scholarly journals Real Time Face Mask Detection using Google Cloud ML and Flutter

2022 ◽  
Vol 2161 (1) ◽  
pp. 012020
Author(s):  
Sohit Kummar ◽  
Asutosh Mohanty ◽  
Jyotsna ◽  
Sudeshna Chakraborty
Keyword(s):  
Machine Learning ◽  
Cloud Service ◽  
Face Mask ◽  
Data Set ◽  
Fine Grained ◽  
Challenging Tasks ◽  
The Face ◽  
Automated Machine Learning ◽  
Precautionary Measures ◽  
Almost All

Abstract Coronavirus (Covid-19) pandemic has impacted the whole world and has forced health emergencies internationally. The contact of this pandemic has been fallen over almost all the development sectors. A lot of precautionary measures have been taken to control the Covid-19 spread, where wearing a face mask is an essential precaution. Wearing a face mask correctly has been essential in controlling the Covid-19 transmission. Moreover, this research aims to detect the face mask with fine-grained wearing states: face with the correct mask and face without mask. Our work has two challenging tasks due to two main reasons firstly the presence of augmented data set available in the online market and the training of large datasets. This paper represents a mobile application for face mask detection. The fully automated Machine Learning Cloud service known as Google Cloud ML API is used for training the model in TensorFlow file format. This paper highlights the efficiency of the ML model. Additionally, this paper examines the advancement of the cloud technology used for machine learning over the traditional coding methods.

scholarly journals Use of Artificial Intelligence for Face Detection with Face Mask in Real Time to Control the Entrance to an Entity

2021 ◽  
Vol 11 (11) ◽  
pp. 68-75
Author(s):  
Enrique Lee Huamaní ◽  
◽  
Lilian Ocares Cunyarachi
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Face Detection ◽  
Face Mask ◽  
Economic Activities ◽  
Facial Mask ◽  
The Face ◽  
The Individual ◽  
A Company ◽  
Selection Of

Due to the pandemic caused by Covid-19, daily life has changed significantly. For this reason, biosecurity measures have been implemented to prevent the spread of the virus as an effective way to reactivate economic activities. In this sense, the present paper focuses on real-time face detection as a measure of control at the entrance to an entity, thus avoiding the spread of the virus while recognizing the identity of workers despite the use of masks and thus reducing the risk of entry of individuals outside the organization. Therefore, the objective is to contribute to the security of a company through the application of machine learning methodology. The selection of methodology is justified due to the adaptation of the same according to the interests of this project. Consequently, algorithms were used in a progressive manner, obtaining as a result the control system that was intended, since each particularity of the face of the individual was recognized in relation to its corresponding identification. Finally, the results of this article benefit the security of organizations regardless of their field or sector. Keywords— Control, Detection, Facial Recognition, Facial Mask, Face recognition, Machine learning.

scholarly journals Analysis of EEG signals using Machine Learning for the Detection and Diagnosis of Epilepsy

2020 ◽  
Vol 10 (1) ◽  
pp. 89-93
Keyword(s):  
Machine Learning ◽  
Epileptic Seizures ◽  
Severe Damage ◽  
Tree Model ◽  
Data Set ◽  
Feature Extraction Method ◽  
Precautionary Measures ◽  
Detection And Diagnosis ◽  
Electroencephalogram Eeg ◽  
Mean Variance

Electroencephalogram (EEG) is one of the most commonly used tools for epilepsy detection. In this paper we have presented two methods for the diagnosis of epilepsy using machine learning techniques.EEG waveforms have five different kinds of frequency bands. Out of which only two namely theta and gamma bands carry epileptic seizure information. Our model determines the statistical features like mean, variance, maximum, minimum, kurtosis, and skewness from the raw data set. This reduces the mathematical complexities and time consumption of the feature extraction method. It then uses a Logistic regression model and decision tree model to classify whether a person is epileptic or not. After the implementation of the machine learning models, parameters like accuracy, sensitivity, and recall have been found. The results for the same are analyzed in detail in this paper. Epileptic seizures cause severe damage to the brain which affects the health of a person. Our key objective from this paper is to help in the early prediction and detection of epilepsy so that preventive interventions can be provided and precautionary measures are taken to prevent the patient from suffering any severe damage

scholarly journals Real Time Detection and Identification of Human Emotions through Live Streaming

