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Mayfly-Algorithm Selected Features for Classification of Breast Histology Images into Benign/Malignant Class 1

9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2021

作者： Kadry, Seifedine Rajinikanth, Venkatesan Srivastava, Gautam Meqdad, Maytham N. Department of Applied Data Science Noroff University College Kristiansand Norway College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Electronics and Instrumentation St. Joseph’s College of Engineering Tamilnadu Chennai600 119 India Department of Mathematics and Computer Science Brandon University 270 18th Street BrandonR7A 6A9 Canada Al-Mustaqbal University College Babil Hillah Iraq

ISBN: (数字)9783031215179

ISBN: (纸本)9783031215162

Incidence rate of Breast Cancer (BC) is rising globally and the early detection is important to cure the disease. The detection of BC consist different phases from verification to clinical level diagnosis. Confirmation of the cancer and its stage is performed normally with breast biopsy. This research aims to develop a framework to identify Benign/Malignant class images from the Breast Histology Slide (BHS). This technique consist the following phases;(i) Cropping and resizing the image slice, (ii) Deep-feature extraction using pre-trained network, (iii) Discrete Wavelet Transform (DWT) feature mining, (iv) Optimal feature selection with Mayfly algorithm, (v) Serial feature concatenation, and (vi) Binary classification and validation. This work considered the test image with dimension 896 × 768 × 3 pixels. During the investigation, every picture is cropped into 25 slices and resized to 224 × 224 × 3 pixels. This work implements the following stages;(i) BC detection with deep-features and (ii) BC recognition with concatenated features. In both the cases, a 5-fold cross validation is employed and the experimental investigation of this research confirms that the proposed work helped to achieve an accuracy of 91.39% with deep-feature and 95.56% with concatenation features. © 2022, Springer Nature Switzerland AG.

关键词： Discrete wavelet transforms

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A Trusted Reputation Management Scheme for Cross-Chain Transactions

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Sensors 2023年第13期23卷 6033-6033页

作者： Chen, Kuongho Lee, Lin-Fa Chiu, Wayne Su, Chunhua Yeh, Kuo-Hui Chao, Han-Chieh ezPay Co. Ltd Taipei11578 Taiwan Department of Information Management National Dong Hwa University Hualien97401 Taiwan Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby2800 Denmark Division of Computer Science University of Aizu Fukushima965-0006 Japan Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan Institute of Computer Science and Innovation UCSI University Kuala Lumpur5600 Malaysia

Blockchain has become a well-known, secured, decentralized datastore in many domains, including medical, industrial, and especially the financial field. However, to meet the requirements of different fields, platforms that are built on blockchain technology must provide functions and characteristics with a wide variety of options. Although they may share similar technology at the fundamental level, the differences among them make data or transaction exchange challenging. Cross-chain transactions have become a commonly utilized function, while at the same time, some have pointed out its security loopholes. It is evident that a secure transaction scheme is desperately needed. However, what about those nodes that do not behave? It is clear that not only a secure transaction scheme is necessary, but also a system that can gradually eliminate malicious players is of dire need. At the same time, integrating different blockchain systems can be difficult due to their independent architectures, and cross-chain transactions can be at risk if malicious attackers try to control the nodes in the cross-chain system. In this paper, we propose a dynamic reputation management scheme based on the past transaction behaviors of nodes. These behaviors serve as the basis for evaluating a node’s reputation to support the decision on malicious behavior and enable the system to intercept it in a timely manner. Furthermore, to establish a reputation index with high precision and flexibility, we integrate Particle Swarm Optimization (PSO) into our proposed scheme. This allows our system to meet the needs of a wide variety of blockchain platforms. Overall, the article highlights the importance of securing cross-chain transactions and proposes a method to prevent misbehavior by evaluating and managing node reputation. © 2023 by the authors.

关键词： Blockchain

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Analysis of the Influence of Students’ Identity on Academic Performance

Analysis of the Influence of Students’ Identity on Academic...

