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HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

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Discourse Analysis of Argumentative Essays of English Learners based on CEFR Level

Discourse Analysis of Argumentative Essays of English Learne...

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2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023

作者： Hanel, Blaise Kosseim, Leila Laboratory Department of Computer Science and Software Engineering Concordia University MontréalQC Canada

ISBN: (纸本)9789544520922

In this paper, we investigate the relationship between the use of discourse relations and the CEFR-level of argumentative English learner essays. Using both the Rhetorical Structure Theory (RST) and the Penn Discourse Tree-Bank (PDTB) frameworks, we analyze essays from The International Corpus Network of Asian Learners (ICNALE), and the Corpus and Repository of Writing (CROW). Results show that the use of the RST relations of EXPLANATION and BACKGROUND, as well as the first-level PDTB sense of CONTINGENCY, are influenced by the English proficiency level of the writer. © 2023 Incoma Ltd. All rights reserved.

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Tropical Cyclone Intensity Prediction Using Deep Learning Techniques- A Survey 4

Tropical Cyclone Intensity Prediction Using Deep Learning Te...

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4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Ilayaraja, V. Rajamohana, S.P. Jagadeesan, S. Aishwarya, V. Rakesh, M. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632 014 India School of Engineering & Technology Pondicherry University Department of Computer Science Puducherry Karaikal India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Tamil Nadu Vellore632 014 India

ISBN: (纸本)9798331528171

Tropical cyclones, characterized by strong winds and heavy rainfall, threaten human life in coastal regions crucial to the economy, including fisheries, agriculture, tourism, and infrastructure. Their frequent occurrence impacts communities reliant on these industries for livelihoods. Accurate estimation of tropical cyclone intensity is vital for disaster preparedness, risk assessment, and timely evacuations. Recent advancements in machine learning and deep learning have been applied to predict cyclone intensity from satellite images, providing insights into cyclone dynamics and enhancing disaster response. This paper analyzes recent research on intensity estimation using various machine learning algorithms and discusses future prospects for improving accuracy and reliability. © 2024 IEEE.

关键词： Tropical cyclone

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A Review of Multi-omics and Machine Learning: Advancements and Applications in Cancer Research 3

A Review of Multi-omics and Machine Learning: Advancements a...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Ayman, Hazem Bassem, Madonna Madkour, Malak Fawzi, Mariam Amr, Nada Yousry, Youmna Elgayar, Ziad Abdelraouf, Ashraf Ain Shams University Faculty of Engineering Department of Computer Engineering and Software Systems Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt

ISBN: (纸本)9798350306231

This comprehensive review starts with diving into the progress and real-world applications of combining multi-omics data analysis with machine learning techniques in cancer research. Multi-omics involves examining various biological data types like genomics, transcriptomics, proteomics, and metabolomics together to enhance our understanding of complex biological systems. By merging machine learning with multiomics data, we highlight the advantages for cancer studies, the deeper insights they yield and increased performance and results. Furthermore, we explore existing literature that showcases the integration of multi-omics and machine learning in cancer research. As part of our study, we conduct an experiment utilizing a multiomics dataset to predict the survival of breast cancer patients. We compare three distinct machine learning methods-ensemble, DeepProg, and DCAP-for survival prediction and conclude that despite the ensemble method that increased the prediction results of DeepProg over DCAP in multi-model setting, but the primitive capacity for DCAP is better in single model setting and achieves higher accuracy than DeepProg with noticeable margin 0.628 to 0.57 on C-Index metric, which strongly recommends using Denoising Autoencoder as the base for dimensionality reduction over the vanilla Autoencoder. Another empirical results conclude that using gaussian mixture model with diagonal covariance matrix for Clustering, which is used in DeepProg, might hinder the process for identifying reasonable clusters due to the assumption of no or zero correlation between different features which might not hold true in our problem. © 2023 IEEE.

关键词： Gaussian distribution

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Clinically Significant Prostate Cancer Detection and Diagnosis in Bi-Parametric MRI with Deep Learning Models 9

Clinically Significant Prostate Cancer Detection and Diagnos...

