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Optimizing the Classification Performance by Fine-Tuning the Machine Learning Hyperparameters and Utilizing PCA and RFE Feature Selection Methods 4th

Optimizing the Classification Performance by Fine-Tuning the...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Adam, Alfrendo Alvian Alfred, Rayner Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Sabah Kota Kinabalu88400 Malaysia

ISBN: (纸本)9789819729760

Breast cancer is among the most common and potentially fatal cancers, especially among women. Breast cancer usually has no obvious early signs, and doctors sometimes have trouble differentiating benign and malignant tumors. Thus, with the help of appropriate Machine Learning (ML) techniques, breast cancer patients can be classified more accurately and help doctors make diagnosis decisions. The research gaps in this topic are that there is no standard ML hyperparameter setting and feature selection method used to improve the ML algorithm performance in breast cancer classification. This paper aims to identify the best hyperparameters settings, identify the best feature selection methods and compare them with past research papers. The experiments conducted in this paper can be divided into two: the first experiment is to identify the best hyperparameter settings, and the second is to identify the best feature selection methods. In this paper, several algorithms are used that includes Logistic Regression (LR), Decision Tree (DT), k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Random Forest (RF), AdaBoost, Hard Voting, and Artificial Neural Network (ANN) to model the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Meanwhile, the feature selection method uses Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE). For comparison with past research, research papers that have used the same dataset and algorithms will be selected to ensure the comparison is fair and has no bias. Based on the results obtained in this paper, Adaboost, combined with the optimized decision tree, is found to be the best algorithm due to high accuracy and sensitivity compared to the other algorithms, which are 98.2% and 99.6%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diseases

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Automatic Deployment to Kubernetes Cluster by Applying a New Learning Tool and Learning Processes 10

Automatic Deployment to Kubernetes Cluster by Applying a New...

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10th Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Golis, Tomáš Dakić, Pavle Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 4 Slovakia Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade Serbia

The rapid growth and widespread adoption of containerization technologies, such as Docker, and the increasing popularity of Kubernetes as a container orchestration platform have significantly shaped the landscape of modern software development and deployment. Containers have created a paradigm shift in application packaging, offering a consistent and portable environment across diverse infrastructure setups. Meanwhile, Kubernetes has emerged as the dominant choice for managing containerized applications at scale, providing features such as automated scaling, load balancing, and fault tolerance. However, despite the numerous advantages containers and Kubernetes offer, deploying applications to a Kubernetes cluster often poses challenges due to the steep learning curve and complex configuration requirements. To address these challenges, this paper aims to introduce a comprehensive learning tool that automates and simplifies the deployment process on Kubernetes clusters. © 2023 Copyright for this paper by its authors.

关键词： CI/CD Container orchestration Docker image Kubernetes

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Modeling heterogeneous behaviors with different strategies in a terrorist attack

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Virtual Reality & Intelligent Hardware 2023年第4期5卷 351-365页

作者： Le BI Tingting LIU Zhen LIU Jason TEO Yumeng ZHAO Yanjie CHAI Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China College of Science&Technology Ningbo UniversityCixi 315300China Faculty of Computing&Informatics Universiti Malaysia SabahKota Kinabalu 88000Malaysia

Existing simulations of terrorist attacks do not consider individual *** overcome this lim-itation,we propose a framework to model heterogeneous behavior of individuals during terrorist *** constructed an emotional model that integrated personality and visual perception for *** emotional model was then integrated with pedestrian relationship networks to establish a decision-making model that sup-ported pedestrians’altruistic behaviors.A mapping model has been developed to correlate antisocial personality traits with attack strategies employed by *** demonstrate that the proposed algorithm can generate practical heterogeneous behaviors that align with existing psychological research findings.

关键词： Terrorist attack simulation Computer animation Big Five personality Intelligent decision making Heterogeneous behaviors

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Covariance Analysis in Selected OOP Languages 47

Covariance Analysis in Selected OOP Languages

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47th ICT and Electronics Convention, MIPRO 2024

作者： Sirotić, Zlatko Sovilj, Siniša Oršulić, Matija Pripužić, Krešimir Istra Tech D.o.o. Pula Croatia Juraj Dobrila University Of Pula Faculty Of Informatics Pula Croatia University Of Zagreb Faculty Of Electrical Engineering And Computing Zagreb Croatia

ISBN: (纸本)9798350382495

This study aims to analyze covariance with non-generic parameters across seven renowned statically-typed object-oriented languages: C++, Eiffel, Java, C#, Scala, Kotlin, and Swift, presented in chronological order from oldest to newest. In the original examples, an attempt is made to apply covariance to two interconnected superclass/subclass pairs, specifically the Patient/HeartPatient and FoodForPatients / FoodForHeartPatients classes. Within a method of the Patient class, a parameter of the FoodForPatients class (or type) is introduced. We asked whether in the aforementioned languages, it is possible to create an overridden method in the HeartPatient subclass that utilizes the FoodForHeartPatients class in place of the FoodForPatients class. It is essential to emphasize that while the utilization of generic parameters and their covariance represents a viable solution, it is not considered the optimal one due to significant practical drawbacks. © 2024 IEEE.

