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10th IEEE Smart World Congress, SWC 2024

作者： Li, Danlei Nair, Nirmal Wang, Kevin I-Kai Sakurai, Kouichi University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Kyushu University Department of Informatics Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9798331520861

Anomaly detection is the identification of instances that substantially deviate from the majority of the data and do not conform to a well-defined normal behavior. Investigating time series anomalies has become increasingly popular in recent years due to widely deployed IoT devices and sensors. Although most existing methods apply image anomaly detection algorithms directly on time series and commonly assume the data is IID (independent and identically distributed). However, in most realworld applications, the underlying data distribution alters over time. Changes in data over time are referred to as concept drift and can significantly degrade the efficacy of an anomaly detection model. Existing research also indicates that the majority of anomaly detection frameworks ignored concept drift, resulting in poor long-term model performance. This paper presents an ensemble-based, unsupervised anomaly detection framework that is adaptive to the concept drift in time series data. Both distribution and performance-based techniques are used to detect concept drift and update the model to ensure the framework's longterm performance reliability. The proposed method is evaluated using a real-world univariate time series data that captures sea surface temperature data over a 2 -year period. By comparing our proposed framework to other state-of-the-art algorithms, we demonstrate our proposed method has obvious benefits in accuracy and long-term stability. © 2024 IEEE.

关键词： Anomaly detection Concept drift adaptation Ensemble method Time series Unsupervised method

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The Novel DeepBoost Multi Output Regressor for Household Finance Intelligence 5

The Novel DeepBoost Multi Output Regressor for Household Fin...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Suwandaratna, S.H.S.V. Priyankara, G.O. Institute of Technology Department of Computer Science and Engineering Informatics No 10 Trelawney PI Colombo00400 Sri Lanka

ISBN: (纸本)9798331530983

This paper forecasts the microeconomic level household expenditures using a novel hybrid deep learning approach. In terms of research significance, household finance control has a major influence on the finance system within the economy. Accurate forecasting of household finances assists in maintaining positive financial behavior among individuals and the economy. The DeepBoost multi-output regressor proposed in this paper is based on the 1D CNN-ANN and the XGBoost. The proposed model in this paper is compared with the R2, MSE, and MAE since it's a regression problem. The experimental results reveal that the proposed DeepBoost multi-output regressor has the best application in forecasting the multiple expenditures of households by outperforming the ANN, 1D CNN-ANN, and Random Forest Regressor models. The proposed DeepBoost multi-output regressor evaluated the housing, food, transportation, healthcare, other necessities, childcare, and tax expenditures that had 0.94, 0.98, 0.83, 0.94, 0.97, 0.97, and 0.99 values for the R2, 9037.71, 2692.12, 9788, 15077.33, 1373.93, 13629.36, and 1904.52 values for the MSE, and 66.07, 34.05, 73.17, 87.05, 26.25, 78.74, and 29.47 MAE values than the ANN, RFR, and 1D CNN-ANN models. © 2025 IEEE.

关键词： Taxation

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"I Have to Use My Son's QR Code to Run the Business": Unpacking Senior Street Vendors' Challenges in Mobile Money Collection in China

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Proceedings of the ACM on Human-computer Interaction 2023年第CSCW1期7卷 1-28页

作者： He, Changyang He, Lu Lu, Zhicong Li, Bo Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Department of Informatics University of California Irvine United States Department of Computer Science City University of Hong Kong Hong Kong Hong Kong

Mobile payment systems have become an infrastructural component in citizens' socio-economic life in China. The rapid shift to a cashless society demands vendors of all ages to quickly adapt themselves to the ubiquitous mobile payment era. However, how this trend may impact senior vendors, a group that typically uses less technology, remains unknown despite its significance in inclusive mobile payment design. This work aims to address this gap by investigating the challenges and strategies of senior vendors in mobile payment adoption. Particularly, we focus on a traditional low-resource setting with a large volume of senior vendors: street vending. We conduct a qualitative study incorporating field observations on 33 senior street vendors and semi-structured interviews with 15 of them (aged 53-78), and take Moneywork as an analytical lens to unpack their challenges in physical and social interactions. We find that senior street vendors are a group passively adopting mobile payments due to business requirements instead of recognizing their advantages. Vendors with relatively low digital literacy have to take an alternative method - using family members' QR codes - to run the business as family-dependent money receivers. With limited considerations for senior vendors' situational vulnerabilities, unexpected difficulties of payment confirmation emerge during transactions, such as reduced confirmation efficacy under noisy surroundings and degraded hearing. Transaction security issues also appear when mobile payment-based frauds target both confirmation interfaces (e.g., fake sounds of successful payment) and trust systems (e.g., showing half-done proof to flee without paying) in street vending. Finally, we raise a less visible yet critical concern of family-dependent vendors on the lost money freedom when their income flows into their families' wallets. We propose design implications for mobile payment systems to support more secure, efficient and accessible money collec

关键词： Electronic money

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Detection of Katokkon Chili Maturity using Convolutional Neural Network with Transfer Learning Model DenseNet169

Detection of Katokkon Chili Maturity using Convolutional Neu...

