International Journal of Advanced Networking and Applications

Gajendra Sharma ¹, Umesh Hengaju ¹

Affiliations
1 Department of Computer Science & Engineering, Kathmandu University, Dhulikhel, Nepal, IN

Abstract

In Data mining, Classification and prediction are the two very essential forms of data analysis. They are widely used for extracting models for describing important data classes. This paper aims in designing classifier models based on five different classification algorithms namely, Decision Tree, K-Nearest Neighbors (KNN), Naive Bayes, Random Forest and Support Vector Machines (SVM), to classify and predict patients with diabetes. These classifiers are experimented with 10 fold Cross Validation and their performances are evaluated by computing Accuracy, Precision, F-Score, Recall and ROC measures. The test experiment shows that the accuracy given by classifier models developed by using Decision Tree, KNN, Naïve Bayes, SVM and Random Forest are 73.82%, 71.65%, 76.30%, 65.10% and 68.74 % respectively. Similarly, their precisions and recall are 0.705, 0.552, 0.759, 0.424, 0.538 and 0.738, 0.763, 0.82, 0.651, 0.804 respectively. Thus, this study shows that the Naïve Bayes algorithm provides the better accuracy in predicting diabetes as compared to other techniques. And, the data set chosen for this study is “Pima Indian Diabetic Dataset” taken from University of California, Irvine (UCI) Repository of Machine Learning databases.

Keywords

Data Mining, Diabetes, Classification, Prediction, KNN, Naive Bayes, Random Forest, SVM, Accuracy, Precision, F-Measure, Recall.

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Bipesh Subedi ¹, Gajendra Sharma ¹

Affiliations
1 Department of Computer Science and Engineering, Kathmandu University, Dhulikhel, Kavre, NP

Abstract

Advancement in IoT and cloud technology has opened room for various application services in different areas. With such popularity, the volume of data increases immensely and it is infeasible for cloud technology to provide real-time services in some cases. Fog computing is an extension of cloud technology which provides real-time and time-sensitive services. Data processing is done at fog nodes that allow seamless connectivity and application services. In this paper, various fog computing architectures, applications, and security issues are discussed. It aims to provide a comprehensive review of various aspects of fog computing.

Keywords

Cloud Computing, Fog Computing, Security, Smart Agriculture, Smart Healthcare.

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