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Proposed Privacy Preservation Technique for Color Medical Images

Intelligent Automation & Soft Computing 2023年第4期36卷 719-732页

作者： Walid El-Shafai Hayam A.Abd El-Hameed Noha A.El-Hag Ashraf A.M.Khalaf Naglaa F.Soliman Hussah Nasser AlEisa Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Nowadays,the security of images or information is very *** paper introduces a proposed hybrid watermarking and encryption technique for increasing medical image ***,the secret medical image is encrypted using Advanced Encryption Standard(AES)***,the secret report of the patient is embedded into the encrypted secret medical image with the Least Signiﬁcant Bit(LSB)watermarking *** that,the encrypted secret medical image with the secret report is concealed in a cover medical image,using Kekre’s Median Codebook Generation(KMCG)***,the stego-image obtained is split into 16 ***,it is sent to the *** adopt this strategy to send the secret medical image and report over a network *** proposed technique is assessed with different encryption quality metrics including Peak Signal-to-Noise Ratio(PSNR),Correlation Coefﬁcient(Cr),Fea-ture Similarity Index Metric(FSIM),and Structural Similarity Index Metric(SSIM).Histogram estimation is used to conﬁrm the matching between the secret medical image before and after *** results demonstrate that the proposed technique achieves good performance with high quality of the received medical image and clear image details in a very short processing time.

关键词： LSB steganography AES algorithm KMCG algorithm

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Combining flow virometry with tree-based machine learning models for rapid virus particle estimation in different wastewater matrices

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Water Research 2025年 284卷 123905页

作者： Myshkevych, Yevhen N'Doye, Ibrahima Medina, Julie Sanchez Aljehani, Fahad K. Xiong, Yanghui Laleg-Kirati, Taous-Meriem Hong, Pei-Ying Environmental Science and Engineering Program Division of Biological and Environmental Science and Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia KAUST Center of Excellence on Smart Health King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Electrical and Computer Engineering Program Division of Computer Electrical and Mathematical Science and Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Paris-Saclay France

Enumerating virus particles (VPs) at different stages of the wastewater treatment process or along the distribution network is essential for ensuring high performance and reducing public health risks. Herein, we aimed to (i) optimize the flow virometry (FVM) protocol for use in wastewater matrices, (ii) correlate FVM data with specific virus genera of interest, and (iii) develop machine learning (ML) models for determining total VP concentration. We identified and tested a comprehensive set of parameters to determine the optimal conditions for wastewater FVM. Specifically, we tested various sample preprocessing steps to enhance FVM detection sensitivity, including the use of different nucleic acid staining dyes, surfactant addition and concentration optimization, glutaraldehyde fixation, and the effect of sample freezing before FVM analysis. Spearman's rank correlation of FVM data with virus genera concentration using a conventional qPCR-based method in 206 samples showed a positive correlation for all five virus genera, ranging from 0.21 to 0.44 (p © 2025 The Author(s)

关键词： Water recycling

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Artificial Neural Network Based Power Quality Improvement For Grid Connected Wind Power System Using PMSG and SEPIC Converter

Artificial Neural Network Based Power Quality Improvement Fo...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Rajendran, E. Raji, V. Kar, Siddheswar Rahiman, Riyaz A. Irfan, Md Mujahid Karthikeyan, D. Skp Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Tiruvannamalai India Skp Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvannamalai India Medi-Caps University Department of Electrical Engineering Indore India Reva University School of Electronics and Communication Bangalore India Sr University Department of Electrical and Electronics Engineering Warangal India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur Chennai India

ISBN: (纸本)9798350393378

Wind energy is attractive into a highly valuable energy source. To meet the grid connections criteria in light of the growing penetration of wind power, changes are needed. The purpose of this chapter is to learn how to control output and discuss the output regulator of a Permanent Magnet Synchronous Generator (PMSG) that is connected to a utility for wind power generation. The unit powered by wind turbine is regulated by a Sepic converter by PMSG. In order to ensure that PMSG's power transmission can be commercially successful, their actual and reactive powers are strictly regulated. PI controller is contrasted with the suggested technique. Using MATLAB/Simulink, the effectiveness of the suggested approach and Sepic converter is proven. © 2023 IEEE.

