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16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Nakanishi, Keiichi Katafuchi, Ryoya Tokunaga, Terumasa Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Iizuka Japan

ISBN: (纸本)9798350377903

Creating pixel-level ground-truth (GT) masks is quite costly for deep learning-based image segmentation. Specialists in areas such as anomaly detection and medical diagnostics face difficulties in producing many GT masks due to limited resources. To reduce this burden, we propose a cost-effective image segmentation framework with point annotations (CoSPA) that performs image segmentation with only point annotations. Point annotations refer to the partial and sparse labeled coordinates in an image. The key idea is to ensure consistency between the predictions for the same coordinates from the two different networks of CoSPA. This new consistency enables the CoSPA to improve segmentation performance and extend to semi-supervised learning. For the MVTec AD dataset, we verified the cost-effectiveness of CoSPA through an anomaly detection task. We demonstrated that the point annotating costs were reduced by 80% compared to creating GT masks. Subsequently, the CoSPA realized by 87% of the mean Intersection over Union (mIoU) achieved using the fully supervised method, DeepLabV3+. Moreover, the mIoU of CoSPA using only 30% of all point annotations defeated that of the unsupervised method, PaDiM. This study offers a new direction for economic anomaly localization. © 2024 IEEE.

关键词： Image segmentation

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A Comparative Study of Deep Learning and Modern Machine Learning Methods for Predicting Australia's Precipitation

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International Journal of Advanced computer science and Applications 2025年第4期16卷 943-966页

作者： Farman, Hira Mastoi, Qurat-Ul-Ain Abbas, Qaiser Ahmad, Saad Alshahrani, Abdulaziz Jan, Salman Syed, Toqeer Ali Department of Computer Science Iqra University Karachi Institute of Economics and Technology Karachi74600 Pakistan School of Computing and Creative Technologies University of the West of England Bristol United Kingdom Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia Faculty of Computer Science IQRA University Karachi Pakistan Faculty of Computer Studies Arab Open University 512 United Kingdom Islamic University of Madinah CO 42351 Madinah Saudi Arabia

Floods are chaotic weather patterns that cause irreversible and devastating harm to people's lives, crops, and the socioeconomic system. It causes extensive property damage, animal mortality, and even human fatalities. To mitigate the risk of flooding, it is imperative to create an early warning system that can accurately forecast the amount of rain that will fall tomorrow. Rainfall forecasting is essential to the lives of people and is absolutely important everywhere in the world. The rainfall prediction model reduces risk and helps to prevent further human deaths. Statistics cannot reliably forecast rainfall since the atmosphere is dynamic. Due to the preceding factors, this study uses machine learning and deep learning techniques to estimate precipitation. The purpose of this study is to develop and evaluate a prediction model for forecasting rainfall of 5 cities of Australia (Darwin, Sydney, Perth airport, Melbourne, Brisbane). The Dataset was gathered from the national meteo-rological organization of Australia is the Australian Government Bureau of Meteorology, also known as the BOM. To monitor and forecast meteorological conditions, climatic trends, and natural calamities like cyclones, storms, floods, the Bureau of Meteorology is essential. The dataset includes 14, 5460 size, 23 features detailed city-specific monthly averages for Australia from 2008 to 2017(10 years). An effective rainfall forecasting was produced by integration of a number of Machine Learning and Deep Learning techniques, including Random Forest model (RF), Decision Tree (DT) and Gradient Boosting classifier (GBC), Artificial Neural Network (ANN), and Recurrent Neural Network (RNN). The models were trained to forecast rainfall, reducing the potential impact of floods. Results indicate that combining neural networks and Random Forests provides the most accurate predictions. © (2025), (science and Information Organization). All rights reserved.

关键词： Rain

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Control Displaying of Web Push Notifications Inducing Users to Phishing Websites 7

Control Displaying of Web Push Notifications Inducing Users ...

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7th IEEE Conference on Dependable and Secure Computing, DSC 2024

作者： Sato, Masaya Mandai, Ryuki Okayama Prefectural University Faculty of Computer Science and Systems Engineering Soja Japan Graduate School of Computer Science and Systems Engineering Okayama Prefectural University Soja Japan

ISBN: (纸本)9798331540289

The number of phishing websites is increasing, and the methods used to direct users to these sites are becoming more diverse. As web push notifications become more widespread, adversaries have begun using them as a method to direct users to phishing websites, known as phishing web push notifications. While protection against other phishing methods has been widely studied, countermeasures against phishing web push notifications remain an ongoing research topic. In this paper, we explore a method to control the display of web push notifications to protect users from phishing attempts. To minimize the cost of implementing and introducing the control mechanism, we utilized a browser extension to manage the display of web push notifications. We employed a keyword-based approach to detect and prevent the display of suspicious web push notifications. Our experimental results demonstrated that the proposed method effectively blocks phishing notifications. © 2024 IEEE.

