Pub Date : 2023-09-13DOI: 10.11113/ijic.v13n1-2.415
Rasha Ali Dihin, Waleed A Mahmoud Al-Jawher, Ebtesam N AlShemmary
Diabetic retinopathy is one of the most dangerous complications for diabetic patients, leading to blindness if not diagnosed early. However, early diagnosis can control and prevent the disease from progressing to blindness. Transformers are considered state-of-the-art models in natural language processing that do not use convolutional layers. In transformers, means of multi-head attention mechanisms capture long-range contextual relations between pixels. For grading diabetic retinopathy, CNNs currently dominate deep learning solutions. However, the benefits of transformers, have led us to propose an appropriate transformer-based method to recognize diabetic retinopathy grades. A major objective of this research is to demonstrate that the pure attention mechanism can be used to determine diabetic retinopathy and that transformers can replace standard CNNs in identifying the degrees of diabetic retinopathy. In this study, a Swin Transformer-based technique for diagnosing diabetic retinopathy is presented by dividing fundus images into nonoverlapping batches, flattening them, and maintaining positional information using a linear and positional embedding procedure. Several multi-headed attention layers are fed into the resulting sequence to construct the final representation. In the classification step, the initial token sequence is passed into the SoftMax classification layer, which produces the recognition output. This work introduced the Swin transformer performance on the APTOS 2019 Kaggle for training and testing using fundus images of different resolutions and patches. The test accuracy, test loss, and test top 2 accuracies were 69.44%, 1.13, and 78.33%, respectively for 160*160 image size, patch size=2, and embedding dimension C=64. While the test accuracy was 68.85%, test loss: 1.12, and test top 2 accuracy: 79.96% when the patch size=4, and embedding dimension C=96. And when the size image is 224*224, patch size=2, and embedding dimension C=64, the test accuracy: 72.5%, test loss: 1.07, and test top 2 accuracy: 83.7%. When the patch size =4, embedding dimension C=96, the test accuracy was 74.51%, test loss: 1.02, and the test top 2 accuracy was 85.3%. The results showed that the Swin Transformer can achieve flexible memory savings. The proposed method highlights that an attention mechanism based on the Swin Transformer model is promising for the diabetic retinopathy grade recognition task.
{"title":"Diabetic Retinopathy Image Classification Using Shift Window Transformer","authors":"Rasha Ali Dihin, Waleed A Mahmoud Al-Jawher, Ebtesam N AlShemmary","doi":"10.11113/ijic.v13n1-2.415","DOIUrl":"https://doi.org/10.11113/ijic.v13n1-2.415","url":null,"abstract":"Diabetic retinopathy is one of the most dangerous complications for diabetic patients, leading to blindness if not diagnosed early. However, early diagnosis can control and prevent the disease from progressing to blindness. Transformers are considered state-of-the-art models in natural language processing that do not use convolutional layers. In transformers, means of multi-head attention mechanisms capture long-range contextual relations between pixels. For grading diabetic retinopathy, CNNs currently dominate deep learning solutions. However, the benefits of transformers, have led us to propose an appropriate transformer-based method to recognize diabetic retinopathy grades. A major objective of this research is to demonstrate that the pure attention mechanism can be used to determine diabetic retinopathy and that transformers can replace standard CNNs in identifying the degrees of diabetic retinopathy. In this study, a Swin Transformer-based technique for diagnosing diabetic retinopathy is presented by dividing fundus images into nonoverlapping batches, flattening them, and maintaining positional information using a linear and positional embedding procedure. Several multi-headed attention layers are fed into the resulting sequence to construct the final representation. In the classification step, the initial token sequence is passed into the SoftMax classification layer, which produces the recognition output. This work introduced the Swin transformer performance on the APTOS 2019 Kaggle for training and testing using fundus images of different resolutions and patches. The test accuracy, test loss, and test top 2 accuracies were 69.44%, 1.13, and 78.33%, respectively for 160*160 image size, patch size=2, and embedding dimension C=64. While the test accuracy was 68.85%, test loss: 1.12, and test top 2 accuracy: 79.96% when the patch size=4, and embedding dimension C=96. And when the size image is 224*224, patch size=2, and embedding dimension C=64, the test accuracy: 72.5%, test loss: 1.07, and test top 2 accuracy: 83.7%. When the patch size =4, embedding dimension C=96, the test accuracy was 74.51%, test loss: 1.02, and the test top 2 accuracy was 85.3%. The results showed that the Swin Transformer can achieve flexible memory savings. The proposed method highlights that an attention mechanism based on the Swin Transformer model is promising for the diabetic retinopathy grade recognition task.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135781465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.11113/ijic.v13n1-2.416
