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Corona Disease Virus (COVID-19) is a rapidly spreading contagious viral disease that causes respiratory contaminations and is currently generating a worldwide medical crisis. It has caused a massive influence on people's lives, general well-being, and the global economy. Henceforth, it is critical to straightaway analyze the positive cases in order to keep the illness from spreading further and to regard infected patients as fast as could really be expected. Both patients and specialists will be benefitted by the early recognizable capability of outrageous COVID-19 by utilizing chest CT to examine biomedical images. RT-PCR (switch record polymerase chain response) based tests help to identify COVID-19, which has numerous limits. In this work, different CNN based Classifier model methodologies are utilized to follow the presence of COVID-19 from chest CT filter images of patients. In true indicative situations, a profound CNN-based methodology could be amazingly valuable in accomplishing quick COVID-19 testing. By utilizing irregularity data obtained from sifted images, image expansion enhances the number of profitable models for creating the CNN model. The proposed model has a grouping exactness of 95% for CT examines utilizing this strategy. With picture expansion, CT check pictures have an affectability of 94.78%and a particularity of 95.98%. The trial results were contrasted with ResNet-18, ResNet-50, and VGG-16 models, with freely available datasets containing CT images.

The improvement of e-learning and online evaluation frameworks is increasing rapidly. The Main Goal is to develop a model which is intended to distinguish the ordinary examples for activities of concern, for example, conversations during a test or the pivoting, processes more exactness and computes more accuracy. Certain presumptions about normal behaviour with regards to delegating tests are made. In the existing system, it takes more computational power and speed is less. Even though it computes not much more accuracy and with the system only able to manage one invigilator for twenty students. Thus, it is important to develop a framework which is high precision and less the manual force. Identification depends on highlights registered utilizing the textural features followed by a Haar Cascade classifier and ADA Boosting calculation and search through explained examples of pre-recorded clips to prepare the framework for train the system for behaviour, the framework is planned as a choice emotionally supportive network to work with programmed administering of tests and distinguishes misbehaviour or malpractice.

The response of the shoreline to natural interfaces like creeks, estuaries, bays and engineering installations such as ports, harbours, groins, breakwaters etc., is of great significance to sustainable shoreline management as it is often observed that these interfaces / structures play a major role in altering the shoreline causing erosion and accretion largely due to their intervention with movement of alongshore sediments. Long term evaluation of the performance of these structures and the corresponding shoreline response is essential to understand the effects on the shoreline and for planning mitigation measures for management of adverse impacts. National Institute of Ocean Technology (NIOT) a Research and Development organization under the Ministry of Earth Sciences (Government of India) had taken an initiative of assessing the effects of various coastal structures on the shoreline along the coast of India. The aim of this study was to assess the impacts of coastal structures on the shoreline behaviour and thereby enable designing and implementation of environmentally friendly coastal infrastructure. This paper discusses the various phases of the study carried out along the coast of Tamilnadu state of India for evaluating the effects of various engineering structures. The coastline from Pulicat in the North to Neerody in the South, stretching across 1076km was covered as part of the study and 260 manmade structures were mapped. Potential sites were identified and behaviour of shoreline was simulated using numerical models which were calibrated using long term field data. The calibrated model was used for evaluating alternative options for shoreline management at select sites for mitigating the impacts of erosion / accretion caused by the structures. The entire work carried out was compiled and published as part of an atlas titled, Digital Information on Shore System Effects due to Manmade Interventions and Natural Alterations by Technological Evaluation (DISSEMINATE).

The edible oil production in India is 72 MT in 2016–2017. Traditionally the vegetable oil has been reused several times before it has been disposed in the drainage. Even 10% of the oil enters in sewage system creates clogs in pipes, foul odour, reduction of oxygen concentration in the water. Several treatment techniques including production of biodiesel from waste vegetable oil has been deployed but due to high cost, it has not been successfully deployed. Hence the special investigation aims in developing the decentralized waste vegetable oil gasification system which can be a substitute for diesel in CI engine. A continuous flow of gasifier for waste vegetable oil has been designed and tested. The producer gas has been analyzed continuously using engine producer gas analyzer for its feasibility in admitting CI engine.

Now a days, BLDC motors are widely used in prosthetic limb applications due to various advantages such as small size and weight, excellent controllability, high torque to weight ratio etc. Due to various diseases, accidents, and congenital defects etc., there is an increasing rate of demand for prosthetic devices. Myoelectric signals that are generated naturally by human muscles are used to control a prosthetic limb. Electromyogram signals from different people are extracted for flexion and extension of elbow and the analysis of an EMG signal help to detect the intention for a particular movement of elbow. A BLDC motor for prosthetic hand is designed in MAXWELL. Modeling of slotted and slotless BLDC motors have done in MATLAB-SIMULINK. EMG signals for flexion and extension of elbow is extracted and processed, and classified using artificial neural network and an entire prosthetic hand drive system is simulated in SIMULINK and the performance comparison of slotted and slotless motor in this application is analyzed.