scholarly journals A Novel Machine Learning Model for the Detection of Epilepsy and Epileptic Seizures Using Electroencephalographic Signals Based on Chaos and Fractal Theories

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zayneb Brari ◽  
Safya Belghith
Keyword(s):  
Machine Learning ◽  
Feature Fusion ◽  
Epileptic Seizures ◽  
Chaotic Signal ◽  
Research Area ◽  
New Method ◽  
Classification Problems ◽  
Learning Framework ◽  
Machine Learning Model ◽  
Electroencephalographic Signals

Machine learning is an expanding research area. Its main application is in the medical field and particularly the detection of epilepsy and epileptic seizures through electroencephalographic signals (EEG). It aims to design an intelligent framework that enables an immediate diagnosis of this disease without neurological consultation and thus saves the lives of the epileptic patients by detecting seizures and warning them before it happens. However, as a real-time application, this kind of framework faces several challenges such as accuracy, fast responses, and optimal memory usage. Within this context, our work was carried out. We propose a new machine learning framework based on chaos and fractal theories. Two main novelties are presented in this paper. Firstly, we propose a new method for signal preprocessing, and we reconstruct new versions of studied EEG signals using derivative determination and chaotic injection. Secondly, we suggest a new method for fractal analysis using Higuchi fractal dimension (HFD). In fact, HFDs extracted from EEG derivatives lead to detect epilepsy, whereas HFDs extracted from EEG with a chaotic signal injection lead to seizure detection. In addition, feature fusion helped to linearize all classification problems. An experimental study using the Bonn EEG database proves the efficiency of our contributions in comparison to published research. An accuracy of 100% was achieved in different classification cases using few features and a simple linear classifier.

scholarly journals Object detection and used car price predicting analysis system (ucpas) using machine learning technique

2021 ◽  
Vol 5 (S2) ◽  
Author(s):  
Anu Yadav ◽  
Ela Kumar ◽  
Piyush Kumar Yadav
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Research Area ◽  
Machine Learning Technique ◽  
Car Detection ◽  
Machine Learning Model ◽  
Implementation Techniques ◽  
Learning Technique ◽  
Used Car ◽  
Analysis System

The highly interesting research area that noticed in the last few years is object detection and find out the prediction based on the features that can be benefited to consumers and the industry. In this paper, we understand the concept of object detection like the car detection, to look into the price of a second-hand car using automatic machine learning methods. We also understand the concept of object detection categories. Nowadays, the most challenging task is to determine what is the listed price of a used car on the market, Possibility of various factors that can drive a used car price. The main objective of this paper is to develop machine learning models which make it possible to accurately predict the price of a second-hand car according to its parameter or characteristics. In this paper, implementation techniques and evaluation methods are used on a Car dataset consisting of the selling prices of various models of  car across different cities of India. The outcome of this experiment shows that clustering with linear regression and Random Forest model yield the best accuracy outcome. The machine learning model produces a satisfactory result within a short duration of time compared to the aforementioned self.

scholarly journals An End-to-End Learning Framework for Battery Capacity-Fade Trajectory Prediction Using Early Life Data

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jinqiang Liu ◽  
Adam Thelen ◽  
Chao Hu ◽  
Xiao-Guang Yang
Keyword(s):  
Machine Learning ◽  
Early Life ◽  
Life Prediction ◽  
Capacity Fade ◽  
Learning Problem ◽  
Learning Framework ◽  
Life Data ◽  
Machine Learning Model ◽  
Li Ion ◽  
End To End

Predicting the capacity-fade trajectory of a lithium-ion (Li-ion) battery cell is a critical task given its broad utility throughout the battery product life cycle. Even more useful is estimating a battery cell’s capacity-fade trajectory when this cell has not exhibited any noticeable capacity degradation. Accurately predicting the entire capacity-fade trajectory using early life data enables more efficient cell design, operation, maintenance, and evaluation for second-life use. To accomplish this challenging task, we propose an end-to-end learning framework combining empirical capacity fade models and data-driven machine learning models, in which the two types of models are closely coupled. First, we evaluate the accuracy of a library of relevant empirical models which have been shown to model the observed capacity fade of Li-ion cells with reasonable accuracy. After selecting a model, we formulate an end-to-end learning problem that simultaneously fits the chosen empirical model to estimate the capacity fade curve and trains a machine learning model to estimate the best-fit parameters of the empirical model. By solving this end-to-end learning problem, rather than sequentially executing the separate tasks of fitting the capacity fade model and training the machine learning model, we achieve a more optimal solution which is shown to better balance these two objectives. Our proposed end-to-end learning framework is evaluated using a publicly available battery dataset consisting of 124 lithium-iron-phosphate/graphite cells charged with various fast-charging protocols. This dataset was split into training, primary test, and secondary test datasets. Our method performs on par with existing early prediction methods in terms of cycle life prediction, attaining root-mean-square errors of 84 cycles and 169 cycles for primary and secondary test datasets, respectively. In addition to the cycle life prediction, our method possesses a unique ability to predict the entire capacity-fade trajectory.

