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27th International Conference on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2024 and the 28th International Conference on Randomization and Computation, RANDOM 2024

作者： Cervenjak, Philip Gan, Junhao Umboh, Seeun William Wirth, Anthony School of Computing and Information Systems The University of Melbourne Australia Melbourne Australia School of Computer Science The University of Sydney Australia

ISBN: (纸本)9783959773485

We consider the Max Unique Coverage problem, including applications to the data stream model. The input is a universe of n elements, a collection of m subsets of this universe, and a cardinality constraint, k. The goal is to select a subcollection of at most k sets that maximizes unique coverage, i.e, the number of elements contained in exactly one of the selected sets. The Max Unique Coverage problem has applications in wireless networks, radio broadcast, and envy-free pricing. Our first main result is a fixed-parameter tractable approximation scheme (FPT-AS) for Max Unique Coverage, parameterized by k and the maximum element frequency, r, which can be implemented on a data stream. Our FPT-AS finds a (1 − Ε)-approximation while maintaining a kernel of size Õ(kr/Ε), which can be combined with subsampling to use Õ(k2r/Ε3) space overall. This significantly improves on the previous-best FPT-AS with the same approximation, but a kernel of size Õ(k2r/Ε2). In order to achieve our first result, we show upper bounds on the ratio of a collection’s coverage to the unique coverage of a maximizing subcollection;this is by constructing explicit algorithms that find a subcollection with unique coverage at least a logarithmic ratio of the collection’s coverage. We complement our algorithms with our second main result, showing that Ω(m/k2) space is necessary to achieve a (1.5 + o(1))/(ln k − 1)-approximation in the data stream. This dramatically improves the previous-best lower bound showing that Ω(m/k2) is necessary to achieve better than a e−1+1/k-approximation. © Philip Cervenjak, Junhao Gan, Seeun William Umboh, and Anthony Wirth.

关键词： approximate kernel data streams maximum coverage Maximum unique coverage

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A unified workflow strategy for analysing large-scale TripAdvisor reviews with BOW model

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International Journal of Business Intelligence and Data Mining 2022年第1期21卷 102-117页

作者： Bektaş, Jale Elsadig, Arwa School of Applied Technology and Management Computer Technology and Information Systems Mersin University Mersin33730 Turkey

Nowadays, firms need to transform customer online reviews data properly into information to achieve goals such as having a competitive edge and improving the quality of service. This paper presents a unified workflow to solve the problems of analysing large-scale data with 710,450 reviews for 1,134 hotels by using text mining methods among the different touristic regions of Turkey. Firstly, a star schema dimensional data mart is built that includes one fact table and two dimensional tables. Then, a series of text mining processes which includes data cleaning, tokenisation, and analysis are applied. Text mining is implemented through standard BOW and the extended BON model. The results show significant findings through this workflow. We propose to build a dimensional model dataset before performing any text mining process, since building such a dataset will optimise the data retrieval process and help to represent the data along with different measures of interest. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Data mining

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Examining the impact of attitude, availability, price, and quality of service on broadband Internet customer satisfaction 9

Examining the impact of attitude, availability, price, and q...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Arin, Ikrar Adinata Murad, Dina Fitria Warnars, Harco Leslie Hendric Spits Meyliana Bina Nusantara University Information System Department School of Information Systems Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University Information Systems Department BINUS Online Learning Jakarta Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University Information System Department School of Information Systems Jakarta Indonesia

ISBN: (纸本)9798350370126

After the COVID-19 pandemic, broadband internet utilization is predicted to continue to grow and experience a significant increase. The pandemic has changed how people work, study, communicate, shop, and carry out many aspects of daily life. Research reported that since 2018, research on broadband internet has continued to increase until now. This quantitative study examines the impact of the four critical variables on the link between attitude, availability, pricing, and quality of service (QoS) on broadband internet customer satisfaction using the Structural Equation Model approach and the Partial Lease Square technique. Three hundred and sixty-one citizen respondents in DKI Jakarta Province were collected using the snowball sampling method facilitated by Google Forms. At the same time, the COVID-19 post-pandemic was still ongoing in Indonesia. Through rigorous statistical analysis, the results show that customer satisfaction with broadband internet services is influenced by all factors, from attitude towards broadband Internet, broadband Internet availability, broadband Internet price, and service quality. The implications of this research are significant for service providers. They provide actionable insights for developing effective strategies to improve customer satisfaction and increase broadband internet subscriptions. © 2023 IEEE.

