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2023 Applied Mathematics, Computational Science and Mechanics: Current Problems, AMCSM 2023

作者： Borisov, Dmitriy Chistyakov, Alexander Don State Technical University Department of Computer Engineering and Automated Systems Software Rostov-on-Don Russia

ISBN: (纸本)9798350348576

The paper presents an analysis of the problem of time series forecasting. The two most common forecasting methods, ARIMA and LSTM, are considered. Since these models independently predict linear and, accordingly, nonlinear dependencies in the best way, a hybrid model is considered using both initial methods in order to combine their strengths, improved by a genetic algorithm for automated model parameterization. A software implementation is built and advantages are described in relation to the original methods based on datasets with different trend, seasonal, non-linear components. In result, an integrative model was compared with ARIMA and LSTM models on 3 datasets and got better results by 6.33% and 8.36% respectively. © 2023 IEEE.

关键词： Genetic algorithms

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Skin cancer detection with MobileNet-based transfer learning and MixNets for enhanced diagnosis

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Neural Computing and Applications 2024年第34期36卷 21383-21413页

作者： Zakariah, Mohammed Al-Razgan, Muna Alfakih, Taha Department of Computer Sciences and Engineering College of Applied Science King Saud University Riyadh11543 Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11345 Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

Skin cancer poses a significant health hazard, necessitating the utilization of advanced diagnostic methodologies to facilitate timely detection, owing to its escalating prevalence in recent years. This paper proposes a novel approach to tackle the issue by introducing a method for detecting skin cancer that uses MixNets to enhance diagnosis and leverages mobile network-based transfer learning. Skin cancer has diverse forms, each distinguishable by its structural attributes, morphological characteristics, texture, and coloration. The pressing demand for accurate and efficient diagnostic instruments has spurred the investigation of novel techniques. The present study utilizes the ISIC dataset, comprising a validation set of 660 images and a training set of 2637 images. Moreover, the research employs a combination of MixNets and mobile network-based transfer learning as its chosen approach. Transfer learning is a technique that leverages preexisting models to enhance the diagnostic capabilities of the proposed system. Integrating MobileNet and MixNets allows for utilizing their respective functionalities, resulting in a dual-model methodology that enhances the comprehensiveness of skin cancer diagnosis. The results demonstrate impressive performance metrics, with MobileNet and MixNets models, and the proposed approach achieves an outstanding accuracy rate of 99.58%. The above findings underscore the efficacy of the dual-model method in effectively discerning between benign and malignant skin lesions. Moreover, the present study aims to examine the potential integration of emerging technologies to enhance the accuracy and practicality of diagnostics within real-world healthcare settings. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Diseases

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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Dynamic Image Deblurring based on Crosshatch Attention Adversarial Network and Hybrid Loss 5

Dynamic Image Deblurring based on Crosshatch Attention Adver...

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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Aslam, Ummama Shaukat, Arslan Azam, Basim Dept. of Computer and Software Engineering H-12 Islamabad Pakistan Institute of Integrated and Intelligent Systems Griffith University Brisbane Australia

ISBN: (纸本)9798350394764

Image deblurring techniques that uses deep learning have shown great potential but due to low generalizability, noise immunity and the correlation among different pixels is not addressed in detail that results in unwanted artifact that appears in the deblurred image. To tackle this problem an end-to-end approach is proposed for the recovery of sharp image from blurred one without the estimation of blur kernel. A special type of attention module known as crosshatch attention is used after Residual Block of Generator model for removing noise and for the collection of correlation of different pixels in an image. Hybrid Loss function is defined which focus on different part of image and improve edges and texture details. The performance of the model for deblurring is measured on GoPro dataset. Our proposed model has slightly higher objective and subjective evaluation i-e PSNR, SSIM value and the visual results. © 2024 IEEE.

关键词： Pixels

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Methods of medical data management based on blockchain technologies

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Journal of Reliable Intelligent Environments 2023年第1期9卷 5-16页

作者： Hovorushchenko, Tetiana Moskalenko, Artem Osyadlyi, Vitaliy Department of Computer Engineering and Information Systems Khmelnytskyi National University Institutska Str. Khmelnytskyi29016 Ukraine Department of Computer Sciences and Software Engineering Academician Yuriy Bugay International Scientific and Technical University Magnitogorsk Lane Kiev02094 Ukraine

The healthcare sector in Ukraine has long been in need of change, and many opportunities of blockchain technology can help it lead the transformation of this sector and ensure compliance with the requirements of the efficiency, safety, novelty, and economic benefits. State-of-the-art on known decisions on the use of blockchain technologies in the information technology for medical data management showed that, despite a large number of different solutions, effective methods and solutions for medical data management based on blockchain technologies are currently lacking. Therefore, improving the efficiency of medical data management by developing methods of medical data management based on blockchain technologies is currently an urgent problem. The paper developed a method for assessing the sufficiency of medical data, which provides: the ability to analyze a variety of medical data for their sufficiency;the ability to quantify the sufficiency of medical data;in case of insufficiency of the medical data, formation of recommendations on supplementing the medical data to be able to upload them to the "Block Data" section of the blockchain block. The paper developed a method of performing transactions on medical data, which consists of the stages of entering information into the blockchain and obtaining information from the blockchain. The developed method of performing transactions on medical data provides the ability to decide on the addition of transactions and medical data from the patient to the blockchain;the ability to decide on the issuance of data from the blockchain on request from a doctor. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Blockchain

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Smart Home User Interface: Development and Comparison

