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Accuracy of Comparison Random Forest, Gradient Boosting Tree, Decision Tree, and Naïve Bayes Algorithms in Predicting the Size of Companies Where data Scientist Works 5

Accuracy of Comparison Random Forest, Gradient Boosting Tree...

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Putra, Dias Perdana Wilson Alexander, Ang Rizal, Sahrian Putra Amin Rais, Muhamad Nathaniel, Dominique Christopher Iskandar Pohan, Husni Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

Therefore, this research discusses the comparison of accuracy between machine learning algorithms such as Naïve Bayes, Decision Tree, Random Forest, and Gradient Boosting Tree for data scientist salary data in predicting the size of the company where they work. This research method involves data from the Kaggle platform. The data includes attributes such as salary paid in the year, salary currency, employee type, company location, job title, salary in USD, experience level in the year, salary, employee residence, company size, and remote ratio. The results of this study show that the three algorithms which is Random Forest, Decision Tree, and Gradient Boosting Tree, have a high level of accuracy compared to Naïve Bayes in predicting the size of the company where data scientists work. However, the performance of Random Forest is less optimal due to unbalanced data which results in the results of Random Forest being less accurate than Decision Tree. A high level of accuracy can affect the classification results so that the better the accuracy level, the better the classification results. Meanwhile, Gradient Boosting Tree obtained excellent accuracy results from the three machine learning algorithms. The conclusion of this research is that Gradient Boosting Tree and Decision Tree can be a good choice to predict the size of the company where data scientists work. However, it is important to ensure that the data is balanced so that the Random Forest algorithm can run more optimally. © 2023 IEEE.

关键词： Decision trees

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Abstractive Indonesian News Summarization Using BERT2GPT 7

Abstractive Indonesian News Summarization Using BERT2GPT

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7th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2023

作者： Nasari, Maulin Maulina, Ayu Girsang, Abba Suganda Computer Science Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350382266

This study examines the necessity of employing BERT2GPT for single-document summarization in the current age of escalating digital data. The primary focus of this work is on the abstractive technique, which tries to generate versatile summaries that surpass the primary sentences of the original content. This study employs two models, namely BERT and GPT -2, for the purpose of the summarization system. The paper introduces the BERT2GPT model, which merges the bidirectional characteristics of BERT with the generative powers of GPT. The findings indicate that the BERT2GPT method successfully catches significant information and linguistic nuances, hence enhancing the quality of the generated summaries. The corresponding average values for Rl, R2, and RL are 0.62, 0.56, and 0.60, respectively. © 2023 IEEE.

关键词： Abstractive Summarization BERT2GPT single-document summarization

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Enhancing Movie Recommendations: A Hybrid Filtering Approach Combining Collaborative and Content-Based Filtering

Enhancing Movie Recommendations: A Hybrid Filtering Approach...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Vemberly, Esther Ancilla, Eugenia Anderies, Anderies Chowanda, Andry Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327472

The exponential growth of information and the widespread use of the internet has increased the importance of recommender algorithms, particularly in movie recommendation systems across various industries. By helping users select movies that match their interests, these systems significantly improve the movie-watching experience. However, a common challenge faced by these systems is the cold-start problem, which makes it difficult to provide accurate recommendations for new users with limited historical data. To address this issue and improve recommendation accuracy, this research paper presents a hybrid approach combining collaborative filtering (CF) and content-based filtering (CBF) to provide movie recommendations, where the model makes use of user ratings and movie datasets. The CF component identifies similar users and suggests popular movies, while the CBF component examines movie attributes such as titles, genres, ratings, and descriptions to recommend movies. The methodology involves data acquisition, cleaning, partitioning, and the design of collaborative, content-based, and hybrid models., where the dataset was partitioned into 60% for training and 40% for testing. The evaluation is based on the MovieLens 100K dataset, using Root Mean Squared Error (RMSE) and Fraction of Concordant Pairs (FCP) metrics. Eventually, the result of the hybrid model obtains a training and testing RMSE of 0.881858 and 0.999145, respectively. For the FCP value, the model achieves a training value of 0.751256 and a testing value of 0.641928. Overall, the proposed hybrid approach highlights the importance of using CF and CBF approaches in recommender algorithms as it gives a balance between accuracy and diversity of the datasets and shows potential in addressing the cold-start problem and providing personalized recommendations. Future studies can focus on improving the hybrid model and exploring other evaluation metrics for a more thorough review. © 2023 IEEE.