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IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics engineering, SIBIRCON

作者： Natalya V. Pustovalova Tatiana V. Avdeenko Dept. Automation and Computer Engineering Novosibirsk State Techinal University Novosibirsk Russian Federation Dept. Applied Mathematics and Computer Science Novosibirsk State Techinal University Novosibirsk Russian Federation

ISBN: (纸本)9781665464819

This paper presents the results of ongoing work. Today, personalization is one of the main trends in education. Universities collect and analyze data about the students' identity and their needs in order to develop specialized systems that support the personalization of the educational process. The purpose of the current work is to verify the applicability of the "Learner model" that was designed previously. This model is one of the main component of universities' intelligent learning system architecture. This model is based on the results of psychometric test. We increased the sample of students by including new observations and new predictors. Having performed preliminary data preparation, we obtained linear regression models. The results of psychometric tests were used as independent predictors (regressors). We tested students from the 2nd to 4th year of the Faculty of applied mathematics and Informatics. The dependent variable is the general ECTS score, as well as the average score for the groups of disciplines (mathematical, work-oriented, humanitarian). We compared the resulting models with those obtained using a smaller sample. In general, our conclusion was confirmed.

关键词： Learning systems Humanities Linear regression Knowledge based systems Predictive models Ontologies Multivariate regression

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University’s Educational Environment Personalization Based on the Ontological Models

University’s Educational Environment Personalization Based ...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Natalya V. Pustovalova Tatiana V. Avdeenko Alexandra V. Pustovalova dept. Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russian Federation dept. Applied Mathematics and Computer Science Novosibirsk State Technical University Novosibirsk Russian Federation

ISBN: (数字)9781665498043

ISBN: (纸本)9781665498050

The paper presents a work in progress. Personalization of the educational environment allows improving the quality of the educational process, raising it to a new level. Our development is intended to diagnose and recommend educational content, depending on the students’ personality. This article describes a system of ontological models (Learner Model, educational content model and cognitive functions model) designed to diagnose students’ characteristics and the subsequent issuance of educational content, taking into account these characteristics. The learner model, implemented as a Protege ontology, is based not only on human professional experience but also on student testing results. This approach, based on the use of various IQ tests, allows reflecting in the ontology the dependencies identified due to linear regression methods, as well as other types of analysis. By this way we will reduce the dependence of the model on the human expert’s personality and make it more objective. Using the Protege tools, we will combine three ontologies in the architecture of a personalized environment through rules and axioms.

关键词： Linear regression Ontologies Electron devices Testing

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Encryption with Image Steganography Based Data Hiding Technique in IIoT Environment

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computers, Materials & Continua 2022年第7期72卷 1323-1338页

作者： Mahmoud Ragab Samah Alshehri Hani A.Alhadrami Faris Kateb Ehab Bahaudien Ashary SAbdel-khalek Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah21589Saudi Arabia Molecular Diagnostic Lab King Abdulaziz University HospitalKing Abdulaziz UniversityJeddah21589Saudi Arabia Special Infectious Agent Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohag82524Egypt

Rapid advancements of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)pose serious security issues by revealing secret ***,security data becomes a crucial issue in IIoT communication where secrecy needs to be guaranteed in real ***,AI techniques can be utilized to design image steganographic techniques in *** addition,encryption techniques act as an important role to save the actual information generated from the IIoT devices to avoid unauthorized *** order to accomplish secure data transmission in IIoT environment,this study presents novel encryption with image steganography based data hiding technique(EISDHT)for IIoT *** proposed EIS-DHT technique involves a new quantum black widow optimization(QBWO)to competently choose the pixel values for hiding secrete data in the cover *** addition,the multi-level discrete wavelet transform(DWT)based transformation process takes ***,the secret image is divided into three R,G,and B bands which are then individually encrypted using Blowfish,Twofish,and Lorenz Hyperchaotic *** last,the stego image gets generated by placing the encrypted images into the optimum pixel locations of the cover *** order to validate the enhanced data hiding performance of the EIS-DHT technique,a set of simulation analyses take place and the results are inspected interms of different *** experimental outcomes stated the supremacy of the EIS-DHT technique over the other existing techniques and ensure maximum security.

