scholarly journals Stress Recognition Using Facial Landmarks and Cnn (Alexnet)

2021 ◽  
Vol 2089 (1) ◽  
pp. 012039
Author(s):  
P Ramesh Naidu ◽  
S Pruthvi Sagar ◽  
K Praveen ◽  
K Kiran ◽  
K Khalandar
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Head Movements ◽  
Psychological Disorder ◽  
Cognitive Stress ◽  
Facial Landmarks ◽  
Non Invasive ◽  
Visual Technology ◽  
Machine Learning Model ◽  
Stress Recognition

Abstract Stress is a psychological disorder that affects every aspect of life and diminishes the quality of sleep. The strategy presented in this paper for detecting cognitive stress levels using facial landmarks is successful. The major goal of this system was to employ visual technology to detect stress using a machine learning methodology. The novelty of this work lies in the fact that a stress detection system should be as non-invasive as possible for the user. The user tension and these evidences are modelled using machine learning. The computer vision techniques we utilized to extract visual evidences, the machine learning model we used to forecast stress and related parameters, and the active sensing strategy we used to collect the most valuable evidences for efficient stress inference are all discussed. Our findings show that the stress level identified by our method is accurate is consistent with what psychological theories predict. This presents a stress recognition approach based on facial photos and landmarks utilizing AlexNet architecture in this research. It is vital to have a gadget that can collect the appropriate data. The use of a biological signal or a thermal image to identify stress is currently being investigated. To address this limitation, we devised an algorithm that can detect stress in photos taken with a standard camera. We have created DNN that uses facial positions points as input to take advantage of the fact that when a person is worried their eye, mouth, and head movements differ from what they are used to. The suggested algorithm senses stress more efficiently, according to experimental data.

scholarly journals A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 219 ◽  
Author(s):  
Sweta Bhattacharya ◽  
Siva Rama Krishnan S ◽  
Praveen Kumar Reddy Maddikunta ◽  
Rajesh Kaluri ◽  
Saurabh Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Comprehensive Evaluation ◽  
Detection System ◽  
Human Life ◽  
Principal Component ◽  
Cyber Attacks ◽  
Classification Model ◽  
Learning Approaches ◽  
Machine Learning Model

The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

scholarly journals Tahapan Desain dan Implementasi Model Machine Learning untuk Sistem Tertanam

2020 ◽  
Vol 12 (2) ◽  
pp. 79-85
Author(s):  
Aminuddin Rizal
Keyword(s):  
Machine Learning ◽  
Embedded System ◽  
Support Vector ◽  
Recognition Experiment ◽  
Machine Learning Model ◽  
Hoeffding Tree ◽  
Simple Logistic ◽  
Stress Recognition ◽  
Simple Stress ◽  
Machine Learning Tool

machine learning and edge computing currently becomes popular technology used in any discipline. Flexibility and adapt to the problem are the main advantages of its technology. In this paper, we explain step-by-step way to make a lightweight machine learning model especially intended for embedded system application. We use open source machine learning tool called as Weka to design the model. Moreover, we performed a simple stress recognition experiment to make our own dataset for evaluation. We evaluate algorithm complexity and accuracy for different well-known classifier such as support vector machine, simple logistic and hoeffding tree.

scholarly journals Reducing U2R and R2L category false negative rates with support vector machines

2014 ◽  
Vol 11 (1) ◽  
pp. 175-188 ◽  
Author(s):  
Nemanja Macek ◽  
Milan Milosavljevic
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
False Negative ◽  
False Negative Rate ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Negative Rate ◽  
Machine Learning Model ◽  
Vector Machines ◽  
Feature Values

The KDD Cup '99 is commonly used dataset for training and testing IDS machine learning algorithms. Some of the major downsides of the dataset are the distribution and the proportions of U2R and R2L instances, which represent the most dangerous attack types, as well as the existence of R2L attack instances identical to normal traffic. This enforces minor category detection complexity and causes problems while building a machine learning model capable of detecting these attacks with sufficiently low false negative rate. This paper presents a new support vector machine based intrusion detection system that classifies unknown data instances according both to the feature values and weight factors that represent importance of features towards the classification. Increased detection rate and significantly decreased false negative rate for U2R and R2L categories, that have a very few instances in the training set, have been empirically proven.

scholarly journals Machine Learning Model for Credit Card Fraud Detection- A Comparative Analysis

