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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 ...

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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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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...

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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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AI-Enabled Edge computing Models: Trends, Developments, and Future Implications 2

AI-Enabled Edge Computing Models: Trends, Developments, and ...

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2nd international conference on Edge computing and Applications, ICECAA 2023

作者： Satyam Geetha, P. Shashikala, K.S. Kumar, N. Ashok School of Engineering Mohan Babu University Department of Ece Tirupati517102 India New Horizon College of Engineering Department of Ai&ml Bangalore560103 India

ISBN: (纸本)9798350347579

This research study examines the ways in which AI-enabled edge computing (AIEC) models are being used across various industries to improve operational efficiency and enable faster decision-making. The study discusses the key trends and developments of shaping the future of these next generation computing models, including the increased adoption of EC, the growing use of IoT devices, the emergence of 5G networks, advancements in machine learning algorithms, and the focus on data security. The research study also highlights case studies from different industries to illustrate the benefits of AIEC models and the potential impacts on businesses. The findings of this research work suggests that these computing models have the potential to transform the way organizations operate, leading to increased efficiency and productivity, better customer experiences, and more informed decision-making. © 2023 IEEE.

关键词： Machine learning

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Machine learning-Based Security Mechanism for Detecting Phishing Attacks

Machine Learning-Based Security Mechanism for Detecting Phis...

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2024 IEEE international conference on Communications, computing, Cybersecurity and Informatics, CCCI 2024

作者： Bhatt, Pragya Obaidat, Mohammad S. Dangwal, Gagan Das, Ashok Kumar Wazid, Mohammad Sadoun, Balqies Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248 002 India University of Science and Technology Beijing Beijing100083 China SRM University Kattankulathur603203 India Amity University Noida201301 India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India College of Engineering Al-Balqa' Applied University Al-Salt19117 Jordan

ISBN: (纸本)9798350349832

Phishing attacks pose a serious threat to online users by impersonatina trustworthy entities and stealing sensitive data. This can result in reputational damage, financial loss, ransomware, or additional spyware outbreaks. Phishing scams take into consideration roughly 22% of all data violations, making it one of the most prominent cybercrimes, in accordance with the FBI's 2021 IC3 report. According to phishing email statistics, roughly 1.2% of all emails received are fraudulent, resulting in 3.4 billion phishing emails per day. One out of every 4,200 emails sent is almost certainly a phishing attempt. Cognitive methods, such as machine learning (ML) and deep learning (DL), are widely used in cybersecurity to extract information and detect possible intrusions and phishing attempts. This research investigates the procedure of employing machine learning methods for recognizing URLs that are used for phishing attacks. We have used the Pristine and Malicious URLs Dataset to train, test, and evaluate the performance of a set of algorithms to develop a robust binary class URL phishing attack detection mechanism. The obtained results show that the proposed mechanism outperforms the other existing competing mechanisms with 99.6% accuracy. © 2024 IEEE.

关键词： Phishing

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Research on human pose and action detection algorithm based on YOLOv8 2

Research on human pose and action detection algorithm based ...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Cheng, Weizhong Wang, Xuefei School of Information Engineering Suihua University Heilongjiang Suihua China School of Electrical Engineering Suihua University Heilongjiang Suihua China

ISBN: (纸本)9781510689053

Human pose and action detection technology has significant applications in areas such as smart security, sports monitoring, and human-computer interaction. The development of deep learning has driven the demand for real-time and efficient detection algorithms in this domain. Although existing human action detection algorithms have improved accuracy, their model complexity and computational demands hinder practical use in portable devices and edge computing platforms. To address this, the paper presents a lightweight human pose and action detection algorithm based on the YOLOv8 framework, designed to meet the high-precision, low-latency needs of edge computing and portable devices. In the feature extraction phase, GhostNet's lightweight structure is used to reduce model parameters, while the CBAM attention mechanism, applied in the feature fusion phase, enhances the model's attention to critical features. This approach significantly decreases computational load while maintaining high accuracy. Experimental results indicate that the algorithm achieves a real-time detection rate exceeding 40 frames per second on edge computing platforms, demonstrating high accuracy and robustness across various human action scenarios. This offers strong technical support for human pose detection in smart security and wearable devices. © 2025 SPIE.

关键词： Portable equipment

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Strategic Patient Education Approaches for Effective Disease Management and Prevention

Strategic Patient Education Approaches for Effective Disease...

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

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

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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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Greensense: IoT-Enabled Smart Monitoring And Early Detection System For Optimal Plant Health In Greenhouse

Greensense: IoT-Enabled Smart Monitoring And Early Detection...

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2024 international conference on Advances in Data engineering and Intelligent computing Systems, ADICS 2024

作者： Venkatesh, R. Aadhira, K.R. Kathiravan, M. Hindustan Institute Of Technology And Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

The emergence of Agriculture 4.0 signifies a transformative shift in traditional farming practices, integrating cutting-edge technologies like IoT, machine learning, and robotics to support productivity and sustainability. This era leverages real-time data and automation for precision farming, predictive analytics, and resource optimization, crucial for addressing global food security challenges while minimizing environmental impact. GreenSense represents progress within Agriculture 4.0. It seeks to revolutionize greenhouse management by integrating IoT and advanced machine learning algorithms. Through sensors, actuators, and a Raspberry Pi 4 board, this system offers a comprehensive solution for monitoring and optimizing plant health in controlled environments. The motivation behind this system stems from the imperative to enhance agricultural practices sustainably in the face of population growth. By enabling real-time monitoring and swift anomaly detection through application, this paper empowers proactive intervention to mitigate risks and optimize growing conditions. Integration of the YOLOv5 algorithm with 97% accuracy enhances precision in leaf disease and pest detection using OpenCV, reducing crop losses effectively. Beyond greenhouse management, this paper contributes to advancing sustainable agriculture. Its scalable and adaptable nature promotes resource-efficient practices and environmental care. Finally, a user-friendly interface is tailored to facilitate efficient data collection from sensors, enabling comprehensive monitoring of crop conditions, including pest and disease detection. © 2024 IEEE.

关键词： learning algorithms

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