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2023 SPE Nigeria Annual International Conference and Exhibition, NAIC 2023

作者： Anumah, Prosper Azadbakht, Saman Department of Energy Systems Engineering Faculty of Engineering andApplied Science University of Regina ReginaSK Canada

ISBN: (纸本)9781959025177

Optimizing well spacing is a critical step in the development of an oilfield. Several approaches havebeen employed over the past decades in an attempt to obtain an efficient and cost-effective well spacingmethodology. The desire to achieve a fast-paced well spacing optimization outcome is not yet realized. This study explores the use of a data-driven surrogate model and metaheuristic optimization algorithms inoptimizing the well spacing in a fractured tight reservoir in Saskatchewan, Canada. The wells considereddrain oil from the Viking Formation located in the Avon Hill field. Multilayer perceptron (MLP) neural network is trained to predict time-series cumulative oil and gasproduction. The training and testing data were obtained from the history matched numerical reservoir modelof the Avon Hill field. The artificial neural network (ANN) surrogate model is then coupled with ParticleSwarm Optimization (PSO) algorithm, and Genetic Algorithm (GA). Maximization of the net present value(NPV) is set as the objective function with well spacing, fracture conductivity, fracture half-length as thecontrol variables. 3 well spacing optimization workflows are then developed comprising of ANN basedproxy only, ANN-PSO and ANN-GA. It is found that the use of the ANN-based proxy model enables various combinations of well spacing andfracture designs to be tested at higher computational speed compared to numerical simulation. The resultsalso show that the PSO outperformed GA in terms of converging at a higher objective function value andat a higher convergence speed. Applying the proposed workflows to the Avon Hill field, it is found that theoptimal well spacing ranges between 124 m to 132 m. This work reveals that variations in fracture geometrydesign significantly affects hydrocarbon recovery and economic performance of a fractured tight reservoirand these variations can be efficiently explored by the use of machine learning techniques. Copyright © 2023 Society of Petroleum Engineer

关键词： Cost effectiveness

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Synchronous Control of H-type Linear Motor System via Super-Twisting Sliding Mode

Synchronous Control of H-type Linear Motor System via Super-...

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2024 China Automation Congress, CAC 2024

作者： Liu, Jiawei Li, Xiaolei Tang, Feilong Kui, Qixin Spasic, Miodrag D. Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Control Systems Faculty of Electronic Engineering University of Nis Nis Serbia

ISBN: (纸本)9798350368604

In this paper, a synchronous control strategy based on super-twisting sliding mode algorithm is proposed to enhance the tracking accuracy and robustness of H-type linear motor systems. Such systems are widely utilized in precision manufacturing owing to their high precision and fast response. Firstly, the dynamic model is established based on the coupling dynamics analysis of the H-type system. Secondly, a super-twisting sliding mode controller (STSMC) with linear terms is designed, which effectively reduces the chattering associated with conventional sliding mode control, while maintaining good robustness. Thirdly, a super-twisting extended state observer (STESO) is introduced to handle large fluctuations in the early estimation process of linear extended state observer (LESO), so as to effectively observe and compensate the uncertainties and external disturbances. Finally, experimental results demonstrate that the STESO-based STSMC greatly improves the anti-disturbance ability and tracking accuracy of the H-type system. © 2024 IEEE.

关键词： Sliding mode control

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The Impact of the Digital Transformation of Education on Literacy 7

The Impact of the Digital Transformation of Education on Lit...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Farkas, Imre Kovari, Attila Rajcsanyi-Molnar, Monika Institute of Computer Engineering University of Dunaujvaros Department of Computer Systems and Control Technology Dunaujvaros Hungary Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Social Sciences University of Dunaujvaros Department of Economics and Management Dunaujvaros Hungary

ISBN: (纸本)9798331531119

During the COVID19 epidemic, people of all ages from all walks of life around the world have become inevitably familiar with and almost dependent on the digital tools of the age and the opportunities they offer. A change had to take place very quickly that was unprecedented. This process has also affected education. During the time of the reading lock-in, there was a proliferation of digital curricular content. The second time around, online education came to the fore, thanks to less or well-functioning online platforms, and has since become part of everyday teaching. What is the effectiveness and usefulness of sharing knowledge in this way? Is literacy still needed at all? Can online and hybrid (a controlled mix of face-to-face and online) education replace the well-established contact and face-to-face education? The study show that the respondents believe that online education has had an impact on literacy and that it is perceived to have a positive impact. The results indicate that online education has a positive impact on the literacy levels of students, as it facilitates knowledge acquisition and enhances digital literacy skills. © 2024 IEEE.

