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Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

Intelligent Automation & Soft computing 2023年第6期36卷 3139-3155页

作者： Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Amgad Atta Abdelmageed Mohammad Mahzari Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah22230Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharj16242Saudi Arabia

applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based *** stay in the leading positions for business as well as personal *** popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email *** filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the *** prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully ***,this study provides a solution related to email message body text automatic classification into phishing and email *** paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on applied Linguistics for Email *** presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam *** the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further ***,the SRRNN method can be useful in recognizing and classifying spam *** hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter *** investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.

关键词： Email classification applied linguistics improved fruitfly optimization deep learning recurrent neural network

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Hydrodynamic Performance of Euplectella aspergillum:Simulating Real Life Conditions in the Abyss

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Communications in Computational Physics 2023年第1期33卷 273-282页

作者： Giacomo Falcucci Giorgio Amati Pierluigi Fanelli Sauro Succi Maurizio Porfiri Department of Enterprise Engineering“Mario Lucertini” University of Rome“Tor Vergata”RomeItaly Department of Physics Harvard UniversityCambridgeMAUSA High Performance Computing Department CINECA Rome SectionRomeItaly DEIM School of EngineeringUniversity of TusciaViterboItaly Italian Institute of Technology Center for Life Nano-and Neuro-ScienceRomeItaly National Research Council of Italy–Institute for Applied Computing(IAC) RomeItaly Department of Biomedical Engineering Tandon School of EngineeringNew York UniversityNew YorkNYUSA Department of Mechanical and Aerospace Engineering Tandon School of EngineeringNew York UniversityNew YorkNYUSA Center for Urban Science and Progress Tandon School of EngineeringNew York UniversityNew YorkNYUSA

We detail some of the understudied aspects of the flow inside and around the Hexactinellid Sponge Euplectella *** leveraging the flexibility of the Lattice Boltzmann Method,High Performance computing simulations are performed to dissect the complex conditions corresponding to the actual environment at the bottom of the ocean,at depths between 100 and 1,000 *** large-scale simulations unveil potential clues on the evolutionary adaptations of these deep-sea sponges in response to the surrounding fluid flow,and they open the path to future investigations at the interface between physics,engineering and biology.

关键词： Lattice Boltzmann method fluid-structure interaction sponge hydrodynamics high performance computing complex boundary conditions

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Strategic Defense Post Allocation Using the Corona Cover Set for Enhanced Military Zone Security

Strategic Defense Post Allocation Using the Corona Cover Set...

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Women in computing (InCoWoCo), International Conference for

作者： Niveditha. P G. Mahadevan C. Sivagnanam Department of Mathematics The Gandhigram Rural Institute (Deemed to be University) Gandhigram Tamilnadu India Mathematics and Computing Skills Unit University of Technology and Applied Sciences-Sur Sultanate of Oman

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Defense forces are vital for national safety, peace-keeping, disaster response, and economic support. They require strong support systems, medical access, rapid deployment, and effective command centers to operate efficiently. Proper placement and a well-structured defense strategy focusing on sustainability and readiness are essential for ensuring safety and stability for both military and civilians. Recently G. Mahadevan *** established the concept of Corona covering number of a graph and obtained many results. A vertex cover set $S\subseteq V(G)$ is a corona cover set if every vertex $v\in S$ such that $d_{\langle S\rangle}(v)=1$ or there exist a vertex $u\in S$ with $d_{\langle S\rangle}(u)=1$ and $uv\in E$ . The least cardinality of a corona cover set is the corona covering number of a graph and it is expressed as $\tau_{C}(G)$ . In this article, the investigation of corona covering number in the strategic placement of defense posts within military zones, aiming to enhance operational efficiency and effectively deter unauthorized incursions has been attained.

关键词： Protected areas Systematics Military computing Focusing Corona Security Resource management Sustainable development Monitoring Resilience

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Ensemble Deep Learning with Chimp Optimization Based Medical Data Classification

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Intelligent Automation & Soft computing 2023年第2期35卷 1643-1655页

作者： Ashit Kumar Dutta Yasser Albagory Majed Alsanea Hamdan I.Almohammed Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Basic Medical Sciences College of Medicine.Almaarefa UniversityAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Eye state classiﬁcation acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so *** modiﬁcations in the cognitive levels can be reﬂected via transforming the electro-encephalogram(EEG)*** deep learning(DL)models automated extract the features and often showcased improved outcomes over the conventional clas-siﬁcation model in the recognition *** paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classiﬁ-cation(EDLCOA-ESC).The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing ***,wavelet packet decomposition(WPD)technique is employed for the extraction of useful features from the EEG *** addition,an ensemble of deep sparse autoencoder(DSAE)and kernel ridge regression(KRR)models are employed for EEG Eye State classiﬁ***,hyperparameters tuning of the DSAE model takes place using COA and thereby boost the classiﬁcation results to a maximum *** extensive range of simulation analysis on the benchmark dataset is car-ried out and the results reported the promising performance of the EDLCOA-ESC technique over the recent approaches with maximum accuracy of 98.50%.

