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A Global Best-guided Firefly Algorithm for engineering Problems

Journal of Bionic engineering 2023年第5期20卷 2359-2388页

作者： Mohsen Zare Mojtaba Ghasemi Amir Zahedi Keyvan Golalipour Soleiman Kadkhoda Mohammadi Seyedali Mirjalili Laith Abualigah Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahrom***FrasIran Department of Electronics and Electrical Engineering Shiraz University of TechnologyShiraz***Iran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehran***Iran Department of Computer Engineering Sari BranchIslamic Azad UniversitySari***Iran Department of Electrical Engineering Urmia BranchIslamic Azad UniversityUrmia571696896Iran Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722South Korea Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan School of Computer Sciences Universiti Sains Malaysia11800George TownPulau PinangMalaysia University Research and Innovation Center Obuda University1034BudapestHungary School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Applied science research center Applied science private universityAmman11931Jordan

The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when *** article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement *** proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best ***,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main ***,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original *** GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha ***,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced *** all cases,GbFA provides the optimal result compared to other *** that the source code of the GbFA algorithm is publicly available at https://***/projects/gbfa.

关键词： Firefly algorithm New movement vector Global best-guided firefly algorithm Global optimization engineering design

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Physically Aware Synthesis Revisited: Guiding Technology Mapping with Primitive Logic Gate Placement 24

Physically Aware Synthesis Revisited: Guiding Technology Map...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Pan, Hongyang Lan, Cunqing Liu, Yiting Wang, Zhiang Shang, Li Zeng, Xuan Yang, Fan Zhu, Keren State Key Lab of Integrated Circuits and Systems School of Microelectronics Fudan University China School of Computer Science Fudan University China Department of Electrical and Computer Engineering University of California San Diego United States

ISBN: (纸本)9798400710773

A typical VLSI design flow is divided into separated front-end logic synthesis and back-end physical design (PD) stages, which often require costly iterations between these stages to achieve design closure. Existing approaches face significant challenges, notably in utilizing feedback from physical metrics to better adapt and refine synthesis operations, and in establishing a unified and comprehensive metric. This paper introduces a new Primitive logic gate placement guided technology MAPping (PigMAP) framework to address these challenges. With approximating technology-independent spatial information, we develop a novel wirelength (WL) driven mapping algorithm to produce PD-friendly netlists. PigMAP is equipped with two schemes: a performance mode that focuses on optimizing the critical path WL to achieve high performance, and a power mode that aims to minimize the total WL, resulting in balanced power and performance outcomes. We evaluate our framework using the EPFL benchmark suites with ASAP7 technology, using the OpenROAD tool for place-and-route. Compared with OpenROAD flow scripts, performance mode reduces delay by 14% while increasing power consumption by only 6%. Meanwhile, power mode achieves a 3% improvement in delay and a 9% reduction in power consumption. © 2024 Copyright is held by the owner/author(s).

关键词： Logic design

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An Overview of Tools and Techniques in Network Forensics 25

An Overview of Tools and Techniques in Network Forensics

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Asassfeh, Mahmoud Al-Mousa, Mohammad Rasmi Al-Dweikat, Hasan Al-Mashagbeh, Malak Hamad Afaneh, Suha Samara, Ghassan Zaid, Ali Abu Khouj, Mohammed Al Daoud, Essam Zarqa University Department of Cybersecurity Zarqa Jordan Applied Science Private University Cybersecurity and Cloud Computing Department Amman Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Zarqa Jordan

ISBN: (纸本)9798331540012

Network forensics pertains to a subfield of digital forensics that deals with network security. It is utilized in conjunction with computer network traffic monitoring and analysis, which acts as an intrusion detection system and firewall and facilitates the collection of data and digital proof for network security. This platform is designed to analyze and monitor data in a real-time network environment. It is a crucial instrument for identifying and resolving instances of real-time network misuse. This research shows the differences between network forensics and digital forensics as well as the tools and techniques used in network forensics. It also discusses building forensics platforms, developing network analysis methodologies, and talking about the future of network forensics. © 2024 IEEE.

