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2024 Abu Dhabi International Petroleum Exhibition and Conference, ADIPEC 2024

作者： Tharayil, Sarafudheen M. Al Qarni, Shouq Al Thubaiti, Leen Al Dossary, Basim Shahada, Marwa Corporate Applications Department Aramco Dhahran Saudi Arabia Department of Mathematics & Computer Science Heriot-Watt University Dubai Campus United Arab Emirates

ISBN: (纸本)9781959025498

The primary objective of this research work is to harness the advanced capabilities of Artificial Intelligence (AI), specifically Deep Learning (DL) and Large Language Models (LLMs), to develop a comprehensive system for detecting and understanding the causes of oil spills. Our approach involves utilizing deep learning algorithms to detect oil spill incidents from images, extracting relevant factors from these images, and feeding these factors into LLMs to determine the causality of the incidents. This research is motivated by the increasing frequency and environmental impact of oil spill events globally, and the lack of existing mechanisms to accurately monitor and explain these incidents. By enabling rapid detection and causality analysis, this system aims to enhance environmental protection efforts and prevent future oil spills through informed decision-making and timely intervention. The methodology of this study involves several critical steps. We began by utilizing an industrial dataset comprising labeled images of oil spills. Initial preprocessing steps included resizing and normalization of the images, followed by extensive data augmentation to enhance the dataset's robustness. We then employed advanced deep learning models, where images are considered as a grid of cells, with bounding boxes. We trained the Convolutional Neural Networks (CNNs) model to identify oil spill incidents by extracting key features from each image. These factors were then fed into a Large Language Model (LLM), to analyze and determine the underlying causes of the oil spills. The study demonstrates the effectiveness of integrating deep learning and LLMs in environmental monitoring and analysis. Our approach achieved a considerable increase in the accuracy of oil spill detection compared to traditional methods. Additionally, we attained a better accuracy rate in identifying contributory factors to oil spills. These results underscore the ecological importance of promptly identifying a

关键词： Oil spills

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Integrating Diverse Approaches in Medical Image Analysis: PAA-CNN and Feature Extraction Fusion for Classification of Psychological Disorders using Anatomical Scans

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Procedia computer science 2025年 260卷 40-47页

作者： Nagashruthi M K Hemanth K S School Of Computer Science and Applications REVA University Bengaluru and 560064 India Department of Computer Science Christ University Bengaluru and 560073 India

A psychological disorder is a condition that impacts a persons behavior. Due to the contemporary way of life, a large number of individuals suffer from disorders like stress, depression, and other similar ones. These might turn into severe issues that would significantly impact a persons quality of life. We present a sample framework that uses an Anatomical scan captured along with fMRI. Anatomical scans were used to extract characteristics, which were then utilized to classify using a random forest classifier. In a follow-up experiment, CNN is applied to features obtained from the piecewise aggregate approximation method for multi-class classification of psychological disorders. This method performs noticeably better than the conventional feature extraction techniques, and with this approach, obtained an accuracy of up to 79%. Combining several approaches may boost the classification and prediction accuracy of medical data.

关键词： anatomical images prominent volumes feature extraction machine learning peicewise aggregate approximation

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Modeling and Prediction of Reference Evapotranspiration (ETo) using Variants of K-Nearest Neighbour Algorithm

Modeling and Prediction of Reference Evapotranspiration (ETo...

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2022 IEEE International Conference on Current Development in Engineering and Technology, CCET 2022

作者： Jain, Satendra Kumar Gupta, Anil Kumar Barkatullah University Computer Science and Applications Department Bhopal India

ISBN: (纸本)9781665454155

Reference evapotranspiration (ETo) is an important climatic factor, and stakeholders like farmers, engineers, and scientists apply it in water resources management, crop water needs, and the analysis of the climatic condition. In this study, the potential of three variants of the k-nearest neighbour algorithm, namely k-nearest neighbour regression (KNNR), fuzzy k-nearest neighbour regression (FKNNR), and kmeans-k-nearest neighbour regression (KM-KNNR) is assessed with climatic information of New Delhi, India, which is located in the northern part of India and its climate lies in between humid subtropical and semi-arid. Simulated outcomes of these variants are compared and calibrated against the worldwide accepted FAO-PM56 method. The empirical results of this study show the remarkable performance of the KNNR and FKNNR with R2 = 0.99 each, whereas the KM-KNNR exhibits insignificant performance even on optimal clusters obtained from the Elbow heuristic method. However, this study demonstrates empirically that similar performance can be achieved by KM-KNNR if more clusters are formed. This study finds that these variants have the potential to estimate the ETo accurately and can be useful tools for stakeholders. © 2022 IEEE.

