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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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Performance Creativity Enhancement Method Based on Emotional Analysis and Concentration

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第3期8卷 1019-1048页

作者： Liu, Chang Zhang, Longfei Huang, Tianyu Wu, Yufeng Zhang, Fuquan Lin, Qi Department of Computer Science and Technology Beijing Institute of Technology 100081 China Beijing Key Laboratory of Digital Performance and Simulation Technology 100081 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Informatics King’s College London LondonWC2R 2LS United Kingdom

Intelligent performance creativity is a new research direction of the intersection of technology and art. At present, the cutting-edge technologies such as computer simulation, emotional computing and machine learning have been applied to the evaluation and enhancement of intelligent performance creativity. The application of these technologies has greatly increased the diversity of performance forms and the complexity of performance content. For the traditional stage performance, due to the lack of timely feedback from the audience, it is difficult for the director to enhance the creativity effec-tively. In order to solve this problem, this paper proposes concentration level enhancement method (CLEM). The CLEM extracts and analyzes the physiological and emotional characteristics of the audience by collecting the multimodal physiological signal data of the audience. Through the analysis of the features, the concentration information of the audience is obtained and the concentration level (CL) is defined. Based on the physiological and emotional induction mechanism, a performance creativity enhancement strategy is proposed, which enhances the performance creativity in two aspects: " Visual-based" and " Content-based". Through the concentration level enhancement method, compare the audience’s performance attention changes before and after the performance creativity enhancement, select the effective strategies to enhance the performance creativity, and provide the director with quantifiable performance creativity enhancement feedback results. The experimental results show that the CLEM proposed in this paper can effectively induce significant changes in the audience’s emotion, and the selected creative enhancement strategy can significantly improve the audience’s concentration and achieve the purpose of enhancing the creativity of the performance. © 2023, Journal of Network Intelligence.

关键词： Physiology

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Reversible Data Hiding Scheme Based on Prediction Error Bit-plane Compression

Reversible Data Hiding Scheme Based on Prediction Error Bit-...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Guo Yuan Chao Heng Zhai Ping Liu Ziqi Li Haibo Li Wenpeng College of computer science and technology Heilongjiang University Harbin China College of Computer and Control Engineering Qiqihar University Qiqihar China

How to maximize embedding capacity is one of the current challenges in the field of reversible data hiding. A reversible data hiding scheme is proposed based on the rearrangement and compression of prediction error bit-planes in this paper. The image holder first predicts the pixels to obtain the error map, then decomposes them into bit-planes, rearranges and compresses them to maximize the embedding space, and embeds the auxiliary information and secret information into the freed space according to certain rules to generate a cryptographic image. After acquiring the cryptographic image, the image receiver extracts the embedded secret information and recovers the original image using the auxiliary information. In this paper, a rearrangement strategy based on full pixel correlation was built to maximize compression efficiency and further compress the auxiliary information to free up more embeddable space and improve embedding capacity. A large number of test has revealed that the embedding rate of this method is 30% higher on average than the current state-of-the-art algorithm.

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An Investigation into Improved YOLOv8-based Target Detection Algorithms for UAV Aerial Imagery

An Investigation into Improved YOLOv8-based Target Detection...

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Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Rui Ming Xinyu Liu Yuxuan Lin Qian Gong College of Computer and Control Engineering Minjiang University Fuzhou China College of Computer and Data Science/College of Software Fuzhou University Fuzhou China

In UAV imagery, the intricate backgrounds combined with the high quantity and compact distribution of minute targets have consistently made target detection a formidable challenge in the realm of computer vision. This study introduces an enhancement over the YOLOv8 algorithm, wherein a sophisticated multi-scale convolutional layer, integrating depth-separable convolution, attention mechanisms, and multi-scale processing techniques, replaces the original model's convolution. Moreover, we introduce an attention mechanism for a Bi-Level Routing within the core component of the base model, and adjustments are made to the original model's loss function. Lastly, to confirm the viability of the enhanced model proposed in this paper, we conducted a validation of the metrics using publicly accessible datasets. The findings illustrate that the improved model outlined in this research substantially enhances target recognition accuracy in UAV images. Furthermore, the model exhibits superior performance in mitigating issues of duplicate detection and target omission.

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Effective Use of Machine Learning for Analyzing Sentiments of Social Media Users

Effective Use of Machine Learning for Analyzing Sentiments o...

