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Simulation and Modelling of Water Injection for Reservoir Pressure Maintenance

Computers, Materials & Continua 2022年第9期72卷 5761-5776页

作者： Rishi Dewan Adarsh Kumar Mohammad Khalid Imam Rahmani Surbhi Bhatia Md Ezaz Ahmed Department of Physics University of Petroleum&Energy StudiesDehradun 248007UttarakhandIndia Department of Computer Science University of Petroleum&Energy StudiesDehradun 248007UttarakhandIndia College of Computing and Informatics Saudi Electronic UniversityRiyadh 11673Saudi Arabia Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityAl Hasa 36362Saudi Arabia

Water injection has shown to be one of the most successful,efficient,and cost-effective reservoir management *** re-injecting treated and filtered water into reservoirs,this approach can help maintain reservoir pressure,increase hydrocarbon output,and reduce the environmental *** goal of this project is to create a water injection model utilizing Eclipse reservoir simulation software to better understand water injection methods for reservoir pressure maintenance.A basic reservoir model is utilized in this *** simulation designs,the reservoir length,breadth,and thickness may be changed to different *** water-oil contact was discovered at 7000 feet,and the reservoir pressure was recorded at 3000 pounds per square inch at a depth of 6900 *** aquifer chosen was of the Fetkovich type and was linked to the reservoir in the j+*** porosity was estimated to be varied,ranging from 9%to 16%.The residual oil saturation was set to 25%and the irreducible water saturation was set at 20%.The vertical permeability was set at 50 md as a *** Volume Temperature(PVT)data was used to estimate the gas and water characteristics.

关键词： Eclipse water injection reservoir modeling simulation oil saturation

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A Survey on Swarm Intelligence Algorithms Employed for Optimizing Machine Learning Techniques Used in Recommendation systems

A Survey on Swarm Intelligence Algorithms Employed for Optim...

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IEEE Conference on systems, Process & Control (ICSPC)

作者： Lim Cher Zet Muhammed Basheer Jasser Richard T.K. Wong Hui Na Chua Bayan Issa Department of Computing and Information Systems Sunway University Sunway City Selangor Malaysia Faculty of Informatics Engineering University of Aleppo Syria

With the exponential growth of digital data, recommendation systems or recommender systems are widely used in various domains assisting users in filtering and decision-making on massive information. Recommendation systems are capable of delivering personalized content to enhance user experience and satisfaction through users' preferences and behaviors. The machine learning algorithms employed in the recommendation systems facilitate the effectiveness of tasks achieved by those systems among which, for example, is providing accurate prediction that matches user preferences. Swarm Intelligence offers robust optimization mechanisms that have been successfully applied in various computational problems including recommendation systems for refining recommendation algorithms. To the best of our knowledge, there is no recent comprehensive survey on swarm intelligence algorithms used for optimizing machine learning techniques when used in recommendation systems. Therefore, this survey presents a survey of the swarm intelligence algorithms used in optimizing the machine learning techniques when employed in recommendation systems. We conducted a literature survey on recommendation systems and swarm intelligence, using relevant keywords and their variants, focusing on recent publications since 2019. Our findings highlight the use of swarm intelligence algorithms primarily in clustering, classification, and feature selection for recommendation systems. Swarm intelligence has significantly enhanced recommendation systems, especially in clustering and classification. However, the balance between computational complexity and processing speed remains a challenge. Future research could focus on refining these algorithms for better efficiency and effectiveness in recommendation systems.

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The Concept of Cognition as Categorization in the Development of New Metaheuristics and Algorithms Inspired by Nature

The Concept of Cognition as Categorization in the Developmen...

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Proceedings of the International Convention MIPRO

作者： Domagoj Tuličić Nikola Ivković Department of Computer Systems and Networks College for Information Technologies Zagreb Hrvatska Department of Computing and Technology Faculty of Organization and Informatics Varaždin Hrvatska

The sensorimotor system is a crucial mechanism that enables organisms to categorize information in their environment, a skill necessary for survival. According to cognitive scientist Steven Harnad’s “To Cognize is to Categorize: Cognition is Categorization all living things are essentially sensorimotor systems. This approach is proposed to serve as a novel concept for the development of algorithms inspired by swarm intelligence. Many authors of such algorithms have drawn inspiration from animal species, attempting to emulate their abilities to interact with and navigate their environment. However, these abilities are essentially rooted in the organisms’ categorization skills, which are a fundamental characteristic of all sensorimotor systems. Therefore, when designing new algorithms and metaheuristics, it is unnecessary to seek out new species with specific behaviors; instead, the entire sensorimotor system can be observed, and its sensor capabilities can be incorporated even if they do not exist in nature. In order to justify this, abstraction was used as a way to demonstrate the creation of concepts. The paper offers a new perspective on the development of swarm intelligence algorithms and promotes a holistic approach to algorithm design based on Steven Harnad’s viewpoint of sensory motor systems.

