Roadmapping is a structured, logic-based method to support strategic planning for organizations and governments. However, it is not necessarily effective to produce destructive changes in the era of uncertainty. Speci...
Roadmapping is a structured, logic-based method to support strategic planning for organizations and governments. However, it is not necessarily effective to produce destructive changes in the era of uncertainty. Specifically, roadmapping is useful for visualizing ideas for better communication among stakeholders but has limitations when creating unconventional knowledge through abduction. Additionally, it lacks the ability to create knowledge considering humanity and well-being to respond to current and future uncertain social situations. Therefore, this study aims to explore the key factors for incorporating the science-fiction (sci-fi) prototyping method into roadmapping, based on the literature review. Roadmapping is a logic-consistent, face-to-face workshop-based method, whereas sci-fi prototyping is a workshop-based method that utilizes fiction in a distributed virtual system. Both methods are used to conceptualize future innovative technologies and policy recommendations. Though they have similar objectives, the resulting future concepts are exactly the opposite. To integrate the two methods effectively, we break them down into modules and consider reconstructing them. After conducting comparative studies based on previous research, we propose a cooperative base of the two methods.
Graph Neural Networks (GNNs) frequently face class imbalance issues, especially in heterogeneous graphs. Existing GNNs often assume balanced class sizes, which isn’t true in many cases. Applying them directly to imba...
Graph Neural Networks (GNNs) frequently face class imbalance issues, especially in heterogeneous graphs. Existing GNNs often assume balanced class sizes, which isn’t true in many cases. Applying them directly to imbalanced data can lead to sub-optimal performance. Traditional oversampling methods, while effective, risk overfitting and face difficulties in reintegrating synthetic samples into the original graph. In this study, we introduce Framework of Imbalanced Node Classification on heterogeneous graph neural network with GAN (FincGAN), a new framework that utilizes oversampling techniques to address class imbalance in heterogeneous graphs. Instead of duplicating existing samples, FincGAN employs a Generative Adversarial Network (GAN) to create synthetic samples and uses deep learning-based edge generators to connect them back to the original graph. Our evaluations on spam user detection in the Amazon and Yelp Review datasets show that FincGAN outperforms baseline models in all essential metrics, including F-score and AUC-PRC score, showing its effectiveness in addressing class imbalance.
Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage...
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(纸本)9798350399080
Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction.
In the past decade, it stated that the discourse on Sustainable Development and the Financial technology(FinTech) role in the financial and banking industries, has attention on researchers, practitioners, regulators, ...
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Indonesia is a country that is rich in artistic diversity. This art can be a song. In addition to traditional songs, Indonesia is rich in songs from various genres. Some of the genres used by the author are pop, balla...
Indonesia is a country that is rich in artistic diversity. This art can be a song. In addition to traditional songs, Indonesia is rich in songs from various genres. Some of the genres used by the author are pop, ballad, dangdut. Song or music has become an interesting area of research in computer science. Music recognition is a computational problem in the field of computer vision. Music introduction aims to classify music into several categories such as genre, mood, or music generation. Although in the last ten years a number of studies have been published, the problem of music recognition is still a difficult problem to solve by computers. In addition, it is necessary to explore several features that represent music, including audio spectrograms, chromagrams, and song lyrics. The author processes the lyrics and audio features in the form of a chromagram using many songs. Music can be a collection of data that can be processed by machine learning. The data used for classification is song data in the form of datasets. The author uses his own dataset or manual methods in collecting and processing datasets. The song is downloaded from YouTube after that it is converted into mp3 and cut. The author processed 1181 songs from the 70’s and 80’s era which only took the chorus part. Lyrics in chorus form are saved in excel. The audio chorus is saved in .wav. The results of the Mood classification used by the author in music consist of 3, namely happy, sad and neutral which are processed using machine learning. Using 2 experiments on Chromagram and word embedding feature has a higher value of 81%.
Music is a blend of the human voice and instruments that bring beauty to the listener. Many people like music to create an atmosphere or atmosphere. In the field of computer science, with a lot of data, music is an ob...
Music is a blend of the human voice and instruments that bring beauty to the listener. Many people like music to create an atmosphere or atmosphere. In the field of computer science, with a lot of data, music is an object that can be studied both through lyrics, audio, biographies and others. This research focuses on the mood of Indonesian music in the 70s and 80s. The data set used consists of text data and audio data. The mood tested consisted of sad, happy and neutral. This area of interest was investigated using the Crisp-DM method derived from business understanding, data understanding, data preparation, modelling, evaluation, deployment. In building the Confusion Matrix, this study uses several parameters, namely epoch, dimensions, set size, learning rate and others. The value generated by the confusion matrix contains a good 85 % value.
Due to the issue of population aging faced by Taiwan, the government is implementing policies on encouraging the elderly to engage in social activities in their life after retirement, so as to enable them to enjoy a c...
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Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolve...
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Global aging is an important issue for global development. Elderly people should not be the burden of individual families, but the assets of the entire society. With the help of life stories or past events that elderl...
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Sports science can have a significant and positive impact on coaches and athletes, as well as for the professional sports organization. According to this situation, more and more organizations are competing to impleme...
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