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MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon)

作者： P. S. Vikhe V. V. Mandhare C. B. Kadu K. S. Nagale Department of Instrumentation & Control Engineering Pravara Rural Engineering College Loni India Department of Computer Engineering Pravara Rural Engineering College Loni India Department of Information Technology Pravara Rural Engineering College Loni India

ISBN: (数字)9798350362879

ISBN: (纸本)9798350362886

The impact of stress and depression on society is significant due to their widespread recognition and handicapping nature. To enhance detection of depression and stress through social networks, automatic monitoring of health play a vital role. The identification of feelings can be achieved through sentiment analysis, which involves use of processing natural language along with content mining approaches. The creation and investigation of systems and devices that have the capability to sense, comprehend, process, and imitate human effects is what computing is all about. Using sentiment analysis and deep learning, it is possible to create powerful technique and frameworks for appraisement and observation of mental issues. This paper focuses on analyzing sentiment through deep learning approach for detecting and monitor depression and stress. A multimodal model for identifying stress and depression is included in fundamental plan, which combines investigation and processing of full- feeling has been proposed in this research work.

关键词： Deep learning Sentiment analysis Art Social networking (online) Computational modeling Natural languages Psychology

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Prospective evaluation of data-driven models to predict daily risk of Clostridioides difficile infection at 2 large academic health centers (vol 2, pg 1, 2022)

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INFECTION control & HOSPITAL EPIDEMIOLOGY 2023年第10期44卷 1705-1705页

作者： Kamineni, Meghana Otles, Erkin Oh, Jeeheh Rao, Krishna Young, Vincent B. Li, Benjamin Y. West, Lauren R. Hooper, David C. Shenoy, Erica S. Guttag, John G. Wiens, Jenna Makar, Maggie Electrical Engineering and Computer Science Department Massachusetts Institute of Technology Cambridge Massachusetts Medical Scientist Training Program University of Michigan Medical School Ann Arbor Michigan Department of Industrial and Operations Engineering University of Michigan College of Engineering Ann Arbor Michigan Division of Computer Science and Engineering University of Michigan College of Engineering Ann Arbor Michigan Department of Internal Medicine Division of Infectious Diseases University of Michigan Medical School Ann Arbor Michigan Infection Control Unit Massachusetts General Hospital Boston Massachusetts Division of Infectious Diseases Massachusetts General Hospital Boston Massachusetts Harvard Medical School Boston Massachusetts

Many data-driven patient risk stratification models have not been evaluated prospectively. We performed and compared the prospective and retrospective evaluations of 2 Clostridioides difficile infection (CDI) risk-prediction models at 2 large academic health centers, and we discuss the models’ robustness to data-set shifts.

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Vessel-Guided Joint Detection and Classification of Bifurcations and Crossings in Retinal Fundus Images

SSRN

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SSRN 2024年

作者： Long, Jun Xie, Yining Zhang, Yuhang Zhang, Kaiwen College of Mechanical and Electrical Engineering Northeast Forestry University Harbin150040 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China

Retinal fundus vessel analysis assumes a pivotal role in the diagnosis of various ocular and systemic diseases. The precise detection and classification of retinal vessel bifurcation and crossing points, as integral features of fundus vessels, are imperative for extracting pertinent biomarkers. In this paper, we propose the Vessel-Guided Joint Bifurcation and Crossing Point Detection Network (VJBC-Net) designed to concurrently detect and classify vessel bifurcation and crossing points. VJBC-Net leverages a vessel-guided module and a vessel feature perception mechanism to provide preliminary guidance for junction point detection and classification. The outcomes of junction point detection and classification are acquired through multi-instance heatmap regression. Experimental results demonstrate the superiority of our proposed method on the DRIVE and IOSTAR datasets for bifurcation and crossing point detection and classification. Specifically, the method attains an F1 score of 0.7349 and 0.6044 for bifurcation and crossing detection, respectively. Moreover, it demonstrates classification accuracies of 0.9602 and 0.9608 for bifurcation and crossing points, surpassing current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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A solar tracking system with light-dependent resistors and a stepper motor controlled by a microcontroller

A solar tracking system with light-dependent resistors and a...

