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Publication Date: 2011
pp. 77-86
This paper presents a novel image security system based on the replacement of the pixel values using recursive Cellular automata (CA) substitution. The advantages of our proposed method are that it is computationally efficient and it is reasonably passing sensibility analysis tests. The proposed method is carried out by using one half of image data to encrypt the other half of the image mutually. Our algorithm can encrypt image in parallel and be also applied to color image encryption. In this proposed method, size of key is dynamic and by changing a bit of security key the image cannot retrieve because our method is key sensitive. Simulation results obtained using color; white-black and gray-level images demonstrate the good performance of the proposed image security system. © 2011 IEEE.
Publication Date: 2012
International Journal of Bio-Inspired Computation (17580374) 4(3)pp. 181-195
High volumes of low-level alerts that are generated by intrusion detection systems (IDSs) are serious obstacle for using them effectively. These high volumes of alerts overwhelm system administrators in such a way that they cannot manage and interpret them. Alert correlation is used to reduce the number of alerts and increase their level of abstraction. It selects a group of low-level alerts and converts them into a higher level attack and then produces a high-level alert for them. In this paper, a new artificial immune system-based alert correlation system is presented, named AISAC. It learns the correlation probability between each pair of alert types and uses this knowledge to extract the attack scenarios. AISAC does not need intensive domain knowledge and rule definition efforts. It also does not need to manually update the extracted knowledge. The computational cost of learning algorithm is linear, and the initial learning is done by a very limited general data in offline mode. AISAC is evaluated by DARPA 2000 and net Forensics Honeynet data. Results show that although it uses a relatively simple algorithm, it generates the attack graphs with acceptable accuracy. © 2012 Inderscience Enterprises Ltd.
Bateni, M. ,
Baraani, A. ,
Ghorbani, A.A. ,
Rezaei, A. Publication Date: 2013
International Journal of Innovative Computing, Information and Control (13494198) 9(1)pp. 231-255
There are many different approaches to alert correlation such as using correlation rules and prerequisite-consequences, using machine learning and statistical methods and using similarity measures. In this paper, iCorrelator, a new AIS-inspired architecture, is presented. It uses a three-layer architecture that is inspired by three types of responses in the human immune system: the innate immune system's response, the adaptive immune system's primary response, and the adaptive immune system's secondary response. In comparison with other correlators, iCorrelator does not need information about different attacks and their possible relations in order to discover an attack scenario. It uses a very limited number of general rules that are not related to any specific attack scenario. A process of incremental learning is used to encounter new attacks. Therefore, iCorrelator is easy to set up and work dynamically without reconfiguration. As a result of using memory cells and improved alert selection policy, the computational cost of iCorrelator is also acceptable even for online correlation. iCorrelator is evaluated by using the DARPA 2000 dataset and a netForensics honeynet data. The completeness, soundness, false correlation rate and execution time are reported. Results show that iCorrelator is able to extract the attack graphs with acceptable accuracy that is comparable to the best known solutions. © 2013 ICIC International.
Publication Date: 2013
International Journal of Network Security (discontinued) (1816353X) 15(3)pp. 160-174
One of the most important challenges facing the intrusion detection systems (IDSs) is the huge number of generated alerts. A system administrator will be overwhelmed by these alerts in such a way that she/he cannot manage and use the alerts. The best-known solution is to correlate low-level alerts into a higher level attack and then produce a high-level alert for them. In this paper a new automated alert correlation approach is presented. It employs Fuzzy Logic and Artificial Immune System (AIS) to discover and learn the degree of correlation between two alerts and uses this knowledge to extract the attack scenarios. The proposed system doesn't need vast domain knowledge or rule definition e®orts. To correlate each new alert with previous alerts, the system first tries to find the correlation probability based on its fuzzy rules. Then, if there is no matching rule with the required matching threshold, it uses the AIRS algorithm. The system is evaluated using DARPA 2000 dataset and a netForensics honeynet data. The completeness, soundness and false alert rate are calculated. The average completeness for LL-DoS1.0 and LLDoS2.0, are 0.957 and 0.745 respectively. The system generates the attack graphs with an acceptable accuracy and, the computational complexity of the probability assignment algorithm is linear.