关键词： Polymorphism

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Designing Relocation Rules with Genetic Programming for the Online Container Relocation Problem 13

Designing Relocation Rules with Genetic Programming for the ...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Durasević, Marko Dumić, Mateja Gil-Gala, Francisco Javier University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia School of Applied Mathematics and Informatics J. J. Strossmayer University of Osijek Osijek Croatia University of Oviedo Department of Computing Gijon Spain

ISBN: (纸本)9798350308365

The container relocation problem (CRP) in shipping terminals is becoming increasingly important due to the growing amount of transferred goods. Until now, the most commonly investigated problem variant has been the offline CRP, in which the order in which the containers need to be retrieved is known beforehand. However, in many real-world situations this is not the case, which is modelled using the online CRP variant. In this variant, not all information is available from the beginning, but rather, it becomes available as the problem is being solved. Unfortunately, many traditional metaheuristic solution methods can not be applied to such a problem variant, which prompts the application of problem-specific heuristics called relocation rules (RRs). However, RRs are challenging to design manually, which prompted the application of genetic programming (GP) to design them automatically. Since GP was used only to design RRs for the offline problem variant, we apply GP to design relocation rules for the online variant in this study. The performance of GP is investigated under different levels of information availability to measure its performance. The results demonstrate that GP can evolve RRs that perform better than existing manually designed ones. Furthermore, the results show that in certain cases, rules generated for one level of information availability perform well for other levels, demonstrating that the evolved rules exhibit a good generalisation capability. © 2024 IEEE.

关键词： Genetic programming

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Constructing Ensembles of Automatically Designed Relocation Rules for the Container Relocation Problem 13

Constructing Ensembles of Automatically Designed Relocation ...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

ISBN: (纸本)9798350308365

Automated design of heuristics with genetic programming (G P) has, in recent years, become an intensively researched research area. One of the most recent applications of this methodology is to generate relocation rules (RRs) for the container relocation problem (CRP). CRP is an important combinatorial optimisation problem that is found in ship ter-minals and warehouses. RRs are simple constructive heuristic methods that provide a good solution quickly, thus representing an alternative to computationally expensive exact or metaheuris-tic methods. Even though the RRs designed by GP provide an improvement over existing manually designed rules, they have limited performance. An efficient way to improve the performance of RRs generated by GP is to use ensemble learning. In this study, we apply ensemble learning on RRs generated by GP for CRP to improve the performance of individual rules. We investigate how different ensemble sizes and combination methods affect the quality of the results, as well as which rules are selected to form ensembles. The experimental study shows that ensembles constructed out of automatically designed RRs significantly improve performance compared to the individual rules. © 2024 IEEE.

关键词： Combinatorial optimization

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Bilingual phrase induction with local hard negative sampling

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 147-159页

作者： Hailong Cao Hualin Miao Weixuan Wang Liangyou Li Wei Peng Tiejun Zhao Faculty of Computing Harbin Institute of TechnologyHarbinHeilongjiangChina School of Informatics University of EdinburghEdinburghScotlandUK Huawei Noah's Ark Lab Hong KongChina IT Innovation and Research Center Huawei Technologies Co.Ltd.ShenzhenGuangdongChina

Bilingual lexicon induction focuses on learning word translation pairs,also known as bitexts,from monolingual corpora by establishing a mapping between the source and target embedding *** recent advancements,bilingual lexicon induction is limited to inducing bitexts consisting of individual words,lacking the ability to handle semantics-rich *** bridge this gap and support downstream cross-lingual tasks,it is practical to develop a method for bilingual phrase induction that extracts bilingual phrase pairs from monolingual corpora without relying on cross-lingual *** this paper,the authors propose a novel phrase embedding training method based on the skip-gram ***,a local hard negative sampling strategy that utilises negative samples of central tokens in sliding windows to enhance phrase embedding learning is *** proposed method achieves competitive or superior performance compared to baseline approaches,with exceptional results recorded for distant ***,we develop a phrase representation learning method that leverages multilingual pre-trained language *** mPLMs-based representations can be combined with the above-mentioned static phrase embeddings to further improve the accuracy of the bilingual phrase induction *** manually construct a dataset of bilingual phrase pairs and integrate it with MUSE to facilitate the bilingual phrase induction task.

