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Development of Robust and Efficient Symmetric Random Keys Model based on the Latin Square Matrix
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Symmetric cryptography forms the backbone of secure data communication and storage by relying on the strength and randomness of cryptographic keys. This increases complexity, enhances cryptographic systems' overall robustness, and is immune to various attacks. The present work proposes a hybrid model based on the Latin square matrix (LSM) and subtractive random number generator (SRNG) algorithms for producing random keys. The hybrid model enhances the security of the cipher key against different attacks and increases the degree of diffusion. Different key lengths can also be generated based on the algorithm without compromising security. It comprises two phases. The first phase generates a seed value that depends on producing a randomly predefined set of key numbers of size n via the Donald E. Knuths SRNG algorithm (subtractive method). The second phase uses the output key (or seed value) from the previous phase as input to the Latin square matrix (LSM) to formulate a new key randomly. To increase the complexity of the generated key, another new random key of the same length that fulfills Shannon’s principle of confusion and diffusion properties is XORed. Four test keys for each 128, 192,256,512, and 1024–bit length are used to evaluate the strength of the proposed model. The experimental results and security analyses revealed that all test keys met the statistical National Institute of Standards (NIST) standards and had high values for entropy values exceeding 0.98. The key length of the proposed model for n bits is 25*n, which is large enough to overcome brute-force attacks. Moreover, the generated keys are very sensitive to initial values, which increases the complexity against different attacks.

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Publication Date
Fri Mar 01 2019
Journal Name
Spatial Statistics
Efficient Bayesian modeling of large lattice data using spectral properties of Laplacian matrix
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Spatial data observed on a group of areal units is common in scientific applications. The usual hierarchical approach for modeling this kind of dataset is to introduce a spatial random effect with an autoregressive prior. However, the usual Markov chain Monte Carlo scheme for this hierarchical framework requires the spatial effects to be sampled from their full conditional posteriors one-by-one resulting in poor mixing. More importantly, it makes the model computationally inefficient for datasets with large number of units. In this article, we propose a Bayesian approach that uses the spectral structure of the adjacency to construct a low-rank expansion for modeling spatial dependence. We propose a pair of computationally efficient estimati

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Publication Date
Sat May 24 2025
Journal Name
Iraqi Journal For Computer Science And Mathematics
Intrusion Detection System for IoT Based on Modified Random Forest Algorithm
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An intrusion detection system (IDS) is key to having a comprehensive cybersecurity solution against any attack, and artificial intelligence techniques have been combined with all the features of the IoT to improve security. In response to this, in this research, an IDS technique driven by a modified random forest algorithm has been formulated to improve the system for IoT. To this end, the target is made as one-hot encoding, bootstrapping with less redundancy, adding a hybrid features selection method into the random forest algorithm, and modifying the ranking stage in the random forest algorithm. Furthermore, three datasets have been used in this research, IoTID20, UNSW-NB15, and IoT-23. The results are compared with the three datasets men

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Facial Emotion Images Recognition Based On Binarized Genetic Algorithm-Random Forest
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Most recognition system of human facial emotions are assessed solely on accuracy, even if other performance criteria are also thought to be important in the evaluation process such as sensitivity, precision, F-measure, and G-mean. Moreover, the most common problem that must be resolved in face emotion recognition systems is the feature extraction methods, which is comparable to traditional manual feature extraction methods. This traditional method is not able to extract features efficiently. In other words, there are redundant amount of features which are considered not significant, which affect the classification performance. In this work, a new system to recognize human facial emotions from images is proposed. The HOG (Histograms of Or

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Publication Date
Mon Sep 03 2012
Journal Name
The International Archives Of The Photogrammetry, Remote Sensing And Spatial Information Sciences
CALIBRATION OF FULL-WAVEFORM ALS DATA BASED ON ROBUST INCIDENCE ANGLE ESTIMATION
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Abstract. Full-waveform airborne laser scanning data has shown its potential to enhance available segmentation and classification approaches through the additional information it can provide. However, this additional information is unable to directly provide a valid physical representation of surface features due to many variables affecting the backscattered energy during travel between the sensor and the target. Effectively, this delivers a mis-match between signals from overlapping flightlines. Therefore direct use of this information is not recommended without the adoption of a comprehensive radiometric calibration strategy that accounts for all these effects. This paper presents a practical and reliable radiometric calibration r

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Publication Date
Thu Jan 01 2009
Journal Name
مجلة العلوم الاحصائية
Robust Estimator for Semiparametric Generalized Additive Model
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Generalized Additive Model has been considered as a multivariate smoother that appeared recently in Nonparametric Regression Analysis. Thus, this research is devoted to study the mixed situation, i.e. for the phenomena that changes its behaviour from linear (with known functional form) represented in parametric part, to nonlinear (with unknown functional form: here, smoothing spline) represented in nonparametric part of the model. Furthermore, we propose robust semiparametric GAM estimator, which compared with two other existed techniques.

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Publication Date
Sun Sep 03 2023
Journal Name
Iraqi Journal Of Computers, Communications, Control & Systems Engineering (ijccce)
Efficient Iris Image Recognition System Based on Machine Learning Approach
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HM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023

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Publication Date
Mon Mar 01 2021
Journal Name
Iop Conference Series: Materials Science And Engineering
An efficient multistage CBIR based on Squared Krawtchouk-Tchebichef polynomials
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Abstract<p>Image databases are increasing exponentially because of rapid developments in social networking and digital technologies. To search these databases, an efficient search technique is required. CBIR is considered one of these techniques. This paper presents a multistage CBIR to address the computational cost issues while reasonably preserving accuracy. In the presented work, the first stage acts as a filter that passes images to the next stage based on SKTP, which is the first time used in the CBIR domain. While in the second stage, LBP and Canny edge detectors are employed for extracting texture and shape features from the query image and images in the newly constructed database. The p</p> ... Show More
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Publication Date
Tue Dec 01 2009
Journal Name
Journal Of Lightwave Technology
A Random Number Generator Based on Single-Photon Avalanche Photodiode Dark Counts
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Publication Date
Sun Sep 11 2022
Journal Name
Electronics
IoT-Based Motorbike Ambulance: Secure and Efficient Transportation
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The predilection for 5G telemedicine networks has piqued the interest of industry researchers and academics. The most significant barrier to global telemedicine adoption is to achieve a secure and efficient transport of patients, which has two critical responsibilities. The first is to get the patient to the nearest hospital as quickly as possible, and the second is to keep the connection secure while traveling to the hospital. As a result, a new network scheme has been suggested to expand the medical delivery system, which is an agile network scheme to securely redirect ambulance motorbikes to the nearest hospital in emergency cases. This research provides a secured and efficient telemedicine transport strategy compatible with the

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Publication Date
Tue Feb 01 2022
Journal Name
Baghdad Science Journal
Retina Based Glowworm Swarm Optimization for Random Cryptographic Key Generation
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The biometric-based keys generation represents the utilization of the extracted features from the human anatomical (physiological) traits like a fingerprint, retina, etc. or behavioral traits like a signature. The retina biometric has inherent robustness, therefore, it is capable of generating random keys with a higher security level compared to the other biometric traits. In this paper, an effective system to generate secure, robust and unique random keys based on retina features has been proposed for cryptographic applications. The retina features are extracted by using the algorithm of glowworm swarm optimization (GSO) that provides promising results through the experiments using the standard retina databases. Additionally, in order t

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