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International Journal of Intelligent Information and Database Systems 2024年第3期16卷 258-285页

作者： Cerjan, Maja Rabuzin, Kornelije Šestak, Martina Faculty of Organization and Informatics Varaždin University of Zagreb Pavlinska 2 Varaždin42000 Croatia Faculty of Electrical Engineering and Computer Science Universiti of Maribor Koroška cesta 46 Maribor2000 Slovenia

Data growth has led to the need to apply new ways to process data. Graph databases are increasing their use, which can be seen over the years, with the Neo4j system being the most common. The biggest problem is the small number of implemented constraints that can be used. One of the shortcomings to be explored is the need to create domains, which are used when large amounts of data are manipulated and where the value needs to be limited or when multiple attributes have the same restrictions and data types. The creation of domains can be applied multiple times. This paper summarises the implementation of domains using Neo4j and the Java programming language. © 2024 Inderscience Enterprises Ltd.

关键词： Graph Databases

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Exploring Link Prediction Techniques in Social Network Analysis for Community Detection 3

Exploring Link Prediction Techniques in Social Network Analy...

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3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Luthra, Snigdha Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Department of CSE Punjab 140413 India University of Kufa Najaf Iraq College of Engineering URUK University Faculty of Computer Science and Mathematics Dept of Computer Science Baghdad Iraq

ISBN: (纸本)9798350361391

The global proliferation of social media networks has sparked significant attention, with platforms such as Facebook, Twitter, Telegram, and WhatsApp enabling widespread interaction among users. Consequently, addressing the challenges associated with social networking sites has become crucial. To tackle this issue, a recommendation model has been developed, merging individuals with similar interests through a community detection algorithm. This statistical approach mines the dynamic social communities based on comment patterns. The model addresses the pressing concern of identifying nodes with a high prevalence of combining based on the interest. The integrated model incorporates link prediction and sentiment analysis features, facilitating the creation of communities, and thereby allowing the identification of individuals with shared interests within a single cluster. To evaluate the performance of the model, a dataset of 5672 Facebook page comments was collected from the Stanford repository. The result shows using the edge-betweeness technique of the community detection algorithm maximum modularity density of 0.2331 is obtained. © 2024 IEEE.

关键词： Tweets

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Assessment of ingestion dose from 226Ra, 228Ra, and 40K in vegetables consumed in Koya district, Iraq

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Environmental science and Pollution Research 2025年第19期32卷 11972-11980页

作者： Saleh, Dedawan Salam Salh, Hemn Smail, Jahfer Majeed Ahmad, Saddon Taha Department of Physics Faculty of Science and Health Koya University Kurdistan Region KoyaKOY45 Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil44001 Iraq Faculty of Medicine Koya University KoyaKOY45 Iraq

The present work comprises the measurement of activity concentrations of three radionuclides, 22⁶Ra, 228Ra, and 4⁰K in 33 species of vegetables commonly used in Koya district, the Iraqi Kurdistan region. The analysis of the samples was performed with high purity germanium (HPGe) gamma spectrometry. Among the analyzed samples, mushrooms presented the highest activity concentrations of 22⁶Ra (3.34 ± 1.18 Bq·kg⁻1), 228Ra (1.24 ± 0.5 Bq·kg⁻1), and 4⁰K (1481.93 ± 14.46 Bq·kg⁻1) while legumes had the lowest levels of these radionuclides. The estimated annual effective dose from the consumption of these vegetables was computed to be 97.216 µSv·y⁻1 and this is much lower than the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) global reference level of 290 µSv·y⁻1. Therefore, it can be concluded that the radiological risk from the consumption of vegetables in this area is very low thus giving a reassurance to the people on the safety implication on the radiological health hazards. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Radiation effects

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Lithium mapping in a Mg-9Li-4Al-1Zn alloy using electron energy-loss spectroscopy

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Journal of Magnesium and Alloys 2023年第4期11卷 1170-1179页

作者： Yu Zhang Laure Bourgeois Yu Chen Zhuoran Zeng Nick Birbilis Philip N.H.Nakashima Department of Materials Science and Engineering Monash UniversityVIC 3800Australia Monash Centre for Electron Microscopy Monash UniversityVIC 3800Australia College of Engineering and Computer Science Australian National UniversityACT 2601Australia Faculty of Science Engineering and Built EnvironmentDeakin UniversityVIC 3125Australia

Magnesium-lithium alloys with high lithium content have been attracting significant attention because of their low density,high formability and corrosion *** properties are dependent on the distribution of lithium,which is difficult to map in the presence of *** this work,a ratio spectrum-imaging method with electron energy-loss spectroscopy(EELS)is demonstrated,which enables the mapping of *** application to LAZ941(Mg-9Li-4Al-1Zn in wt.%),this technique revealed that a key precipitate in the microstructure,previously thought by some to be Mg_(17)Al_(12),is in fact rich in *** result was corroborated with a structural investigation by high-angle annular dark-field scanning transmission electron microscopy(HAADF-STEM),showing this phase to be Al_(1-x)Zn_(x)Li,with x<<*** work indicates the potential offered by this technique for mapping lithium in materials.

