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Encryption with Image Steganography Based Data Hiding Technique in IIoT Environment

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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Dt2f-Tlnet: A Novel Text-Independent Writer Identification Model Using a Combination of Deep Type-2 Fuzzy Architecture and Transfer Learning Networks Based on Handwriting Data

SSRN

引用

SSRN 2023年

作者： Yang, Jing Shokouhifar, Mohammad Yee, Por Lip Khan, Abdullah Ayub Awais, Muhammad Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Department of Electrical & Computer Engineering Shahid Beheshti University Tehran*** Iran Department of Computer Science and Information Technology Benazir Bhutto Shaheed University Lyari Karachi75660 Pakistan COMSATS University Islamabad Pakistan

Identifying and verifying the identity of people based on scanned images of handwritten documents is an applicable biometric modality with applications in forensic and historic document investigation, and it is an important study area within the research field of behavioral biometrics. Despite this, there are few studies in this field. Furthermore, there are very few standard datasets for identifying and verify handwritten documents. Also, handwritten documents lose their character during time because of ink spread and drying. Therefore, it is necessary to provide a method that can identify and verify handwritten documents under various uncertainties. In this study, a text-independent writer identification and verification model in offline state under different experimental conditions is developed using a combination of Deep Type-2 Fuzzy architecture and Transfer Learning networks (DT2F-TLNet). So, a right-to-left dataset has been collected. The proposed DT2F-TLNet model is validated using both the designed dataset and other benchmark datasets. The proposed model is distinguished by the fact that it is developed to be independent of the textual content of the handwritten cases and can be used for various languages. The study's findings show that the developed DT2F-TLNet model can learn properties from heterogeneous handwriting data and results in higher accuracy than other comparable approaches. © 2023, The Authors. All rights reserved.

关键词： Biometrics

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Low-error Reconstruction of Directional Functions with Spherical Harmonics

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Vlnas, Michal Milet, Tomas Zemcik, Pavel Brno University of Technology Department of Computer Graphics and Multimedia Faculty of Information Technology Czech Republic Lappeenranta-Lahti University of Technology School of Engineering Science Lappeenranta53850 Finland

This paper proposes a novel approach for the low-error reconstruction of directional functions with spherical harmonics. We introduce a modified version of Spherical Gaussians with adaptive narrowness and amplitude to represent the input data in an intermediate form. This representation is then projected into spherical harmonics using a closed-form analytical solution. Because of the spectral properties of the proposed representation, the amount of ringing artifacts is reduced, and the overall precision of the reconstructed function is improved. The proposed method is more precise comparing to existing methods. The presented solution can be used in several graphical applications, as discussed in this paper. For example, the method is suitable for sparse models such as indirect illumination or reflectance functions. © 1995-2012 IEEE.

关键词： Visualization

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Security of Cloud-Assisted BANs Using Digital Twin

Security of Cloud-Assisted BANs Using Digital Twin

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International Conference on information technology in Asia (CITA)

作者： Najm Us Sama Kartinah Zen Aziz Ud Din Nazia Azim Atiq Ur Rahman Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Kota Samarahan Sarawak Malaysia Shaikh Zayed Islamic Center University of Peshawar Peshawar Pakistan Abdul Wali Khan University Mardan Pakistan Faculty of Computer Information Science Higher Colleges of Technology United Arab Emirates

Wireless Sensor Networks (WSNs) that monitor individuals' physical and medical conditions are called Body Area Networks (BANs). This technology uses wireless sensors to monitor vital signs from the human body. In order to overcome processing and storage limitations, the BAN is often connected to the cloud. Wireless Body Area Network (WBAN), however, opens up the possibility of various security threats when using the cloud. The digital twin (DT) can provide users with hundreds of thousands of interpretations of the physical device without interfering with its normal functionality. It provides an accurate depiction of a cyberattack before time, which maximizes efficiency by reflecting and simulating the physical devices and their relationships with the environment. This paper proposes an integrated security framework for cloud-assisted BANs (CBANs), which will use DT to detect cyberattacks. By combining digital and physical models, cyber-physical security (CPS) can be optimized in advance at low risk and cost. The results of the study show that designing and evaluating techniques for the protection of CBAN using digital twin will help security experts to use an optimized solution without physical testing that will also save time and resources.

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Residual and Attention Mechanism-Based Network for Aluminum-Silicon Alloy Microstructure Image Classification

Residual and Attention Mechanism-Based Network for Aluminum-...

