Floods, common natural disasters in Indonesia, bring substantial damage and economic losses. Jakarta, Indonesia's capital, faces frequent flooding, mainly due to the overflowing Ciliwung River. Effective flood prevention, like timely warnings, is crucial to reduce losses. Yet, the existing flood early warning system managed by the Ciliwung-Cisadane River Center has limitations. Its hydrological models for short-term predictions lack accuracy, leading to prolonged computational times. To enhance this system, a machine learning approach was introduced for better water level prediction. This model utilizes rainwater and runoff data from four river segments upstream. Two neural network methods, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), were compared. RNN-LSTM outperformed ANFIS, displaying lower error rates and faster computation times. It excelled in predictive capabilities for three of four river segments, evident through improved Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Despite a minor setback in the third segment's performance, both methods scored well in the coefficient of determination (R2). RNN-LSTM emerged superior due to its minimal error and efficient computational speed, making it the preferred choice for water level prediction. Additionally, RNN-LSTM showcases enhanced predictive ability concerning water level fluctuations, as reflected in its larger standard deviation.
Topic popularity prediction has always been a popular research field. In previous studies, traditional methods are mostly used to predict the popularity of topics characterized by long text, long period, and large amounts of data. These models do not apply to hot topics generated on social network platforms. Hot topics on social platforms are characterized by rapid generation, development, explosion, and vanishing, resulting in topics often disappearing within a few days or even hours, significantly reducing the amount of data available. According to these features, this paper collects blogs related to the hot topic “2023 Hangzhou Asian Games” on Sina Micro-blog, and designs a hotness formula to measure the topic's popularity within a certain period. Subsequently, two baseline models were trained by combining convolutional neural networks (CNN) and long short-term memory (LSTM). The prediction results are compared using two evaluation metrics, RMSE and MAE. The model proposed in this paper outperforms the two baseline models in prediction.
In order to promote viability of the circular economy (CE), circular design in waste management, needs to be highlighted. This study proposes first, situating circular economy design/ development in Value Sensitive Design to increase empathy, and open (instead of closed) innovations; second, incorporating feasibility testing, via SWOT analysis, chain-of-effects mind map and chain-of- effects prioritization, mediated and moderated by economic factors; third, gamifying awareness/contributions, authenticating via blockchain, and analysing/monitoring via a dashboard; fourth, triangulating 3 HCI instruments, to derive data-driven bigger pictures/insights into the technology acceptance of the Figma prototype. By building on open standards and platforms, over time, real-time what-if scenarios and analyses, and knowledge bases can develop; increasing opportunities for business expansion, and better quality of life. Findings from alpha-beta testing are promising, towards enhancing interconnections among (eco)systems, and sustainability/growth. However, since sample size is small, findings cannot be generalized.
This paper examines the comparative effectiveness of three distinct Flipped Classroom (FC) teaching methods in the context of Power and Electrical (PE) engineering education: Traditional Flipped Classroom (TFC), Video-Assisted Teaching Methods (VAFC), and Virtual Reality Flipped Classroom (VRFC). The study incorporates valuable feedback from students who experienced these methods and provides an evaluation of their perceptions. A cross-over methodology for comparing these FC methods is presented. The VAFC method is perceived to be an effective approach and preferred over TFC and VRFC by students. The paper also provides Teachers' perception in implementing above FC methods, which outlines a process for developing VAFC and VRFC teaching resources. Operational difficulties in implementing such methods are highlighted, especially in the context of fast-paced, privately-catered undergraduate education sector striving for implementing non-conventional teaching methods.
In the context of Kun Khmer, a traditional combat technique in Cambodia, the challenge of correcting trainees' postures and movements is significant due to its impact on performance and outcomes. Moreover, incorrect movements during training can lead to physical injuries. This paper presents a machine learning-based system that aims to recognize Kun Khmer postures and potentially facilitate posture correction. The proposed solution utilizes anatomical landmarks and explores multiple classifiers, including Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine. Experimental results demonstrate a recognition rate of 96.32% using the Support Vector Machine classifier, with an average recall and precision of 96.22% and 96.32% respectively.
