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23rd International Conference on Transparent Optical Networks, ICTON 2023

作者： Tsintzos, Simeon I. Tsimvrakidis, Konstantinos Sinani, Alexandra Bogris, Adonis Gates, James C. Smith, Peter G.R. Elshaari, Ali W. Zwiller, Val Riziotis, Christos Theoretical & Physical Chemistry Institute National Hellenic Research Foundation Athens11635 Greece University of West Attica Department of Informatics and Computer Engineering Egaleo12243 Greece University of Southampton Optoelectronics Research Centre SouthamptonSO17 1BJ United Kingdom Department of Applied Physics Stockholm106 91 Sweden Institute for Advanced Modelling and Simulation University of Nicosia NicosiaCY-2417 Cyprus

ISBN: (纸本)9798350303032

The paper presents current design issues, limitations, and fabrication challenges towards the development of hybrid integrated optical circuits with embedded single photon sources, which are critical components for future quantum computing, sensing, and communication systems. Optical nanowires with embedded quantum dots (NWQD) are very promising single photon sources, with several reported results of integration in high index platforms like silicon or silicon nitride. Silica on silicon is a favorable platform for optical computing, providing the high degree of customization when combined with direct laser writing techniques for definition of optical structures with refractive index modification. However, the low ~10-3 achievable refractive index contrast imposes strict limitations on the compatibility with NWQD resulting in general in low coupling efficiency as shown in this work. Consideration of several design and fabrication issues demonstrates the potential for NQWD integration in laser written silica based optical circuits with adequate efficiency for practical applications. © 2023 IEEE.

关键词： Fabrication

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Recent Advances of Chimp Optimization Algorithm:Variants and Applications

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Journal of Bionic engineering 2023年第6期20卷 2840-2862页

作者： Mohammad Sh.Daoud Mohammad Shehab Laith Abualigah Mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva Mohammad AAlia Raed Abu Zitar College of Engineering Al Ain University112612Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Haj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio'n Basada en la Informacio'n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500Petaling JayaMalaysia

Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence *** has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial ***,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable ***,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short ***,in this review paper,several research publications using ChOA have been overviewed and ***,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual *** main operations of ChOA are procedurally discussed,and the theoretical foundation is ***,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled *** main applications of ChOA are also thoroughly *** applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,*** of ChOA is also *** review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and *** well,it is wealthy in research on health,environment,and public ***,it will aid those who are interested by providing them with potential future research.

关键词： Artificial intelligence Nature-inspired optimization algorithms Chimp optimization algorithm Optimization problems

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Construction of Countably Infinite Programs that Evade Malware/Non-Malware Classification for Any Given Formal System

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SSRN 2024年

作者： Stamatiou, Yannis C. Liagkou, Vasia Spirakis, Paul G. Nastou, Panayiotis E. Computer Technology Institute and Press - "Diophantus" University of Patras Campus 26504 Greece Department of Computer Science University of Liverpool UK and Computer Engineering and Informatics Department University of Patras 26504 Greece Department of Business Administration University of Patras 26504 Greece University of Ioannina Department of Informatics and Telecommunications Koatakioi Arta 47100 Greece Department of Mathematics University of the Aegean Applied Mathematics and Mathematical Modeling Laboratory Samos Greece

The formal study of computer malware was initiated in the seminal work of Fred Cohen in the mid-80s who applied elements of Computation Theory in the investigation of the theoretical limits of using the Turing Machine formal model of computation in detecting viruses. Cohen gave a simple but realistic, formal, definition of the characteristic actions of a computer virus as a Turing Machine that replicates itself and proved that detecting this behaviour, in general. is an undecidable problem. In this paper, we complement Cohen's approach by providing a simple generalization of his definition of a computer virus so as to model any type of malware behaviour and showing that the malware/nonmalware classification problem is, again, undecidable. Most importantly, beyond Cohen's work, our work provides a generic theoretical framework for studying anti-malware applications and identifying, at an early stage, before their deployment, several of their inherent vulnerabilities which may lead to the construction of zero-day exploits and malware strains with stealth properties. To this end, we show that for any given formal system, which can be seen as an anti-malware formal model, there are infinitely many, effectively constructible, programs for which no proof can be produced by the formal system that they are either malware or non-malware programs. Moreover, infinitely many of these programs are, indeed, malware programs which evade the detection powers of the given formal system. © 2024, The Authors. All rights reserved.

