Abstract-The standard office or business in Botswana hosts their resources in-house. This means that a company will have their hardware, software and support staff as part of their daily work operations. Technology has brought a shift to the office environment with Cloud Computing. Botswana has seen the growth of the Cloud Technologies, within its own boundaries where companies have embraced the new technology to mobilize and push their operational agenda with the same tenacity as the rest of the world using the technology. Cloud computing has taken root in Botswana and it shows that a lot of SMEs are using cloud computing, whilst some are non-adopters to the technology. Edgar Tsimane reported the take up on cloud computing in Botswana. Botswana uses the National ICT policy to guide on technological advances and development, i.e the Maitlamo policy. This paper is considering aspects influencing the company's decisions on utilizing the cloud as a service, both opportunistic and challenges. Some of the questions to address for the study are: how effective is cloud computing for businesses in Botswana; what challenges and successes these companies have had, is there any particular framework they had to follow to guide them in adopting the services? Finally, the paper was to take consideration in recommending a framework that can be adopted within the Botswana.
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.
This paper proposes the application of various types of highlight extractors method to perceive the airplane utilizing the satellite picture. Acknowledgment of article (Aircraft) in a picture dependent on the mix of highlight extractors which contain nonsubsampled contourlet change and SPIHT change notwithstanding the relationship on shape examination. Additionally, an article can be perceived with the assistance of surface or appearance includes through Scale-invariant component change SPIHT To legitimize the right measure of each element extractor, we perform per of the referenced changes to enter pictures, decisively. The pre-owned classifier right now Detect Fuzzy Clustering and the aftereffects of this test appear, that the correct acknowledgment pace of airplane right now, at the hour of utilizing bend, let change and all Contourlet coefficients are 100%. And the identifying of the aircraft using SPIHT and NSCT finalized images are processed in the Content-Based Image Retrieval method and the Aircraft is identified.
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