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Analyzing Nickel Stock Price Prediction: A Comparative Study of Long Short-Term Memory and Convolutional Neural Network Models

Analyzing Nickel Stock Price Prediction: A Comparative Study...

引用

2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Irwanto, Ferry Hafizh Sasongko, Muhammad Farrell Febrian, Stevanus Santoso Gunawan, Alexander Agung Setiawan, Karli Eka Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350353464

This research delves to predict PT Vale Indonesia Tbk stock price as an experiment on Indonesian stock using three models: naïve, LSTM, and 1D-CNN. Our analysis emphasizes the importance of matching model architectures to data properties. We compare models' performance with fiveday window for predict one-day prediction output. Interestingly, the single-layer LSTM outperforms the 1D-CNN even with similar hyperparameters, showcasing its strength in capturing long-term temporal dependencies crucial for nickel prices. While the 1D-CNN excels at identifying short-term patterns, its limited receptive field hinders long-term dependence. Recognizing the potential of both models, we encourage exploring hybrid architectures combining LSTM and CNN strengths for further improvement in financial forecasting. The experiment result shows single-layer LSTM outperforms a 1D-CNN with similar settings. ©2024 IEEE.

关键词： Convolutional neural networks

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Interpretable Machine Learning in Leveraging Customer Information for Success Direct Marketing Campaigns 10

Interpretable Machine Learning in Leveraging Customer Inform...

引用

10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Octavia Tjandra, Clarissa Shirley Cam, Grace Agung Santoso Gunawan, Alexander Eka Setiawan, Karli School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350363104

A bank's marketing campaign execution is very important. Undoubtedly, a well-crafted marketing plan will contribute to the bank's increased revenue. Marketing is crucial because it can be used to connect with consumers and inform them of the advantages of making a deposit with the bank, which will increase the number of deposits the bank receives. Thus, the goal of this research is to determine the best customer information that affects their decision on subscribing a term deposit and providing the best model for predicting the outcome of future marketing campaign. By determining the significance of each feature, this research constructed machine learning models using methods like Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Logistic Regression (LR) as our research contribution. According to the research's findings, out of the three machine learning models, Random Forest (RF) has the highest accuracy scores. Additionally, the feature importance model reveals that a customer's decision to make a deposit or not is greatly influenced by factors such as the age of the customer, how much does the customer has in their bank account, did the bank have contacts with longer call durations, and did it has a positive outcomes on the previous marketing campaigns. The customers who are contacted more frequently and have subscribed before are more likely to subscribe in the future marketing campaign. © 2024 IEEE.

关键词： bank marketing classification direct marketing machine learning predictive analysis

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Viral Melodies: Exploring the Factors Influencing Music Virality in TikTok Engagement 4

Viral Melodies: Exploring the Factors Influencing Music Vira...

引用

4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Rahardjo, Evelyn Zefanya Alifa, Jasmine Mutia Setiawan, Sharon Zefanya Gunawan, Alexander Agung Santoso Setiawan, Karli Eka Bina Nusantara University Data Science Program Computer Science Department School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376111

In the digital era and the evolution of social media platforms like TikTok, understanding the factors influencing content virality has become increasingly crucial. Therefore, this research aims to delve into the musical variables contributing to the popularity of content on TikTok, including aspects such as artist, beat of music, and the number of shares. Focusing on content that successfully garnered over 5, 0 0 0 likes, this study employed a data analysis approach involving the librosa library for music tempo extraction and logistic regression to identify relationships among these variables. The research findings indicate that the most significant variable influencing the virality of music content on TikTok is the number of shares. This highlights that the phenomenon of virality is not solely dependent on musical characteristics like artist or beat but is more influenced by social interactions through video sharing among users. The findings suggest that when someone shares a video with others who may have similar music preferences, the video is likely to receive likes, even if it does not appear on the user's For You Page (FYP). Thus, the virality of content on TikTok can be explained by the existence of social connections among friends that trigger the viral spread of the video. These results provide valuable insights into the mechanisms behind the success of conten/t on this platform. © 2024 IEEE.

关键词： content virality data analysis music selection tiktok user engagement

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Breast Cancer Image Classification Obtained Through Dynamic Thermography using Deep Learning 4

Breast Cancer Image Classification Obtained Through Dynamic ...

