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Two-Mode Biomedical Sensor Build-up:Characterization of Optical Amplifier

computers, Materials & Continua 2022年第3期70卷 5487-5501页

作者： Usman Masud Fathe Jeribi Mohammed Alhameed Faraz Akram Ali Tahir Mohammad Yousaf Naudhani Faculty of Electrical and Electronics Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Electrical Communication Engineering University of KasselKassel34127Germany College of Computer Science&Information Technology Jazan UniversityJazan45142Saudi Arabia Faculty of Engineering and Applied Sciences Riphah International UniversityIslamabad44000Pakistan Faculty of Information&Communication Technology Balochistan University of Information TechnologyEngineering and Management SciencesQuetta

Intracavity absorption spectroscopy is a strikingly sensitive technique that has been integrated with a two-wavelength setup to develop a sensor for human *** factors are considered in such a scenario,out of which Relative Intensity Noise(RIN)has been exploited as an important parameter to characterize and calibrate the said *** the performance of an electrical based assessment arrangement which has been developed in the laboratory as an alternative to the expensive Agilent setup,the optical amplifier plays a pivotal role in its development and operation,along with other components and their ***,the investigation and technical analysis of the amplifier in the system has been explored in *** algorithm developed for the automatic measurements of the system has been effectively deployed in terms of the laser’s *** this in perspective,a frequency dependent calibration has been pursued in depth with this scheme which enhances the sensor’s efficiency in terms of its *** this way,our investigation helps us in a better understanding and implementation perspective of the proposed system,as the outcomes of our analysis adds to the precision and accuracy of the entire system.

关键词： Biomedical sensor cavity optomechanics spectroscopy relative intensity noise semiconductor laser diode

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Proposing A Modified Proof of Stake System to Counter 51% Attack in Blockchain 2

Proposing A Modified Proof of Stake System to Counter 51% At...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Khatoon, Naghma Rishu, Rishav Verma, Sushant Pranav, Prashant Usha Martin University Faculty of Computing and Information Technology Ranchi India Birla Institute of Technology Department of Computer Science and Engineering Mesra Ranchi India

ISBN: (纸本)9798350372847

Blockchain technology has revolutionized many sectors in the past few years. From its application in health care, digital currencies to building smart cities, many sectors have incorporated blockchain based mechanisms to produce better and efficient solutions to many of their long-lasting problems. The technology itself is fast and the reliance of some sectors on it has improved the overall performance of the applications. One of the most important and sought after applications of blockchain is in digital currencies. Cryptocurrencies are used in many different areas now a days and have become the go to investment destination for many individuals. Although blockchain is secured but it has many underlying security issues which need to be addressed to make this emerging technique more reliable. One of the issues with blockchain is double spending or 51% attack in which the miners authenticate suspicious transactions which result in heavy loss. In this paper, a new consensus mechanism called Modified Proof of Stake to defy 51% attack in blockchain is proposed. © 2024 IEEE.

关键词： Blockchain

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Cough Audio Analysis for COVID-19 Diagnosis

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SN computer Science 2023年第2期4卷 125页

作者： Kapoor, Teghdeep Pandhi, Tanya Gupta, Bharat Department of Computer Science and Engineering Jaypee Institute of Information Technology Sector-62 Uttar Pradesh Noida 201309 India Faculty of Computer Science and Engineering Jaypee Institute of Information Technology Sector-62 Uttar Pradesh Noida 201309 India

Humanity has suffered catastrophically due to the COVID-19 pandemic. One of the most reliable diagnoses of COVID-19 is RT-PCR (reverse-transcription polymer chain reaction) testing. This method, however, has its limitations. It is time consuming and requires scalability. This research work carries out a preliminary prognosis of COVID-19, which is scalable and less time consuming. The research carried out a competitive analysis of four machine-learning models namely, Multilayer Perceptron, Convolutional Neural Networks, Recurrent Neural Networks with Long Short-Term Memory, and VGG-19 with Support Vector Machines. Out of these models, Multilayer Perceptron outperformed with higher specificity of 94.5% and accuracy of 96.8%. The results show that Multilayer Perceptron was able to distinguish between positive and negative COVID-19 coughs by a robust feature embedding technique. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Cough diagnosis COVID-19 COVID-19 preliminary diagnosis Deep learning LSTM Machine learning MLP RNN SVM

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Whole Slide Image Annotation Support for Estimating Lesion Proportions 23

Whole Slide Image Annotation Support for Estimating Lesion P...

