Underwater image enhancement and object detection has great potential for studying underwater environments. It has been utilized in various domains, including image-based underwater monitoring and Autonomous Underwate...
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Underwater image enhancement and object detection has great potential for studying underwater environments. It has been utilized in various domains, including image-based underwater monitoring and Autonomous Underwater Vehicle (AUV)-driven applications such as underwater terrain surveying. It has been observed that underwater images are not clear due to several factors such as low light, the presence of small particles, different levels of refraction of light, etc. Extracting high-quality features from these images to detect objects is a significant challenging task. To mitigate this challenge, MIRNet and the modified version of YOLOv3 namely Underwater-YOLOv3 (U-YOLOv3) is proposed. The MIRNet is a deep learning-based technology for enhancing underwater images. while using YOLOv3 for underwater object detection it lacks in detection of very small objects and huge-size objects. To address this problem proper anchor box size, quality feature aggregation technique, and during object classification image resizing is required. The proposed U-YOLOv3 has three unique features that help to work with the above specified issue like accurate anchor box determination using the K-means++ clustering algorithm, introduced Spatial Pyramid Pooling (SPP) layer during feature extraction which helps in feature aggregation, and added downsampling and upsampling to improve the detection rate of very large and very small size objects. The size of the anchor box is crucial in detecting objects of different sizes, SPP helps in aggregation of features, while down and upsampling changes sizes of objects during object detection. Precision, recall, F1-score and mAP are used as assessment metrics to assess proposed work. The proposed work compared with SSD, Tiny-YOLO, YOLOv2, YOLOv3, YOLOv4, YOLOv5, KPE-YOLOv5, YOLOv7, YOLOv8 and YOLOv9 single stage object detectors. The experiment on the Brackish and Trash ICRA19 datasets shows that our proposed method enhances the mean average precision for b
Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and C...
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Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict softwa...
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Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.
Link prediction in complex networks is a fundamental problem with applications in diverse domains, from social networks to biological systems. Traditional approaches often struggle to capture intricate relationships i...
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Due to recent expansion of wireless communications, it has become impossible to cope with the allotment of the precious spectrum while resources for wireless communication are bounded and finite. Hence, the cognitive ...
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Recently, image inpainting has been proposed as a solution for restoring the polluted image in the field of computer vision. Further, face inpainting is a subfield of image inpainting, which refers to a set of image e...
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Advancements in cloud computing and virtualization technologies have revolutionized Enterprise Application Development with innovative ways to design and develop complex *** Architecture is one of the recent technique...
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Advancements in cloud computing and virtualization technologies have revolutionized Enterprise Application Development with innovative ways to design and develop complex *** Architecture is one of the recent techniques in which Enterprise Systems can be developed as fine-grained smaller components and deployed *** methodology brings numerous benefits like scalability,resilience,flexibility in development,faster time to market,*** the advantages;Microservices bring some challenges *** microservices need to be invoked one by one as a *** most applications,more than one chain of microservices runs in parallel to complete a particular requirement To complete a user’s *** results in competition for resources and the need for more inter-service communication among the services,which increases the overall latency of the application.A new approach has been proposed in this paper to handle a complex chain of microservices and reduce the latency of user requests.A machine learning technique is followed to predict the weighting time of different types of *** communication time among services distributed among different physical machines are estimated based on that and obtained insights are applied to an algorithm to calculate their priorities dynamically and select suitable service instances to minimize the latency based on the shortest queue waiting *** were done for both interactive as well as non interactive workloads to test the effectiveness of the *** approach has been proved to be very effective in reducing latency in the case of long service chains.
Potatoes are one of the world's most popular and economically important crops. For many uses in agriculture, breeding, and trading, accurate recognition of potato breeds is important. In recent years, deep learnin...
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In a cloud environment,graphics processing units(GPUs)are the primary devices used for high-performance *** exploit flexible resource utilization,a key advantage of cloud *** users share GPUs,which serve as coprocesso...
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In a cloud environment,graphics processing units(GPUs)are the primary devices used for high-performance *** exploit flexible resource utilization,a key advantage of cloud *** users share GPUs,which serve as coprocessors of central processing units(CPUs)and are activated only if tasks demand GPU *** a container environment,where resources can be shared among multiple users,GPU utilization can be increased by minimizing idle time because the tasks of many users run on a single ***,unlike CPUs and memory,GPUs cannot logically multiplex their ***,GPU memory does not support over-utilization:when it runs out,tasks will ***,it is necessary to regulate the order of execution of concurrently running GPU tasks to avoid such task failures and to ensure equitable GPU sharing among *** this paper,we propose a GPU task execution order management technique that controls GPU usage via time-based *** technique seeks to ensure equal GPU time among users in a container environment to prevent task *** the meantime,we use a deferred processing method to prevent GPU memory shortages when GPU tasks are executed simultaneously and to determine the execution order based on the GPU usage *** the order of GPU tasks cannot be externally adjusted arbitrarily once the task commences,the GPU task is indirectly paused by pausing the *** addition,as container pause/unpause status is based on the information about the available GPU memory capacity,overuse of GPU memory can be prevented at the *** a result,the strategy can prevent task failure and the GPU tasks can be experimentally processed in appropriate order.
Wireless Sensor Network(WSNs)is an infrastructure-less wireless net-work deployed in an increasing number of wireless sensors in an ad-hoc *** the sensor nodes could be powered using batteries,the development of WSN e...
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Wireless Sensor Network(WSNs)is an infrastructure-less wireless net-work deployed in an increasing number of wireless sensors in an ad-hoc *** the sensor nodes could be powered using batteries,the development of WSN energy constraints is considered to be a key *** wireless sensor networks(WSNs),wireless mobile chargers(MCs)conquer such issues mainly,energy *** proposed work is to produce an energy-efficient recharge method for Wireless Rechargeable Sensor Network(WRSN),which results in a longer lifespan of the network by reducing charging delay and maintaining the residual energy of the *** this algorithm,each node gets sorted using the K-means technique,in which the data gets distributed into various *** mobile charges execute a Short Hamiltonian cycle opposite direction to reach each cluster’s anchor *** position of the anchor points is calculated based on the energy distribution using the base *** this case,the network will act as a spare MC,so that one of the two MCs will run out of energy before reaching the *** the current tours of the two MCs terminate,regression analysis for energy prediction initiates,enabling the updating of anchor points in the upcoming *** on thefindings of the regression-based energy prediction model,the recommended algorithm could effectively refill network energy.
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