This paper presents an advanced method for addressing the inverse kinematics and optimal path planning challenges in robot manipulators. The inverse kinematics problem involves determining the joint angles for a given...
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The rapid growth of digital payment platforms like OVO, ShopeePay, and GoPay in Indonesia has driven the need for businesses to optimize marketing strategies by analyzing customer interactions through social media. Th...
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ISBN:
(数字)9798331506490
ISBN:
(纸本)9798331506506
The rapid growth of digital payment platforms like OVO, ShopeePay, and GoPay in Indonesia has driven the need for businesses to optimize marketing strategies by analyzing customer interactions through social media. This study leverages Social Network Analysis (SNA) and Power BI to extract insights from Twitter data, offering a comprehensive view of how these platforms engage with consumers and shape brand perceptions. By applying sentiment analysis and classification using IndoBERT results, this study aims to identify key influencers, assess the sentiment around these brands, and visualize marketing communication patterns. The integration of SNA and BI tools provides a detailed, data-driven approach to improving business decision-making in the competitive digital payment market, by utilizing them to evaluate e-wallet tweet activity and user interactions in Indonesia and identifying key periods and regions for targeted marketing. Strategies like loyalty programs and segmented campaigns are recommended to enhance user engagement, address security concerns, and maximize market reach during culturally significant periods such as Eid and the holiday season.
We consider the trajectory planning of a 6-Degree-of-Freedom (DOF) robot manipulator using computer algebra, with controlling the orientation of the end-effector. As a first step towards the objective, we present a so...
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Today's society is different from the past, where the speed of the internet is getting more sophisticated, and the existing gadget technology is getting faster, and of course, this is also changing the lifestyle o...
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This empirical study involved volunteers who played a game featuring NPCs specially developed for the research. The research investigated the influence and behaviour of NPC appearance on some factors regarding the pla...
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With the advancement of technology, information systems have become increasingly necessary in almost all areas, including healthcare. One of the technologies used to facilitate this progress is electronic medical reco...
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We propose a method for solving the speech direction estimation problem by computer algebra. The method is based on the function approximation using the minimax polynomial. The minimax polynomial is obtained by an ite...
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A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches...
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Hate speech is one of the most challenging problem internet is facing today. With increasing numbers of users online, hate speech also rise and takes time to be classified manually particularly in languages other than...
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In this study, we initiated an approach to predict Protein Secondary Structure Prediction (PSSP) by using n-grams modeling, namely $n$ adjacent amino acid sequences, to represent the amino acid sequence and 1-Dimens...
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ISBN:
(数字)9798350373332
ISBN:
(纸本)9798350373349
In this study, we initiated an approach to predict Protein Secondary Structure Prediction (PSSP) by using n-grams modeling, namely
$n$
adjacent amino acid sequences, to represent the amino acid sequence and 1-Dimensional Convolutional Neural Network (CNN). We compare n-grams with a one-hot encoding approach and find that n-grams provide better performance. The analysis results show that bigrams are more effective than trigrams in describing protein amino acid sequence patterns. We also combine it with the PSSM profile feature to improve model performance. The results of this study reveal that the n-grams approach, especially bigrams, in combination with PSSM, can produce more accurate protein secondary structure predictions than previous models, with superior accuracy evaluations of Q8 68.1 % and Q3 82.75
%
on the CB513 dataset.
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