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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Dayong, Zhang Wahab, Nur Haliza Abdul Kadir, Khairunnisa A. Aldhaqm, Arafat Nasir, Haslinah Mohd Wong, Keng Yinn Universiti Teknologi Malaysia Faculty of Computing Johor Malaysia Universiti Teknikal Malaysia Melaka Electronics & Computer Engineering Technology Department Durian Tunggal Melaka Malaysia Universiti Teknologi Malaysa Faculty of Mechanical Johor Malaysia

ISBN: (纸本)9798350305333

Blockchain is a distributed database technology developed because of cryptocurrency, which has the characteristics of open and transparent ledger data. However, attackers will analyze ledger data to obtain sensitive information, threatening transaction privacy of users. Cryptography is the underlying technology of the blockchain to ensure the integrity and legitimacy of transactions in the blockchain. Providing privacy protection for the blockchain by studying cryptography has become a research hotspot in the blockchain. This paper introduces the cryptography technology in the blockchain, summarizes the privacy protection problems existing in the current blockchain, and expounds the existing cryptography solutions and the advantages and disadvantages of each scheme. Finally, the future research directions of cryptography in blockchain are summarized and prospected. © 2023 IEEE.

关键词： Blockchain

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Enhancing Student Performance Prediction with Greylag Goose Optimization Algorithm

Enhancing Student Performance Prediction with Greylag Goose ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Rizk, Faris H. Mohamed, Mahmoud Elshabrawy Sameh, Basant Zaki, Ahmed Mohamed Eid, Marwa M. El-Kenawy, El-Sayed M. Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt

ISBN: (纸本)9798350351408

The current paper addresses the central role of hyperparameter optimization in improving the predictive power of the MLP Regressor for forecasting student performance in Portuguese secondary schools. The uniqueness of this research lies in its exploration of metaheuristic optimization algorithms, specifically highlighting GGO (Greylag Goose Optimization) for enhancement. The study utilized a dataset crucial for understanding and predicting student performance, with a special focus on its distinctive features. By comprehensively tuning the MLP Regressor, the paper demonstrates remarkable improvements in various performance measures, as evident in the enclosed tables. Specifically, the MSE values calculated for the MLP Regressor both before and after GGO optimization are compared. Without optimization, the MLP Regressor had an MSE of 0.0103. After GGO optimization, the MSE significantly improved to 0.0060, indicating enhanced accuracy with GGO in the model. These findings emphasize that hyperparameter optimization, particularly the GGO technique, is crucial for refining the MLP Regressor in predicting student performance. The paper not only delves into the technical aspects but also concludes by highlighting the broader consequences of these outcomes. The potential educational applications are substantial, as improved models can provide more accurate predictions, empowering educators and policymakers to make informed decisions in education. This paper establishes a foundation for future research directions, contributing to the pool of ideas for educational predictive modeling. © 2024 IEEE.

关键词： Optimization algorithms

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Automated COVID-19 Detection Based on Single-Image Super-Resolution and CNN Models

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computers, Materials & Continua 2022年第1期70卷 1141-1157页

作者： Walid El-Shafai Anas M.Ali El-Sayed M.El-Rabaie Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

In developing countries,medical diagnosis is expensive and time ***,automatic diagnosis can be a good cheap *** task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks(CNNs).These tools can be used on medical images to speed up the diagnosis process and save the efforts of *** deep CNNs allow direct learning from the medical ***,the accessibility of classified data is still the largest challenge,particularly in the field of medical *** learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs to medical image ***,because of the inhomogeneity and enormous overlap in intensity between medical images in terms of features in the diagnosis of Pneumonia and COVID-19,transfer learning is not usually a robust ***-Image Super-Resolution(SISR)can facilitate learning to enhance computer vision functions,apart from enhancing perceptual image ***,it helps in showing the main features of *** by the challenging dilemma of Pneumonia and COVID-19 diagnosis,this paper introduces a hybrid CNN model,namely SIGTra,to generate super-resolution versions of X-ray and CT *** depends on aGenerative Adversarial Network(GAN)for the super-resolution reconstruction ***,Transfer learning with CNN(TCNN)is adopted for the classification of *** different categories of chest X-ray and CT images can be classified with the proposed model.A comparison study is presented between the proposed SIGTra model and the other relatedCNNmodels for COVID-19 detection in terms of precision,sensitivity,and accuracy.