Publication Date: 2015
Malaysian Journal Of Computer Science (01279084) 28(1)pp. 46-58
Image reconstruction is an important part of computed tomography imaging systems, which converts the measured data into images. Because of high computational cost and slow convergence of iterative reconstruction algorithms, these methods are not widely used in practice. In this paper, we propose a hybrid iterative algorithm by combining multigrid method,Tikhonov regularization and Simultaneous Iterative Reconstruction Technique (SIRT) for reconstruction of the computed tomography image that reduces this drawback. To do so, we reduce the time and the volume of computations considerably by finding astable and appropriate starting point. The experimental results indicate that the proposed iterative algorithm has more rapid convergence and reconstructs high quality images in short computational time than the classical ones.
Publication Date: 2016
Scalable Computing (18951767) 17(4)pp. 331-349
Nested loops are one of the most time-consuming parts and the largest sources of parallelism in many scientific applications. In this paper, we address the problem of 3-dimensional tiling and scheduling of three-level perfectly nested loops with dependencies on heterogeneous systems. To exploit the parallelism, we tile and schedule nested loops with dependencies by awareness of computational power of the processing nodes and execute them in pipeline mode. The tile size plays an important role to improve the parallel execution time of nested loops. We develop and evaluate a theoretical model to estimate the parallel execution time of tilled nested loops. Also, we propose a tiling genetic algorithm that used the proposed model to find the near-optimal tile size, minimizing the parallel execution time of dependence nested loops. We demonstrate the accuracy of theoretical model and effectiveness of the proposed tiling genetic algorithm by several experiments on heterogeneous systems. The 3D tiling reduces the parallel execution time by a factor of 1.2× to 2× over the 2D tiling, while parallelizing 3D heat equation as a benchmark. © 2016 SCPE.
Publication Date: 2017
Concurrency and Computation: Practice and Experience (15320626) 29(5)
Nested loops are the largest source of parallelism in many data-parallel scientific applications. Heterogeneous distributed systems are popular computing platforms for data-parallel applications. Data partitioning is critical in exploiting the computational power of such systems, and existing data partitioning algorithms try to maximize performance of data-parallel applications by finding a data distribution that balances the workload between the processing nodes while minimizing communication costs. This paper addresses the problem of 3-dimensional data partitioning for 3-level perfectly nested loops on heterogeneous distributed systems. The primary aim is to minimize the execution time by improving the load balancing and minimizing the internode communications. We propose a new data partitioning algorithm using dynamic programming, build a theoretical model to estimate the execution time of each partition, and select a partition with minimum execution time as a near-optimal solution. We demonstrate the effectiveness of the new algorithm for 2 data-parallel scientific applications on heterogeneous distributed systems. The new algorithm reduces the execution time by between 7% and 17%, on average, compared with leading data partitioning methods on 3 heterogeneous distributed systems. Copyright © 2016 John Wiley & Sons, Ltd.
Publication Date: 2017
2018pp. 1-6
Modern GPUs employ simultaneous kernel executions (SKE), an equivalent to multitasking in CPUs, to maximize the hardware utilization and enhance the resulted performance. SKE paradigm is not yet fully explored by the research community. In this study, a reuse-distance (RD) based analysis approach, called SKERD, is proposed to analyze the effect of SKE scenarios on the kernel data reuse and GPU cache memories performance. Only two simultaneous kernels were considered in this work. Moreover, Three types of coarse-grained SM (streaming multiprocessor) partitioning schemes were investigated including an even SM to kernel partitioning and two SM partitioning schemes that assign the SMs to the kernels based on the kernel workloads. The simulation results show that none of the mentioned partitioning schemes always functions better than the others. Further, for some memory intensive kernels, SKE resulted in cache contentions and hit ratio degradation. Consequently, the effects of SKE on cache memories should be carefully considered. © 2017 IEEE.
