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Data security and privacy computing in artificial intelligence

Journal of Information and Intelligence 2024年第2期2卷 99-101页

作者： Dengguo Feng Hui Li Rongxing Lu Zheli Liu Jianbing Ni Hui Zhu Institute of Software Academy of SciencesBeijing 100190China State Key Laboratory of Integrated Services Networks School of Cyber EngineeringXidian UniversityXi’an 710071China Faculty of Computer Science University of New BrunswickFredericton E3B 5A3Canada College of Computer Science Nankai UniversityTianjin 300071China Department of Electrical and Computer Engineering Queen’s UniversityKingston K7L 3N6Canada

Artificial intelligence(AI)is a field of computer science dedicated to creating systems and algorithms that can perform tasks typically requiring human intelligence,such as learning,prob.em-solving,language understanding,and decision-making,contributing to a wide array of applications across diverse *** development of AI,such as machine learning and deep learning,has revolutionized data processing and *** transformation is rapidly changing human life and has allowed for many practical AI based applications,including biometric recognition,text/sentimental analysis,and attack detection in the fields of health care,finance,autonomous vehicles,personalized ***,the potential benefits of AI are hindered by issues,such as insecurity and privacy violations in data processing and communication.

关键词： such has finance

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Deep Learning-based Feature Recognition for enhancing Mobile Robot Vision

SSRN

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SSRN 2023年

作者： Aoun, Muhammad Nadeem, Muhammad Asgher Mazhar, Tehseen Shahzad, Tariq Ahmad, Wasim Ali, Syeda Rija Hamam, Habib Department of Computer Science Ghazi University Dera Ghazi Khan32100 Pakistan Department of Computer Science University of Sargodha Sargodha40100 Pakistan Department of Computer Science Virtual University of Pakistan Lahore55150 Pakistan Department of Computer Sciences COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Information Technology University of Malakand Pakistan Department of Computer Science NCAB&E Multan Pakistan College of Computer Science and Engineering University of Ha’il Ha’il55476 Saudi Arabia Faculty of Engineering University of de Moncton MonctonNBE1A3E9 Canada Commune d'Akanda B.P. Libreville1989 Gabon

To improve the efficiency and accuracy of mobile robot visual image feature recognition, a mobile robot visual image feature recognition method based on machine vision is proposed in this paper. Firstly, the development of mobile robot vision is analyzed, and the specific functions of the robot visual feature recognition method are designed;Then, the Fourier series method is used to collect the mobile robot visual image, and the matrix associated with the autocorrelation function is calculated according to the Harris algorithm to complete the edge feature extraction of the mobile robot visual image;SIFT feature points of mobile robot visual image are classified, and mobile robot visual image feature recognition is realized through machine vision. The experimental results show that when the number of images is 600, the accuracy of image feature recognition and the loss value of image edge feature extraction of this method are 96.98% and 6.38%, respectively, and the number of iterations is 500. The time of visual image feature recognition of this method is only 3 minutes;The technique has the lowest error mean and error variance under different noise conditions. This method can effectively improve the efficiency and accuracy of image feature recognition. © 2023, The Authors. All rights reserved.

关键词： Feature extraction

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Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids

arXiv

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arXiv 2023年

作者： Hassani, Hossein Hallaji, ehsan Razavi-Far, Roozbeh Saif, Mehrdad The Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada The Faculty of Computer Science Canadian Institute for Cybersecurity University of New Brunswick FrederictonNBE3B 5A3 Canada

The performance of fault diagnosis systems are highly affected by data quality in cyber-physical power systems. These systems generate massive amounts of data that overburden the system with excessive computational costs. Another issue is the presence of noise in recorded measurements, which prevents building a precise decision model. Furthermore, the diagnostic model is often provided with a mixture of redundant measurements that may deviate it from learning normal and fault distributions. This paper presents the effect of feature engineering on mitigating the aforementioned challenges in cyber-physical systems. Feature selection and dimensionality reduction methods are combined with decision models to simulate data-driven fault diagnosis in a 118-bus power system. A comparative study is enab.ed accordingly to compare several advanced techniques in both domains. Dimensionality reduction and feature selection methods are compared both jointly and separately. Finally, experiments are concluded, and a setting is suggested that enhances data quality for fault diagnosis. © 2023, CC bY.

