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Image encryption based on 2DNA encoding and chaotic 2D logistic map

Journal of engineering and applied Science 2023年第1期70卷 60页

作者： Alrubaie, Asmaa Hasan Khodher, Maisa’a Abid Ali Abdulameer, Ahmed Talib Computer Sciences Department University of Technology-Iraq Baghdad Iraq Computer Engineering Department University of Technology-Iraq Baghdad Iraq IT Department Middle Technical University Baghdad Iraq

DNA has recently been investigated as a possible medium concerning ultra-compact information storage and ultra-scale computation. The development of secure image encryption systems has recently received a certain effective and new direction from chaos-based cryptographic algorithms. This paper proposes a novel image encryption algorithm, 2DNALM, based on double-dynamic DNA sequence encryption and a chaotic 2D logistic map. The three phases regarding the suggested approach are as follows: the first phase involves permuting the positions of the pixels using a position key-based scrambling operation. The second phase involves double DNA encoding on scrambled images using various rules by DNA cryptography concept to produce an encoded image, and in the final step, an image which has been encoded is encrypted using XOR operation and chaotic keys created through a chaotic 2D logistic map. The entropy analysis and experimental findings show that the suggested scheme exhibits great encryption and withstands several common attacks. © 2023, The Author(s).

关键词： Cryptography

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Investigation of mechanical properties of Kofke stone used in historical structures in isparta by nondestructive and destructive methods

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Journal of Building Pathology and Rehabilitation 2024年第2期9卷 1-10页

作者： Evci, Pınar Usta Davraz, Metin Uysal, Nesibe Isparta University of Applied Sciences Technology Faculty Civil Engineering Department Isparta Turkey

The seismic behavior of historical structures and possible damages that may occur in the structure can be determined by software such as finite element models. However, for an accurate simulation, the properties of masonry building materials should be determined with high precision. In many cases, it is inconvenient to conduct experiments that may destroy historical structures. Therefore, non-destructive methods are also needed to determine material properties. In this study, the mechanical properties of Kofke stone used in historical masonry structures in Isparta province, many of which are still actively used, were investigated. Non-destructive and destructive methods were used in the investigation and these methods were correlated with each other. In the study, properties of kofke stone such as dry-unit volume mass, porosity, water absorption by mass, ultrasonic seismic velocity, Schmidt tensile strength, dynamic modulus of elasticity, compressive strength, splitting-tensile strength, static modulus of elasticity were determined. All tests were performed by the applicable national and international standards. The dry unit volume mass of Kofke stone is 1434 kg/m³, water absorption by mass is 18.5%, longitudinal seismic wave velocity (avg.) was 1806 m/s, Schmidt hammer strength was 17.6 MPa, dynamic modulus of elasticity was 4.85 GPa and Poisson's ratio was 0.19. In the destructive tests, the compressive strength was determined as 6.57 MPa, the splitting-tensile strength as 0.71 MPa, and the modulus of elasticity as 1.66 GPa. Empirical equations for the prediction of the compressive strength and static modulus of elasticity of kofke stone by non-destructive methods were also presented. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Destructive and non-destructive methods Historical building Kofke stone Physical and mechanical properties

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Considerations for using tree-based machine learning to assess causation between demographic and environmental risk factors and health outcomes

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Environmental Science and Pollution Research 2024年第51期31卷 60927-60935页

作者： Galatro, Daniela Di Nardo, Alessia Pai, Varun Trigo-Ferre, Rosario Jeffrey, Melanie Jacome, Maria Costanzo-Alvarez, Vincenzo Bazylak, Jason Amon, Cristina H. Department of Chemical Engineering and Applied Chemistry University of Toronto Toronto Canada Department of Mechanical and Industrial Engineering University of Toronto Toronto Canada Faculty of Applied Science and Engineering University of Toronto Toronto Canada Centre for Indigenous Studies University of Toronto Toronto Canada Faculty of Applied Sciences and Technology Humber Institute of Technology and Advanced Learning Toronto Canada