Publication Date: 2017
2017pp. 260-265
Performance modeling plays an important role for optimal hardware design and optimized application implementation. This paper presents a very low overhead performance model, called VLAG, to approximate the data localities exploited by GPU kernels. VLAG receives source code-level information to estimate per memory-access instruction, per data array, and per kernel localities within GPU kernels. VLAG is only applicable to kernels with regular memory access patterns. VLAG was experimentally evaluated using an NVIDiA Maxwell GPU. For two different Matrix Multiplication kernels, the average errors of 7.68% and 6.29%, was resulted, respectively. The slowdown of VLAG for MM was measured 1.4X which, comparing with other approaches such as trace-driven simulation, is negligible. © 2017 IEEE.
Publication Date: 2019
International Journal of Computer Network and Information Security (20749104) 11(9)pp. 9-17
Malware poses one of the most serious threats to computer information systems. The current detection technology of malware has several inherent constraints. Because signature-based traditional techniques embedded in commercial antiviruses are not capable of detecting new and obfuscated malware, machine learning algorithms are applied in identifing patterns of malware behavior through features extracted from programs. There, a method is presented for detecting malware based on the features extracted from the PE header and section table PE files. The packed files are detected and then unpacke them. The PE file features are extracted and their static features are selected from PE header and section tables through forward selection method. The files are classified into malware files and clean files throughs different classification methods. The best results are obtained through DT classifier with an accuracy of 98.26%. The results of the experiments consist of 971 executable files containing 761 malware and 210 clean files with an accuracy of 98.26%. © 2019 MECS.
Publication Date: 2019
Simulation Modelling Practice and Theory (1569190X) 91pp. 102-122
Memory footprint is a metric for quantifying data reuse in memory trace. It can also be used to approximate cache performance, especially in shared cache systems. Memory footprint is acquired through memory footprint analysis (FPA). However, its main limitation is that, for a memory trace of n accesses, the all-window FPA algorithm requires O(n3) time. Therefore, in this paper, we propose an analytical algorithm for FPA, whereby the average footprints are calculated in O(n2). The proposed algorithm can also be employed for window distribution analysis. Moreover, we propose a framework to enable the application of FPA to GPU kernels and model the performance of L1 cache memories. The results of experimental evaluations indicate that our proposed framework functions 1.55X slower than the Xiang's formula, as a fast average FPA method, while it can also be utilized for window distribution analysis. In the context of FPA-based cache performance estimation, the experimental results indicate a fair correlation between the estimated L1 miss rates and those of the native GPU executions. On average, the proposed framework has 23.8% error in the estimation of L1 cache miss rates. Further, our algorithm runs 125X slower than the reuse distance analysis (RDA) when analyzing a single kernel. However, the proposed method outperforms RDA in modeling shared caches and multiple kernel executions in GPUs. © 2018 Elsevier B.V.
Publication Date: 2019
ACM Transactions on Architecture and Code Optimization (15443973) 15(4)
Reuse distance analysis (RDA) is a popular method for calculating locality profiles and modeling cache performance. The present article proposes a framework to apply the RDA algorithm to obtain reuse distance profiles in graphics processing unit (GPU) kernels. To study the implications of hardware-related parameters in RDA, two RDA algorithms were employed, including a high-level cache-independent RDA algorithm, called HLRDA, and a detailed RDA algorithm, called DRDA. DRDA models the effects of reservation fails in cache blocks and miss status holding registers to provide accurate cache-related performance metrics. In this case, the reuse profiles are cache-specific. In a selection of GPU kernels, DRDA obtained the L1 miss-rate breakdowns with an average error of 3.86% and outperformed the state-of-the-art RDA in terms of accuracy. In terms of performance, DRDA is 246,000×slower than the real GPU executions and 11×faster than GPGPUSim. HLRDA ignores the cache-related parameters and its obtained reuse profiles are general, which can be used to calculate miss rates in all cache sizes. Moreover, the average error incurred by HLRDA was 16.9%. © 2018 Association for Computing Machinery.
