test the performance of algorithm implementations in applications. …”
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Student attendance system using facial recognition based on deep learning / Syahila Aina Haris and Zulfikri Paidi by Haris, Syahila Aina, Paidi, Zulfikri

“…In this suggested system, K-NN is used to categorize student faces, deep metric learning is used to build facial embedding, and Convolutional Neural Network (CNN) is used to detect faces in photos. …”
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Intelligent deep machine learning cyber phishing URL detection based on BERT features extraction by Muna Elsadig, Ashraf Osman Ibrahim Elsayed, Shakila Basheer, Manal Abdullah Alohali, Sara Alshunaifi, Haya Alqahtani, Nihal Alharbi, Wamda Nagmeldin

“…In this paper, a novel URL phishing detection technique based on BERT feature extraction and a deep learning method is introduced. …”
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Efficient ML technique in blockchain-based solution in carbon credit for mitigating greenwashing by Raja Segaran, Bama, Mohd Rum, Siti Nurulain, Hafez Ninggal, Mohd Izuan, Mohd Aris, Teh Noranis

“…However, while blockchain ensures transparency, it lacks real-time anomaly detection capabilities. ML algorithms, particularly supervised models such as Random Forest, XGBoost, and Neural Networks, are well-suited for detecting fraudulent patterns and verifying the authenticity of forest carbon credit transactions. …”
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Java based expert system for selection of natural fibre composite materials by Ahmed Ali, Basheer A., Salit, Mohd Sapuan, Zainudin, Edi Syams, Othman, Mohamed

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Implementation of (AES) Advanced Encryption Standard algorithm in communication application by Moh, Heng Huong

“…The concept of ABS algorithm was firstly studied, including the definition, historical background, and a brief comparison was made between the ABS algorithm with other types of algorithm. …”
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Discovering optimal features using static analysis and a genetic search based method for Android malware detection by Firdaus, Ahmad, Anuar, Nor Badrul, Karim, Ahmad, Razak, Mohd Faizal Ab

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An enhanced android botnet detection approach using feature refinement by Anwar, Shahid

“…Although the existing analyses have been obtained a good accuracy, but the attackers find novel ways of skipping the detection while performing harmful activities. Furthermore, the existing detection techniques can detect only malicious Android applications, while they are unable to detect the Android botnet applications. …”
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Classification System for Heart Disease Using Bayesian Classifier by Magendram, Anusha

“…This system was developing base on to three main part which is data processing, testing and implementation of the algorithm. In this system a Bayesian algorithm was used in order to implement the system. …”
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