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Application of VGIS Digital Twin Platform for Enhanced Oilfield Disaster Management and Environmental Protection

Informatica (Slovenia) 2024年第14期48卷 27-42页

作者： Alabbasi, Awab Aljabery, Maalim A. Bölük, Pınar Sarısaray Faculty of Engineering and Natural Sciences Electrical and Computer Engineering Dept. Bahcesehir University Istanbul34353 Turkey Faculty of Computer Science and Information Technology Computer Science Dept. University of Basrah Basrah61004 Iraq Faculty of Engineering and Natural Sciences Software Engineering Dept. Bahçeşehir University Istanbul34353 Turkey

VGIS (Virtual Geographic Information System) Platform is a unified oilfield operations management platform based on MaaS (Management as a Service) that integrates advanced technologies such as AIoT (Artificial Intelligence & Internet of Things), GIS (Geographic Information System), Digital Twin, and AR (Augmented Reality)/ VR (Virtual Reality). The VGIS platform connects, collects, integrates, analyses, and acts on disparate data sources to provide the oilfield with a centralized view to help make informed decisions fast, bring operational visibility across all assets to improve safety and operational efficiency, reduce costs and improve margins, provide comprehensive analytics and decision support, increase business agility, and reduce management complexity. This is a case study of the application of the VGIS platform Majnoon oilfield, Basrah, Iraq. VGIS digital transformation, production process, and KPIs (Key performance indicators) are highly managed taking oilfield management to a higher level globally, in this study, the objective is to address and resolve the issue of high costs associated with using cloud services by utilising a local Data Center (a large group of networked computer servers typically used by organizations for the remote storage, processing, or distribution of large amounts of data). This approach involves directly uploading 3D maps to ensure faster service on the intranet, eliminating the need to pay for cloud *** aims providing a platform for researching and implementing monitoring and controlling processes in a more convenient and remote manner. The vGIS platform has emerged as a crucial instrument in the realm of disaster response and crisis management. This technology enables responders to rapidly evaluate, analyse, and respond to crucial information in real-time. This research examined the application of vGIS technology in disaster response and analyse the potential advantages and obstacles associated with its utilisation. © 2

关键词： Information management

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CNN-based Community Detection: A Comparison Study 5

CNN-based Community Detection: A Comparison Study

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5th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2023

作者： Bekkair, Abdelfateh Oulad-Naoui, Slimane Bellaouar, Slimane Krimat, Rababe Roumaissa Haddaoui, Alla Universite de Ghardaia Dept. of Mathematics and Computer Science Algeria

ISBN: (纸本)9798350381450

Community detection is indispensable for comprehending the structural organization and functional modules of complex networks. Convolutional neural networks (CNNs) have emerged as powerful instruments for graph data analysis in recent years. In this paper we start by providing a classification of CNN community detection approaches in networks based on node and edge transformations. Then we conduct a comparative analysis of these two categories. We assess the performance of the two CNN models on diverse network datasets using normalized mutual information a widely accepted evaluation metric. According to the findings of the study, CNN models can identify cohesive communities within networks. Moreover, they demonstrate that the node-based transformation method is more straightforward than the edge-based transformation method, while still producing satisfactory community detection results. This indicates that the node-based transformation method simplifies community detection and requires fewer computational resources. © 2023 IEEE.

关键词： Convolutional neural networks

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IoT and Edge Computing Using Virtualized Low-Resource Integer Machine Learning with Support for CNN, ANN, and Decision Trees 18

IoT and Edge Computing Using Virtualized Low-Resource Intege...

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Bosse, Stefan University of Bremen Dept. Mathematics and Computer Science Bremen Germany

ISBN: (纸本)9788396744784

Data-driven models used for predictive classification and regression tasks are commonly computed using floating point arithmetic preserving accuracy by automatic scaling even in high non-linear functions. With respect to distributed sensor networks like the IoT, sensor data is acquired on low-resource embedded systems and delivered to data servers characterized by big data volumes. In specific use cases and domains, local predictive modelling on low-power devices is desired or required. But heterogeneity of host platforms and dynamic programming disables machine code deployment. This work addresses Tiny ML on very low-resource devices (microcontrollers, less than 32 kB RAM and ROM) by using a stack-based Tiny Virtual Machine providing core ML operations to implement Decision Trees (DT), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN). VM program code is always provided in textual format and compiled just-in-time to Bytecode to ensure portability, servicability, and mobility. Two damage diagnostics use-cases demonstrate the suitability of the VM approach, and even time consuming computational tasks do not compromise the overall responsiveness of the platform by using a real-time approach. This work addresses the underlying integer arithmetic operations required to implement efficient and fast computable ML models on microncontrollers. © 2023 Polish Information Processing Society.

关键词： Decision trees

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Thiagarajan, Priya Sundarakantham, K. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350394528

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better. © 2024 IEEE.

