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Pipeline Leak Detection System Using Machine Learning

Pipeline Leak Detection System Using Machine Learning

2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Jaswanth, Chitrada Shiva, Golla Pavan Raj, Grandhi Naga Sai Rishi Thirunavukkarasan, M. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350382693

Leak detection is a critical concern across various sectors due to its potential environmental, economic, and safety implications. This paper presents a machine learning-based approach aimed at enhancing leak detection strategies, focusing on predicting the distance and diameter of leaks in pipelines. Leveraging a comprehensive dataset sourced from experimental setups featuring deliberate leaks, our model utilizes pressure and flow measurements to accurately identify and localize leaks. Through rigorous evaluation of multiple machine learning algorithms, including K-Nearest Neighbors, XGBoost, Decision Tree, Random Forest, and Support Vector Machine, we identify XGBoost for predicting leak distances and Random Forest for estimating leak diameters as optimal choices. The proposed approach demonstrates promising results in accurately detecting and quantifying pipeline leaks, offering valuable insights for ensuring pipeline integrity and safety. © 2024 IEEE.

关键词： Information leakage

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Hybrid Water Quality Prediction With Multimodal Low-Rank Fusion and Localized Attention

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IEEE Internet of Things Journal 2025年第12期12卷 21158-21169页

作者： Bi, Jing Li, Yibo Yuan, Haitao Wang, Mengyuan Wang, Ziqi Zhang, Jia Zhou, Mengchu Beijing University of Technology College of Computer Science Beijing Laboratory of Smart Environmental Protection Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University School of Energy and Power Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

Water quality prediction methods forecast the short- or long-term trends of its changes, providing proactive advice for preventing and controlling water pollution. Existing water quality prediction methods typically fail to capture water quality’s nonlinear characteristics accurately and only consider historical time series data. However, meteorology and other factors also significantly impact water quality indicators. Therefore, considering only historical data of water quality time series is not feasible. To solve this problem, this work proposes a hybrid water quality prediction model called CMLIP, which integrates convNeXt V2, multimodal bottleneck transformer, low-rank multimodal fusion, iTransformer, and PatchTST. CMLIP inputs water quality time series and meteorological remotely sensed rainfall images into a multimodal fusion module before prediction. Specifically, CMLIP integrates the model of ConvNeXt V2 to extract image features. Its multimodal fusion module combines a multimodal bottleneck transformer and the low-rank multimodal fusion to fuse the time series and images. Furthermore, CMLIP combines iTransformer and PatchTST to form an improved prediction module that realizes the prediction of fused features. Experimental results with real-life water quality time series and remotely sensed rainfall images demonstrate that CMLIP when fusing meteorological data, achieves an average improvement of 17% in water quality forecasting accuracy compared to forecasts using only water quality time series. Moreover, CMLIP outperforms other state-of-the-art algorithms in both data fusion and prediction, with an average enhancement of 6% in fusion effectiveness and an average improvement of 22% in prediction accuracy. © 2014 IEEE.

关键词： Rain

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

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Intelligent Automation & Soft Computing 2023年第1期35卷 1151-1163页

作者： M.Mohan P.C.Kishore Raja P.Velmurugan A.Kulothungan Department of Computer Science&Engineering SRM UniversitySonepatHaryanaIndia Department of Electronics&Communication Engineering SRM UniversitySonepatHaryanaIndia Department of Computing Technologies SRM Institute of Science and TechnologyChennaiIndia Department of Computer Science&Engineering SRM Institute of Science and TechnologyModinagarGhaziabadIndia

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of *** proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different *** dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next *** model uses 3 main concepts for forecasting *** one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning *** value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters *** second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback *** third concept is Recommendation System whichfilters and predict the rating based on the different factors.

关键词： Stock market stock market prediction time series forecasting efficient market hypothesis National stock exchange India smoothing observation trend level seasonal factor

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Advancing Emotion Recognition in Visual Data with Artificial Intelligence

Advancing Emotion Recognition in Visual Data with Artificial...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Anusree, K. Bhuvaneshvari, B. Balakrishnan, D. Nandhagopal, S. Chellakannu, Aarthy Logeshwari, S. K S R Institute for Engineering and Technology Department of Information Technology Namakkal India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India K P R Institute of Engineering and Technology Department of Computer Science Engineering Coimbatore India Dhanalakshmi Srinivasan University Department of Computer Science & Engineering Tiruchirappalli India

ISBN: (纸本)9798350359688

The burgeoning discipline of affective computing, which sits at the nexus of AI and psychology, aims to improve our capacity to comprehend and analyze human emotions as they manifest themselves in visual data. This abstract provides a succinct overview of the problem, highlighting the key elements that characterize the discipline's significance and ramifications. Emotion recognition in visual data requires sophisticated artificial intelligence techniques, including computer vision algorithms and deep learning models. These technologies have made it feasible to accurately deduce emotional states from facial expressions, body language, and even speech sentiment. Facial expression analysis has gained popularity in particular because it may be used to recognize a variety of emotions © 2024 IEEE.

