This study, using the Naïve Bayes classifier, proposes a new descriptive model for conducting a comparative review analysis on the tourism domain. The proposed model seeks to improve the understanding of tourists...
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This study, using the Naïve Bayes classifier, proposes a new descriptive model for conducting a comparative review analysis on the tourism domain. The proposed model seeks to improve the understanding of tourists' experiences by visually representing their opinions between various tourist destinations. The model predicts the sentiment of tourist reviews using the Naive Bayes classifier, which is then used to generate visualizations that facilitate comparative analysis. The comparison takes two tourism sites as experimental examples. The results, in terms of accuracy, show 80% and 64% for the first and the second site respectively. The study discusses the model's implementation, effectiveness, and potential applications in the tourism industry. The findings of the study suggest that the proposed model can provide valuable insights into tourist experiences and help tourism stakeholders, such as the managements of tourism sites, the future or soon coming tourists to make informed decisions based on comparative analysis.
The term "personality" may be expressed in terms of the individual differences in characteristics pattern of thinking, feeling, and behavior. This work presents several machine learning techniques including ...
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Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and e...
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Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear
Based on the policy of independent study and independent campus, where students can participate in part of the study period and load from within the campus, as well as others outside the campus, so that they can compl...
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The development of the internet made new problems within the scope of copyright. The internet is used to download copyrighted content and use it without permission from the creators. The legal protection of copyright ...
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Sonic wave travel-time prediction is an important task in oil and gas exploration as it provides important information on the content and lithography of the rocks. Travel-time data, however, are not always accessible ...
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ISBN:
(纸本)9781665486644
Sonic wave travel-time prediction is an important task in oil and gas exploration as it provides important information on the content and lithography of the rocks. Travel-time data, however, are not always accessible due to practical considerations. Currently, machine learning methods have been used to infer these values. In this paper, we look at the application of machine learning in predicting sonic wave travel-time, specifically in terms of challenges, benchmarks, and datasets. In addition, we present some preliminary results of sonic wave travel-time prediction using existing machine learning regression methods, namely curve fitting artificial neural network and multiple linear regression. Finally, this paper is aimed to act as a ”bridge” between machine learning practitioners and domain-specific oil and gas engineers.
A test of quantumness is a protocol where a classical user issues challenges to a quantum device to determine if it exhibits nonclassical behavior, under certain cryptographic assumptions. Recent attempts to implement...
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A test of quantumness is a protocol where a classical user issues challenges to a quantum device to determine if it exhibits nonclassical behavior, under certain cryptographic assumptions. Recent attempts to implement such tests on current quantum computers rely on either interactive challenges with efficient verification or noninteractive challenges with inefficient (exponential time) verification. In this paper, we execute an efficient noninteractive test of quantumness on an ion-trap quantum computer. Our results significantly exceed the bound for a classical device's success.
This research raises problems among Baitul Mal aid recipients regarding aid to micro businesses. The process of assisting is an important activity to develop micro-businesses so that they can continue to operate and d...
This research raises problems among Baitul Mal aid recipients regarding aid to micro businesses. The process of assisting is an important activity to develop micro-businesses so that they can continue to operate and develop. Selection is also needed to prevent aid from being distributed incorrectly. Therefore, the proposed solution is to utilize data mining techniques, especially classification methods (clustering) to identify potential aid recipients who truly deserve to receive aid. This research also uses the Principal Component Analysis (PCA) method to reduce attribute dimensions in the dataset to increase the accuracy of the classification process. By optimizing the classification process through appropriate attribute reduction, this research is expected to make a positive contribution to increasing the efficiency and accuracy of credit risk assessment in the banking sector. The results of this research are: accuracy after the dataset has been reduced by 2 (two) attributes has a higher value when compared to the dataset that has not been reduced, this happens because the attribute reduction process is carried out to reduce attributes that are not or less relevant. So it can be said that the reduction process influences the accuracy of the C5.0 algorithm classification results.
Since the RSA algorithm is no longer secure and a lot of research has been done to break down RSA security, it is necessary to do research by making a combination of modifications of RSA algorithms, in this case using...
Since the RSA algorithm is no longer secure and a lot of research has been done to break down RSA security, it is necessary to do research by making a combination of modifications of RSA algorithms, in this case using enhanced RSA using fake modules, with RCCM (RC4 Chaotic Map) algorithms to produce a Hybrid Cryptosystem that is expected to be able to improve the security of secret messages. The combination process is done by encrypting the messages using the RCCM algorithm first, then the messages that have been encrypted back in the encryption using the enhanced RSA algorithm with fake modules. So the security of messages has been improved. As for the results of this study, the key generation process is automatically generated which produces keystream for RCCM and fake public key and fake private key for enhanced RSA. In addition, what distinguishes enhanced RSA from ordinary RSA is the use of four primes p, q, r, and s. as well as the application of chaotic maps to RCCM gives the advantage of the unnecessary use of generating keys manually. With these keys, the process of encryption and decryption can be carried out to generate a secret message. In addition, the program resulting from this research applies the kraitchik factorization algorithm as a medium of attack against the key. The conclusion of this study is that by doing a hybrid cryptosystem enhanced RSA and RCCM can be safer due to the occurrence of double encryption processes. In addition, an enhanced RSA algorithm determines a fake module where the value of X is a prime number so that if attacked using a kraitchik factorization algorithm it is difficult to find a factorization of X values that corresponds to the number of p, q, r, and s.
Facial image super-resolution is a crucial preprocessing for facial image analysis, face recognition, and image-based 3D face reconstruction. Convolutional neural networks were earlier used to produce high-resolution ...
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