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Content Validity Assessment of Metaverse Characteristics Instrument for High School Teachers' Adoption

Content Validity Assessment of Metaverse Characteristics Ins...

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International Conference on Smart computing & Communications (ICSCC)

作者： Cadelina Cassandra Mohamad Noorman Masrek Fadhilah Aman School of Information Science College of Computing Informatics and Media University Teknologi MARA Shah Alam Selangor MALAYSIA Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

This study's aim is to investigate the content validity of an instrument designed to evaluate the Metaverse characteristic assessment for high school teachers' adoption. The content validity of the instrument was explored based on the perspectives of five experts who were chosen based on their expertise and experiences. The Content Validity use content validity index (I-CVI) and scale content validity index (S-CVI). In the pre-testing process, two contexts were tested: the relevant context and clarity context. In the Relevant context, the mean I-CVI ranged from 0.9 to 1, the S-CVI/UA ranged from 0.5 to 1, and the S-CVI/AVE ranged from 0.9 to 1 for the construct of Interactivity, Persistency and Corporeity, the result shows three items need to be revised. In the Clarity context, the mean I-CVI ranged from 0.95 to 1, the S-CVI/UA ranged from 0.75 to 1, and the S-CVI/AVE ranged from 0.95 to 1 for the construct of Interactivity, Persistency and Corporeity, the result show one items need to be revised. The revised items are also proposed in this study for better instruments.

关键词： Measurement errors Metaverse Instruments Indexes Reliability

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Revolutionizing Human–Computer Interaction: Unraveling the Power of Deep Learning Convolutional Neural Networks in Face Recognition 4th

Revolutionizing Human–Computer Interaction: Unraveling the ...

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4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Baharum, Aslina Halamy, Suhaida Ismail, Rozita Abdul Rahim, Emelia Mat Noor, Noorsidi Aizuddin Deris, Farhana Diana Department of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Selangor Malaysia School of Information Science College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Sarawak Kota Samarahan Malaysia College of Computing and Informatics Universiti Tenaga Nasional Putrajaya Campus Kajang Malaysia Faculty of Business Management Universiti Teknologi MARA Cawangan Sarawak Sarawak Kota Samarahan94300 Malaysia Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Real Estate Department Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Johor Bahru Malaysia Faculty of Social Sciences and Humanities Universiti Teknologi Malaysia Johor Johor Bahru Malaysia

ISBN: (纸本)9789819729760

Face recognition, being one of the most effective applications of image analysis, has recently received a lot of attention due to the huge implication in human–computer interaction (HCI). As the availability and eligibility to detect a person’s facial features, face recognition technology has been used in biometric detection applications as uses certain aspects of a person’s physiology to identify them. In addition, Deep Learning, under the subset of machine learning, can solve various problems, especially in image processing and face recognition. The advancement and development of Deep Learning can also enhance the use of the Convolution Neural Network (CNN) as the predominant model in the field of face recognition. The paper discusses the systems based on CNN approaches and algorithms and provides a review of the CNN face recognition approach. Furthermore, each paper’s details, such as used datasets, techniques, architecture, and obtained findings, hence the application are fully summarized and analyzed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Breast Cancer Detection on X-Tray Mammogram Images 3

Breast Cancer Detection on X-Tray Mammogram Images

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Azmi, Muhammad Arrif Amirul Mustafa, Wan Azani Alquran, Hiam Aziz, Azwa Abdul Mansor, Muhammad Naufal Alzubaidi, Laith H. Hussein, A.H.A. Universiti Malaysia Perlis Faculty of Electrical Engineering Technology UniCITI Alam Campus Sungai Chuchuh Padang Besar Perlis Malaysia Yarmouk University Department of Biomedical Systems and Informatics Engineering Irbid Jordan Faculty of Informatics & Computing Terengganu Malaysia The Islamic University Faculty of Engineering Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

ISBN: (纸本)9798350317022

Breast cancer (BC) is a common cancer affecting women everywhere in world. Mammography is identified and efficient technique to detect primary BC. The aim of project is for detect BC on mammogram in order to categorize disease by image processing when comparing with a previous technique. Through utilizing conventional methods, it creates it complex for radiology to detect cancer from patient's breast. In addition, there are environmental disturbances and technical problems if using the old method. Image processing techniques was separated to numerous elements. The elements are input, pre-processing, segmentation, morphological, object classification and classification. First pre-processing was done through Weiner and Median filter. Then, thresholding method on segmentation and finally, morphological will eliminate limitations at a segmentation. The image classified into 2 classes like normal and tumor. Both type of images analyzed based on elements. Additionally, it comprises a building of Graphical User Interface (GUI) which is utilized to generate the system as user-friendly. The developed model attain accuracy of 93.71 %, specificity of 82.53 % sensitivity of 94.36% for tumorous images. © 2023 IEEE.