Publication Date: 2019
International Journal of Innovative Technology and Exploring Engineering (discontinued) (22783075) 8(11)pp. 4258-4265
In a social network the individuals connected to one another become influenced by one another, while some are more influential than others and able to direct groups of individuals towards a move, an idea and an entity. These individuals are named influential users. Attempt is made by the social network researchers to identify such individuals because by changing their behaviors and ideologies due to communications and the high influence on one another would change many others' behaviors and ideologies in a given community. In information diffusion models, at all stages, individuals are influenced by their neighboring people. These influences and impressions thereof are constructive in an information diffusion process. In the Influence Maximization problem, the goal is to finding a subset of individuals in a social network such that by activating them, the spread of influence is maximized. In this work a new algorithm is presented to identify most influential users under the linear threshold diffusion model. It uses explicit multimodal evolutionary algorithms. Four different datasets are used to evaluate the proposed method. The results show that the precision of our method in average is improved 4.8% compare to best known previous works. © BEIESP.
Publication Date: 2019
Computing and Informatics (25858807) 38(2)pp. 421-453
In the present paper, we propose RDGC, a reuse distance-based performance analysis approach for GPU cache hierarchy. RDGC models the thread-level parallelism in GPUs to generate appropriate cache reference sequence. Further, reuse distance analysis is extended to model the multi-partition/multi-port parallel caches and employed by RDGC to analyze GPU cache memories. RDGC can be utilized for architectural space exploration and parallel application development through providing hit ratios and transaction counts. The results of the present study demonstrate that the proposed model has an average error of 3.72 % and 4.5 % (for L1 and L2 hit ratios, respectively). The results also indicate that the slowdown of RDGC is equal to 47 000 times compared to hardware execution, while it is 59 times faster than GPGPU-Sim simulator. © 2019 Slovak Academy of Sciences. All rights reserved.
Publication Date: 2019
Journal of Circuits, Systems and Computers (17936454) 28(14)
Modern GPUs can execute multiple kernels concurrently to keep the hardware resources busy and to boost the overall performance. This approach is called simultaneous multiple kernel execution (MKE). MKE is a promising approach for improving GPU hardware utilization. Although modern GPUs allow MKE, the effects of different MKE scenarios have not adequately studied by the researchers. Since cache memories have significant effects on the overall GPU performance, the effects of MKE on cache performance should be investigated properly. The present study proposes a framework, called RDMKE (short for Reuse Distance-based profiling in MKEs), to provide a method for analyzing GPU cache memory performance in MKE scenarios. The raw memory access information of a kernel is first extracted and then RDMKE enforces a proper ordering to the memory accesses so that it represents a given MKE scenario. Afterward, RDMKE employs reuse distance analysis (RDA) to generate cache-related performance metrics, including hit ratios, transaction counts, cache sets and Miss Status Holding Register reservation fails. In addition, RDMKE provides the user with the RD profiles as a useful locality metric. The simulation results of single kernel executions show a fair correlation between the generated results by RDMKE and GPU performance counters. Further, the simulation results of 28 two-kernel executions indicate that RDMKE can properly capture the nonlinear cache behaviors in MKE scenarios. © 2019 World Scientific Publishing Company.
Publication Date: 2019
Journal of Parallel and Distributed Computing (07437315) 129pp. 14-35
Nested loops are main source of the parallelism in many scientific applications. Partitioning the iteration space of nested loops with data dependencies into tiles and assigning them to processing nodes for parallel execution is essential for achieving high performance. Although most of the previous work focused on tiling on fully connected homogeneous distributed systems, some studies have been devoted to tiling on partially connected distributed systems. In this paper, we address the parallelization of perfectly nested loops with dependencies on partially connected heterogeneous distributed systems and present a topology and computational-power aware tile mapping. This work aims to take into account not only the node's computational power when tiling iteration space of nested loops but also the exploitation of the network topology when mapping tiles to processing nodes. This approach allows minimizing the parallel execution time by improving the load balancing and minimizing the communication costs. We demonstrate the performance of proposed method by comparing it with the computational-power aware tile mapping and the topology aware tile mapping. The experimental results show that the proposed method improves the parallel execution time by up to 62% and 28% compared with the computational-power aware tile mapping and the topology aware tile mapping, respectively. © 2019 Elsevier Inc.