scholarly journals Bot Net Detection for Network Traffic using Ensemble Machine Learning Method

2020 ◽  
Vol 10 (1) ◽  
pp. 201-204
Keyword(s):  
Machine Learning ◽  
Denial Of Service ◽  
Research Area ◽  
Network Activity ◽  
Machine Learning Techniques ◽  
Dos Attacks ◽  
Imbalance Problem ◽  
Ensemble Machine Learning ◽  
Private Data ◽  
Machine Learning Model

In todays era the need of security is raising due to hike in security risks discovered every day. A new vulnerability can be found in any software or product by the attacker as it launches in the market. Botnet carried out various attacks in distributed manner which results in extensive disruption of network activity through information and identity theft, email spamming, click fraud DDoS (Distributed Denial of Service) attacks, virtual deceit and distributed resource usage for cryptocurrency mining.The main aim f botnet is to steal private data of clients,sendind spam and viruses and DOS attacks in the network. The detection of Botnet like Rbot ,Virut and Neris are still vigorous research area due to unavailability of any technique to detect the entire ecosystem of botnet. As they are comprised of different configurations and profoundly armored by malwares writers to dodge detection systems by utilizing complicated dodging techniques. Hence only solution is to discover the infected botnets to control over the services and ports. This work aims to contribute in the botnet detection with its overview and existing methods. The study focuses on techniques like one-hot encoding and variance thresholding. These techniques are utilized to clean the botnet dataset. The performance of the machine learning model can be improved with feature selection methods. The work explores the dataset imbalance problem with the help of ensemble machine learning techniques. The performance is evaluated on the best received model that is trained and tested on datasets of various attacks.

scholarly journals CarEnvision: A Data-Driven Machine Learning Framework for Automated Car Value Prediction

2021 ◽  
Author(s):  
TianGe (Terence) Chen ◽  
Angel Chang ◽  
Evan Gunnell ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Model ◽  
Retail Price ◽  
Data Driven ◽  
Value Prediction ◽  
Learning Framework ◽  
Machine Learning Model ◽  
Negative Sentiment

When people want to buy or sell a personal car, they struggle to know when the timing is best in order to buy their favorite vehicle for the best price or sell for the most profit. We have come up with a program that can predict each car’s future values based on experts’ opinions and reviews. Our program extracts reviews which undergo sentiment analysis to become our data in the form of positive and negative sentiment. The data is then collected and used to train the Machine Learning model, which will in turn predict the car’s retail price.

scholarly journals 6mAPred-MSFF: A Deep Learning Model for Predicting DNA N6-Methyladenine Sites across Species Based on a Multi-Scale Feature Fusion Mechanism

2021 ◽  
Vol 11 (16) ◽  
pp. 7731
Author(s):  
Rao Zeng ◽  
Minghong Liao
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Feature Fusion ◽  
Experimental Comparison ◽  
Scale Feature ◽  
Learning Framework ◽  
Multi Scale ◽  
Fold Cross Validation

