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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Rusdyputra, William Chowanda, Andry Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Reinforcement learning has been implemented to model a task by doing the task repeatedly to get the maximum results based on the reward and punishment policy. It has been implemented in the game and agent-based modelling. In the game, the game agent or Non-Players character can be modelled using several techniques to achieve the goal (e.g. reinforcement learning, deep neural network and Monte Carlo Tree Search). Deep neural networks and Monte Carlo Tree Search, two more sophisticated techniques in reinforcement learning algorithms, assisted the present reinforcement learning in resolving more challenging issues. However, this area has two challenges: the minimum number of data to model and generalization to different environments. Determining a minimum number of data required by the architecture to train the model is quite a cumbersome task to be applied to real-world jobs and situations since it demands substantial data to be explicitly provided and trial-and-error re-configuration. This work proposes a data-Efficient Reinforcement Learning model by augmenting the data and implementing episodic memory. To illustrate the effectiveness of the proposed model, this research compares it to several models, such as the Deep Q-Network (DQN) with episodic memory to the same model with data augmentation and episodic memory. The model adds to the observations before being stored in the agent's memory, causing the agent to use the same logic and take the same action in comparable situations. The outcome demonstrates that the augmented model can surpass the fundamental model in speed (with an improvement of 50% quicker). © 2023 The Authors. Published by Elsevier B.V.

关键词： data Efficient Deep Learning Deep Reinforcement Learning Reinforcement Learning

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Reliable Entropy-induced Anchor Learning for Incomplete Multi-view Subspace Clustering

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5293-5306页

作者： Shen, Qiangqiang Guo, Zihou Wang, Hanzhang Xu, Yanhui Chen, Yongyong Wang, Shiqi Liang, Yongsheng City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong School of Science Guangdong Shenzhen518055 China Nanjing University of Posts and Telecommunications College of Electronic and Optical Engineering Nanjing210023 China School of Computer Science and Technology Guangdong Shenzhen518055 China School of Electronics and Information Engineering Guangdong Shenzhen518055 China Shenzhen Technology University College of Big Data and Internet Guangdong Shenzhen518118 China

Under large-scale data with missing views, fast incomplete multi-view clustering (IMVC) with anchor learning is of critical importance due to its linear complexity O(n). However, existing anchor-based methods only explore the column orthogonality of anchor points, where their arbitrary column orthogonal basis vectors have weak constraint relationships with real samples and significant deviations from more representative anchors, thereby impeding the precise representation of sample similarities. To solve this issue, we propose a Reliable Entropy-induced anchor learning for incomplete Multi-view subspace Clustering (REMC), which performs an entropy approximation term to learn more representative anchors, and we prove that the information entropy minimization can be relaxed into the 2, 1-norm paradigm. Specifically, the proposed REMC first integrates anchor learning and subspace clustering to produce multiple view-specific bipartite graphs and capture the high-order correlations by imposing these bipartite graphs with the tensor nuclear norm. Then, we fuse all the view-specific bipartite graphs to build a consensus bipartite graph with entropy approximation regularization, and hence the proposed REMC can produce a more discriminative similarity graph, preserving each non-zero element in its column close to 1, while the other elements are approaching 0. Besides, an efficient algorithm is designed to solve the proposed REMC. Numerous results show the superior performance of our method on both the complete and incomplete data. © 1991-2012 IEEE.

关键词： Graph theory

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Exploring XGBoost as an Effective Machine Learning Algorithm for Stellar Spectral data Classification in Astronomy

Exploring XGBoost as an Effective Machine Learning Algorithm...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Yoshino, Erio Juarto, Budi Kurniadi, Felix Indra Bina Nusantara University School of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350339215

The discipline of astronomy has experienced a substantial metamorphosis due to the advent of contemporary telescopes, which facilitate the accumulation of extensive spectral information. The processing of data through traditional manual means has become unfeasible owing to its vast volume, thereby necessitating the adoption of automated classification. The advent of machine learning algorithms and artificial intelligence has enabled the efficient and accurate processing of spectral data, thereby facilitating a more comprehensive analysis of celestial objects. Prior research has investigated the efficacy of diverse machine learning algorithms in the context of stellar classification endeavors, as well as the significance of pre-processing spectral data. The present investigation centers on the utilization of XGBoost, a machine learning algorithm that exhibits the ability to handle heterogeneous data types and address imbalanced datasets, thus rendering it a viable option for classification endeavors. In this research, we found that Random Forest and XGBoost gave good result compared to other classifier. In contrast, reduction dimension using PCA are lowering the achieved result. © 2023 IEEE.