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9th International Conference on computer Science and engineering, UBMK 2024

作者： Asoh-Itambi, Clinton Binda Yüzkat, Mecit Varli, Songül Yildiz Technical University Faculty of Electrical and Electronics Department of Computer Engineering Istanbul Turkey Muş Alparslan University Health Institutes of Türkiye Faculty of Engineering and Architecture Department of Software Engineering Istanbul Turkey Yildiz Technical University Health Institutes of Türkiye Faculty of Electrical and Electronics Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350365887

This study presents a significant improvement in the detection and diagnosis of clinically significant prostate cancer (csPCa) in bi-parametric magnetic resonance imaging (bpMRI) by adapting the nnU-Net framework. We address the inherent limitations of traditional imaging analysis techniques by modifying the loss function used in nnU-Net, replacing the default combination of Cross-Entropy and soft Dice loss with a novel integration of Cross-Entropy loss and Focal loss. This modification targets class imbalance and enhances the detection sensitivity for less represented, clinically significant lesions, which are crucial for effective csPCa management while minimizing false diagnosis. Employing a semi-supervised learning approach, the modified nnU-Net was trained and validated on the PI-CAI (Prostate Imaging: Cancer AI) Public Training dataset (1500 cases), the current benchmark dataset for csPCa detection and diagnosis. It was also tested on the PI-CAI Hidden Testing cohort dataset consisting of 100 unseen cases. These datasets offer a comprehensive and diverse collection of prostate MRI exams, providing a robust foundation for model training and testing. We conducted a rigorous 5-fold cross-validation to ensure the robustness and reproducibility of our findings. The model's performance was evaluated with Average Precision (AP) at the lesion level and Area Under the Receiver Operating Characteristics curve (AUROC) at the patient level. Our model with AUROC and AP of 0.824 and 0.603 respectively on the Hidden Tuning cohort, outperformed the state-of-the-art U-Net, nnDetection models, and other nnU-Net variants. This work contributes to ongoing efforts to refine diagnostic tools in medical imaging, offering the potential for more accurate and timely prostate cancer screenings. © 2024 IEEE.

关键词： Magnetic resonance imaging

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General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

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engineering 2024年第2期33卷 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Department of Cognitive Robotics Faculty of MechanicalMaritime and Materials Engineering(3mE)Delft University of TechnologyDelft 2628Netherlands Department of Software Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft 2628Netherlands Xi’an Institute of High-Technology Xi’an 710051China

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

关键词： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

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Mask Wearing Detection System for Epidemic Control Based on STM32 6th

Mask Wearing Detection System for Epidemic Control Based on ...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Luoli Yadav, Amit Khan, Asif Varish, Naushad Singh, Priyanka Thakkar, Hiren Kumar Department of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University DarwinNT Australia Department of Computer Application Integral University Lucknow India Department of Computer Science and Engineering GITAM University Hyderabad502329 India Department of Computer Science and Engineering SRM University Andhra Pradesh Amravati522502 India Department of Computer Science and Engineering Pandit Deendayal Energy Univrsity Gujarat Gandhinagar382007 India

ISBN: (纸本)9789819940707

This paper designs an epidemic prevention and control mask wearing detection system based on STM32, which is used to monitor the situation of people wearing masks. Tiny-YOLO detection algorithm is adopted in the system, combined with image recognition technology, and two kinds of image data with and without masks are used for network training. Then, the trained model can be used to carry out real-time automatic supervision on the wearing of masks in the surveillance video. When the wrong wearing or not wearing masks are detected, the buzzer will send an alarm, so as to effectively monitor the wearing of masks and remind relevant personnel to wear masks correctly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image recognition

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Parameter-Efficient Federated Cooperative Learning for 3D Object Detection in Autonomous Driving

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IEEE Internet of Things Journal 2025年第12期12卷 20314-20325页

作者： Chi, Fangyuan Wang, Yixiao Nasiopoulos, Panos Leung, Victor C.M. University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada Toronto AI Lab LG Electronics TorontoONM5V 1M3 Canada Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

In the rapidly evolving field of autonomous driving, accurately detecting and understanding dynamic environments remains a challenge. Federated learning (FL) offers a promising approach by integrating decentralized models from multiple Connected Autonomous Vehicles (CAVs) to enhance the performance of Deep Learning (DL) based object detection methods. However, traditional FL faces hurdles such as extensive data synchronization requirements, limited data variance, and high communication costs. This paper introduces a federated cooperative learning framework that addresses these challenges by combining data from both CAVs and Roadside Units (RSUs). The framework combines local cooperative perception with global federated learning through a parameter-efficient federated learning adapter (PEFLA) and a lazy communication (LazyComm) strategy, improving DL-based object detection capabilities across diverse driving scenarios while significantly reducing bandwidth requirements. We also present a novel multi-agent-multi-town dataset V2X-Fed, specifically developed to validate the effectiveness of our approach under various conditions. Notably, our framework retains 97.51% of the detection accuracy achieved by full-model federated learning, while utilizing only 1.4% of the bandwidth typically required, demonstrating substantial improvements over conventional federated learning strategies. This study underscores the potential of our tailored approach to substantially enhance autonomous vehicle technologies with minimal resource utilization. © 2014 IEEE.