Publication Date: 2020
Multimedia Tools and Applications (13807501) 79(33-34)pp. 24993-25022
In this paper, we propose a novel image encryption scheme based on a hybrid model of DNA computing, chaotic systems and hash functions. The significant advantage of the proposed scheme is high efficiency. The proposed scheme consists of the DNA level permutation and diffusion. In the DNA level permutation, a mapping function based on the logistic map is applied on the DNA image to randomly change the position of elements in the DNA image. In the DNA level diffusion, not only we define two new algebraic DNA operators, called the DNA left-circular shift and DNA right-circular shift, but we also use a variety of DNA operators to diffuse the permutated DNA image with the key DNA image. The experimental results and security analyses indicate that the proposed image encryption scheme not only has good encryption effect and able to resist against the known attacks, but also is sufficiently fast for practical applications. The MATLAB source code of the proposed image encryption scheme is publicly available at the URL: https://github.com/EbrahimZarei64/ImageEncryption. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Publication Date: 2020
Biomedical Signal Processing and Control (17468108) 60
Background and objectives: Data compression techniques have been used in order to reduce power consumption when transmitting electrocardiogram (ECG) signals in wireless body area networks (WBAN). Among these techniques, compressed sensing allows sparse or compressible signals to be encoded with only a small number of measurements. Although ECG signals are not sparse, they can be made sparse in another domain. Numerous sparsifying techniques are available, but when signal quality and energy consumption are important, existing techniques leave room for improvements. Methods: To leverage compressed sensing, we increased the sparsity of an ECG frame by removing the redundancy in a normal frame. In this study, by framing a signal according to the detected QRS complex (R peaks), consecutive frames of the signal become highly similar. This helps remove redundancy and consequently makes each frame sparse. In order to increase detection performance, different frames that symptomize a cardiovascular disease are sent uncompressed. Results: For evaluating and comparing our proposed technique with different state-of-the-art techniques two datasets that contained normal and abnormal ECG: MIT-BIH Arrhythmia Database and MIT-BIH Long Term Database were used. For performance evaluation, we performed heart rate variability (HRV) analysis as well as energy-based distortion analysis. The proposed method reaches an accuracy of 99.9%, for a compression ratio of 25. For MIT-BIH Long Term Database, the average percentage root-mean squared difference (PRD) is less than 10 for all compression ratios. Conclusion: Removing the redundancy between successive similar frames and exact transmission of dissimilar frames, the proposed method proves to be appropriate for heart rate variability analysis and abnormality detection. © 2020
Publication Date: 2021
Computer Networks (13891286) 196
In the present article, we propose a virtual machine placement (VMP) algorithm for reducing power consumption in heterogeneous cloud data centers. We propose a novel model for the estimation of power consumption of datacenter's network. The proposed model is employed to estimate power consumption of a Fat-Tree network. It calculates the traffic of each network layer and uses the results to estimate the average power consumption of each switch in the network, which is used for network power calculation. Further, we employ the chemical reaction optimization (CRO) algorithm as a meta-heuristic algorithm to obtain a power-efficient mapping of virtual machines (VMs) to physical machines (PMs). Moreover, two kinds of solution encoding schemes, namely permutation-based and grouping-based encoding schemes, were utilized for representing individuals in CRO. For each encoding scheme, we designed proper operators required by the CRO for manipulating the molecules in search of more optimal solution candidates. Additionally, we modeled VMs with east–west and north–south communications, and PMs with constrained CPU, memory, and bandwidth capacity. Our network power model is integrated into the CRO algorithms to enable the estimation of both PMs and network power consumption. We compared our proposed methods with a number of similar methods. The evaluation results indicate that the proposed methods perform well and the CRO algorithm with the grouping-based encoding outperforms the rest of the methods in terms of power consumption. The evaluation results also show the significance of network power consumption. © 2021 Elsevier B.V.
