Analysis of Enrollment Criteria in Secondary Schools Using Machine Learning and Data Mining Approach

Abideen, Zain Ul; Mazhar, Tehseen; Razzaq, Abdul; Haq, Inayatul; Ullah, Inam; Alasmary, Hisham; Mohamed, Heba G.

doi:10.3390/electronics12030694

Cited by 14 publications

(9 citation statements)

References 45 publications

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“…In the last stage of an attack, the attacker uses a specific attack technique, like a backdoor, virus, or Trojan horse, to get unrestricted access to the target system. Installing a backdoor or other undetected malware enables quick and easy access to the target ( Abideen et al, 2023 ). Let’s say the enemy successfully surrounds and controls the SCADA server.…”

Section: Resultsmentioning

confidence: 99%

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Security risk models against attacks in smart grid using big data and artificial intelligence

Yasin Ghadi,

Mazhar,

Aurangzeb

et al. 2024

PeerJ Computer Science

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The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend against cyber threats. Therefore, a more flexible approach is required to assess data sets and identify hidden risks. This is possible for vast amounts of data due to recent developments in artificial intelligence, machine learning, and deep learning. Due to adaptable base behavior models, machine learning can recognize new and unexpected attacks. Security will be significantly improved by combining new and previously released data sets with machine learning and predictive analytics. Artificial Intelligence (AI) and big data are used to learn more about the current situation and potential solutions for cybersecurity issues with smart grids. This article focuses on different types of attacks on the smart grid. Furthermore, it also focuses on the different challenges of AI in the smart grid. It also focuses on using big data in smart grids and other applications like healthcare. Finally, a solution to smart grid security issues using artificial intelligence and big data methods is discussed. In the end, some possible future directions are also discussed in this article. Researchers and graduate students are the audience of our article.

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Section: Resultsmentioning

confidence: 99%

“…It is important to remember that certain variables, such as the coupling, surroundings, and cable age, cannot be recreated under any circumstances. The programmable logic controller (PLC) technology ( Abideen et al, 2023 ) was made. This innovation was made possible by the technology.…”

Section: Related Workmentioning

confidence: 99%

Security risk models against attacks in smart grid using big data and artificial intelligence

Yasin Ghadi,

Mazhar,

Aurangzeb

et al. 2024

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

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“…Systems based on the IoT are having trouble because of poor management. The problem is that most of the time, software engineers try to figure out how to extract useful data from sensors [ 138 ]. They do not care how data are gathered, just that it is.…”

Section: Resultsmentioning

confidence: 99%

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Mazhar

Talpur

Shloul³

et al. 2023

Brain Sciences

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The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website’s content as a technical resource and reference.

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“…EDM utilizes data collected from educational environments to reveal valuable and relevant knowledge [12]. Educational Data Mining (EDM) uses data mining (DM) methods to gain a better understanding of student behavior and learning environments [13]. DM enables education practitioners to identify the key factors that affect student performance.…”

Section: Introductionmentioning

confidence: 99%

Students Final Academic Score Prediction Using Boosting Regression Algorithms

Muhammady,

Nugraha,

Nastiti

et al. 2024

J. Ilm. Tek. Elektro Komput. Dan Inform

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Academic grades are crucial in education because they assist students in acquiring the knowledge and skills necessary to succeed in school and their future. Accurately predicting students' final academic performance grade score is important for educational decision-makers. However, creating precise prediction models based on students' historical data can be challenging due to the complex nature of academic data. This research analyzes student academic data totaling 649 Portuguese language course student data that has been processed according to data requirements which are then predicted using XGBoost Regressor, Light Gradient Boosting Machine (LGBM), and CatBoost. This research aims to develop a robust prediction model that can effectively predict students' final academic performance. This research offers valuable insights into the factors that influence academic success and provides practical implications for educational institutions looking to improve their decision-making processes. The prediction requires identifying key predictors of academic performance, such as previous grades, attendance records, and socio-economic background. The research makes a contribution by improving the matrix MAE in this research is less than the previous research from 2.2 average each algorithm to 0.22 average, this less MAE means the better model. The research achieved MAE score of 0.22 average. In conclusion, this research is expected to address the challenge of predicting student academic performance through the application of advanced machine learning techniques. The results provide valuable insights for decision-makers in education and highlight the importance of a data-driven approach to improving academic performance. By utilizing machine learning algorithms, educational institutions can effectively support student learning and success.

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Analysis of Enrollment Criteria in Secondary Schools Using Machine Learning and Data Mining Approach

Cited by 14 publications

References 45 publications

Security risk models against attacks in smart grid using big data and artificial intelligence

Security risk models against attacks in smart grid using big data and artificial intelligence

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Students Final Academic Score Prediction Using Boosting Regression Algorithms

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