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Applications of Cloud computing and Artificial Intelligence in the Oil and Gas Industry: A Review 6

Applications of Cloud Computing and Artificial Intelligence ...

引用

6th IEEE international conference on Image Processing, Applications and Systems, IPAS 2025

作者： Jellah, Alfitouri Ibrahim Mohamed, M. Moaad, I. Bani-Waleed University Oil and Gas Engineering Department Bani-Waleed Libya

ISBN: (纸本)9798331506520

This paper reviews the latest machine learning and artificial intelligence technology, showcasing its potential to revolutionize the oil and gas industry. Despite the current limitations, the industry is on the brink of a digital transformation that promises to enhance operations and increase overall production. With the help of AI and cloud computing, the oil and gas industry's future is one of optimism and potential. The review showed a significant increase in production when applying AI technology, particularly with EOR. The paper summarized the limitations and future challenges of this technology. © 2025 IEEE.

关键词： Gas industry

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Detection of Face Mask and Social Distance Calculation Using Deep learning 9

Detection of Face Mask and Social Distance Calculation Using...

引用

9th international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Sudhakaran, Pradeep Sindhu, V. Gupta, Sarthak Preetha, M. School of Computing Srm Institute of Science And Technology Department of Computing Technologies Chengalpattu District Tamil Nadu Kattankulathur603203 India Sri Krishna College of Engineering And Technology Department of Information Technology Tamil Nadu Coimbatore India Srm Institute of Science And Technology Career Development Centre Chengalpattu District Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350365092

The COVID 19 epidemic is causing a worldwide health disaster. The epidemic caused governments all over the world to institute lockdowns in order to limit viral spread. According to study data, wearing a face mask and keeping social distance in public places significantly reduces the risk of transmission. The project's ultimate goal is the development of a real-time, very accurate system that can detect any non-masked faces anywhere and compulsively require people to wear masks in the interest of public health. The proposed system combines state-of-the-art deep learning algorithms with geometric methods to provide a reliable modal that is effective at detection, tracking, and validation. Consequently, the proposed method is helpful to society because it reduces the spread of the corona virus while saving time. In light of the current situation, where the lockdown is being removed to permit inspections at public gatherings, retail malls, and other areas, this may prove to be effective. Automated inspection saves resources since it reduces the need for human inspectors. In addition, it is applicable in any circumstance. © 2024 IEEE.

关键词： learning algorithms

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Delay-Optimized Task Scheduling Based on Deep Reinforcement learning in computing Power Networks 3rd

Delay-Optimized Task Scheduling Based on Deep Reinforcement ...

引用

3rd international conference on Machine learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Tang, Qinqin Xu, Deyun Xie, Renchao Feng, Li Chen, Tianjiao Huang, Tao Beijing University of Posts and Telecommunications Beijing China China Mobile Research Institute Beijing China

ISBN: (纸本)9789819624676

With the rapid advancement of information and communication technology, there arises an increasing demand for heightened computing capabilities to facilitate a myriad of intelligent services. The computing power network (CPN) amalgamates an array of resources including cloud and edge devices to enable collaborative sensing, routing, and scheduling. This integration significantly amplifies the utilization of computing network resources while concurrently augmenting service quality. In the realm of multi-node distributed joint computing within CPNs conventional scheduling methodologies frequently neglect the interplay between computing and the network, resulting in suboptimal task scheduling. To address this issue, this study proposes a delay-optimized task scheduling mechanism based on deep reinforcement learning. Simulation results demonstrate that this framework achieves faster convergence speed, particularly under dual-depth networks, outperforming other algorithms in reducing system delays, energy and enhancing scheduling efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computing power

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Next-Generation Intrusion Detection Systems with Machine learning and Deep learning-Based Firewalls 3

Next-Generation Intrusion Detection Systems with Machine Lea...

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Rani, Ritu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331515911

The growing complexity of cyber-attacks has underlined the requirement of strong Intrusion Detection Systems (IDS) able to instantly recognize a variety of hazards. This work offers a strategy for improving Next-Generation Intrusion Detection Systems (NGIDs) combined with firewalls by use of machine learning (ML) and deep learning (DL) approaches. Using the NSL-KDD dataset - which includes both normal and attack network traffic - the work creates a system able to classify traffic patterns into "Normal"and "Attack"categories. The proposed method consists in four phases: data collecting, data preprocessing, feature selection and extraction, and classifier application. In the preprocessing stage, the dataset is cleaned and normalized;hence, suitable feature selection follows to increase classification accuracy. Trained and tested for classification using performance criteria including accuracy, precision, recall, and F1-score, naïve bayes and support vector machine (SVM) models are then compared in efficacy. The results reveal that the SVM classifier has low misclassification and great accuracy (97.29%), therefore proving its prospective use in advanced IDS. The article underlines how several models could be merged inside a firewall system to offer real-time network protection. Future studies will look at using deep learning techniques and ensemble methods to raise detection accuracy even more, therefore allowing NGIDS to be more adaptable and efficient in countering evolving cyber threats. © 2025 IEEE.