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2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Kaswar, Andi Baso Ilham, Muhammad Ryan Tahir, Renisa Amalia Mappalotteng, Abdul Muis Djawad, Yasser Abdul Andayani, Dyah Darma State University of Makassar Department of Computer Engineering Makassar Indonesia State University of Makassar Department of Informatics and Computer Engineering Makassar Indonesia State University of Makassar Department of Electronics Engineering Makassar Indonesia

ISBN: (纸本)9798350382914

Chili is a plant that originates from America and is included in the Solonaceae and Capsicum genera. One type of chili that is included in the large chili category is Katokkon chili, commonly referred to as Toraja chili, which comes from the Tana Toraja district, South Sulawesi, Indonesia. Research related to the detection of fruit ripeness and quality has also been conducted. However, the research that has been conducted still uses relatively few images, and the accuracy needs to be improved. In addition, no research has discussed the detection of Katokkon chili maturity level using images or images of Katokkon chili fruit taken directly from the tree. Therefore, research was conducted with the title Detection of the Maturity Level of Katokkon Chili using the CNN pretrained DenseNet169 model method. In this study, the level of maturity of chili peppers was classified into three categories: raw chilies, half-ripe chilies, and ripe chilies. Based on the results of several tests that have been carried out, the best accuracy results are obtained in testing using the DenseNet169 method, with an accuracy of 95.03%, a loss value of 16.72%, a recall and specificity value of 95.03% and 97.51% respectively, a precision value of 95.03%, and an accuracy CF of 96.69%. This shows that the DenseNet169 method with a combination (Swish, Swish, and Adamax) can be considered the best choice for classifying or detecting the level of maturity of Toraja chili compared other methods that have been tested. © 2023 IEEE.

关键词： Convolutional neural networks

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NDAS: Noise-Decomposed Abnormal Segmentation for Robust Medical Image Retrieval 19

NDAS: Noise-Decomposed Abnormal Segmentation for Robust Medi...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Kim, Ju-Chan Van Nguyen, Pham Le, Duc-Tai Choo, Hyunseung College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

This paper presents NDAS (Noise-Decomposed Abnormal Segmentation), an innovative framework for robust medical image retrieval and segmentation. By explicitly decomposing noise and abnormal features, NDAS enhances retrieval accuracy and segmentation precision. Experimental results on the BraTS 2019 dataset validate NDAS’s superiority over CBMIR and SBMIR methods, achieving the highest Top-5 accuracy and Dice coefficient. This demonstrates its effectiveness in handling noise, isolating pathological regions, and retrieving diagnostically relevant medical images. NDAS offers a significant advancement in medical imaging analysis, emphasizing its potential for clinical applications requiring precision and reliability. © 2025 IEEE.

关键词： Image segmentation

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The role of explainable Artificial Intelligence in high-stakes decision-making systems: a systematic review

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7827-7843页

作者： Sahoh, Bukhoree Choksuriwong, Anant School of Informatics Walailak University Tha Sala Nakhon Si Thammarat80160 Thailand Department of Computer Engineering Faculty of Engineering Prince of Songkla University Songkla Had Yai90112 Thailand

A high-stakes event is an extreme risk with a low probability of occurring, but severe consequences (e.g., life-threatening conditions or economic collapse). The accompanying lack of information is a source of high-stress pressure and anxiety for emergency medical services authorities. Deciding on the best proactive plan and action in this environment is a complicated process, which calls for intelligent agents to automatically produce knowledge in the manner of human-like intelligence. Research in high-stakes decision-making systems has increasingly focused on eXplainable Artificial Intelligence (XAI), but recent developments in prediction systems give little prominence to explanations based on human-like intelligence. This work investigates XAI based on cause-and-effect interpretations for supporting high-stakes decisions. We review recent applications in the first aid and medical emergency fields based on three perspectives: available data, desirable knowledge, and the use of intelligence. We identify the limitations of recent AI, and discuss the potential of XAI for dealing with such limitations. We propose an architecture for high-stakes decision-making driven by XAI, and highlight likely future trends and directions. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Bayesian networks

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Performance Evaluation of Deep Dense Layer Neural Network for Diabetes Prediction

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computers, Materials & Continua 2023年第7期76卷 347-366页

作者： Niharika Gupta Baijnath Kaushik Mohammad Khalid Imam Rahmani Saima Anwar Lashari Department of Computer Science and Engineering Shri Mata Vaishno Devi UniversityKatraJ&KIndia College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia

Diabetes is one of the fastest-growing human diseases worldwide and poses a significant threat to the population’s longer *** prediction of diabetes is crucial to taking precautionary steps to avoid or delay its *** this study,we proposed a Deep Dense Layer Neural Network(DDLNN)for diabetes prediction using a dataset with 768 instances and nine *** also applied a combination of classical machine learning(ML)algorithms and ensemble learning algorithms for the effective prediction of the *** classical ML algorithms used were Support Vector Machine(SVM),Logistic Regression(LR),Decision Tree(DT),K-Nearest Neighbor(KNN),and Naïve Bayes(NB).We also constructed ensemble models such as bagging(Random Forest)and boosting like AdaBoost and Extreme Gradient Boosting(XGBoost)to evaluate the performance of prediction *** proposed DDLNN model and ensemble learning models were trained and tested using hyperparameter tuning and K-Fold cross-validation to determine the best parameters for predicting the *** combined ML models used majority voting to select the best outcomes among the *** efficacy of the proposed and other models was evaluated for effective diabetes *** investigation concluded that the proposed model,after hyperparameter tuning,outperformed other learning models with an accuracy of 84.42%,a precision of 85.12%,a recall rate of 65.40%,and a specificity of 94.11%.