Evaluation of the heterogeneous treatment effect (HTE) allows for the assessment of the causal effect of a therapy or intervention while considering heterogeneity in individual factors within a population. Machine learning (ML) methods have previously been employed for HTE evaluation, addressing the limitations associated with modelling complex systems. In this work, three tree-based ML algorithms, causal random forest (CRF), causal Bayesian additive regression trees (CBART), and causal rule ensemble (CRE), are used to analyze the potential causation of benzene exposure to cause childhood acute myeloid leukemia (AML). Data for this analysis is generated by drawing samples from a previously developed model that estimates AML probability given as input demographic information and benzene exposure. Comparison is drawn between the three tree-based algorithms in terms of the predicted average treatment effect (ATE), the regression coefficient of determination, and the computational time of each algorithm. Minimal difference is reported between the three tree-based algorithms in terms of the ATE, as well as the regression coefficient of determination. However, CRF outperforms CBART in terms of algorithm computational time. Moreover, CRF allows for both continuous and binary treatment variables, as opposed to CBART and CRE, making it better suited to environmental health studies, where exposure levels of pollutants shall be considered continuous. Following the comparison of all three algorithms, the influence of adding Gaussian noise to the treatment and outcome variables, as well as outliers, is investigated using CRF. A set of considerations is drawn to guide researchers in using these algorithms. These considerations detail the simulation settings, applications, and results interpretation and aim to provide prompt information in decision-making surrounding the establishment of pollutant exposure thresholds in environmental risk assessments. © The Author(s), under exclus

关键词： Risk assessment

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Evaluation of Hotel Performance with Sentiment Analysis by Deep Learning Techniques

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第9期17卷 70-87页

作者： Hameed, Rafeef A. Abed, Wael J. Sadiq, Ahmed T. Computer Sciences Department University of Technology Baghdad Iraq Computer Techniques Engineering Department Al-Mustaqbal University College Hillah Iraq

The subject of sentiment analysis through social media sites has witnessed significant development due to the increasing reliance of people on social media in advertising and marketing, especially after the Corona pandemic. There is no doubt that the prevalence of the Arabic language makes it considered one of the most important languages all over the world. Through human comments, it can know things if they are positive or negative. But in fact, the comments are many, and it takes work to evaluate the place or the product through a detailed reading of each comment. Therefore, this study applied deep learning approaches to this issue to provide final results that could be utilized to differentiate between the comments in the dataset. Arabic Sentiment Analysis was used and gave a percentage for each positive and negative commentary. This work used eight methods of deep learning techniques after using Fast Text as embedding, except Ara BERT. These techniques are the transformer (AraBERT), RNN (Long short-term memory (LSTM), Bidirectional long-short term memory (BILSTM), Gated recurrent units (GRUs), Bidirectional Gated recurrent units (BIGRU)), CNN (like ALEXNET, proposed CNN), and ensemble model (CNN with BI-GRU). The Hotel Arabic Reviews Dataset was utilized to test the models. This paper obtained the following results. In the Ara BERT model, the accuracy is 96.442%. In CNN, like the Alex Net model, the accuracy is 93.78%. In the suggested CNN model, the accuracy is 94.43%. In the suggested LSTM model, the accuracy is 95%. In the suggested BI-LSTM model, the accuracy is 95.11%. The accuracy of the suggested GRU model is 95.07%. The accuracy of the suggested BI-GRU model is 95.02%. The accuracy is 94.52% in the Ensemble CNN with BI-GRU model that has been proposed. Consequently, the AraBERT outperformed the other approaches in terms of accuracy. Because the AraBERT has already been trained on some Arabic Wikipedia entries. The LSTM, BI-LSTM, GRU, and BI-GRU, on the o

关键词： Sentiment analysis

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Current Advancements in Radar Communication and its Future Research Directions

Current Advancements in Radar Communication and its Future R...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Judice, Antony Livin, Joel James, Aaron Bharathi, Subha University of Technology and Applied Sciences Department of Engineering Ibri Oman University of Technology and Applied Sciences Department of Engineering Ibra Oman