关键词： Network intrusion

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A Deep learning-Enabled Approach for Real-Time Monitoring of Learner Activities in Adaptive E-learning Environments 7

A Deep Learning-Enabled Approach for Real-Time Monitoring of...

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Ahamed, H. Riaz Kerana Hanirex, D. Bharath Institute of Higher Education and Research Department of Computer Applications Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350372816

A DL-enabled strategy might improve learner outcomes in settings that are adaptable to online instruction. Before and during assesements, it closely monitored important metrics including learning progress, time on task, quiz accuracy, completion rates, and average engagement. After the intervention, significant gains were seen in every indicator. In particular, completion rates increased from 0.85 to 0.90, average engagement increased from 0.72 to 0.82, and quiz accuracy increased from 0.78 to 0.84. Most significantly, learning progress increased significantly, rising from 65% to 78%. These results highlight the ability of our DL-enabled approach to dynamically adjust the learning process based on real-time input, leading to increased student understanding, engagement, and overall development. Our research highlights how DL techniques may improve the flexibility and adaptation of e-learning environments, leading to more effective and individualized learning results. Future research projects should focus on expanding and improving our methods to handle new challenges and complexity present in adaptive e-learning environments. The ultimate goal is to support each student's academic progress and achievement by providing a more individualized and optimum learning experience. © 2024 IEEE.

关键词： Adversarial machine learning

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Strategic Patient Education Approaches for Effective Disease Management and Prevention

Strategic Patient Education Approaches for Effective Disease...

引用

2024 international conference on Sustainable computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Aravinda, K. Rangaraju, Tejaswini Singla, Atul Shrivastava, Anurag Lavanya, C. Al-Rubaye, Taqi Mohammed Khattab New Horizon College of Engineering Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Department of Civil GRIET Bachupally Telangana Hyderabad India Department of Medical Laboratory Technology College of Medical Technologies The Islamic University Najaf Iraq

ISBN: (纸本)9798350355338

This study presents and assesses the Adaptive learning Reinforcement (ALR) approach, a new patient education strategy that improves illness prevention and treatment. Behavioral incentives and AI provide a dynamic and individualized learning environment in ALR. ALR consistently outperformed competitors in key areas. ALR's Patient Profiling Algorithm (PPA) customizes lesson plans depending on patient personality. Adaptability allows health information to be tailored to each patient's health and education. ALR emphasizes education involvement. The Reinforcement learning Algorithm (RLA) changed material based on patient responses, keeping them engaged. RLA's regular feedback helps individuals recall and utilize health knowledge. ALR's culture awareness distinguishes them. The Cultural Sensitivity Algorithm ensures that all students can understand and apply to courses by considering cultural backgrounds. Cultural relevance creates a healthcare environment that values and welcomes diversity. ALR was forward-thinking by using modern technology. The system proved straightforward to integrate with AI, virtual models, and real-time feedback. Thus, this addresses tech-savvy youth requirements and increases learning. Finally, ALR improves patient education by providing high-level customization, continuing participation, cultural understanding, technology, and peer support. © 2024 IEEE.

关键词： Contrastive learning

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Soil Health Analysis Using Few-Shot learning and Deep learning 8

Soil Health Analysis Using Few-Shot Learning and Deep Learni...

引用

8th IEEE international conference on computing, Communication, Control and Automation, ICCUBEA 2024

作者： Vispute, Sushma Rajeswari, K. Kumare, Vinay Naikawadi, Muhafij Patekar, Abhishek Padavale, Pravin Pimpri Chinchwad College of Engineering Department of Computer Engineering Nigdi Pune India

ISBN: (纸本)9798350391770

This paper presents an innovative approach to soil health analysis specifically tailored for farmers in a Pune division. We addressing the unique agricultural needs of this region by leveraging advanced machine learning techniques and data-driven methodologies. Our research focused on the development of machine learning models for crop recommendation and soil classification, utilizing diverse datasets including soil nutrient information, soil card details, weather records, and soil image visuals. The first aspect of our investigation involves the implementation of a few-shot learning model for personalized crop suggestions. This model considering the unique characteristics of the soil in the Pune division, incorporating data from soil card filings, weather reports, and soil images specific to the region. By providing tailor recommendations for optimal crop selection, our model aiming to maximize agricultural productivity and sustainability for Pune division farmers. In the second phase, we employing convolutional neural networks (CNNs) for soil picture classification. This model analyzing soil visuals to accurately categorize soil types, assisting in soil classification procedures crucial for various agricultural and environmental applications within the Pune division. Our emphasis throughout this research is on developing robust and accurate machine learning models that cater to the agricultural needs of Pune division farmers. By combining cutting-edge machine learning techniques with region-specific agricultural data, including soil image data, this research aims to empower Pune division farmers with actionable insights. Our goal is to promote sustainable farming practices and contribute to the agricultural development of the region. © 2024 IEEE.

