An Efficient Internet of Things (IoT)-Enabled Skin Lesion Detection Model using Hybrid Feature Extraction with Extreme Machine Learning Model

2021 ◽  
pp. 275-282
Author(s):  
B. Pushpa
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Skin Lesion ◽  
Internet Of Things ◽  
Learning Model ◽  
Lesion Detection ◽  
Detection Model ◽  
Machine Learning Model ◽  
Hybrid Feature Extraction

scholarly journals A Multilayer Hybrid Machine Learning Model for Diabetes Detection

2020 ◽  
Vol 32 ◽  
pp. 03032
Author(s):  
Sahil Parab ◽  
Piyush Rathod ◽  
Durgesh Patil ◽  
Vishwanath Chikkareddi
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Model ◽  
Point Of View ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Case Scenario ◽  
Detection Model ◽  
Machine Learning Model ◽  
Hybrid Machine

Diabetes Detection has been one of the many challenges which is being faced by the medical as well as technological communities. The principles of machine learning and its algorithms is used in order to detect the possibility of a diabetic patient based on their level of glucose concentration , insulin levels and other medically point of view required test reports. The basic diabetes detection model uses Bayesian classification machine learning algorithm, but even though the model is able to detect diabetes, the efficiency is not acceptable at all times because of the drawbacks of the single algorithm of the model. A Hybrid Machine Learning Model is used to overcome the drawbacks produced by a single algorithm model. A Hybrid Model is constructed by implementing multiple applicable machine learning algorithms such as the SVM model and Bayesian’s Classification model or any other models in order to overcome drawbacks faced by each other and also provide their mutually contributed efficiency. In a perfect case scenario the new hybrid machine learning model will be able to provide more efficiency as compared to the old Bayesian’s classification model.

scholarly journals IFed: A novel federated learning framework for local differential privacy in Power Internet of Things

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772091969
Author(s):  
Hui Cao ◽  
Shubo Liu ◽  
Renfang Zhao ◽  
Xingxing Xiong
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Differential Privacy ◽  
Learning Model ◽  
Wireless Sensor ◽  
Trade Off ◽  
Privacy Requirements ◽  
Learning Framework ◽  
Machine Learning Model ◽  
Internet Of Thing

Nowadays, wireless sensor network technology is being increasingly popular which is applied to a wide range of Internet of Things. Especially, Power Internet of Things is an important and rapidly growing section in Internet of Thing systems, which benefited from the application of wireless sensor networks to achieve fine-grained information collection. Meanwhile, the privacy risk is gradually exposed, which is the widespread concern for electricity power consumers. Non-intrusive load monitoring, in particular, is a technique to recover state of appliances from only the energy consumption data, which enables adversary inferring the behavior privacy of residents. There can be no doubt that applying local differential privacy to achieve privacy preserving in the local setting is more trustworthy than centralized approach for electricity customers. Although it is hard to control the risk and achieve the trade-off between privacy and utility by traditional local differential privacy obfuscation mechanisms, some existing obfuscation mechanisms based on artificial intelligence, called advanced obfuscation mechanisms, can achieve it. However, the large computing resource consumption to train the machine learning model is not affordable for most Power Internet of Thing terminal. In this article, to solve this problem, IFed was proposed—a novel federated learning framework that let electric provider who normally is adequate in computing resources to help Power Internet of Thing users. First, the optimized framework was proposed in which the trade-off between local differential privacy, data utility, and resource consumption was incorporated. Concurrently, the following problem of privacy preserving on the machine learning model transport between electricity provider and customers was noted and resolved. Last, users were categorized based on different levels of privacy requirements, and stronger privacy guarantee was provided for sensitive users. The formal local differential privacy analysis and the experiments demonstrated that IFed can fulfill the privacy requirements for Power Internet of Thing users.

scholarly journals Lesion Detection of Cerebral Infarction by a Machine Learning Model that Learned ‘Normal’ [Presidential Award Proceedings]

2020 ◽  
Vol 40 (1) ◽  
pp. 26-29
Author(s):  
Akihiko WADA ◽  
Yuya SAITO ◽  
Shinpei KATO ◽  
Akifumi HAGIWARA ◽  
Shohei FUJITA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebral Infarction ◽  
Learning Model ◽  
Lesion Detection ◽  
Machine Learning Model

scholarly journals A Machine Learning Model to Identify Duplicate Questions in Social Media Forums

2020 ◽  
Vol 9 (4) ◽  
pp. 370-373
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Feature Extraction ◽  
Good Accuracy ◽  
Learning Model ◽  
Learning Models ◽  
Single Model ◽  
Machine Learning Model ◽  
Repetitive Nature ◽  
Machine Learning Models

In recent years, digital platform forums where question and answers are being discussed are attracting more number of users. Many discussions on these forums would be repetitive nature. Such duplicate questions were provided by Quora as a competition on Kaggle. It is observed that the dataset provided by Quora, requires many modifications before training machine learning models to obtain a good accuracy. These modifications include feature extraction, vectorization and tokenization after which the data is ready for training desired models. While analyzing each model after prediction, it gives plenty of information about its efficiency and many other factors. Later, these information of different models are compared and helps to choose the best model. These models later can be combined and used as a single model with best accuracy. In this paper, a Machine Learning model which will predict duplicate questions is proposed

A Hybrid Machine Learning Model for Demand Prediction of Edge-Computing-Based Bike-Sharing System Using Internet of Things

2020 ◽  
Vol 7 (8) ◽  
pp. 7345-7356 ◽  
Author(s):  
Tiantian Xu ◽  
Guangjie Han ◽  
Xingyue Qi ◽  
Jiaxin Du ◽  
Chuan Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Model ◽  
Edge Computing ◽  
Demand Prediction ◽  
Machine Learning Model ◽  
Bike Sharing ◽  
Hybrid Machine
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