Ali Hakem Alsaeedi, Yarub Alazzawi, Suha Mohammed Hadi
Images captured in dusty environments suffering from poor visibility and quality. Enhancement of these images such as sand dust images plays a critical role in various atmospheric optics applications. In this work, proposed a new model based on Color Correction and New Fuzzy Intensification Operators to enhance san dust images. The proposed model consists of three phases: correction of color shift, removal of haze, and enhancement of contrast and brightness. The color shift is corrected using a fuzzy intensification operator to adjust the values of U and V in the YUV color space. The Adaptive Dark Channel Prior (A-DCP) is used for haze removal. The stretching contrast and improving image brightness are based on Contrast Limited Adaptive Histogram Equalization (CLAHE). The proposed model tests and evaluates through many real sand dust images. The experimental results show that the proposed solution is outperformed the current studies in terms of effectively removing the red and yellow cast and provides high quality and quantity dust images.
{"title":"Fast Dust Sand Image Enhancement Based on Color Correction and New Fuzzy Intensification Operators","authors":"Ali Hakem Alsaeedi, Yarub Alazzawi, Suha Mohammed Hadi","doi":"10.11113/ijic.v13n1-2.416","DOIUrl":"https://doi.org/10.11113/ijic.v13n1-2.416","url":null,"abstract":"Images captured in dusty environments suffering from poor visibility and quality. Enhancement of these images such as sand dust images plays a critical role in various atmospheric optics applications. In this work, proposed a new model based on Color Correction and New Fuzzy Intensification Operators to enhance san dust images. The proposed model consists of three phases: correction of color shift, removal of haze, and enhancement of contrast and brightness. The color shift is corrected using a fuzzy intensification operator to adjust the values of U and V in the YUV color space. The Adaptive Dark Channel Prior (A-DCP) is used for haze removal. The stretching contrast and improving image brightness are based on Contrast Limited Adaptive Histogram Equalization (CLAHE). The proposed model tests and evaluates through many real sand dust images. The experimental results show that the proposed solution is outperformed the current studies in terms of effectively removing the red and yellow cast and provides high quality and quantity dust images.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135689720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.11113/ijic.v13n1-2.414
Maryam I Mousa Al-Khuzaay, Waleed A. Mahmoud Al-Jawher
The transformation model plays a vital role in medical image processing. This paper proposed new two Mixed Transforms models that are the hybrid combination of linear and nonlinear Transformations techniques. The first mixed transform is computed in three steps: calculate 2D discrete cosine transform (DCT) of the image, and applying Arnold Transform (AT) on the DCT coefficients, and applying the discrete Wavelet Transform (DWT) on the result to get which was abbreviated as (CAW). The second mixed transform consists of firstly computing the discrete Fourier transform (DFT), net applying the Arnold Transform (AT), and finally, the computation of discrete Wavelet Transform (DWT) which was abbreviated as (FAW). These transforms have superior directional representations as compared to other multiresolution representations such as DWT or DCT and work as non-adaptive mixed transformations for multi-scale object analysis. Due to their relationship to the wavelet idea, they are finding increasing use in areas like image processing and scientific computing. These transforms are tested in medical image classification task and their performances are compared with that of the traditional transforms. CAW and FAW transforms are used in the feature extraction stage of a classification VGG16 deep learning (DNN) task of Tumor MRI medical image. The numerical findings favor CAW and FAW over the wavelet transform for estimating and classifying pictures. From the results obtained it was shown that the CAW and FAW transform gave e much higher classification rate than that achieved with the traditional transforms, namely DCT, DFT and DWT. Furthermore, this combination leads to a family of directional and multi-transformation bases for image processing.