关键词： Lithium mapping Magnesium alloys Electron energy-loss spectroscopy(EELS) High-angle annular dark-field scanning transmission electron microscopy(HAADF-STEM)

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A NOTE ON THE QUALITATIVE BEHAVIOUR OF AN ODE MODEL RELATED TO ENDOTOXIN TOLERANCE

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第4期86卷 127-130页

作者： Olteanu, Mircea Ştefan, Radu Department of Mathematical Methods and Models Faculty of Applied Sciences University "Politehnica" of Bucharest Romania Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science University "Politehnica" of Bucharest Romania

关键词： Differential equations

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Myorcadial Infarction Classification with 15 Lead Electrocardiogram (ECG) Signal using Hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BILSTM) 8

Myorcadial Infarction Classification with 15 Lead Electrocar...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Mirza, Ahmad Haidar Nurmaini, Siti Partan, Radiyati Umi Herdiansyah, Muhammad Izman Universitas Sriwijaya Faculty of Computer Science Universitas Bina Darma Faculty of Engineering Palembang Indonesia University of Sriwijaya Intelligent System Research Group Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty of Medicine Palembang Indonesia Universitas Bina Darma Faculty of Computer Science Palembang Indonesia

ISBN: (纸本)9798350342604

More than three branches of death due to cardiovascular disease. Myocardial infarction (MI) is one of the most dangerous forms of coronary heart disease, with the highest mortality rate among other types of coronary heart disease. MI is caused by reduced oxygen demand in heart muscle tissue when oxygen is pumped throughout the body. To detect MI, experts can use ECG signals. ECG is a tool that can be used to record the activity of biological signals formed due to the electrical activity of the heart. Various studies have been conducted using deep learning to help detect and predict MI. This research proposes a hybrid model of CNN-LSTM and CNN-BILSTM model techniques to predict MI. The MI dataset from the Physiobank (PTB) ECG database consisting of 52 normal subjects and 148 patients was used for training models. The proposed deep learning model uses automatic feature extraction. This study suggests a simple deep learning architecture with automatic feature extraction to learn from data. Evaluation of model performance resulted in an average accuracy of 98.80% for the CNN-LSTM model and 99.90% for the CNN-BILSTM model. © 2023 IEEE.

关键词： Long short-term memory

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Application of Machine Learning for Detection and Prediction of Malaria: A Review 3

Application of Machine Learning for Detection and Prediction...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Apetorgbor, Mawuli Wankhede, Vidhi Dakhare, Kajal Verma, Prateek Raut, Yudhishthir Tripathi, Chanchla Department of Science and Technology Maharashtra Wardha India Faculty of Engineering and Technology Maharashtra Wardha India Pimpri Chichwad University Department of Computer Science and Engineering Maval Sate Pune India Yeshwantrao Chavan College of Engineering Department of Computer Science and Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350381931

Mosquitoes carries a parasitic infection that affects millions of peoples globally. The initial and meticulous prediction of outbursts, patient verdict, and treatment enhancement can all considerably diminish the consequences of malaria. Machine learning is a promising method to tackle this problem with its capability to unveil and understand complex patterns from broad and multifaceted dataset. Accuracy of drug dosage and disease management in healthcare industry has proven phenomenal accuracy with use of machine learning algorithm. The current analysis explores the application of machine learning for prediction of malaria cases. This paper showcase the inclusion of numerous data source, comprising environmental factors, medical history and epidemiologic information to demonstrate how machine learning influences this to abundance of data to enhance prediction accuracy. This study also enhances the major advancements in prior malaria detection that have been made feasible and practicable by machine learning driven symptomatic technologies such prompt analytical test and image analysis of blood taints. Concerns like model comprehensibility, data secrecy, and equitable distribution of healthcare assets are also concealed in this review study. © 2024 IEEE.

关键词： Forecasting

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Hybrid-Optimized Model for Deepfake Detection

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International Journal of Advanced computer science and Applications 2025年第4期16卷 148-160页

作者： Mancy, H. Elpeltagy, Marwa Eldahshan, Kamal Ismail, Aya Department of Computer Science College of Engineering and Computer Sciences Prince Sattam Bin Abdulaziz University Alkharj11942 Saudi Arabia Al-Azhar University Cairo Egypt Systems and Computers Department Al-Azhar University Egypt Mathematics Department Faculty of Science Al-Azhar University Egypt Mathematics Department Tanta University Egypt