关键词： Phishing

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A Study on DDoS Attacks Detection on IoT Devices Using Machine Learning for Microcontrollers

A Study on DDoS Attacks Detection on IoT Devices Using Machi...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Mwaura, Jedidah Araki, Shunsuke Kakizaki, Ken'Ichi Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering FuKuoka Japan Kyushu Institute of Technology Faculty of Computer Science and Systems Engineering Fukuoka Japan

ISBN: (纸本)9798350324136

In recent years, there has been a proliferation of Internet of Things (IoT) devices, and so has been the attacks on them. In this paper we will propose a methodology to detect Distributed Denial of Service (DDoS) attacks on IoT devices using Machine Learning for Microcontrollers. We will discuss a model which we made for Arduino Nano 33 BLE Sense using Machine Learning for Microcontrollers. Additionally, we will discuss results of our proposal in detecting DDoS attacks on IoT devices. Lastly, we will describe the feasibility of our model on IoT devices. © 2024 IEEE.

关键词： DDoS IoT Machine Learning TinyML

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Deepfake detection using convolutional vision transformers and convolutional neural networks

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Neural Computing and Applications 2024年第31期36卷 19759-19775页

作者： Soudy, Ahmed Hatem Sayed, Omnia Tag-Elser, Hala Ragab, Rewaa Mohsen, Sohaila Mostafa, Tarek Abohany, Amr A. Slim, Salwa O. Department of Computer Science Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Department of Information Systems Faculty of Computers and Information Kafrelsheikh University Kafrelsheikh Egypt

Deepfake technology has rapidly advanced in recent years, creating highly realistic fake videos that can be difficult to distinguish from real ones. The rise of social media platforms and online forums has exacerbated the challenges of detecting misinformation and malicious content. This study leverages many papers on artificial intelligence techniques to address deepfake detection. This research proposes a deep learning (DL)-based method for detecting deepfakes. The system comprises three components: preprocessing, detection, and prediction. Preprocessing includes frame extraction, face detection, alignment, and feature cropping. Convolutional neural networks (CNNs) are employed in the eye and nose feature detection phase. A CNN combined with a vision transformer is also used for face detection. The prediction component employs a majority voting approach, merging results from the three models applied to different features, leading to three individual predictions. The model is trained on various face images using FaceForensics++ and DFDC datasets. Multiple performance metrics, including accuracy, precision, F1, and recall, are used to assess the proposed model’s performance. The experimental results indicate the potential and strengths of the proposed CNN that achieved enhanced performance with an accuracy of 97%, while the CViT-based model achieved 85% using the FaceForences++ dataset and demonstrated significant improvements in deepfake detection compared to recent studies, affirming the potential of the suggested framework for detecting deepfakes on social media. This study contributes to a broader understanding of CNN-based DL methods for deepfake detection. © The Author(s) 2024.

关键词： Face recognition

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Determining the Effect of Feedback Quality on User Engagement on Online Idea Crowdsourcing Platforms Using an AI model

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Proceedings of the ACM on Human-computer Interaction 2024年第CSCW2期8卷 1-26页

作者： Imam, Sana Hassan Metz, Christopher A. Hornuf, Lars Drechsler, Rolf Institute of Computer Science University of Bremen Bremen Germany Faculty of Business Studies and Economics University of Bremen Bremen Germany Faculty of Business and Economics Dresden University of Technology Dresden Germany Bremen Bremen Germany

The success of idea crowdsourcing platforms relies on fostering a collaborative environment that encourages active user participation, measured by the quality and quantity of contributions and interactions. However, understanding the impact of peer interactions on user engagement and the innovation process remains challenging. While previous studies have focused on sentiment analysis, the contextual interpretation of peer feedback and its effects on engagement and innovation have yet to be explored. To address this knowledge gap, we propose a feature-based AI model that categorizes peer feedback based on its quality and polarity. Our model achieves 96% accuracy in identifying feedback quality (constructive vs. non-constructive and toxic vs. non-toxic) and feedback polarity (negative vs. positive). This contextual feedback categorization provides a foundation for quantitatively analyzing the impacts of feedback on user engagement and the innovation process. Our results show that positive and constructive peer feedback has a significantly positive effect, while toxic and negative peer feedback has a significantly negative impact on the process of idea generation, idea evaluation, and idea selection. Our results also indicate that inexperienced users are more susceptible to toxic feedback than experienced users. Based on these findings, we suggest a recommendation and reward system for incentivizing constructive feedback and preventing toxic feedback. © 2024 Owner/Author.

关键词： Crowdsourcing

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Enhancing Performance of Machine Learning Models in Healthcare: An Analytical Framework for Assessing and Improving Data Quality 5th

Enhancing Performance of Machine Learning Models in Healthca...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Al-Hgaish, Areen Atan, Rodziah Yaakob, Razali Osman, Mohd Hafeez Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang44330 Malaysia