关键词： Collaborative filtering

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Microstructure Estimation Using Synergistic Dual-path Hybrid Network

Microstructure Estimation Using Synergistic Dual-path Hybrid...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Jiquan Cao, Jian Yang, Junqing Wang, Siqi Chen, Geng Heilongjiang University Department of Computer Science and Technology Harbin China Northwestern Polytechnical University School of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798350386226

White matter microstructure plays a pivotal role in the diagnosis and study of brain disorders. Deep learning-based estimation of white matter microstructural indices from diffusion MRI (dMRI) data has gained increasing research attention in recent years. To conduct effective learning in the heterogeneous space (i.e., x-space and q-space) of dMRI data, hybrid neural networks were proposed and have shown great potential. To this end, we propose a new hybrid neural network, called synergistic dual-path hybrid convolutional neural network (SDH-Net), for more effective microstructure estimation. In our SDH-Net, we propose a dual-path architecture that takes bidirectional asymmetric learning in x-space and q-space to enhance feature representation in heterogeneous domains. Firstly, 3D patches are extracted from each diffusion gradient as vertices, and then a graph is constructed based on the correlation between diffusion angles. In the x-q learning branch, an effective representation is embedded in x-space to enhance learning in q-space, while vice versa in the q-x learning branch. Extensive experiments on data from the human connectome project demonstrate that our SDH-Net outperforms the existing state-of-the-art models. © 2024 IEEE.

关键词： Convolutional neural networks

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Behavioral insights into compression: a study of Move-to-Front-or-Middle deterministic online algorithm through sequence classification and characterization

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International Journal of Information Technology (Singapore) 2025年第1期17卷 189-203页

作者： Baisakh Mohapatra, Hitesh Guru, Abhishek Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Sambalpur India Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to Be University Bhubanseswar India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Since the list update problem was applied to data compression as an effective encoding technique, numerous deterministic algorithms have been studied and analyzed. A powerful strategy, Move-to-Front (MTF), involves moving an accessed item to the front of the list immediately. Theoretical analysis has demonstrated MTF’s potential, showing it to be 2-competitive through competitive analysis. This success has inspired researchers to develop various intriguing MTF variants and assess their performance both theoretically and experimentally. One such variant, Move-to-Front-or-Middle (MFM), has been experimentally shown that MFM performed better than the classic MTF on Calgary and Cantebury datasets. However, the experimental performance alone does not provide sufficient insight into the internal behavior of the algorithm or explain why its performance surpasses MTF on certain datasets. Therefore, in this paper, we conduct a detailed behavioral study of MFM through sequence classification and characterization to gain deeper understanding of the algorithm and aims to reconcile the disparity between theoretical and experimental outcomes. This paper also by classifying and characterizing MFM’s performance across various request sequences, considering both the presence and absence of locality of reference. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Competitive analysis Deterministic list update algorithm List update problem Sequence classification

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Weather Prediction in Jakarta: An Analysis of Climate data and Regional Influences using LSTM and GRU

Weather Prediction in Jakarta: An Analysis of Climate Data a...

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2023 International Conference on data science and Its Applications, ICoDSA 2023

作者： Respaty, William Arga Hong, Castin Putra, Nicholas Kurniawan Kurniadi, Felix Indra Riccosan Bina Nusantara University School of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350305197

This research investigates meteorological predictions in Jakarta using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, focusing on precipitation forecasting. Meteorological data, encompassing wind speed, temperature, humidity, and precipitation, were sourced from the Kemayoran Meteorological Station in Jakarta. After data pre-processing to ensure suitability for machine learning training, the study contrasted LSTM and GRU models' performances using Root Mean Square Error (RMSE) and R-squared (R2) as evaluation metrics. Findings showed the GRU model marginally outperformed during testing, though the difference wasn't statistically significant. The LSTM model exhibited greater consistency, suggesting its potential for more dependable weather predictions given the right dataset. The research underscores the necessity of considering regional factors, climatological data, and data time intervals for accurate forecasting. It offers insights into machine learning's potential in meteorological predictions. Specifically, at a learning rate of 0.0002, GRU had an RMSE of 18.034, slightly better than LSTM's 18.075. However, at 0.0003, LSTM outperformed with an RMSE of 17.972 compared to GRU's 17.988, highlighting the nuanced performances of these models. © 2023 IEEE.

关键词： Long short-term memory

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An In-Depth Review of Machine Learning, Blockchain, and Deep Learning Models for Intelligent Security and Resilience Enhancement in Smart Cities

An In-Depth Review of Machine Learning, Blockchain, and Deep...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Lanjewar, Umesh Khekare, Ganesh Wahi, Amitabh Bhagwant University Department of Computer Science & Engineering Ajmer India School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331517953

