Browsing by Author "Karthikeyan S."

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Heuristic Technique to�Find Optimal Learning Rate of�LSTM for�Predicting Student Dropout Rate
    (Springer Science and Business Media Deutschland GmbH, 2024) Singh A.K.; Karthikeyan S.
    Predictive analytics is being increasingly recognized as being important for evaluating university students� academic achievement. Utilizing big data analytics particularly students� demographic information, offers valuable insights to bolster academic success and enhance completion rates. For instance, learning analytics is a vital element of big data within university settings, offering strategic decision-makers the chance to conduct time series analyses on learning activities. We have used semesters first and second records of Polytechnic Institute of Portalegre students. Advanced deep learning methods, such as the Long short-term memory (LSTM) model are employed to analyze students at risk of retention issues. Typically, the best design for a deep neural network model was found by trial and error, which is a laborious and exponential combinatorial challenge. Hence we proposed the heuristic technique to configure the parameters of the neural network. The parameter taken into account in this work is the learning rate of the Adam optimizer. In this study, we have presented the ant colony optimization (ACO) technique to determine the ideal learning rate for model training. Experimental results obtained with the predictive model indicated that prediction of student retention is possible with a high level of accuracy using ACO-LSTM approach. � The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
  • Thumbnail Image
    Item
    Leveraging Grover�s Algorithm for Quantum Searchable Encryption in Cloud Infrastructure and its application in AES Resource Estimation
    (Springer, 2024) Joshi M.; Mishra M.K.; Karthikeyan S.
    Designing efficient techniques to search over encrypted data space has always been an intriguing security challenge, although many solutions based on classical searching methods have been proposed. Grover�s algorithm, a quantum counterpart of searching algorithms, has proven to provide quadratic speedup over any classical search technique on an unsorted database. However, this algorithm is unable to search over encrypted data space. This study proposed an extension of Grover�s algorithm to enable search over encrypted dataspace, allowing clients with limited-capability quantum resources to delegate complex search operations to an untrusted server. The blindness of data in this protocol is achieved by encrypting qubits using Pauli�s rotation gates that maximally mix the outgoing states. The empirical estimation of the overhead of the computation due to the introduction of this technique has been analyzed. This estimate has been used for comparative analysis, showing the efficiency of the proposed protocol. A practical application of the proposed searchable encryption technique has been utilized to estimate the increase in resources needed to carry out a brute-force attack on AES encryption using secure Grover�s algorithm. Furthermore, an extensive experimental analysis of the effect of noise has been studied using four different noise models: amplitude damping, phase damping, depolarizing noise, and bit-flip noise. The investigation provided useful insight into the behavior of the proposed algorithm under noisy conditions and also estimated the tolerance thresholds of the proposed algorithm under different noise models. � The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.