关键词： digital education digital literacy digital-informatics literacy

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Deep Fake Detection Using computer Vision-Based Deep Neural Network with Pairwise Learning

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Intelligent Automation & Soft Computing 2023年第2期35卷 2449-2462页

作者： R.Saravana Ram M.Vinoth Kumar Tareq M.Al-shami Mehedi Masud Hanan Aljuaid Mohamed Abouhawwash Department of Electronics and Communication Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Computer Science and Engineering Anna UniversityUniversity College of EngineeringDindigul624622India Department of Mathematics Faculty of ScienceSana’a UniversitySana’a 13509Yemen Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Deep learning-based approaches are applied successfully in manyﬁelds such as deepFake identiﬁcation,big data analysis,voice recognition,and image *** is the combination of deep learning in fake creation,which states creating a fake image or video with the help of artiﬁcial intelligence for political abuse,spreading false information,and *** artiﬁcial intel-ligence technique has a wide demand,increasing the problems related to privacy,security,and *** paper has analyzed the features related to the computer vision of digital content to determine its *** method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction *** the proposed deep belief network with loss handling,the manipulation of video/image is found by means of a pairwise learning *** proposed approach has improved the accuracy of the detection rate by 98%on various datasets.

关键词： Deep fake deep belief network fuzzy clustering feature extraction pairwise learning

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Toward quantum unmanned systems

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science China(Technological sciences) 2024年第7期67卷 2277-2280页

作者： CHENG ShuMing QI HongSheng SUN Yue LIU LiJun XIE ShaoRong REN XiaoQiang Department of Control Science and Engineering Tongji UniversityShanghai 200092China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China Cipher Cloud Core Ltd. Beijing 100085China College of Mathematics and Computer Science Shanxi Normal UniversityLinfen 041081China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444China

1 Quantum information technology Quantum information technology utilizes physical systems at the microscopic level, such as photon, atom, ion, and superconducting, to accomplish information-processing tasks that are impossible for the classical macroscopic world. During the past decade, significant process has been achieved in the pursuit of quantum technology into practical applications,generating great research interest from various domains, with the potential to radically change our information infrastructure [1–3].

关键词： quantum utilize generating

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Multithreaded Haplotype Block Partitioning of Rheumatoid Arthritis Genomic Data

Multithreaded Haplotype Block Partitioning of Rheumatoid Art...

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2023 IEEE EMBS Special Topic Conference on Data science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Al-Khalifa, Azza M. Saad, Mohamed N. Rushdi, Muhammad A. Eldeib, Ayman M. Minia University Faculty of Engineering Biomedical Engineering Department Egypt Cairo University Faculty of Engineering Department of Biomedical Engineering and Systems Giza12613 Egypt Southern New Hampshire University School of Engineering Technology and Aeronautics Computer Science Department ManchesterNH United States

ISBN: (纸本)9798350383386

Haplotype block partitioning methods have been widely employed in the analysis of genetic patterns of common and complex diseases. The existing methods suffer from high time and memory complexities due to the key bottleneck of computing the linkage disequilibrium (LD) for all or most pairs of single-nucleotide polymorphisms (SNPs). In this work, we propose a multithreaded variant of the confidence internal test (CIT) method for haplotype block partitioning implementations. The usefulness of the proposed method in reducing the time and memory costs is demonstrated through experiments on the North American Rheumatoid Arthritis Consortium (NARAC) dataset. Our simulation results show 11-35% savings in time and 8-36% savings in memory requirements with only 3-6% increase in CPU *** Relevance: This work seeks time and memory savings in genetic analysis for the prediction of diseases with complex factors (e.g., rheumatoid arthritis in our case). These savings can streamline and accelerate relevant clinical workflows and reduce demand for computational resources. © 2023 IEEE.

关键词： Diseases

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Enhanced Physical Layer Security for Full-Duplex Symbiotic Radio with AN Generation and Forward Noise Suppression

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IEEE Transactions on Communications 2024年第7期72卷 3905-3918页

作者： Jin, Chi Chang, Zheng Hu, Fengye Chen, Hsiao-Hwa Hamalainen, Timo University of Jyväskylä Faculty of Information Technology Jyväskylä40014 Finland University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Jilin University College of Communication Engineering Changchun130012 China National Cheng Kung University Department of Engineering Science Tainan70101 Taiwan

Due to the constraints on power supply and limited encryption capability, data security based on physical layer security (PLS) techniques in backscatter communications has attracted a lot of attention. In this work, we propose to enhance PLS in a full-duplex symbiotic radio (FDSR) system with a proactive eavesdropper, which may overhear the information and interfere legitimate communications simultaneously by emitting attack signals. To deal with the eavesdroppers, we propose a security strategy based on pseudo-decoding and artificial noise (AN) injection to ensure the performance of legitimate communications through forward noise suppression. A novel AN signal generation scheme is proposed using a pseudo-decoding method, where AN signal is superimposed on data signal to safeguard the legitimate channel. The phase control in the forward noise suppression scheme and the power allocation between AN and data signals are optimized to maximize security throughput. The formulated problem can be solved via problem decomposition and alternate optimization algorithms. Simulation results demonstrate the superiority of the proposed scheme in terms of security throughput and attack mitigation performance. 0090-6778 © 2024 IEEE.