关键词： Bandwidth

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CardioNetFusion: A Deep Learning and Explainable AI Integrated System for Cardiovascular Disease Detection 24

CardioNetFusion: A Deep Learning and Explainable AI Integrat...

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

作者： Raja, Gunasekaran Essaky, Selvam Muthu, Surya Raj Suresh, Babith Sarish Cho, Jaehyuk Yoo, Seohyun Easwaramoorthy, Sathishkumar Veerappampalayam Anna University NGNLab Department of Computer Technology Chennai India Jeonbuk National University Department of Software Engineering Division of Electronics and Information Engineering Jeonju-si Korea Republic of Sunway University School of Engineering and Technology Malaysia

ISBN: (纸本)9798331530631

Early detection of Cardiovascular Diseases (CVD) plays a vital role in effective treatment and management. However, traditional methods for analyzing ECG signals are often limited in both accuracy and interpretability. In this work, we introduce a novel model, CardioNetFusion, designed to address these limitations by incorporating advancements in Deep Learning (DL) and Explainable AI (XAI). The model leverages the strengths of Convolutional Neural Networks (CNN), MobileNetV2, and VGG16 to improve the robustness of ECG signal interpretation. To further enhance transparency and provide healthcare professionals with actionable insights, saliency maps are utilized to visualize model predictions. Additionally, by integrating an Application programming Interface (API) within an IoT-based sensor fusion framework, the model supports real-time cardiovascular health monitoring. Achieving a classification accuracy of 98.7% in detecting arrhythmias, CardioNetFusion surpasses existing methodologies and offers a practical, reliable solution for early CVD diagnosis and management. Traditional methods of ECG analysis often have limitations in terms of diagnostic accuracy and interpretability. This paper proposes a CardioNetFusion model to address the challenges of conventional methods of ECG data analysis by leveraging advances in Deep Learning (DL) and Explainable AI (XAI). The proposed CardioNetFusion model integrates Convolutional Neural Networks (CNN), MobileNetV2, and VGG16 models to improve the robustness of ECG signal interpretation. Additionally, we utilized saliency maps to enhance model transparency and provide clear, actionable insights for healthcare professionals. Integrating Application programming Interface (API) in the IoT-based sensor fusion approach to our proposed model facilitates seamless real-time monitoring of cardiovascular health. With an impressive 98.7% accuracy in arrhythmia classification, CardioNetFusion outperformed existing methods and ensured reliab

关键词： Convolutional neural networks

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Alzheimer's Disease Classification Approach Using MRI Imaging 3

Alzheimer's Disease Classification Approach Using MRI Imagin...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Khaled, Eman Amr, Farah Essam, Farah Nouh, Maryam Sherif, Youssef Yakan, Zyad Gayar, Ziad El Abdelraouf, Ashraf Ain Shams University Faculty of Engineering Department of Computer Engineering and Software Systems Cairo Egypt Misr International University Faculty of Computer Scinece Cairo Egypt

ISBN: (纸本)9798350306231

Alzheimer's disease (AD) is a slowly progressing, irreversible brain condition that weakens memory and negatively affects the patient's quality of life. Alzheimer's disease (AD) can be identified using Magnetic Resonance Imaging (MRI) data. For an early diagnosis of the disease, various medical and diagnostic approaches are being investigated. Even while MRI is a useful tool for locating AD-related brain symptoms, the acquisition process is time-consuming, largely because workflow bottlenecks must be manually evaluated. In order to find the best effective method for detecting the disease, this research examines the basic technique for analyzing MRI images. To carry out our study to slow progression of the disease by the use of Alzheimer's disease (AD) prognosis, a dataset from The Alzheimer's Disease Neuroimaging Initiative (ADNI) will be imported and fitted. The outcomes highlight the tremendous potential of integrating imaging data for automated categorization of Alzheimer's disease (AD) using multidisciplinary AI techniques. With a deep three-dimensional convolutional network (3D CNN) being used to handle the three-dimensional MRI input and a Transformer encoder being applied to manage the genetic sequence input, the suggested solution merges machine learning, bioinformatics, and other image processing techniques. After various experiments by checking the results accuracy, it is stated that the CNN model is never enough to provide us with the desired accuracy either by training on both skull stripped data or the GM tissue segmented data. Although, it is relatively better at the skull stripped dataset training, but the results accuracy and predicted classes show that inferring some classifiers after extracting the features from the CNN would increase the accuracy and results. After applying Support Vector Machine SVM-RBF, SVM-POLY, and XGBoost, it is concluded that the training of the Skull Stripped Dataset with features extracted from the CNN model we provided an

关键词： Magnetic resonance imaging

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