DNA methylation is one of the most extensive epigenetic modifications. DNA N6-methyladenine (6mA) plays a key role in many biology regulation processes. An accurate and reliable genome-wide identification of 6mA sites is crucial for systematically understanding its biological functions. Some machine learning tools can identify 6mA sites, but their limited prediction accuracy and lack of robustness limit their usability in epigenetic studies, which implies the great need of developing new computational methods for this problem. In this paper, we developed a novel computational predictor, namely the 6mAPred-MSFF, which is a deep learning framework based on a multi-scale feature fusion mechanism to identify 6mA sites across different species. In the predictor, we integrate the inverted residual block and multi-scale attention mechanism to build lightweight and deep neural networks. As compared to existing predictors using traditional machine learning, our deep learning framework needs no prior knowledge of 6mA or manually crafted sequence features and sufficiently capture better characteristics of 6mA sites. By benchmarking comparison, our deep learning method outperforms the state-of-the-art methods on the 5-fold cross-validation test on the seven datasets of six species, demonstrating that the proposed 6mAPred-MSFF is more effective and generic. Specifically, our proposed 6mAPred-MSFF gives the sensitivity and specificity of the 5-fold cross-validation on the 6mA-rice-Lv dataset as 97.88% and 94.64%, respectively. Our model trained with the rice data predicts well the 6mA sites of other five species: Arabidopsis thaliana, Fragaria vesca, Rosa chinensis, Homo sapiens, and Drosophila melanogaster with a prediction accuracy 98.51%, 93.02%, and 91.53%, respectively. Moreover, via experimental comparison, we explored performance impact by training and testing our proposed model under different encoding schemes and feature descriptors.

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 Machine learning building price prediction with green building determinant

2020 ◽  
Vol 9 (3) ◽  
pp. 379
Author(s):  
Thuraiya Mohd ◽  
Syafiqah Jamil ◽  
Suraya Masrom
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Model Building ◽  
Green Building ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Classification Problems ◽  
Price Prediction ◽  
Machine Learning Model ◽  
Housing Issue

In the era of Industrial 4.0, many urgent issues in the industries can be effectively solved with artificial intelligence techniques, including machine learning. Designing an effective machine learning model for prediction and classification problems is an ongoing endeavor. Besides that, time and expertise are important factors that are needed to tailor the model to a specific issue, such as the green building housing issue. Green building is known as a potential approach to increase the efficiency of the building. To the best of our knowledge, there is still no implementation of machine learning model on GB valuation factors for building price prediction compared to conventional building development. This paper provides a report of an empirical study that model building price prediction based on green building and other common determinants. The experiments used five common machine learning algorithms namely Linear Regression, Decision Tree, Random Forest, Ridge and Lasso tested on a set of real building datasets that covered Kuala Lumpur District, Malaysia. The result showed that the Random Forest algorithm outperforms the other four algorithms on the tested dataset and the green building determinant has contributed some promising effects to the model.

scholarly journals Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction

2020 ◽  
Vol 6 (6) ◽  
pp. 37
Author(s):  
Emmanuel Pintelas ◽  
Meletis Liaskos ◽  
Ioannis E. Livieris ◽  
Sotiris Kotsiantis ◽  
Panagiotis Pintelas
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Prediction Accuracy ◽  
Classification Problems ◽  
Cancer Prediction ◽  
Learning Framework ◽  
Box Models ◽  
Black Box Models

Image classification is a very popular machine learning domain in which deep convolutional neural networks have mainly emerged on such applications. These networks manage to achieve remarkable performance in terms of prediction accuracy but they are considered as black box models since they lack the ability to interpret their inner working mechanism and explain the main reasoning of their predictions. There is a variety of real world tasks, such as medical applications, in which interpretability and explainability play a significant role. Making decisions on critical issues such as cancer prediction utilizing black box models in order to achieve high prediction accuracy but without provision for any sort of explanation for its prediction, accuracy cannot be considered as sufficient and ethnically acceptable. Reasoning and explanation is essential in order to trust these models and support such critical predictions. Nevertheless, the definition and the validation of the quality of a prediction model’s explanation can be considered in general extremely subjective and unclear. In this work, an accurate and interpretable machine learning framework is proposed, for image classification problems able to make high quality explanations. For this task, it is developed a feature extraction and explanation extraction framework, proposing also three basic general conditions which validate the quality of any model’s prediction explanation for any application domain. The feature extraction framework will extract and create transparent and meaningful high level features for images, while the explanation extraction framework will be responsible for creating good explanations relying on these extracted features and the prediction model’s inner function with respect to the proposed conditions. As a case study application, brain tumor magnetic resonance images were utilized for predicting glioma cancer. Our results demonstrate the efficiency of the proposed model since it managed to achieve sufficient prediction accuracy being also interpretable and explainable in simple human terms.

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