{"title":"New Proposed Mixed Transforms: CAW and FAW and Their Application in Medical Image Classification","authors":"Maryam I Mousa Al-Khuzaay, Waleed A. Mahmoud Al-Jawher","doi":"10.11113/ijic.v13n1-2.414","DOIUrl":"https://doi.org/10.11113/ijic.v13n1-2.414","url":null,"abstract":"The transformation model plays a vital role in medical image processing. This paper proposed new two Mixed Transforms models that are the hybrid combination of linear and nonlinear Transformations techniques. The first mixed transform is computed in three steps: calculate 2D discrete cosine transform (DCT) of the image, and applying Arnold Transform (AT) on the DCT coefficients, and applying the discrete Wavelet Transform (DWT) on the result to get which was abbreviated as (CAW). The second mixed transform consists of firstly computing the discrete Fourier transform (DFT), net applying the Arnold Transform (AT), and finally, the computation of discrete Wavelet Transform (DWT) which was abbreviated as (FAW). These transforms have superior directional representations as compared to other multiresolution representations such as DWT or DCT and work as non-adaptive mixed transformations for multi-scale object analysis. Due to their relationship to the wavelet idea, they are finding increasing use in areas like image processing and scientific computing. These transforms are tested in medical image classification task and their performances are compared with that of the traditional transforms. CAW and FAW transforms are used in the feature extraction stage of a classification VGG16 deep learning (DNN) task of Tumor MRI medical image. The numerical findings favor CAW and FAW over the wavelet transform for estimating and classifying pictures. From the results obtained it was shown that the CAW and FAW transform gave e much higher classification rate than that achieved with the traditional transforms, namely DCT, DFT and DWT. Furthermore, this combination leads to a family of directional and multi-transformation bases for image processing.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135689440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.11113/ijic.v13n1-2.420
Saadi Mohammed Saadi, Waleed Ameen Mahmoud Al-Jawher
Videos made by artificial intelligence (A.I.) seem real, but they are not. When making DeepFake videos, face-swapping methods are frequently employed. The misuse of technology when using fakes, even though it was fun at first, these videos were somewhat recognizable to human eyes. However, as machine learning advanced, it became simpler to produce profound fake videos. It's practically impossible to tell it apart from actual videos now. Using GANs (Generative Adversarial Networks) and other deep learning techniques, DeepFake videos are output technology that may mislead people into thinking something is real when it is not. This study used a MultiWavelet transform to analyze the type of edge and its sharpness to develop a blur inconsistency detecting system. With this capability, it can assess whether or not the facial area is obscured in the video. As a result, it will detect fake videos. This paper reviews DeepFake detection techniques and discusses how they might be combined or altered to get more accurate results. A detection rate of more than 93.5% was obtained, which is quite successful.