2021 ◽  
Author(s):  
Pratyush Sharma ◽  
Souradeep Banerjee ◽  
Devyanshi Tiwari ◽  
Jagdish Chandra Patni
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Credit Card ◽  
Detection System ◽  
Fraud Detection ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Machine Learning Model ◽  
Artificial Neural Network Ann

In today's world, we are on an express train to a cashless society which has led to a tremendous escalation in the use of credit card transactions. But the flipside of this is that fraudulent activities are on the increase; therefore, implementation of a methodical fraud detection system is indispensable to cardholders as well as the card-issuing banks. In this paper, we are going to use different machine learning algorithms like random forest, logistic regression, Support Vector Machine (SVM), and Neural Networks to train a machine learning model based on the given dataset and create a comparative study on the accuracy and different measures of the models being achieved using each of these algorithms. Using the comparative analysis on the F_1 score, we will be able to predict which algorithm is best suited to serve our purpose for the same. Our study concluded that Artificial Neural Network (ANN) performed best with an F_1 score of 0.91.

Design of Fault Detection System for Automobile Power Train Using Digital Signal Processing and Soft Computing Techniques

2014 ◽  
Vol 4 (3) ◽  
pp. 50-63
Author(s):  
Karthik V. Shankar ◽  
Kailasnath K ◽  
S. Babu Devasenapati
Keyword(s):  
Machine Learning ◽  
Combustion Engine ◽  
Detection System ◽  
Digital Signal ◽  
Transmission System ◽  
Random Forest Algorithm ◽  
Power Train ◽  
Machine Learning Model ◽  
Soft Computing Techniques ◽  
Catastrophic Failures

The increasing dependence of internal combustion engine in multitudes of application has mandated a detailed study on most of its subsystems. This paper focuses on predictive maintenance using machine learning based models. The transmission system of any power pace is often challenged due to sudden variation in applied load. Any fault in the transmission system could lead to the catastrophic failures hence need for this work. This paper deals with the identification of various fault conditions that happen in a transmission system using vibration signals acquired by an accelerometer. The acquired signals are processed to extract the statistical and spectral features. These features are used to build a machine learning model using decision tree or Random forest algorithm. The best combination of features and algorithm is evaluated and the results are presented.

scholarly journals A Novel Non-Invasive Estimation of Respiration Rate from Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Md Nazmul Islam Shuzan ◽  
Moajjem Hossain Chowdhury ◽  
Md Shafayet Hossain ◽  
Muhammad E.H. Chowdhurya ◽  
Mamun Bin Ibne Reaz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Respiration Rate ◽  
Learning Model ◽  
Non Invasive ◽  
Machine Learning Model

scholarly journals Non-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modeling Approach

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3335 ◽  
Author(s):  
Sigfredo Fuentes ◽  
Eden Jane Tongson ◽  
Roberta De Bei ◽  
Claudia Gonzalez Viejo ◽  
Renata Ristic ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Management Practices ◽  
Near Infrared ◽  
Detection System ◽  
Screening Tools ◽  
Unmanned Aerial Systems ◽  
Non Invasive ◽  
Aerial Systems ◽  
Non Destructive

Bushfires are becoming more frequent and intensive due to changing climate. Those that occur close to vineyards can cause smoke contamination of grapevines and grapes, which can affect wines, producing smoke-taint. At present, there are no available practical in-field tools available for detection of smoke contamination or taint in berries. This research proposes a non-invasive/in-field detection system for smoke contamination in grapevine canopies based on predictable changes in stomatal conductance patterns based on infrared thermal image analysis and machine learning modeling based on pattern recognition. A second model was also proposed to quantify levels of smoke-taint related compounds as targets in berries and wines using near-infrared spectroscopy (NIR) as inputs for machine learning fitting modeling. Results showed that the pattern recognition model to detect smoke contamination from canopies had 96% accuracy. The second model to predict smoke taint compounds in berries and wine fit the NIR data with a correlation coefficient (R) of 0.97 and with no indication of overfitting. These methods can offer grape growers quick, affordable, accurate, non-destructive in-field screening tools to assist in vineyard management practices to minimize smoke taint in wines with in-field applications using smartphones and unmanned aerial systems (UAS).

scholarly journals An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2653 ◽  
Author(s):  
William Taylor ◽  
Syed Aziz Shah ◽  
Kia Dashtipour ◽  
Adnan Zahid ◽  
Qammer H. Abbasi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Recognition System ◽  
Learning Model ◽  
Wearable Devices ◽  
Human Motion ◽  
Direct Care ◽  
Non Invasive ◽  
Human Motion Detection ◽  
Machine Learning Model

Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.

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