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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Parambath, Shameem A Puthiya Alfahad, Saleh Abdullah M Anagnostopoulos, Christos Kolomvatsos, Kostas University of Glasgow School of Computing Science United Kingdom University of Thessaly Department of Informatics and Telecommunications Greece

ISBN: (纸本)9798331530631

In this paper, we propose a Thompson Sampling based sequential block elimination approach for dynamic pricing within pure-exploration Multi-Armed Bandit (MAB) setting. Given an l-dimensional action space, which represents various price ranges, our objective is to identify the optimal prices for different alternatives or variants of a product in order to maximize the total revenue. Our contribution lies in the development of a novel block elimination-based MAB algorithm. The proposed algorithm begins by discretizing the continuous action space into a finite set of discrete actions. Subsequently, a recursive block elimination procedure is employed to progressively remove sub-optimal actions from the set. The elimination process leverages the calculation of confidence bounds over the blocks of actions, enabling the efficient exclusion of sub-optimal choices. We conduct extensive experiments on a dynamic pricing problem in the logistics domain to evaluate the effectiveness of our proposed approach. The experimental results demonstrate that our method is able to identify the optimal arms within the given budget. © 2024 IEEE.

关键词： Budget control

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Bias analysis of AI models for undergraduate student admissions

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Neural computing and Applications 2025年第12期37卷 7785-7795页

作者： Van Busum, Kelly Fang, Shiaofen Department of Computer Science and Software Engineering Butler University IndianapolisIN United States Computing and Engineering Indiana University Luddy School of Informatics Indiana University Indianapolis IndianapolisIN United States

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors Van Busum and Fang (Proceedings of the 38th ACM/SIGAPP Symposium on Applied computing, 2023) to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the school of science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Students

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A-MASA: Arabic Multi-Domain Aspect-Based Sentiment Analysis Datasets 6

A-MASA: Arabic Multi-Domain Aspect-Based Sentiment Analysis ...

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6th International Conference on AI in Computational Linguistics, ACLing 2024

作者： Eid, Yomna Zayed, Hala Medhat, Walaa School of Information Technology and Computer Science Nile University Giza Egypt Centre for Informatics Science Nile University Giza Egypt School of Computing and Information Sciences Egypt University of Informatics Cairo Egypt

The rapid growth of Natural Language Processing (NLP) applications has gained interest in various languages, including Arabic. One critical NLP task is Aspect-Based Sentiment Analysis (ABSA). ABSA involves identifying the sentiment expressed toward specific aspects or attributes of entities mentioned in a piece of text, rather than determining the overall sentiment. Despite the increased use of Semitic languages like Arabic in NLP over the past decade, there remains a lack of resources and datasets in Arabic, particularly in dialectical Arabic. Additionally, existing ABSA datasets are often limited to a single domain, which does not support all types of ABSA, such as multi-domain ABSA, nor do they allow for testing a model's generalization capabilities. This study addresses these limitations by constructing multi-domain ABSA datasets in Arabic for the tasks of aspect extraction and aspect polarity detection. The datasets include both real and synthetic data, covering dialectical and modern standard Arabic, and comprising 6,500 records across five domains. These datasets can be utilized in single-domain, cross-domain, and multi-domain experiments for ABSA. Furthermore, we evaluated the constructed datasets using various transformer-based models and assessed the impact of generating synthetic data on the diversity and balance of the data, as well as on model performance. The results reported in this research demonstrate that the data is valid for use in ABSA tasks. Moreover, the models used for evaluation achieved competitive or superior results compared to existing models on various datasets. Additionally, the annotation guidelines we created are presented as recommended practices applicable to different tasks. All datasets used in this study are publicly available for research purposes. © 2024 Elsevier B.V.. All rights reserved.

关键词： Data assimilation

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Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

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Computers, Materials & Continua 2024年第2期78卷 2441-2468页

作者： Vaishnawi Priyadarshni Sanjay Kumar Sharma Mohammad Khalid Imam Rahmani Baijnath Kaushik Rania Almajalid Department of Computer Science Gautam Buddha UniversityGreater NoidaUttar PradeshIndia College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia School of Computer Science Shri Vaishno Devi UniversityKatraIndia

Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in *** detection and effective treatment of BC can help save women’s *** an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection *** paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography(CBIS-DDSM)data *** novelty of the proposed framework lies in the integration of various techniques,where the fusion of deep learning(DL),traditional machine learning(ML)techniques,and enhanced classification models have been deployed using the curated *** analysis outcome proves that the proposed enhanced RF(ERF),enhanced DT(EDT)and enhanced LR(ELR)models for BC detection outperformed most of the existing models with impressive results.

