An Effective Prediction Factors for Coronary Heart Disease using Data Mining based Classification Technique

Identification of diseases are very challenging task in field of medical science. Heart disease is very critical issues facing by the people. In our proposed work we have used data mining based classification techniques for analysis and classification of different level of heart disease namely Cleveland, Switzerland, Hungarian and Long Beach. We have used WEKA and Rapid miner data mining tools for analysis of heart disease data set and compared the performance of different classification techniques with four heart disease data set using WEKA and Rapid Miner data mining tool. The proposed SVM gives better accuracy as 66.67% with Hungarian data set in case of WEKA data mining tool while Decision Stump gives better accuracy as 63.94% with same Hungarian data set in case of Rapid miner data mining tool. The Hungarian data set gives better performance with our proposed data mining tools and classification techniques which can help the people to predict effective factors about Coronary Heart Disease

Intelligent intrusion detection system in smart grid using computational intelligence and machine learning

Smart grid systems enhanced the capability of traditional power networks while being vulnerable to different types of cyber-attacks. These vulnerabilities could cause attackers to crash into the network breaching the integrity and confidentiality of the smart grid systems. Therefore, an intrusion detection system (IDS) becomes an important way to provide a secure and reliable services in a smart grid environment. This article proposes a feature-based IDS for smart grid systems. The proposed system performance is evaluated in terms of accuracy, intrusion detection rate (DR), and false alarm rate (FAR). The obtained results show that the random forest and neural network classifiers have outperformed other classifiers. We have achieved a 0.5% FAR on KDD99 dataset and a 0.08% FAR on the NSLKDD dataset. The DR and the testing accuracy on average are 99% for both datasets

New and efficient design of multimode piezoelectric vibration energy harvester for MEMS application

The major challenges in a piezoelectric energy harvester (PEH) are high operating frequency, narrow bandwidth and low output generation. We propose a new and efficient design concept based on optimal geometry shape and optimal segmentation of piezoelectric layer at strain nodes of higher vibration modes. The analytical model of the proposed design concept is developed and comparative analysis is performed to compare with conventional rectangular PEH and non-segmented trapezoidal PEH. For a mode 3 harvester design, the simulation result shows that there are three resonant peaks for voltage generation, at the fundamental, 2nd and 3rd resonant frequencies to enable multi-frequency operation that widens the operating frequency range of PEH. The parallel connection of the piezoelectric PZT segments to a common load resistance yields 21.6 mW, 0.23 mW and 0.15 mW power output for three resonant frequencies at 0.5 g input acceleration for optimal load of 21 kΩ. The proposed device shows a performance improvement and reduction in operating resonating frequencies of the higher modes of vibration compared to conventional rectangular and a non-segmented trapezoidal shaped PEH. The harvester provides an alternative to complex and inefficient device design of multimodal energy harvesters

Not Available

Not AvailableThe aim of the presented study was to characterise the development of the cellular part of the immune system in pigs from 1 d to 20 weeks of age. Haematological examination and flow cytometry were used to establish the relative and absolute counts of various leukocyte subsets. During the first 5 months of pig life, a significant age-dependent increase in lymphocyte and granulocyte counts was noted. Moreover, a decrease in relative size of lymphocytes connected with increasing proportion of granulocytes was observed. The absolute size of CD3+ and CD21+ increased approximately two-fold during the analysed period. The absolute number of CD4+CD8- , CD4-CD8+ and CD4+CD8+ cells increased almost twice from birth till the 6th week of age. After this time, only CD4+CD8- subset remained stable, while the number of CD4-CD8+ and CD4+CD8+ subsets gradually increased 1.5- and 2.5-fold from 6 weeks to 5 months of age for CD4-CD8+ and CD4+CD8+ , respectively. In conclusion, changes in the absolute size of lymphocyte subsets are not always consistent with changes in their relative size. Moreover, because there are age-related differences in leukocyte subsets in porcine blood, there is a need to use appropriate age matched control groups, especially in experiments, which include immunophenotyping of lymphocytes. We suggest that immunophenotyping of lymphocytes, especially for diagnostic purposes, should be based on the absolute size rather than on the percentage of lymphocyte subpopulations.Not Availabl