Multiclass Classification of Disease Using CNN and SVM of Medical Imaging

Parasitic computing is a method in which a normal authorised interactions with the help of another computer through the unauthorised interactions or we can say that the parasitic computing is a type of exploit or security exploit in which the program which is implementing the parasitic computing is unauthorised to the resources available in the computer at that time as we know that in today's era the name hacking plays a vital role in which the hacker wants an unauthorised access to the resources available in the computer of the host.

Section: Implementation Of Parasitic Computing Using Tcp/ipmentioning

confidence: 99%

“…Which interns making the TCP IP connection between the user and the hacker. [14] talks about the use of machine learning algorithms for medical purpose.…”

Section: Introduction Figure 1: Prototype Parasitic Computingmentioning

confidence: 99%

Parasitic Computing: A Review

2021

“…Classical ML methods, profound learning, and neural organizations are a portion of the classes for AI models. Multiclass classification is discussed in [6,7].…”

Section: Introduction Machine Learningmentioning

confidence: 99%

Implementation of Traditional Vs. Transformer Machine Learning Models

2021

Classical models have been mostly used in NLP tasks. There are several classical models like Naïve Bayes, SVM, CNN etc. In classical model we need to build the model from scratch. The model is trained, developed and then lastly deployed. With the advent of transformer models the process have been simplified. Transformer models are pretrained on larger datasets and just needs labelled dataset. It saves time, energy and resources. This report discusses the mechanism involved in workflow of classical and transformer ml models. Implementation of models have been also done for comparison.

“…Introduce a semantic role labelling convolutional neural network. In [13,14] there are several ways talked about CNN modeling.…”

Section: Introductionmentioning

confidence: 99%

Text Classfication Using CNN and CNN-LSTM

2021

Text classification, language modelling, and machine translation are some of the applications. CNNs excel at retrieving local and position-invariant characteristics, but RNNs excel at classification based on a long-range semantic dependency instead of local key-value pairs. It shows out that CNNs perform admirably when used to NLP problems. The basic (Bow) model is a clear oversimplified based on faulty beliefs, but it has been the usual technique for years and has produced reasonable accuracy. The speed of CNNs is a major selling point. In this paper, Text classification is carried out by using a deep learning model that is CNN and a hybrid model using CNN-LSTM and compare the performance of two models.