Since cloud computing becoming the trend, the way servers being implemented slowly moves to the cloud. Companies did not need to buy a physical server machine to deploy an app. Having a private server on cloud infrast...
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The scenario of online learning is a very urgent need in the world of future knowledge. Since the Corona Virus Disease-19 pandemic, the world economy has started to plummet and caused many adults to lose their jobs. T...
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With the large amount of information available on the internet, recommendation tasks have grown to be more crucial than ever. Businesses that store digital media on the internet such as video streaming and music strea...
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Social media is an online media that functions as a platform for users to participate, share, create, and exchange information through various forums and social networks. The rapid increase in social media activity ca...
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Competitive programming (CP) is a mind sports activity where people solve problems using command-line computerprograms to provide correct output for the given test cases. Competitors need to practice problem-solving ...
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Competitive programming (CP) is a mind sports activity where people solve problems using command-line computerprograms to provide correct output for the given test cases. Competitors need to practice problem-solving and mathematics as well as study algorithms and data structures to perform well in CP. This study aims to provide an original way to perform a trend analysis in CP, distinguishing topics frequently used in CP contests. To fulfill our goal, we create topic models based on previous topic modeling works to do natural language processing tasks using Latent Dirichlet Allocation (LDA) and Biterm Topic Model (BTM). For our dataset, we constructed a corpus from Codeforces blog posts, a popular website for competitive programmers, by extracting its content and user comments. Our results indicate that BTM is powerful enough to do trend analysis in CP. The trend analysis recognized that dynamic programming and complexity analysis have been the most prominent topics for the last ten years. Data structures and string algorithms are runners-up that may have potential trends in the future. This study opens up further research on other methods to perform trend analysis using better topic models and corpora.
Manufacturing, energy, finance, education, transportation, smart home, and medicine employ IoT technology. IoT solutions can efficiently manage hospital patients and mobile assets to provide high-quality medical servi...
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This study evaluates the effectiveness of using the EfficientNetB4 model architecture for deepfake image detection and identifies the main challenges in developing an accurate model for deepfake image detection. Deepf...
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This research tries to detect mental illness using sentiment analysis on Reddit data, as well as comparing the performance of the k-Nearest Neighbors (k-NN), Random Forest, and Neural Network models. Using text post d...
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This research tries to detect mental illness using sentiment analysis on Reddit data, as well as comparing the performance of the k-Nearest Neighbors (k-NN), Random Forest, and Neural Network models. Using text post data from the pre-pandemic and post-pandemic periods, we concluded that the Random Forest model had the highest overall performance with an F1 Score, accuracy, recall and precision of 80.6%, making it quite effective in detecting depression. Even though the Neural Network model shows slightly lower accuracy, namely 79%, in fact this model has the lowest error rate, namely 0.06496. The k-NN model showed the lowest accuracy and higher error rate. These findings highlight the potential of sentiment analysis and machine learning in identifying mental health issues on social media and suggest that better models can improve early detection and intervention efforts.
This systematic literature review (SLR) study aims to analyze the role of New Media as a Tool to Improve Creative Thinking, with relevant articles from 2018 to 2023 taken from reputable international journals. It uses...
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As of 2023, Indonesia ranks as the second largest waste-producing country globally. The waste produced is not segregated properly, leading to the vast amount of waste piling up in local landfills. Traditional methods,...
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As of 2023, Indonesia ranks as the second largest waste-producing country globally. The waste produced is not segregated properly, leading to the vast amount of waste piling up in local landfills. Traditional methods, such as manual sorting, have been widely used to segregate waste but suffer from inefficiencies and inaccuracies. In contrast, deep learning models offer an alternative solution for waste classification, overcoming the limitations of traditional methods. A deep learning approach using YOLOv8 was proposed to classify waste into six distinct categories. Three different YOLOv8 variants: nano, small, and medium, are trained after the dataset has been augmented into 3,500 labeled images. The results indicate that these models were able to achieve high accuracy in classifying images, with the nano variant having the least training time and an accuracy of approximately 89%.
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