关键词： IIoT security data hiding technique image steganography encryption secure communication

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On the existence of highly organized communities in networks of locally interacting agents

arXiv

引用

arXiv 2023年

作者： Liagkou, V. Nastou, P.E. Spirakis, P. Stamatiou, Y.C. 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 Kostakioi Arta47100 Greece Department of Mathematics University of the Aegean Applied Mathematics and Mathematical Modeling Laboratory Samos Greece Center for Applied Optimization University of Florida Gainesville United States

In this paper we investigate phenomena of spontaneous emergence or purposeful formation of highly organized structures in networks of related agents. We show that the formation of large organized structures requires exponentially large, in the size of the structures, networks. Our approach is based on Kolmogorov, or descriptional, complexity of networks viewed as finite size strings. We apply this approach to the study of the emergence or formation of simple organized, hierarchical, structures based on Sierpinski Graphs and we prove a Ramsey type theorem that bounds the number of vertices in Kolmogorov random graphs that contain Sierpinski Graphs as subgraps. Moreover, we show that Sierpinski Graphs encompass close-knit relationships among their vertices that facilitate fast spread and learning of information when agents in their vertices are engaged in pairwise interactions modelled as two person games. Finally, we generalize our findings for any organized structure with succinct representations. Our work can be deployed, in particular, to study problems related to the security of networks by identifying conditions which enable or forbid the formation of sufficiently large insider-subnetworks with malicious common goal to overtake the network or cause disruption of its operation. © 2023, CC BY.

关键词： Graphic methods

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Enhanced Mini-YOLOv7: A AI Approach for Safety Helmet Detection in the Construction Industry

Enhanced Mini-YOLOv7: A AI Approach for Safety Helmet Detect...

引用

IEEE International WIE Conference on Electrical and computer engineering (WIECON-ECE)

作者： A. Alice Blessie R. S. Sankarasubramanian Joseph John S. Krishnapriya A. Bhuvanesh R. Keerthanadevi Department of Electronics and Communication Engineering Government College of Engineering Tirunelveli India Department of Mathematics PSG Institute of Technology and Applied Research Coimbatore India Department of Mechnanical Engineering Gregorian Institute of Technology Kottayam India Department of English Sri Eshwar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535483

Wearing safety helmets is an effective way to keep construction workers safe on the job. Workers often opt to remove their helmets due to discomfort or a lack of security knowledge, leaving them vulnerable to unsaid threats. A worker's risk of injury from falls, both physical and otherwise, increases when they do not wear a safety helmet. Because detecting whether workers are wearing safety helmets is an important aspect of managing safety on construction sites, a fast and accurate detector is required. However, standard manual monitors aren't particularly popular, and attaching sensors to a safety helmet is a laborious operation. With that in mind, this study presents EM-YOLOv7, a DL method for autonomous safety helmet recognition in real-time that has been developed for use on construction sites. A multi-scale non-local attention module (MSNA) is incorporated into the system to improve detection accuracy in complicated situations. This module enhances feature recovery by aggregating semantic context across multiple scales. Further, to improve detection performance and handle mutual occlusion, a loss function named Wise-IoUv3 (WIoUv3) is used. This method adjusts the loss depending on the overlap between the predicted and ground truth bounding boxes. To train and test the model, we will utilize the safety helmet-wearing (SHWD) dataset, which includes a wide variety of construction workers and whether or not they wore helmets. The goal of this technique is to provide a 99% accurate solution for real-time helmet recognition, which would greatly improve safety management and decrease the likelihood of accidents on construction sites.

关键词： Accuracy Semantics Safety management Helmets Real-time systems Safety Sensors Artificial intelligence Standards Construction industry

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Two-dimensional computer Modelling of Contaminant Migration in Unsaturated Porous Media with Nanoparticles

Two-dimensional Computer Modelling of Contaminant Migration ...

