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Optimization of Feature Selection in Support Vector Machines (SVM) Using Recursive Feature Elimination (RFE) and Particle Swarm Optimization (PSO) for Heart Disease Detection

Optimization of Feature Selection in Support Vector Machines...

引用

International Conference on Mechatronics (ICOM)

作者： Luhur Bayuaji Kusnadi Mochamad Yamin Amzah Dwi Pebrianti Faculty of Data Science & Information Technology INTI International University Nilai Malaysia Faculty of Information Technology Universitas Budi Luhur Jakarta Indonesia Department of Mechanical & Aerospace Engineering Kulliyyah of Engineering International Islamic University Malaysia Gombak Malaysia

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

Heart disease is one of the main health problems throughout the world. Early and accurate detection of heart disease is very important to reduce the death rate caused by this condition. One effective approach to detect heart disease is to use Support Vector Machine (SVM) as a machine learning algorithm. However, when using SVM, selecting the right features is very important to improve prediction performance. Inappropriate feature selection can lead to excess dimensions, high computing time, as well as the possibility of adding irrelevant information. Apart from that, the choice of C and gamma parameters can affect SVM performance. The RFE method is used to select the most informative and relevant subset of features from a given feature set. RFE combines a dimensionality reduction process with a machine learning process where the least important features are iteratively removed until the best feature subset is obtained, while the PSO method is used to optimize the C and gamma parameters of SVM. The results of the SVM-RFE PSO model trial showed increased accuracy in classifying heart disease. The accuracy value increased from 86.41% to 89.13%, indicating that this approach compares favorably with conventional SVM models or even SVM with RFE alone. These results illustrate that using the combination of RFE and PSO together is effective in improving the ability of SVM in classifying heart disease.

关键词： Support vector machines Heart Dimensionality reduction Accuracy Mechatronics Machine learning algorithms Feature extraction

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A Review of Multi-Agent Path Finding Algorithms 11

A Review of Multi-Agent Path Finding Algorithms

引用

11th International Conference on information Systems and Computing technology, ISCTech 2023

作者： Wu, Mengdie Yan, Wenyao Hasan, Haslin Jamaluddin, Robiatul A'Dawiah Xi'an Innovation College of Yan'an University School of Data Science and Engineering Xi'an China Infrastructure University Kuala Lumpur Faculty of Creative Media and Innovative Technology De Centrum City Malaysia Infrastructure University Kuala Lumpur Faculty of Business Information and Human Sciences De Centrum City Malaysia

ISBN: (纸本)9798350342406

In the context of Industry 4.0, Autonomous Guided Vehicles (AGVs) and other types of Multi-Agent System (MAS) have been widely used in large-scale warehouses and unmanned factories. Coordination of AGVs in shared space is drawing increasing attention of researchers, especially in the field of Multi-Agent Path Finding (MAPF). The MAPF problem has been studied a lot, and there are many kinds of solutions. Not all kinds of MAPF solvers can be applied in the field of AGV. Few literature focused on which MAPF solutions are suitable for AGV navigation. This review categorized existing MAPF algorithms into five branches, Conflict-Based Search (CBS) algorithms, Rule-Based algorithms, Priority-Based algorithms, Numerical Optimization-based algorithms and Learning-based algorithms. Furthermore, we evaluate these algorithms in terms of their optimality, lifelong property, scalability and computational cost. Finally, we analyzed which MAPF solvers are suitable for AGV navigation. © 2023 IEEE.

关键词： Optimization

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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Realizing the Potential of Metaverse to Revolutionize the Future of Healthcare

Realizing the Potential of Metaverse to Revolutionize the Fu...

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Cyber Resilience (ICCR), International Conference on

作者： Abdelraouf Ishtaiwi Wael Hadi Ahmad Al-Qerem Department of Data Science and Artificial Intelligence University of Petra Amman Jordan Department of Information Security University of Petra Amman Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The emergence of metaverse technology, underpinned by virtual reality, augmented reality, and artificial intelligence, holds profound implications for transforming healthcare. This paper reviews the current applications of metaverse in medical education and training, mental healthcare, remote patient monitoring, surgical procedures, and biomedical research. It analyzes the challenges around adopting this technology, spanning technical limitations, ethical dilemmas, threats to privacy, and regulatory gaps. Finally, it proposes strategies and future directions to harness the metaverse's potential to increase healthcare access, efficiency, personalization and democratization. This encompasses research on innovative use cases, developing ethical governance frameworks, and policy reforms to incentivize equitable metaverse adoption across the healthcare ecosystem. A nuanced, evidence-based approach can enable the metaverse to usher a new paradigm of patient-centric healthcare delivery.

关键词： Training Ethics Privacy Uncertainty Metaverse Reviews Surgery

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Moving towards FAIR mappings and crosswalks

Moving towards FAIR mappings and crosswalks

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Martínková, Jana Juty, Nick Beltran, Alejandra Gonzalez Goble, Carole Le Franc, Yann e-Science Data Factory S.A.S.U. Montpellier France Czech Technical University in Prague Faculty of Information Technology Prague160 00 Czech Republic The University of Manchester Department of Computer Science Oxford Road ManchesterM13 9PL United Kingdom Scientific Computing Science and Technology Facilities Council Harwell Campus OX11 0QX United Kingdom

Mappings and crosswalks are key elements to ensure semantic interoperability as well as metadata and data integration between different information systems. Designing FAIR compliant systems requires making sure all the elements that constitute the systems are themselves FAIR to support machine-actionability and automation. This paper describes the ongoing European and international effort to build a framework for FAIR mappings and crosswalks. This framework aims to be generic enough to capture the diverse set of use cases and methodologies across domains and communities. It should be composed of a set of technical recommendations to aid compliance with FAIR principles, a set of models for machine-actionable mappings and crosswalks as well as a practical framework with aligned good practices to support the creation of mappings by scientific communities. Developed in the context of FAIR IMPACT, a Horizon Europe project, this work will be pursued within a more international context as a Research data Alliance Working Group. © 2024 Copyright for this paper by its authors.

