Imaging studies in dentistry and maxillofacial pathology have recently concentrated on detecting the inferior alveolar nerve (IAN) canal. In spite of the minor dimensions of 3D maxillofacial datasets, deep learning-based algorithms have shown encouraging consequences in this study area. This study describes a mandibular cone-beam CT (CBCT) dataset with 2D and 3D hand comments. It is huge and freely available. It was possible to utilise this dataset by applying the residual neural network (IANSegNet), which consumed less GPU memory and computational complexity. As an encoder, IANSegNet uses the computationally efficient 3D ShuffleNetV2 network to reduce graphics processing unit (GPU) memory usage and improve efficiency. After that, a decoder with leftover blocks is added to keep the quality high. To address network convergence and data inequity, Dice’s loss and cross-entropy loss were created. Optimized postprocessing techniques are also recommended for fine-tuning the coarse segmentation findings that are generated by IANSegNet. The results of the validation show that IANSegNet outperformed other deep learning models in a variety of criteria.

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ABSTRACT:Cloud Computing provides scalable computing and storage resources. More and more data-intensive applications are developed in this computing environment.Evaluation of cloud data center performance is a key attribute in propagation of IaaS cloud services. This evaluation is necessary to quantify the cost benefit and Quality Of Service (QoS) of Cloud services. Cloud system managers find it difficult to maintain performance data due to the huge number of parameters that need to be monitored in a cloud system. Different applications have different quality-ofservice (QoS) requirements. In this paper, we present an analytical model, based on Stochastic Reward Nets (SRNs), that is both scalable to model systems composed of thousands of resources and flexible to represent different policies and cloud-specific strategies. Several performance metrics are defined and evaluated to analyze the behavior of a Cloud data center: utilization, availability, waiting time, and responsiveness. We are also notifying the resiliency analysis to take into account the load bursts. Finally, a general approach is presented that, starting from the concept of system capacity, can help system managers to opportunely set the data center parameters under different working conditions. KEYWORDS:Cloud computing, stochastic reward nets, cloud-oriented performance metrics, resiliency, responsiveness. 1.INTRODUCTIONCloud computing is the use of computing resources (hardware and software) that are delivered as a service over a network (typically the Internet). The name comes from the common use of a cloud-shaped symbol as an abstraction for the complex infrastructure it contains in system diagrams. Cloud computing entrusts remote services with a user's data, software and computation. Cloud computing consists of hardware and software resources made available on the Internet as managed third-party services. These services typically provide access to advanced software applications and high-end networks of server computers. The goal of cloud computing is to apply traditional supercomputing, or high-performance computing power, normally used by military and research facilities, to perform tens of trillions of computations per second, in consumer-oriented applications such as financial portfolios, to deliver personalized information, to provide data storage or to power large, immersive computer games. The cloud computing uses networks of large groups of servers typically running low-cost consumer PC technology with specialized connections to spread data-processing chores across them. This shared IT infrastructure contains large pools of systems that are linked together. Often, virtualization techniques are used to maximize the power of cloud computing. Ondemand self-service is a consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service's provider. Resource pooling is where the provider's computing resources are pooled to serve mu...

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