关键词： Quality of service

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Addressing the Libyan Arabic Dialect Identification: A Comparative Study of Ensemble Classification Methods 4

Addressing the Libyan Arabic Dialect Identification: A Compa...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Ghumeid, Najah A. Essgaer, Mansour Sebha University Faculty of Sciences Computer Science Department Sebha Libya Sebha University Faculty of Information Technology Information Systems Department Sebha Libya

ISBN: (纸本)9798350372632

Dialect identification presents a challenging task within the realm of Natural Language Processing. The growing utilization of Arabic dialects in written form, particularly on social media platforms, has created new demands for accurate identification of these dialects. In multilingual datasets, distinguishing the Libyan Arabic dialect from neighboring Arabic dialects has emerged as a crucial goal. To address this, we conducted experiments using different lengths of word n-gram features to determine the most suitable combination of word n-grams and term frequency-inverse document frequency. Our aim was to capture contextual information effectively while mitigating linguistic similarities between Arabic dialects of neighboring countries. The QCRI Arabic Dialects Identification dataset was employed, and we evaluated the performance of three ensemble tree-based classification methods: Random Forest, Gradient Boost Machine, and eXtreme Gradient Boosting. The evaluation, based on the f-measure metric, yielded respective scores of 68%, 37%, and 52% for Random Forest, Gradient Boost Machine, and eXtreme Gradient Boosting. These results underscore the effectiveness of our approach in identifying the Libyan Arabic dialect and highlight the potential of ensemble tree-based methods in Arabic dialect identification tasks. © 2024 IEEE.

关键词： Computational linguistics

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Understanding the Definitions of Microcredentials in Higher Education: Systematic Literature Review

Understanding the Definitions of Microcredentials in Higher ...

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2024 International Conference on TVET Excellence and Development, ICTeD 2024

作者： Wihendro Warnars, Harco Leslie Hendric Spits Prabowo, Harjanto Sfenrianto Bina Nusantara University BINUS Graduate Program- Computer Science Information Systems Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Business School Undergraduate Program Management Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Information Systems Management Information Systems Management Department Jakarta Indonesia

ISBN: (纸本)9798331516413

The growing focus on microcredentials emphasizes the urgent need for precise and widely accepted definitions, as existing uncertainties hinder their effective implementation. This research aims to investigate the comprehension of microcredentials definitions in the context of higher education by conducting a systematic literature review. The goal is to identify current definitions of microcredentials to facilitate standardization initiatives. Following The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology to systematically search in Scopus database within 2019-2024 resulting 940 articles, by removing the duplicates, 280 articles were initially reviewed, with 29 selected for detailed thematic analysis based on specific inclusion and exclusion criteria. By adapting the six phases of thematic analysis, selected articles were identified analyzed, organized, described, and themes found the data set were reported. Key findings reveal that, while a universally accepted definition is lacking, common themes emerge: microcredentials are competency-based, modular, portable, stackable, and often include quality assurance. These attributes highlight microcredentials as valuable for upskilling and reskilling in a flexible manner. The study concludes that establishing standard definitions can increase the recognition and utility of microcredentials across educational and professional sectors and recommends further research to strengthen core elements for consistency. © 2024 IEEE.

关键词： Competency-based Higher Education Microcredentials Modular Reskilling Upskilling

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A Transfer Learning for Intelligent Prediction of Lung Cancer Detection

A Transfer Learning for Intelligent Prediction of Lung Cance...

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2023 Al-Sadiq International Conference on Communication and information Technology, AICCIT 2023

作者： Al-Shouka, Tabreer T. Alheeti, Khattab M. Ali University of Anbar College of Computer and Information Technology Department of Computer Sciences Anbar Iraq Technology University of Anbar College of Computer and Information Department of Computer Networking Systems Iraq

ISBN: (纸本)9798350341881

Cancer is a widespread and potentially lethal illness. There are many different types of cancer. The most common type of cancer is lung cancer. High mortality rates from lung cancer are major public health concerns. Therefore, early detection of cancer reduces the possibility of dying from the disease. A CT scan is used to determine the best course of treatment. Screening with CT has been shown to reduce mortality from lung cancer by detecting the disease at its earliest stages when it manifests as pulmonary nodules. Artificial Intelligence (AI) systems, which employ transfer-learning models, have shown great potential in enhancing the precision and speed of lung cancer diagnosis and treatment. Transfer learning enables adapting pre-existing models to new tasks, facilitating more efficient and effective medical data analysis. Additionally, these systems can assist clinicians in identifying the specific type and stage of lung cancer, which can guide treatment decisions. Our study explores the use of CNN transfer learning with RESNET, MobileNetV2, Xception, and VGG16 models for medical image analysis tasks;res-net achieved the highest testing accuracy in our research, at 0.94, with a testing loss of0.16, which has demonstrated the potential to enhance the accuracy and efficiency of healthcare AI systems. © 2023 IEEE.