引用

4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Timadius, Ethan Daniel Wongso, Reyner Baihaqi, Thirza Gunawan, Alexander Agung Santoso Setiawan, Karli Eka Bina Nusantara University School of Computer Science Data Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376111

This paper addresses the vital task of diagnosing breast cancer early and accurately. It acknowledges the difficulty in detecting minor variations in breast cancer patterns through visual examination alone, particularly when analyzing gray scale images. To overcome this challenge, our research concentrates on using image processing models, particularly Convolutional Neural Networks (CNNs), and enhances them with a refined approach to classification. The research methodology encompasses rigorous data collection, preprocessing, and strategic feature selection to enhance model robustness. To address challenges related to data intricacies, the study incorporates transfer learning, refining the model's training process for improved accuracy. The paper not only seeks to elevate diagnostic precision and minimize errors but also aims to provide healthcare professionals with efficient tools for breast cancer detection. Building upon prior research, the study underscores the potential of deep learning methodologies in advancing breast cancer diagnosis. Furthermore, the fine-tuning process involves adapting a pre-trained model to the intricacies of breast cancer patterns, optimizing its performance. The ultimate goal is to contribute to the development of reliable diagnostic tools that empower medical professionals in the fight against breast cancer. The paper concludes by summarizing key findings. Additionally, the model achieved an accuracy of 84%, highlighting its effectiveness in breast cancer detection. The paper also emphasizes the broader significance of leveraging deep learning for enhanced breast cancer detection. © 2024 IEEE.

关键词： Breast Cancer Convolutional Neural Network Deep Learning Image Classification Thermal Image

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Leveraging XGBoost and Feature Importance for NBA Salary Classification

Leveraging XGBoost and Feature Importance for NBA Salary Cla...

引用

2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Rafif, Musyaffa Ayman Daksa, Rayhan Prawira Indrajaya, Gede Sanjaya Gunawan, Alexander A S Setiawan, Karli Eka Bina Nusantara University Data Science Program Computer Science Department School of Computer Science Jakarta11480 Indonesia Bina Nusantara University Computer Science Department School of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9798350376210

Focusing on feature importance, this study utilized the XGBoostClassifier to predict NBA player salaries within categorized salary bands. Oversampling techniques ensured balanced class representation, while feature importance analysis identified Minutes Played, Age, Points Scored, Games Started, and Turnovers as key factors influencing model predictions. The XGBoostClassifier achieved a balanced accuracy of 83% across all salary tiers, demonstrating its ability to navigate NBA player valuation complexities. Focusing on these key features further boosted average accuracy to 85%, highlighting the potential for optimization through prioritizing impactful attributes. Our findings suggest valuable avenues for future research, including leveraging feature importance for model refinement, expanding the dataset size, incorporating more relevant features, and investigating outliers for deeper insights into the multifaceted dynamics of NBA player salary classification. © 2024 IEEE.

关键词： Wages

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Enhancing Online Learning Experience: An Ensemble Approach for Classifying Student Adaptivity Level 4

Enhancing Online Learning Experience: An Ensemble Approach f...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Mutiara, Belinda Florencia Ariyanto, Gabrielle Felicia Gunawan, Alexander Agung Santoso Setiawan, Karli Eka Bina Nusantara University Data Science Program Computer Science Department School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350376111

The study investigates the increasing demand of online learning as a means of addressing education issues in the context of the COVID-19 epidemic. Online learning requires several adaptations for teaching methods, learning methodologies, and devices needs. The flexibility of both teachers and students is essential to these adaptations. This research uses dataset collected from a survey about student adaptability level in online education. Carrying out the preprocessing is a challenge as the data used in this research has imbalanced value on the target category. Based on this problem, the aim of this research was created, namely to categorize students' adaptivity levels in online learning and also focuses on finding out approaches that can provide solutions to overcome imbalanced values in the dataset. The model uses ensemble methods - Bagging, Boosting, and Voting with machine learning algorithms. There are two models that stand out, with Soft Voting obtaining the best performance with 9 0% accuracy. © 2024 IEEE.

关键词： Federated learning

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Determining environmental indicators related to the propagation of contagious diseases and health issues: A Systematic Literature Review 18

Determining environmental indicators related to the propagat...