2019 ◽  
Vol 8 (4) ◽  
pp. 12842-12845
Keyword(s):  
Skin Color ◽  
Communication Complexity ◽  
Opinion Mining ◽  
Training Data ◽  
Live Streaming ◽  
Data Set ◽  
Detection And Identification ◽  
Challenging Tasks ◽  
The Face ◽  
Video Input

Automating the analysis of facial expressions of individuals is one of the challenging tasks in opinion mining. In this work, the proposed technique for identifying the face of an individual and the emotions, if present from a live camera. Expression detection is one of the sub-areas of computer visions which is capable of finding a person from a digital image and identify the facial expression which are the key factors of nonverbal communication. Complexity involves mainly in two cases viz., 1)if more than one emotions coexist on a face. 2) expressing same emotion between individuals is not exactly same. Our aim was to make the processes automatic by identify the expressions of people in a live video. In this system OpenCV library containing face recognizer module for detecting the face and for training the model. It was able to identify the seven different expressions with 75-85% accuracy. The expressions identified are happy, sadness, disgust, fear, anger, surprise and neutral. The this an image frame from is captured from the video, locate the face in it and then test it against the training data for predicting the emotion and update the result. This process is continued till the video input exists. On top of this the data set for training should be in such a way that , it prediction should be independent of age, gender, skin color orientation of the human face in the video and also the lamination around the subject of reference

A rule-based automated machine learning approach in the evaluation of recommender engine

2020 ◽  
Vol 27 (10) ◽  
pp. 2721-2757
Author(s):  
Rajat Kumar Behera ◽  
Pradip Kumar Bala ◽  
Rashmi Jain
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Small Data ◽  
Data Set ◽  
Rule Based ◽  
Content Type ◽  
Online Evaluation ◽  
Phase Study ◽  
Best Fitting ◽  
Automated Machine Learning

PurposeAny business that opts to adopt a recommender engine (RE) for various potential benefits must choose from the candidate solutions, by matching to the task of interest and domain. The purpose of this paper is to choose RE that fits best from a set of candidate solutions using rule-based automated machine learning (ML) approach. The objective is to draw trustworthy conclusion, which results in brand building, and establishing a reliable relation with customers and undeniably to grow the business.Design/methodology/approachAn experimental quantitative research method was conducted in which the ML model was evaluated with diversified performance metrics and five RE algorithms by combining offline evaluation on historical and simulated movie data set, and the online evaluation on business-alike near-real-time data set to uncover the best-fitting RE.FindingsThe rule-based automated evaluation of RE has changed the testing landscape, with the removal of longer duration of manual testing and not being comprehensive. It leads to minimal manual effort with high-quality results and can possibly bring a new revolution in the testing practice to start a service line “Machine Learning Testing as a service” (MLTaaS) and the possibility of integrating with DevOps that can specifically help agile team to ship a fail-safe RE evaluation product targeting SaaS (software as a service) or cloud deployment.Research limitations/implicationsA small data set was considered for A/B phase study and was captured for ten movies from three theaters operating in a single location in India, and simulation phase study was captured for two movies from three theaters operating from the same location in India. The research was limited to Bollywood and Ollywood movies for A/B phase, and Ollywood movies for simulation phase.Practical implicationsThe best-fitting RE facilitates the business to make personalized recommendations, long-term customer loyalty forecasting, predicting the company's future performance, introducing customers to new products/services and shaping customer's future preferences and behaviors.Originality/valueThe proposed rule-based ML approach named “2-stage locking evaluation” is self-learned, automated by design and largely produces time-bound conclusive result and improved decision-making process. It is the first of a kind to examine the business domain and task of interest. In each stage of the evaluation, low-performer REs are excluded which leads to time-optimized and cost-optimized solution. Additionally, the combination of offline and online evaluation methods offer benefits, such as improved quality with self-learning algorithm, faster time to decision-making by significantly reducing manual efforts with end-to-end test coverage, cognitive aiding for early feedback and unattended evaluation and traceability by identifying the missing test metrics coverage.

scholarly journals HOW CAN MACHINE LEARNING HELP THE AUTHORITIES?