关键词： Backscattering

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Remote Sensing Data-Based Satellite Image Analysis in Water Quality Detection for Public Health Data Modelling

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Remote Sensing in Earth systems sciences 2024年第4期7卷 532-541页

作者： Balakrishnan, S. Preetam Raj, P Michael Somasekar, J. Kumar, Kambala Vijaya Amutha, S. Sangeetha, A. Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology (AVIT) Vinayaka Mission’s Research Foundation (Deemed to be University) Chennai India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be-University) Karnataka Bengaluru 562112 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology TamilNadu Tholurpatti India

One of the most important aspects of environmental sustainability is water quality monitoring. The ecology is impacted by poor water quality in addition to aquatic life. With the volume of data being collected from satellite systems, the issue of effectively and automatically extracting water bodies is growing more pressing. One of the most important things to do for managing and safeguarding water resources is to conduct extensive monitoring of water quality parameters, or WQPs. It is still difficult to monitor optically inactive WQPs in inland waters, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP). Water quality (WQ) classification and prediction can be greatly enhanced with the use of artificial intelligence (AI). This research proposes novel method in healthcare detection by water quality analysis based on classification utilising ML model. Input is collected as healthcare dataset based on water quality as well as processed for noise removal and normalisation. The processed data features are extracted and classified utilising reinforcement Gaussian stacked layer network with variational fuzzy Markov basis neural network. In order to suggest best course of action for the water bodies, it is imperative that any study look into the spatiotemporal changes of the dominant water quality parameters (WQPs). WQP concentrations have typically been determined by extensive fieldwork. These findings could help identify and regulate non-point source pollution, offer a large-scale spatial picture of the water quality, and pinpoint vulnerable locations and times of water pollution. Experimental analysis is carried out for various water sample dataset in terms of detection accuracy, mean precision, recall, F-1 score and AUC. The proposed technique obtained 97% of detection accuracy, 95% of recall, 91% of F-1 score, 96% of AUC, and 94% of MAP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification Fuzzy Markov basis Healthcare detection Machine learning model Water quality analysis

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Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

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computer Modeling in engineering & sciences 2024年第2期138卷 1981-2004页

作者： Shaik Mahaboob Basha Victor Hugo Cde Albuquerque Samia Allaoua Chelloug Mohamed Abd Elaziz Shaik Hashmitha Mohisin Suhail Parvaze Pathan Department of Electronics and Communication Engineering N.B.K.R.Institute of Science&TechnologyVidyanagarAndhra Pradesh524413India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Department of Avionics Indian Institute of Space Science and TechnologyThiruvananthapuramKerala695547India Department of Veterinary Clinical Sciences University of MinnesotaSt.PaulMinnesota55108USA

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 ***,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning *** study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal *** information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering *** learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding *** ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different ***,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased *** analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered ***,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

关键词： Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis

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A Convnet Model-Based Deep Learning Approach for Pneumonia Recognition from Chest X-ray Images 2

A Convnet Model-Based Deep Learning Approach for Pneumonia R...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Venkateswarulu Naik, B. Unhelkar, Bhuvan Chakrabarti, Prasun Ghantasala, G.S. Pradeep Vidyullatha, Pellakuri Pyla, Meghana Department of Information Systems and Decision Sciences Muma College of Business University of South Florida South Florida United States Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (纸本)9798350383867

Pneumonia diagnosis using chest X-ray (CXR) images shows a decisive part in early detection and handling of the disease. Within this research, proposes a convolutional neural network (ConvNet) model for automated pneumonia diagnosis based on CXR images. Our model leverages the deep learning capabilities of ConvNets to analyze features in CXR images and categorize them as pneumonia-positive or pneumonia-negative. We conducted experimentations on a comprehensive dataset of CXR images, achieving auspicious outcomes in terms of efficiency and accuracy. The proposed ConvNet model demonstrates prospective for an accurate and appropriate pneumonia diagnosis, contributing to improved healthcare outcomes and resource optimization in medical settings. ConvNet model is implemented using tensorflow keras with preprocessing using Image data generator. The models are deployed like sequential, Conv2D with tuning parameters of Relu, dropout, max pool 2d and adam optimizer. For training the images with 150, 150 pixels, train sample as are 1000 and input shape is 3, epochs are 50, batch size is 20. Applications of the categorical cross-entropy loss function for multi-class division with Adam optimizer employed for parameter optimization. The Model trained for a stable amount of epochs through early based on validation loss toward prevent overfitting and learning rate, batch size, and dropout rate optimized through hyperparameter tuning. © 2024 IEEE.

关键词： Convolutional neural networks

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