关键词： Machine learning

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A CNN-GA Hybrid Approach for Efficient Analysis of Size and Shape of Nanoparticle

A CNN-GA Hybrid Approach for Efficient Analysis of Size and ...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Sushma, K.S.N. Sarkar, Argha Sanyal, Gautam School Of Computer Science And Engineering Reva University Bengaluru India National Institute Of Technology Department Of Computer Science And Engineering Durgapur India

ISBN: (纸本)9798350371567

Addressing the challenge of manually analyzing nanoparticle characteristics, such as size and shape, researchers have increasingly turned to deep learning methodologies. This research paper introduces a novel approach, a synergistic combination of CNN-GA architecture, to efficiently analyze the physical properties of nanoparticles. The integration of CNN for feature extraction from image data and GA for fine-tuning hyperparameters and attributes proves to be a powerful tool. Unlike the traditional manual design of CNN architectures, this semi-automatic approach allows researchers to design robust architectures without necessitating in-depth CNN expertise, facilitating the achievement of benchmark results in nanoparticle characterization. © 2024 IEEE.

关键词： Genetic algorithms

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Diabetic Retinopathy Diagnosis System Based on Retinal Biomarkers Using EfficientNet-B0 for Android Devices 12

Diabetic Retinopathy Diagnosis System Based on Retinal Bioma...

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12th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2023

作者： Matthew, Alvin Gunawan, Alexander Agung Santoso Kurniadi, Felix Indra Bina Nusantara University Jakarta School of Computer Science Computer Science Department 11480 Indonesia

ISBN: (纸本)9798350341102

Currently, Diabetic Retinopathy has become one of the main causes of vision loss for the working-class population (20-65 years) and the disease is an asymptomatic disease which makes detection of the disease without medical assistance more difficult. Along with the development of the era, many new methods have been found in diagnosing diseases, one of which is computer Aided Diagnosis (CAD) which relies on computer technology to assist medical personnel in diagnosing diseases. This research uses CAD with machine learning to classify Diabetic Retinopathy using transfer learning on the EfficientNet-B0 model which is implemented in a program for mobile devices for android. The program was designed with the aim of helping medical personnel to be able to diagnose Diabetic Retinopathy in areas that do not have sufficient medical facilities using only a smartphone, 20D lens, and several other drugs supports. The results showed that the EfficientNet-B0 model for classifying Diabetic Retinopathy was able to produce an accuracy of 91.85 percent in classifying three classes namely no DR, Non-proliferative DR, and Proliferative DR. This research was done to contribute in the growth of Artificial intelligence involvement in the medical world and also to help areas that lack the medical equipment to have better treatment. © 2023 IEEE.

关键词： Android (operating system)

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Classification of Alzheimer's Disease using Random Oversampling and Albumentations on Convolutional Neural Network 8

Classification of Alzheimer's Disease using Random Oversampl...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Kamardi, Calvin Laksana, I Kadek Perry Bagus Anggreainy, Maria Susan Fitriawati, Nora Yonatan, Michiko Amaya Mangkang, Tesalonika Abigail Eikwine Pengesa, Vianny Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350342604

Alzheimer's Disease (AD) is a brain disorder that causes dementia and affects the memory, cognitive, and behavioral function. Early detection for AD can help to reduce the symptoms and slow down AD progression. Deep learning, particularly Convolutional Neural Network (CNN), a component of artificial intelligence, has demonstrated efficacy in resolving image-related issues and has gained widespread use in the analysis of medical images. CNN works well on large datasets, however, in imbalanced datasets, it can perform poorly by misclassifying minority classes. In this study, the random over-sampling technique is used to address the imbalance in the dataset, while Albumentations is used for image augmentation to prevent overfitting. The results demonstrate that the combination of these techniques leads to improved classification performance compared to traditional approaches. The proposed method has the potential to improve the accuracy of AD diagnosis and contribute to the development of effective treatments for this debilitating condition. © 2023 IEEE.

关键词： Convolutional neural networks

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Optimizing Multilayer Perceptron Classifiers for Predictive Heart Disease Diagnosis: A Hyperparameter Tuning Approach 8

Optimizing Multilayer Perceptron Classifiers for Predictive ...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Vadlamudi, Anvitha Talasila, Smeeth Althaph, Bollapalli Challa, Nagendra Panini Vadlamudi, Gnana Abhinay School of Computer Science and Engineering VIT-AP University Amaravati India Department of Computer Science and Engineering New York United States

ISBN: (纸本)9798331505462

Heart diseases, collectively known as cardiovascular diseases (CVDs), remain one of the foremost health challenges across the globe, taking millions of lives each year. This study evaluates the effectiveness of Multilayer Perceptron (MLP) classifiers in predicting CVDs using the Heart Disease dataset from Kaggle. We implemented a structured approach using data pre-processing, model training with baseline MLP classifiers, and hyperparameter optimization utilizing Random and Grid Search techniques. The optimized models, particularly the MLP Classifier trained with Grid Search, outperformed the baseline, achieving up to 98.54 % accuracy and 100% precision. However, analysis of learning curves indicated the need for further model refinement to enhance generalization on unseen data. This study underscores the importance of hyperparameter tuning in developing effective MLP classifiers for CVDs prediction and suggests directions for future research, including exploring advanced optimization methods and regularization techniques. © 2024 IEEE.