Publication Date: 2021
Journal of Supercomputing (15730484) 77(5)pp. 5120-5147
Graphics processing units (GPUs) are powerful in performing data-parallel applications. Such applications most often rely on the GPU’s memory hierarchy to deliver high performance. Designing efficient memory hierarchy for GPUs is a challenging task because of its wide architectural space. To moderate this challenge, this paper proposes a framework, called stack distance-analytic modeling (SDAM), to estimate memory performance of the GPU in terms of memory cycle counts. Providing the input data to the model is crucial in terms of the accuracy of the input data, and the time spent to obtain them. SDAM employs the stack distance analysis method and analytical modeling to obtain the required input accurately and swiftly. Further, it employs a detailed analytical model to estimate memory cycles. SDAM is validated against real GPU executions. Further, it is compared with a cycle accurate simulator. The experimental evaluations, performed on a set of memory-intensive benchmarks, prove that SDAM is faster and more accurate than cycle-accurate simulation, thus it can facilitate the GPU cache design-space exploration. For a selection of data-intensive benchmarks, SDAM showed a 32% average error in estimating memory data transfer cycles in a modern GPU, which outperforms cycle-accurate simulation, while it is an order of magnitude faster than the cycle-accurate simulation. Finally, the applicability of SDAM in exploring cache design-space in GPUs is demonstrated through experimenting with various cache designs. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Publication Date: 2022
Biomedical Signal Processing and Control (17468108) 78
Proposing a practical method for high-performance emotion recognition could facilitate human–computer interaction. Among existing methods, deep learning techniques have improved the performance of emotion recognition systems. In this work, a new multimodal neural design is presented wherein audio and visual data are combined as the input to a hybrid network comprised of a bidirectional long short term memory (BiLSTM) network and two convolutional neural networks (CNNs). The spatial and temporal features extracted from video frames are fused with Mel-Frequency Cepstral Coefficients (MFCCs) and energy features extracted from audio signals and BiLSTM network outputs. Finally, a Softmax classifier is used to classify inputs into the set of target categories. The proposed model is evaluated on Surrey Audio–Visual Expressed Emotion (SAVEE), Ryerson Audio–Visual Database of Emotional Speech and Song (RAVDESS), and Ryerson Multimedia research Lab (RML) databases. Experimental results on these datasets prove the effectiveness of the proposed model where it achieves the accuracy of 99.75%, 94.99%, and 99.23% for the SAVEE, RAVDESS, and RML databases, respectively. Our experimental study reveals that the suggested method is more effective than existing algorithms in adapting to emotion recognition in these datasets. © 2022 Elsevier Ltd
Publication Date: 2022
PLoS ONE (19326203) 17(2 February)
Differentiating between shockable and non-shockable Electrocardiogram (ECG) signals would increase the success of resuscitation by the Automated External Defibrillators (AED). In this study, a Deep Neural Network (DNN) algorithm is used to distinguish 1.4-second segment shockable signals from non-shockable signals promptly. The proposed technique is frequency-independent and is trained with signals from diverse patients extracted from MIT-BIH, MIT-BIH Malignant Ventricular Ectopy Database (VFDB), and a database for ventricular tachyarrhythmia signals from Creighton University (CUDB) resulting, in an accuracy of 99.1%. Finally, the raspberry pi minicomputer is used to load the optimized version of the model on it. Testing the implemented model on the processor by unseen ECG signals resulted in an average latency of 0.845 seconds meeting the IEC 60601-2-4 requirements. According to the evaluated results, the proposed technique could be used by AED’s. Copyright: © 2022 Nasimi, Yazdchi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Publication Date: 2022
PLoS ONE (19326203) 17(2 February)
The demand for long-term continuous care has led healthcare experts to focus on development challenges. On-chip energy consumption as a key challenge can be addressed by data reduction techniques. In this paper, the pseudo periodic nature of ElectroCardioGram (ECG) signals has been used to completely remove redundancy from frames. Compressing aligned QRS complexes by Compressed Sensing (CS), result in highly redundant measurement vectors. By removing this redundancy, a high cluster of near zero samples is gained. The efficiency of the proposed algorithm is assessed using the standard MIT-BIH database. The results indicate that by aligning ECG frames, the proposed technique can achieve superior reconstruction quality compared to state-of-the-art techniques for all compression ratios. This study proves that by aligning ECG frames with a 0.05% unaligned frame rate(R-peak detection error), more compression could be gained for PRD > 5% when 5-bit non-uniform quantizer is used. Furthermore, analysis done on power consumption of the proposed technique, indicates that a very good recovery performance can be gained by only consuming 4.9μW more energy per frame compared to traditional CS. Copyright: © 2022 Nasimi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Schuermans A. Publication Date: 2023