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23rd International Conference on Cyberworlds, CW 2024

作者： Takano, Nana Nishiguchi, Satoshi Toyoura, Masahiro Osaka Institute of Technology Graduate School of Information Science and Technology Osaka Japan Osaka Institute of Technology Faculty of Information Science and Technology Osaka Japan University of Yamanashi Department of Computer Science and Engineering Yamanashi Japan

ISBN: (纸本)9798331527174

The annotation of medical images for the purpose of segmentation demands a high level of expertise and experience, and the generation of suitable datasets represents a significant challenge. In this study, we propose a method that facilitates annotation on a whole slide image (WSI) without requiring detailed specification of the lesion area. The objective is to enable annotation without the need for specialised knowledge. In particular, the WSI is divided into smaller regions for annotation purposes, with the proportion of lesions in each region being compared with reference images of similar regions, for which the proportion of lesions is known. The proportion of lesions in the reference small region image deemed to be most analogous is then designated as the annotation. The outcomes of an annotation experiment on multiple subjects based on this approach indicated that as the level of experience of the annotator increased, the variability in the accuracy rate diminished. This suggests that the annotators had acquired knowledge and skills through the process of annotation. © 2024 IEEE.

关键词： Medical imaging

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Design Principles-Based Interactive Learning Tool for Solving Nonlinear Equations

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computer Systems Science & engineering 2022年第3期40卷 1023-1042页

作者： Ahad Alloqmani Omimah Alsaedi Nadia Bahatheg Reem Alnanih Lamiaa Elrefaei Computer Sciences Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairoEgypt

Interactive learning tools can facilitate the learning process and increase student engagement,especially tools such as computer programs that are designed for human-computer ***,this paper aims to help students learn five different methods for solving nonlinear equations using an interactive learning tool designed with common principles such as feedback,visibility,affordance,consistency,and *** also compares these methods by the number of iterations and time required to display the *** study helps students learn these methods using interactive learning tools instead of relying on traditional teaching *** tool is implemented using the MATLAB app and is evaluated through usability testing with two groups of users that are categorized by their level of experience with *** with no knowledge in root-finding confirmed that they understood the root-finding concept when interacting with the designed *** positive results of the user evaluation showed that the tool can be recommended to other users.

关键词： Graphical user interface(GUI) interactive learning tool design principles nonlinear equations experimental design

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Intelligent System for Intrusion Detection Based on Machine Learning 6

Intelligent System for Intrusion Detection Based on Machine ...

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6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Ragab, Mohamed Ibrahim Fouad, Khaled M. School of Information Technology and Computer Science Nile University Giza Egypt New Mansoura University and Nile University Faculty of Computer Science and Engineering Mansoura Egypt

ISBN: (纸本)9798350378511

In today's digital age, security challenges threaten user privacy as networks face increased vulnerability to malicious attacks due to large data volumes. Intrusion Detection Systems (IDS) play a crucial role in identifying cyber-attacks and protecting system resources and users. This study utilizes machine learning classifiers (MLC) to analyze the NSL-KDD dataset, optimizing by preprocessing to remove irrelevant features. System performance is assessed using four attribute subsets, comparing model accuracy across DoS, Probe, U2L, and R2L attack classes to determine the best algorithm for each class. Using Random Forest with 20 features successfully achieved an accuracy of up to 99 % in intrusion detection. © 2024 IEEE.

关键词： Adversarial machine learning

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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

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computers, Materials & Continua 2022年第10期73卷 865-881页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Abdelhameed Ibrahim Said H.Abd Elkhalik Shady Y.El-Mashad Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

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Exploring the Usage of Mobile Phones in the Assessment Process: A Case Study 47

Exploring the Usage of Mobile Phones in the Assessment Proce...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Kovačević, Željko Bubnic, Bostjan Kosar, Tomaž Zagreb University Of Applied Sciences Department Of Computer Science And Information Technology Zagreb Croatia University Of Maribor Faculty Of Electrical Engineering And Computer Science Maribor Slovenia

