Oral cavity and upper gastrointestinal cancers predominated in both genders. In females, cervical cancer predominated over breast cancer. Thyroid cancers were relatively more common in this region especially in females.

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World is facing stress due to unpredicted pandemic of novel COVID-19. Daily growing magnitude of confirmed cases of COVID-19 put the whole world humanity at high risk and it has made a pressure on health professionals to get rid of it as soon as possible. So, it becomes necessary to predict the number of upcoming cases in future for the preparation of future plan-of-action and medical set-ups. The present manuscript proposed a hybrid fuzzy time series model for the prediction of upcoming COVID-19 infected cases and deaths in India by using modified fuzzy C-means clustering technique. Proposed model has two phases. In phase-I, modified fuzzy C-means clustering technique is used to form basic intervals with the help of clusters centroid while in phase-II, these intervals are upgraded to form sub-intervals. The proposed model is tested against available COVID-19 data for the measurement of its performance based on mean square error, root mean square error and average forecasting error rate. The novelty of the proposed model lies in the prediction of COVID-19 infected cases and deaths for next coming 31 days. Beside of this, estimation for the approximate number of isolation beds and ICU required has been carried out. The projection of the present model is to provide a base for the decision makers for making protection plan during COVID-19 pandemic.

Tasks allocation is an important step for obtaining high performance in distributed computing system (DCS). This article attempts to develop a mathematical model for allocating the tasks to the processors in order to achieve optimal cost and optimal reliability of the system. The proposed model has been divided into two stages. Stage-I, makes the ‘n' clusters of set of ‘m' tasks by using k-means clustering technique. To use the k-means clustering techniques, the inter-task communication costs have been modified in such a way that highly communicated tasks are clustered together to minimize the communication costs between tasks. Stage-II, allocates the ‘n' clusters of tasks onto ‘n' processors to minimize the system cost. To design the mathematical model, executions costs and inter tasks communication costs have been taken in the form of matrices. To test the performance of the proposed model, many examples are considered from different research papers and results of examples have compared with some existing models.

Distributed computing systems [DCSs] offer the potential for improved performance and resource sharing. To make the best use of the computational power available, it is essential to assign the tasks dynamically to that processor whose characteristics are most appropriate for the execution of the tasks in distributed processing system. We have developed a mathematical model for allocating “M” tasks of distributed program to “N” multiple processors (M>N) that minimizes the total cost of the program. Relocating the tasks from one processor to another at certain points during the course of execution of the program that contributes to the total cost of the running program has been taken into account. Phasewise execution cost [EC], intertask communication cost [ITCT], residence cost [RC] of each task on different processors, and relocation cost [REC] for each task have been considered while preparing a dynamic tasks allocation model. The present model is suitable for arbitrary number of phases and processors with random program structure.

Microscopic evaluation of a peripheral blood smear is one of the most benefi cial test. But anticoagulant induced artefacts could lead to misinterpretation of the smears. The present study was undertaken to identify the anticoagulant induced artefacts and avoid misinterpretation of peripheral blood smears. The blood samples were collected using Ethylene Diaminetetraacetic acid (EDTA) and Sodium citrate, mixed thoroughly and smears were made immediately as well as 1hr apart for 6 hrs, stained and examined under oil immersion microscope. Direct smears were used as controls. Signifi cant morphological artefacts were observed in our study. Artefacts were marked at the end of 2 hrs with EDTA but seen almost immediately with citrate blood. At 6 hrs, artefacts were marked but more severe with citrates than EDTA. Thus the practice of making blood smears before addition of anticoagulant is recommended and a delay up to 1hr is permissible with EDTA blood but not beyond.

Defuzzification is a process that converts a fuzzy set or fuzzy number into a crisp value or number. Defuzzification is used in fuzzy modeling and in fuzzy control system to convert the fuzzy outputs from the systems to crisp values. This process is necessary because all fuzzy sets inferred by fuzzy inference in the fuzzy rules must be aggregated to produce one single number as the output of the fuzzy model.There are numerous techniques for defuzzifying a fuzzy set; some of the more popular techniques are included in fuzzy logic system. In the present chapter some recent defuzzification methods used in the literature are discussed with examples.