Artificial intelligence (AI) development across the health sector has recently been the most crucial. Early medical information, identification, diagnosis, classification, then analysis, along with viable remedies, are always beneficial developments. Precise and consistent image classification has critical in diagnosing and tactical decisions for healthcare. The core issue with image classification has become the semantic gap. Conventional machine learning algorithms for classification rely mainly on low-level but rather high-level characteristics, employ some handmade features to close the gap, but force intense feature extraction as well as classification approaches. Deep learning is a powerful tool with considerable advances in recent years, with deep convolution neural networks (CNNs) succeeding in image classification. The main goal is to bridge the semantic gap and enhance the classification performance of multi-modal medical images based on the deep learning-based model ResNet50. The data set included 28378 multi-modal medical images to train and validate the model. Overall accuracy, precision, recall, and F1-score evaluation parameters have been calculated. The proposed model classifies medical images more accurately than other state-of-the-art methods. The intended research experiment attained an accuracy level of 98.61%. The suggested study directly benefits the health service.

Internet usage is rapidly increasing in every field of life. Internet coverage is wide either it is textile, pharmaceutical or education sectors. In every sectors where internet envisions are exist, there objects must be connected with each other. When we discussed the objects are connected with each other, a word internet of things (IoT) must exist there. IoT covers iPads, mobile phones, digital camera etc. These devices provide us our basic needs when they connected each other. Sharing of services or concept of virtualization is also part of IoT and also called cloud computing. Cloud computing is said to be sharing of services or resources using internet as a connection. Cloud based resources can be distributed all over the world. It is among most popular technology in developed countries and they are trying to made green solution of its resources. Datacenters of cloud computing systems are wasting a terrific amount of energy, hence emits carbon dioxide. Under developed counties like Pakistan where financial and energy issues are common in every sector especially in education where IT utilizations are increasing day by day. Although concept of cloud computing are exist there but there is no awareness of green solution. In this paper, we suggested a scheduling approach for efficient resource management which can be helpful for green solution at universities of Faisalabad, Pakistan. This approach can be helpful to boost up concept of cloud and green cloud computing.Â