关键词： Breast Cancer Classification Graphical User Interface Image Processing MATLAB

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Model of Visualization and Analytics for Open Data (Case: Election Voters & Kids Disability Category)

Model of Visualization and Analytics for Open Data (Case: El...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Ripto Mukti Wibowo Bahjat Fakieh Muhammad Sher Ramzan Ahmed Saeed Alzahrani Muazzam Siddiqui Bander Alzahrani Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Luddy School of Informatics Computing and Engineering Indiana University Bloomington

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 *** this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities.

关键词： Technological innovation Data analysis Pandemics Voting Data visualization Big Data Data models

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Two Heads are Better than One: Geometric-Latent Attention for Point Cloud Classification and Segmentation 32

Two Heads are Better than One: Geometric-Latent Attention fo...

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32nd British Machine Vision Conference, BMVC 2021

作者： Cuevas-Velasquez, Hanz Gallego, Antonio Javier Fisher, Robert B. School of Informatics University of Edinburgh Edinburgh United Kingdom Department of Software and Computing Systems University of Alicante Alicante Spain

We present an innovative two-headed attention layer that combines geometric and latent features to segment a 3D scene into semantically meaningful subsets. Each head combines local and global information, using either the geometric or latent features, of a neighborhood of points and uses this information to learn better local relationships. This Geometric-Latent attention layer (Ge-Latto) is combined with a sub-sampling strategy to capture global features. Our method is invariant to permutation thanks to the use of shared-MLP layers, and it can also be used with point clouds with varying densities because the local attention layer does not depend on the neighbor order. Our proposal is simple yet robust, which allows it to achieve competitive results in the ShapeNetPart and ModelNet40 datasets, and the state-of-the-art when segmenting the complex dataset S3DIS, with 69.2% IoU on Area 5, and 89.7% overall accuracy using K-fold cross-validation on the 6 areas. © 2021. The copyright of this document resides with its authors.

关键词： Geometry

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SEIMR: Secure and Ethical IoT Mixed Reality Integration Framework for Metaverse Experiences

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IEEE Transactions on Consumer Electronics 2025年

作者： Uddin, Mueen Abdelhaq, Maha Alsufyani, Hamed Alanazi, Fuhid Alsaqour, Raed University of Doha for Science and Technology College of Computing and IT 24449 Qatar Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology Riyadh11671 Saudi Arabia Saudi Electronic University College of Computing and Informatics Department of Computer Science Riyadh11673 Saudi Arabia Islamic University of Madinah Faculty of Computer and Information Systems Madinah42351 Saudi Arabia Saudi Electronic University College of Computing and Informatics Department of Information Technology Riyadh93499 Saudi Arabia

Mixed Reality (MR) environments are increasingly integral to various applications, necessitating robust security and privacy measures. Current frameworks often fail to address the comprehensive integration of privacy preservation, security resilience, interoperability efficiency, and ethical compliance. This study aims to develop the SEIMR framework, which seamlessly integrates the Contextual Data Anonymization Protocol (CDAP), the Dynamic Threat Response Algorithm (DTRA), the Unified IoT-MR Interaction Protocol (UIMRIP), and the Adaptive Ethical Decision-Making Algorithm (AEDMA). CDAP ensures that user data are contextually anonymized, while DTRA dynamically identifies and mitigates security threats. UIMRIP facilitates the seamless integration of heterogeneous IoT devices with MR platforms, and AEDMA maintains ethical standards through adaptive decision making. SEIMR has practical applications in healthcare and education Metaverse systems, addressing privacy and interoperability challenges effectively. The framework was evaluated using the CICIDS2017 dataset, demonstrating superior performance with a 95% detection rate, 90% privacy preservation, 88% integration efficiency and 85% ethical compliance. © 1975-2011 IEEE.

关键词： Differential privacy

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Education inequality in underserved regions: Exploring the role of technology to promote diversity and inclusivity 18

Education inequality in underserved regions: Exploring the r...

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18th IEEE International Conference on e-Business Engineering, ICEBE 2022

作者： Mavangere, Nicholas Edifor, Ernest Edem Adedoyin, Festus Apeh, Edward Owusu, Acheampong Bournemouth University Department of Computing and Informatics Bournemouth United Kingdom Manchester Metropolitan University Operations Technology Events and Hospitality Management Manchester United Kingdom University of Ghana Department of Operations and Management Information Systems Legon Ghana

ISBN: (纸本)9781665492447

In underserved regions, the educational inequality gap between better-served children and under-served children is wide. At the same time, the digital inequality gap in such countries is narrowing;less endowed families are now able to access some digital technologies. Therefore, there is the need to use technologies for educational purposes to support less-endowed children in promoting diversity and inclusivity. This research is explorative research to highlight issues in the use of technologies for education, such as adoption, barriers, challenges and benefits in African context. In doing so, we seek to promote diversity and inclusivity for education in underserved regions and bridge the education inequality gap. This research will contribute to academic research relating to inclusivity to support under-served children in developing countries. It will push forward educational technology research and contribute to policymaking. The outcome of this research would prepare the foundation for a future large-scale implementation across Sub-Saharan Africa. © 2022 IEEE.

关键词： Developing countries

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