{"title":"Proposed DeepFake Detection Method Using Multiwavelet Transform","authors":"Saadi Mohammed Saadi, Waleed Ameen Mahmoud Al-Jawher","doi":"10.11113/ijic.v13n1-2.420","DOIUrl":"https://doi.org/10.11113/ijic.v13n1-2.420","url":null,"abstract":"Videos made by artificial intelligence (A.I.) seem real, but they are not. When making DeepFake videos, face-swapping methods are frequently employed. The misuse of technology when using fakes, even though it was fun at first, these videos were somewhat recognizable to human eyes. However, as machine learning advanced, it became simpler to produce profound fake videos. It's practically impossible to tell it apart from actual videos now. Using GANs (Generative Adversarial Networks) and other deep learning techniques, DeepFake videos are output technology that may mislead people into thinking something is real when it is not. This study used a MultiWavelet transform to analyze the type of edge and its sharpness to develop a blur inconsistency detecting system. With this capability, it can assess whether or not the facial area is obscured in the video. As a result, it will detect fake videos. This paper reviews DeepFake detection techniques and discusses how they might be combined or altered to get more accurate results. A detection rate of more than 93.5% was obtained, which is quite successful.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135781280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.11113/ijic.v13n1-2.412
Afrah U Mosa, Waleed A Mahmoud Al-Jawher
Data fusion is a “formal framework in which are expressed the means and tools for the alliance of data originating from different sources.” It aims at obtaining information of greater quality; the exact definition of 'greater quality will depend upon the application. It is a famous technique in digital image processing and is very important in medical image representation for clinical diagnosis. Previously many researchers used many meta-heuristic optimization techniques in image fusion, but the problem of local optimization restricted their searching flow to find optimum search results. In this paper, the Grey Wolf Optimization (GWO) algorithm with the help of the Shuffled Frog Leaping Algorithm (SFLA) has been proposed. That helps to find the object and allows doctors to take some action. The optimization algorithm is examined with a demonstrated example in order to simplify its steps. The result of the proposed algorithm is compared with other optimization algorithms. The proposed method's performance was always the best among them.
{"title":"Image Fusion Algorithm using Grey Wolf optimization with Shuffled Frog Leaping Algorithm","authors":"Afrah U Mosa, Waleed A Mahmoud Al-Jawher","doi":"10.11113/ijic.v13n1-2.412","DOIUrl":"https://doi.org/10.11113/ijic.v13n1-2.412","url":null,"abstract":"Data fusion is a “formal framework in which are expressed the means and tools for the alliance of data originating from different sources.” It aims at obtaining information of greater quality; the exact definition of 'greater quality will depend upon the application. It is a famous technique in digital image processing and is very important in medical image representation for clinical diagnosis. Previously many researchers used many meta-heuristic optimization techniques in image fusion, but the problem of local optimization restricted their searching flow to find optimum search results. In this paper, the Grey Wolf Optimization (GWO) algorithm with the help of the Shuffled Frog Leaping Algorithm (SFLA) has been proposed. That helps to find the object and allows doctors to take some action. The optimization algorithm is examined with a demonstrated example in order to simplify its steps. The result of the proposed algorithm is compared with other optimization algorithms. The proposed method's performance was always the best among them.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135781281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.11113/ijic.v13n1-2.422
Raad Ahmed Mohamed, Karim Q Hussein
There are at least three hundred and fifty million people in the world that cannot hear or speak. These are what are called deaf and dumb. Often this segment of society is partially isolated from the rest of society due to the difficulty of dealing, communicating and understanding between this segment and the rest of the healthy society. As a result of this problem, a number of solutions have been proposed that attempt to bridge this gap between this segment and the rest of society. The main reason for this is to simplify the understanding of sign language. The basic idea is building program to recognize the hand movement of the interlocutor and convert it from images to symbols or letters found in the dictionary of the deaf and dumb. This process itself follows mainly the applications of artificial intelligence, where it is important to distinguish, identify, and extract the palm of the hand from the regular images received by the camera device, and then convert this image of the movement of the paws or hands into understandable symbols. In this paper, the method of image processing and artificial intelligence, represented by the use of artificial neural networks after synthesizing the problem under research was used. Scanning the image to determine the areas of the right and left palm. Non-traditional methods that use artificial intelligence like Convolutional Neural Networks are used to fulfill this part. YOLO V-2 specifically was used in the current research with excellent results. Part Two: Building a pictorial dictionary of the letters used in teaching the deaf and dumb, after generating the image database for the dictionary, neural network Dark NET-19 were used to identify (classification) the images of characters extracted from the first part of the program. The results obtained from the research show that the use of neural networks, especially convolution neural networks, is very suitable in terms of accuracy, speed of performance, and generality in processing the previously unused input data. Many of the limitations associated with using such a program without specifying specific shapes (general shape) and templates, hand shape, hand speed, hand color and other physical expressions and without using any other physical aids were overcome through the optimal use of artificial convolution neural networks.
