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MOAC: Multi-Objective Adaptive Clustering Optimization Algorithm Based on Cluster Adaptation

MOAC: Multi-Objective Adaptive Clustering Optimization Algor...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hossam M. J. Mustafa Dheeb Albashish Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper presents a novel Multi-Objective Adaptive Clustering (MOAC) algorithm that addresses current limitations in existing multi-objective optimization methods. While current algorithms often struggle with premature convergence and diversity preservation, the MOAC algorithm proposes a novel clustering-based approach to search space by partitioning. The algorithm combines cluster evolution with an adaptive parameter control mechanism to achieve significant improvements in balancing exploration-exploitation and diversity maintenance. MOAC employs k-means clustering for population partitioning, combined with cluster-based genetic operators, and an efficient diversity preservation scheme. Extensive experiments across different ZDT, MMF, and UF test suites demonstrate that MOAC's performance is superior, which achieves outstanding hypervolume and IGD values in complex multi-modal problems. The algorithm shows strength in handling complex Pareto fronts, and comprehensive statistical analysis through the Friedman rankings test with a 2.21 rank. Moreover. The results reveal that MOAC is a robust approach for complex multi-objective optimization problems, particularly maintaining solution diversity and achieving consistent convergence across different problems.

关键词： Ranking (statistics) Statistical analysis Metaheuristics Clustering algorithms Optimization methods Evolutionary computation Partitioning algorithms Maintenance Convergence Genetic operators

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Advancements in Skin Cancer Detection: A Comprehensive Review of Convolutional Neural Network Approaches

Advancements in Skin Cancer Detection: A Comprehensive Revie...

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International Conference on Software technology and Engineering (ICSTE)

作者： Rajermani Thinakaran J. Somasekar Vikram Neerugatti Krishna Ganga Faculty of Data Science and Information Technology INTI International University Negeri Sembilan Malaysia Department of Computer Science and Engineering Jain (Deemed-to-be University) Banglore Karnataka India

ISBN: (数字)9798350378955

ISBN: (纸本)9798350378962

Skin cancer is one of the most prevalent types of cancer globally, with its incidence steadily rising over the past decades. Early and accurate detection of skin cancer plays a pivotal role in improving patient outcomes and reducing mortality rates. In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for automated image analysis and classification, offering promising results in various medical imaging tasks, including skin cancer detection. This paper provides a comprehensive review and analysis of CNN-based approaches for skin cancer classification. We discuss the challenges associated with traditional methods of skin cancer diagnosis and highlight the potential of CNNs in addressing these challenges. Furthermore, we survey state-of-the-art CNN architectures, dataset collections, preprocessing techniques, and evaluation metrics commonly used in skin cancer classification tasks. We also delve into the transfer learning and data augmentation strategies employed to improve model generalization and robustness. Additionally, we review recent advancements in interpretability techniques for CNNs, which enhance the transparency and trustworthiness of automated skin cancer diagnosis systems. Finally, we discuss the limitations and future directions of CNN-based approaches in skin cancer classification, emphasizing the need for large-scale, diverse datasets, standardized evaluation protocols, and interdisciplinary collaborations to facilitate the clinical translation and deployment of CNN-based diagnostic tool.

关键词： Surveys Translation Protocols Reviews Transfer learning Mortality Software Robustness Convolutional neural networks Skin cancer

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Intelligent diagnosis of Alzheimer’s disease based on BP neural network 3

Intelligent diagnosis of Alzheimer’s disease based on BP ne...