关键词： Diabetes prediction hyperparameter tuning k-fold validation machine learning neural network

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A Simulation-based Education Platform for Security Investments Through Culture and Knowledge Convergence 18

A Simulation-based Education Platform for Security Investmen...

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18th European Conference on Games Based Learning, ECGBL 2024

作者： Filippidis, Adam P. Hilas, Constantinos S. Politis, Anastasios Rantos, Konstantinos Mouratidis, Haralambos Department of Computer Informatics and Telecommunications Engineering International Hellenic University Serres Campus Serres Greece Department of Computer Science Democritus University of Thrace Kavala Campus Greece Department of Computer and Systems Sciences Stockholm University Sweden

ISBN: (纸本)9781917204194

Cybersecurity is one of the most critical sectors of information systems and is considered a strategic priority for many Organizations. Information security investments are a crucial aspect of cybersecurity. However, it often happens that the economic decisions, which aim to improve information security, have poor efficiency. One reason is the lack of background on economics of the Chief Information Security Officers who are often not familiar with the evaluation of the non-technical aspects of security, e.g. economics. The other reason is the cultural gap between the different persons related to business management and administration. In this paper we present an interactive - simulation software designed to familiarize the aforementioned persons with information security investments. The software is actually a game that aims to boost learning for all the stakeholders who are involved in the investment process. Α better understanding of the processes, the technical, economic and human factors, along with the cultural convergence of the stakeholders will help boost the efficiency of investments. Design Science is used as the research method. The creative method, a brainstorming technique, is combined with the five steps of Design Science, i.e. explicate the problem, define the requirements, design and develop the artefact, demonstrate the artefact, and evaluate the artefact, to develop a process framework that will be able to cope with this problem. The artefact is an interactive-simulation platform in which a person could take the decision maker role and (a) deal with events that happen and (b) investigate different aspects of information security investment complexity. The evaluation of the artefact is made with a case study in the Industry, where real world data are used to feed the simulation and real-world decision makers play the game in order to better understand the different aspects of the problem and help us trim and enhance the functioning of the platform.

关键词： Efficiency

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Cross Feature Fusion of Fundus Image and Generated Lesion Map for Referable Diabetic Retinopathy Classification 17th

Cross Feature Fusion of Fundus Image and Generated Lesion ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Mok, Dahyun Bum, Junghyun Tai, Le Duc Choo, Hyunseung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9789819609000

Diabetic Retinopathy (DR) is a primary cause of blindness, necessitating early detection and diagnosis. This paper focuses on referable DR classification to enhance the applicability of the proposed method in clinical practice. We develop an advanced cross-learning DR classification method leveraging transfer learning and cross-attention mechanisms. The proposed method employs the Swin U-Net architecture to segment lesion maps from DR fundus images. The Swin U-Net segmentation model, enriched with DR lesion insights, is transferred to generate a lesion map. Both the fundus image and its segmented lesion map are used as complementary inputs for the classification model. A cross-attention mechanism is deployed to improve the model’s ability to capture fine-grained details from the input pairs. Our experiments, utilizing two public datasets, FGADR and EyePACS, demonstrate a superior accuracy of 94.6%, surpassing current state-of-the-art methods by 4.4%. To this end, we aim for the proposed method to be seamlessly integrated into clinical workflows, enhancing accuracy and efficiency in identifying referable DR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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i-NVMe: Isolated NVMe over TCP for a Containerized Environment 42

i-NVMe: Isolated NVMe over TCP for a Containerized Environme...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Lee, Seongho Yeom, Ikjun Kim, Younghoon College of Computing and Informatics Sungkyunkwan University Department of Computer Science and Engineering Suwon Korea Republic of

ISBN: (纸本)9798350334142

Non-Volatile Memory Express (NVMe) over TCP is an efficient technology for accessing remote Solid State Drives (SSDs);however, it may cause a serious interference issue when used in a containerized environment. In this study, we propose a CPU isolation scheme for NVMe over TCP in such an environment. The proposed scheme measures the CPU usage of the NVMe over TCP worker, charges it to containers in proportion to their NVMe traffic, and schedules containers to ensure isolated sharing of the CPU. However, because the worker runs with a higher priority than normal containers, it may not be possible to achieve CPU isolation with container scheduling alone. To solve this problem, we also control the CPU usage of the worker by throttling NVMe over TCP traffic. The proposed scheme is implemented on a real testbed for evaluation. We perform extensive experiments with various workloads and demonstrate that the scheme can provide CPU isolation even in the presence of excessive NVMe traffic. © 2023 IEEE.

关键词： Containers

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