The advancement of deep learning models has led to the creation of novel techniques for image and video synthesis. One such technique is the deepfake, which swaps faces among persons and then produces hyper-realistic videos of individuals saying or doing things that they never said or done. These deepfake videos pose a serious risk to everyone and countries if they are exploited for extortion, scamming, political disinformation, or identity theft. This work presents a new methodology based on a hybrid-optimized model for detecting deepfake videos. A Mask Region-based Convolutional Neural Network (Mask R-CNN) is employed to detect human faces from video frames. Then, the optimal bounding box representing the face region per frame is selected, which could help to discover many artifacts. An improved Xception-Network is proposed to extract informative and deep hierarchical representations of the produced face frames. The Bayesian optimization (BO) algorithm is employed to search for the optimal hyperparameters' values in the extreme gradient boosting (XGBoost) classifier model to properly discriminate the deepfake videos from the genuine ones. The proposed method is trained and validated on two different datasets;CelebDF-FaceForencics++ (c23) and FakeAVCeleb, and tested also on various datasets;CelebDF, DeepfakeTIMIT, and FakeAVCeleb. The experimental study proves the superiority of the proposed method over the state-of-the-art methods. The proposed method yielded %97.88 accuracy and %97.65 AUROC on the trained CelebDF-FaceForencics++ (c23) and tested CelebDF datasets. Additionally, it achieved %98.44 accuracy and %98.44 AUROC on the trained CelebDF-FaceForencics++ (c23) and tested DeepfakeTIMIT datasets. Moreover, it yielded %99.50 accuracy and %99.21 AUROC on the FakeAVCeleb visual dataset. © (2025), (science and Information Organization). All Rights Reserved.

关键词： Convolutional neural networks

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A Novel Approach on Advanced Fruit Disease Identification Using RCNN

A Novel Approach on Advanced Fruit Disease Identification Us...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Kumar, R. Boopathi, P. Rajan, M. Sundar Bangalore India Shri Nehru College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India Arba Minch Institute of Technology Arba Minch University Faculty of Electrical and Computer Engineering Ethiopia

ISBN: (纸本)9798331509828

The early diagnosis of diseases in fruits holds immense importance for agricultural industries, as it directly impacts production quality and quantity. This study introduces a novel approach utilizing Recursive Convolutional Neural Network (RCNN) for automated citrus plant disease detection. Through a comprehensive methodology involving data collection, image pre-processing, segmentation, feature extraction using Linear Principal Component Analysis (LPCA), and classification, the proposed RCNN algorithm showcases promising results. Experimental evaluation demonstrates the efficacy of the RCNN-based approach, with precision averaging at 0.929, recall at 0.946, accuracy at 0.945, F-measure at 0.943, and minimal time complexity. Compared to traditional manual inspection methods, RCNN offers a more accurate and efficient solution, significantly reducing the burden on farmers and experts while enhancing fruit disease detection capabilities. Overall, the proposed RCNN-based approach presents a significant advancement in fruit disease identification, offering precise and timely detection to mitigate agricultural losses. With its superior performance metrics and streamlined process, RCNN stands as a promising tool for revolutionizing fruit disease diagnosis and improving agricultural productivity. © 2025 IEEE.

关键词： Convolutional neural networks

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Deep learning and ensemble methods for anomaly detection in ICS security

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1761-1775页

作者： Hossain, Md. Alamgir Hasan, Tahmid Karovic, jr, Vincent Abdeljaber, Hikmat A. M. Haque, Md. Alimul Ahmad, Sultan Zafar, Aasim Nazeer, Jabeen Mishra, B.K. Department of Computer Science and Engineering State University of Bangladesh South Purbachal Kanchan Dhaka 1461 Bangladesh Department of Computer Science and Engineering Prime University Dhaka Bangladesh Department of Information Systems Faculty of Management Comenius Bratislava Odbojárov 10 Bratislava 25 82005 Slovakia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman 11931 Jordan MEU Research Unit Middle East University Amman 11831 Lebanon Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Computer Science Aligarh Muslim University U.P Aligarh 202002 India P.G. Department of Physics Veer Kunwar Singh University Bihar Ara India

This research addresses the escalating threats to industrial control systems by introducing a novel approach that combines deep learning for feature selection with a robust ensemble-based classification technique to enhance anomaly detection. Our method utilizes a tailored autoencoder architecture to efficiently select features, followed by a Random Forest classifier to ensure reliable and generalizable detection of anomalies. Evaluated on the HAI Security Dataset, the proposed approach demonstrates exceptional performance, with significant improvements in detecting anomalous activities in ICS. The results highlight the method’s potential to enhance ICS security by providing a scalable and adaptable framework that evolves with emerging threats. This study not only advances cybersecurity methodologies for industrial systems but also lays the groundwork for future research, emphasizing the need for innovative and effective security solutions in the face of evolving cyber challenges. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anomaly detection in ICS Cyber-resilience in critical infrastructure Deep learning for feature selection Industrial control systems security Random forest classifier for ICS

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