关键词： k-nearest neighbors

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Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes 10

Summit Vitals: Multi-Camera and Multi-Signal Biosensing at H...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Ke Tang, Jiankai Jiang, Zhang Wang, Yuntao Liu, Xiaojing Li, Dong Shi, Yuanchun Qinghai University Department of Computer Technology and Applications Qinghai810016 China National Key Laboratory of Human Factors Engineering Beijing100094 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

ISBN: (纸本)9798331520861

Video photoplethysmography (vPPG) is an emerging method for non-invasive and convenient measurement of physiological signals, utilizing two primary approaches: remote video PPG (rPPG) and contact video PPG (cPPG). Monitoring vitals in high-altitude environments, where heart rates tend to increase and blood oxygen levels often decrease, presents significant challenges. To address these issues, we introduce the SUMS dataset comprising 80 synchronized non-contact facial and contact finger videos from 10 subjects during exercise and oxygen recovery scenarios, capturing PPG, respiration rate (RR), and SpO2. This dataset is designed to validate video vitals estimation algorithms and compare facial rPPG with finger cPPG. Additionally, fusing videos from different positions (i.e., face and finger) reduces the mean absolute error (MAE) of SpO 2 predictions by 7. 6 % and 1 0. 6 % compared to only face and only finger, respectively. In cross-subject evaluation, we achieve an MAE of less than 0.5 BPM for HR estimation and 2. 5 % for SpO 2 estimation, demonstrating the precision of our multi-camera fusion techniques. Our findings suggest that simultaneous training on multiple indicators, such as PPG and blood oxygen, can reduce MAE in SpO 2 estimation by 1 7. 8 %. © 2024 IEEE.

关键词： Photoplethysmography

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Travelling Salesman Problem Using GA-ACO Hybrid Approach: A Review 2nd

Travelling Salesman Problem Using GA-ACO Hybrid Approach: A ...

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2nd International Conference on Data science, Machine Learning and applications, ICDSMLA 2020

作者： Ankita Department of Computer Science and Applications Kurukshetra University Kurukshetra India

ISBN: (纸本)9789811636899

Travelling Salesman Problem is the most widely studied combinatorial optimization problem which has attracted many researchers in the last few years. It is recognized as one of popular NP-Hard problem that has broad search space. TSP is a difficult problem which can't be solved by conventional methods particularly when the number of cities increases. So, to solve this problem efficiently, heuristic approach is the most feasible solution. Genetic Algorithm and Ant Colony Optimization are commonly used metaheuristics to solve TSP successfully. To solve TSP, this paper considers the three eminent heuristic approaches namely ACO, GA and hybrid GA-ACO. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Helmholtz Solving and Performance Optimization in Global/Regional Assimilation and Prediction System

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Tsinghua science and Technology 2021年第3期26卷 335-346页

作者： Jianqiang Huang Wei Xue Haodong Bian Wenxin Yan Xiaoying Wang Wenguang Chen Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Department of Computer Technology and Applications Qinghai UniversityXining 810016China