Smart Home User Interface: Development and Comparison

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2023 International Conference on Industrial engineering, Applications and Manufacturing, ICIEAM 2023

作者： Faizrakhmanov, R. Platunov, A. Bahrami, M. Innopolis University Cyberphysical Systems Laboratory Kazan Russia Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia

ISBN: (纸本)9781665475952

The active development of the Internet of Things (IoT) in recent years has increased people's need for control smart devices for home. At the same time, the complexity of these devices is growing. Therefore, Smart Home systems require a simple and convenient interface for control. However, methods for evaluating and comparing interfaces are not clear. In this paper, we described a method to comparing and evaluating different types of Smart Home interfaces. In addition, we evaluate results experimentally. For this comparison, we used a popular Smart Home control software on the market. The results showed that Smart Home graphical user interfaces are more convenient than a messenger or voice. Voice interface is suitable for controlling a Smart Home but is not informative enough in feedback. On the other hand, the familiar interface of the messenger turned out to be the least convenient. This study will help both researchers and practitioners evaluate and choose the right Smart Home interface. © 2023 IEEE.

关键词： Graphical user interfaces

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Smart Home User Interface: Overview

Smart Home User Interface: Overview

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2023 International Conference on Industrial engineering, Applications and Manufacturing, ICIEAM 2023

作者： Faizrakhmanov, Renat Platunov, Alexey Bahrami, Mohammad Reza Innopolis University Cyberphysical Systems Laboratory Kazan Russia Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia

ISBN: (纸本)9781665475952

This paper overviews the Smart Home User Interfaces (UI) in the following aspects: general definition of Human-Machine Interface (HMI), types of common Smart Home UI, market analysis of HMI, voice assistants and chatbots. This study was conducted to determine existed Smart Home UI and evaluate them in the future works. This comparing and evaluation needed to choose appropriate and convenient Smart Home UI for certain application. The overview leaded to the following findings: Smart Home market is growing;certain global trends influenced on this market and on HMI in general;certain groups of people are more interested in Smart Home technologies;each type of Smart Home User Interface has its leaders. This study can help researchers learn about the main types of Smart Home User Interfaces and trends in this market. © 2023 IEEE.

关键词： User interfaces

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CAC 2.0: A Corrupt and Correct Logic Locking Technique Resilient to Structural Analysis Attacks 25

CAC 2.0: A Corrupt and Correct Logic Locking Technique Resil...

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25th IEEE Latin American Test Symposium, LATS 2024

作者： Aksoy, Levent Yasin, Muhammad Pagliarini, Samuel National University Of Sciences And Technology Department Of Computer And Software Engineering Islamabad Pakistan Tallinn University Of Technology Department Of Computer Systems Tallinn Estonia Carnegie Mellon University Department Of Electrical And Computer Engineering Pittsburgh United States

ISBN: (纸本)9798350365559

Logic locking proposed to protect integrated circuits from serious hardware threats has been studied extensively over a decade. In these years, many logic locking techniques have been proven to be broken. The state-of-the-art logic locking techniques, including the prominent corrupt and correct (CAC) technique, are resilient to satisfiability (SAT)-based and removal attacks, but vulnerable to structural analysis attacks. To overcome this drawback, this paper introduces an improved version of CAC, called CAC 2.0, which increases the search space of structural analysis attacks using obfuscation. To do so, CAC 2.0 locks the original circuit twice, one after another, on different nodes with different number of protected primary inputs using CAC, while hiding original protected primary inputs among decoy primary inputs. This paper also introduces an open source logic locking tool, called HIID, equipped with well-known techniques including CAC 2.0. Our experiments show that CAC 2.0 is resilient to existing SAT-based, removal, and structural analysis attacks. To achieve this, it increases the number of key inputs at most 4× and the gate-level area between 30.2% and 0.8% on circuits with low and high complexity with respect to CAC. © 2024 IEEE.

关键词： Structural analysis

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Ensemble Approach Combining Deep Residual Networks and BiGRU with Attention Mechanism for Classification of Heart Arrhythmias

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computers, Materials & Continua 2024年第7期80卷 341-359页

作者： Batyrkhan Omarov Meirzhan Baikuvekov Daniyar Sultan Nurzhan Mukazhanov Madina Suleimenova Maigul Zhekambayeva Department of Information Systems Al-Farabi Kazakh National UniversityAlmaty050040Kazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmaty050040Kazakhstan Department of Software Engineering Satbayev UniversityAlmaty050013Kazakhstan

This research introduces an innovative ensemble approach,combining Deep Residual Networks(ResNets)and Bidirectional Gated Recurrent Units(BiGRU),augmented with an Attention Mechanism,for the classification of heart *** escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and *** model leverages the deep hierarchical feature extraction capabilities of ResNets,which are adept at identifying intricate patterns within electrocardiogram(ECG)data,while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG *** integration of an Attention Mechanism refines the model’s focus on critical segments of ECG data,ensuring a nuanced analysis that highlights the most informative features for arrhythmia *** on a comprehensive dataset of 12-lead ECG recordings,our ensemble model demonstrates superior performance in distinguishing between various types of arrhythmias,with an accuracy of 98.4%,a precision of 98.1%,a recall of 98%,and an F-score of 98%.This novel combination of convolutional and recurrent neural networks,supplemented by attention-driven mechanisms,advances automated ECG analysis,contributing significantly to healthcare’s machine learning applications and presenting a step forward in developing non-invasive,efficient,and reliable tools for early diagnosis and management of heart diseases.

关键词： CNN BiGRU ensemble deep learning ECG arrhythmia heart disease

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