A smart city is a fast-moving terrain that requires efficient and smart security mechanisms with resilience for solving the intricate challenges of modern urbanism. The current paper presents the critical review of machine learning, blockchain, and deep learning models in strengthening security and making the urban environment at smart cities more resilient. Not all the review articles heretofore successfully integrated these three pivotal domains, which lowered their practical applicability and insight depth. This paper reviews recent state-of-the-art models, including supervised and unsupervised machine learning algorithms, blockchain frameworks, and deep advanced learning architectures. The important machine learning models reviewed include Random Forest, Support Vector Machines, and K-means clustering. These were chosen for their already proved effectiveness in anomaly detection, predictive analytics, and classification tasks. On the other hand, blockchain models of Ethereum and Hyperledger Fabric would be evaluated for decentralized security features, immutability, and capability to improve data integrity and transparency. Deep learning models can handle large-scaled, unstructured data and extract complex patterns. Their integration, therefore, shows great promise in enhancing the security and resilience of smart cities. This paper contributes to a holistic, multidisciplinary viewpoint, filling literature gaps by providing a basic framework for further research and implementation of smart city initiatives. Such insights synthesized across these domains set a course for innovation in the formulation of solutions that fortify urban infrastructures against emerging threats and enhance overall urban resilience. (Abstract) © 2024 IEEE.

关键词： Adversarial machine learning

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Identifying Facial and Body Movement Through CNN And Monocular Tracking Algorithm 6

Identifying Facial and Body Movement Through CNN And Monocul...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Fu, Agnes Fortuna Wibisono, Samantha Rochelle Angela, Eryn Pratama, Galih Dea Hasana, Silviya Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315639

Facial Expression movement could analyze the true meaning behind the person's intention, but people could lie and fabricate their real expression by restraining the facial muscle. However, multitasking and restraining, both facial and body movement could reveal their true intention. Facial and body movement studies have been conducted to study the true meaning behind someone's movement. Through these studies, we will subtract a few methods that are accurate to the true result through the testing that have been conducted. We proposed the utilization of Seetaface method and a hybrid of deep learning approach with inertial sensors to analyze human true emotions. Through the Seetaface method, we will analyze the person's facial movement, which consists of the important facial features such as eyes, nose and mouth. Subsequently, the hybrid of deep data and facial features will detect and analyze a person's body movement through a camera and record it to the body models data. After successfully processing both of the methods, our findings show that facial detection is capable of pointing to the facial feature and expression of important features, however hindered by face shifting and obstruction. In body detection, the true action matched 100% successfully with the prediction of the action result after further evaluation using hybrid best results. © 2023 IEEE.

关键词： computer vision

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Detection of Cardiovascular Disease Using Machine Learning Algorithms and Principal Component Analysis 15

Detection of Cardiovascular Disease Using Machine Learning A...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Narfian, Pradipta Syifa Halim, Dylan Christiandi Sembiring, Albert Nathan Fredyan, Renaldy Pranoto, Hady School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350383829

Cardiovascular disease is a family of diseases that are the leading cause of death worldwide. Late diagnosis and treatment contribute to the high number of deaths caused by this family of diseases. To increase accessibility for the general public to get diagnosed and treatment, a cardiovascular disease detection system powered by artificial intelligence (AI) can be developed and used. The healthcare sector has already utilized AI for many purposes and has proven to be as reliable as trained professionals. This study aims to develop an AI model that can detect whether a person has cardiovascular disease through analyzing their clinical data. Both the support vector machine and the decision tree classifier algorithm were trained with the Cleveland heart disease dataset and a version of the dataset that has undergone dimensionality reduction through the principal component analysis (PCA) algorithm, with a 75:25 ratio of training and testing data. The performance of the two algorithms was then evaluated using the confusion matrix which found that the PCA algorithm enhanced the performance of both algorithms and that the SVM model paired with PCA achieved the highest performance of 86.67% accuracy, 90% precision, and 79.41$ recall. © 2023 IEEE.

关键词： Decision trees

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Experiments on IndoBERT Implementation for Detecting Multi-Label Hate Speech with data Resampling through Synonym Replacement Method 8

Experiments on IndoBERT Implementation for Detecting Multi-L...

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8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023

作者： Darmawan, Michael Adriel William Boentoro, Nathanael Surya, Kevin Christian Sutoyo, Rhio School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315516

The freedom to share information online leads to increased online hate speech. Addressing this issue is crucial to creating better online communities. A study in 2019 used machine learning approaches to detect multi-label hate speech in the Indonesian language, achieving an accuracy of 66.12%. Recently, several researchers have shown improved performance when using the deep learning method. Looking at the opportunity, this work is developing an Indonesian hate speech model using IndoBERT. Various word preprocessing techniques and dataset stratification were explored for optimal results. In addition, this work also tried to solve the imbalance dataset problem in the multi-label dataset through the synonym replacement method. As a result, the IndoBERT model of this research achieved an improved accuracy of 88.23%. It was also implemented into a web-based application to show the potential usage. © 2023 IEEE.

关键词： Deep learning

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