A Novel Image Encryption Technique Based on Cyclic Codes over Galois Field

Asif, Muhammad; Asamoah, Joshua Kiddy K.; Hazzazi, Mohammad Mazyad; Alharbi, Adel R.; Ashraf, Muhammad Usman; Alghamdi, Ahmed Mohammed

doi:10.1155/2022/1912603

Cited by 12 publications

(9 citation statements)

References 17 publications

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“…Be that as it may, the primary downside of the GoogleNet was its heterogeneous geography that should be modified from module to module. Another constraint of GoogleNet was a portrayal bottleneck that definitely decreases the element feature in the next layer and hence at times may prompt loss of helpful data [41][42][43][44][45][46][47].…”

Section: Related Workmentioning

confidence: 99%

COVID Detection Using Chest X-ray Images & Deep Learning CNN Mechanism

Aljahdali¹

2023

IJETAE

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As we know that how rapidly is corona virus spreading, starting from China, to all over the world. There are more than 5 million suspected cases and almost 0.33 million deaths according to the statistics of world meterinfo, and there are limited test kits available in hospitals because of cases are increasing rapidly on the daily bases. So, it is compulsory to build an authentic automatic detection system which gives maximum high performance to check whether the patient is Covid-19 suspect or not as an alternative to slow down the spread of coronavirus among people.In this research, we have used deep learning’s (DL) Convolutional neural network (CNN) and ResNet models. With a critical analysis, we conclude that every ResNet layered model has the high performance with error rate less than 3% on approximately all kinds of datasets of chest Xray images whether it includes rib shadow & clivade or after segmentation. We have proposed a new solution for existing model and to enhance the ResNet model by applying layered architecture style by adding more layers to our ResNet which will help to minimize the error rate. Further, to boost the performance of ResNet by tune up the batch size and learning rate, we achieve the learning rate 0.00001 that has higher accuracy as compared to the other learning rates 0.1, 0.01, 0.001 and 0.0001. The proposed study is promising framework for the covid detection that assist us to deal the COVID decease

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Section: Related Workmentioning

confidence: 99%

COVID Detection Using Chest X-ray Images & Deep Learning CNN Mechanism

Aljahdali¹

2023

IJETAE

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“…These estimations recommend that CNNs might have exceptionally huge execution contrasts when distinguishing the presence of spatial examples. The case wherein the sign can show up in one of a few areas and found that CNN spatial affectability compares to IO [27][28][29][30][31][32][33]. Nonetheless, CNN affectability was far underneath ideal in distinguishing some complicated surface examples.…”

Section: A Challengesmentioning

confidence: 99%

A New Framework for Accelerating Magnetic Resonance Imaging using Deep Learning along with HPC Parallel Computing Technologies

Aljahdali¹

2022

IJACSA

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MRI (magnetic resource imaging) has played a vital role in emerging technologies because of its non-invasion principle. MR equipment is traditional procedure being used for imaging biological structures. In medical domain, MRI is a most important tool being used for staging in clinical diagnosis that has ability to furnish rich physiological and functional information and radiation and non-ionizing nature. However, MRI is highly demanding in several clinical applications. In this paper, we have proposed a novel deep learning based method that accelerates MRI using a huge number of MR images. In proposed method, we used supervised learning approach that performs network training of given datasets. It determines the required network parameters that afford an accurate reconstruction of under-sampled acquisitions. We also designed offline based neural network (NN) that was trained to discover the relationship between MR images and K-space. All the experiments were performed over advanced NVIDIA GPUs (Tesla k80 and GTX Titan) based computers. It was observed that the proposed model outperformed and attained <0.2% error rate. With our best knowledge, our method is the best approach that can be considered as leading model in future.

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“…In addition, its accuracy of results is much higher than other various algorithms. [42], [64][65][66][67][68][69][70].…”

Section: Estimation Model/regression Techniques a Random Forestmentioning

confidence: 99%

Comparative Analysis of Machine Learning Techniques for Predicting Air Pollution

Ashraf¹,

Akram²,

Usman³

2022

Research Journal Of Computer Science And Information Technology

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The modern and motorized way of life has cultured air pollution. Air pollution has become the biggest rival of robust living. This situation is becoming more lethal in developing countries and so in Pakistan. Hence, this inquiry was carried out to propose an architecture design that could make real-time prediction of air pollution with another purpose of scanning the frequently adopted algorithm in past investigations. In addition, it was also intended to narrate the toxic effects of air pollution on human health. So, this research was carried out on a large dataset of Seoul as an adequate dataset of Pakistan was not attainable. The dataset consisted of three years (2017-2019) including 647,512 instances and 11 attributes. The four distinctive algorithms termed Random Forest, Linear Regression, Decision Tree and XGBoosting were employed. It was inferred that XGB is more promising and feasible in predicting concentration level of NO2, O3, SO2, PM10, PM2.5 and CO with the lowest RMSE and MAE values of 0.0111, 0.0262, 0.0168, 49.64, 41.68 and 0.1856 and 0.0067, 0.0096, 0.0017, 12.28, 7.63 and 0.0982 respectively. Furthermore, it was found out as well that the Random Forest was preferred mostly in the previous studies related to air pollution prophecy while many probes supported that air pollution is very detrimental to human health especially long-lasting exposure causes lung cancer, respiratory and cardiovascular diseases.

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A Novel Image Encryption Technique Based on Cyclic Codes over Galois Field

Cited by 12 publications

References 17 publications

COVID Detection Using Chest X-ray Images & Deep Learning CNN Mechanism

COVID Detection Using Chest X-ray Images & Deep Learning CNN Mechanism

A New Framework for Accelerating Magnetic Resonance Imaging using Deep Learning along with HPC Parallel Computing Technologies

Comparative Analysis of Machine Learning Techniques for Predicting Air Pollution

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