Diabetes is a widespread chronic condition that impacts people all over the globe and requires a clear and timely diagnosis. Untreated diabetes leads to retinopathy, nephropathy, and damage to the nervous system. In t...
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Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural network...
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Alzheimer’s Disease(AD)is a progressive neurological *** diagnosis of this illness using conventional methods is very *** Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance an...
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Alzheimer’s Disease(AD)is a progressive neurological *** diagnosis of this illness using conventional methods is very *** Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast *** disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age *** light of research investigations,it is vital to consider age as one of the key criteria when choosing the *** younger subjects are more susceptible to the perishable side than the older *** proposed investigation concentrated on the younger *** research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages *** proposed work is executed in three *** 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)*** Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of *** model was trained and tested to classify the five stages of *** ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.
Dynamic resource discovery in a network of dispersed computing resources is an open problem. The establishment and maintenance of resource pool information are critical, which involves both the polymorphic migration o...
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Dynamic resource discovery in a network of dispersed computing resources is an open problem. The establishment and maintenance of resource pool information are critical, which involves both the polymorphic migration of the network and the time and energy costs resulting from node selection and frequent interactions of information between nodes. The resource discovery problem for dispersed computing can be considered a dynamic multi-level decision problem. A bi-level programming model of dispersed computing resource discovery is developed, which is driven by time cost, energy consumption and accuracy of information acquisition. The upper-level model is to design a reasonable network structure of resource discovery, and the lower-level model is to explore an effective discovery mode. Complex network topology features are used for the first time to analyze the polymorphic migration characteristics of resource discovery networks. We propose an integrated calibration method for energy consumption parameters based on two discovery modes(i.e., agent mode and self-directed mode). A symmetric trust region based heuristic algorithm is proposed for solving the system model. The numerical simulation is performed in a dispersed computing network with multiple modes and topological states, which proves the feasibility of the model and the effectiveness of the algorithm.
Blockchain as a decentralized storage technology is widely used in many *** has extremely strict requirements for reliability because there are many potentially malicious ***,blockchain is a chain storage structure fo...
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Blockchain as a decentralized storage technology is widely used in many *** has extremely strict requirements for reliability because there are many potentially malicious ***,blockchain is a chain storage structure formed by interconnecting blocks1),which are stored by full replication method,where each node stores a replica of all blocks and the data consistency is maintained by the consensus *** decrease the storage overhead,previous approaches such as BFT-Store and Partition Chain store blocks via erasure ***,existing erasure coding based methods utilize static encoding schema to tolerant f malicious nodes,but in the typical cases,the number of malicious nodes is much smaller than f as described in previous *** redundant parities to tolerate excessive malicious nodes introduces unnecessary storage *** solve the above problem,we propose Dynamic-EC,which is a Dynamic Erasure Coding method in permissioned blockchain *** key idea of Dynamic-EC is to reduce the storage overhead by dynamically adjusting the total number of parities according to the risk level of the whole system,which is determined by the number of perceived malicious nodes,while ensuring the system *** demonstrate the effectiveness of Dynamic-EC,we conduct several experiments on an open source blockchain software *** results show that,compared to the state-of-the-art erasure coding methods,Dynamic-EC reduces the storage overhead by up to 42%,and decreases the average write latency of blocks by up to 25%,respectively.
Scene text removal is a recent development in computer vision that replaces text patches in natural images with the appropriate background. Text removal is a difficult process leading to faulty areas of text cont...
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Scene text removal is a recent development in computer vision that replaces text patches in natural images with the appropriate background. Text removal is a difficult process leading to faulty areas of text containing text strokes with their hazy backgrounds. Text in the real world uses a variety of font kinds, some of which are difficult to localize due to their chaotic shapes, varied shading degrees, and orientation *** text erasing may include the subtasks of text detection as well as text inpainting. Both subtasks require a large amount of data to be successful;but, existing approaches were limited by insufficient real-world data for scene-text elimination. Eventhough the existing works produced considerable performance improvement in scene text removal, they often leave many text remains like text strokes, thus producinglow-quality visual outcomes. Therefore, this paper proposes an automatic text inpainting and video quality elevation model by using the Improved Convolutional Network-based ***, the video samples are collected from the diverse datasets and then converted into frames. Next, the frames are deblurred using an enhanced Convolutional Neural Network (CNN) model that has three convolutional layers for accurately localizing the texts in frames. Subsequently, the texts are detected by utilizing the CLARA-based VGG-16 network. Afterward, the text strokes are removed using a convolutional Encoder and decoder network to eliminate the presence of text on complex backgrounds and textures. Here, the coordinates of text in the deblurred frames are used to crop out the text stroke regions. So, the texts are in-painted, and then, the text in-painted regions are pasted back to their original positions in the frames. Furthermore, the video quality is elevated with the help of the DenseNet-centric Enhancement network. The experimental outcomes demonstrate that the proposed model effectively removed scene texts and enhanced the video qu
Polycystic Ovary Syndrome (PCOS) is a recurring endocrine disorder that primarily affects women of reproductive age. It is difficult to diagnose due to its heterogeneous characteristics and overlapping symptoms with o...
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Heart disease increases the strain on the heart by reducing its ability to pump blood throughout the body, which can lead to heart attacks and strokes. Heart disease is becoming a global threat to the world due to peo...
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The big data clustering is a requisite for generating the data in the digitalised globe. The old-fashioned clustering approaches are not large sized and highly unorganised big data. Thus, to obtain the efficiency of b...
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The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart ***,these applications act as the building blocks of IoT-enabled ...
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The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart ***,these applications act as the building blocks of IoT-enabled smart *** high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for ***,there is a high computation latency due to the presence of a remote cloud *** computing,which brings the computation close to the data source is introduced to overcome this *** an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay *** efficient resource allocation at the edge is helpful to address this *** this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation ***,we presented a three-layer network architecture for IoT-enabled smart ***,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization *** Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource *** extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.
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