Application of Machine Learning algorithms for betterment in education system

Halde, Radhika R

doi:10.1109/icacdot.2016.7877759

Cited by 20 publications

(16 citation statements)

References 12 publications

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“…Spor alanında bir çalışma da ise araştırmacılar futbolcuların pozisyonlarını belirlemek için yığılmış topluluk öğrenme modeli önermişler ve 83.9% sınıflandırma başarımı elde etmişlerdir [26]. Eğitim alanında gerçekleştiren bir başka çalışmada ise akademik enstitüler tarafından toplanan büyük miktarda veri üzerinde farklı Makine Öğrenimi algoritmalarının uygulanmasına ilişkin bir literatür taraması sunulmuştur [27].…”

Section: Elektroensefalografiunclassified

EEG sinyallerini kullanarak Alzheimer hastalığının otomatik tespiti için bilgisayar destekli tanı sistemi

Aslan

2022

DÜMF MD

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Alzheimer beyindeki bozulmalardan kaynaklı bilişsel ve davranışsal eksiklikler gibi semptomlarla kendini gösteren önemli bir nörolojik hastalıktır. Alzheimer hastalığının kesin bir tedavi yöntemi bulunmamaktadır. Ancak hastalığın erken teşhisi ile hastalığın ilerlemesinin yavaşlatılması amaçlanmaktadır. Bu durum hastanın yaşam standartlarının korunmasında önem arz etmektedir. Ayrıca hastalığın tam olarak teşhisi deneyimli bir uzman tarafından değerlendirilecek olan maliyetli testler ve yorucu bir teşhis aşaması gerektirmektedir. Bu motivasyonla önerilen yöntemle Alzheimer hastalığının EEG sinyallerinden otomatik olarak gerçekleştirilmesini amaçlayan yeni bir bilgisayar destekli tanı sistemi sunulmaktadır. Sunulan çalışmada öncelikle ham EEG verilerine önişlem uygulanarak var olan gürültüler giderilmiştir. Sonraki aşamada ise her bir kanaldan alınan verilere dalgacık dönüşümü uygulandıktan sonra istatistiksel özellikler hesaplanmıştır. Elde edilen özelliklerin k-en yakın komşu (kNN) sınıflandırıcısı ile sınıflandırılmasıyla sağlıklı katılımcılar ile Alzheimer hastası katılımcılar 91.12% doğrulukla ayırt edilmiştir.

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Section: Elektroensefalografiunclassified

EEG sinyallerini kullanarak Alzheimer hastalığının otomatik tespiti için bilgisayar destekli tanı sistemi

Aslan

2022

DÜMF MD

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“…Recently, many machine learning (ML)-based approaches have been proposed for device simulation to shorten the turnaround time (TAT). ML has emerged as an efficient method that is having applications in different fields such as healthcare [20], agriculture [21], cybersecurity [22], education [23], data governance [24], finance [25], marketing [26], etc. In the past few years, it has also been used significantly in the semiconductor industry to solve modeling and optimization problems [27]- [33].…”

Section: A Preliminaries and Related Workmentioning

confidence: 99%

A Machine Learning Approach to Modeling Intrinsic Parameter Fluctuation of Gate-All-Around Si Nanosheet MOSFETs

Butola

Kola

2022

IEEE Access

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The sensitivity of semiconductor devices to any microscopic perturbation is increasing with the continuous shrinking of device technology. Even the small fluctuations have become more acute for highly scaled nano-devices. Therefore, these fluctuations need to be addressed extensively in order to continue further device scaling. In this paper, we mainly focus on three intrinsic parameter fluctuation sources, work function fluctuation (WKF), random dopant fluctuation (RDF), and interface trap fluctuation (ITF) for gate-all-around (GAA) silicon nanosheet (NS) MOSFETs. Generally, the effect of these fluctuations is analyzed using a time-consuming device simulation process. A machine learning (ML) based powerful and efficient artificial neural network (ANN) model is used to accelerate this process. Firstly, the effects of fluctuation sources are analyzed individually by using the ANN model and results have been presented that show the WKF variations dominate the threshold voltage (VTH), off-state current (IOFF), and on-state current (ION) variations among other fluctuation sources. Next, we examine the combined effect of all three fluctuation sources. It is crucial because considering only one fluctuation can result in unexpected variations due to other fluctuations present in the device. Therefore, the ANN model is used to estimate the combined effects as well. The results show that the proposed model predicts the outputs with an R 2 -score of 99% and an error rate of less than 1%. In addition, the ML is also utilized to calculate the permutation importance of input variables as a measure to investigate the contribution of each fluctuation source.

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“…Halde [20] gave analysis of various ways in which machine learning can be applied by educational institutions to predict students' performance and the critical characteristics that have to be considered while making such predictions.…”

Section: Related Literaturementioning

confidence: 99%

Prospects and Challenges of Using Machine Learning for Academic Forecasting

Onyema

Almuzaini

Onu

et al. 2022

Computational Intelligence and Neuroscience

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The study examines the prospects and challenges of machine learning (ML) applications in academic forecasting. Predicting academic activities through machine learning algorithms presents an enhanced means to accurately forecast academic events, including the academic performances and the learning style of students. The use of machine learning algorithms such as K-nearest neighbor (KNN), random forest, bagging, artificial neural network (ANN), and Bayesian neural network (BNN) has potentials that are currently being applied in the education sector to predict future events. Many gaps in the traditional forecasting techniques have greatly been bridged by the use of artificial intelligence-based machine learning algorithms thereby aiding timely decision-making by education stakeholders. ML algorithms are deployed by educational institutions to predict students’ learning behaviours and academic achievements, thereby giving them the opportunity to detect at-risk students early and then develop strategies to help them overcome their weaknesses. However, despite the benefits associated with the ML approach, there exist some limitations that could affect its correctness or deployment in forecasting academic events, e.g., proneness to errors, data acquisition, and time-consuming issues. Nonetheless, we suggest that machine learning remains one of the promising forecasting technologies with the power to enhance effective academic forecasting that would assist the education industry in planning and making better decisions to enrich the quality of education.

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Application of Machine Learning algorithms for betterment in education system

Cited by 20 publications

References 12 publications

EEG sinyallerini kullanarak Alzheimer hastalığının otomatik tespiti için bilgisayar destekli tanı sistemi

EEG sinyallerini kullanarak Alzheimer hastalığının otomatik tespiti için bilgisayar destekli tanı sistemi

A Machine Learning Approach to Modeling Intrinsic Parameter Fluctuation of Gate-All-Around Si Nanosheet MOSFETs

Prospects and Challenges of Using Machine Learning for Academic Forecasting

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