关键词： Recurrent neural networks

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Heuristic Algorithms for Software Modules Clustering

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Procedia computer science 2025年 258卷 2159-2169页

作者： Ali H. Ali Dujan B. Taha Software Dept. College of Computer Science and Mathematics Mosul University Iraq Computer Science Dept. College of Computer Science and Mathematics Mosul University Mosul Iraq

In the fields of software engineering, the maintenance phase takes the most effort. Software Module Clustering (SMC), as a part of reverse engineering, is used for the purpose of creating structural models from the source code enhancing its comprehensibility. Finding the best clusters is an NP-hard problem. The method of software module clustering aims to extract the best clusters, which involves Increase cohesion and reduce coupling. This re-search addresses a set of problems with the aim of solving them, which are drawback found in most previous studies. This paper adopts three main methods to solve Software Modules Clustering problem (SMCP), a Teaching-Learning-Based-optimization (TLBO) algorithm, a Black Widow optimization (BWO) algorithm, and the third is a hybrid algorithm combining the previous two methods. These algorithms were used to cluster software modules and were evaluated based on MQ (Modularity Quality), cohesion, coupling, and stability. To determine the optimal number of clusters for each reference program, the Louvain algorithm is used, which works on detecting communities in networks. The study also experintly examined the impact of the chaos, random, Lorenzo methods in the initial generation of solutions on the quality of the results and stability. Considering the results of the experiments conducted on eight standard applications, the hybrid algorithm performs better in SMC problems than the TLBO and BWO. The performance of these algorithms also improves significantly, when their initial populations are generated using the logistic Lorenz and Chaos method rather than a random one. 6.644548537, 6.318318013, and 4.375766992 are the average MQ of the clusters that were formed for the selected casses set using the Hybrid method, BWO, and TLBO, respectively.

关键词： Software Modules clustering Software Clustering Clustering optimization

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Bio-inspired Heuristic Optimization-based Cascaded Network for Diabetic Retinopathy Screening 3

Bio-inspired Heuristic Optimization-based Cascaded Network f...

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3rd International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Shuvo, Eshat Ahmad Rahman, Wahidur Hossain, Md Shakhawat Islam, Md. Tarequl Iqbal, Md. Sadiq Bangladesh University Dept. of Computer Science and Engineering Adabor Bangladesh Uttara University Dept. of Computer Science and Engineering Uttara Dhaka Bangladesh Center Independent University Dept. of Computer Science & Engineering & RIoT Bangladesh Khwaja Yunus Ali University Dept. of Computer Science & Engineering Sirajganj Bangladesh

ISBN: (纸本)9798350388282

Diabetic retinopathy (DR) is a common yet fatal complication of diabetic patients in which high levels of blood sugar damage the blood vessels in the retina, the light-sensitive eye tissue crucial for human vision. Early detection and timely intervention are essential to managing DR and preventing severe vision loss. Traditionally, the examination is performed by ophthalmologists manually examining the retinal fundus images to check for signs of DR. This approach is helpful but subjective, time-consuming and tedious. Artificial intelligence (AI)-guided computer vision has recently become very compelling and practical for image analysis and diagnosis. Existing AI-based methods achieved sufficient accuracy at the cost of high computing resources and large datasets. This paper proposes a cascaded network incorporating deep learning and the traditional machine learning approaches with a bio-inspired heuristic optimization algorithm for DR detection from fundus images. The proposed method achieved sufficient accuracy (97.1%) when trained using limited data and low computing machines. The AI models for the cascaded networks were selected through an exhaustive search in which five popular CNN models were used for extracting features;the Bacterial foraging optimization (BFO) was used to determine optimal features, and seven traditional machine models were used to detect the DR. The ResNet50-BFO-SVC cascaded network was found to be most suitable in this study. The proposed cascaded network brings efficiency, accuracy, scalability, and robustness to DR screening. © 2024 IEEE.

关键词： Large datasets

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Automated Road Extraction in Urban and Rural Regions from Remote Sensing Imagery with U-Net and ResNet-Based Architectures 1

Automated Road Extraction in Urban and Rural Regions from Re...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Palvi Kumar, Rakesh Gupta, Meenu Obaid, Ahmed J. Chandigarh University Dept. of Computer Science & Engineering Punjab India Chandigarh University Dept. of Computer Science & Engineering Punjab India University of Kufa Faculty of Computer Science and Mathematics Najaf Iraq National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350356816

Accurate road extraction from Remote Sensing Imagery (RSI) is crucial for various applications, including urban planning, navigation, and disaster response. This study investigates automated road extraction in rural, semi-urban and urban regions using deep learning architectures, specifically UNet and Deep Residual U-Net (ResNet) models. We leverage High-Resolution Satellite Imagery (HRSI) to train and validate our models, focusing on the distinct characteristics and challenges presented by urban and rural environments. Our methodology involves comprehensive data preprocessing and augmentation techniques to enhance model robustness. We evaluate the performance of both architectures using metrics and achieved an accuracy of 90.29%, recall of 80.95%, precision of 74.89%, and F1- score of 77.80% for the Deep Residual U-Net. Experimental results demonstrates that while both models perform well, the Deep Residual U-Net exhibits superior accuracy and generalization, particularly in complex urban landscapes. © 2024 IEEE.