{"title":"Real-Time Hand Gesture Recognition Using YOLO and (Darknet-19) Convolution Neural Networks","authors":"Raad Ahmed Mohamed, Karim Q Hussein","doi":"10.11113/ijic.v13n1-2.422","DOIUrl":"https://doi.org/10.11113/ijic.v13n1-2.422","url":null,"abstract":"There are at least three hundred and fifty million people in the world that cannot hear or speak. These are what are called deaf and dumb. Often this segment of society is partially isolated from the rest of society due to the difficulty of dealing, communicating and understanding between this segment and the rest of the healthy society. As a result of this problem, a number of solutions have been proposed that attempt to bridge this gap between this segment and the rest of society. The main reason for this is to simplify the understanding of sign language. The basic idea is building program to recognize the hand movement of the interlocutor and convert it from images to symbols or letters found in the dictionary of the deaf and dumb. This process itself follows mainly the applications of artificial intelligence, where it is important to distinguish, identify, and extract the palm of the hand from the regular images received by the camera device, and then convert this image of the movement of the paws or hands into understandable symbols. In this paper, the method of image processing and artificial intelligence, represented by the use of artificial neural networks after synthesizing the problem under research was used. Scanning the image to determine the areas of the right and left palm. Non-traditional methods that use artificial intelligence like Convolutional Neural Networks are used to fulfill this part. YOLO V-2 specifically was used in the current research with excellent results. Part Two: Building a pictorial dictionary of the letters used in teaching the deaf and dumb, after generating the image database for the dictionary, neural network Dark NET-19 were used to identify (classification) the images of characters extracted from the first part of the program. The results obtained from the research show that the use of neural networks, especially convolution neural networks, is very suitable in terms of accuracy, speed of performance, and generality in processing the previously unused input data. Many of the limitations associated with using such a program without specifying specific shapes (general shape) and templates, hand shape, hand speed, hand color and other physical expressions and without using any other physical aids were overcome through the optimal use of artificial convolution neural networks.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135741114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.11113/ijic.v13n1-2.418
Ahmed Hussein Salman, Waleed Ameen Mahmoud Al-Jawher
In machine learning, feature selection is crucial to increase performance and shorten the model's learning time. It seeks to discover the pertinent predictors from high-dimensional feature space. However, a tremendous increase in the feature dimension space poses a severe obstacle to feature selection techniques. Study process to address this difficulty, the authors suggest a hybrid feature selection method consisting of the Multiwavelet transform and Gray Wolf optimization. The proposed approach minimizes the overall downsides while cherry picking the benefits of both directions. This notable wavelet transform development employs both wavelet and vector scaling functions. Additionally, multiwavelets have orthogonality, symmetry, compact support, and significant vanishing moments. One of the most advanced areas of study of artificial intelligence is optimization algorithms. Grey Wolf Optimization (GWO) here produced artificial techniques that yielded good performance results and were more responsive to current needs. Keywords — About four key words or phrases in order of importance, separated by commas, used to compile the subject index for the last issue for the year.