关键词： Network intrusion

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications 4

Adaptive Deep Reinforcement Learning-based Resource Manageme...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karne, Niharika Ramagundam, Chandra Shekar Patnala, Ranga Rao Ramagundam, Shashishekhar Tadiboina, Sai Nitisha Sr. Principal Systems Engineer in C&RS Platform Services University of South Alabama Department of Electrical Engineering Masters in Electrical Engineering Amtrak DT United States Software Engineer in Cloud Platform Services City of Philadelphia Pennsylvania United States Vice President Neuberger Berman United States Software Development & Engineering in Content Discovery & AI Troy University Department of Computer Science Masters in Computer Science AL Comcast Montgomery Campus United States Department of Information Technology GEICO - Chevy Chase Chevy ChaseMD20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Deep reinforcement learning

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Cancellable Multi-Biometric Template Generation Based on Arnold Cat Map and Aliasing

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computers, Materials & Continua 2022年第8期72卷 3687-3703页

作者： Ahmed M.Ayoup Ashraf A.M.Khalaf Walid El-Shafai Fathi E.Abd El-Samie Fahad Alraddady Salwa M.Serag Eldin Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Electronics and Electrical Communications Engineering Department Faculty of Electronic EngineeringMenoufia University32952MenoufEgypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of EngineeringTanta UniversityTantaEgypt

The cancellable biometric transformations are designed to be computationally difficult to obtain the original biometric *** paper presents a cancellable multi-biometric identification scheme that includes four stages:biometric data collection and processing,Arnold’s Cat Map encryption,decimation process to reduce the size,and finalmerging of the four biometrics in a single generated ***,a 2D matrix of size 128×128 is created based on Arnold’s Cat Map(ACM).The purpose of this rearrangement is to break the correlation between pixels to hide the biometric patterns and merge these patterns together for more *** decimation is performed to keep the dimensions of the overall cancellable template similar to those of a single template to avoid data ***,some sort of aliasing occurs due to decimation,contributing to the intended distortion of biometric *** hybrid structure that comprises encryption,decimation,andmerging generates encrypted and distorted cancellable *** simulation results obtained for performance evaluation show that the system is safe,reliable,and feasible as it achieves high security in the presence of noise.

关键词： Aliasing technique selective encryption ACM decimation process

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A Novel Hyper Chaotic-Cellular Automata Based Medical Image Encryption Technique 3

A Novel Hyper Chaotic-Cellular Automata Based Medical Image ...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： El-Shafai, Walid Naguib, Ahmed Shokair, Mona Prince Sultan University Security Engineering Lab Computer Science Department Riyadh11586 Saudi Arabia Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt 6 October University Faculty of Electrical Engineering Egypt

ISBN: (纸本)9798350323511

The security of healthcare and telemedicine systems stands as a matter of paramount importance, necessitating extensive research. In the upcoming years, the medical industry envisions widespread adoption of advanced telemedicine applications. With healthcare smart devices becoming increasingly interconnected through the Internet, enabling convenient access from any location, the protection of sensitive and private information, such as personal medical images, emerges as a critical focal point. As a result, the encryption of medical images assumes a pivotal role within the domain of telemedicine and healthcare applications. This article introduces a highly efficient cryptographic system designed to enhance the protection of medical images by harnessing the power of cellular automata techniques in conjunction with hyper-chaotic procedures. In the proposed medical image encryption technique, a layered cellular automata approach is embraced, and a pseudorandom sequence derived from a four-dimensional hyper-chaotic system is employed as the cryptographic key. The study encompasses both grayscale and color images. Several assessment criteria, including the Number of Changing Pixel Rate (NPCR), Unified Averaged Changed Intensity (UACI), average entropy, correlation coefficients, Structural Similarity (SSIM), Feature Similarity (FSIM), and Peak Signal-to-Noise Ratio (PSNR), are employed to evaluate the suggested cryptosystem. These evaluations vividly underscore the robust security performance exhibited by the proposed approach. © 2023 IEEE.

关键词： Entropy

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X-ray Image-Based COVID-19 Patient Detection Using Machine Learning-Based Techniques

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computer Systems science & engineering 2022年第11期43卷 671-682页

作者： Shabana Habib Saleh Alyahya Aizaz Ahmed Muhammad Islam Sheroz Khan Ishrat Khan Muhammad Kamil Department of Information Technology College of ComputerQassim UniversityBuraidah52571Saudi Arabia Department of Electrical Engineering College of Engineering&Information TechnologyOnaizah CollegesOnaizah51911Saudi Arabia Digital Image Processing Lab Islamia CollegePeshawar25120KPKPakistan Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electrical Engineering National University of Computer and Emerging SciencesPeshawarPakistan

In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Organization(WHO)declared the outbreak a global pandemic *** the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected *** COVID-19 using Machine Learning(ML)technologies can help the health care unit through assistive diagnostic suggestions,which can reduce the health unit's burden to a certain *** paper investigates the possibilities of ML techniques in identifying/detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral *** approach includes preprocessing,feature extraction,and ***,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature ***,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is *** results show that the diagnostic accuracy of random forest classifier(RFC)on extracted HOG plusLBP features is as high as 94%followed by SVM at 93%.The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%.Overall,the predicted approach has shown higher classification accuracy and effective diagnostic *** is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.

