Visual saliency techniques based on Convolutional Neural Networks (CNNs) exhibit an excessive performance for saliency fixation in a scene, but it is harder to train a network in view of their complexity. The imparting Residual Network Model (ResNet) that is more capable to optimize features for predicting salient area in the form of saliency maps within the images. To get saliency maps, an amalgamated framework is presented that contains two streams of Residual Network Model (ResNet-50). Each stream of Reset-50 that is used to enhance the low level and high level semantics features and build a network of 99 layers at two different image scales for generating the normal saliency attention. This model is trained with transfer learning for initialization that is pre-trained on ImageNet for object detection and with some modification to minimize prediction error. At the end, the two streams integrate the features by fusion at low and high scale dimensions of images. This model is fine-tuned on four commonly used datasets and examines both qualitative and quantitative evaluation metrics with state of the art deep saliency models outcomes.
There are many jobs adverts on the internet, even on reputable job posting sites, that never appear to be false. However, following the selection, the so-called recruiters begin to seek money and bank information. Many candidates fall into their traps and lose a lot of money as well as their existing job. As a result, it is preferable to determine whether a job posting submitted on the site is genuine or fraudulent. Manually identifying it is extremely difficult, if not impossible! An automated online tool (website) based on machine learning-based categorization and algorithms are presented to eliminate fraudulent job postings on the internet. It aids in the detection of bogus job postings among the vast number of postings on the internet.
The field of Ophthalmology is well suited to the implementation of Virtual Reality (VR) and Augmented Reality (AR) technologies, due to the fine microsurgical skills required. Most postgraduate Ophthalmology training programmes utilise VR stimulators, such as the EyeSi Surgicaland MicroVisTouch. The Eyesi Surgical is the most extensively assessed VR simulator in the literature for intraocular surgical training [1]. It consists of a mannequin head which contains a model eye connected to an operating microscope. Surgical instrument movement is tracked by internal sensors, and results in a virtual image which can be viewed in the microscope and separate screen. The developed software trains individuals through the steps of cataract and vitreoretinal surgeries, and provides feedback. MicroVisTouch differs from the EyeSi, as it has integrated tactile feedback [2]. Over the last decade, there have been rapid advancements in the technology underpinning VR, and the range of clinical and surgical applications in the specialty are now vast. For example, portable VR devices are being trialled in home environments, to monitor disease progression (e.g. glaucoma). VR based approaches are also being developed for use in the operating room, e.g. the Da Vinci surgical system which facilitates minimally invasive surgeries
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