关键词： Cardiology

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Dual-Optimized Adaptive Graph Reconstruction for Multi-View Graph Clustering 24

Dual-Optimized Adaptive Graph Reconstruction for Multi-View ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wen, Zichen Wu, Tianyi Ren, Yazhou Ling, Yawen Cui, Chenhang Pu, Xiaorong He, Lifang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science and Engineering Lehigh University BethlehemPA United States

ISBN: (纸本)9798400706868

Multi-view clustering is an important machine learning task for multi-media data, encompassing various domains such as images, videos, and texts. Moreover, with the growing abundance of graph data, the significance of multi-view graph clustering (MVGC) has become evident. Most existing methods focus on graph neural networks (GNNs) to extract information from both graph structure and feature data to learn distinguishable node representations. However, traditional GNNs are designed with the assumption of homophilous graphs, making them unsuitable for widely prevalent heterophilous graphs. Several techniques have been introduced to enhance GNNs for heterophilous graphs. While these methods partially mitigate the heterophilous graph issue, they often neglect the advantages of traditional GNNs, such as their simplicity, interpretability, and efficiency. In this paper, we propose a novel multi-view graph clustering method based on dual-optimized adaptive graph reconstruction, named DOAGC. It mainly aims to reconstruct the graph structure adapted to traditional GNNs to deal with heterophilous graph issues while maintaining the advantages of traditional GNNs. Specifically, we first develop an adaptive graph reconstruction mechanism that accounts for node correlation and original structural information. To further optimize the reconstruction graph, we design a dual optimization strategy and demonstrate the feasibility of our optimization strategy through mutual information theory. Numerous experiments demonstrate that DOAGC effectively mitigates the heterophilous graph problem. © 2024 ACM.

关键词： Graph neural networks

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Cost-Effective Authenticated Solution (CAS) for 6G-Enabled Artificial Intelligence of Medical Things (AIoMT)

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IEEE Internet of Things Journal 2024年第13期11卷 23977-23984页

作者： Mahmood, Khalid Obaidat, Mohammad S. Shamshad, Salman Alenazi, Mohammed J. F. Kumar, Gulshan Anisi, Mohammad Hossein Conti, Mauro National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Communication and Computing Engineering Beijing100083 China Amity University Amity School of Engineering and Technology Noida201301 India University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh12311 Saudi Arabia University of Padua Department of Mathematics Padua35131 Italy Lovely Professional University Department of Computer Science and Engineering Phagwara144411 India University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

The Internet of Things (IoT) is a network of interconnected objects, which congregate and exchange gigantic amounts of data. Usually, predeployed embedded sensors sense this massive data. Soon, several applications of IoT are anticipated to exploit emerging 6G technology. Healthcare is one of them, where the 6G-inspired paradigm may facilitate the users to exchange information through hundreds of sensors under the assumption of Artificial Intelligence of Things (AIoT). Integration of medical sensors with AIoT is known as Artificial Intelligence of Medical Things (AIoMT). The secure and seamless interactions among 6G-enabled AIoMT users should be the primary challenge. Furthermore, resource-constrained wearable sensing devices, with their inability to execute complex security solutions, provide an ideal attraction for malicious entities to launch diverse attacks. These challenges have motivated us to design a cost-effective authenticated solution (CAS) for 6G-enabled AIoMT healthcare applications. Our CAS protocol not only prevents cyber threats like impersonation session key secrecy but it can also prevent physical threats like hardware tampering. We observe formal and informal security validations to endorse its robustness and effectiveness. Performance comparison reveals that the CAS protocol offers maximum security enrichment. Moreover, CAS is cost-effective as it has achieved 33% and 60% reduction in computation and communication overheads, respectively, compared to contemporary competing related protocols. © 2014 IEEE.

关键词： Authentication

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Brain Stroke Prediction Using Random Forest Method with Tuning Parameter 4

Brain Stroke Prediction Using Random Forest Method with Tuni...

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4th International Conference on Artificial Intelligence and data sciences, AiDAS 2023

作者： Felice, Nicole Johan, Jefferson Natthannael, Jevent Gozal, Michael Baptista Jovannie, Charlene Anggreainy, Maria Susan School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Brain stroke is a disease that contributes to the number of deaths in Indonesia. The number of victims of brain stroke is still increasing to this day, so a solution is needed that can predict brain stroke attacks in patients. This research is useful so that patients can be given appropriate medical treatment to prevent brain stroke. To predict brain stroke in patients, researchers designed an application that utilizes machine learning to provide accurate predictions based on several variables. The researcher used a dataset consisting of 4981 patient data with 10 non-labeled attributes who had/not had a brain stroke. By using random forest algorithm which then performed parameter tuning, researchers managed to get an accuracy of 98.98% in predicting the occurrence of brain stroke in patients. © 2023 IEEE.

关键词： Forecasting

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