关键词： Failure analysis

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Deep Learning for Precision Grade-Quality Classification of Pomegranates

Deep Learning for Precision Grade-Quality Classification of ...

引用

Innovation and Novelty in engineering and Technology (INNOVA), International Conference on

作者： RajinderKumar M. Math Nagaraj V. Dharwadkar Vijaykumar Sajjanar M. N. Deshmukh Department of Electronics and Communication Engineering B.L.D.E.A's V.P Dr. P.G Halakatti College of Engineering and Technology Vijayapur Karnataka India Department of Computer Science Central University of Karnataka Kadaganchi Kalaburgi India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

The conventional methods of manual grading of fruits after agricultural harvest have always b.en laborious, time-consuming, and prone to human errors. In order to speed up the grading process without human intervention, an automatic system is the need of the hour. This research has led to the development a Convolutional Neural Network (CNN) based deep learning technique to provide real-time grading of pomegranate based on its visual features. This automatic grading technique would help farmers in minimizing their efforts and time spent on manual sorting, at the same time ensuring error-free and uniform sorting and packaging of quality fruits that would, in turn, enhance the storage time post packaging. The methodology uses nine pre-trained models to train on a custom pomegranate dataset with twelve grade-quality classes of pomegranates. The grade-quality grading is obtained using the fine-tuning approach in transfer learning, reducing the computational resource requirements and drastically improving inference speed, which is necessary for real-time model deployment scenarios. The results reveal that the fine-tuning of DenseNet-169 model outperformed the other models by achieving highest values of 99.20% and 1 for (Accuracy, Precision, Recall, and Fl-score) and AUC, respectively.

关键词： Deep learning Visualization Technological innovation Computational modeling Transfer learning Manuals Packaging Real-time systems Convolutional neural networks Sorting

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Vision based umpire detection method for event extraction in cricket video based on HOG and color image segmentation 1

Vision based umpire detection method for event extraction in...

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1st Ieee International Conference on Advances in electrical, Computing, Communications and Sustainab.e Technologies, ICAeCT 2021

作者： Nandyal, Suvarna Kattimani, Suvarna L. Department of Computer Science and Engineering P. D. A. College of Engineering Karnataka Kalaburgi585102 India Department of Computer Science and Engineering B.L.D.E.A's V.P. Dr.P.G.Halakatti College of Engg. and Tech. Karnataka Vijayapur586103 India

ISBN: (纸本)9781728157900

As of late, there has b.en expanded interest in Video Summarization and Automatic Highlights age. One of the most watched and a played sport is cricket, especially in South Asian Countries. In cricket sport video, Umpire has the force for settling on huge choices about functions in the field. In this work, the manually chosen Recorded Video of Some International Twenty-Twenty cricket match video (T20*20) is used as dataset for Umpire identification inside the sport of cricket. The proposed dataset is assessed as a starter help for creating frameworks to consequently produce cricket features. With the growing increase of the utilization of technology in sports, this paper points basically at the Umpire Identification utilizing Area-Of-interest based Histogram of Oriented Gradients (AOI-HOG) Feature extraction strategy for Human Identification. It expects to give Color Image Segmentation technique utilizing pixel wise color information to support vector machine (SVM) classification for Umpire Identification while protecting the arrangement of highlights. Test proof shows that the proposed technique accomplishes the Accuracy of 94.57%. Proposed one efforts towards successful segmentation results and computational conduct, and lessens the time and expands the norm of color image segmentation. © 2021 Ieee

关键词： Support vector machines

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evaluating Feature Selection Methods to enhance Diabetes Prediction with Random Forest 23

Evaluating Feature Selection Methods to Enhance Diabetes Pre...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Pushpa Negi AnishKumar Dhablia Hrishikesh bhanudas Vanjari Jayashree Tamkhade Sharayu Ikhar Shrinivas T. Shirkande Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) India Engineering Manager Altimetrik India Pvt Ltd India E&TC Engineering Dept BVCOE Lavale SPPU Pune India E&TC VIIT India Department of Information technology Vishwakarma Institute of Information technology India Computer Science & Engineering S.B.Patil College of Engineering Indapur India