Journal of the American College of Cardiology (7351097) (25)pp. 2350-2473
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Hay, S.I. ,
Vos, T. Publication Date: 2023
The Lancet (1406736) (10397)pp. 203-234
Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. Findings: In 2021, there were 529 million (95% uncertainty interval [UI] 500–564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8–6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7–9·9]) and, at the regional level, in Oceania (12·3% [11·5–13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1–79·5) in individuals aged 75–79 years. Total diabetes prevalence—especially among older adults—primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1–96·8) of diabetes cases and 95·4% (94·9–95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5–71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5–30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22–1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1–17·6) in north Africa and the Middle East and 11·3% (10·8–11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%. Interpretation: Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers. Funding: Bill & Melinda Gates Foundation. © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
Publication Date: 2023
International Journal of Business Intelligence and Data Mining (17438195) 23(2)pp. 150-165
Due to the importance of identifying influential nodes in different applications, many methods have been proposed for it. Some of them are not accurate enough or have high temporal complexity. In this paper, a method named new GLS (NGLS) is developed based on the global and local search (GLS) algorithm. GLS, despite its high accuracy compared to other methods is not fast and efficient enough. NGLS is developed to improve the efficiency and scalability of GLS. To reach this goal, the number of common neighbours of each node is counted only up to a radius of two. The execution time of NGLS on average has been reduced by 85% in real-world networks and 97% on simulated networks, while the accuracy of NGLS is the same as GLS accuracy. Therefore, NGLS is applicable for larger real-world networks. © 2023 Inderscience Enterprises Ltd.
Alamatsaz, N. ,
Tabatabaei, L. ,
Yazdchi, M. ,
Payan, H. ,
Alamatsaz, N. ,
Nasimi, F. Publication Date: 2024
Biomedical Signal Processing and Control (17468108) 90
Objective: Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used for monitoring heart electrical signals and evaluating its functionality. The human heart can suffer from a variety of diseases, including cardiac arrhythmias. Arrhythmia is an irregular heart rhythm that in severe cases can lead to stroke and can be diagnosed via ECG recordings. Since early detection of cardiac arrhythmias is of great importance, computerized and automated classification and identification of these abnormal heart signals have received much attention for the past decades. Methods: This paper introduces a light Deep Learning (DL) approach for high accuracy detection of 8 different cardiac arrhythmias and normal rhythms. To employ DL techniques, the ECG signals were preprocessed using resampling and baseline wander removal techniques. The classification was performed using an 11-layer network employing a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). Results: In order to evaluate the proposed technique, ECG signals are chosen from the two physionet databases, the MIT-BIH arrhythmia database and the long-term AF database. The proposed DL framework based on the combination of CNN and LSTM showed promising results than most of the state-of-the-art methods. The proposed method reaches the mean diagnostic accuracy of 98.24%. Conclusion: A trained model for arrhythmia classification using diverse ECG signals were successfully developed and tested. Significance: This study presents a lightweight classification technique with high diagnostic accuracy compared to other notable methods, making it a potential candidate for implementation in Holter monitor devices for arrhythmia detection. Finally, we used SHapley Additive exPlanations (SHAP), the most popular Explainable Artificial Intelligence (XAI) method to understand how our model make predictions. The results indicate that those features (ECG samples) that have contributed the most to a prediction are consonant with clinicians’ decisions. Therefore, the use of interpretable models increases the trust of clinicians in AI and thus leads to decreasing the number of misdiagnoses of cardiovascular diseases. © 2023 Elsevier Ltd
Publication Date: 2024
Multimedia Tools and Applications (13807501) 83(3)pp. 7939-7979