2021 ◽  
Vol 2020 (1) ◽  
pp. 370-376
Author(s):  
Samuel Ady Sanjaya ◽  
Suryo Adi Rakhmawan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Action Planning ◽  
Face Mask ◽  
Public Place ◽  
Communication Tool ◽  
Health Crisis ◽  
Surveillance Camera ◽  
Proposed Model ◽  
The Face

Corona Virus Desease (COVID-19) pandemic is causing health crisis in every region in the world, especially in Indonesia. One of the effective methods against the virus is wearing face mask in public place as the regulation made by the authorities. This paper introduces face mask detection that can be used by the authorities to make mitigation, evaluation, prevention, and action planning against COVID-19. On the other hand, this solution can be used as communication tool to evaluate people’s habit on wearing face mask. The face mask recognition in this study is developed with machine learning algorithm through the image classification method: MobileNetv2. The proposed model can be integrated with surveillance camera to impede the Covid-19 transmission by allowing the detection of people who are not wearing face mask. After the training, validation, and testing phase, the model can provide the percentage of people using face mask in some cities with high accuracy. The data produced also have a strong correlation to the vigilance index of COVID-19.

scholarly journals Machine Learning Approach to Select Optimal Task Scheduling Algorithm in Cloud

2021 ◽  
Vol 12 (6) ◽  
pp. 2565-2580
Author(s):  
Chinmai Shetty, Dr. Sarojadevi H, Suraj Prabhu
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Service Provider ◽  
Scheduling Algorithm ◽  
Cloud Service ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Cloud Service Provider ◽  
Incoming Request

The flexibility provided by the cloud service provider at reduced cost popularized the cloud tremendously. The cloud service provider must schedule the incoming requests dynamically. In a cloud environment tasks must be scheduled such that proper resource utilization is achieved. Hence task scheduling plays a significant role in the functionality and performance of cloud computing systems. While there exist many approaches for boosting the task scheduling in the cloud, it is still an unresolved issue. In this proposed framework we attempt to optimize the usage of cloud computing resources by applying machine learning techniques. The new proposed framework dynamically selects the scheduling algorithm for the incoming request rather than arbitrary assigning a task to the scheduling algorithm. The scheduling algorithm is predicted dynamically using a neural network which is the best for the incoming request. The proposed framework considers scheduling parameters namely cost, throughput, makespan and degree of imbalance. The algorithms chosen for scheduling are 1) MET 2) MCT 3) Sufferage 4)Min-min 5) Min-mean 6) Min-var. The framework includes 4 neural networks to predict the best algorithm for each scheduling parameters considered for optimization. PCA algorithm is used for extracting relevant features from the input data set. The proposed framework shows the scope for the overall system performance by dynamically selecting precise scheduling algorithms for each incoming request from the user. 

scholarly journals AI Enabled Face Mask Detector

2021 ◽  
Vol 9 (VI) ◽  
pp. 2980-2986
Author(s):  
Shivraj Patil
Keyword(s):  
Machine Learning ◽  
Global Health ◽  
Image Classification ◽  
Learning Algorithm ◽  
Face Mask ◽  
Classification Method ◽  
Machine Learning Algorithm ◽  
Health Crisis ◽  
The Face

Covid-19 pandemic is causing a global health crisis. To battle against the virus everyone should wear a face mask. The face mask detector in this study is developed with a machine learning algorithm called MobileNetV2 which is an image classification method. The steps to build the model are collecting the data, pre-processing, split the data, testing the model and implement the model.

scholarly journals Automatic Attendance System with Face Recognition using Machine Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 3642-3646
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Recognition Algorithm ◽  
Machine Learning Techniques ◽  
Project Work ◽  
Data Set ◽  
Second Stage ◽  
Learning Techniques ◽  
The Face ◽  
Face Detection And Recognition

