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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chu-Chun Yu Ming-Yi Hong Chiok-Yew Ho Che Lin Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Program of Data Science National Taiwan University and Academia Sinica Taiwan Department of Electrical Engineering National Taiwan University Taiwan

Push notifications efficiently deliver real-time messages, boosting user engagement and website traffic. However, users often passively receive notifications without active interaction in recommendation contexts. Consequently, for precise recommendations, Click-Through Rate (CTR) prediction for push notifications requires addressing challenges such as user temporal and contextual preferences, the dynamic nature of user click behavior, and limited interactions between users and items. We propose Push4Rec, a novel push notification recommendation model designed explicitly for news articles. Push4Rec integrates pivotal learners to extract information adeptly. It assesses click behavior, captures preferences, and comprehends trends’ influence. A fusion function and gating network ensure versatile extraction of user click preferences. We assessed Push4Rec using a real-world push notification dataset from our partnering company. Push4Rec outperformed benchmark models, delivering state-of-the-art results across all evaluation metrics. Thus, we believe that Push4Rec, with its novel approach, sets a new standard in push notification services, driving forward the field of personalized recommendation systems.

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Lung Cancer Diagnosis Based on Object Detection Technology 14th

Lung Cancer Diagnosis Based on Object Detection Technology

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14th International Conference on Frontier Computing, FC 2024

作者： Xu, Jia-Lang Wang, Pei-Yun Huang, Tzu-Hsuan Hsu, Ying-Lin Huang, Kuo-Yang Chen, Mu-Yen Artificial Intelligence Development Center Changhua Christian Hospital Changhua Taiwan Doctoral Program in Data Science and Industrial Analytics National Chung Hsing University Taichung Taiwan Department of Applied Mathematics and Institute of Statistics National Chung Hsing University Taichung Taiwan Division of Chest Medicine Department of Internal Medicine Changhua Christian Hospital Changhua Taiwan Department of Engineering Science National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9789819623945

With the rise of artificial intelligence, many people nowadays use artificial intelligence to help solve some problems in life, and the medical field is also with the rise of artificial intelligence, many people are slowly applying artificial intelligence to medical images to assist in decision-making, this study proposes a Yolov8 algorithm for object tracking of CT images of lung cancer, which can quickly tell the doctor where the cancer is and what the type of cancer is. The results of this study show that the Box_Loss, Cls_Loss, and Dfl_loss of the Yolov8 algorithm in detecting the location of lung cancer are 1.3689, 0.4552, and 1.5569 in the validation data, respectively, and thus this study suggests that this method can help physicians know the location of the tumor and what kind of tumor it belongs to. Therefore, this study suggests that this method can help physicians know the location of the tumor and the type of tumor. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Bayesian Parameter Estimation for Partially Observed McKean-Vlasov Diffusions Using Multilevel Markov chain Monte Carlo

arXiv

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arXiv 2025年

作者： Jasra, Ajay Wu, Amin School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this article we consider Bayesian estimation of static parameters for a class of partially observed McKean-Vlasov diffusion processes with discrete-time observations over a fixed time interval. This problem features several obstacles to its solution, which include that the posterior density is numerically intractable in continuous-time, even if the transition probabilities are available and even when one uses a time-discretization, the posterior still cannot be used by adopting well-known computational methods such as Markov chain Monte Carlo (MCMC). In this paper we provide a solution to this problem by using new MCMC algorithms which can solve the aforementioned issues. This MCMC algorithm is extended to use multilevel Monte Carlo (MLMC) methods. We prove convergence bounds on our parameter estimators and show that the MLMC-based MCMC algorithm reduces the computational cost to achieve a mean square error versus ordinary MCMC by an order of magnitude. We numerically illustrate our results on two models. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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IoT for Water Quality Categorization 5

IoT for Water Quality Categorization

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

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Identifying Prognostic Markers of Non-small Cell Lung Carcinoma Using Bioinformatics

Identifying Prognostic Markers of Non-small Cell Lung Carcin...

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International Conference on Industrial Instrumentation and Control,ICI2C 2021

作者： Khan, Siladitya Chakraborty, Debamitra Department of Biomedical Engineering University of Rochester RochesterNY14627 United States Goergen Institute for Data Science University of Rochester RochesterNY14627 United States Materials Science Graduate Program University of Rochester RochesterNY14627 United States

ISBN: (纸本)9789811670107

We present a transcriptomics pipeline for performing the functional analysis of array expression profiling data of normal and adenocarcinoma lung tissue. Our aims are twofold, firstly to elucidate molecular processes that transform normal tissue, to a lung adenocarcinoma. Following this, we perform functional analysis to screen potential hub genes and demonstrate their value in cancer prognosis. Analysis was performed on a total of 500 differentially expressed genes (DEG), screened for their regulation and transcriptional modulation in a female population carrying non–small cell lung carcinoma (NSCLC). Key pathways like cytokine-cytokine receptors, ECM receptor interactions, and TNF signaling were identified suggesting a potential role in lung tumorigenesis. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Bioinformatics

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CNN-Based Task State Estimation for Safer Automation of Oxy-Fuel Metal Cutting

CNN-Based Task State Estimation for Safer Automation of Oxy-...