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2023 International Conference on computer, Electronics and Electrical engineering and their Applications, IC2E3 2023

作者： Sayeedurrahman Sabir, Bushra Khan, Rashid Ahmed Masood, Dawood Nat, Aslam Aligarh Muslim University Z.H. College of Engineering & Technology Department of Electrical Engineering Aligarh202002 India National Taiwan University of Science and Technology Department of Electrical Engineering Taipei10607 Taiwan Sapienza University of Rome Department of Computer Control Management Engineering 00185 Italy

ISBN: (纸本)9798350338003

Solar energy is a clean and simple method of electricity generation. This electricity is utilized for various purposes, ranging from modest electrical gadgets and cars to major enterprises, satellites, and space exploration. The title of this project, "Solar tracking system with light-dependent resistors and a stepper motor controlled by a microcontroller"alludes to the project's purpose, which is to harvest as much solar energy as possible from a solar panel throughout the day using a specific Sun tracking mechanism. To do this, a mechanism was developed that tracks the sun's position and rotates the solar panel correspondingly so that it receives maximum sunlight at all times of the day. This is accomplished by mounting a stepper motor and a couple of light-dependent resistors (LDRs) on the solar panel, which adjusts its orientation based on the location of the sun. This project has three primary components: the solar panel, the stepper motor, and the LDRs. Microcontroller, sensor, and stepper motor. For this aim, an Arduino UNO microcontroller has been utilized. It accepts an output signal from a sensor as input, processes it, and controls stepper motors based on the supplied program. A stepper motor is attached to the shaft on which a panel is positioned to raise and lower the panel. Since maximal solar rays always strike the surface of the solar panel module, it is possible to obtain maximum power output, increasing its total efficiency as compared to the static panel. © 2023 IEEE.

关键词： Microcontrollers

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Robust Parameter Estimation with Orthogonal Regression Using Interval Constraint Satisfaction Technique

Robust Parameter Estimation with Orthogonal Regression Using...

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Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference

作者： Raval, Sejal K. Raval, Kamlesh N. Makwana, Dipesh P. Kotak, Paresh Instrument and Control Department Government Polytechnic Ahmedabad India Computer Engineering Department R.C.Technical Institute Ahmedabad India I.C. Department L.D. College of Engineering Ahmedabad India S. and S. Gandhi College Surat India

ISBN: (纸本)9783031397769

Parameter estimation is a first and foremost task to design a proper mathematical model. Outliers in a data can often lead to improper parameter estimation. In present work a novel technique using interval constraint satisfaction technique is suggested for getting robust parameter estimation of a system using linear orthogonal regression. In this proposed method we used M estimator with tukey’s biweight function and modified it for orthogonal regression. Tukey’s biweight is generally computed using IRLS which has computational issues of convergence and local minima. Here, benefits of interval analysis based method is utilized to develop the proposed method. Two well-known examples of datasets are used to validate the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Parameter estimation

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Detecting Credit Card Fraud using Machine Learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2021年第24期15卷 108-122页

作者： Almuteer, Arjwan H. Aloufi, Asma A. Alrashidi, Wurud O. Alshobaili, Jowharah F. Ibrahim, Dina M. Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Computers and Control Engineering Department Faculty of Engineering Tanta University Tanta Egypt

Credit card is getting increasingly more famous in budgetary exchanges, simultaneously frauds are likewise expanding. In the past, fraud practitioners were identified using rule-based master frameworks, which ignored a variety of variables, including the outlandishly imbalanced nature of positive and negative cases. Using named information, we provide an approach to fraud detection that uses Convolutional Neural Networks (CNNs) and is based on CNNs. An element lattice speaks to a plethora of interchange information and uses a convolutional neural organization to recognize a large number of idle examples for each of those examples. A considerable business bank's boss presentation is compared with several best-in-class techniques in trials on truly monstrous exchanges. Our objective is to combine CNN with LSTM and Auto-encoder to increase credit card fraud detection while improving the previous models' performance. By using these four models;CNN, AE, LSTM, and AE&LSTM. each of these models is trained by different parameter values highest accuracy has been achieved where the AE model has accuracy = 0.99, the CNN model has accuracy = 0.85, the accuracy of the LSTM model is 0.85, and finally, the AE&L-STM model obtained an accuracy of 0.32 by 400 epoch. It is concluded that the AE classifies the best result between these models. © 2021. All Rights Reserved.

关键词： Budget control

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