ISBN: (纸本)9798350333244

Radars are playing a vital role in the field of electronic warfare, ground observation, different military applications, speed recognition of an objects, air traffic management, etc. It is an inescapable part for Tele-Communications. All of these systems must be capable of carrying out various jobs and have significant power gains, adaptive beamforming, faster scanning rates, low detection probabilities, high dependability, and low maintenance costs. Many of the updated radar systems and methods are taking the place of the outdated ones. This examination will deliver a thorough idea of the developments in radars, and current trends. Current radar systems, such as sophisticated scanning methods, GPS-based landing systems, passive radars with multiliterate, 3D radars, Long and short-range Radars for Autonomous Vehicles, and satellite monitoring radars, are briefly discussed in this paper. The Radars being used in the advanced jet fighters also discussed. Certainly, all the secondary Radars would be replaced by Passive Radars and Satellites in near future. All the current research potential areas in modern Radar Communication are mentioned in this paper that would helpful for the early researchers in this vibrant field. © 2023 IEEE.

关键词： Optical radar

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A Recurrent Neural Network for Multimodal Anomaly Detection by Using Spatio-Temporal Audio-Visual Data

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computers, Materials & Continua 2024年第11期81卷 2493-2515页

作者： Sameema Tariq Ata-Ur-Rehman Maria Abubakar Waseem Iqbal Hatoon S.Alsagri Yousef A.Alduraywish Haya Abdullah AAlhakbani Department of Electrical Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Electrical Engineering National University of Science and TechnologyNational University of Sciences and TechnologyIslamabad24090Pakistan Department of Business and Computing Ravensbourne University LondonRavensbourne UniversityLondonSE100EWEngland Electrical and Computer Engineering Department College of EngineeringSultan Qaboos UniversityMuscat123Oman College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11673Saudi Arabia

In video surveillance,anomaly detection requires training machine learning models on spatio-temporal video ***,sometimes the video-only data is not sufficient to accurately detect all the abnormal ***,we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video *** paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual *** proposed model is trained to produce low reconstruction error for normal data and high error for abnormal data,effectively distinguishing between the two and assigning an anomaly *** is conducted on normal datasets,while testing is performed on both normal and anomalous *** anomaly scores from the models are combined using a late fusion technique,and a deep dense layer model is trained to produce decisive scores indicating whether a sequence is normal or *** model’s performance is evaluated on the university of California,San Diego Pedestrian 2(UCSD PED 2),university of Minnesota(UMN),and Tampere university of technology(TUT)Rare Sound Events datasets using six evaluation *** is compared with state-of-the-art methods depicting a high Area Under Curve(AUC)and a low Equal Error Rate(EER),achieving an(AUC)of 93.1 and an(EER)of 8.1 for the(UCSD)dataset,and an(AUC)of 94.9 and an(EER)of 5.9 for the UMN *** evaluations demonstrate that the joint results from the combined audio-visual model outperform those from separate models,highlighting the competitive advantage of the proposed multi-modal approach.

关键词： Acoustic-visual anomaly detection sequence-to-sequence autoencoder reconstruction error late fusion regularity score

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Vulnerabilities and Challenges in the Development of PUF-Based Authentication Protocols on FPGAs: A Brief Review 7

Vulnerabilities and Challenges in the Development of PUF-Bas...

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7th IEEE Conference on Dependable and Secure Computing, DSC 2024

作者： Yavuz, Sinan Naroska, Edwin Daniel, Kai University of Siegen Department of Electrical Engineering and Computer Science Siegen Germany Niederrhein University of Applied Sciences Department of Electrical Engineering and Computer Science Krefeld Germany Institute of Measurement and Sensor Technology University of Applied Sciences Ruhr West Mülheim Germany

ISBN: (纸本)9798331540289

The security of IoT (Internet of Things) devices and the protection of sensitive information processed by these devices such as personal data, sensor values, process-related information is an important and difficult challenge. A major task in IoT communication is secure identification of devices. Unfortunately, traditional cryptographic methods are often not suitable for IoT devices due to their limited hardware resources. However, typical methods are computationally intensive, require a large amount of memory, and have a high-power consumption. On the other hand, Physical Unclonable Functions (PUFs) are low-cost and lightweight hardware-based primitives that can be used as a security component to protect data against third parties and thus increase security of a device. In this paper, the challenges and vulnerabilities in the development of PUF-based authentication protocols are presented. To this end, a security analysis of different approaches known from literature are discussed. Furthermore, possible attack vectors and prevention techniques are also considered. © 2024 IEEE.