引用

information Systems and Computing technology (ISCTech), International Conference on

作者： Xiao Yang Tao Shen Jiancheng Yin Yingli Liu Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China Faculty of Materials Science and Engineering Kunming University of Science and Technology Kunming China

To enhance the accuracy of aluminum-silicon alloy microstructure image classification, we propose an aluminum-silicon alloy microstructure image classification model based on an improved residual network, incorporating attention mechanisms and multi-scale thinking. The model is built upon the ResNet18 architecture, utilizing enhanced basic residual blocks to select informative features. Additionally, a multi-scale attention module is incorporated into the network to fuse multi-scale features and further extract effective features. Experimental results on an aluminum-silicon alloy microstructure image classification dataset demonstrate that compared to the best-performing model currently available, the proposed model achieves an increase in classification accuracy, precision, recall, and F1 score by 3.17%, 0.74%, 5.53%, and 3.26%, respectively, reaching 94.97%, 96.39%, 93.97%, and 95.17%. These results indicate that the proposed model exhibits strong capabilities in classifying aluminum-silicon alloy microstructure images and can facilitate further research on the properties of aluminum-silicon alloys.

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Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts 22

Using Artificial Neural Networks to Determine Ontologies Mos...

引用

22nd Conference information Technologies - Applications and Theory, ITAT 2022

作者： Korel, Lukáš Behr, Alexander S. Kockmann, Norbert Holeňa, Martin Faculty of Information Technology CTU Prague Czech Republic Faculty of Biochemical and Chemical Engineering TU Dortmund University Germany Institute of Computer Science Czech Academy of Sciences Prague Czech Republic Leibniz Institute for Catalysis Rostock Germany

This paper provides an insight into the possibility of how to find ontologies most relevant to scientific texts using artificial neural networks. The basic idea of the presented approach is to select a representative paragraph from a source text file, embed it to a vector space by a pre-trained fine-tuned transformer, and classify the embedded vector according to its relevance to a target ontology. We have considered different classifiers to categorize the output from the transformer, in particular random forest, support vector machine, multilayer perceptron, k-nearest neighbors, and Gaussian process classifiers. Their suitability has been evaluated in a use case with ontologies and scientific texts concerning catalysis research. From results we can say the worst results have random forest. The best results in this task brought support vector machine classifier. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Decision trees

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Designing Robust Deep Learning Models for RealTime Data Analysis in Machine Learning 3

Designing Robust Deep Learning Models for RealTime Data Anal...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Borade, Monali R. Gupta, Mukur Faiz, Ayaan Acharjya, Kalyan Rajesh, A. Nunciya, P.M. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India

ISBN: (纸本)9798350319125

The developing call for real-time statistics analysis and selection-making has been a primary driver of improving deep studying models for the device getting to know. These models permit the evaluation of massive statistics sets, the generation of predictions from the statistics, and the speedy studying of new behaviors and patterns. Deep mastering fashions have been efficaciously used for various tasks, including natural language processing, computer imagination and prescient, and reinforcement studying. However, because of their complexity and a massive variety of parameters, they can be unreliable and volatile when uncovered to real-global records. This paper outlines strategies and techniques to lay out deep mastering fashions that can be robust to actual-world records. We also provide high-quality practices for deploying and retaining deep learning models in manufacturing packages. Those strategies include regularization techniques, model architectures, facts pre-processing techniques, and model validation and monitoring techniques. We additionally speak of the numerous strategies for assessing and improving the reliability of deep getting-to-know fashions. Subsequently, we provide a case study of a product that uses deep studying fashions to stumble on and classify objects in near-actual time in a manufacturing place. © 2023 IEEE.

关键词： improving maintaining production regularization techniques

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ClinicLens: Visual Analytics for Exploring and Optimizing the Testing Capacity of Clinics given Uncertainty

arXiv

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arXiv 2023年

作者： Dong, Yu Liang, Jie Cao, Longbing Catchpoole, Daniel The School of Computer Science University of Technology Sydney Australia The Faculty of Engineering and Information Technology University of Technology Sydney Australia The Children's Hospital Westmead Australia

Clinic testing plays a critical role in containing infectious diseases such as COVID-19. However, one of the key research questions in fighting such pandemics is how to optimize testing capacities across clinics. In particular, domain experts expect to know exactly how to adjust the features that may affect testing capacities, given that dynamics and uncertainty make this a highly challenging problem. Hence, as a tool to support both policymakers and clinicians, we collaborated with domain experts to build ClinicLens, an interactive visual analytics system for exploring and optimizing the testing capacities of clinics. ClinicLens houses a range of features based on an aggregated set of COVID-19 data. It comprises Back-end Engine and Front-end Visualization that take users through an iterative exploration chain of extracting, training, and predicting testing-sensitive features and visual representations. It also combines AI4VIS and visual analytics to demonstrate how a clinic might optimize its testing capacity given the impacts of a range of features. Three qualitative case studies along with feedback from subject-matter experts validate that ClinicLens is both a useful and effective tool for exploring the trends in COVID-19 and optimizing clinic testing capacities across regions. The entire approach has been open-sourced online: https://***/YuDong5018/clinic-lens. © 2023, CC BY.