关键词： Wind power

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Introduction to the Special Issue on computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph

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computer Modeling in engineering & sciences 2022年第9期132卷 707-709页

作者： Shuihua Wang Zheng Zhang Yuankai Huo School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK School of Computer Science and Technology Harbin Institute of TechnologyHarbin150006China Electrical and Computer Engineering University of VanderbiltNashville37240USA

The chest radiograph has been one of the most frequently performed radiological investigation *** clinical medicine,the chest radiograph can provide technical basis and scientific instruction to recognize a series of thoracic diseases(such as Atelectasis,Nodule,and Pneumonia,etc.).Importantly,it is of paramount importance for clinical screening,diagnosis,treatment planning,and efficacy ***,it remains challenging for automated chest radiograph diagnosis and interpretation at the level of an experienced *** recent years,many studies on biomedical image processing have advanced rapidly with the development of artificial intelligence especially deep learning techniques and *** to build an efficient and accurate deep learning model for automatic chest radiograph processing is an important scientific problem that needs to be solved.

关键词： diagnosis clinical artificial

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An Optimal Framework for SDN Based on Deep Neural Network

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computers, Materials & Continua 2022年第10期73卷 1125-1140页

作者： Abdallah Abdallah Mohamad Khairi Ishak Nor Samsiah Sani Imran Khan Fahad RAlbogamy Hirofumi Amano Samih M.Mostafa Department of Industrial Engineering School of Applied Technical Sciences German Jordanian UniversityAmman35247Jordan School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and TechnologyThe National University of Malaysia(UKM)Bangi43600SelangorMalaysia Department of Electrical Engineering University of Engineering and TechnologyPeshawar814Pakistan Turabah University College Computer Sciences ProgramTaif UniversityTaif21944Saudi Arabia Research Institute for Information Technology Kyushu UniversityFukuoka819-0395Japan Computer Science-Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt

Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network ***,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in *** address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is *** approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection *** initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on *** assaults were found when suspected irregular traffic was *** reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy *** false alarm rate(FAR)is much lower than that of the information entropy-based detection ***,the proposed framework can shorten the detection time and improve the resource utilization efficiency.

关键词： Deep neural network computer networks data security optimization

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Recent Advancement in Autonomous Vehicle and Driver Assistance Systems

Recent Advancement in Autonomous Vehicle and Driver Assistan...

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2023 IEEE International Students' Conference on electrical, Electronics and computer science, SCEECS 2023

作者： Mishra, Abhishek Purohit, Jahanvi Nizam, Maruf Gawre, Suresh Kumar Maulana Azad National Institute of Technology Department of Mechanical Engineering MP Bhopal India Maulana Azad National Institute of Technology Department of Computer Science & Engineering MP Bhopal India Maulana Azad National Institute of Technology Department of Electrical Engineering MP Bhopal India

ISBN: (纸本)9798350398748

In past two decades, the popularity and use of autonomous vehicles and driver assistance systems has accelerated due to the advancement in artificial intelligence techniques, big data analysis, machine learning, computer vision, smart sensors and Internet of Things (IoT). Autonomous vehicles (AVs) are gaining popularity because it reduces traffic congestion along with increase in safety and fuel efficiency. The present paper focuses on the functionality of the driver-less car and Advanced Driver & Driving Assistance Systems (ADDAS). Driver assistance systems help drivers to driving safely by avoiding and minimising distractions by observing their actions. Artificial Intelligence can be incorporated when AVs drive alongside human-driven vehicles (HVs), also known as the era of Mixed Autonomy. The Markov decision process (MDP) can be used to represent the communication between the driver-less vehicles and the surroundings, where, MDP is a discrete-time imaginary command process. MDP is utilized in various fields like automation, economics, manufacturing, and automatic control. This paper provides a detailed review on recent research and developments on autonomous vehicles and ADDAS. Some onfield examples of ADDAS are Automatic Parking, Automatic Braking System (ABS), Blind Spot Monitors, Adaptive Cruise Control (ACC), etc. © 2023 IEEE.

关键词： Autonomous vehicles

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Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing

Energy-Optimized with Multi-Population Differential Annealed...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wu, Ruixuan Shi, Yuliang Yuan, Haitao Bi, Jing Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Mobile devices (MDs) cannot fully run all computation/delay-sensitive tasks due to their limited computing resources. Mobile edge computing (MEC) meets the demand by providing massive resources for MDs and offloading task partitions to MEC servers. However, task offloading also brings communication delay and energy consumption. Therefore, it is challenging to associate resource-constrained MDs with appropriate MEC servers to minimize power consumption. To address this problem, a constrained mixed integer nonlinear program is formulated to optimize the total energy consumption of the system including MDs and MEC servers. To solve this problem, this work designs an improved meta-heuristic optimization algorithm called Self-adaptive and Multi-population Differential Annealed Optimization (SMDAO). Experimental results demonstrate that compared with its two state-of-the-art peers, the proposed SMDAO yields the best solution with the smallest total energy consumption in the least time. © 2023 IEEE.