关键词： Medical images SIGTra GAN CT and X-ray images SISR TCNN

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Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving engineering Applications

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computers, Materials & Continua 2024年第6期79卷 3631-3678页

作者： Ibraheem Abu Falahah Osama Al-Baik Saleh Alomari Gulnara Bektemyssova Saikat Gochhait Irina Leonova OmParkash Malik Frank Werner Mohammad Dehghani Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Software Engineering Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Science and Information Technology Software EngineeringJadara UniversityIrbid21110Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Symbiosis Institute of Digital and Telecom Management Constituent of Symbiosis International Deemed UniversityPune412115India Neuroscience Research Institute Samara State Medical UniversitySamara443001Russia Faculty of Social Sciences Lobachevsky UniversityNizhny Novgorod603950Russia Department of Electrical and Software Engineering University of CalgaryCalgaryAB T2N 1N4Canada Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization(FLO),which emulates the unique hunting behavior of frilled lizards in their natural *** draws its inspiration from the sit-and-wait hunting strategy of these *** algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases:(i)an exploration phase,which mimics the lizard’s sudden attack on its prey,and(ii)an exploitation phase,which simulates the lizard’s retreat to the treetops after *** assess FLO’s efficacy in addressing optimization problems,its performance is rigorously tested on fifty-two standard benchmark *** functions include unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions,as well as the challenging CEC 2017 test ***’s performance is benchmarked against twelve established metaheuristic algorithms,providing a comprehensive comparative *** simulation results demonstrate that FLO excels in both exploration and exploitation,effectively balancing these two critical aspects throughout the search *** balanced approach enables FLO to outperform several competing algorithms in numerous test ***,FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems,further validating its robustness and versatility in solving real-world optimization ***,the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

关键词： Optimization engineering bio-inspired metaheuristic frilled lizard exploration exploitation

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Single Gateway Placement in Wireless Mesh Networks

arXiv

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arXiv 2024年

作者： Turlykozhayeva, D.A. Waldemar, W. Akhmetali, A.B. Ussipov, N.M. Temesheva, S.A. Akhtanov, S.N. Lublin University of Technology Faculty of Electrical Engineering and Computer Science Department of Electronics and Information Technology Lublin Poland

Wireless Mesh Networks (WMNs) remain integral to various sectors due to their adaptability and scalability, providing robust connectivity where traditional wired networks are impractical. From facilitating smart city initiatives to enabling disaster recovery efforts and supporting industrial automation, WMNs play a pivotal role in modern networking applications. Their versatility extends to rural connectivity, underscoring their relevance across diverse scenarios. Recent research in WMNs has honed in on optimizing gateway placement and selection to bolster network performance and ensure efficient data transmission. This paper introduces a novel approach to maximize average throughput by strategically positioning gateways within the mesh topology. Inspired by Coulomb's law, which has been previously employed in network analysis, this approach aims to improve network performance by strategically positioning gateways for optimization. Comprehensive simulations and analyses carried out in this research demonstrate the effectiveness of the proposed method in enhancing both throughput and network efficiency. By leveraging physics-based models such as Coulomb's law, the study offers an objective means to optimize gateway placement—a critical component in WMN design. These findings provide invaluable insights for network designers and operators, guiding informed decision-making processes for gateway deployment across a spectrum of WMN deployments. As such, this research contributes significantly to the ongoing evolution of WMN optimization strategies, reaffirming the indispensable role of gateway placement in establishing resilient and efficient wireless communication infrastructures. © 2024, CC BY.

关键词： Network performance

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Efficient Deep-Learning-Based Autoencoder Denoising Approach for Medical Image Diagnosis

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computers, Materials & Continua 2022年第3期70卷 6107-6125页

作者： Walid El-Shafai Samy Abd El-Nabi El-Sayed MEl-Rabaie Anas M.Ali Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Effective medical diagnosis is dramatically expensive,especially in third-world *** of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia,themedical diagnosis of these diseases is a significant ***,transfer learning represents a promising solution in transferring knowledge from generic tasks to specific ***,experimentation and utilization of different models of transfer learning do not achieve satisfactory *** this study,we suggest the implementation of an automatic detectionmodel,namelyCADTra,to efficiently diagnose pneumonia-related *** model is based on classification,denoising autoencoder,and transfer ***,pre-processing is employed to prepare the medical *** depends on an autoencoder denoising(AD)algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features,in order to improve the diagnosis ***,classification is performed using a transfer learning model and a four-layer convolution neural network(FCNN)to detect *** proposed model supports binary classification of chest computed tomography(CT)images and multi-class classification of chest X-ray ***,a comparative study is introduced for the classification performance with and without the denoising *** proposed model achieves precisions of 98%and 99%for binary classification and multi-class classification,respectively,with the different ratios for training and *** demonstrate the efficiency and superiority of the proposed CADTra model,it is compared with some recent state-of-the-art CNN *** achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases and improve the diagnostic efficiency compared to the exis

