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Mathematical Study for Proving Correctness of the Serial Graph-Validation Queue Scheme Salsabila, Fitra Nuvus; Bukhari, Fahren; Nurdiati, Sri
Journal of the Indonesian Mathematical Society Vol. 31 No. 2 (2025): JUNE
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.v31i2.1592

Abstract

Numerous studies have been conducted to develop concurency control schemes that can be applied to client-server systems, such as the Validation Queue (VQ) scheme, which uses object caching on the client side. This scheme has been modified into the Serial Graph-Validation Queue (SG-VQ) scheme, which employs validation algorithms based on queues on the client side and graphs on the server side. This study focuses on verifying the correctness of the SG-VQ scheme by using serializability as a mathematical tool. The results of this study demonstrate that the SG-VQ scheme can execute its operations correctly, in accordance with Theorem 4.16, which states that every history (H) of SG-VQ is serializable. Implementing a cycle-free transaction graph is a necessary and sufficient condition to achieve serializability. To prove Theorem 4.16, mathematical statements involving ten definitions, two propositions, and three lemmas have been formulated.
PERFORMANCE COMPARISON OF GRADIENT-BASED CONVOLUTIONAL NEURAL NETWORK OPTIMIZERS FOR FACIAL EXPRESSION RECOGNITION Nurdiati, Sri; Najib, Mohamad Khoirun; Bukhari, Fahren; Revina, Refi; Salsabila, Fitra Nuvus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1086.562 KB) | DOI: 10.30598/barekengvol16iss3pp927-938

Abstract

A convolutional neural network (CNN) is one of the machine learning models that achieve excellent success in recognizing human facial expressions. Technological developments have given birth to many optimizers that can be used to train the CNN model. Therefore, this study focuses on implementing and comparing 14 gradient-based CNN optimizers to classify facial expressions in two datasets, namely the Advanced Computing Class 2022 (ACC22) and Extended Cohn-Kanade (CK+) datasets. The 14 optimizers are classical gradient descent, traditional momentum, Nesterov momentum, AdaGrad, AdaDelta, RMSProp, Adam, Radam, AdaMax, AMSGrad, Nadam, AdamW, OAdam, and AdaBelief. This study also provides a review of the mathematical formulas of each optimizer. Using the best default parameters of each optimizer, the CNN model is trained using the training data to minimize the cross-entropy value up to 100 epochs. The trained CNN model is measured for its accuracy performance using training and testing data. The results show that the Adam, Nadam, and AdamW optimizers provide the best performance in model training and testing in terms of minimizing cross-entropy and accuracy of the trained model. The three models produce a cross-entropy of around 0.1 at the 100th epoch with an accuracy of more than 90% on both training and testing data. Furthermore, the Adam optimizer provides the best accuracy on the testing data for the ACC22 and CK+ datasets, which are 100% and 98.64%, respectively. Therefore, the Adam optimizer is the most appropriate optimizer to be used to train the CNN model in the case of facial expression recognition.
PROVING THE CORRECTNESS OF THE EXTENDED SERIAL GRAPH-VALIDATION QUEUE SCHEME IN THE CLIENT-SERVER SYSTEM Salsabila, Fitra Nuvus; Bukhari, Fahren; Nurdiati, Sri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1359-1368

Abstract

Numerous studies have been conducted to develop concurrency control schemes that can be applied to client-server systems, such as the Extended Serial Graph-Validation Queue (SG-VQ) scheme. Extended SG-VQ is a control concurrency scheme in client-server system which implements object caching on the client side and locking strategy on the server side. This scheme employs validation algorithms based on queues on the client side and graphs on the server side. This research focuses on the mathematical analysis of the correctness of the Extended SG-VQ scheme using serializability as the criterion that needs to be achieved. Implementing a cycle-free transaction graph is a necessary and sufficient condition to achieve serializability. In this research, the serializability of the Extended SG-VQ scheme has been proven through the exposition of ten definitions, two propositions, three lemmas, and one theorem.