关键词： Mapping

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Examining the Impact of data Mining on Decision-Making Processes 3

Examining the Impact of Data Mining on Decision-Making Proce...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Singh, Vaishali Pandey, Pratik Aggarwal, Prateek Reena, R. Bhagwat, Suvarna R. Sunil, M.P. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

Statistics mining has emerged as a crucial device for agencies in the past two decades that significantly impact selection-making methods. It can be used to gain insights from massive datasets and might assist with an employer's marketing approach, operational performance, customer segmentation, etc. This paper will explore the impact information mining has on decision-making approaches, especially regarding coming across new styles and relationships. It will discuss the blessings of using records mining and how it may help decorate choice-making. It will also look at various methods used to extract statistics from massive datasets and how they facilitate the selection-making method. Finally, it will discuss a number of the constraints of information mining and the ability of answers to overcome them. Standard, this paper aims to pick out the effect of information mining on the choice-making system and how it may be leveraged as a practical device. © 2023 IEEE.

关键词： discuss extract information overcome relationships

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Investigating the Benefits of Machine Learning Algorithms for Text Classification 3

Investigating the Benefits of Machine Learning Algorithms fo...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Gowrishankar, J. Vinithra Banu, T. Parikshit, N. Singh, Vandana Kalra, Hitesh Sahoo, Girija Shankar Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

This paper affords a research into using gadget studying algorithms for textual content category duties, with a focal point on understanding the importance of these algorithms and their ability to enhance the accuracy of textual content classification. it's miles located that, while conventional techniques, which include rule based models and hand-engineered features, can attain first-class outcomes, there are compelling motives to explore the capability of device getting to know algorithms to further improve the accuracy and performance of textual content class. The paper discusses the 3 maximum famous gadget gaining knowledge of algorithms for this project: the NaÏve Bayes classifier, help Vector Machines, and synthetic Neural Networks, and presents a top level view of the latest research findings that demonstrate the prevalence of these algorithms over traditional methods. The paper additionally presents an outline of the distinctive approaches for introducing era into text type, and discusses modern-day strategies for implementing and deploying those algorithms. Finally, the paper outlines ability areas for destiny research associated with information the results of system studying algorithms for this mission, and suggests viable instructions for exploration. © 2023 IEEE.

关键词： Classification (of information)

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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Entrodeduper: data Deduplication Using Entropy and Secure Hash Algorithm

Entrodeduper: Data Deduplication Using Entropy and Secure Ha...

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Computer Applications (ICCA), IEEE Conference on

作者： Tin Thein Thwel Hsu Mon Maung Faculty of Information Science University of Computer Studies Yangon Yangon Myanmar Data Center Department Myanmar Institute of Information Technology Mandalay Myanmar

Being a data science era, any bulk of data concerned with specific fields need to maintain in data centers with the help of rack server. For reducing storage for data center, the need to implement the data deduplication mechanism and keeping backup of those data in data center is becoming more important. In the case of reducing redundant data from the various servers in data center, the huge amount of data become the biggest challenge. As the data deduplication mechanism not only it can reduce the size of data to be stored or to be transmit but also it can give the backup and recovery features. This paper proposed the efficient data deduplication mechanism called EntroDeduper, which is the hybrid of entropy, secure hash algorithm and indexing. The input files are divided into blocks using file separator and the redundant block is removed by applying the entropy gain theory, which is used to get not only the significant reduction rate but also the full block coverage for reconstruction.

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The Classification Status of River Water Quality in Riau Province Using Modified K-Nearest Neighbor Algorithm with STORET Modeling and Water Pollution Index

The Classification Status of River Water Quality in Riau Pro...

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2020 Annual Conference on science and technology Research, ACOSTER 2020

作者： Mustakim Rosdina Ramadhani, Dian Afdal, M. Rahmawita, Medyantiwi Departement of Information System Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Information System Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Information System Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Information System Puzzle Research Data Technology Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Departement of Information System Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

The Department of Environment and Forestry, Pollution and Environmental Damage Control Division, has an active role in monitoring water quality in Riau Province. The rivers that are still monitored and managed are Kampar River, Siak River and Indragiri River. Division of Environment Pollution calculates river quality status manually using Microsoft Excel, this is not maximally done since this important information should be processed quickly. Division of water pollution must determine the right calculation to get the results of the water quality status. Because of many calculation formulas set by the government, the commonly used method is the STORET method and the Pollution Index. So, in overcoming the problem of classification, the researcher proposes the use of learning methods that can predict or determine the status of water quality with classification techniques on data mining that is Modified K-Nearest Neighbor (MKNN) which is a modification of K-NN. The calculation of the MKNN algorithm produced the highest accuracy of 85.10% at K = 5 using STORET result data as training data. While, using the Pollution Index data results, the highest accuracy is 76.92% at K = 1. Based on the analysis with attribute analysis, the attributes that influence the determination of river water quality are BOD, COD, NH3, Fecal Coli and Total Coli. This result can be taken into consideration by the Division of Environmental Pollution in the process of overcoming and reducing pollutant overload that exceeds quality standards. © Published under licence by IOP Publishing Ltd.

关键词： Rivers

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