ISBN: (纸本)9798400709418

Abstract - The issue of diabetes is a significant and urgent matter in global health, necessitating the development of precise predictive models to facilitate early intervention and effective management. The ob.ective of this study is to assess the effectiveness of different feature selection techniques in improving the accuracy of diabetes prediction when employing the Random Forest algorithm. The present study evaluates the impact of three feature selection methods, namely Recursive Feature elimination (RFe), Mutual Information, and L1 Regularization (Lasso), on the performance of the model. This study employs a comprehensive dataset obtained from Kaggle, which includes various attributes such as age, blood pressure, and laboratory measurements. The primary ob.ective of this research is to systematically evaluate and compare the predictive performance of Random Forest models when integrated with different feature selection techniques. The evaluated metrics encompass accuracy, precision, recall, and F1-Score. The findings demonstrate significant enhancements in the accuracy of predictions when employing feature selection techniques. Specifically, the utilization of L1 Regularization (Lasso) resulted in the highest level of accuracy, reaching 98.45%. Furthermore, the features chosen by each method provide insight into the variab.es that have the greatest impact on predicting diabetes. This study offers significant findings regarding the significance of feature selection in healthcare applications, thereby enabling more accurate and effective diagnosis and treatment of diabetes. The results of this study not only make a valuab.e contribution to improving the accuracy of diabetes prediction, but also highlight the importance of carefully selecting relevant features when developing predictive models for healthcare datasets. This study promotes the need for additional investigation into feature selection methodologies and their implementation in the field of healthcare

关键词： Diabetes Prediction

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Medical Image Denoising and brain Tumor Detection Using CNN and U-Net

Medical Image Denoising and Brain Tumor Detection Using CNN ...

引用

International Conference on Innovative Sustainab.e Computational Technologies (CISCT)

作者： basu Dev Shivahare S. K. Gupta Alok Nath Katiyar Punitha S Prabhishek Singh Manoj Diwakar Dr. APJ Abdul Kalam Technical University Lucknow India Deptt. of CSE B.I.E.T Jhansi India Amazon web services India Department of Computer Science Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

The process of medical imaging, which incorporates images produced by X-rays, ultrasound imaging, angiography, etc., is intricate. During the imaging process, image noise is also captured, some of which are very corrosive, causing a disturbance that impairs the quality of the image. The challenge of removing destructive Gaussian additive white noise from medical images while maintaining the fine features is addressed by the suggested approach. The concept of method noise is combined with a deep learning-based framework for a convolutional neural network (CNN), using method noise as a post-processing operation, to create the proposed technique. brain tumor detection over noisy images is much difficult with better accuracy. Hence in this paper we proposed a method for denoising as well as for brain tumor detection using CNN and U-Net. by explicitly adding Gaussian additive white noise at various noise variance values (= 10, 15, 20, 25, 30), distorted images are produced. The generated denoised images' aesthetic appeal and quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), are next assessed. The results are also contrasted with those of other non-traditional methods to evaluate the effectiveness of the suggested methodology further. The findings of the critical examination demonstrate the proposed methodology's admirab.e effectiveness in degaussing the medical images that had b.en tainted by Gaussian noise. This method can be applied in a variety of real-world contexts in the realm of medical image processing.