关键词： computation offloading energy optimization Mobile edge computing multi-population optimization simulated annealing

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Research on hybrid flow-shop scheduling based on improved genetic algorithm 18

Research on hybrid flow-shop scheduling based on improved ge...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Chen, Haopeng Fang, Zaojun Zhong, Caiming Ningbo University Ningbo Institute of Materials & Technology Engineering Chinese & Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo China Chinese & Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials & Technology Engineering Ningbo China Ningbo University College of Science & Technology Ningbo China

ISBN: (纸本)9798350312201

Aiming at the scheduling problem in the production process of hybrid flow-shop, a hybrid flow-shop scheduling model with the objective of minimizing the makespan is established, and a neighborhood search adaptive genetic algorithm (NSAGA) based on heuristic decoding strategy is proposed. An encoding strategy based on the first stage processing sequence and a decoding strategy based on a heuristic rule are used to guide the algorithm to find the optimal solution quickly. In order to speed up the convergence of the algorithm, the elitism strategy is introduced into the genetic operation, and adaptive crossover and mutation strategy based on sigmoid function is designed. In order to enhance the local search capability of the algorithm, a neighborhood search strategy is designed. Finally, the effectiveness and superiority of NSAGA for solving the hybrid flow-shop scheduling problem (HFSP) with different scales are verified by simulation experiments. © 2023 IEEE.

关键词： Genetic algorithms

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The Role of Artificial Intelligence (AI) in the Accounting and Auditing Professions 10

The Role of Artificial Intelligence (AI) in the Accounting a...

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10th IEEE Uttar Pradesh Section International Conference on electrical, Electronics and computer engineering, UPCON 2023

作者： Kaur, Harshdeep Arora, Geeta Salaria, Archana Singh, Arun Rakhra, Manik Dhiman, Gaurav Lovely Professional University Department of Mathematics Phagwara India University Institute of Engineering and Technology Department of Computer Science and Engineering Kathua India School of Computer Science and Engineering Lovely Professional University Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Lebanese American University Department of Electrical and Computer Engineering Lebanon School of Sciences and Emerging Technologies Jagat Guru Nanak Dev Punjab State Open University Patiala India

ISBN: (纸本)9798350382471

The fast growth of business, science, and technology has ushered in the age of artificial intelligence, which has had far-reaching effects in every sphere of human existence. This article explains how AI may be used to detect and prevent accounting fraud and enhance the reliability of financial statements. Accounting and auditing firms may benefit or suffer from the advancement and use of artificial intelligence (AI). This article explains how AI can be used to reduce accounting fraud and boost the credibility of financial statements. The use of AI in these fields is not without its merits and drawbacks. Change is inevitable, and the accounting and auditing industry will undergo significant change in the near future. The report concludes by stressing the importance, in the broader context of AI, of accounting staff members developing their own expertise and proficiency. This research highlights the possible impacts of artificial intelligence (AI) on the accounting industry and the steps that need to be taken to prepare for a workforce in the future where AI plays a larger role. © 2023 IEEE.

关键词： component formatting insert style styling

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Automated Detection of Oropharyngeal Carcinoma Using Super-Resolution Generative Adversarial Network and Convolutional Neural Network

Automated Detection of Oropharyngeal Carcinoma Using Super-R...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Prasanth, A. Ragavi, B. Jayachitra, S. Kuntavai, T. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India Dr.N.G.P Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Dindigul India Adhipara Sakthi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331528126

Oropharyngeal carcinoma is one of the prevalent forms of abnormal cancer. Detecting cancer at an early stage and getting proper treatment is the only way to reduce the mortality rate. The key intention of this work is to enhance the image quality and to classify the images with distinct test cases. The first approach is to implement an Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) in the input stage for improving the quality of images. The second approach is to implement the U-Net architecture in a proposed method to enhance the accuracy rate and precise outcome for early prediction. After implementing a U-Net architecture, the cancerous image and non-cancerous images are identified and classified accurately with lesser computational time. The proposed methodology executes preprocessing of input images using de-gamma correction. Thereafter, the Convolutional Neural Network (CNN) model are employed to extract all the features pertained from the image which facilitates to classify the input images. The unique validation dataset consists of 1050 images utilized to analyze the proposed ESRGAN-CNN model. The simulated results have been tested under 2 test cases with different accuracy rates along with cancerous and non-cancerous categorized images. When compared to existing works, the suggested work produces a high-resolution image with superior accuracy of 92.51%. © 2024 IEEE.

关键词： Generative adversarial networks

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