21 results for "Supriadi Rustad"
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Journal Article
Optimizing aspect-based sentiment analysis using sentence embedding transformer, bayesian search clustering, and sparse attention mechanism
The aspect-based sentiment analysis of hotel reviews represents a critical yet underexplored area of research. Such reviews encompass a range of expressions that extend beyond simple positive, negative, or neutral sentiments, delving into specific aspects discussed within the review. For instance…
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Journal Article Full text
One-pot reductive amination of carbonyl compounds with nitro compounds over a Ni/NiO composite
Easily prepared Ni/NiO acts as a heterogeneous catalyst for the one-pot reductive amination of carbonyl compounds with nitroarenes to afford secondary amines with H(2) as a hydride source. This catalytic system does not require a special technique to avoid air-exposure, in contrast to the common…
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Journal Article
Improve semantic similarity based on statistical approach and LLM based transformer model for extractive summarization
This study addresses the need for effective extractive text summarization methods specifically designed for Indonesian news texts, driven by the increasing volume of digital information and persistent challenges related to semantic drift and coherence instability. A key challenge in extractive…
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Journal Article
Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors
This scientific paper aims to investigate the best machine learning (ML) for predicting the corrosion inhibition efficiency (CIE) value of amino acid compounds. The study applied a quantitative structure–property relationship (QSPR) model based on an ML approach to predict the CIE values of three…
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Variational quantum circuit-based quantum machine learning approach for predicting corrosion inhibition efficiency of pyridine-quinoline compounds
This work used a variational quantum circuit (VQC) in conjunction with a quantitative structure-property relationship (QSPR) model to completely investigate the corrosion inhibition efficiency (CIE) displayed by pyridine-quinoline compounds acting as corrosion inhibitors. Compared to conventional…
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Journal Article
Channel-spatial dual-attention for plant disease detection: CBAM-ECA integrated CNN models with visual explainability
Early detection of plant leaf diseases is critical for minimizing crop losses and supporting precision agriculture. While Convolutional Neural Networks (CNNs) have demonstrated high accuracy in image-based diagnosis, conventional architectures may not optimally balance spatial localization and…
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A feature restoration for machine learning on anti-corrosion materials
Materials informatics often struggles with small datasets. Our study introduces the Gaussian Mixture Model Virtual Sample Generation (GMM-VSG) approach to enhance feature correlation by generating virtual samples. Applied to six small and one large dataset of 218 N-heterocyclic compounds, GMM-VSG…
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Journal Article
A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds
In this investigation, a quantitative structure-property relationship (QSPR) model coupled with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating quantum chemical properties (QCP) features reduced computational burden…
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Journal Article
Review of automatic text summarization techniques & methods
Text summarization automatically produces a summary containing important sentences and includes all relevant important information from the original document. One of the main approaches, when viewed from the summary results, are extractive and abstractive. An extractive summary is heading towards…
Also available at: DOAJ
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Journal Article
Review of ambiguity problem in text summarization using hybrid ACA and SLR
Text summarization is the process of creating a text summary that contains important information from a text document. In recent years, significant progress has been made in the field of text summarization research, along with the challenges that drive research progress in the field at large. The…
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Journal Article
Inverted LSB image steganography using adaptive pattern to improve imperceptibility
Various image steganography methods have been developed to improve the stego image quality, i.e. imperceptibility, capacity, and security. This research proposes an adaptive method that can select the most optimal pattern to minimize the error ratio due to message embedding. This adaptive pattern…
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Journal Article
iLDA: A new dimensional reduction method for non-Gaussian and small sample size datasets
High-dimensional non-Gaussian data is widely found in the real world, such as in face recognition, facial expressions, document recognition, and text processing. Linear discriminant analysis (LDA) as dimensionality reduction performs poorly on non-Gaussian data and fails on high-dimensional data…
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Journal Article
Data enhancement to improve CNN accuracy for early detection of colorectal cancer
Multifactorial colorectal cancer is a frequently diagnosed disease that is a leading cause of cancer-related mortality worldwide. Optimizing the patient's chances of recovery requires early detection. This research aims to implement a Convolutional Neural Network (CNN) model that can classify colon…
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Journal Article
A machine learning approach for corrosion small datasets
Abstract In this work, we developed a QSAR model using the K-Nearest Neighbor (KNN) algorithm to predict the corrosion inhibition performance of the inhibitor compound. To overcome the small dataset problems, virtual samples are generated and added to the training set using a Virtual Sample…
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Journal Article Full text
Out-of-Time-Order Correlators as Indicators of Entropy in the Quantum Kicked Rotor
We show that Out-of-Time-Ordered Correlator (OTOC) growth in the Quantum Kicked Rotor (QKR) quantifies information scrambling rather than entropy production. Numerical simulations reproduce the quadratic OTOC scaling at resonance (ℏeff=4π) and its suppression under detuning. Bitstreams derived from…
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Journal Article
Synergistic interaction between a corrugated surface and hydrogen bonding affects selectivity in bond cleaving reaction: A case of coadsorbed hydrazine-OH on Ni(211) system
Investigating how a surface affects first N–H and N–N bond cleaving in hydrazine (N2H4) is a critical step towards designing a selective catalyst. In this study, the first N–H and N–N bond cleaving in hydrazine (N2H4) and hydroxyl (OH) coadsorption system on the Ni(211) surface is investigated…
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Journal Article
Acceptable noise level of quantum circuit for encrypting plaintext
This study investigates quantum cryptography using quantum circuits for encrypting and decrypting plaintext data. Various quantum gates were combined to form encryption and decryption circuits, demonstrating the potential of quantum cryptography to provide a robust security mechanism compared to…
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Journal Article
Stacking classical-quantum hybrid learning approach for corrosion inhibition efficiency of N-heterocyclic compounds
This study introduces the stacking classical-quantum model (SCQM) as a novel approach to predicting N-heterocyclic compounds' corrosion inhibition efficiency (CIE). SCQM integrates classical models such as Multi-Layer Perceptron Neural Network (MLPNN) and Random Forest (RF) as base learners, with…
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Journal Article
Quantum machine learning for corrosion resistance in stainless steel
This study evaluates the efficacy of quantum machine learning (QML) models in predicting stainless steel corrosion behaviour. Using two datasets, the quantum support vector classifier (QSVC) outperformed classical models, achieving accuracies of 95.46 % and 94.80 % for Dataset A and Dataset B…