关键词： COVID-19

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Heart Disease Prediction Using Machine Learning Algorithms

Heart Disease Prediction Using Machine Learning Algorithms

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International Conference on Smart Communications and Networking (SmartNets)

作者： Dina Jrab Derar Eleyan Amna Eleyan Tarek Bejaoui Department of Computer Science Faculty of Information Technology Palestine Technical University - Kadoorie Tulkarem State of Palestine Faculty of Telecomm. and Information Technology Nablus University for Technical and Vocational Education Nablus Palestine Department of Computing and Mathematics Manchester Metropolitan University Manchester Uk Computer Engineering Department University of Carthage Tunisia

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

Heart disease is a prevalent and complex condition that affects numerous individuals worldwide. Timely and accurate diagnosis of heart disease is of utmost importance in cardiology. In this research article, we propose an efficient and precise system for heart disease diagnosis, employing machine learning techniques. The system is designed based on various classification algorithms, including Support Vector Machine, Logistic Regression, Decision Tree, and Random Forest. Standard feature selection algorithms such as ANDV A, Chi-Squared, and Mutual information Feature Selection (MIFS) are utilized to eliminate unrelated features. Furthermore, we introduce a novel fast experiment that contains a conditional mutual information feature selection algorithm, ANDVA feature selection algorithms, and a Chi-squared feature selection algorithm to address the feature selection challenge. These feature selection algorithms enhance classification model accuracy and reduce the compile time in the classification ML model. The cross-validation method evaluates the models and optimizes hyperparameters, ensuring reliable model assessment. Performance measuring metrics are utilized to assess the classifiers' performance. The classifiers are estimated based on the selected features determined by the feature selection algorithms. The experimental results show that the ANDV A F -test feature selection algorithm along with the Support Vector Machine classifier, is a viable approach for developing an advanced intelligent system that can identify heart disease. The proposed model can also be easily implemented in healthcare to facilitate heart disease identification.

关键词： Heart Support vector machines Accuracy Machine learning algorithms System performance Feature extraction Prediction algorithms

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Forecast of Students' Mental Health Combining an Artificial Intelligence Technique and Fuzzy Inference System

Forecast of Students' Mental Health Combining an Artificial ...

引用

2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Ramasamy, Lakshmana Kumar Khan, Firoz Joghee, Shanmugan Dempere, Juan Garg, Punnet Higher Colleges of Technology Computer Information Science Ras al Khaimah Women's Campus United Arab Emirates Higher Colleges of Technology Dubai Men's Campus United Arab Emirates Skyline University School of Business United Arab Emirates Higher Colleges of Technology Faculty of Business Ras al Khaimah Women's Campus United Arab Emirates St.Andrews Institute of Technology & Management Department of Cse India

ISBN: (纸本)9798350382723

Mental health prediction is one of the most necessary parts of decreasing the probability of severe mental illness. In the meantime, mental health prediction could present a hypothetical base for the Department of Public Health to work out mental interference tactics for students. This paper intends to forecast students' mental health using Artificial Intelligence (AI). This paper uses the Survey Student Responses dataset from the Kaggle repository, 19 factors of 1182 students of different age groups from different educational institutions in Delhi National Capital Region (NCR) through a cross-sectional survey, with the outcomes of the Self-reporting Inventory, was used to distinguish mental health. This paper proposes a new forecasting technique using the Mamdani Fuzzy Inference System based on the Min-Max Method (for data labelling) and Pearson's correlation coefficient (for feature selection), which could choose and order the very significant features that impact the student's mental health. In addition, we utilize Multinomial Logistic Regression (MLR) and Multilayer Perceptron (MLP) (for ML) algorithm and the Dl4jMlpClassifier (for DL) algorithm to forecast the mental health of students. The outcomes demonstrate that the prediction accuracy of the proposed technique is high, which is superior to the previous algorithms. Therefore, this technique could be used to forecast the student's mental health efficiently. © 2024 IEEE.

关键词： Students

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