Despite efficient parallelism in the solution of physical parameterization in the Global/Regional Assimilation and Prediction System(GRAPES),the Helmholtz equation in the dynamic core,with the increase of resolution,can hardly achieve sufficient parallelism in the solving process due to a large amount of communication and irregular *** this paper,optimizing the Helmholtz equation solution for better performance and higher efficiency has been an urgent *** optimization scheme for the parallel solution of the Helmholtz equation is proposed in this ***,the geometrical multigrid optimization strategy is designed by taking advantage of the data anisotropy of grid points near the pole and the isotropy of those near memory equator in the Helmholtz equation,and the Incomplete LU(ILU)decomposition preconditioner is adopted to speed up the convergence of the improved Generalized Conjugate Residual(GCR),which effectively reduces the number of iterations and the computation *** overall solving performance of the Helmholtz equation is improved by thread-level parallelism,vectorization,and reuse of data in the *** experimental results show that the proposed optimization scheme can effectively eliminate the bottleneck of the Helmholtz equation as regards the solving *** the test results on a 10-node two-way server,the solution of the Helmholtz equation,compared with the original serial version,is accelerated by 100,with one-third of iterations reduced.

关键词： Global/Regional Assimilation and Prediction System(GRAPES) Helmholtz equation Generalized Conjugate Residual(GCR) performance optimization Incomplete LU(ILU)

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Blood Cell Classification and Counting in Hematology through Deep Learning 4

Blood Cell Classification and Counting in Hematology through...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Ananthi, P. Devi, K. Nirmala Ramya, T.E. Shanmugapriya, P. Aradhya, T. Kongu Engineering College Department of Computer Technology Perundurai Tamil Nadu Erode India Madanapalle Institute of Technology & Science Department of Computer Applications India Kongu Engineering College Perundurai Tamil Nadu Erode India

ISBN: (纸本)9798331540364

Medical diagnosis is one of the areas that has been greatly influenced by the progress of computer vision technologies. This study presents a unique approach for the detection and counting of blood cells in hematology: You Only Look One algorithm version 7 (YOLOv7). YOLO is designed for real-time object detection, making it ideal for applications such as blood vessel detection where speed is critical. The architecture allows the model to process images faster than other recognition models, such as R-CNN or SSD, which is very important for situations that require fast results, such as analysis automatic blood at clinical sites. The purpose of the proposed method is to overcome the shortcomings of conventional methods and to accurately and quickly identify blood cells in mechanical images. The YOLOv7 model has been utilizedbecause it can detect objects in real-time with highprecision and speed. The methodology for bloodcell counting involves the use of YOLOv7 togenerate bounding boxes around each detectedblood cell in an image, where the count of these bounding boxes directly corresponds to the number of cells. This advancement in hematology not onlyimproves blood cell analysis efficiency but also expedites the process of diagnosing and planningtreatment in various medical situations. This paper details how YOLOv7 was modified and adjusted to meet the specific requirements of hematologicalimage analysis, including training models, preparing datasets, and evaluating performance. The study also addresses the potential impact of this technology on clinical workflows, highlightinghow it might help medical practitioners make decisions more quickly and intelligently. Hematology analysis systems including YOLOv7 are able to improve laboratory diagnostics and help patients by providing better care and results. This workconcludes by demonstrating the revolutionary potential of YOLOv7 in blood cell identificationand counting in hematology, paving the way for accurate and better

关键词： Diagnosis

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Enhancing Gas Leak Detection with IoT Technology: An Innovative Approach 2

Enhancing Gas Leak Detection with IoT Technology: An Innovat...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Babu, Tina Nair, Rekha R. Kishore, S. Vineeth, M. Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte University Mangalore India Buisness Intelligent Consultant Zellis HR Infopark Ernakulam India

The presence of a natural gas leak within a household carries the potential for fires and poses a risk of natural gas poisoning. Similar to how we approach other hazardous energy sources such as electricity and gasoline, it is crucial to exercise caution when dealing with natural gas exposure. To prevent potential hazards and dangerous situation, identified a smart gas detection system for rapid and accurate detection of gas leaks. The proposed gas leakage detection system combines advanced sensor technology, real-time monitoring, and automated alert mechanisms to ensure timely identification and response to gas leaks. The MQ2 sensor helps in detection of gas leakage. The MQ2 sensor possesses the capability to detect a wide range of gases, including methane, propane, carbon monoxide, and hydrogen. This versatility makes it an invaluable tool in ensuring safety and protecting against potential hazards and early identification of gas leaks. The collected data is analyzed using sophisticated algorithms to distinguish between normal background gas levels and potential leaks. NodeMCU, equipped with its Wi-Fi capabilities, functions as the central control unit of the system. The NodeMCU gathers real-time data from the gas sensors, constantly monitoring gas levels. It processes this data and sends it to the cloud or a central server using its internet connection. The system's ability to monitor in real-time ensures that any gas leaks detected are quickly reported to the relevant personnel or authorities through automated alerts. The fast notification system allows for quick actions, reducing the risks of accidents, saving lives, preventing property damage, and mitigating harm to the environment. © 2024 Elsevier B.V.. All rights reserved.