关键词： Turing machines

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ML_Recognition: A Robust Model for Handwritten Digit Recognition Using Machine Learning Classifiers 3

ML_Recognition: A Robust Model for Handwritten Digit Recogni...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Zeid, Magdy Abd-Elghany Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Ibrahim Abd Elatif Gomaa Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9798350306231

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: Random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%. © 2023 IEEE.

关键词： Support vector machines

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AI Based Prediction for Heart Disease: A Comparative Analysis and an Improved Machine Learning Approach

AI Based Prediction for Heart Disease: A Comparative Analysi...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Jay Raval Jai Prakash Verma Sardar N. M. Islam Rachna Jain Narina Thakur Computer Science Engineering Department Institute of Technology Nirma University Ahmedabad India Applied Informatics Research Victoria University Melbourne Australia Department of Computer Science and Engineering Bhagwan Parshuram Institute of Technology Delhi India

Heart disease problems are growing day by day in the world. Many factors are responsible for increasing the chance of heart attack and any other disease. Many countries have a low level of cardiovascular competence in predicting heart disease-related issues. Finding the best accurate machine learning classifiers for various diagnostic uses by data mining and machine learning techniques aids in predicting whether or not the heart disease-related issue will occur. To predict heart disease, a number of supervised machine-learning algorithms are used and their effectiveness are evaluated. With the exceptionof MLP and KNN, all applied algorithms had their estimated feature significance scores for each feature. This helps to find the main factors affecting heart disease and the accuracy of the model, which helps to get the best prediction. At the end of the research the support vector machine gives us 87.91 % highest testing accuracy compare with all applied machine learning algorithm.

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Multiclass skin lesion classification using deep learning networks optimal information fusion

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Discover applied Sciences 2024年第6期6卷 1-13页

作者： Khan, Muhammad Attique Hamza, Ameer Shabaz, Mohammad Kadry, Seifeine Rubab, Saddaf Bilal, Muhammad Abdullah Akbar, Muhammad Naeem Kesavan, Suresh Manic Department of Computer Science HITEC University Taxila Pakistan Model Institute of Engineering and Technology Jammu India Department of Applied Data Science Noroff University College Oslo Norway Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of CS SEECS NUST Islamabad Pakistan National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical and Communication Engineering National University of Science and Technology Muscat Sultanate of Oman

A serious, all-encompassing, and deadly cancer that affects every part of the body is skin cancer. The most prevalent causes of skin lesions are UV radiation, which can damage human skin, and moles. If skin cancer is discovered early, it may be adequately treated. In order to diagnose skin lesions with less effort, dermatologists are increasingly turning to machine learning (ML) techniques and computer-aided diagnostic (CAD) systems. This paper proposes a computerized method for multiclass lesion classification using a fusion of optimal deep-learning model features. The dataset used in this work, ISIC2018, is imbalanced; therefore, augmentation is performed based on a few mathematical operations. After that, two pre-trained deep learning models (DarkNet-19 and MobileNet-V2) have been fine-tuned and trained on the selected dataset. After training, features are extracted from the average pool layer and optimized using a hybrid firefly optimization technique. The selected features are fused in two ways: (i) original serial approach and (ii) proposed threshold approach. Machine learning classifiers are used to classify the chosen features at the end. Using the ISIC2018 dataset, the experimental procedure produced an accuracy of 89.0%. Whereas, 87.34, 87.57, and 87.45 are sensitivity, precision, and F1 score respectively. At the end, comparison is also conducted with recent techniques, and it shows the proposed method shows improved accuracy along with other performance measures.

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An application of some machine learning methods for biological data modeling 15

An application of some machine learning methods for biologic...

