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the credential part of the paper reviews about the advanced methods for the treatment of cancer using nano devices and nano technology technique. The advanced technology which could be the best to treat cancer is the treatment using nano-technology the molecular scale properties and the interface between the chemical, biological and the physical sciences are the important concerns of nano-technology. The application in the treatment of cancer using nano- technology is promising more than the ordinary treatments. This paper also proposes the use of nano-robots in medical field is the fast, best, and safe method for the treatment of different diseases in the human body. The main stress given in the paper is the comparison of the latest treatment in curing cancer using nano-technology.
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A. Jennifer Aparna, K. Ganesh Kumar, C. Gomathi, S. Gomathy and A. Deepak. "Various Approach for the Treatment of Cancer - using Nano-Technology." International Journal for Scientific Research and Development 2.7 (2014): 680-683.
The vital for many safety critical applications is the testable fault tolerant system. Due to its less heat dissipating characteristics, the reversible logic gaining interest in the recent times. Any Boolean logic function can be implemented using reversible gates. The credential part of the paper proposes a technique to convert any reversible logic gate to a testable gate that is also reversible. The resultant reversible testable gate can detect online any single bit errors that include Single Stuck Faults and Single Event Upsets S. Karp et.al. The proposed technique is illustrated using an example that converts a reversible decoder circuit to an online testable reversible decoder circuit.
S. Gowtham, K. Ganesh Kumar, R. Arvind, Y. Azain Abdul Kadhar and P. Dass. "An Efficient Construction of Online Testable Circuits using Reversible Logic Gates." International Journal for Scientific Research and Development 2.7 (2014): 684-687.
Database schema integration is an important discipline for constructing heterogeneous multidatabase systems. Fuzzy information has also been introduced into relational databases and has been extensively studied. However, the issues of integrating local fuzzy relations are rarely addressed. In this paper, we identify new types of conflicts that may occur in schemas and data due to the inclusion of fuzzy relational databases. We propose a methodology that resolves these new types of conflicts in a specific order to minimize the execution time of integration process.
Sanjay Kumar, Dr. A. K. Sharma and Kamal Kant. "Semantic Conflicts and Solutions in Integration of Fuzzy Relational Databases." International Journal for Scientific Research and Development 2.7 (2014): 688-693.
Healthcare facilities have at their disposal vast amounts of cancer patients’ data. Medical practitioners require more efficient techniques to extract relevant knowledge from this data for accurate decision-making. However the challenge is how to extract and act upon it in a timely manner. If well engineered, the huge data can aid in developing expert systems for decision support that can assist physicians in diagnosing and predicting some debilitating life threatening diseases such as cancer. Expert systems for decision support can reduce the cost, the waiting time, and liberate medical practitioners for more research, as well as reduce errors and mistakes that can be made by humans due to fatigue and tiredness. The process of utilizing health data effectively however, involves many challenges such as the problem of missing feature values, the curse of dimensionality due to a large number of attributes, and the course of actions to determine the features that can lead to more accurate diagnosis. Effective data mining tools can assist in early detection of diseases such as cancer. In This paper, we propose a new approach called IGANFIS. This approach optimally minimizes the number of features using the information gain (IG) algorithm which is usually used in text categorization to select the quality of text. The IG will be used for selecting the quality of cancer features by virtue of reducing them in number. The reduced number quality features dataset will then be applied to the Adaptive Neuro Fuzzy Inference System (ANFIS) to train and test the proposed approach. ANFIS method of training is ideally the hybrid learning algorithm which uses the gradient descent method and Least Square Estimate (LSE) for computing the error measure for each training pair. Each cycle of the ANFIS hybrid learning consists of a forward pass to present the input vector calculating the node outputs layer by layer repeating the process for all data and a backward pass using the steepest descent algorithm to update parameters, a process called back propagation.
Benard Nyangena Kiage. "Iganfis Data Mining Approach for Forecasting Cancer Threats." International Journal for Scientific Research and Development 2.7 (2014): 694-698.
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