关键词： EEG eye state data classiﬁcation deep learning medical data analysis chimp optimization algorithm

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An Elastic LatticeKrig Generative Model for Non-ideal Spatial Datasets

An Elastic LatticeKrig Generative Model for Non-ideal Spatia...

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Smart World Congress (SWC), IEEE

作者： Gang Xu Di Wu Christina Hellevik Yushan Pan Department of Applied Mathematics Xi’an Jiaotong-Liverpool University China Department of ICT and Natural Sciences Norwegian University of Science and Technology Norway Department of International Business Norwegian University of Science and Technology Norway Department of Computing Xian Jiaotong-Liverpool University China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

When datasets have missing parts, we typically use data characteristics to select a suitable model to generate these missing parts. However, the poor quality of real data often affects the fitting accuracy of the ideal model, leading to lower-thanexpected results. In this paper, we improve the LatticeKrig model by using local basis function spanning. This enhancement aims to improve the model’s fitting ability and robustness to poor quality data while avoiding potential overfitting. In our experiment, we demonstrated the model’s fitting ability with ideal data and used it to fit the missing parts of garbage collection data from the Norwegian coast. The results show that our improved model achieves better fitting performance with non-ideal data.

关键词： Accuracy Fitting Lattices Data models Spatial databases Robustness Overfitting

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Feature Extraction and Classification Stage on Facial Expression: A Review 6

Feature Extraction and Classification Stage on Facial Expres...

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6th International Conference on Informatics and Computational Sciences, ICICoS 2022

作者： Shidiq, Muchamad Bachram Ernawan, Ferda Khubrani, Mousa Nugroho, Fajar Agung Universitas Nusa Mandiri Faculty of Computer Science Jakarta Indonesia Universiti Malaysia Pahang Faculty of Computing College of Computing & Applied Sciences Pahang Malaysia Jazan University Faculty of Computer Science & Information Technology Jazan Saudi Arabia Diponegoro University Department of Informatics Semarang Indonesia

ISBN: (数字)9781665460996

ISBN: (纸本)9781665460996

Human facial expression becomes an important technology in recent years. As information technology and networks have grown, identification and authentication have become more frequent in people's daily lives, especially using biometric technology. Human facial recognition involves face detection, feature extraction, and classification. A lot of experiments showed that there are various techniques for extracting facial features and classifying facial expressions. This paper reviews and analyze the various optimization techniques on extract feature and classification stage for human facial expression recognition. This review will compare two kinds of extract features methods and one classification method. The first technique of extracting features is the optimization technique using the K-Mean algorithm, which helps to increase recognition accuracy. The second extract feature is an optimization technique using improved Gradient Local Ternary Pattern (GLTP) which is beneficial for computational resources efficiency. Lastly, the optimization technique for image classification using a three-staged Support Vector Machine (SVM) is very helpful for increasing accuracy and eliminating error. The modified GLTP is able to obtain an accuracy of 97%. © 2022 IEEE.

关键词： Classification (of information)

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SkinMarkNet: an automated approach for prediction of monkeyPox using image data augmentation with deep ensemble learning models

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Multimedia Tools and Applications 2025年第18期84卷 20177-20193页

作者： Akram, Aqsa Jamjoom, Arwa A. Innab, Nisreen Almujally, Nouf Abdullah Umer, Muhammad Alsubai, Shtwai Fimiani, Gianluca Bahawalpur Pakistan Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Diriyah13713 Saudi Arabia 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 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 Computer Science University of Salerno Fisciano84084 Italy

Monkeypox, a rare but potentially fatal viral disease, poses a significant public health challenge due to its potential for outbreaks and complications. Detecting monkeypox lesions early and accurately is vital to effectively manage and control the disease. This study introduces a novel method for classifying monkeypox lesions, employing data augmentation methods and a framework based on an ensemble of three transfer learning models called "SkinMarkNet". The dataset used in this research consists of skin lesion images collected from the Kaggle data repository, encompassing diverse demographics and lesion characteristics. This research uses image data augmentation techniques to tackle the scarcity of annotated data. This augmentation enriches the training dataset, thereby improving the model’s ability to perform effectively. Moreover, the novelty of this research work lies in the usage of three popular transfer learning models(Inception, Xception, and ResNet) for feature extraction and ensemble learning. The SkinMarkNet achieves promising results showing an accuracy of 90.615% for monkeypox lesion classification, outperforming traditional machine learning and deep learning methods utilized in recent research works. In addition, thorough comparative analysis is done with machine learning models and contemporary approaches to validate the efficacy of the proposed method. Overall, the findings underscore the potential of leveraging advanced deep learning architectures and data augmentation strategies for improving monkeypox lesion classification, thereby facilitating early diagnosis and intervention in clinical settings and public health surveillance efforts. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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An overview of common emotion models in computer systems

