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Early detection of all types of lung nodules with different characters in medical modality images using computer-aided detection is the best acceptable remedy to save the lives of lung cancer sufferers. But accuracy of different types of nodule detection rates is based on chosen segmented procedures for parenchyma and nodules. Separation of pleural from juxta-pleural nodules (JPNs) is difficult as intensity of pleural and attached nodule is similar. This research paper proposes a fully automated method to detect and segment JPNs. In the proposed method, lung parenchyma is segmented using iterative thresholding algorithm. To improve the nodules detection rate separation of connected lung lobes, an algorithm is proposed to separate connected left and right lung lobes. The new method segments JPNs based on lung boundary pixels extraction, concave points extraction, and separation of attached pleural from nodule. Validation of the proposed method was performed on LIDC-CT images. The experimental result confirms that the developed method segments the JPNs with less computational time and high accuracy.

In the world various methods are being adopted to create a systematic identification for their citizens. In year 2009, UIDAI a government body of India initiated a 12-digit number called Aadhar number, which is generated out of biometric and demographic fusion of an individual and their identity. In order to ensure a highest level of data protection as well as privacy preservation of the Aadhar card propagation through a network, it requires an efficient model of security that becomes synchronous with the cloud infrastructure. This paper initially investigates the existing approaches and their limitations towards the Aadhar card security proposes a security model namely ECrypto-AaDhaar based on the cryptography approach synchronous to the cloud architecture. The privacy preservation of the AAdhar card associative demographic and biometric information is performed considering a statistical crypto-blocking operation prior propagating it through the network in the context of cloud infrastructure. The study later also presented an experimental analysis to demonstrate the performance of ECrypto-AaDhaar technique from a time complexity perspective.

Early detection of all kinds of lung nodules with different characters in patient’s medical modality images is the best acceptable remedy to save the life of lung cancer sufferers. Even though day by day the prominence of Computer-Aided Detection/Diagnosis (CADe/x) systems have been increasing as a part of medical routine in detection of different types of lung nodules, but detection rate performance depends on accuracy of lung parenchyma and nodule segmentation procedures. Segmentation of Juxta-Vascular nodules attached very complex. In this paper new fully automated CAD system is developed to detect and classify Juxta-Vascular nodules. In proposed methodology, lung parenchyma is segmented using iterative thresholding algorithm and lung nodules are segmented using proposed modified region growing algorithm. Since in vascular nodules, separation of blood vessel from nodule is difficult as intensity feature of attached blood vessel and nodule is same. Two new methods nodule segmentation method and vessel removal based on multi features to separate the vascular nodule part from the attached blood vessels are developed. To achieve the higher nodule-vessel separation accuracy, nodule-vessel attached region is refined. Validation of proposed method is performed on LIDC-CT lung images. A fully automated method segments the vascular nodules with less computational time and high accuracy.

Lung cancer health care community depends on lung cancer Computer Aided Detection system to draw useful lung cancer details from Computed Tomography lung images. Nodules growth rate indicates the severity of the disease, which can be periodically radiologist analyzed by nodule segmentation and classification. Main challenges in analyzing nodules growth rate are lung nodules of different type requires special methods for segmentation, their irregular shape, and boundary. In this paper, automatic three-phase framework for lung nodules and nodules of ground glass opacity detection followed by classification is proposed. In this work, nodule segmentation framework uses proposed automatic region growing algorithm that selects set of black pixels as seed points automatically from output binary image for lung parenchyma segmentation followed by artifacts removal to reduce disease search space. Nodules are segmented based on nodule candidates center pixels identification and intensity feature of lung nodule candidates. Segmented nodules are classified using SVM classifier and classification results are compared with other considered classifiers KNN, boosting and decision tree. In the evaluation step, it was found that SVM classifier’s performance is outstanding compared to other considered classifiers in this work. Complete automation in nodule detection within very less time is the key feature of the proposed method. CT images are taken from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database to evaluate the performance of proposed work. An accuracy of 98% (45/46) with less computational time is achieved. The experimental results demonstrated that the proposed method achieve efficient and accurate segmentation of lung nodules and ground glass opacity nodules with less computation time.

In the recent era, the research interest has increased among different computer and communication societies towards microarray gene expression detection and profiling. Despite of having a wide range of applications, the more emphasize has been kept towards cancer and its sub-type classifications. It has been seen in the past that the existing data mining approaches impose more cost of computation during pattern discovery and correlation establishment. Thereby, it is needed to address this shortcoming to strengthen the reliable cancer detection and classification process cost-effectively. An efficient machine learning tool has a better scope of optimization towards handling margin and error factors. Addressing this open research issue, the current study has come up with a novel method namely Optimal Framework for Microarray Data Classification (OFDMC) which incorporates Eigenvector decomposition to perform dimension reduction of gene expression data without compromising the complexity and accuracy aspects. The study also validates the performance of the proposed system by introducing a numerical analysis.