ISBN: (纸本)9798350382495

technology is changing how students learn in the 21st century significantly. Integrating mobile devices in teaching, learning, and assessment processes has emerged as an important strategy for improving teaching methodologies and students' learning. However, adopting mobile devices presents several challenges, including understanding the students' performance, impact on time, and efficiency in the assessment process. This study explores this area with an empirical study of using mobile devices and compares the results to those using traditional desktop computers. In the educational environment, we investigated students' effective use while interacting with mobile and desktop versions of the Second Introductory programming summative assessment. Multinational/multi-institutional research involved a comparative analysis between two student groups undertaking identical tasks on mobile phones and desktop computers. Our results suggest that the students' results are not affected significantly by using the mobile version of the assessment application. These results highlight the usability of mobile devices as a flexible and effective tool for various educational applications, strengthening their role in the modern learning environment. © 2024 IEEE.

关键词： Object oriented programming

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Blockchain-based color medical image cryptosystem for industrial Internet of Healthcare Things (IoHT)

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Multimedia Tools and Applications 2024年 1-55页

作者： Khallaf, Fatma El-Shafai, Walid El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Ahram Canadian University 6th October City Giza Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

In recent years, the proliferation of smart devices and associated technologies, such as the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Internet of Medical Things (IoMT), has witnessed a substantial growth. However, the limited processing power and storage capacity of smart devices make them vulnerable to cyberattacks, rendering traditional security and cryptography techniques inadequate. To address these challenges, blockchain (BC) technology has emerged as a promising solution. This study introduces an efficient framework for the Internet of Healthcare Things (IoHT), presenting a novel cryptosystem for color medical images using BC technology in conjunction with the IoT, Secure Hash Algorithm 256-bit (SHA256), shuffling, and bitwise XOR operations. The encryption scheme is specifically designed for an IIoT grid network computing system, relying on diffusion and confusion principles. In this paper, the proposed cryptosystem strength is evaluated against differential attacks with several comprehensive metrics. Simulation results and theoretical analysis demonstrate the cryptosystem effectiveness, showcasing its ability to provide high levels of security and immunity to data leakage. The proposed cryptosystem offers a versatile range of technical solutions and strategies that are adaptable to various scenarios. The evaluation metrics, with approximate values of 99.61% for Number of Pixels Change Rate (NPCR), 33.46% for Unified Average Changed Intensity (UACI), and 8 for information entropy, closely align with the desired ideal outcomes. Consequently, this paper contributes to the advancement of secure and private systems for medical image encryption based on BC technology, potentially mitigating the risks associated with cyberattacks on smart medical devices. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Selective feature-based ovarian cancer prediction using MobileNet and explainable AI to manage women healthcare

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Multimedia Tools and Applications 2024年 1-22页

作者： Almujally, Nouf Abdullah Alzahrani, Abdulrahman Hakeem, Abeer M. Attiah, Afraa Umer, Muhammad Alsubai, Shtwai Polsinelli, Matteo Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Ovarian cancer is a global health concern due to the unavailability of an effective screening strategy and is often diagnosed at a late stage with approximately 70% of the case which reduces the survival chances of patients. Initial diagnosis is challenging due to inconspicuous symptoms in its initial stages which complicate its timely diagnosis. Regular screenings, such as pelvic exams, ultrasounds, and blood tests targeting specific biomarkers can be helpful for early diagnosis. In addition, the use of machine learning models can help automate this process thereby assisting medical experts for accurate diagnosis. This study aims at timely and accurate detection of ovarian cancer using a transfer learning approach that uses the MobileNet model. Moreover, the Chi-square technique is used to extract the most impactful features for better accuracy. For experiments, the Soochow University ovarian cancer dataset is employed. Extensive experiments are performed using all features from the dataset, as well as, using Chi-square-based selective features. The best accuracy of 90.88% is achieved with the Xception model when all features are used. Experimental results show a substantial increase when selective features are used indicating a 98.49% accuracy using the MobileNet model with 20 most important features. In addition, precision, recall, and F1 scores of 99.13%, 99.27%, and 99.20% are obtained showing the model’s robustness and generalization. This study also employs Shapley additive explanations to explain the importance of various features toward the model’s output thereby providing transparency for the model’s decision-making process. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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