引用

3rd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big data Forum, AIBDF 2023

作者： Zhao, Jia Huang, Jilin Cheng, Jiejia Cai, Shiyuan Faculty of Information Engineering Guangxi Polytechnic of Construction Nanning530007 China School of Arts and Sciences China University of Petroleum-Beijing At Karamay Karamay834000 China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300401 China

ISBN: (纸本)9798400716362

Alzheimer’s disease (AD) is a progressive neurodegenerative disease, and its onset is unknown. It usually occurs in the elderly and dies due to complications 10 to 20 years after onset. Therefore, early and accurate diagnosis of AD and mild cognitive impairment is of great significance. Firstly, preprocess the data, manually remove the data with no specific information, and then use Python to remove duplicate feature variables with the same name and data with more than 10% missing features. Fill in the remaining data with missing values and fill in the average values, leaving 16 sample labels;Then, the processed data is divided into training and testing sets to train a random forest model, and the correlation scores between data features and Alzheimer’s disease diagnosis are calculated separately. Some results are as follows: LDELTOTAL_ BL-18.81%, PACCDigit 14.30%, and combined with the existing dataset with nonlinear data features for comprehensive analysis. Based on the advantages of BP neural network nonlinear mapping function and existing feature labels, a model is established to intelligently diagnose Alzheimer’s disease. Through testing and analysis, when the number of hidden neurons is 9 and the model training method is Bayesian regularization, the solution effect is better. Some of the optimal intelligent diagnosis schemes are: FAQ: 0.0-30.0;ADAS13:0.0-54.68;AGE: 50.4-91.4;MMSE: 16.0-30.0. © 2023 Copyright held by the owner/author(s).

关键词： Diagnosis

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Optimal LZ-End Parsing Is Hard 34

Optimal LZ-End Parsing Is Hard

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34th Annual Symposium on Combinatorial Pattern Matching, CPM 2023

作者： Bannai, Hideo Funakoshi, Mitsuru Kurita, Kazuhiro Nakashima, Yuto Seto, Kazuhisa Uno, Takeaki M&D Data Science Center Tokyo Medical and Dental University Japan Department of Informatics Kyushu University Fukuoka Japan Japan Society for the Promotion of Science Tokyo Japan Nagoya University Japan Faculty of Information Science and Technology Hokkaido University Sapporo Japan National Institute of Informatics Tokyo Japan

ISBN: (纸本)9783959772761

LZ-End is a variant of the well-known Lempel-Ziv parsing family such that each phrase of the parsing has a previous occurrence, with the additional constraint that the previous occurrence must end at the end of a previous phrase. LZ-End was initially proposed as a greedy parsing, where each phrase is determined greedily from left to right, as the longest factor that satisfies the above constraint [Kreft & Navarro, 2010]. In this work, we consider an optimal LZ-End parsing that has the minimum number of phrases in such parsings. We show that a decision version of computing the optimal LZ-End parsing is NP-complete by showing a reduction from the vertex cover problem. Moreover, we give a MAX-SAT formulation for the optimal LZ-End parsing adapting an approach for computing various NP-hard repetitiveness measures recently presented by [Bannai et al., 2022]. We also consider the approximation ratio of the size of greedy LZ-End parsing to the size of the optimal LZ-End parsing, and give a lower bound of the ratio which asymptotically approaches 2. © 2023 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： data compression

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A Conversational Human-Computer Interface for Smart Energy System Simulation Environments

A Conversational Human-Computer Interface for Smart Energy S...

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2023 Winter Simulation Conference, WSC 2023

作者： Dengler, Gabriel Bazan, Peter German, Reinhard Lalbakhsh, Pooia Liebmann, Ariel Friedrich-Alexander-Universität Erlangen-Nürnberg Laboratory of Computer Networks and Communication Systems Martensstr. 3 BY Erlangen91058 Germany Monash University Faculty of Information Technology Department of Data Science and Ai ClaytonVIC3168 Australia

ISBN: (纸本)9798350369663

This paper introduces a conversational framework that enhances the usability of smart energy system simulations. This study is centered around OpenAI's Generative Pre-trained Transformer (GPT), a fine-tuned conversational model that allows users to communicate with the system in a natural way. Therefore, users can describe their simulation scenarios in plain language and GPT seamlessly translates these descriptions into Python scripts, used as inputs to the simulation environment, in our case, AnyLogic Simulation Software. Our framework is based on the i7-AnyEnergy core framework to compute distribution flows and relevant statistics. The proposed human-machine interface facilitates and accelerates simulation modeling, as demonstrated through the two scenarios we have provided in this paper. Overall, our conversational framework has the potential to significantly improve the user experience of smart energy system simulation environments. By simplifying the interaction between users and complex simulation models, we enable users to obtain valuable insights rapidly and more easily. © 2023 IEEE.