关键词： Gas detectors

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Weather Forecasting Using Advanced Artificial Intelligence Techniques Based on Swin Transformer Network 2

Weather Forecasting Using Advanced Artificial Intelligence T...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kavitha, S. Nagamani, H.S. Sumanth, S. Pareek, Piyush Kumar Mageswari, P. Uma Nitte Meenakshi Institute of Technology Department of Mca Bangalore India Govt. Smt Vhd Central Institute of Home Science Department of Computer Science Bengaluru India Government College for Women Department of Computer Science and Applictaions Kolar India Nitte Meenakshi Institute of Technology Department of AI/ML Bangalore India Presidency College Department of Computer Applications Bangalore India

ISBN: (纸本)9798350382952

It is still difficult for weather prediction (NWP) representations to exactly anticipate rainfall, especially the highest rainfall events. The severely intermittent character rainfall, together with subgrid-scale are critical to the multi-scale dynamics that produce rainfall, might be the reasons for this. Because of this inherent fallibility of weather prediction model projections, it is standard practice to fix these forecasts by post-processing the model's output. To improve weather prediction, the study suggests using a Spectral Swin Transformer Network (SSTN). An operating high-resolution model system, the PRUFS model, which runs for East China at 3 km grid intermissions, was used to test the model. The 311 conventional ground-based weather stations to collect data on 2 m temperature, 2 m relative speed. A year's worth of historical PRUFS perfect outputs and surface comments make up the training dataset for SSTN, while two real-time SSTN outputs form the basis of the act evaluation. © 2024 IEEE.

关键词： Weather forecasting

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MMDFD- A Multimodal Custom Dataset for Deepfake Detection 2023

MMDFD- A Multimodal Custom Dataset for Deepfake Detection

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15th International Conference on Contemporary Computing, IC3 2023

作者： Asha, S. Vinod, P. Menon, Varun G. Department of Computer Science & Engineering SCMS School of Engineering & Technology APJ Abdul Kalam Technological University Trivandrum India Department of Computer Applications Cochin University of Science & Technology Cochin India Department of Computer Science & Engineering SCMS School of Engineering & Technology Ernakulam India

ISBN: (纸本)9798400700224

A multi-modal deepfake dataset is relevant in addressing the growing concern of deepfake misuse, which poses a significant security and privacy threat. Deepfakes are becoming increasingly sophisticated, and their potential to deceive individuals and organizations is a significant issue. The ability to generate synthesized human voices using deep learning models and inserting fake subtitles has added to this problem, making it more challenging to detect deepfakes accurately. A superior quality dataset is essential to developing a competent deepfake detector. However, existing datasets are limited and often biased, making it difficult to detect deepfakes accurately. A multi-modal deepfake dataset, such as the proposed multi-modal Audio-Video-Text Deepfake dataset (MMDFD) addresses this gap by providing a more realistic and unbiased dataset. Such a dataset helps develop more accurate and effective deepfake detection methods, which can detect audio, video, and textual deepfakes simultaneously. The proposed dataset is more reflective of situations in the real world since it contains actual YouTube recordings of celebrities from four different racial origins. This helps to avoid the creation of deepfake detectors that are biased toward certain racial or ethnic groups. Overall, a multi-modal deepfake dataset is essential in addressing the growing concerns of deepfake misuse and developing effective detection methods that can detect deepfakes accurately, regardless of the medium. © 2023 ACM.

关键词： Deep neural networks

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