关键词： Agricultural Sustainability CNN Crop Recommendation Few-Shot learning Soil Classification Soil Health Analysis

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Overcoming Challenges in DevOps Education through teaching Methods 23

Overcoming Challenges in DevOps Education through Teaching M...

引用

IEEE/ACM 45th international conference on Software engineering - Software engineering Education and Training (ICSE-SEET)

作者： Ferino, Samuel Fernandes, Marcelo Cirilo, Elder Agnez, Lucas Batista, Bruno Kulesza, Uira Aranha, Eduardo Treude, Christoph Univ Fed Rio Grande do Norte Natal Brazil Fed Inst Rio Grande do Norte Mossoro Brazil Univ Fed Sao Joao del Rei Sao Joao Del Rei Brazil Univ Melbourne Parkville Australia

ISBN: (纸本)9798350322590

DevOps is a set of practices that deals with coordination between development and operation teams and ensures rapid and reliable new software releases that are essential in industry. DevOps education assumes the vital task of preparing new professionals in these practices using appropriate teaching methods. However, there are insufficient studies investigating teaching methods in DevOps. We performed an analysis based on interviews to identify teaching methods and their relationship with DevOps educational challenges. Our findings show that project-based learning and collaborative learning are emerging as the most relevant teaching methods.

关键词： DevOps teaching methods challenges mixed methods

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Research on teaching Reform of Spark Big Data Analysis Course Based on TOPCARES-OBE 4

Research on Teaching Reform of Spark Big Data Analysis Cours...

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4th international conference on Big Data engineering and Education, BDEE 2024

作者： Luo, Guotao Chengdu Neusoft University Department of Intelligent Science and Engineering Chengdu China

ISBN: (纸本)9798350388268

Focusing on the issues of students' low efficiency in learning real-time big data processing and analysis techniques, as well as their weakness in solving comprehensive real-time big data problems, this article, guided by the TOPCARES teaching method and based on the OBE and student-centered educational philosophy, takes the Spark Big Data Analysis course as an example to reform theoretical teaching methods and practical teaching modes. In theoretical teaching, we adopt an outcome-oriented online and offline hybrid teaching method. In practical teaching, we use a teaching mode that combines OBE-CDIO-based classroom and extracurricular smart cloud platform project practice. This can not only consolidate students' theoretical foundation of Spark big data but also improve their ability to solve comprehensive big data problems. Finally, we study the evaluation mechanism of course goal achievement and calculate the degree of course goal achievement. By comparing the average values of course goal achievement between the 20201-20204 big data classes and the 19201-19204 big data classes, it is found that the average achievement rates of course goal 1, goal 2, and goal 3 of the 20201-20204 classes are 3%, 4%, and 2% higher than those of the 19201-19204 classes respectively. Meanwhile, a questionnaire survey was conducted among students, and the average satisfaction rate of students with the course was 92.55, indicating that the teaching reform has achieved certain results and can provide references for other similar curriculum reforms. © 2024 IEEE.

关键词： Data centers

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StraightLine: An End-to-End Resource-Aware Scheduler for Machine learning Application Requests 3

StraightLine: An End-to-End Resource-Aware Scheduler for Mac...

引用

3rd international conference on Artificial Intelligence, Internet of Things and Cloud computing Technology, AIoTC 2024

作者： Ching, Cheng-Wei Guan, Boyuan Xu, Hailu Hu, Liting University of California Santa Cruz Department of Computer Science and Engineering Santa CruzCA United States Center of Florida International University MiamiFL United States California State University Department of Computer Engineering and Computer Science Long BeachCA United States

ISBN: (纸本)9798331530280

The life cycle of machine learning (ML) applications consists of two stages: model development and model deployment. However, traditional ML systems (e.g., training-specific or inference-specific systems) focus on one particular stage or phase of the life cycle of ML applications. These systems often aim at optimizing model training or accelerating model inference, and they frequently assume homogeneous infrastructure, which may not always reflect real-world scenarios that include cloud data centers, local servers, containers, and serverless platforms. We present StraightLine, an end-to-end resource-aware scheduler that schedules the optimal resources (e.g., container, virtual machine, or serverless) for different ML application requests in a hybrid infrastructure. The key innovation is an empirical dynamic placing algorithm that intelligently places requests based on their unique characteristics (e.g., request frequency, input data size, and data distribution). In contrast to existing ML systems, StraightLine offers end-to-end resource-aware placement, thereby it can significantly reduce response time and failure rate for model deployment when facing different computing resources in the hybrid infrastructure. © 2024 IEEE.

关键词： Resource allocation

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