Abstract -- Determining of blood types is very important during emergency situation before administering a blood transfusion. Presently, these tests are performed manually by technicians, which can lead to human errors. Determination of the blood types in a short period of time and without human errors is very much essential. A method is developed based on processing of images acquired during the slide test. The image processing techniques such as thresholding and morphological operations are used. The images of the slide test are obtained from the pathological laboratory are processed and the occurrence of agglutination are evaluated. Thus the developed automated method determines the blood type using image processing techniques. The developed method is useful in emergency situation to determine the blood group without human error.

Abstract — This paper detects the fault which arises in the electronic circuits by comparing with MISR.MISR is an output response analyzer which accelerates the testing methodology by compacting multi-bit streams into single signature. Here, the most important aspect is to generate the bit streams using pseudo- random pattern generators (PRPG). It is comprised of a phase shifter and the ROM, has the expected output then it is compared with the MISR. It gives the result as the output is either same as expected or different. When the expected output is same there is no fault in the circuit otherwise it is fault. This is the way of fault detection by the pseudo random patterns.

Abstract -- Pregnant women in rural areas still strive to do their regular check-ups during their gestation period in under developing countries ,since the medical system are not well developed for sharing medical information. This increases the death rate of the neonatal and the mother .So we have proposed a system that continuously monitor the neonatal and the mother and sends a real time data to health care worker. Important parameter like heartbeat, temperature of the women, kick count of the baby and contraction rate of the uterine will be monitored. Arduino helps in pre-processing the measured data. Using GSM, Alert message will be send if an abnormality is detected. For future reference and remote access the acquired data will be gathered and stored in cloud .This system is highly sensitive, Accurate and reliable .It can act as an perfect In home monitoring device.

Abstract -- This paper provides a unique reversible gate implementation based on Quantum Dot Cellular Automata (QCA). Because of its small size (nanometer), extreme low power consumption, and better clock rate (Terahertz range), QCA has been explored as a FET alternative. Reversible computation, on the other hand, is a new ideology in which all logic operations are imminent. This property is critical for a variety of technologies, including quantum computing, adiabatic circuits, and low-power computing. QCA has been viewed as a viable technology for approaching the thermodynamic limit of computation due to its low power usage. Comparators are crucial in segregating fault patterns from good ones in industrial automation. The goal of this project is to provide an efficient QCA implementation of the R-CNOT reversible Gate based on direct interaction between QCA cells. In addition, using the R-CNOT reversible gate, a 1-bit comparator is also built. QCA designer tool version 2.0.3 has been used to analyze the efficiency of the suggested work. Finally, the energy dissipation results for the proposed area-efficient reversible gate have been computed utilizing the accurate E-QCA power estimator tool.

Abstract -- Corona Virus Disease (COVID-19) has been labeled a worldwide epidemic and is quickly spreading. Many persons who are infected with disease may be saved if the virus is caught early. Unfortunately, it is easy to confuse as pneumonia or lung cancer and fit spreads swiftly through the chest cells, it can kill the patient. The most common imaging modality of diagnosis for all three diseases is X-ray. This study proposes a multi-classification deep learning model for detecting those disorders from chest images. Furthermore, augmentation increases the dataset size, which improves classification accuracy. The performance of architecture, specifically VGG19+CNN & VGG19 is examined in this study. A detailed evaluation of deep learning architectures is offer during three types of datasets. The VGG19 model outperforms the other models, according to the findings of the experiments. TheVGG19 model has an accuracy of 96%, a recall of 96 %, a precision of 96 % and 99 % area under curve with X-ray images.