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15th Conference of the Euro-American Consortium for Promoting the Application of Mathematics in Technical and Natural Sciences, AMiTaNS 2023

作者： Sapundzhi, Fatima Lazarova, Meglena Dzimbova, Tatyana Georgiev, Slavi Department of Communication and Computer Engineering Faculty of Engineering South-West University Neofit Rilski 66 Ivan Myhailov Str. Blagoevgrad2700 Bulgaria Faculty of Applied Mathematics and Informatics Technical University of Sofia 8 St. Kliment Ohridski Blvd. Sofia1000 Bulgaria Institute of Molecular Biology Bulgarian Academy of Sciences Acad. G.Bonchev Str bl. 21 Sofia1113 Bulgaria Department of Information Modeling Institute of Mathematics and Informatics Bulgarian Academy of Sciences Acad. Georgi Bonchev Str. bl. 8 Sofia1113 Bulgaria Department of Applied Mathematics and Statistics Faculty of Natural Sciences and Education University of Ruse 8 Studentska Str. Ruse7004 Bulgaria

The development of fast and reliable methods for predicting the biological activity of the substances in computational biology is of a great importance. This improves the development of some new compounds while keeping costs low. Among many scientists, an attractive target for docking experiments is the Delta-opioid receptor (DOR) and delta-opioid ligands (DOL). Their biological efficacy can be measured by various techniques, which could facilitate the establishment of the relationship between the structure of the compounds and their biological effect. The relationship between the results of the computer experiments and the biological activity of these compounds is modelled by using machine learning regressors. The primary goal of this study is to determine the most appropriate neural network for modelling the relationship between in vitro and in silico results for DOR and delta-opioid ligands. © Published under licence by IOP Publishing Ltd.

关键词： Bioactivity

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Promoting Accurate Image Reconstruction via Synthetic Noise for Unsupervised Screw Anomaly Detection and Location

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Chen, Jian Wen Lin, Wan Ju Li, Kuan Ming Mrozek, Dariusz Hung, Che Lun National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan National Kaohsiung University of Science and Technology Department of Mold and Die Kaohsiung80778 Taiwan National Taiwan University Department of Mechanical Engineering Taipei106319 Taiwan Silesian University of Technology Department of Applied Informatics Poland National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei11221 Taiwan

With residuals-based unsupervised models, defects can be detected using only normal samples, boosting both efficiency and practicality in industrial anomaly detection (AD) applications. While these approaches offer benefits, they also present challenges in detecting anomalies in cylindrical objects, such as imprecise image reconstruction and camera blind spots. As a result, this paper introduces ScrewNet, an unsupervised anomaly detection framework designed specifically for cylindrical objects like screws. To improve the accuracy of reconstruction in the AD network, the synthetic noise module and pair training approach are employed during the training stage. The synthetic noise module, utilizing one of the CycleGAN generators, produces defects that match real-world distributions, thereby enabling the AD network to extract valuable features while preserving image structure. The pair training approach is designed to enable the AD network to achieve accurate reconstruction using similar background input-output pairs. To tackle inspection blind spots problem, an efficient, cost-effective inspection system using multiple mirrors is developed for the inference stage. This approach enables effective anomaly detection across the entire 360-degree surface of the screw. In experiments, three different types of screw datasets containing both damaged and undamaged screws are provided. Numerous experiments demonstrate the superiority of our approach over several state-of-the-art (SOTA) unsupervised AD networks. For instance, ScrewNet outperforms baselines by 4 dB in average Peak signal-to-noise ratio (PSNR) for image reconstruction quality and by 32% in average Area Under the Receiver Operating Characteristic curve (AUROC) for pixel-level anomaly segmentation. Additionally, image-level detection achieves 98.3% accuracy across the three screw datasets. The code is available at https://***/vitowen9580/ScrewNet. © 2025 IEEE.

关键词： Image reconstruction

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Impact of tourist demand, from COVID-19, on the sustainable Inca infrastructure of Ollantaytambo, Urubamba – Cusco – Peru 10

Impact of tourist demand, from COVID-19, on the sustainable ...

