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A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

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Archives of Computational Methods in engineering 2025年第1期32卷 269-310页

作者： Hosseinzadeh, Mehdi Rahmani, Amir Masoud Husari, Fatimatelbatoul Mahmoud Alsalami, Omar Mutab Marzougui, Mehrez Nguyen, Gia Nhu Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia School of Computer Science Duy Tan University Da Nang Viet Nam Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of

In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2024.

关键词： Optimization

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Multi-modal classification of breast cancer lesions in Digital Mammography and contrast enhanced spectral mammography images

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computers in Biology and Medicine 2024年 183卷 109266-109266页

作者： Bouzarjomehri, Narjes Barzegar, Mohammad Rostami, Habib Keshavarz, Ahmad Asghari, Ahmad Navid Azad, Saeed Talatian Department of Computer Engineering Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr*** Iran Department of Electrical Engineering Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr*** Iran

Breast cancer ranks as the second most prevalent cancer in women, recognized as one of the most dangerous types of cancer, and is on the rise globally. Regular screenings are essential for early-stage treatment. Digital mammography (DM) is the most recognized and widely used technique for breast cancer screening. Contrast-Enhanced Spectral Mammography (CESM or CM) is used in conjunction with DM to detect and identify hidden abnormalities, particularly in dense breast tissue where DM alone might not be as effective. In this work, we explore the effectiveness of each modality (CM, DM, or both) in detecting breast cancer lesions using deep learning methods. We introduce an architecture for detecting and classifying breast cancer lesions in DM and CM images in Craniocaudal (CC) and Mediolateral Oblique (MLO) views. The proposed architecture (JointNet) consists of a convolution module for extracting local features, a transformer module for extracting long-range features, and a feature fusion layer to fuse the local features, global features, and global features weighted based on the local ones. This significantly enhances the accuracy of classifying DM and CM images into normal or abnormal categories and lesion classification into benign or malignant. Using our architecture as a backbone, three lesion classification pipelines are introduced that utilize attention mechanisms focused on lesion shape, texture, and overall breast texture, examining the critical features for effective lesion classification. The results demonstrate that our proposed methods outperform their components in classifying images as normal or abnormal and mitigate the limitations of independently using the transformer module or the convolution module. An ensemble model is also introduced to explore the effect of each modality and each view to increase our baseline architecture's accuracy. The results demonstrate superior performance compared with other similar works. The best performance on DM images

关键词： Image enhancement

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Precision Health: Maximizing Well-being with IAHN Integration 3

Precision Health: Maximizing Well-being with IAHN Integratio...

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3rd International Conference on Automation, Computing and Renewable systems, ICACRS 2024

作者： Jumlesha, Shaik Raju, S. Hrushikesava Adinarayna, S. Sesadri, U. Choudhury, Nabanita Jadala, Vijaya Chandra AP Tirupati517501 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Guntur522502 India Department of Computer Science and Systems Engineering College of Engineering Andhra University Visakhapatnam531162 India Department of CSE Vardhaman College of Engineering Telangana Hyderabad India Faculty of Computer Technology Assam down town University Assam Guwahati India School of Computer Science and Artificial Intelligence SR University Ananthasagar Hasanparthy Hanumakonda Telangana Warangal506371 India

ISBN: (纸本)9798331532420

Precision health is emerging as a significant trend in healthcare, emphasizing the customization of medical care and interventions to align with the unique characteristics of each patient, ultimately maximizing health outcomes and well-being. Traditional approaches to personalized care are often time-consuming and lack the necessary focus on individual patient needs, leading to inadequate monitoring of disease progression and insufficient patient engagement. To address these challenges, the proposed approach employs an Improved Adaptive Health Navigator (IAHN) that continuously monitors each patient and formulates ecofriendly medications tailored to their specific symptoms. This innovative process incorporates various factors such as lifestyle choices, genomic data, family history, and other relevant parameters to inform precision medicine recommendations. The resulting precision medicine approach is designed to enhance recovery speed and improve patient outcomes. By adopting a proactive strategy, this model facilitates early detection of health issues, reduces the incidence of illnesses, enhances quality of life, and optimizes resource allocation. The IAHN plays a crucial role in the ongoing development of personalized medicine by ensuring continuous health monitoring, empowering healthcare providers, and enabling diagnostic tools to make informed decisions for disease prevention. © 2024 IEEE.