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NFT Appraisal Using Machine Learning 23

NFT Appraisal Using Machine Learning

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5th Asia Pacific information technology Conference, APIT 2023

作者： Dawod, Ahmed Dawod Mohammed Munkhdalai, Lkhagvadorj Park, Kwang Ho Ryu, Keun Ho Pham, Van Huy Database and Bioinformatics Laboratory College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of Data Science Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9781450399500

Non-Fungible Tokens (NFTs) are digital assets based on a blockchain and those are characterized as unique cryptographic tokens and non-interchangeable. To date, research into the NFT marketplace has been relatively limited. As it is an emerging platform with many unique elements, The NFT market has been impacted due to recent fluctuations in crypto-asset markets more broadly. This current bear market cycle has shed light on concerns around the value of NFTs, profit-based motivation, and environmental sustainability. However, periods of volatility and cyclicality are to be expected with any nascent technology as it develops a product-market fit. consequently, the appraisal of real-price for NFT collections is essential for individual financial security and investment making. In this study, we evaluate the machine learning algorithms to appraise their real-price based on NFT item's characteristics, market event information, and their rarity score data acquired by retrieved from the biggest marketplace OpenSea. Furthermore, the procedures were applied to meet the objectives of this study we built prediction models based on various machine-learning algorithms ranging from Random Forest, XGBoost, SVM, Lasso, ElasticNet, Ridge, Linear Polynomial Regression, TabNet, CatBoost, and LightGBM models. From the results, LightGBM regression model outperformed the other by RMSE around 0.905. The best R2 is only found in this model, which has a value of 0.917. © 2023 ACM.

关键词： Blockchain

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Investigating Digital Herbal Platforms and Health data Innovations in the Context of Thailand

Investigating Digital Herbal Platforms and Health Data Innov...

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2024 International Workshop on Artificial Intelligence for Signal, Image Processing and Multimedia, AI-SIPM 2024

作者： Wittayakhom, Nawarat Chalermdit, Jiragorn Chumuang, Narumol Ganokratanaa, Thittaporn Phetcharat, Thanatchaphan Ketcham, Mahasak Dept. Digital Media Technology Industrial Technonlogy Faculty Muban Chombueng Rajabhat University Ratchaburi Thailand dept. of Mathematics King Mongkut s University of Technology Thonburi Bangkok Thailand Dept. Data Science and Innovation College of Interdisciplinary Studies Thammasat University Bangkok Thailand dept. of Information Technology Management King Mongkut's University of Technology North Bangkok Thailand

ISBN: (纸本)9798400705489

Currently, waste management is one of the most critical issues in In this research, factors influencing the acceptance of digital technology in the digital herbal platform and innovative health data management for healthcare in Thailand were thoroughly investigated. The focus was on individuals interested in herbal treatment and alternative healthcare, including the younger generation unfamiliar with herbal usage. The main objective was to develop a central platform that encompasses herbal distribution, supports community participation, and promotes online medical tourism. The analysis of data from users interested in herbal remedies and alternative healthcare enabled us to apply Inbound Marketing technology in developing the "Digital Herbal Hub."This platform emphasizes convenience and inclusivity for all genders and ages, aiming to promote online medical tourism in Thailand. © 2024 ACM.

关键词： Computer vision

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Multilingual BERT Cross-Lingual Transferability with Pre-trained Representations on Tangut: A Survey 5

Multilingual BERT Cross-Lingual Transferability with Pre-tra...