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10th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2021

作者： Esenarro, Doris Santos, Jaqueline Teodoro, Christian Pandey, Bishwajeet Rodriguez, Ciro Geographical Environmental Ecoturiism Engineering Universidad Nacional Federico Villarreal Lima Peru Computer Science & Engineering Birla Institute of Applied Science Bhimtal India Faculty of Electronics Informatics and Mechatronics Engineering Universidad Nacional Federico Villarreal Lima Peru

ISBN: (纸本)9780738105239

The objective of the study is to analyze how tourism demand impacts, in the sustainable Inca infrastructure of Ollantaytambo in Cusco during 2020 in COVID-19. Based on bibliographic reviews presented in other research, regulations, and registration of updated data from the last 10 years in the tourism sector, such as the registration of visitors, flights, and others. There was relying on GIS tools to determine its location and accessibility. For its development, there were stages of recognition and description of the study area, characterization of the physical and demographic aspects, analysis of the data collected and tourist demand, and identifying the impacts from COVID-19. It was found that By 2020, a considerable increase in the tourist demand of Ollantaytambo was expected. However, the repercussion caused by the COVID-19 pandemic, the visitor registration in Ollantaytambo fell by 73% compared to 2019 and flights fell by 71, 7%, thus concluding that tourism has had a significant drop, with uncertain prognostic. A change in the current tourist dynamics allows controlling the capacity, respecting the spaces, taking care of the visitor, and generating a greater organization for developing the tourist activity. © 2021 IEEE.

关键词： Tourism

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Darcy-Forchheimer mangetized flow based on differential type nanoliquid capturing Ohmic dissipation effects

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Propulsion and Power Research 2023年第3期12卷 443-455页

作者： M.Waqas Yunjie Xu M.Nasir Md Mottahir Alam Amjad Ali Pasha Kashif Irshad Bandar M.Fadhl M.S.Kausar NUTECH School of Applied Science and Humanities National University of TechnologyIslamabad 44000Pakistan Department of Mechanical Engineeing Lebanese American UniversityBeirutLebanon School of Engineering Huzhou UniversityHuzhou 313000China Faculty of Informatics and Computing University Sultan Zainal AbidinBesut Campus22200 BesutTereng-ganuMalaysia Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Aerospace Engineering Department King Abdulaziz UniversityJeddah 21589Saudi Arabia Interdisciplinary Research Centre for Renewable Energy and Power System(IRC-REPS) Research InstituteKing Fahd University of Petroleum and Minerals(KFUPM)Dhahran 31261Saudi Arabia Mechanical Engineering Department College of Engineering and Islamic ArchitectureUmm Al-Qura UniversityP.O.Box 5555Makkah 21955Saudi Arabia

Hydromagnetic nanoliquid establish an extraordinary category of nanoliquids that unveil both liquid and magnetic *** interest in the utilization of hydromagnetic nanoliquids as a heat transporting medium stem from a likelihood of regulating its flow along with heat transportation process subjected to an externally imposed magnetic *** analysis reports the hydromagnetic nanoliquid impact on differential type(second-grade)liquid from a convectively heated extending *** well-known Darcy-Forchheimer aspect capturing porosity characteristics is introduced for nonlinear *** conditions elaborating heat-mass transportation effect are *** addition,Ohmic dissipation and suction/injection aspects are also a part of this *** analysis is done by implementing the basic relations of fluid *** modeled physical problem is simplified through order *** resulting systems(partial differential expressions)are rendered to the ordinary ones by utilizing the apposite *** solutions are constructed employing homotopy *** and numeric result are addressed comprehensively to elaborate the nature of sundry parameters against physical *** velocity profile is suppressed with increasing Hartmann number(magnetic parameter)whereas it is enhanced with increment in material parameter(second-grade).With the elevation in thermophoresis parameter,temperature and concentration of nanoparticles are accelerated.

关键词： Darcy-Forchheimer magnetized flow Differential type nanoliquid Ohimc dissipation Robin conditions Homotopy algorithm

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