ISBN: (纸本)9789819761050

The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concern in this domain is the data quality that forms the basis for these models, which is essential for their effectiveness. While numerous studies are dedicated to leveraging Machine Learning (ML) to enhance patient care, a substantial gap exists in understanding and evaluating the quality of training data before training ML models. This issue is exacerbated by the lack of well-defined standards to ensure that data characteristics align with the specific goals of ML in healthcare. To address this, our study introduces a conceptual three-dimensional framework focusing on data accuracy, completeness, and consistency aimed at assessing and improving the quality of training data. This framework is designed to enhance the performance, predictability, and interpretability of healthcare-specific ML models and reduce biases, thus bridging current research gaps and laying the groundwork for future developments in ML-driven healthcare systems. This paper presents the initial step for our proposed Data Quality Framework (DQF), which addresses the three dimensions mentioned above. It discusses its potential implications and applications in healthcare data, and its main goal is to provide a comprehensive overview of the proposed DQF, illustrating its significance in advancing healthcare data quality and ultimately improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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LSM: A Comprehensive Metric for Assessing the Safety of Lane Detection systems in Autonomous Driving 100

LSM: A Comprehensive Metric for Assessing the Safety of Lane...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Gamerdinger, Jorg Teufel, Sven Amann, Stephan Volk, Georg Bringmann, Oliver University of Tübingen Faculty of Science Embedded Systems Group Department of Computer Science Germany

ISBN: (纸本)9798331517786

Comprehensive perception of the vehicle's environment and correct interpretation of the environment are crucial for the safe operation of autonomous vehicles. The perception of surrounding objects is the main component for further tasks such as trajectory planning. However, safe trajectory planning requires not only object detection, but also the detection of drivable areas and lane corridors. While first approaches consider an advanced safety evaluation of object detection, the evaluation of lane detection still lacks sufficient safety metrics. For assessing safety, additional factors such as the semantics of the scene with road type and road width, the detection range as well as the potential causes of missing detections, incorporated by vehicle speed, should be considered for the evaluation of lane detection. Therefore, we propose the Lane Safety Metric (LSM), which takes these factors into account in order to evaluate the safety of lane detection systems by determining an easily interpretable safety score. We evaluate our offline safety metric on various virtual scenarios using different lane detection approaches and compare it with state-of-the-art performance metrics. © 2024 IEEE.

关键词： Vehicle safety

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A novel hybrid deep learning framework for pedestrian detection based on thermal infrared and visible spectrum images

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Multimedia Tools and Applications 2025年 1-27页

作者： Defaoui, Mahassine Koutti, Lahcen El Ansari, Mohamed Lahmyed, Redouan Masmoudi, Lhoussaine LabSIV Department of Computer Science Faculty of Science Ibn Zohr University Agadir80000 Morocco Informatics and Applications Laboratory Faculty of Science My Ismail University Meknes Morocco Conception and Systems Laboratory Faculty of Sciences Mohamed V University Rabat Morocco

For many intelligent systems, designing accurate pedestrian detection approaches is a fundamental task. This paper describes a novel hybrid system to detect pedestrians using both visible and thermal infrared sensors. The proposed method is achieved in two primary stages. The first stage, hypotheses generation (HG), aims to extract the regions that represent suspected pedestrians from the image. It is performed using both thermal and visible images of the same scene. Here, an improved deep learning-based solution with a convolutional neural network named PedTherm-U-net is proposed. It segments the thermal images and maps the segmentation results into the corresponding visible image to extract the ROIs (candidate pedestrians). The second stage, hypotheses verification (HV), classifies the ROIs extracted from the first stage into pedestrians and non-pedestrians. In the HV step, a novel PedVis-ResNet framework to classify the extracted ROIs is introduced. It is a modified version of ResNet model dedicated to classify the visible pedestrian samples. The proposed method has been tested on the publicly accessible OSU Color-Thermal dataset, and the obtained results demonstrate the effectiveness of the proposed method when compared to the recent state of the art pedestrian detection approaches. © The Author(s), under exclusive licence to Springer science+business Media, LLC, part of Springer Nature 2025.

关键词： Intelligent systems

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SerIoT: The Interface That Speaks Upgradeability by Default 13

SerIoT: The Interface That Speaks Upgradeability by Default

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13th International Conference on the Internet of Things, IoT 2023

作者： Lebloch, Kaspar Rafetseder, Albert Faculty of Computer Science Research Group Cooperative Systems University of Vienna Austria and Doctoral School Computer Science Austria Faculty of Computer Science Research Group Cooperative Systems University of Vienna Austria

ISBN: (纸本)9798400708541

Our Framework (frw) proposes to separate concerns in IoT devices in a new way, offering upgradeability by default. We explain the design of the frw, and demonstrate a full-stack implementation. Typical IoT appliances integrate physical and network capabilities inseparably. Therefore, when either fails or becomes obsolete, the whole appliance turns useless. This is a major source of consumer inconvenience, and has severe environmental impacts. Our design splits functionalities in IoT appliances differently: Digital control is mediated through the simple, self-describing SerIoT (siot) interface whose protocol draws on the Web of Things recommendation's Thing Description (TD) and JSON. Different Clients (clients) such as network adapters have this one single interface to attach to, on every compatible appliance, and are replaceable whenever the network-facing connectivity technology changes. Our practical demonstrator leverages an existing device abstraction codebase for an IoT-retrofit espresso maker, connects it to the proposed siot interface, and then bridges it to a Home Assistant instance over WLAN by means of a network adapter that accesses the siot interface. © 2023 Owner/Author.

关键词： Internet of things

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