Heart disease is a leading global cause of death, highlighting the need for accurate and efficient risk assessment methods. Traditional models often fail to address the uncertainty and vagueness in medical data. A Fuz...
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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
Skin tones come in a diverse range of shades and are often necessary for various computer vision tasks. While skin detection is a well-studied focus, skin tone classification is not. Most works also use the Fitzpatric...
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Backdoor attacks threaten federated learning (FL) models, where malicious participants embed hidden triggers into local models during training. These triggers can compromise crucial applications, such as autonomous sy...
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Backdoor attacks threaten federated learning (FL) models, where malicious participants embed hidden triggers into local models during training. These triggers can compromise crucial applications, such as autonomous systems, when they activate specific inputs, causing a targeted misclassification in the global *** recommend a strong defense mechanism that combines statistical testing, model refinement, and adversarial training methods. The primary goal is to develop a robust defense against backdoor attacks in federated learning (FL), where malicious participants embed hidden triggers into local models. This defense aims to preserve the integrity of the global model and ensure high reliability in real-world FL deployments, even when facing sophisticated adversarial strategies. Our defense strategy incorporates "Messy" samples with obvious triggers and "wrap" samples with similar but nonidentical triggers during adversarial training. This dual approach enhances the model’s ability to detect and resist hidden manipulations. We facilitate applying neuron pruning to remove compromised neurons, further refining the model architecture for improved security. Continuous statistical testing, including variance analysis and cosine similarity checks, ensures that only legitimate and significant updates are integrated into the global model. A key innovation of our method is a significance-based filtering mechanism that effectively identifies and excludes malicious updates, preventing backdoor triggers from affecting the global model. This iterative defense process adapts to attack strategies, maintaining the model’s robustness. Empirical results confirm that this defense mechanism significantly improves FL models’ resilience to sophisticated backdoor attacks while preserving high accuracy and reliability. Balancing defensive strategies from adversarial training and sample diversification to model pruning provides a dependable framework for safeguarding FL models where integ
The efforts for data transparency and open government initiatives have resulted in a large amount of data being published on open data portals. These portals are organized to enhance published data accessibility by pr...
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Earth observation (EO) data have seen a constant surge in volume, necessitating efficient storage, retrieval, and sharing mechanisms. Cartographic projections play a vital role in transforming spheroidal surface data ...
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Fine-Tuning of large language models is often demanding in terms of computational resources and memory. Consequently, there is a need to explore new methods that can effectively fine-Tune these models without compromi...
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Open data initiatives have resulted in a large amount of data being published on open data portals. In order to make published data more accessible these portals provide search mechanisms based on metadata like catego...
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An intelligent robotic vehicle with an ultrasonic sensor that can avoid obstacles in its path is the research idea. This sensor recognizes obstructions, permitting the vehicle to perform activities like halting, turni...
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This systematic literature review delves into the dynamic realm of graphical passwords, focusing on the myriad security attacks they face and the diverse countermeasures devised to mitigate these threats. The core obj...
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