The current paper proposes LSIE, a fast and secure Latin square-based image encryption scheme using SHA256 hash function and chaotic systems. LSIE uses a 3-tier architecture consisting of diffusion-confusion-diffusion based on Latin squares to design an efficient cryptographic algorithm. Firstly, the initial values and parameters of the 3D exponent chaotic map (3D-ECM) is obtained from the SHA256 hash value of the external secret key and the plain image. Next, two orthogonal Latin squares are constructed using chaotic sequences generated by the 3D-ECM. In the first diffusion phase, one of the Latin squares is considered as a key image to Exclusive-OR (XOR) with the plain image. In the confusion phase, a 2D permutation based on the orthogonal Latin squares is presented to permute pixel positions of the diffused image. In the second diffusion phase, another Latin square is considered as a key image to XOR with the permuted image. The analysis and simulation results indicate that the proposed LSIE could efficiently resist common security attacks, as also that it is a fast method for real-time applications. The MATLAB source code of the proposed LSIE is available at the URL: https://github.com/EbrahimZarei64/LSIE . © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Publication Date: 2024
Journal of Information Security and Applications (22142134) 83
Today, due to the unparalleled growth of multimedia data sharing, especially digital images, between users over insecure channels in real-time applications, cryptography algorithms have gained increasing attention for the secure and efficient transmission. In classical chaos-based image cryptosystems, the confusion and diffusion operations are often applied as two separate and independent phases, which threatens the cryptosystem security. To address these problems, in this paper, a fast image cryptosystem based on parallel simultaneous diffusion–confusion strategy has been proposed using Latin squares, called PSDCLS. It consists of three main steps. First, the initial parameters of the Hénon-Sine chaotic map are produced from SHA256 of both the plain image content and the user's secret key. Second, a chaos-based random Latin square is constructed by employing the chaotic sequence produced through the Hénon-Sine chaotic map. Third, a parallel simultaneous diffusion–confusion scheme is proposed by using Latin square and vectorization technique to overcome the problems of computational complexity and high risk of separable and iterative confusion–diffusion operations in the classical chaos-based image cryptosystems. To analyze and evaluate the security and performance of PSDCLS cryptosystem, we conducted extensive simulations and experiments on various benchmark images. Experimental results and analyses show that PSDCLS achieves excellent scores for information entropy (>7.99), correlation coefficients close to 0, key space (2512), NPCR (>99.60%), UACI (>33.46%). The encryption time for test images of size 512 × 512 and 512×512×3 was around 0.026 and 0.081 s, respectively. Therefore, PSDCLS is highly robust against common cryptographic attacks and serves as a swift cryptosystem for real-time encryption applications. The source code of PSDCLS is accessible at: https://github.com/EbrahimZarei64/PSDCLS. © 2024 Elsevier Ltd
Publication Date: 2024
BMC Emergency Medicine (1471227X) 24(1)
Background: Although unplanned deliveries in ambulances are uncommon, Emergency Medical Services (EMS) providers may encounter this situation before reaching the hospital. This research aims to gather insights from Emergency Medical Technicians (EMTs), midwives, and expectant mothers to examine the causes of giving birth in ambulances and the challenges EMTs, pregnant women, and midwives face during delivery. Methods: A qualitative study was conducted, and 28 EMTs, midwives, and pregnant women who had experience with pre-hospital births in the ambulance were interviewed. Data were analyzed using thematic content analysis. The MAXQDA/10 software was employed for data analysis and code extraction. Results: The analysis of the interviews revealed two main categories: factors that cause delivery in the ambulance and its challenges. The factors include cultural problems, weak management, and inaccessibility to facilities. The challenges consist of fear and anxiety, native culture, and lack of resources. Conclusions: Several approaches should be implemented to reduce the number of births in ambulances and Pre-hospital Emergency Medical Services (PEMS). These include long-term community cultural activities, public education, awareness campaigns, education and follow-up for pregnant women, and improved accessibility to health facilities. Additionally, EMTS need to receive proper education and training for ambulance deliveries. Enhancing ambulance services and supporting EMTs in dealing with litigation claims are also critical. © The Author(s) 2024.
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University of Isfahan
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