Data Mining - Concepts and Techniques.

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Advances In Kernel Methods Support Vector Learning

Modified Alexnet architecture for classification of diabetic retinopathy images

https://research.edgehill.ac.uk/ws/files/29195215/Manuscript1.pdf

Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model

On the Relation between Education and Crime

People give more importance concerning the overall quality of the modernized ecosystem. The pollution of air is one of the significant problems to be resolved as it restricted the ecological transformation of the modernized ecosystem. Therefore, it is fundamental to evaluate the implication of these ecological issues to enhance the urban ecosystem. This vital purpose of this research is to propose a canonical correlation analysis based hyper basis feedforward neural network classification (CCA-HBFNNC) model for evaluating sustainable urban environmental quality. The CCA-HBFNNC model initially acquires a large size of U.S. air pollution dataset as input. Then, a canonical correlative analysis based feature selection algorithm is applied in the CCA-HBFNNC model to select the key pollutant features, which bear fundamental implications to the modernize air pollution to maintain the level of urban sustainability. After the feature selection process, the CCA-HBFNNC model applies the HYPER BASIS FEEDFORWARD NEURAL NETWORK CLASSIFICATION (HBFNNC) algorithm in order to classify input air data based on chosen pollutants features. During the classification process, the HBFNNC algorithm used three critical layers namely hidden, output and input layers for efficiently categorizing each input data as higher or lower pollution level with higher accuracy. If the level of air pollution on the urban environment is higher, finally CCA-HBFNNC model significantly reduces the pollution level. In this way, the CCA-HBFNNC model attains improved urban sustainability levels when compared to sophisticated operation. An experimental evaluation of the CCA-HBFNNC model is determined in terms of CCA-HBFNNC model, time complexity and false-positive rate in consideration of the diversified number of air data retrieved from the big data sets. An investigational result shows that the proposed CCA-HBFNNC model can increases the sustainability level and minimizes the time complexity of urban development when contrasted with contemporary works.

https://link.springer.com/content/pdf/10.1007/s11063-020-10327-3.pdf

Canonical Correlation Analysis Based Hyper Basis Feedforward Neural Network Classification for Urban Sustainability

Security and channel noise management in cognitive radio networks

The early and accurate prediction of liver disease in patients is still a challenging task among medical practitioners even with latest advanced technologies. The support vector machines are widely used in medical domain. It has proved its efficiency on producing good diagnostic parameters. These results can be further improved by optimizing the hyperparameters of support vector machines. The proposed work is based on optimizing support vector machines with crow search algorithm. This optimized support vector machine classifier (CSA-SVM) is used for accurate diagnosis of Indian liver disease data. The various similar state of art algorithms are taken for comparison with proposed approach to prove its efficient. The performance of CSA-SVM is found to be outstanding among all other approaches in terms of all metrics taken for comparison. It has yielded the classification accuracy of 99.49%.

/pdf/efficient-diagnosis-of-liver-disease-using-support-vector-5ceew5ehp0.pdf

Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm

The mobile networks seem to have a steady future in the direction of the recent emergence of socially aware cognitive mobile networks. Their style and design are specifically made in improving shared spectrum space access, in cooperative spectrum sensing, and in enhancing device-to-device communications. Socially aware mobile networks do have enough potential to amass sufficient returns in the efficacy of the spectrum and also to march and gain a considerable amount of increase in the capacity of the network. Even though there are lot of gains in its potency to be reaped yet, still there seems to be enough challenges that are both businessand technical-related that have to be taken care of. This chapter delves into the cognitive radio (CR) and its social relations and also makes sufficient exploits in establishing a scheme that will be based on social-based cooperative sensing scheme (SBC).

Social Aware Cognitive Radio Networks: Effectiveness of Social Networks as a Strategic Tool for Organizational Business Management

The detection of cancer in the breast is done using mammograms (x-ray images). The authors propose a CAD framework for distinguishing little changes in mammogram which may demonstrate malignancies which are too little to be felt either by the lady herself or by a radiologist. In this chapter, they build up a framework for analysis, visualization, and prediction of cancer in breast tissue by utilizing Intelligent based wavelet classifier. Intelligent-based wavelet classifier is a new approach constructed using texture value and wavelet neural network. The proposed framework is applied to the genuine clinical database of 160 mammograms gathered from mammogram screening focuses. The execution of the CAD framework is examined utilizing ROC curve. This will help the specialists in determination of the breast tissues either cancerous or noncancerous in an accurate way.

Anandakumar Haldorai

Papers

Canonical Correlation Analysis Based Hyper Basis Feedforward Neural Network Classification for Urban Sustainability

Security and channel noise management in cognitive radio networks

Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm

Social Aware Cognitive Radio Networks: Effectiveness of Social Networks as a Strategic Tool for Organizational Business Management

An Intelligent-Based Wavelet Classifier for Accurate Prediction of Breast Cancer