Object and Face detection and recognition is one of the mostly researched area in computer vision. This particular field of work is widely used in mobile phones and laptops for unlocking the system by the user. Recently this field gained importance in the automatic attendance system in schools, colleges and institution. The institutions are moving from biometric based attendance to face recognition based attendance system. In this project work, I have used machine learning techniques to create a complete system of automatic attendance system which can be implemented very easily. There are majorly four steps involved in the system. Firstly, the datasets can be created instantly using webcam and in the second stage the created data set have to be trained and the trainer algorithm will create the trainer.yml document. As a next step, the face recognition algorithm have to be performed in order to recognize the faces of various students and teacher. In the final step, the attendance of the students will be updated in the CSV file or Excel. The proposed work is very much suited for the real time applications like automatic attendance system. HaarCascade is very eff

Accuracy of automated machine learning in classifying retinal pathologies from ultra-widefield pseudocolour fundus images

2021 ◽  
pp. bjophthalmol-2021-319030
Author(s):  
Fares Antaki ◽  
Razek Georges Coussa ◽  
Ghofril Kahwati ◽  
Karim Hammamji ◽  
Mikael Sebag ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Binary Classification ◽  
Image Data ◽  
Fundus Images ◽  
Learning Models ◽  
Data Set ◽  
Discriminative Performance ◽  
Automated Machine Learning ◽  
Good Diagnostic Accuracy

AimsAutomated machine learning (AutoML) is a novel tool in artificial intelligence (AI). This study assessed the discriminative performance of AutoML in differentiating retinal vein occlusion (RVO), retinitis pigmentosa (RP) and retinal detachment (RD) from normal fundi using ultra-widefield (UWF) pseudocolour fundus images.MethodsTwo ophthalmologists without coding experience carried out AutoML model design using a publicly available image data set (2137 labelled images). The data set was reviewed for low-quality and mislabeled images and then uploaded to the Google Cloud AutoML Vision platform for training and testing. We designed multiple binary models to differentiate RVO, RP and RD from normal fundi and compared them to bespoke models obtained from the literature. We then devised a multiclass model to detect RVO, RP and RD. Saliency maps were generated to assess the interpretability of the model.ResultsThe AutoML models demonstrated high diagnostic properties in the binary classification tasks that were generally comparable to bespoke deep-learning models (area under the precision-recall curve (AUPRC) 0.921–1, sensitivity 84.91%–89.77%, specificity 78.72%–100%). The multiclass AutoML model had an AUPRC of 0.876, a sensitivity of 77.93% and a positive predictive value of 82.59%. The per-label sensitivity and specificity, respectively, were normal fundi (91.49%, 86.75%), RVO (83.02%, 92.50%), RP (72.00%, 100%) and RD (79.55%,96.80%).ConclusionAutoML models created by ophthalmologists without coding experience can detect RVO, RP and RD in UWF images with very good diagnostic accuracy. The performance was comparable to bespoke deep-learning models derived by AI experts for RVO and RP but not for RD.

scholarly journals Estimating soil-water characteristic curve based on soil type and best-fitting regressions derived from a simplified method using Aburra Valley dataset

2021 ◽  
Vol 337 ◽  
pp. 02002
Author(s):  
Johnatan Ramos-Rivera ◽  
Daniel Parra-Holguín ◽  
Yamile Valencia-González ◽  
Oscar Echeverri-Ramírez
Keyword(s):  
Machine Learning ◽  
Soil Water ◽  
Soil Mechanics ◽  
Characteristic Curve ◽  
Computer Code ◽  
Data Set ◽  
Soil Water Characteristic Curve ◽  
Fine Grained ◽  
Simplified Method ◽  
Unsaturated Soil Mechanics

In unsaturated soil mechanics, many attempts have been made to estimate the SWCC based on soil texture and grain-size distribution. This paper proposes a simplified method to estimate the soil-water characteristic curve (SWCC) for both coarse and fine-grained soils using SWCC data and machine learning computer code in the Aburra Valley. Fredlund and Xing parameters has been used to estimate the SWCC correlations. Soil samples collected from field survey were subjected to laboratory testing, SWCCs were estimated using filter paper method. Each SWCC data set from Aburra Valley was fitted with Fredlund and Xing curve using multiple regression analysis, correlations were derived for those four parameters based on predictors derived from machine learning. The proposed method gives a good estimation and low residual errors of the SWCC.

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