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Domination based graph neural networks

International Journal of computers and Applications 2024年第11期46卷 998-1005页

作者： Meybodi, Mohsen Alambardar Safari, Mahdi Davoodijam, Ensieh Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan Iran Department of Software Engineering University of Kashan Kashan Iran

Graph Neural Networks (GNNs) have emerged as a widely used and effective method across various domains for learning from graph data. Despite the abundance of GNN variants, many struggle with effectively propagating messages over long distances. This paper introduces a novel hierarchical message passing framework for graph learning, specifically designed to address the challenge of long-distance message propagation in graphs. By constructing smaller graphs from the main graph using the concept of domination, a fundamental principle in graph theory, we facilitate more efficient message passing within each subgraph. Subsequently, we employ a Graph Attention Network (GAT) to aggregate these features and propagate them to distant nodes across the graph. Experimental results on standard node classification datasets validate that the proposed architecture achieves performance comparable to or better than conventional GNNs. Additionally, our model consistently performs better on graphs with missing edges. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Neural network models

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Diabetes Prediction Using Hybrid 1D CNN and PeeHole Long Short-Term Memory Algorithm 5

Diabetes Prediction Using Hybrid 1D CNN and PeeHole Long Sho...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Behera, Mandakini Mishra, Debahuti Sarangi, Archana Sarangi, Shubhendu Deemed to be University School of Computer Engineering Bhubaneswar India University Department of Computer Science & Engineering Bhubaneswar India University Department of Electronics and Telecommunications Bhubaneswar India University Faculty of Engineering Bhubaneswar India

ISBN: (纸本)9798331522100

According to the survey of International Diabetes Federation (IDF) the number of persons with diabetes is continuously increasing. There are currently 3820 million diabetics worldwide, and during the next 15 years, this figure might double. Diabetes can create several life-threatening diseases, including heart attack, renal failure, and stroke. To sustain a healthy life, early diabetes identification is crucial. machine learning (ML) is a approach which automatically learns from experience to improve performance and produce more accurate predictions, that can be used to forecast diabetes. Deep learning is a branch of machine learning, and the human brain inspires both its architecture and the way it *** manuscript presents a hybrid method that combines peephole LSTM and 1D CNN to improve the accuracy of diabetes prediction. Performance is evaluated based on F1 score, accuracy, specificity, sensitivity, and epoch-wise accuracy and *** Pima Indian diabetes dataset is utilized for experimental validation. Experimental results demonstrate high prediction accuracy of 96.74% on training and 75.32% on testing for diabetes prediction. © 2025 IEEE.

关键词： Deep learning

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Evaluation of Shallow Convolutional Neural Network in Open-World Chart Image Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第6期48卷 185-198页

作者： Bajić, Filip Habijan, Marija Nenadić, Krešimir University Computing Centre University of Zagreb Zagreb10000 Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek31000 Croatia

Data’s role is pivotal in the era of internet technologies, but unstructured data poses comprehension challenges. Data visualizations like charts have emerged as crucial tools for condensing complex information. Classifying charts and applying various processing techniques are vital to interpreting visual data. Traditional chart image classification methods rely on predefined rules and have limited accuracy. The advent of support vector machines (SVMs) and convolutional neural networks (CNNs) significantly improved the accuracy of these methods. This research evaluates our previously introduced Shallow convolutional neural network (SCNN) architecture for chart image classification, comprising four convolutional layers, two max-pooling layers, and one fully-connected layer. The network achieves state-of-the-art results, requiring smaller datasets and reduced computational resources. When two networks are combined into Siamese SCNN (SSCNN), emphasizing generalization, it achieves high accuracy with small datasets and excels in open-set classification. The evaluation process encompasses the utilization of six publicly available datasets. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Data visualization

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Optimized Invoker Scheduling for Open-Source Serverless IoT Actions 7th

Optimized Invoker Scheduling for Open-Source Serverless IoT ...

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7th International Conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Ganeshan, M. Rajesh, A. Department of Computer Science and Engineering Jain Deemed to Be University Bangalore India Faculty of Engineering and Technology Department of Computer Science and Engineering Jain University Bangalore India

ISBN: (纸本)9789819907687

Serverless computing has emerged as a promising research subject in computer science as it grows in favor among developers because of its low cost and elasticity. However, one primary concern that has cropped up in recent months with serverless computing is vendor lock-in: It can be hard to port to another vendor's platform without considerable effort and cost. Serverless on AWS is a new way to build cloud-native IoT systems that are highly performant, highly resilient, and low maintenance. We'll implement exciting design concepts for serverless systems on AWS in this paper using the open-source OpenWhisk software. To provide low-cost and maintenance-free IoT services, traditional cloud service providers offer platforms that are progressively migrating toward the serverless approach. OpenWhisk takes care of the infrastructure, servers, and scaling with Docker and containers. Because Apache Open Whisk’s components are built using containers, it provides a wide range of deployment options in cloud environments. Many of today’s prominent Container frameworks such as Kubernetes are available as options. The Kubernetes framework is a well-known open-source container-orchestration system widely used in industrial and academic fields. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Containers