关键词： Personalized medicine

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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System

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Multimedia Tools and Applications 2024年第21期83卷 59887-59913页

作者： Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Zitar, Raed Abu Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Elminaam, Diaa Salama Abd Elaziz, Mohamed Abd Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wenzhou-Kean University Wenzhou China Department of Computer Information System Zarqa University P.O. Box 13132 Zarqa Jordan School of Information Engineering Sanming University Sanming365004 China Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha12311 Egypt Computer Science Department Faculty of Computer Science Misr International University Cairo12585 Egypt Faculty of Computer Science & Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Network security

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Investigation of Effective Electrode Structure for Metal Removal from Composite Materials Using Pulsed Discharge 10

Investigation of Effective Electrode Structure for Metal Rem...

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10th Euro-Asian Pulsed Power Conference, 25th International Conference on High-Power Particle Beams and 20th International Symposium on Electromagnetic Launch Technology, EAPPC-BEAMS-EML 2024

作者： Doi, Takuto Oishi, Shota Sakugawa, Takashi Nishimura, Kaisei Shobako, Shinichiro Shimasaki, Shinichi Yamashita, Tomohiko Kumamoto University Graduate School of Science and Technology Department of Computer Science and Electrical Engineering Kumamoto Japan Kagawa College National Institute of Technology Department of Electro-Mechanical Systems Engineering Takamatsu Japan

ISBN: (纸本)9789038661353

The authors investigate the potential of pulsed power technology in recycling of E-waste. Applying the pulsed discharge can separate composite materials into plastic and metal. In this study, pulsed discharge was applied to indium tin oxide (ITO) coated plastic films. We used two electrode types: rod-To-rod electrode and a pair of flat plate electrodes to investigate the effect of electrode structure on the metal removal area in detail. A series of single pulse discharges were applied at various electrode distances. As a result, it was revealed that metal can be removed over a wide range by the pulsed discharge using a pair of flat plate electrodes. When the gap between the electrodes was 30 mm, the removal area by the flat plate electrodes was approximately 3.4 times that by the rod electrodes. Analysis of the current density also revealed that the metal removal area was greatly affected by the current density. © 2024 Eindhoven University of Technology.

关键词： Tin oxides

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TATOO: A Flexible Hardware Platform for Binary-Only Fuzzing 24

TATOO: A Flexible Hardware Platform for Binary-Only Fuzzing

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wu, Jinting Zheng, Haodong Wang, Yu Yue, Tai Zhang, Fengwei Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9798400706011

Hardware-based tracing, being efficient, can be a good alternative to the computationally-expensive software-based instrumentation in binary-only greybox fuzzing. However, it only records all branches within a specified address range, lacking the flexibility to re-filter them. To overcome these limitations, this paper introduces Tatoo, a hardware platform that employs tagged architectures and hardware tracing to enable users to perform instruction-level tagging, which can significantly reduce the volume of traced data and improve fuzzing efficiency. Tatoo also supports recording the dataflow information for smart mutations. Implemented on a real hardware FPGA platform, Tatoo demonstrates a mere 8.7% performance overhead. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Digital storage

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Deep Learning-Based Classification of Areca Nut Yellow Leaf Disease with ResNet-50 CNN

Deep Learning-Based Classification of Areca Nut Yellow Leaf ...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Veeresha, R.K. Lathish Kumar, N.D. Shetty, Samarth S. Prasad, Shrajan G. Poojary, Swaroop S. Karegoudra, Shilpa Koten, Hasan Department of Robotics and Artificial Intelligence Udupi574110 India Department of Computer Science and Engineering Udupi574110 India Istanbul Medeniyet University Faculty of Engineering and Pure Sciences Department of Mechanical Engineering Istanbul Turkey

ISBN: (纸本)9798350388602

Detecting healthy arecanut leaves, yellow leaf disease in arecanut, and differentiating these from other types of leaves using deep learning involves designing an advanced neural network model for precise image classification. The model is trained on a dataset comprising images of healthy arecanut leaves, arecanut leaves affected by yellow leaf disease, and various other leaves from different plant species. Convolution Neural Networks (CNNs) are leveraged to extract and analyze intricate patterns in the images, enabling the model to effectively classify each leaf type. Transfer learning techniques might be utilized to improve the model's performance and adaptability. The model's effectiveness is evaluated using accuracy, precision, recall, and F1 score on a dedicated test dataset. This paper explores the methodology and results of employing image detection for the identification of healthy arecanut leaves, yellow leaf disease, and other leaf types, with potential applications in agricultural diagnostics, disease management, and crop monitoring. © 2024 IEEE.

