Approximate string matching algorithms, which permit mismatched characters, are extensively employed in software featuring search tools, database management systems, and various applications and online services. Conse...
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This work proposes a novel and improved Butterfly Optimization Algorithm (BOA), known as LQBOA, to solve BOA’s inherent limitations. The LQBOA uses Lagrange interpolation and simple quadratic interpolation techniques...
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Image enhancement is a widely used technique in digital image processing that aims to improve image aesthetics and visual quality. However, traditional methods of enhancement based on pixel-level or global-level modif...
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Image enhancement is a widely used technique in digital image processing that aims to improve image aesthetics and visual quality. However, traditional methods of enhancement based on pixel-level or global-level modifications have limited effectiveness. Recently, as learning-based techniques gain popularity, various studies are now focusing on utilizing networks for image enhancement. However, these techniques often fail to optimize image frequency domains. This study addresses this gap by introducing a transformer-based model for improving images in the wavelet domain. The proposed model refines various frequency bands of an image and prioritizes local details and high-level features. Consequently, the proposed technique produces superior enhancement results. The proposed model’s performance was assessed through comprehensive benchmark evaluations, and the results suggest it outperforms the state-of-the-art techniques.
Energy and environmental concerns have fostered the era of electric vehicles (EVs) to take over and be welcomed more than ever. Fuel-powered vehicles are still predominant;however, this trend appears to be changing so...
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Energy and environmental concerns have fostered the era of electric vehicles (EVs) to take over and be welcomed more than ever. Fuel-powered vehicles are still predominant;however, this trend appears to be changing sooner than we might expect. Countries in Europe, Asia, and many states in America have already made the decision to transition to a fully EV industry in the next few years. This looks promising;however, drivers still have concerns about the battery mileage of such vehicles and the anxiety that such driving experiences! Indeed, driving with the probability of having insufficient battery charge that may be involved in guaranteeing the delivery to the trip destination imposes a level of anxiety on the vehicle drivers. Therefore, for an alternative to traditional fuel-powered vehicles to be convincing, there needs to be sufficient coverage of charging stations to serve cities in the same way that fuel stations serve traditional vehicles. The current navigation models select routes based solely on distance and traffic metrics, without taking into account the coverage of fuel service stations that these routes may offer. This assumption is made under the belief that all routes are adequately covered. This might be true for fuel-powered vehicles, but not for EVs. Hence, in this work, we are presenting AFARM, a routing model that enables a smart navigation system specifically designed for EVs. This model routes the EVs via paths that are lined with charging stations that align with the EV’s current charge requirements. Different from the other models proposed in the literature, AFARM is autonomous in the sense that it determines navigation paths for each vehicle based on its make, model, and current battery status. Moreover, it employs Dijkstra’s algorithm to accommodate varying least-cost navigation preferences, ranging from shortest-distance routes to routes with the shortest trip time and routes with maximum residual battery capacities as well. According to t
Delay Tolerant Networks (DTNs) have the ability to make communication possible without end-to-end connectivity using store-carry-forward technique. Efficient data dissemination in DTNs is very challenging problem due ...
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The enormous developments of gaming devices as well as mobile apps have increased the demand of bandwidth. Development of wireless applications has been affected because of the insufficient spectrum resources in the 3...
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Chronic Kidney Cancer (CKC) is a disease that hindrances the blood-filtering mechanism of the kidney and is increasing at an alarming rate in the recent few years. As CKC does not show any earlier symptoms, the earlie...
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An information system stores outside data in the backend database to process them efficiently and protects sensitive data from illegitimate flow or unauthorised users. However, most information systems are made in suc...
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It has been widely proven that Augmented Reality (AR) brings numerous benefits in learning experiences, including enhancing learning outcomes and motivation. However, not many studies investigate how different forms o...
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Brain hemorrhage is a serious and life-threatening condition. It cancause permanent and lifelong disability even when it is not fatal. The wordhemorrhage denotes leakage of blood within the brain and this leakage ofbl...
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Brain hemorrhage is a serious and life-threatening condition. It cancause permanent and lifelong disability even when it is not fatal. The wordhemorrhage denotes leakage of blood within the brain and this leakage ofblood from capillaries causes stroke and adequate supply of oxygen to thebrain is hindered. Modern imaging methods such as computed tomography(CT) and magnetic resonance imaging (MRI) are employed to get an idearegarding the extent of the damage. An early diagnosis and treatment can savelives and limit the adverse effects of a brain hemorrhage. In this case, a deepneural network (DNN) is an effective choice for the early identification andclassification of brain hemorrhage for the timely recovery and treatment of anaffected person. In this paper, the proposed research work is divided into twonovel approaches, where, one for the classification and the other for volumecalculation of brain hemorrhage. Two different datasets are used for twodifferent techniques classification and volume. A novel algorithm is proposedto calculate the volume of hemorrhage using CT scan images. In the firstapproach, the ‘RSNA’ dataset is used to classify the brain hemorrhage typesusing transfer learning and achieved an accuracy of 93.77%. Furthermore,in the second approach, a novel algorithm has been proposed to calculate thevolume of brain hemorrhage and achieved tremendous results as 1035.91mm3and 9.25 cm3, using the PhysioNet CT scan tomography dataset.
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