关键词： Image pre-processing detection classification x-ray images filter

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An Enhanced Deep Super-Resolution Generative Adversarial Network Approach for Skin Disease Image Regeneration and Assessment 3

An Enhanced Deep Super-Resolution Generative Adversarial Net...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： El-Fattah, Ibrahim Abd Taha, Taha E. El-Shafai, Walid Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt Department Electronics and Electrical Communications Engineering Alexandria Egypt Prince Sultan University Security Engineering Lab Computer Science Department Riyadh11586 Saudi Arabia

ISBN: (纸本)9798350323511

Skin cancer is a critical medical concern, posing significant challenges in accurate diagnosis. Algorithmic approaches have seen remarkable advancements across various occupations, including skin disease assessment. This study introduces a distinctive Improved Deep Super-Resolution Generative Adversarial Network (I_DSUR-GAN) methodology for regenerating low-resolution (LOR) skin disease images into super-resolution (SUR) format. Additionally, a modified SUR-dataset inspired by HAM10000 is presented for skin disease applications. The proposed approach incorporates a novel loss function design to provide supplementary information, facilitating the creation of high-quality SUR images. Experimental results demonstrate that our method outperforms existing approaches on the HAM10000 dataset. A comprehensive evaluation and employing sensitive metrics is conducted to assess the effectiveness, training periods, and memory requirements of the proposed framework. The outcomes reveal that the suggested model excels in restoring and identifying hue and texture compared to conventional and earlier models. © 2023 IEEE.

关键词： Generative adversarial networks

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Deep Learning-and Transfer Learning-based Models for COVID-19 Detection using Radiography Images

Deep Learning-and Transfer Learning-based Models for COVID-1...

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2023 International Conference on Advances in Electronics, Control and Communication Systems, ICAECCS 2023

作者： Mazari, Ahmed Cherif Kheddar, Hamza University of Medea Lsea Lab Dept. of Mathematics and Computer Science Faculty of Science Medea2600 Algeria University of Medea Lsea Lab Dept. of Electrical Engineering Faculty of Technology Medea2600 Algeria

ISBN: (纸本)9781665463102

Image classification and segmentation techniques are still very popular in the medical field (for healthcare), in which the medical image plays an important role in the detection and screening of diseases. Recently, the spread of new viral diseases, namely Covid-19, requires powerful computer models and rich resources (datasets) to fight this phenomenon. In this study, we propose to examine the CNN Deep Learning algorithm and two Transfer Learning models, namely RestNet50 and MobileNetV2 using the pretrained model of the ImageNet database, experimented on the new dataset (COVID-QU-Ex Dataset 2022) offered by the University of Qatar. These models are tested to classify radiography images into two classes (Covid19 and Normal). The results achieved by CNN (Acc =95.97%), ResNet50 (Acc =95.53%) and MobileNetV2 (Acc =97.32%) show that these algorithms are promising in order to combat this Covid-19 disease by detecting it through thoracic images (Chest X-ray type). © 2023 IEEE.

关键词： Image classification

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Information Gathering of Earthquake Disasters by Mobile Crowd Sourcing in Smart Cities 5

Information Gathering of Earthquake Disasters by Mobile Crow...

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5th International Conference on Internet of Things and Applications, IoT 2021

作者： Vahdat-Nejad, Hamed Bahadori, Hamid Abiri, Ali University of Birjand Faculty of Electrical and Computer Engineering Pervasive and Cloud Computing Lab Birjand Iran

ISBN: (纸本)9781665444484

Natural disasters such as earthquakes have always threatened and victimized the lives of humans and other creatures. After each earthquake, the environmental conditions change and various information is required to carry out the rescue operation. A major part of this information cannot be obtained from the knowledge that had already existed about the disaster area. However, the disaster management organizations as well as rescue teams need comprehensive information in the shortest possible time to be able to operate. Given the progress of Internet of Things and ubiquity of smartphones, it is possible to obtain this comprehensive information in the shortest time through crowd sourcing. In this article, the requirement analysis of the earthquake information gathering system has been performed. The requirements are generally classified into 4 groups including victims, facilities and livelihood, security and health, and maps. Afterwards, a layered architecture is designed for the earthquake information gathering system, which includes 4 layers including sensing, fog, cloud and application. The scenario-based approach validates that the proposed architecture satisfies the identified requirements. Finally, as selfish users could corrupt the whole information of crowd sourcing systems, a simulation reveals that the system is able to detect and eliminate these users. © 2021 IEEE.

关键词： Architecture

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