关键词： Autoencoder breast cancer deep neural network convolutional neural network image processing machine learning deep learning

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Natural Language Processing Revolution From Humble Beginnings to Deep Learning and Beyond 4

Natural Language Processing Revolution From Humble Beginning...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Smarts, Nyalalani Selvaraj, Rajalakshmi Kuthadi, Venumadhav Botswana International University of Science and Technology School of Pure and Applied Sciences Department of Computing and Informatics Palapye Botswana

ISBN: (纸本)9798331518981

This paper explores the intricate development of Natural Language Processing (NLP), a domain which is centered at the core of computational intelligence and human communication. This research is inspired by the need to understand the foundations of NLP and how it has influenced the development of human-computer interactions, language comprehension systems and information retrieval techniques. It explores the transformational effect of deep learning and attention-based models such as the third-generation Generative Pre-trained Transformer (GPT-3), Text-to-Text Transfer Transformer (T5) and Bidirectional Encoder Representations from Transformers (BERT). The models mentioned above have greatly improved the capability to comprehend and generate human-like language using computers. The originality of the work is also reflected on its comprehensive overview of key NLP concepts, tracing chronological advancements, exploring deep learning innovations, and discussing effective pruning techniques to optimize model performance. By reviewing the history of NLP, this research not merely brings the past into focus but suggests future developments for scholars to investigate and find solutions for current challenges in NLP. Its results not only act as a guide for practitioners today but also suggest new directions for research and development that will help meet the challenges of tomorrow in this dynamic discipline of NLP. © 2024 IEEE.

关键词： Deep learning

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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Visualizing NASDAQ Composite Index Historical Data Using Tableau 6th

Visualizing NASDAQ Composite Index Historical Data Using Tab...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Safuan, Halawati Abd Jalil Nyein, Chit Ko Ko Riskhan, Basheer Ashfaq, Farzeen School of Computing and Informatics Albukhary International University Alor Setar Malaysia School of Computer Science SCS Taylors University Subang Jaya Malaysia

ISBN: (纸本)9789819780891

The study explores the visualization and analysis of historical data for the NASDAQ Composite Index (^IXIC) using Tableau. It examines trends in daily closing prices and volume distribution and compares performance with the NYSE index. Findings reveal significant fluctuations in the NASDAQ index over 22 years, suggesting potential future price changes. Forecasted graphs offer insights into potential economic decline and recovery, highlighting the dynamic nature of the market. Volume distribution analysis identifies periods of heightened trading activity. Comparative performance analysis emphasizes different trends observed in the NASDAQ and NYSE indices. Due to market unpredictability and external influences, additional research is crucial for validation. This study showcases the value of data visualization and analysis in finance through Tableau. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inflation

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of Computer science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Web-Based Mental Health Predicting System Using K-Nearest Neighbors and XGBoost Algorithms 8th

Web-Based Mental Health Predicting System Using K-Nearest Ne...

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8th International Visual informatics Conference, IVIC 2023

作者： Zulkefli, Nurul Farhanaa Diah, Norizan Mat Ismail, Azlan Hanum, Haslizatul Fairuz Mohamed Ibrahim, Zaidah Arif, Yunifa Miftachul School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia Informatics Engineering Faculty of Science and Technology Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia

ISBN: (纸本)9789819973385

Problems with mental health are common presently and have been a worry for a long time. Mental health problems, like anxiety, depression, and panic attacks, can be caused by numerous things. Therefore, recognising the start of mental disease is becoming increasingly crucial to maintaining a good life balance. This study uses machine learning to identify any possible mental health disorders in an individual to attain this goal. The investigation employed supervised machine learning to predict mental health status, namely K-Nearest Neighbors (KNN) and XGBoost, with performance evaluation criteria including accuracy, precision, recall, and F1 score. When these two algorithms were compared, it was discovered that XGBoost produced a more effective prediction model, which was then employed to develop a web-based mental health prediction system. The web-based method creates a questionnaire for mental health issues. Based on the user’s responses to the questions, the system will predict his or her mental health status as normal, depression, anxiety, stress, loneliness, or regularity. Every component of the system, including buttons and forms, has been successfully tested using functionality tests. Moreover, the system’s advantages, weaknesses, and future study directions are identified. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Nearest neighbor search

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IoT: Communication protocols and security threats

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Internet of Things and Cyber-Physical Systems 2023年第1期3卷 1-13页

作者： Gerodimos, Apostolos Maglaras, Leandros Ferrag, Mohamed Amine Ayres, Nick Kantzavelou, Ioanna School of Computer Science and Informatics University of Thessaly Lamia Greece School of Computing at Edinburgh Napier University Edinburgh United Kingdom Technology Innovation Institute Abu Dhabi United Arab Emirates School of Computer Science and Informatics De Montfort University Leicester United Kingdom School of Engineering Dept.of Informatics and Computer Engineering University of West Attica Athens Greece

In this study, we review the fundamentals of IoT architecture and we thoroughly present the communication protocols that have been invented especially for IoT technology. Moreover, we analyze security threats, and general implementation problems, presenting several sectors that can benefit the most from IoT development. Discussion over the findings of this review reveals open issues and challenges and specifies the next steps required to expand and support IoT systems in a secure framework. © 2023 The Authors

关键词： Network architecture

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