The detection of neurological disorders and diseases is aided by automatically identifying brain tumors from brain magnetic resonance imaging (MRI) images. A brain tumor is a potentially fatal disease that affects humans. Convolutional... more
The detection of neurological disorders and diseases is aided by automatically identifying brain tumors from brain magnetic resonance imaging (MRI) images. A brain tumor is a potentially fatal disease that affects humans. Convolutional neural networks (CNNs) are the most common and widely used deep learning techniques for brain tumor analysis and classification. In this study, we proposed a deep CNN model for automatically detecting brain tumor cells in MRI brain images. First, we preprocess the 2D brain image MRI image to generate convolutional features. The CNN network is trained on the training dataset using the GoogleNet and AlexNet architecture, and the data model's performance is evaluated on the test data set. The model's performance is measured in terms of accuracy, sensitivity, specificity, and AUC. The algorithm performance matrices of both AlexNet and GoogLeNet are compared, the accuracy of AlexNet is 98.95, GoogLeNet is 99.45 sensitivity of AlexNet is 98.4, and G...
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Rice is grown almost everywhere in the world, especially in Asian countries, because it is part of the diets of about half of the world's population. However, farmers and planting experts have faced several persistent agricultural... more
Rice is grown almost everywhere in the world, especially in Asian countries, because it is part of the diets of about half of the world's population. However, farmers and planting experts have faced several persistent agricultural obstacles for many years, including many rice diseases. Severe rice diseases might result in no grain harvest; hence, in the field of agriculture, a fast, automatic, less expensive, and reliable approach to identifying rice diseases is widely needed. This paper focuses on how to build a lightweight deep learning model to detect rice plant diseases more precisely. To achieve the above objective, we created our own CNN model "LW17" to detect rice plant disease more precisely in comparison to some of the pre-trained models, such as VGG19, InceptionV3, MobileNet, Xception, DenseNet201, etc. Using the proposed methodology, we took UCI datasets for disease detection and tested our model with different layers, different training–testing ratios, diff...
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In this study, by making the use of q-analogous of the hyperbolic tangent function and a Sălăgean q-differential operator, a new class of q-starlike functions is introduced. The prime contribution of this study covers the derivation of... more
In this study, by making the use of q-analogous of the hyperbolic tangent function and a Sălăgean q-differential operator, a new class of q-starlike functions is introduced. The prime contribution of this study covers the derivation of sharp coefficient bounds in open unit disk U, especially the first three coefficient bounds, Fekete–Szego type functional, and upper bounds of second- and third-order Hankel determinant for the functions to this class. We also use Zalcman and generalized Zalcman conjectures to investigate the coefficient bounds of a newly defined class of functions. Furthermore, some known corollaries are highlighted based on the unique choices of the involved parameters l and q.
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The stationary hierarchical network faces considerable challenges from hotspots and faster network breakdowns, especially in smart monitoring applications. As a solution to this issue, mobile sinks were recommended since they are... more
The stationary hierarchical network faces considerable challenges from hotspots and faster network breakdowns, especially in smart monitoring applications. As a solution to this issue, mobile sinks were recommended since they are associated with huge and balanced ways to transfer data and energy across the network. Again, due to the mobile sink node advertisement around the network latency and the energy utilization overheads introduced across the network, ring routing reduces the control overhead while preserving the benefits of the mobile sink, thereby optimizing the energy and improving the network life span. Consequently, we suggested a novel, distributed advanced ring routing strategy, in this work, for the mobile wireless sensor network. Extensive simulations and performance evaluation, in comparison to previous distributed mobile approaches, reveal a 37% and 40% boost in the network throughput and end-to end delay, respectively. Additionally, the lifespan of a network is dete...
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Crack detection at an early stage is necessary to save people’s lives and to prevent the collapse of building/bridge structures. Manual crack detection is time-consuming, especially when a building structure is too high. Image processing,... more
Crack detection at an early stage is necessary to save people’s lives and to prevent the collapse of building/bridge structures. Manual crack detection is time-consuming, especially when a building structure is too high. Image processing, machine learning, and deep learning-based methods can be used in such scenarios to build an automatic crack detection system. This study uses a novel deep convolutional neural network, 3SCNet (3ScaleNetwork), for crack detection. The SLIC (Simple Linear Iterative Clustering) segmentation method forms the cluster of similar pixels and the LBP (Local Binary Pattern) finds the texture pattern in the crack image. The SLIC, LBP, and grey images are fed to 3SCNet to form pool of feature vector. This multi-scale feature fusion (3SCNet+LBP+SLIC) method achieved the highest sensitivity, specificity, an accuracy of 99.47%, 99.75%, and 99.69%, respectively, on a public historical building crack dataset. It shows that using SLIC super pixel segmentation and LB...