关键词： Authentication Protocol

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Deploying Hybrid Ensemble Machine Learning Techniques for Effective Cross-Site Scripting(XSS)Attack Detection

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computers, Materials & Continua 2024年第10期81卷 707-748页

作者： Noor Ullah Bacha Songfeng Lu Attiq Ur Rehman Muhammad Idrees Yazeed Yasin Ghadi Tahani Jaser Alahmadi School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430073China Department of Computer Science and Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain12555Abu Dhabi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving sophistication of cyber *** paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack *** approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in *** demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant ***,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time *** for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current *** innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.

关键词： Cross-site scripting machine learning XSS detection stacking ensemble learning hybrid learning

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An Enhanced Integrated Method for Healthcare Data Classification with Incompleteness

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computers, Materials & Continua 2024年第11期81卷 3125-3145页

作者： Sonia Goel Meena Tushir Jyoti Arora Tripti Sharma Deepali Gupta Ali Nauman Ghulam Muhammad Electrical and Electronics Engineering(EEE)Department Maharaja Surajmal Institute of TechnologyNew-Delhi110058India Information Technology(IT)Department Maharaja Surajmal Institute of TechnologyNew-Delhi110058India Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpuraPunjab140401India Department of Computer Science and Engineering Yeungnam UniversityGyeongsan-si38541Republic of Korea Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11421Saudi Arabia

In numerous real-world healthcare applications,handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification *** approaches often rely on statistical methods for imputation,which may yield suboptimal results and be computationally *** paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved *** classification methods are ill-suited for incomplete medical *** enhance efficiency without compromising accuracy,this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete ***,the linear interpolation imputation method alongside an iterative Fuzzy c-means clustering method is applied and followed by a classification *** effectiveness of the proposed approach is evaluated using multiple performance metrics,including accuracy,precision,specificity,and *** encouraging results demonstrate that our proposed method surpasses classical approaches across various performance criteria.

关键词： Incomplete data nearest neighbor linear interpolation imputation clustering classification

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Next generation hybrid based flying squirrel search optimization approach for cubic boost converter used in solar photovoltaic system

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International Journal of Information technology (Singapore) 2024年第8期16卷 4967-4980页

作者： Jadhav, Veerabhadra Rao, S. Nagaraja Faculty of Engineering & amp Technology Department of Electrical Engineering M S Ramaiah University of Applied Sciences

Renewable energy sources (RES) reveal potential for the near future since they are sustainable and generate clean energy. Currently grid-connected solar photovoltaic (SPV) systems are becoming increasingly significant in meeting energy demand and contributing to clean energy production. The power electronic converter (PEC) plays a significant role in conversion, regulating and controlling the flow of power from RES. This research primarily focuses on high-gain cubic boost converter (HG-CBC) and simulated with the help of MATLAB/Simulink. The results are evaluated and contrasted with both traditional and other high-gain boost converters, focusing on the boost factor (B) and the total part count. It is essential to integrate SPV modules with MPPT system to capture the peak power available under varying temperature (T) and solar irradiation (G) levels. This research introduces a new hybrid based flying squirrel search optimization (FSSO) approach combined with Perturb & Observe (P&O) MPPT approach which allows for faster MPP, lesser oscillations at the output and high accuracy with less convergence time in contrast with P&O and FSSO MPPT. The proposed hybrid based HFSSO with P&O MPPT with HG-CBC exhibits low output voltage ripples of 0.039 % and a contrast lower rising time of 0.264 s and settled at 0.6 s. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Boost factor FSSO HG-CBC MPPT RES and SPV

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