关键词： Medical images CADTra AD CT and X-ray images autoencoder

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Efficient Deep CNN Model for COVID-19 Classification

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computers, Materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in *** Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death *** X-Ray(CXR)and computerized Tomography(CT)screening of infected persons are essential in diagnosis *** are numerous ways to identify positive COVID-19 *** of the fundamental ways is radiology imaging through CXR,or CT *** comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high ***,automated classification techniques are required to facilitate the diagnosis *** Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical *** deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image *** this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is *** activation functions as well as three optimizers are tested and compared for this *** proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train *** performance is tested and investigated on the CT and CXR *** activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch *** optimizers are studied with different batch sizes and a constant learning ***,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is ***,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early *** proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR an

关键词： COVID-19 image classification CNN DL activation functions optimizers

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Surveillance and Intruder Detection using Pyramid Vision Transformer You Only Look Once Enhanced with Gradient Weighted Class Activation Mapping 4

Surveillance and Intruder Detection using Pyramid Vision Tra...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Arunachalam, Sripriya Almusawi, Muntather Rafikiran, Shaik. Sivagami, S. Praveena, Hirald Dwaraka Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Science and Applications Vadapalani City Campus Chennai India College of Technical Engineering The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq Sri Venkateswara College of Engineering Department of Electrical & Electronics Engineering Tirupati India New Prince Shri Bhavani College of Engineering and Technology Department of Chemistry Chennai India Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350352931

In this era, wireless networks are economically feasible and flexible to establish in healthcare, defense, surveillance, and traffic monitoring, fire detection. Effective intruder detection systems utilize advanced technologies like Artificial Intelligence (AI), sensors, and cameras. Traditional approaches for surveillance and intruder detection had faced several challenges which include false alarms, high costs, privacy concerns, and vulnerabilities. To overcome these challenges, a Gradient-weighted Class Activation Mapping (Grad CAM) with Pyramid Vision Transformer-You Only Look Once Enhanced (PViT-YOLOE) namely GRADCAM-PViT-YOLOE is proposed to detect intruders in public places using images and videos. For preprocessing, noise reduction, image resizing, and image segmentation are considered to reduce false positives, enhance memory efficiency, and to reduce complexity. Then, PViT-YOLOE is considered for feature extraction to enhance detection of small objects, enhance robustness to variations. At last, Grad CAM is employed as classifier to detect intruders in public places with improved interpretability, robustness to noise, and flexibility. The results demonstrate the efficacy of the proposed GRADCAM-PViT-YOLOE in terms of accuracy (99.25%), precision (96.75%) recall (89.82%) and F1-Score (87.92%) when compared to the existing method like Faster Regions Convolutional Neural Networks (FR-CNN). © 2024 IEEE.

关键词： Noise abatement

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios engineering

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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Predictive Modeling of Portuguese Student Performance: Comparative Machine Learning Analysis

Predictive Modeling of Portuguese Student Performance: Compa...

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2024 International Telecommunications Conference, ITC-Egypt 2024

ISBN: (纸本)9798350351408

Such an analysis of different machine learning methods for predicting the achievement levels of students in Portuguese secondary education makes this essay. The research highlights the importance of accurate expectations of learners' results for education system administrations and respective policymakers. The current study makes use of the 'Student Performance in Portuguese Secondary education' dataset and employs machine learning algorithms, namely MLPRegressor, XGBoost, DecisionTreeRegressor, CatBoost, and KNeighborsRe-gressor, to the corpus. Performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), etc., are used to judge every model's performance. The conclusion that can be drawn from the data is that the MLPRegressor model leads among the competitors, having an MSE equivalent of 0.0103, which is superior to others. The findings of this study are of great significance for educational institutions and policymakers as they work to make appropriate contact with students' performance prediction. © 2024 IEEE.

关键词： Contrastive Learning

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