Dijlah University College

Abstract: Today, most of the people are affected by lung cancer, mainly because of the genetic changes of the tissues in the lungs. Other factors such as smoking, alcohol, and exposure to dangerous gases can also be considered the contributory causes of lung cancer. Due to the serious consequences of lung cancer, the medical associations have been striving to diagnose cancer in its early stage of growth by applying the computer-aided diagnosis process. Although the CAD system at healthcare centers is able to diagnose lung cancer during its early stage of growth, the accuracy of cancer detection is difficult to achieve, mainly because of the overfitting of lung cancer features and the dimensionality of the feature set. Thus, this paper introduces the effective and optimized neural computing and soft computing techniques to minimize the difficulties and issues in the feature set. Initially, lung biomedical data were collected from the ELVIRA Biomedical Data Set Repository. The noise present in the data was eliminated by applying the bin smoothing normalization process. The minimum repetition and Wolf heuristic features were subsequently selected to minimize the dimensionality and complexity of the features. The selected lung features were analyzed using discrete AdaBoost optimized ensemble learning generalized neural networks, which successfully analyzed the biomedical lung data and classified the normal and abnormal features with great effectiveness. The efficiency of the system was then evaluated using MATLAB experimental setup in terms of error rate, precision, recall, G-mean, F-measure, and prediction rate.

Abstract: Diabetes mellitus is a serious health problem affecting the entire population all over the world for many decades. It is a group of metabolic disorder characterized by chronic disease which occurs due to high blood sugar, unhealthy foods, lack of physical activity and also hereditary. The sorts of diabetes mellitus are type1, type2 and gestational diabetes. The type1 appears during childhood and type2 diabetes develop at any age, mostly affects older than 40. The gestational diabetes occurs for pregnant women. According to the statistical report of WHO 79% of deaths occurred in people under the age of 60, due to diabetes. With a specific end goal to deal with the vast volume, speed, assortment, veracity and estimation of information a scalable environment is needed. Cloud computing is an interesting computing model suitable for accommodating huge volume of dynamic data. To overcome the data handling problems this work focused on Hadoop framework along with clustering technique. This work also predicts the occurrence of diabetes under various circumstances which is more useful for the human. This paper also compares the efficiency of two different clustering techniques suitable for the environment. The predicted result is used to diagnose which age group and gender are mostly affected by diabetes. Further some of the attributes such as hyper tension and work nature are also taken into consideration for analysis.

Abstract: In this work, we report enhanced specific capactance and cycle stability of Fe-SnO2@CeO2 electrode for supercapacitor applications. The Fe-SnO2@CeO2 nanocomposite was synthesized through a simple chemical co-precipitation method. The electrochemical performances of the Fe-SnO2@CeO2 nanocomposite electrode are assessed by using cyclic voltammetry, galvanostatic charge/discharge and impedance spectroscopy techniques. In three electrode system, the Fe-SnO2@CeO2 nanocomposite electrode is exhibited a maximum specific capacitance of 348 F/g at a current density of 1 A/g. Further, the asymmetric supercapacitor (ASC) device performance has been evaluated and the ASC device produces a specific energy and specific power of 32.2 W h kg−1 and 747 W kg−1 at a current density of 1A/g, respectively. The device exhibited the capacitance retention of 85.05 % over 5000 cycles of operation. This study confirms that the Fe-SnO2@CeO2 is an alternative electrode material for high energy storage supercapacitor applications.

Abstract: Making an accurate diagnosis of nasopharyngeal carcinoma (NPC) disease is a challenging task that involves many parties such as radiology specialists often times need to delineate NPC boundaries on various tumor-bearing endoscopic images. It is a tedious and time-consuming operation exceedingly based on doctors and experience of radiologist. NPC has complex and irregular structures which makes it difficult to diagnose even by an expert physician. However, the diagnosis accuracy results of such methods are still insignificant and need improvement in order to manifest robust solution. The study aim is to develop and propose a new automatic classification of NPC tumor using machine learning techniques and feature-based decision-level fusion scheme from endoscopic images. We have implemented the fusion of the three image texture-based schemes (local binary patterns, the first-order statistics histogram properties, and histogram of gray scale) at the decision level and tested the performance of this scheme using the same experimental setup in the previous section for simple score-level fusion, but for comparison, We used the classifiers methods which are support vector machines (SVM), k-nearest neighbors’ algorithm, and artificial neural network (ANN). The results demonstrate that the majority rule for decision-based fusion is outperformed considerably by the single best performing feature scheme (FFGF) for the SVM classifier, but for the ANN and KNN classifier it is significantly outperformed by each of the components features. The classifiers approaches were listed a high accuracy of 94.07%, the sensitivity of 92.05%, and specificity of 93.07%.