关键词： Health care

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Improved constrained social network rating-based neural network technique for recommending products in E-commerce environment

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International Journal of Intelligent Networks 2022年 3卷 80-86页

作者： Ottikunta, Lohith Department of Computer and Information Systems University of Houston Clear Lake HoustonTX77058 United States

In the modern world, the essentiality in the utilization of the e-commerce contents like movies, music and electronic goods becomes indispensable with diversified items searched over the internet. The relevant results of the items search are made feasible through the enforcement of filtering techniques since it determines relevant data for recommendation of an item. A diversified number of filtering schemes are available of filtering the data instead of accessing each data available on the internet for deriving associated results. The data access and efficiency, the process of identifying relevant results based on users’ preferences is challenging task. In this paper, the proposed Constrained Social Network Rating-based Neural Network Technique (CSNR-NNT) is presented with the key significances and implementation processes. This proposed CSNR-NNT significantly concentrates on the exploration of trustee information that aids in social content persuading selection process for facilitating superior recommendation. The proposed CSNR-NNT scheme utilized the benefits of neural learning for ensuring recommendation through the incorporation of distrust and trustee relation. This proposed CSNR-NNT scheme also aids in categorizing the positive and negative recommendation of the trustee based on the process of the prediction. © 2022 The Author

关键词： Electronic commerce

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A Novel Framework for Gauging information Extracted from Smartphones Using Neutrosophic Logic

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Neutrosophic Sets and systems 2025年 76卷 154-171页

作者： Abou Alzahab, R.M. Ismail, Amr Abd Elkhalik, S.H. Shams, M.Y. El-Bakry, H.M. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Department. of Communication and Computer Engineering Air Defense University Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Department of Information Systems. Faculty of Computer and Information Science Mansoura University Egypt

Smartphones contain a vast amount of information about their users, which can be used as evidence in criminal cases. However, the sheer volume of data can make it challenging for forensic investigators to identify and use the most relevant information. Neutrosophic logic is a generalization of fuzzy logic that allows for uncertainty and vagueness, making it a more potent tool for dealing with the ambiguity of real-world data. The proposed framework aims to identify and utilize the most relevant information for forensic investigators, making it easier to solve criminal cases using Neutrosophic logic. This novel approach provides a more powerful tool for dealing with the ambiguity of smartphone data, ultimately improving the accuracy and effectiveness of forensic investigations. Our research has utilized Neutrosophic logic to evaluate the degree of truth, falsity, or Indeterminacy of this information. Additionally, this study analyzes conversations between individuals using Excel’s fuzzy lookup add-in to determine the percentage of truth and false in each conversation. The results were compiled into a dataset and utilized a Neutrosophic Python code to evaluate the information. The results indicate the percentage of truth, false, and Indeterminacy in each conversation and which can be used to determine its admissibility as evidence and which not. © 2025, University of New Mexico. All rights reserved.

关键词： Smartphones

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An early prediction model for toddler nutrition based on machine learning from imbalanced data 9th

An early prediction model for toddler nutrition based on mac...

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9th International Conference on computer Science and Computational Intelligence, ICCSCI 2024

作者： Miranda, Eka Aryuni, Mediana Zakiyyah, Alfi Yusrotis Kurniawati, Yulia Ery Sano, Albert Verasius Dian Kumbangsila, Meyske Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Sawah Besar Community Health Service Jakarta Indonesia

The aim is to develop a machine-learning model that uses uneven data to predict child nutrition. Methods: We utilized an Electronic Medical Record (EMR) that included weight, height, sex, and age data related to toddlers. We used Random Forest (RF), XGBoost, and Support Vector Machine (SVM) in our analysis. Imbalanced data was handled using the Synthetic Minority Oversampling Technique (SMOTE). Results: SMOTE-balanced data with RF performed better on almost every performance metric, including f1-score, accuracy, precision, recall, and ROCAUC, by 84.70%, 88.90%, 80.00%, 84.20%, and 93.00%, in that order. In conclusion, SMOTE improved the classifier system's ROC AUC, f1-score, accuracy, precision, recall, and recall, according to our research. © 2024 The Authors.

关键词： Adversarial machine learning

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Detection of hate speech in Arabic tweets using deep learning

Detection of hate speech in Arabic tweets using deep learnin...

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作者： Al-Hassan, Areej Al-Dossari, Hmood Information Systems Department College of Computer Science and Information Systems King Saud University Riyadh Saudi Arabia

Nowadays, people are communicating through social networks everywhere. However, for whatever reason it is noticeable that verbal misbehaviors, such as hate speech is now propagated through the social networks. One of the most popular social networks is Twitter which has gained widespread in the Arabic region. This research aims to identify and classify Arabic tweets into 5 distinct classes: none, religious, racial, sexism or general hate. A dataset of 11 K tweets was collected and labelled and SVM model was used as a baseline to be compared against 4 deep learning models: LTSM, CNN + LTSM, GRU and CNN + GRU. The results show that all the 4 deep learning models outperform the SVM model in detecting hateful tweets. Although the SVM achieves an overall recall of 74%, the deep learning models have an average recall of 75%. However, adding a layer of CNN to LTSM enhances the overall performance of detection with 72% precision, 75% recall and 73% F1 score. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

关键词： Support vector machines

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