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Hybrid Feedback Control for Global and Optimal Safe Navigation

arXiv

引用

arXiv 2024年

作者： Cheniouni, Ishak berkane, Soulaimane Tayebi, Ab.elhamid The Department of Electrical Engineering Lake-head University Thunder BayONP7B 5E1 Canada The Department of Electrical and Computer Engineering Western University LondonONN6A 3K7 Canada The Department Computer Science and Engineering University of Quebec OutaouaisQCJ8X 3X7 Canada

We propose a hybrid feedback control strategy that safely steers a point-mass robot to a target location optimally from all initial conditions in the n-dimensional euclidean space with a single spherical obstacle. The robot moves straight to the target when it has a clear line-of-sight to the target location. Otherwise, it engages in an optimal obstacle avoidance maneuver via the shortest path inside the cone enclosing the obstacle and having the robot’s position as a vertex. The switching strategy that avoids the undesired equilibria, leading to global asymptotic stability (GAS) of the target location, relies on using two appropriately designed virtual destinations, ensuring control continuity and shortest path generation. Simulation results illustrating the effectiveness of the proposed approach are presented. Copyright © 2024, The Authors. All rights reserved.

关键词： Location

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experimental Assessment of Grounding System Impacts on Ground Currents and Transient Overvoltage

Experimental Assessment of Grounding System Impacts on Groun...

引用

作者： Saleh, Saleh A. Jewett, Danielle Al-Durra, Ahmed Kanukollu, Saikrishna Cardenas-barrera, Julian Valdes, Marcelo e. Meng, Julian Panetta, Sergio A. R. University of New Brunswick Department of Electrical and Computer Engineering FrederictonNBE3B 5A3 Canada Khalifa University of Science Technology Electrical and Computer Engineering Abu Dhabi127788 United Arab Emirates Abb Electrical Solutions Chapel HillNC27511 United States I-Gard Corporation MississaugaONL4T 1L2 Canada

In this article, experimental performance assessment and comparison are presented for solid, low impedance, high impedance, frequency-selective, and isolated grounding (IG) systems. The typical design of a grounding system (for low and medium voltage generation, transmission, and distribution systems) is based on selecting an impedance (ZG), which is used to connect the neutral point and the ground. The impedance ZG is typically composed from a combination of R, L, and C elements. The combination type (series or parallel), along with the values of R, L, C elements, allow estimating the possib.e effects of ZG on ground currents and potentials during ground faults. In this article, solid, low impedance, high impedance, frequency-selective, and IG systems are designed for a 35-kVA 3φ transformer and a 5-kVA 3φ synchronous generator, for purposes of assessing and comparing their effects on ground currents and potentials during ground faults. The laboratory transformer and generator are tested for line-to-ground and doub.e line-to-ground faults with all designed grounding systems under different loading levels. experimental results show that some grounding systems can effectively reduce ground currents, and other grounding systems can effectively reduce ground potentials. These capabilities and features can be used to achieve certain system and operation mandates, including ground capacity, maximum allowed overvoltage, and service continuity. © 1972-2012 Ieee.

关键词： electric grounding

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AlexNet Model for Sign Language Recognition 10th

AlexNet Model for Sign Language Recognition

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10th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2022

作者： Singh, Shreya bhateja, Vikrant Srivastav, Shivangi Pratiksha Lin, Jerry Chun-Wei Travieso-Gonzalez, Carlos M. Department of Electronics and Communication Engineering Shri Ramswaroop Memorial College of Engineering and Management Faizabad Road Uttar Pradesh Lucknow226028 India Dr. A. P. J. Abdul Kalam Technical University Uttar Pradesh Lucknow India Department of Electronics Engineering Veer Bahadur Singh Purvanchal University Shahganj Road Uttar Pradesh Jaunpur222003 India Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Science Bergen Norway Campus Universitario de Tafira sn. Telecommunication. Pabellon B. D-111 Las Palmas de Gran CanariaE-35017 Spain

ISBN: (纸本)9789811975233

In recent years, for recognizing sign language, several hardware approaches have b.en developed using the leap motion controller and Kinect sensors. The sensor-based approaches were costly, and the complexity of designing was high. In machine learning approaches, it was found that less accuracy of prediction occurs as compared to other approaches. As a solution, machine learning-based approaches using image processing evolved as an approach with better prediction and accuracy. In this paper, deep learning approach for sign language recognition (SLR) system has b.en proposed using AlexNet model. AlexNet is pretrained model, and it is being tested on Indian Sign Language (ISL) Dataset pf Stanford University comprising of alphabets (A–Z) and numerals (0–9). The proposed model has yielded an accuracy of 99.6%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image processing

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