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Your search keyword '"Stamenkovic, Z."' showing total 114 results
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114 results on '"Stamenkovic, Z."'

2. A Novel Ensemble Machine Learning Algorithm for Predicting the Suitable Crop to Cultivate Based on Soil and Environment Characteristics.

Author

Mariammal, G., Suruliandi, A., Stamenkovic, Z., and Raja, S. P.

Subjects
MACHINE learning, SUPPORT vector machines, ENVIRONMENTAL soil science, PREDICTION models, CLIMATE change
Abstract

Research in agriculture is a promising field, and crop prediction for particular land areas is especially critical to agriculture. Such prediction depends on the soil, minerals, and environment, the last of which has been short-changed by changing climatic conditions. Consequently, crop prediction for a particular zone presents difficulties for farmers. This is where machine learning (ML) steps in with techniques that are widely applied in agriculture. This work proposes a weighted stacked ensemble (WSE) method for the crop prediction process. It combines two base learners or classifiers to construct the WSE, which is a single predictive ensemble model, using weighted instances. The experimental outcomes show that the proposed WSE outperforms other classification and ensemble techniques in terms of improved crop prediction accuracy. [ABSTRACT FROM AUTHOR]

Published
2024
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6. A Feature Extraction Approach for the Detection of Phishing Websites Using Machine Learning.

Author

Gundla, Sri Charan, Karthik, M. Praveen, Reddy, Middi Jashwanth Kumar, Gourav, Pankaj, Ashutosh, Stamenkovic, Z., and Raja, S. P.

Subjects
PHISHING, UNIFORM Resource Locators, PHISHING prevention, FEATURE extraction, MACHINE learning, CYBERTERRORISM
Abstract

In this growing world of the internet, most of our daily routine tasks are somehow connected to the internet, from smartphones to internet of things (IoT) devices to cloud networks. Internet users are growing rapidly, and the internet is accessible to everyone from anywhere. Data phishing is a cyber security attack that uses deception to trick internet users to get their content and information. In this attack, malicious users try to steal personal data such as login credentials, credit card details, health care information, etc., of the users on the internet. They exploit users' sensitive information using vulnerabilities. Information stealers are known as phishers. Phishers use different techniques for phishing. One of the most common methods is to direct the users to a false website to enter their login credentials and their details on these phishing sites. Phishing websites look like the original websites. Phishers use these details to get access to the user's accounts and hijack them for monetary purposes. Many internet users fall for this trap of phishing sites and share their personal and sensitive details. In this paper, we will analyze and implement machine learning (ML) techniques to detect phishing attacks. There are different methods to identify phishing attacks, one of them is by checking the uniform resource locator (URL) address using ML. ML is used to teach a machine to differentiate between phishing and original site URLs. There are many different techniques to overcome this attack. This research paper aims to provide accurate and true phishing detection with less time complexity. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Burden of alcohol use disorder in Balkan countries

Author

Todorovic, J, primary, Santric- Milicevic, M, additional, Terzic-Supic, Z, additional, Stevanovic, A, additional, Stamenkovic, Z, additional, Terzic, N, additional, Scepanovic, L, additional, Albreht, T, additional, and Stoisavljevic, S, additional

Published
2022
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8. Burden of Depressive disorders in Balkan countries

Author

Todorovic, J, primary, Santric-Milicevic, M, additional, Stevanovic, A, additional, Terzic-Supic, Z, additional, Stamenkovic, Z, additional, Albreht, T, additional, Stoisavljevic, S, additional, Terzic, N, additional, and Scepanovic, L, additional

Published
2022
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18. Modeling of integrated circuit yield loss mechanisms

Author

Stamenkovic, Z., Stojadinovic, N., and Dimitrijev, S.

Subjects
Integrated circuit fabrication -- Planning, Yield-line analysis -- Models, Production planning -- Models, Industrial productivity -- Models, Business, Computers, Electronics, Electronics and electrical industries
Published
1996

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