关键词： Convolutional neural networks

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Consensus⁃Based Cryptographic Framework for Side⁃Channel Attack Resilience in Cloud Environments

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Journal of Harbin Institute of technology(New Series) 2025年第2期32卷 90-104页

作者： I.Nasurulla K.Hemalatha P.Ramachandran S.Parvathi Department of MCA VEMU Institute of TechnologyChittoor District 517112Andhra PradeshIndia Department of Computer Science and Engineering(AI) Madanapalle Institute of Technology&ScienceMadanapalle 517325India Department of MCA Parul institute of Engineering and TechnologyVadodara 391760India Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerundurai 638057India

Cloud environments are essential for modern computing,but are increasingly vulnerable to Side-Channel Attacks(SCAs),which exploit indirect information to compromise sensitive *** address this critical challenge,we propose SecureCons Framework(SCF),a novel consensus-based cryptographic framework designed to enhance resilience against SCAs in cloud *** integrates a dual-layer approach combining lightweight cryptographic algorithms with a blockchain-inspired consensus mechanism to secure data exchanges and thwart potential side-channel *** framework includes adaptive anomaly detection models,cryptographic obfuscation techniques,and real-time monitoring to identify and mitigate vulnerabilities *** evaluations demonstrate the framework's robustness,achieving over 95%resilience against advanced SCAs with minimal computational *** provides a scalable,secure,and efficient solution,setting a new benchmark for side-channel attack mitigation in cloud ecosystems.

关键词： Cloud computing side channel attacks HAVAL cryptographic hash Wilcoxon signed⁃rank test consensus mechanism improved schmidt⁃samoa cryptography

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IoT-Based Classification of COVID-19 Cases with Cardiovascular Disease Using Deep Convolutional Decision Trees 14th

IoT-Based Classification of COVID-19 Cases with Cardiovascul...

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14th EAI International Conference on Broadband Communications, Networks, and Systems, BROADNETS 2024

作者： Amudha, R. Kavitha, M.S. Karthik, S. Biju, Balakrishnan Department of Information Technology Hindusthan College of Engineering and Technology TamilNadu Coimbatore641032 India Department of Computer Science and Engineering SNS College of Technology TamilNadu Coimbatore641035 India Department of Computer Science and Engineering Chennai Institute of Technology Kundrathur Chennai600069 India

ISBN: (纸本)9783031811678

The COVID-19 has underscored the need for advanced healthcare solutions. This research addresses the intersection of COVID-19 and cardiovascular disease (CVD) through the lens of Internet of Things (IoT) and deep learning. Patients with pre- existing cardiovascular conditions face elevated risks when infected with COVID-19. Current diagnostic methods often lack the precision required to identify specific health risks in this vulnerable population. This research aims to bridge this gap by developing a sophisticated model that combines IoT data and deep learning techniques for robust classification of COVID-19 cases with concurrent cardiovascular disease. While existing studies explore either COVID-19 classification or the relationship with cardiovascular conditions separately, there is a noticeable research gap in the integration of IoT and deep convolutional decision trees for a comprehensive analysis. This study fills this void by proposing a novel approach that harnesses the potential of both technologies to improve diagnostic accuracy. With the aim of enhancing classification accuracy, we propose an IoT-based framework that leverages deep convolutional decision trees. Our methodology involves the collection of diverse IoT data streams, including vital signs and patient activity, to create a comprehensive dataset. Deep convolutional decision trees are then employed to extract intricate patterns and relationships from the data. The model is trained on a well-curated dataset, optimizing its ability to accurately classify COVID-19 cases in individuals with pre-existing cardiovascular conditions. The results demonstrate a significant improvement in classification accuracy compared to traditional methods. The model exhibits enhanced sensitivity and specificity, showcasing its potential for early and precise identification of COVID-19 cases in individuals with cardiovascular disease. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engin

关键词： COVID-19

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Data-Driven Insights: Leveraging Analytics for Predictive Modeling in Finance 4

Data-Driven Insights: Leveraging Analytics for Predictive Mo...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Purwar, Mitali Deka, Upasana Raj, Himadri Ritu Upes Department of Computer Science & Engineering Dehradun India Noida Institute of Engineering & Technology Department of Computer Science & Engineering Greater Noida India Chandigarh College of Engineering Chandigarh Group of Colleges Department of Computer Science & Engineering Jhanjeri140307 India