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Deep learning-based method for detection of copy-move forgery in videos

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Neural Computing and Applications 2025年第14期37卷 8451-8464页

作者： Elbarougy, Reda Abdelfatah, Osama Behery, G.M. El-Badry, Noha M. Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Ha’il Ha’il81481 Saudi Arabia Department of Information Technology Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Computer Science Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Mathematics Faculty of Science Damietta University New Damietta Kafr Saad Egypt

Video forgery is one of the most serious problems affecting the credibility and reliability of video content. Therefore, detecting video forgery presents a major challenge for researchers due to the diversity of forgery types, the modernity of the programs used in forgery operations, and the abundance of information and content present in videos. The seriousness of this issue arises from the widespread use of videos in vital fields that require very high accuracy with no room for doubt or error, such as courtrooms, journalism, and others. Copy-move forgery is one of the most common and dangerous types of video forgery because of the difficulty in identifying it with the naked eye and the great diversity of forgery techniques involved in this particular type. One of the biggest challenges researchers have faced in the past is the complexity of the steps required to detect forgery in videos, leading to significant computational complexity and time consumption. The proposed method also aims to achieve better results than previous methods while reducing computational operations. Ultimately, forgery is detected with great efficiency. Compared to previous methods used for detecting copy-move forgery in the Rewind dataset, the proposed method achieves the highest F1, reaching 0.86, with a significant difference of 0.13 compared to the best result of the previous methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep learning

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Social and Non-social Reward Learning Contexts for Detection of Major Depressive Disorder Using EEG: A Machine Learning Approach 16th

Social and Non-social Reward Learning Contexts for Detecti...

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16th International Conference on Brain Informatics, BI 2023

作者： Ghattas, Philopateer Gamal, Mai Eldawlatly, Seif Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt

ISBN: (纸本)9783031430749

Major Depressive Disorder (MDD) is a leading cause of disability globally and a major cause of suicide deaths. Improving our understanding of MDD is expected to inspire better objective diagnostic and treatment tools which may decrease the burden of disease worldwide. MDD is associated with social impairments and reward processing aberrations. However, the interaction between social cognition and reward learning in MDD is not well understood. In this work, we aim to study the effect of integrating social information with reward learning in MDD using an EEG-based machine learning approach. We recorded EEG data from subjects during their participation in a reward learning experiment in social and non-social contexts. We then extracted linear and nonlinear features from the EEG data to detect MDD using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers. Our results show that the data collected during social contexts achieved broadly higher classification accuracies compared to non-social contexts reaching 80% using multi-channels. Moreover, single-channel data achieved comparable and even better accuracies than multi-channels with also superior performance in social contexts reaching 85.7%. In the sub-band classification analysis, beta and alpha bands were considerably better than theta and delta bands. Surprisingly, the non-social context had the highest accuracy in the beta band while the social context had the highest accuracy in the alpha band. The results were consistent when using single features compared to combining the features. These findings show the notable role of social information with reward processing in advancing our understanding of MDD and its subtypes which can guide the improvement of diagnostic and personalized treatment tools. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nearest neighbor search

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Robustness of generative AI detection: adversarial attacks on black-box neural text detectors

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International Journal of Speech Technology 2024年第4期27卷 861-874页

作者： Fishchuk, Vitalii Braun, Daniel Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands Department of High-tech Business and Entrepreneurship University of Twente Enschede Netherlands

The increased quality and human-likeness of AI generated texts has resulted in a rising demand for neural text detectors, i.e. software that is able to detect whether a text was written by a human or generated by an AI. Such tools are often used in contexts where the use of AI is restricted or completely prohibited, e.g. in educational contexts. It is, therefore, important for the effectiveness of such tools that they are robust towards deliberate attempts to hide the fact that a text was generated by an AI. In this article, we investigate a broad range of adversarial attacks in English texts with six different neural text detectors, including commercial and research tools. While the results show that no detector is completely invulnerable to adversarial attacks, the latest generation of commercial detectors proved to be very robust and not significantly influenced by most of the evaluated attack strategies. © The Author(s) 2024.