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International computer sciences and Information Technologies (CSIT)

作者： Viktor Zhukovskyy Nataliia Zhukovska Jan Perhac Department of Computer Science and Applied Mathematics The National University of Water and Environmental Engineering Rivne Ukraine Department of Computers and Informatics Technical University of Kosice Kosice Slovak Republic

ISBN: (纸本)9798350334326

The complicated physical problem of computer modeling pollutant migration in the zone of suspended water with nanoparticles to the catcher filter is discussed in this study. The brand-new mathematical model of nonlinearity in two dimensions is offered. The generalized Darcy-Clute laws for mass and moisture movement in various subregions of soil are taken into careful consideration in the model. The finite difference method was used to find a numerical solution to the boundary value problem and to provide an algorithm for computer implementation. Utilizing a newly created Nanosurface program solution, computer modeling was carried out along with the analysis of numerical experiments. The nanoparticle influence was demonstrated.

关键词： Nanoparticles Cloud computing Boundary value problems Computational modeling Soil Predictive models Mathematical models

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Towards End-to-End Structure Solutions from Information-Compromised Diffraction Data via Generative Deep Learning

arXiv

引用

arXiv 2023年

作者： Guo, Gabe Goldfeder, Judah Lan, Ling Ray, Aniv Yang, Albert Hanming Chen, Boyuan Billinge, Simon J.L. Lipson, Hod Columbia University Department of Computer Science New YorkNY10027 United States Columbia University Department of Applied Physics and Applied Mathematics New YorkNY10027 United States Duke University Department of Mechanical Engineering and Materials Science DurhamNC27708 United States Columbia University Department of Mechanical Engineering New YorkNY10027 United States

The revolution in materials in the past century was built on a knowledge of the atomic arrangements and the structure-property relationship. The sine qua non for obtaining quantitative structural information is single crystal crystallography. However, increasingly we need to solve structures in cases where the information content in our input signal is significantly degraded, for example, due to orientational averaging of grains, finite size effects due to nanostructure, and mixed signals due to sample heterogeneity. Understanding the structure property relationships in such situations is, if anything, more important and insightful, yet we do not have robust approaches for accomplishing it. In principle, machine learning (ML) and deep learning (DL) are promising approaches since they augment information in the degraded input signal with prior knowledge learned from large databases of already known structures. Here we present a novel ML approach, a variational query-based multi-branch deep neural network that has the promise to be a robust but general tool to address this problem end-to-end. We demonstrate the approach on computed powder x-ray diffraction (PXRD), along with partial chemical composition information, as input. We choose as a structural representation a modified electron density we call the Cartesian mapped electron density (CMED), that straightforwardly allows our ML model to learn material structures across different chemistries, symmetries and crystal systems. When evaluated on theoretically simulated data for the cubic and trigonal crystal systems, the system achieves up to 93.4% average similarity with the ground truth on unseen materials, both with known and partially-known chemical composition information, showing great promise for successful structure solution even from degraded and incomplete input data. The approach doesn’t presuppose a crystalline structure and the approach are readily extended to other situations such as nanomaterials and te

关键词： Carrier concentration

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Modelling and Economic Optimal Control for a Laboratory-scale Continuous Stirred Tank Reactor for Single-cell Protein Production

arXiv

引用

arXiv 2022年

作者： Nielsen, Marcus Krogh Dynesen, Jens Dragheim, Jess Christensen, Ib Jørgensen, Sten Bay Huusom, Jakob Kjøbsted Gernaey, Krist V. Jørgensen, John Bagterp Department of Applied Mathematics and Computer Science Denmark Department of Chemical and Biochemical Engineering the Technical University of Denmark Kgs. Lyngby Denmark Unibio A/S Roskilde Denmark

In this paper, we present a novel kinetic growth model for the micro-organism Methylococcus capsulatus (Bath) that couples growth and pH. We apply growth kinetics in a model for single-cell protein production in a laboratory-scale continuous stirred tank reactor inspired by a physical laboratory fermentor. The model contains a set of differential algebraic equations describing growth and pH-dynamics in the system. We present a method of simulation that ensures non-negativity in the state and algebraic variables. Additionally, we introduce linear scaling of the algebraic equations and variables for numerical stability in Newton’s method. Finally, we conduct a numerical experiment of economic optimal control for single-cell protein production in the laboratory-scale reactor. The numerical experiment shows non-trivial input profiles for biomass growth and pH tracking. Copyright © 2022, The Authors. All rights reserved.

关键词： Laboratories

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