An overview of common emotion models in computer systems

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Proceedings of the International Convention MIPRO

作者： M. Horvat A. Stojanović Ž . Kovač ević Department of Applied Computing Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Computer Science and Information Technology Zagreb University of Applied Sciences Zagreb Croatia

ISBN: (数字)9789532331035

ISBN: (纸本)9781665484343

Emotions are an omnipresent and important factor in the interaction and communication between people. Since emotions are an indispensable part of human life, it would accelerate the progress of artificial intelligence and other fields of science that require data about emotions if they could be adequately described by computer systems. Today there are many different theories of affect, but few of them are used in affective computing. Other areas of computing also benefit from structured and expressive data models of the affective domain, such as human-computer interaction and brain-computer interfaces. Typical tasks include automated recognition and analysis of emotional states, mental fatigue, individual motivation, vigilance and stress resilience. In this paper four often used models of emotion and cognitive behavior are listed and their properties explained: discrete, dimensional, appraisal and action tendency models. For each model, algorithms are provided for similarity measures that can be used to determine the relatedness between different stimulation and estimation artefacts in their respective emotion spaces. The goal of this article is to help professionals find the optimal emotion model for their research and quickly become familiar with data modelling of affective states.

关键词： Emotion recognition Computational modeling Estimation Human factors Brain modeling Fatigue Extraterrestrial measurements

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Deep Neural Cellular Potts Models

arXiv

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arXiv 2025年

作者： Minartz, Koen d’Hondt, Tim Hillmann, Leon Starruß, Jörn Brusch, Lutz Menkovski, Vlado Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Department of Applied Physics and Science Education Eindhoven University of Technology Eindhoven Netherlands Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands Center for Information Services and High Performance Computing TUD Dresden University of Technology Dresden Germany

The cellular Potts model (CPM) is a powerful computational method for simulating collective spatiotemporal dynamics of biological cells. To drive the dynamics, CPMs rely on physics-inspired Hamiltonians. However, as first principles remain elusive in biology, these Hamiltonians only approximate the full complexity of real multicellular systems. To address this limitation, we propose NeuralCPM, a more expressive cellular Potts model that can be trained directly on observational data. At the core of NeuralCPM lies the Neural Hamiltonian, a neural network architecture that respects universal symmetries in collective cellular dynamics. Moreover, this approach enables seamless integration of domain knowledge by combining known biological mechanisms and the expressive Neural Hamiltonian into a hybrid model. Our evaluation with synthetic and real-world multicellular systems demonstrates that NeuralCPM is able to model cellular dynamics that cannot be accounted for by traditional analytical Hamiltonians. Copyright © 2025, The Authors. All rights reserved.

关键词： Potts model

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Evaluating the Knowledge in Test Engineering Education using a Solar-powered Remote-controlled Flying Probe-Inspired In-Circuit Tester

Evaluating the Knowledge in Test Engineering Education using...

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International Symposium for Design and technology of Electronics Packages (SIITME)

作者： Sorin Liviu Jurj Raul Rotar Ioana Brîncovan Flavius Oprițoiu Mircea Vlăduțiu Computers and Information Technology Department Advanced Computing Systems and Architectures (ACSA) Laboratory “Politehnica” University of Timisoara Timisoara Romania Department of Automation and Applied Informatics Aurel Vlaicu University Arad Romania

ISBN: (数字)9798331539511

ISBN: (纸本)9798331539528

This paper presents a solar-powered remote-controlled Flying Probe-Inspired In-Circuit Tester (FPICT) designed to improve individuals’ knowledge and practical skills in the Test Engineering Education (TEE) domain. The proposed system consists of a user-friendly modern website that offers three main functions: (a) access to TEE-related documentation and tests, (b) a real-time webcam feed for remote monitoring, access, control, and execution of real-time tests on multiple Printed Circuit Boards (PCBs) using the FPICT platform, and (c) performance assessment of users’ TEE knowledge improvement. The effectiveness of the FPICT in enhancing TEE knowledge is evaluated by comparing the score distribution of users before and after the initial tests. Additionally, the power consumption of the FPICT platform is measured, and the feasibility of powering the system with solar energy is investigated. Experimental results demonstrate that the remote-controlled FPICT successfully increases the TEE-related knowledge of web users by an average of $26 \%$ over an average duration of 50 minutes, highlighting its efficacy in teaching theoretical concepts of In-Circuit Testing (ICT) and developing practical skills in the Flying Probe Testing (FPT) domain.

关键词： Knowledge engineering Power measurement Webcams Printed circuits Solar energy Real-time systems Probes Engineering education Remote monitoring Testing

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