关键词： Generative adversarial networks

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Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset

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IEEE Access 2024年 12卷 9369-9381页

作者： Ainan, Ummey Hany Por, Lip Yee Chen, Yen-Lin Yang, Jing Ku, Chin Soon Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The challenge of bankruptcy prediction, critical for averting financial sector losses, is amplified by the prevalence of imbalanced datasets, which often skew prediction models. Addressing this, our study introduces the innovative hybrid model XGBoost+ANN, designed to leverage the strengths of both ensemble learning and artificial neural networks. This model integrates a comprehensive set of features with parameters optimized through genetic algorithms, eschewing traditional feature selection approaches. Our research focuses on an unbalanced dataset of Polish companies and reveals that the XGBoost+ANN model, in particular, exhibits outstanding performance. Optimized using genetic algorithms and without feature selection, this model achieved the highest AUC (0.958), sensitivity (0.752), and accuracy (0.983) scores, surpassing other models in our study. This remarkable outperformance, along with the robust results, marks a substantial advancement in the field of bankruptcy prediction. It underscores the efficacy of our approach in addressing the persistent challenge of data imbalance, offering a more reliable and accurate solution for financial risk assessment. © 2013 IEEE.

关键词： Feature extraction

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Comparative Study of Facial Expression Recognition Using Facial Landmark in e-Learning System 11

Comparative Study of Facial Expression Recognition Using Fac...

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11th International Conference on Electrical engineering, computer science and Informatics, EECSI 2024

作者： Bachtiar, Fitra Abdurrachman Nainggolan, Cesilia Natasya Soetedjo, Riza Setiawan Rokhmawati, Retno Indah Muflikhah, Lailil Brawijaya University Department of Informatics Engineering Faculty of Computer Science Malang Indonesia Brawijaya University Faculty of Computer Science Department of Information Systems Malang Indonesia

ISBN: (纸本)9798350355314

The e-Iearning system or distance learning system has become important, especially during the COVID-19 pan-demic. Several tertiary institutions have made the e-Iearning system an alternative teaching and learning activity. Research on the use of e-Iearning needs to be explored to obtain innovative, creative, and fun learning. One aspect that can be examined in e-learning is students' emotional response to the learning activities. The emotional state of students directly or indirectly affects the learning process. The student's emotional responses can be measured by their facial expressions. In this study, an exploration was carried out on the facial expressions shown by students in HSS Learning, an e-Iearning for learning web programming. The student's facial expressions were extracted using 68 pairs of facial landmarks and additional 6 facial feature vectors. The total number of features used is 142. Machine learning methods for classification are applied to determine the performance of several classification models, including the k-Nearest Neighbor (k- NN) algorithm, Logistic Regression, and Support Vector Machine (SVM). Machine learning performance evaluation is done using the Fl-score and confusion matrix. The results show that the accuracy of the k-NN, Logistic Regression, and SVM are 91.4%, 92.2%, and 92.1 %, respectively. However, the Fl-score macro values are relatively low due to data imbalance with the k- NN, Logistic Regression, and SVM models are 0.250, 0.272, and 0.243, respectively. While the study provides insight into the student learning experience in e-Iearning settings, this study needs to be explored more. © 2024 IEEE.

关键词： Contrastive Learning

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Battery Monitoring System on IoT-Based Smart Micro Grid 4

Battery Monitoring System on IoT-Based Smart Micro Grid

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4th International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2024

作者： Iksan, Nur Widodo, Djoko Adi Djuniadi, Djuniadi Putri, Riana Defi Mahadji Suryanto, Agus Udayanti, Erika Devi Universitas Negeri Semarang Faculty of Engineering Department of Electrical Engineering Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Department of Information System Indonesia

ISBN: (纸本)9798350378368

The use of batteries as an energy storage medium has a very important role in the installation of renewable energy power plants, such as photovoltaics to overcome intermittency in photovoltaics and to maintain stable power quality. In this study, the installation of solar plants that was used as the research object was a smart micro grid that integrates photovoltaic electrical energy sources with the Utility electricity network for residential scale. The battery monitoring system is used to manage the performance of existing batteries on the electricity grid through analysis of the battery's electrical values such as current, voltage, power and energy, so that it can function optimally in storing electrical energy and supplying electricity to users. Internet of things (IoT) technology is used to provide easy transfer of data obtained through the process of data acquisition to data visualization provided to be accessed by users in real time. This research aims to develop an IoT-based battery monitoring system. The development carried out focuses on the data acquisition process, the data transfer process using IoT technology, the process of analyzing and visualizing data on the IoT cloud and remote access to users. © 2024 IEEE.

关键词： Battery IoT Monitoring Smart Micro Grid

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