An optimized K-Nearest Neighbor based breast cancer detection

In this research, a grid search is employed to find the optimal parameter and an optimized K-Nearest Neighbor (KNN) based breast cancer detection model is proposed. The grid search is used to find the best combinations of parameters that could produce better breast cancer detection accuracy. Moreover, this study explored the effect of parameter tuning on the performance of KNN algorithm foe breast cancer detection. The findings of this research reveals that parameter tuning has a significant effect on the performance of the proposed model. The effect of parameter tuning on the performance of KNN algorithm is experimentally tested using Wisconsin breast cancer dataset collected from kaggle data repository. Finally, we have compared the performance of the KNN algorithm with the tuned hyper-parameter and with default hyper-parameter. The result analysis on the performance of the KNN algorithm on breast cancer detection on the test dataset reveals that the accuracy of the proposed optimized model is 94.35% and the performance of the KNN algorithm with the default hyper-parameter is 90.10%

Automated Chronic Kidney Disease Detection Model with Knearest Neighbor

Chronic kidney disease is one of the most common disease in the world today. Kidney disease causes death if the patient is not threated at early stage. One of the challenge in kidney disease treatment is accurate identification of kidney disease at an early stage. Moreover, detecting kidney disease requires experienced nephrologist. However, in developing nations lack of medical specialist or nephrologist for identifying chronic kidney disease makes the problem more challenging. As alternative solution to kidney disease identification, researchers have developed many intelligent models using K-nearest Neighbors (KNN) algorithm. However, the accuracy of the existing KNN model has scope for improvement. Thus, this study proposed KNN based model for accurate identification of kidney disease at early stage. To develop optimized KNN model, we have employed error plot to find most favorable K value to obtain more accurate result than the existing models. To conduct experiments, study employed kidney disease dataset collected form publically available Kaggle data repository for training and testing the proposed model. Finally, we have evaluated the proposed model against predictive accuracy. The experimental result on the proposed model appears to prove that the predictive accuracy of the model is 99.86%

Software defined network emulation with OpenFlow protocol

In software defined network the network infrastructure layer where the entire network devices, like switches and routers reside is connected with the separate controller layer with the help of standard called OpenFlow. The open flow standard enables different vendor devices like juniper, cisco and Huawei switch to connect to the controller or a software program. The software program controls and manages the network devices. Therefore, software defined network architecture makes the network flexible, cost effective and manageable, enables dynamic provisioning of bandwidth, dynamic scale out and dynamic scale in compared to the traditional network. In this study, the architectures and principles of software defined network is explored by emulating the software defined network employing a mininet

Performance analysis of emulated software defined wireless network

Software defined wireless network is a networking architecture and an emerging networking principle that is based on software defined network. A software defined network is a fundamental networking concept which separates the networking devices used in communication network form the program that runs on the top of these devices. In this paper we will explore the modeling tools used for software defined wireless network in our literature survey and based on the survey we have used mininet-Wi-Fi for modeling software defined wireless network, which is the best and most widely used emulation tool for software defined wireless network. Lastly, we have evaluated the performance of TCP traffic bandwidth for different number of emulated end stations and access points

The Performance of Machine Learning for Chronic Kidney Disease Diagnosis

This paper aims to review the performance of different machine learning (ML) models and develop models for the automated diagnosis of chronic kidney disease. To detect chronic kidney disease with better precision, selecting the right and better-performing ML model is significant as it improves the precision and accuracy of the chronic kidney disease diagnosis. The study uses the Joana Briggs Institute (JBI) scoping review methodology, which involves different steps such as searching relevant literature, conducting the review, and reporting the review result. In the search, the year of publication and the indexing of journals where the studies are published is used as inclusion and exclusion criteria. The review result shows that the current chronic kidney disease detection has focused on the development of ensemble-based and deep-learning methods. The deep learning method can achieve a higher accuracy of 99.75%

Improving network performance with an integrated priority queue and weighted fair queue scheduling

Quality of service (QoS) is the measure of network service availability and transmission. There are many factors influencing QoS among which one is the increasing number of network service users. The increase in the number of network service users and communication traffic causes network congestion. And the traffic congestion results in delay or packet loss and jitter variation. As a result, an organization’s network quality deteriorates and or even becomes unavailable. Therefore, to deliver a high quality network service to the users, a solution that avoids network traffic congestion is needed. In this study, the causes for network traffic congestion and the best solutions to eliminate traffic congestion in a network with congestion management and avoidance using an integrated priority queue (PQ) and weighted fair queue (WFQ) packet scheduling algorithms is proposed

Handwritten digits recognition with decision tree classification: a machine learning approach

Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9

The performance of Gauss Markov’s mobility model in emulated software defined wireless mesh network

Wireless mesh networks (WMNs) are a new trend in wireless communication promising greater flexibility, reliability, and performance over traditional wireless local area networks (WLANs).Test bed analysis and emulation plays an important role in evaluation of wireless networks and node mobility is the prominent feature of next generation wireless network. In this paper we will focus on the models of wireless station mobility and discuss their importance within the software defined wireless mesh network performance evaluation. The existing mobility models for the next generation software defined wireless network will be explored. Finlay, we will present the mobility models in the mininet-Wi-Fi test bed, and evaluate the performance of the model

Breast cancer prediction model with decision tree and adaptive boosting

In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased to the benign observation and results in poor performance on predicting the malignant observation. To improve the performance of the decision tree on the malignant observation, boosting algorithm namely, the adaptive boosting is employed. Finally, the predictive performance of the decision tree and adaptive boosting is analyzed. The analysis on predictive performance of the model on the kaggle breast cancer data repository shows that, adaptive boosting has 92.53% accuracy and the accuracy of decision tree is 88.80%, Overall, the adaboost algorithm performed better than decision tree