引用

18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Portillo, Janeth Dominguez Peralta, Laura M. Rodriguez Sampaio, Paulo N. M. Nunes, Eldman De Oliveira Faculty of Information Technologies and Data Science Mexico Graduate Program in Computing and Systems Bahia Salvador Brazil

ISBN: (纸本)9789893347928

The environmental indicators provide significant information in order to describe the state of a phenomenon, environment or area [1]. This paper addresses the indicators present in the air and their relationship in the accommodation and transmission of viruses, as well as how these environmental indicators can lead to health problems. Therefore, the main goal of this work is to review the scientific contributions in the literature in order to determine and select the potential environmental indicators present in indoor environments which have an impact on the health of people who work and live within these spaces. For this purpose, we carried out a Systematic Literature Review (SLR) related to environmental monitoring, COVID-19, Internet of things, air pollution and environmental indicators within indoor spaces. This SLR allowed us to identify indicators that according to their level of presence can represent a high risk for people's health. Therefore, the process of planning, conducting and reporting the Systematic Literature Review enabled us not only to identify some environmental indicators, but also their corresponding monitoring advantages and disadvantages, and multiple perspectives and approaches of each contribution in the literature. Further on, this study will be the basis for the proposal of an automatic and intelligent platform based on Internet of Things for monitoring indoor environments in order to detect high risk levels for the contamination of contagious diseases. © 2023 ITMA.

关键词： COVID-19

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On the Continuity of Moore-Penrose Inverse of the Graph Laplacian 13

On the Continuity of Moore-Penrose Inverse of the Graph Lapl...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Lin, Tse-Yu Tsai, Yen-Lung National Taiwan University Data Science Degree Program Taipei Taiwan National Chengchi University Department of Mathematical Sciences Taipei Taiwan

ISBN: (纸本)9798350355079

Graph Laplacian is a fundamental tool in various fields such as spectral clustering, network analysis, image processing, and, deep learning recently for studying message passing in graph neural network models. To support the theoretical use of the graph Laplacian in these fields, in this work, we study the continuity of the Moore-Penrose generalized inverse of the graph Laplacian. We provide a graph-theoretical proof of this continuity in terms of the connectivity of the underlying graph associated with a given graph Laplacian matrix. © 2024 IEEE.

关键词： Laplace transforms

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A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detection 2

A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detectio...

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2nd International Conference on Software Engineering and Information Technology, ICoSEIT 2024

作者： Karnyoto, Andrea Stevens Isnan, Mahmud Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350317503

There is still a severe malaria problem worldwide, particularly in regions with limited access to diagnostic tools. It is crucial to develop a system for detecting malaria in blood cells. This paper presents a hybrid Convolutional Neural Network (CNN), and as a classifier, we use a Support Vector Machine (SVM) framework for the automated detection of malaria parasites in blood cell images. The proposed system leverages the strengths of CNNs in feature extraction and representation learning from images, combined with the discriminative power of SVMs for classification. Initially, CNN extracts intricate features from blood cell images, capturing essential patterns indicative of malaria infection. Subsequently, the extracted features are used to train an SVM classifier, enabling accurate discrimination between parasitized and uninfected blood cells. Experimental dataset evaluations were obtained from the Lister Hill National Center for Biomedical Communications website of the National Library of Medicine. The proposed model achieves a better f1-score, outperforming individual CNN or SVM models, around 0.015 compared to individual CNN models and 0.27 compared to individual SVM models. This hybrid CNN-SVM methodology offers a promising solution for accurately and efficiently detecting malaria parasites in blood cell images. © 2024 IEEE.

关键词： Support vector machines

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Federated Approach to Online Pairwise Comparison Ranking Problem

Federated Approach to Online Pairwise Comparison Ranking Pro...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Lin, Tse-Yu Tsai, Yen-Lung National Taiwan University Data Science Degree Program Taipei Taiwan National Chengchi University Department of Mathematical Sciences Taipei Taiwan

ISBN: (纸本)9798350324136

In this work, we propose a federated learning (FL) framework for the online HodgeRank problem to obtain a global ranking based on the pairwise comparison data provided by users while respecting users' data privacy. HodgeRank is a statistical ranking method that views pairwise comparison data as an edge-weighted directed graph, and, vertex weight function can be viewed as a ranking that recovers edge weight using difference. A critical assumption of the HodgeRank is the connectivity of the comparison graph. This assumption relies on the contribution of users' data to form a weakly connected directed graph between items to be ranked. In this paper, we aim to find a framework to compute HodgeRank with the minimum usage of users' data. © 2024 IEEE.

关键词： federated learning HodgeRank pairwise comparison

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