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In the COVID-19 era, it may be possible to detect COVID-19 by detecting lesions in scans, i.e., ground-glass opacity, consolidation, nodules, reticulation, or thickened interlobular septa, and lesion distribution, but it becomes difficult... more
In the COVID-19 era, it may be possible to detect COVID-19 by detecting lesions in scans, i.e., ground-glass opacity, consolidation, nodules, reticulation, or thickened interlobular septa, and lesion distribution, but it becomes difficult at the early stages due to embryonic lesion growth and the restricted use of high dose X-ray detection. Therefore, it may be possible for a patient who may or may not be infected with coronavirus to consider using high-dose X-rays, but it may cause more risks. Conclusively, using low-dose X-rays to produce CT scans and then adding a rigorous denoising algorithm to the scans is the best way to protect patients from side effects or a high dose X-ray when diagnosing coronavirus involvement early. Hence, this paper proposed a denoising scheme using an NLM filter and method noise thresholding concept in the shearlet domain for noisy COVID CT images. Low-dose COVID CT images can be further utilized. The results and comparative analysis showed that, in mo...
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Medical images and patient information are routinely transmitted to a remote radiologist to assist in diagnosis. It is critical in e-healthcare systems to ensure that data are accurately transmitted. Medical images of a person’s body can... more
Medical images and patient information are routinely transmitted to a remote radiologist to assist in diagnosis. It is critical in e-healthcare systems to ensure that data are accurately transmitted. Medical images of a person’s body can be used against them in many ways, including by transmitting them. Copyright and intellectual property laws prohibit the unauthorized use of medical images. Digital watermarking is used to prove the authenticity of the medical images before diagnosis. In this paper, we proposed a hybrid watermarking scheme using the Slantlet transform, randomized-singular value decomposition, and optimization techniques inspired by nature (Firefly algorithm). The watermark image is encrypted using the XOR encryption technique. Extensive testing reveals that our innovative approach outperforms the existing methods based on the NC, SSIM, and PSNR. The SSIM and NC values of watermarked image and extracted watermark are close to or equal to 1 at a scaling factor of 0.06...
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Consistently, influenza has become a major cause of illness and mortality worldwide and it has posed a serious threat to global public health particularly among the immuno-compromised people all around the world. The development of... more
Consistently, influenza has become a major cause of illness and mortality worldwide and it has posed a serious threat to global public health particularly among the immuno-compromised people all around the world. The development of medication to control influenza has become a major challenge now. This work proposes and analyzes a structured model based on two geographical areas, in order to study the spread of influenza. The overall underlying population is separated into two sub populations: urban and rural. This geographical distinction is required as the immunity levels are significantly higher in rural areas as compared to urban areas. Hence, this paper is a novel attempt to proposes a linear and non-linear mathematical model with adaptive immunity and compare the host immune response to disease. For both the models, disease-free equilibrium points are obtained which are locally as well as globally stable if the reproduction number is less than 1 (R01 < 1 & R02 < 1) and th...
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In recent years, scholars have found content-based image recovery to be a particularly interesting and exciting field. This field develops quick image recovery algorithms that are very similar to pictures from various data sources. There... more
In recent years, scholars have found content-based image recovery to be a particularly interesting and exciting field. This field develops quick image recovery algorithms that are very similar to pictures from various data sources. There is currently a lot of space and sources for data storage. Finally, once data sources are obtained, there is just competition for the exact and best result. The result is filtered using the heuristic correlation based on the above features of the photographs. The overall matching scores are calculated by adding all these individual feature ratings. The recommended method will be used to retrieve all images containing the content of the query image. The scores will be used to rate the match. The results of the combined method simulation demonstrate that the strategy is successful. This proposed model will improve the accuracy of search results. This research model, like the other metadata models on the web, is interactive and familiar for searching pi...
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The theory of dynamical systems and their widespread applications involve, for example, the Lane–Emden-type equations which are known to arise in initial- and boundary-value problems with singularity at the time t=0. The main objective of... more
The theory of dynamical systems and their widespread applications involve, for example, the Lane–Emden-type equations which are known to arise in initial- and boundary-value problems with singularity at the time t=0. The main objective of this paper is to make use of some mathematical analytic tools and techniques in order to numerically solve some reaction–diffusion equations, which arise in spherical catalysts and spherical biocatalysts, by applying the Chebyshev spectral collocation method. The proposed scheme has good accuracy. The results are demonstrated by means of illustrative graphs and numerical tables. The accuracy of the proposed method is verified by a comparison with the results which are derived by using analytical methods.
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The purpose of the present paper is to introduce and study a sequence of positive linear operators defined on suitable spaces of measurable functions on $[0,\infty )$[0,∞) and continuous function spaces with polynomial weights. These... more
The purpose of the present paper is to introduce and study a sequence of positive linear operators defined on suitable spaces of measurable functions on $[0,\infty )$[0,∞) and continuous function spaces with polynomial weights. These operators are Kantorovich type generalization of Jakimovski–Leviatan operators based on multiple Appell polynomials. Using these operators, we approximate suitable measurable functions by knowing their mean values on a sequence of subintervals of $[0,\infty )$[0,∞) that do not constitute a subdivision of it. We also discuss the rate of convergence of these operators using moduli of smoothness.