关键词： Convolutional neural networks

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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

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computer Modeling in engineering & sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema Kavitha L.Francis Raj Sandeep Kautish Abdulaziz S.Almazyad Karam M.Sallam Ali Wagdy Mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine *** enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images *** image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye *** degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal *** proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between *** methodology was used to clarify the input images and make them adequate for the process of glaucoma *** objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were *** the peak regions were identified,the recurrence relationships among those peaks were then *** partitioning was done due to varying degrees of similar and dissimilar concentrations in the *** and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and *** distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural Network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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Symbiotic Organisms Search with Deep Learning Driven Biomedical Osteosarcoma Detection and Classification

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computers, Materials & Continua 2023年第4期75卷 133-148页

作者： Abdullah M.Basahel Mohammad Yamin Sulafah M.Basahel Mona M.Abusurrah K.Vijaya Kumar E.Laxmi Lydia Faculty of Economics and Administration King Abdulaziz UniversityJeddahSaudi Arabia E-Commerce Department College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia Department of Management Information Systems College of Business AdministrationTaibah UniversityAl-MadinahSaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVisakhapatnam CampusGITAM(Deemed to be a University)India Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia

Osteosarcoma is one of the rare bone cancers that affect the individualsaged between 10 and 30 and it incurs high death rate. Early diagnosisof osteosarcoma is essential to improve the survivability rate and treatmentprotocols. Traditional physical examination procedure is not only a timeconsumingprocess, but it also primarily relies upon the expert’s *** this background, the recently developed Deep Learning (DL) models canbe applied to perform decision making. At the same time, hyperparameteroptimization of DL models also plays an important role in influencing overallclassification performance. The current study introduces a novel SymbioticOrganisms Search with Deep Learning-driven Osteosarcoma Detection andClassification (SOSDL-ODC) model. The presented SOSDL-ODC techniqueprimarily focuses on recognition and classification of osteosarcoma usinghistopathological images. In order to achieve this, the presented SOSDL-ODCtechnique initially applies image pre-processing approach to enhance the qualityof image. Also, MobileNetv2 model is applied to generate a suitable groupof feature vectors whereas hyperparameter tuning of MobileNetv2 modelis performed using SOS algorithm. At last, Gated Recurrent Unit (GRU)technique is applied as a classification model to determine proper class *** order to validate the enhanced osteosarcoma classification performance ofthe proposed SOSDL-ODC technique, a comprehensive comparative analysiswas conducted. The obtained outcomes confirmed the betterment of SOSDLODCapproach than the existing approaches as the former achieved a maximumaccuracy of 97.73%.

关键词： Osteosarcoma medical imaging deep learning feature vectors computer aided diagnosis image classification

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Counterfactual discriminative micro-expression recognition

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Visual Intelligence 2024年第1期2卷 350-359页

作者： Yong Li Menglin Liu Lingjie Lao Yuanzhi Wang Zhen Cui Department of Computer Science City University of Hong KongHong Kong999077China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China

Micro-expressions are spontaneous,rapid and subtle facial movements that can hardly be suppressed or ***-expression recognition(MER)is one of the most challenging topics in affective *** aims to recognize subtle facial movements which are quite difficult for humans to perceive in a fleeting ***,many deep learning-based MER methods have been ***,how to effectively capture subtle temporal variations for robust MER still perplexes *** propose a counterfactual discriminative micro-expression recognition(CoDER)method to effectively learn the slight temporal variations for video-based *** explicitly capture the causality from temporal dynamics hidden in the micro-expression(ME)sequence,we propose ME counterfactual reasoning by comparing the effects of the facts *** ME sequences and the counterfactuals ***-revised ME sequences,and then perform causality-aware prediction to encourage the model to learn those latent ME temporal *** experiments on four widely-used ME databases demonstrate the effectiveness of CoDER,which results in comparable and superior MER performance compared with that of the state-of-the-art *** visualization results show that CoDER successfully perceives the meaningful temporal variations in sequential faces.

关键词： Affective computing Micro-expression recognition Temporal variation Counterfactual reasoning Causal graph

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