关键词： artificial intelligence local hard negative sampling natural language processing phrase embedding pre-trained language models

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Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors

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Computers, Materials & Continua 2023年第10期77卷 583-599页

作者： Fadhil Mukhlif Norafida Ithnin Roobaea Alroobaea Sultan Algarni Wael Y.Alghamdi Ibrahim Hashem Information Assurance and Security Research Group(IASRG) Faculty of ComputingUniversiti Teknologi Malaysia(UTM)Johor BahruJohor81310Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia College of Computing and Informatics Department of Computer ScienceUniversity of SharjahSharjah27272United Arab Emirates

The World Health Organization(WHO)refers to the 2019 new coronavirus epidemic as COVID-19,and it has caused an unprecedented global crisis for several *** every country around the globe is now very concerned about the effects of the COVID-19 outbreaks,which were previously only experienced by Chinese *** of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare *** time the pandemic surprises them by providing new values for various parameters,all the connected research groups strive to understand the behavior of the pandemic to determine when it will *** prediction models in this research were created using deep neural networks and Decision Trees(DT).DT employs the support vector machine method,which predicts the transition from an initial dataset to actual figures using a function trained on a *** short-term memory networks(LSTMs)are a special sort of recurrent neural network(RNN)that can pick up on long-term *** an added bonus,it is helpful when the neural network can both recall current events and recall past events,resulting in an accurate prediction for *** provided a solid foundation for intelligent healthcare by devising an intelligence COVID-19 monitoring *** developed a data analysis methodology,including data preparation and dataset *** examine two popular algorithms,LSTM and Decision tree on the official ***,we have analysed the effectiveness of deep learning and machine learning methods to predict the scale of the *** issues and challenges are discussed for future *** is expected that the results these methods provide for the Health Scenario would be reliable and credible.

关键词： Healthcare framework AI COVID-19 machine&deep learning LSTM RNN decision tree

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Steam players count prediction using GRU optimized by modified PSO 32

Steam players count prediction using GRU optimized by modifi...

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32nd Telecommunications Forum, TELFOR 2024

作者： Jovanovic, Luka Zivkovic, Miodrag Zivkovic, Tamara Bacanin, Nebojsa Dubljanin, Dobrivoje Malisic, Snezana Singidunum University Faculty of Technical Sciences Belgrade Serbia Singidunum University Faculty of Informatics and Computing Belgrade Serbia University of Belgrade School of Electrical Engineering Belgrade Serbia University of Belgrade Technical Faculty in Bor Bor Serbia

ISBN: (纸本)9798350391053

Accurate prediction of Steam players count is vital for efficient resource management, informed decision-making, and ability to maximize the potential of each game over its entire lifecycle. From the business point of view, this can directly affect profitability, player retention and strategic planning. This paper examines potential of GRU tuned by an improved version of renowned particle swarm optimization algorithm. The proposed model was placed into comparative simulations with other potent optimization techniques, and obtained experimental outcomes clearly pointed out the supreme performance of the suggested technique. © 2024 IEEE.

关键词： GRU metaheuristics algorithms players count prediction PSO Steam

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MSSThreD: Multitask Social Media Sentiment Analysis with Sarcasm for Public Security Threat Detection 11

MSSThreD: Multitask Social Media Sentiment Analysis with Sar...

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11th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2024

作者： Suhaimin, Mohd Suhairi Md Hijazi, Mohd Hanafi Ahmad Moung, Ervin Gubin Politeknik Kota Bharu College Education Department of Polytechnic and Community Kelantan Kota Bharu Malaysia Universiti Malaysia Sabah Data Technology and Applications Research Group Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Sabah Malaysia Universiti Malaysia Sabah Data Technology and Applications Research Group Faculty of Computing and Informatics Sabah Malaysia

ISBN: (纸本)9798350389289

Public security threat detection is critical for maintaining safety of citizens from possible natural or non-natural disasters. Social media, with its vast and diverse content, serves as a significant source for communication and information dissemination. Early detection of threats through social media analysis can greatly benefit in prevention before the event, or response and rescue during the event. However, the noisy textual with linguistic nature of social media content presents substantial challenges for sentiment analysis. Coupling with sarcasm can reverse the polarity of sentiments, complicating threat detection efforts. To address these challenges, we introduce an application framework for public security threat detection that embeds multitask learning, incorporating sentiment analysis and sarcasm detection. This application utilized preprocessing and feature engineering techniques based on Natural Language Processing to capture the nuance of sentiment and sarcasm, with deep learning network model of Bidirectional Long Short-Term Memory and Gated Recurrent Unit combination. The sentiment filtering and identifying threat-related keywords, producing monitoring reports that highlight threats earlier than traditional web news sources, as evidenced by our case study results. This application demonstrates the practical utility and impact of the framework in enhancing public security through timely threat detection. © 2024 IEEE.

关键词： Long short-term memory

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