{"title":"A Hybrid Multiwavelet Transform with Grey Wolf Optimization Used for an Efficient Classification of Documents","authors":"Ahmed Hussein Salman, Waleed Ameen Mahmoud Al-Jawher","doi":"10.11113/ijic.v13n1-2.418","DOIUrl":"https://doi.org/10.11113/ijic.v13n1-2.418","url":null,"abstract":"In machine learning, feature selection is crucial to increase performance and shorten the model's learning time. It seeks to discover the pertinent predictors from high-dimensional feature space. However, a tremendous increase in the feature dimension space poses a severe obstacle to feature selection techniques. Study process to address this difficulty, the authors suggest a hybrid feature selection method consisting of the Multiwavelet transform and Gray Wolf optimization. The proposed approach minimizes the overall downsides while cherry picking the benefits of both directions. This notable wavelet transform development employs both wavelet and vector scaling functions. Additionally, multiwavelets have orthogonality, symmetry, compact support, and significant vanishing moments. One of the most advanced areas of study of artificial intelligence is optimization algorithms. Grey Wolf Optimization (GWO) here produced artificial techniques that yielded good performance results and were more responsive to current needs. Keywords — About four key words or phrases in order of importance, separated by commas, used to compile the subject index for the last issue for the year.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135689445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.11113/ijic.v13n1-2.419
Ali Akram Abdul-Kareem, Waleed Ameen Mahmoud Al-Jawher
Chaotic systems have become widely adopted as an effective way for secure data communications, because of its simple mathematical complexity and good security. The relationship between encryption algorithms and chaos systems has gained a lot of attention in the past few years, since it avoids the data spreading as well as lower the transmission delay and costs. In this paper a novel 3D discrete chaotic map is proposed for data encryption and secure communication and named as WAM. For secure communication, the Pecora and Carroll (P-C) method was utilized to achieve synchronization between the master system and the slave system. The simulation results of WAM 3D discrete chaotic map showed that the system has a chaotic behavior and a characteristic randomness and can pass 0-1, Lyapunov exponent (LE) and NIST tests which are usually used to check chaotic behavior. The statistical outcomes of the LE test were 0.0193, the frequency test (FT) was 0.4237, and the run test (RT) yielded a value of 0.0607. As a result, it enrich the theoretical basis of the equations and implementation of chaos, and it is superior for encryption algorithms and communication security applications.
{"title":"WAM 3D Discrete Chaotic Map for Secure Communication Applications","authors":"Ali Akram Abdul-Kareem, Waleed Ameen Mahmoud Al-Jawher","doi":"10.11113/ijic.v13n1-2.419","DOIUrl":"https://doi.org/10.11113/ijic.v13n1-2.419","url":null,"abstract":"Chaotic systems have become widely adopted as an effective way for secure data communications, because of its simple mathematical complexity and good security. The relationship between encryption algorithms and chaos systems has gained a lot of attention in the past few years, since it avoids the data spreading as well as lower the transmission delay and costs. In this paper a novel 3D discrete chaotic map is proposed for data encryption and secure communication and named as WAM. For secure communication, the Pecora and Carroll (P-C) method was utilized to achieve synchronization between the master system and the slave system. The simulation results of WAM 3D discrete chaotic map showed that the system has a chaotic behavior and a characteristic randomness and can pass 0-1, Lyapunov exponent (LE) and NIST tests which are usually used to check chaotic behavior. The statistical outcomes of the LE test were 0.0193, the frequency test (FT) was 0.4237, and the run test (RT) yielded a value of 0.0607. As a result, it enrich the theoretical basis of the equations and implementation of chaos, and it is superior for encryption algorithms and communication security applications.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135689858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.11113/ijic.v13n1-2.421
Sajjad H. Hendi, Karim Q. Hussein, Hazeem B. Taher
Video summarization has arisen as a method that can help with efficient storage, rapid browsing, indexing, fast retrieval, and quick sharing of the material. The amount of video data created has grown exponentially over time. Huge amounts of video are produced continuously by a large number of cameras. Processing these massive amounts of video requires a lot of time, labor, and hardware storage. In this situation, a video summary is crucial. The architecture of video summarization demonstrates how a lengthy film may be broken down into shorter, story-like segments. Numerous sorts of studies have been conducted in the past and continue now. As a result, several approaches and methods—from traditional computer vision to more modern deep learning approaches—have been offered by academics. However, several issues make video summarization difficult, including computational hardware, complexity, and a lack of datasets. Many researchers have recently concentrated their research efforts on developing efficient methods for extracting relevant information from videos. Given that data is gathered constantly, seven days a week, this study area is crucial for the advancement of video surveillance systems that need a lot of storage capacity and intricate data processing. To make data analysis easier, make it easier to store information, and make it easier to access the video at any time, a summary of video data is necessary for these systems. In this paper, methods for creating static or dynamic summaries from videos are presented. The authors provide many approaches for each literary form. The authors have spoken about some features that are utilized to create video summaries.