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IEEE International Conference on Automation science and engineering (CASE)

作者： James Akl Shreedhar Kodate Berk Calli Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA Data Science Program Worcester Polytechnic Institute Worcester MA USA

The industrial operation of oxy-fuel metal cutting via gas torches involves tasks such as ignition, preheating, and combustion along the target surface. Automated oxy-fuel cutting systems are exposed to risks and anomalies that can lead to incorrect actions and safety hazards. In this paper, we develop a classifier for online task state estimation to assess the cutting robot's actions, detect anomalies, and reduce the risk of hazards. Using representative footage from our robotic cutting experiments, we curate an image dataset labeled with four types of cutting task states. Using deep learning methods, we design and train a convolutional neural network model for classifying the cutting task state from input images. The classifier architecture is optimized for rapid inferences during online estimation. After evaluation, our classifier achieves an overall accuracy of 93.8 % with high inference speeds on two types of representative hardware. Our ‘Oxy-fuel Cutting Task State’ (OCTS) dataset is available at ***/10.5281/zenodo.7734951.

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The Impact of Different Modes of Augmented Reality Information in Assisted Aircraft Cable Assembly 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Ariansyah, Dedy Xi, Khaizuo Erkoyuncu, John Ahmet Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia School of Aerospace Transport and Manufacturing Cranfield University BedfordMK43 0AL United Kingdom Centre of Digital Engineering and Manufacturing School of Aerospace Transport and Manufacturing Cranfield University BedfordMK43 0AL United Kingdom Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Aircraft avionics systems are complicated systems which involves high number of components and complex cable assembly procedure. To deal with this challenge, Augmented Reality (AR) has been proposed to be an effective method to improve assembly efficiency and to reduce error rates. In this paper, the authors developed AR-based guidance cable assembly and compare the impact of different modes of AR information. The results show that although AR outperformed the paper document assembly guidance, the design of AR information has distinct influence on the successful completion of the cables assembly process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Augmented reality

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Sentiment Analysis: Indonesia Netflix User's Comment Using Multiple Lexicon-Based Dictionaries

Sentiment Analysis: Indonesia Netflix User's Comment Using M...

引用

Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Sebastianus Adi Santoso Mola Tiwuk Widiastuti Rosa V. K. Isabela Ohe Roma Andrea Stevens Karnyoto Bens Pardamean Faculty of Science and Engineering University of Nusa Cendana Kupang Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

A subscription movie streaming service from the United States, Netflix, has been present in Indonesia since 2016 and provides a wide variety of films without showing any single advertisements that can be viewed from anywhere, as long as there is access to the internet. Despite these advantages, many users have complained through the Google Play Store's comments column. Some of the common complaints include frequent buffering and connectivity issues, dissatisfaction with the limited selection of Indonesian movies, lack of subtitles for specific languages, or pricing concerns have also been raised. In this study developed two combined scenario methods using the InSet and SentiStrength_id dictionary to obtain better performance and compare them against independent InSet and SentiStrength_id. This study collected users' comments for the Netflix app in the Google Play Store as a dataset using web scraping techniques through the Google Collaboration tool. The dataset contains 3250 rows spanning the period from January 22, 2024, through June 6, 2024. To ensure proper processing of the text, data cleaning, lowercasing, normalization, tokenization, stemming, and stopwords removal are conducted. The results show that most user opinions are negative. The InSet dictionary has an accuracy of 92%, SentiStrength_id 78%, Combined scenario 1 is 87%, and scenario 2 reaches the highest among others which is 92.52%.

关键词： Sentiment analysis Dictionaries Accuracy Films Collaboration Pricing Motion pictures Tokenization Cleaning Internet

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Multilevel Particle Filters for Partially Observed McKean-Vlasov Stochastic Differential Equations

arXiv

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arXiv 2024年

作者： Awadelkarim, Elsiddig Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia School of Data Science The Chinese University of Hong Kong Shenzhen China

In this paper we consider the filtering problem associated to partially observed McKean-Vlasov stochastic differential equations (SDEs). The model consists of data that are observed at regular and discrete times and the objective is to compute the conditional expectation of (functionals) of the solutions of the SDE at the current time. This problem, even the ordinary SDE case is challenging and requires numerical approximations. Based upon the ideas in [3, 12] we develop a new particle filter (PF) and multilevel particle filter (MLPF) to approximate the afore-mentioned expectations. We prove under assumptions that, for ϵ > 0, to obtain a mean square error of O(ϵ2) the PF has a cost per-observation time of O(ϵ−5) and the MLPF costs O(ϵ−4) (best case) or O(ϵ−4 log(ϵ)2) (worst case). Our theoretical results are supported by numerical experiments. © 2024, CC BY.

关键词： Mean square error

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Constructing a Segmentation Model for Patients with Leukoaraiosis Using Deep Learning Algorithms 24

Constructing a Segmentation Model for Patients with Leukoara...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Lo, Li-Ting Tsai, Yi-Chia Chang, Yi-Jou Ting, Hsien-Wei Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Many patients with leukoaraiosis (LA) exhibit mild and difficult-to-detect symptoms in the early stages, and due to the lack of effective detection methods, the optimal timing for treatment is often missed. This not only makes later treatment more difficult but, in severe cases, can even lead to mild to severe dementia. Therefore, in addition to relying on radiologists to manually interpret magnetic resonance imaging (MRI) scans, finding more effective detection methods has become extremely urgent. This study aimed to combine image processing and deep learning technologies to develop a model capable of accurately identifying and segmenting white matter hyperintensities (WMHs) to assist doctors in the early diagnosis of LA. We first performed efficient preprocessing on MR image slices from two different series and used the ResNet18 model to identify key slices containing WMHs, ultimately achieving precise segmentation of WMH areas with the U-Net model. This research focused on developing innovative preprocessing techniques, improving the accuracy by 12.3% with a limited dataset through preprocessing. Ultimately, the ResNet18 model achieved an F1 score of 0.829, while the U-Net model’s intersection over union (IoU) and Dice coefficient reached 0.750 and 0.857, respectively, showing the potential of this architecture. We propose improvements in the final chapter to construct a more comprehensive model. © 2024 Copyright ACM.

关键词： Deep learning

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