ISBN: (纸本)9798350387490

A distinct gap in the most recent business intelligence literature is addressed by this research, which discusses the possibility of radical changes in the market based on the application of predictive analytics. The key goal is to evaluate the impact of the prediction models on ethics, expansiveness, precision and reliability. The evaluation is of a qualitative nature, and topic analysis along with case histories is used to acquire specific, contextualised data. Basic findings indicate that employment of market forecasting skills is enhanced through the use of predictive models since decisions are made much more deliberately. The analysis reveals that these models remain prone to change in a variety of contexts and demonstrates their capacity to deal with large data efficiently. Ethical issues is stressed out, which leads to the conclusion that there should be stricter rules for personal data protection and more openness for procedures to gain the compliance and trust. The conclusions underline the major competitive advantages that, if applied line by line, predictive analytics can offer, calling for more adaptability and sensitivity to the market trends. This report which proposes a 50-50 mix of value creation and integrity does, nonetheless;provide notice of potential ethical issues and data privacy. The guidelines for enhancing the accuracy of the prediction, which complies with the ethical standard are strengthening the information management system and developing novel predictive model technologies. Future research must continue to analyze shifts in predictive analytics understanding new technologies and possible changes in the market. © 2024 IEEE.

关键词： Prediction models

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A Deep Learning Based Approach for Sugarcane Disease Detection 3

A Deep Learning Based Approach for Sugarcane Disease Detecti...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Gupta, Vishan Kumar Sharma, Garima Vidisha Singh, Mukesh Kumar Amity School of Engineering & Technology Amity University Punjab Mohali India Department of Computer Science & Engineering Uttarakhand Dehradun India Galgotias College of Engineering and Technology Computer Science & Engineering Department Greater Noida India

ISBN: (纸本)9798331518592

The world's largest producers of sugarcane, which is used to make both sugar and bioethanol, are Brazil and India. The crop is primarily grown in tropical and subtropical regions. These nations produce 40% of the world's bioethanol and 80% of its sugar. Sugarcane is susceptible to a wide range of diseases, including bacterial blight, mosaic, red rot, rust, and yellow, due to a multitude of environmental factors. An accurate and dependable automated disease detection system is needed for sugarcane disease diagnosis. In this work, we created the convolution neural network model EfficientNetB0 utilizing data augmentation, evaluated its effectiveness in identifying diseases affecting sugarcane plants using leaf pictures, and compared its performance. The precision, recall, and F1 score values of the EfficientNetB0 model are 0.9812, 0.9821, and 0.9816. This model is a helpful tool that growers may use to prevent sugarcane infections from interfering with the harvesting process, as it has an accuracy rate of 98.12%. © 2024 IEEE.

关键词： Plant diseases

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Enhancing MRI-Based Brain Tumor Classification Accuracy through EfficientNetB3 and Advanced Image Data Augmentation Techniques 2

Enhancing MRI-Based Brain Tumor Classification Accuracy thro...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Vanitha, K. Mahesh, R.T. Aafiya Thahaseen, A. Manivannan, B. Anitha, K. Thakur, Arastu Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Department of Computer Science and Engineering Karnataka Bangalore India Al-Ameen Engineering College Department of Computer Science and Engineering Erode India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering India CSI College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798331534950

This work analyzes the possibilities of the EfficientNetB3 architecture, reinforced by modern image data augmentation methods, in the classification of brain cancers from MRI scans. Our key objective was to greatly boost the accuracy and reliability of brain tumor diagnosis by combining the advantages of a pre-trained EfficientNetB3 model, along with a custom-designed data augmentation strategy geared at effectively resolving the inherent variabilities in MRI images. To achieve this, comprehensive preprocessing protocols were applied to the MRI datasets designated for training, validation, and testing. These protocols included the resizing and normalization of images to ensure uniformity and compatibility with the neural network. Further, our approach utilized real-time image augmentation techniques such as rotations, shifts, and flips during the training process, thereby enhancing the model's exposure to a wide range of imaging scenarios and increasing its robustness against variations not present in the training set. The outcome of this research was a notable enhancement in the model's classification accuracy, achieving up to 99% accuracy in identifying various types of brain tumors. These results not only demonstrate the effectiveness of integrating sophisticated convolutional network architectures with extensive image augmentation in medical diagnostics but also suggest potential pathways for setting new standards in the accuracy and reliability of medical image analysis, particularly in the critical domain of brain tumor diagnosis. © 2024 IEEE.

关键词： Deep learning

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