关键词： Urban planning

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Self-attention-enhanced 3D Convolutional Dual-stream Pyramid Registration Neural Network

Self-attention-enhanced 3D Convolutional Dual-stream Pyramid...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yin, Yunfei Yuan, Zheng Yuan, Zhiwei Bao, Xianjian Chongqing University Dept. of Computer Science Chongqing China Maharishi University of Management Dept. of Computer Science Fairfield United States

ISBN: (纸本)9798350386226

Medical image registration is mainly used for 3D reconstruction of images, which has important clinical research significance. Therefore, accurate medical image registration is an important and highly challenging research topic. Current state-of-the-art methods employ pyramid registration neural network models to learn the anatomical relationship between moving images and fixed images, achieving breakthrough progress. However, these methods overlook the spatial relationship between non-adjacent elements in the images, leading to a small receptive field problem. To address this problem, we propose a self-attention enhanced 3D convolution dual-stream pyramid registration neural network model based on Transformer-ConvNet, named TransPyramid++. In TransPyramid++, Self-attention Blocks based on attention mechanisms and PatchMerging based on image block fusion are exploited to capture the anatomical relationship between moving images and fixed images, considering the non-adjacent elements. Then, by utilizing the upsampling and skip connections to decode this relationship step by step, while maintaining the residual relationship between the encoder and decoder. Finally, we propose the PR++ Module based on 3D convolution operations to calculate correlations between feature maps at different positions, which is an enhancement for the relation between non-adjacent elements. As a result, the problem of small receptive fields is alleviated. We conducted extensive experiments on the publicly available brain image registration datasets, Mindboggle101 and LPBA40. The experimental results show that compared to state-of-the-art methods, the performance of our model exceeds that of existing models, with an average improvement of 6.7% on the ASSD (Average Symmetric Surface Distance) index. © 2024 IEEE.

关键词： Image registration

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Secure Automated Inventory Management system using Abstracted System Design Microservice Architecture 14

Secure Automated Inventory Management system using Abstracte...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Darshan, M. Raswanth, S.R. Chaitanya, V.S.S.K. Kalavalapalli, Venkata Sridhar Sai Kumar, Priyanka Srivastava, Gautam Amrita School of Computing Dept. of Computer Science and Engineering Coimbatore India University of Texas Dept. of Computer Science San Antonio United States Brandon University Dept. of Mathematics and Computer Science Brandon Canada

ISBN: (纸本)9798350335095

Managing inventory has forever been a difficult problem for e-commerce sellers. Even today manual labor is used for the major part of the job. Without much automation in this industry growth has been stagnant because of the same *** proposed system works on a microservice-based architecture that enables automated inventory management with the help of a social-media messaging bot in an abstract manner. The bot comprises an integrated hybrid recommender system at the customer's end for better recommendations based on image and textual data. The entire system is deployed on the cloud following a serverless architecture for resilient service. The proposed solution plays a key role in taking research forward in the direction of micro-service-based system design involving real-world data. The results of the experiment prove to be beneficial for customers and retailers since they are now connected using the micro-service. © 2023 IEEE.

关键词： Systems analysis

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A Taxonomic Classification of Insider Threats: Existing Techniques, Future Directions & Recommendations

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Journal of Cyber Security and Mobility 2023年第2期12卷 221-252页

作者： Rauf, Usman Mohsen, Fadi Wei, Zhiyuan Dept. of Math. & Computer Science Mercy College NY United States Information Systems Group Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen9712 CP Netherlands

In the last two decades, the number of rapidly increasing cyber incidents (i.e., data theft and privacy breaches) shows that it is becoming enormously difficult for conventional defense mechanisms and architectures to neutralize modern cyber threats in a real-time situation. Disgruntled and rouge employees/agents and intrusive applications are two notorious classes of such modern threats, referred to as Insider Threats, which lead to data theft and privacy breaches. To counter such state-of-the-art threats, modern defense mechanisms require the incorporation of active threat analytics to proactively detect and mitigate any malicious intent at the employee or application level. Existing solutions to these problems intensively rely on co-relation, distance-based risk metrics, and human judgment. Especially when humans are kept in the loop for access-control policy-related decision-making against advanced persistent threats. As a consequence, the situation can escalate and lead to privacy/data breaches in case of insider threats. To confront such challenges, the security community has been striving to identify anomalous intent for advanced behavioral anomaly detection and auto-resiliency (the ability to deter an ongoing threat by policy tuning). Towards this dimension, we aim to review the literature in this domain and evaluate the effectiveness of existing approaches per our proposed criteria. According to our knowledge, this is one of the first endeavors toward developing evaluation-based standards to assess the effectiveness of relevant approaches in this domain while considering insider employees and intrusive applications simultaneously. There have been efforts in literature towards describing and understanding insider threats in general. However, none have addressed the detection and deterrence element in its entirety, hence making our contribution one of a kind. Towards the end of this article, we enlist and discuss the existing data sets. The data sets can help

关键词： Machine learning

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