{"title":"Digital Video Summarization: A Survey","authors":"Sajjad H. Hendi, Karim Q. Hussein, Hazeem B. Taher","doi":"10.11113/ijic.v13n1-2.421","DOIUrl":"https://doi.org/10.11113/ijic.v13n1-2.421","url":null,"abstract":"Video summarization has arisen as a method that can help with efficient storage, rapid browsing, indexing, fast retrieval, and quick sharing of the material. The amount of video data created has grown exponentially over time. Huge amounts of video are produced continuously by a large number of cameras. Processing these massive amounts of video requires a lot of time, labor, and hardware storage. In this situation, a video summary is crucial. The architecture of video summarization demonstrates how a lengthy film may be broken down into shorter, story-like segments. Numerous sorts of studies have been conducted in the past and continue now. As a result, several approaches and methods—from traditional computer vision to more modern deep learning approaches—have been offered by academics. However, several issues make video summarization difficult, including computational hardware, complexity, and a lack of datasets. Many researchers have recently concentrated their research efforts on developing efficient methods for extracting relevant information from videos. Given that data is gathered constantly, seven days a week, this study area is crucial for the advancement of video surveillance systems that need a lot of storage capacity and intricate data processing. To make data analysis easier, make it easier to store information, and make it easier to access the video at any time, a summary of video data is necessary for these systems. In this paper, methods for creating static or dynamic summaries from videos are presented. The authors provide many approaches for each literary form. The authors have spoken about some features that are utilized to create video summaries.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135689848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.11113/ijic.v13n1-2.413
Qutaiba K Abed, Waleed A Mahmoud Al-Jawher
In this paper, a modified robust image encryption scheme is developed by combining block compressive sensing (BCS) and Wavelet Transform. It was achieved with a balanced performance of security, compression, robustness and running efficiency. First, the plain image is divided equally and sparsely represented in discrete wavelet transform (DWT) domain, and the coefficient vectors are confused using the coefficient random permutation strategy and encrypted into a secret image by compressive sensing. In pursuit of superior security, the hyper-chaotic Lorenz system is utilized to generate the updated secret code streams for encryption and embedding with assistance from the counter mode. This scheme is suitable for processing the medium and large images in parallel. Additionally, it exhibits superior robustness and efficiency compared with existing related schemes. Simulation results and comprehensive performance analyses are presented to demonstrate the effectiveness, secrecy and robustness of the proposed scheme. The compressive encryption model using BCS with Walsh transform as sensing matrix and WAM chaos system, the scrambling technique and diffusion succeeded in enhancement of secure performance.
{"title":"A Robust Image Encryption Scheme Based on Block Compressive Sensing and Wavelet Transform","authors":"Qutaiba K Abed, Waleed A Mahmoud Al-Jawher","doi":"10.11113/ijic.v13n1-2.413","DOIUrl":"https://doi.org/10.11113/ijic.v13n1-2.413","url":null,"abstract":"In this paper, a modified robust image encryption scheme is developed by combining block compressive sensing (BCS) and Wavelet Transform. It was achieved with a balanced performance of security, compression, robustness and running efficiency. First, the plain image is divided equally and sparsely represented in discrete wavelet transform (DWT) domain, and the coefficient vectors are confused using the coefficient random permutation strategy and encrypted into a secret image by compressive sensing. In pursuit of superior security, the hyper-chaotic Lorenz system is utilized to generate the updated secret code streams for encryption and embedding with assistance from the counter mode. This scheme is suitable for processing the medium and large images in parallel. Additionally, it exhibits superior robustness and efficiency compared with existing related schemes. Simulation results and comprehensive performance analyses are presented to demonstrate the effectiveness, secrecy and robustness of the proposed scheme. The compressive encryption model using BCS with Walsh transform as sensing matrix and WAM chaos system, the scrambling technique and diffusion succeeded in enhancement of secure performance.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135689301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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