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

A review on software defined network security risks and challenges

Software defined network is an emerging networking architecture that separates the traditional integrated control logic and data forwarding functionality into different planes, namely the control plane and data forwarding plane. The data plane does and end to end data delivery. And the control plane does the actual network traffic forwarding and routing between different network segments. In software defined network the networking infrastructure layer where the entire networking device, such as switches and routers reside is connected with the separate controller layer with the help of standard called OpenFlow protocol. It is a standard protocol that allows different vendor devices like juniper switches, cisco switches and huawei switches to be connected to the controller. The centralization of the SDN controller made the network more flexible, manageable and dynamic, such as provisioning of bandwidth, dynamic scale out and scale in compared to the traditional communication network, however the centralized SDN controller is more vulnerable to security risk factors such as DDOS and flow rule poisoning attack. In this paper we will explore the architectures and principles of software defined network and security risks with the centralized SDN controller and possible ways to mitigate these risks

An image-based gangrene disease classification

Gangrene disease is one of the deadliest diseases on the globe which is caused by lack of blood supply to the body parts or any kind of infection. The gangrene disease often affects the human body parts such as fingers, limbs, toes but there are many cases of on muscles and organs. In this paper, the gangrene disease classification is being done from the given images of high resolution. The convolutional neural network (CNN) is used for feature extraction on disease images. The first layer of the convolutional neural network was used to capture the elementary image features such as dots, edges and blobs. The intermediate layers or the hidden layers of the convolutional neural network extracts detailed image features such as shapes, brightness, and contrast as well as color. Finally, the CNN extracted features are given to the Support Vector Machine to classify the gangrene disease. The experiment results show the approach adopted in this study performs better and acceptable

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 models</jats:p

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.</jats:p

An image-based gangrene disease classification

Gangrene disease is one of the deadliest diseases on the globe which is caused by lack of blood supply to the body parts or any kind of infection. The gangrene disease often affects the human body parts such as fingers, limbs, toes but there are many cases of on muscles and organs. In this paper, the gangrene disease classification is being done from the given images of high resolution. The convolutional neural network (CNN) is used for feature extraction on disease images. The first layer of the convolutional neural network was used to capture the elementary image features such as dots, edges and blobs. The intermediate layers or the hidden layers of the convolutional neural network extracts detailed image features such as shapes, brightness, and contrast as well as color. Finally, the CNN extracted features are given to the Support Vector Machine to classify the gangrene disease. The experiment results show the approach adopted in this study performs better and acceptable.</jats:p

Fake News Detection Models and Performances

Fake News detection is a hard problem for decades after the advent of social media. As misinformation, so called fake news continues to be rapidly distributing on internet, the reality has becoming increasingly shaped by false information. Time after time we have consumed or being exposed to inaccurate information. The last few years have been talking about guarding against misinformation and not progressed much in this direction. The social media is one of the medium where the fake news spreads so rapidly and impact many in a lesser span of time. Machine Learning and Natural Language processing are the core techniques to detect the fake news and stopping from spreading on social media. Many researchers putting their effort in this new challenge to curb down. This paper provides an insight on feature extraction techniques used for fake news detection on soft media. Text feature extraction works with extracting the document information which represent the whole document without loss of the sole information but words which are considered irrelevant were ignored for the purpose of improving the accuracy. Term Frequency Inverse Document Frequency (TF-IDF), BoW(Bag of Words) are some of the important techniques used in text feature extraction. These techniques are discussed with their significance in this paper. One of the important approach, Automated Readability Index is used to test the readability of the text to build the model also discussed in this paper. This paper will play a significant role for the researchers who are interested in the area of fake news Identification.</jats:p