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5th International Conference on Natural Language Processing, ICNLP 2023

作者： Lu, Xiaoming Liu, Wenjian Jiang, Shengyi Liu, Changqing City University of Macau Faculty of Data Science China Guangdong University of Foreign Studies School of Interpreting and Translation Studies Guangzhou China Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou China Guangdong University of Foreign Studies Lexicography Research Center Guangzhou China

ISBN: (纸本)9798350302219

Natural Language Processing (NLP) systems have three main components including tokenization, embedding, and model architectures (top deep learning models such as BERT, GPT-2, or GPT-3). In this paper, the authors attempt to explore and sum up possible ways of fine-tuning the Multilingual BERT (mBERT) model and feeding it with effective encodings of Tangut characters. Tangut is an extinct low-resource language. We expect to introduce a tailored embedding layer into Tangut as part of the fine-tuning procedure without altering mBERT internal structure. The initial work is listed on. By reviewing existing State of the Art (SOTA) approaches, we hope to further analyze the performance boost of mBERT when applied to low-resource languages. © 2023 IEEE.

关键词： Embeddings

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Predictive Maintenance in Aerospace Industry Using Convolutional Neural Network

Predictive Maintenance in Aerospace Industry Using Convoluti...

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International Conference on Mechatronics (ICOM)

作者： Dwi Pebrianti Zulhakim Khalani Rusdah Luhur Bayuaji Dept. of Mechanical & Aerospace Engineering International Islamic University Malaysia Kuala Lumpur Malaysia Faculty of Information Technology Universitas Budi Luhur Jakarta Indonesia Faculty of Data Science & Information Technology INTI International University Nilai Malaysia

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

Predictive maintenance offers many benefits compared to reactive and preventive maintenance. Reactive maintenance is often cosidered too late due to its characteristic to fix failure after the failures happened and preventive maintenance is considered too early in maintenance process due to its characteristic to substitute spareparts far before the failure happens. The benefit of predictive maintenance includes enhanced safety, reduced downtime, improved staff planning, reduced operational costs, enhanced asset management and improved regulatory compliances. This paper presents an approach of machine learning in predictive maintenance to improve the current corrective maintenance used in the aviation industry. The goal of this study is to develop a predictive failure model using Convolutional Neural Network (CNN) to provide an early detection of engine breakdown before its occurrence. The data used in the study is the data of the aircraft engine operation cycle measured by 21 sensors. The prediction of failure is made on the Remaining Useful Life (RUL) of the engine. The failure prediction results will be compared with Recurrent Neural Network (RNN) at the end of this paper to see whether the proposed approach could outperform the existing method being applied or not. The results showed that CNN achieved greater accuracy, precision, recall, and F1-score compared to Recurrent neural network (RNN) in failure prediction. The result shows that the proposed method accurately predicts the failures in the system with 98% of accuracy, 96% of precision and 96% of Recall. In conclusion, this study demonstrates that employing a Convolutional Neural Network (CNN) for predictive maintenance in the aviation industry significantly enhances failure prediction accuracy, thereby contributing to improved safety and operational efficiency.

关键词： Industries Accuracy Recurrent neural networks Atmospheric modeling NASA Predictive models Maintenance

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Medical Material Allocation Using Multi-Queue Scheduling During Pandemics

Medical Material Allocation Using Multi-Queue Scheduling Dur...

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science, technology, Engineering and Management (ICSTEM), International Conference on

作者： D. Divya Priya Suthashini Subramaniam P.Vinoth Kumar Lim Jit Theam Ramesh Kumar Ayyasamy Kesavan Krishnan Department of Computer Science Engineering MLR Institue of Technology Hyderabad India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Perak Malaysia Department of Artificial Intelligence and Data Science St Joseph's Institute of Technology OMR CHENNAI Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

In this study, we implemented a multi-level queue scheduling algorithm for a hospital with three wards: General, Pandemic, and Arogya Sree. The Pandemic ward uses priority scheduling, while the others use FCFS. It also calculates distances to nearby airports based on geolocation to identify oxygen airlift points. This work allows us to eliminate human interference in resource allocation by automating the process based on priority derived from the doctor's diagnosis. This ensures that maximum efficiency is reached. The common people faced many problems during the pandemic, mainly related to costs incurred and resource availability. We want to solve this by removing the human factor from the equation.

关键词： Costs Pandemics Hospitals Scheduling algorithms Geology Interference Human factors

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