As the prevalence of diabetes mellitus rises, more families are being impacted. Most diabetics don't know much about their health situation or the risks they face before getting a diagnosis. A unique data mining-b...
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The lack of symptoms in the early stages of liver disease may cause wrong diagnosis of the disease by many doctors and endanger the health of patients. Therefore, earlier and more accurate diagnosis of liver problems ...
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The faculty of computerscience, Universitas Brawijaya (Filkom UB) is committed to providing quality services for the users especially internal and external stakeholders, one of which is through the HaloFilkom service...
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ISBN:
(纸本)9798350379914
The faculty of computerscience, Universitas Brawijaya (Filkom UB) is committed to providing quality services for the users especially internal and external stakeholders, one of which is through the HaloFilkom service. HaloFilkom services have limitations in terms of time. HaloFilkom services are not available 24 hours due to limited working hours. Questions asked by users are not answered directly. This weakness in the HaloFilkom system can be overcome by using a chatbot system. Chatbot is an interactive system that works with natural human language and can work 24 hours. Thus, the current study explores the basic chatbot model by classifying the Q&A in the closed domain knowledge. The dataset in this research is in the form of pairs of questions and answers regarding various topics at the Filkom UB. The knowledge is preprocessed using text preprocessing which includes case folding, tokenization, padding, and tensorization. One of the chatbot models is a generative model. Creating a generative chatbot model can be done using the Seq2Seq model mechanism which consists of an encoder and decoder. The model created consists of four different architectures, namely a model with an LSTM encoder without attention and with attention and a BiLSTM model encoder without attention and with attention. Hyperparameter tuning was conducted to obtain the best hyperparameter combination. The experiment results show the best hyperparameter combination obtained is hidden size 448, drop out rate 0.5, learning rate 0.001, batch size 64, and teacher force 0. The model with the best loss is obtained with a BiLSTM encoder architecture without an attention mechanism with a train loss of 0.120. The model with the highest BLEU Score was obtained by a model with a BiLSTM encoder architecture without an attention mechanism with a BLEU Score of 0.8587 on the training data. Testing using prompt testing obtained an average BLEU Score of 0.3745 on the BiLSTM encoder without an attention mechanism mo
In this work, we propose a hybrid method to fast solve the optimization of array located on the PEC carrier inside the dielectric radome with parameters variation. By constructing a reusable low-rank reduced-order mod...
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Mobile Edge Computing (MEC) has become an indispensable way to reduce the execution delay of devices. However, for some devices located far away from the MEC server, the transmission delay of communication with MEC is...
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Traditional volume surface integral equation for analysis of metal-dielectric composites requires the target mesh to be conformal, which leads to over-meshing of the multi-scale model and reduces the solution efficien...
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To expedite the characteristic mode analysis (CMA) of electromagnetic target structures, this paper combines frequency- and material-independent reactions (FMIR) with characteristic mode analysis using the volume-surf...
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This tertiary systematic literature review examines 29 systematic literature reviews and surveys in Explainable Artificial Intelligence (XAI) to uncover trends, limitations, and future directions. The study explores c...
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In this work we consider a scheme for unsourced random access (uRA) in cell free (CF) wireless networks, which is conceptually reminiscent of the 2-step RACH scheme defined in 3GPP for cellular networks. During the de...
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With the breakthrough of convolutional neural networks, deep hashing methods have demonstrated remarkable performance in large-scale image retrieval tasks. However, existing deep supervised hashing methods, which rely...
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