Background: COVID-19 diagnosis in symptomatic patientsis an important factor for arranging the necessary lifesaving facilities like ICU care and ventilator support. For this purpose, we designed a computer-aided diagnosis and severity detection method by using transfer learning and a back propagation neural network. Method: To increase the learning capability, we used data augmentation. Most of the previously done works in this area concentrate on private datasets, but we used two publicly available datasets. The first section diagnose COVID-19 from the input CT image using the transfer learning of the pre-trained network ResNet-50. We used ResNet-50 and DenseNet-201 pre-trained networks for feature extraction and trained a back propagation neural network to classify it into High, Medium, and Low severity. Results: The proposed method for COVID-19 diagnosis gave an accuracy of 98.5% compared with the state-of-the-art methods. The experimental evaluation shows that combining the ResNet-50 and DenseNet-201 features gave more accurate results with the test data. The proposed system for COVID-19 severity detection gave better average classification accuracy of 97.84% compared with the state-of-the-art methods. This enables medical practitioners to identify the resources and treatment plans correctly. Conclusions: This work is useful in the medical field as a first-line severity risk detection that is helpful for medical personnel to plan patient care and assess the need for ICU facilities and ventilator support. A computer-aided system that is helpful to make a care plan for the huge amount of patient inflow each day is sure to be an asset in these turbulent times.

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Renal transplantation has become the treatment of choice for most patients with end-stage renal disease. Recent advances in renal transplantation notably, the matching of Major Histocompatibility Complex (MHC) and improved immunosuppressants have improved short-term and long-term graft survival rates. In light of recent developments optimization of kidney transplant outcomes is paramount to further augment the graft survival time and the quality of life of the patient. An intuitive understanding of the post transplantation interaction mechanisms involving graft and host is intricate and on account of this prognosis of planned organ transplantation outcomes is an involved problem. Consequently, machine learning approaches based on donor and recipient data are indespensible for improved prognosis of graft outcomes. This study proposes improved data miningbased models for variable filtering and for prediction of graft status and survival period in renal transplantation using the patient profile information prior to the transplantation.

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An intelligent agent refers to an autonomous entity directing its activity towards achieving goals, acting upon an environment using data obtained with the help of a sensory mechanism. Intelligent agent software is a software system that performs tasks independently on behalf of a user in a networking environment based on user interface and past experiences. By the design of an intelligent sensing software program we can regulate the flow of traffic in a transportation infrastructure network. The problems leading to inefficiencies like loss of time, decrease in safety of vehicles and pedestrians, massive pollution, high wastage of fuel energy, degradation in the quality of life can be achieved by the optimized design. Ant Colony Optimization (ACO) has proven to be a very powerful optimization model for combinatorial optimization problems. The algorithm has the objective of regulating high real time traffic enabling every vehicle in the network with increased efficiency to minimize factors like time delay and traffic congestion.