Gene expression data is popularized for its capability to disclose various disease conditions. However, the conventional procedure to extract gene expression data itself incorporates various artifacts that offer challenges in diagnosis a complex disease indication and classification like cancer. Review of existing research approaches indicates that classification approaches are few to proven to be standard with respect to higher accuracy and applicable to gene expression data apart from unaddresed problems of computational complexity. Therefore, the proposed manuscript introduces a novel and simplified model capable using Graph Fourier Transform, Eigen Value and vector for offering better classification performance considering case study of microarray database, which is one typical example of gene expression data. The study outcome shows that proposed system offers comparatively better accuracy and reduced computational complexity with the existing clustering approaches.

The significant contribution of the clustering algorithm for diagnosis of the clinical condition through medical data consideration is must in the healthcare sector. The currently existing techniques implement the Fuzzy Logic in clustering and have been found by research gap which describes that less focus on the medical data clustering. Thus, this paper introduced a novel algorithm where the enhancement of fuzzy logic is performed to achieve better computational ability in the processing of highly complex medical data such as microarray data. The introduced algorithm is implemented for disease diagnosis and classification. The outcomes of the proposed algorithm are compared with recent approaches like the genetic algorithm, support vector machine (SVM), and artificial neural network (ANN). On analyzing these comparative results found that the proposed clustering model achieved significant performance in response time and classification of disease with better accuracy.

The sucesssive growth of collabrative applications prodcuing Bigdata on timeline leads new opprutinity to setup commodities on cloud infrastructure. Mnay organizations will have demand of an efficient data storage mechanism and also the efficient data analysis. The Big Data (BD) also faces some of the security issues for the important data or information which is shared or transferred over the cloud. These issues include the tampering, losing control over the data, etc. This survey work offers some of the interesting, important aspects of big data including the high security and privacy issue. In this, the survey of existing research works for the preservation of privacy and security mechanism and also the existing tools for it are stated. The discussions for upcoming tools which are needed to be focused on performance improvement are discussed. With the survey analysis, a research gap is illustrated, and a future research idea is presented

Priority based scheduling algorithms plays a very important role in multiprocessor real time systems. Our algorithm results in improved through put and minimal response time which intern results in better utilization of system resources. In this paper utilization of fuzzy scheduling algorithm FZEDZL is compared with other priority based algorithms like EDF-US [m/ (2m-1)], EDZL and shown that our algorithm successfully schedules on m processors with utilization at most m2 / (2m-1) for periodic task set.

As the technology is improving, today’s era is focusing towards big data. In that sense, various organizations are demanding an efficient data storage mechanism and also the efficient data analysis. The Big Data (BD) also faces some of the security issues for the important data or information which is shared or transferred over the cloud. These issues include the tampering, losing control over the data, etc. This survey work offers some of the interesting, important aspects of big data including the high security and privacy issue. In this, the survey of existing research works for the preservation of privacy and security mechanism and also the existing tools for it are stated. The discussions for upcoming tools which are needed to be focused on performance improvement are discussed. With the survey analysis, a research gap is illustrated, and a future research idea is presented.

In many diseases classification an accurate gene analysis is needed, for which selection of most informative genes is very important and it require a technique of decision in complex context of ambiguity. The traditional methods include for selecting most significant gene includes some of the statistical analysis namely 2-Sample-T-test (2STT), Entropy, Signal to Noise Ratio (SNR). This paper evaluates gene selection and classification on the basis of accurate gene selection using structured complex decision technique (SCDT) and classifies it using fuzzy cluster based nearest neighborclassifier (FC-NNC). The effectiveness of the proposed SCDT and FC-NNC is evaluated for leave one out cross validation metric(LOOCV) along with sensitivity, specificity, precision and F1-score with four different classifiers namely 1) Radial Basis Function (RBF), 2) Multi-layer perception(MLP), 3) Feed Forward(FF) and 4) Support vector machine(SVM) for three different datasets of DLBCL, Leukemia and Prostate tumor. The proposed SCDT &FC-NNC exhibits superior result for being considered more accurate decision mechanism.

In this paper, a method has been presented to convert finite automaton to fuzzy automaton as fuzzy automaton is better than finite automaton for strings comparison when individual levels of similarity for particular pairs of symbols or sequences of symbols are defined. A finite automaton is useful in determining whether a given string is accepted or not whereas fuzzy automaton determines the extent to which the string is accepted.