A Novel Approach for Identification of Tomato Flu using Deep Learning

The study proposes a novel deep learning-based model for early and accurate detection of the Tomato Flu virus, also known as tomato fever, which has recently emerged in children under the age of five in the Indian state of Kerala. The model utilizes a deep learning method to classify skin pictures and check whether a person is suffering from the virus or not, with an accuracy of 100% and a validation loss of 0.2463. Additionally, an API is developed for easy integration into various web/app frameworks. The authors highlight the importance of careful management of rare viral diseases, especially in the context of the ongoing COVID-19 pandemic. © 2023 Bharati Vidyapeeth, New Delhi.

In Real Time Face Mask Detection Alert System using CNN Model

Today's current scenario of the coronavirus pandemic (Covid19), where in the future there will be a need for efficient applications of real-time mask detection. Because, nowadays it is very difficult for doctors to handle patients infected with corona virus. Our major purpose of building a face-mask detection alert system using OpenCV that can detect individual person's if he/she is wearing a face mask or not wearing a face-mask using CCTV Camera, with quite a good accuracy. And also building and training the Convolutional Neural Network (CNN) using keras framework. After that, He / She refused to go to the locations or the regions wherever the officials were strictly asked to wear face-mask. After denying way in to the individual, the officers or the authorized person will receive an email in real time where the photograph of the person can be attached. In away screen panels could be installed at the entrances where the person's denied access can see a pop-up warning message. Where he/she would be advised to wear a face mask before getting access. This type of face mask detection alert system has some applications in schools, colleges, malls, theaters, offices and also other major crowded places or areas where it expects large public gathering. © 2022 IEEE.

Biomedical Image Classification Using Convolutional Neural Networks

Recent research trends in the field image processing have focussed on challenges and few techniques for processing and classification tasks related to it. Image classification aims at classifying images based on several predefined categories. Several research works have been carried out to overcome shortcomings in image classification, nevertheless the output was restricted to the elementary low-level picture. Several deep neural network techniques are employed for image classification such as Convolutional Neural Network, Machine Learning Algorithms like Random Forest, SVM, etc. In this paper, we aim at designing a COVID-19 detection using the CNN model with support of Open-Source software such as Keras, Python, Google Colab, Google Drive, Kaggle, and Visual Studio for aggregate, design, create, train, visualize, and analyze bulk load of data on the cloud after programing a Deep neural network without a need for high-end processing hardware. We have made use of weights to test and analyse the accuracy, visualize and predict the condition of a lung using chest X-Rays at certain accuracy. This will help in identifying the problems of the patients at a faster rate, thus giving an appropriate treatment at an early stage itself to saving one life. © 2023 IEEE.

An Efficient Deep Learning based Approach for the Detection of Brain Tumors

Deep learning has stretched out its roots even more in our daily lives. As a society, we are witnessing small changes in lifestyle such as self-driving cars, Google Assistant, Netflix recommendations, and spam email detection. Similarly, deep learning is also evolving in healthcare, and today many doctors often use it more comfortably. Using deep learning models we can detect severe brain tumors with the help of MRI scans, in fact in the Covid era, deep learning evolved majorly to detect the disease with the help of Lung X-Rays. Magnetic Resonance Imaging (MRI) is used when a person has a brain tumor to detect it. Brain tumors can fall into any category, and MRI scans of these millions of people are needed to determine if they have the disease and if so, which category they belong to. Determining the type of brain tumor can be a rigid task and deep learning models play an important role here. For the proposed deep learning model, we have implemented convolution neural networks (CNN) through which our model has achieved a testing accuracy of 96.5%. Also, along with this, the libraries of Keras and Tensorflow have been explored by the authors in this research. © 2022 IEEE.

Application of AI techniques for Face Mask Detection

Covid-19 is a completely new problem, and we have seen it move to a brand new level. After the 3rd wave of Covid-19 in India and predictions of another wave this year it is a major concern and still many people are not following basic precautionary measures like wearing a mask in public locations this can be solved by our face mask detection program we want to be short a good way to respond to new facts, which they are all around us. Growing a secure environment can be paramount the human to make lifestyles as smooth as ever. Alternatives have to be taken to protect all who go back and to maintain them our loved ones who have no troubles. New era packages are being made each day to satisfy regulations and regulations but, the face mask becomes a new well known used for regular existence, but, to create a more secure surroundings that contributes to public protection, a want to be diagnosed at some stage in date and motion towards people who do not put on masks in public locations or offices. Many sections of the general public appear to simply accept Covid adherence protection gear. A face masks detector is among the most crucial equipment. This software allows one to find out who does not have the desired face masks. Those applications with them current tracking systems and neural network algorithm to see if an individual has put on a mask or not. About this, we'll do discussion in short the synthetic intelligence and its small additives specifically device gaining knowledge of and in-intensity analysing, in-intensity reading frameworks followed with the aid of the usage of simplicity implementation of face masks detection machine. © 2022 IEEE.

Deep Learning based Face Mask Detection system with Automatic Indigation

The COVID-19 pandemic has quickly had an impact on our day-to-day lives, as well as on the movement of goods and people around the world. It has recently been common practice to shield one's face by using a mask. In the not too distant future, many businesses that provide public services will need their clients to correctly wear masks in order for them to receive those services. As a result, the detection of face masks has evolved into an important mission in the service of worldwide society. In this post, a relatively straightforward approach to achieving this goal is presented using basic machine learning tools like TensorFlow, Keras, OpenCV, and Scikit-Learn. The suggested method accurately locates the face inside the image before determining whether or not it is covered by a mask. While doing a surveillance task, it is capable of detecting a mask as well as a moving face. To properly detect the presence of masks without over-fitting, we look into numerous options for optimizing the values of the parameters in the Sequential Convolutional Neural Network model. © 2022 IEEE.

Identifying Face Masks with OpenCv and Deep Learning

During the course of this epidemic, the Corona virus had a significant influence not only regular lives but also on international business. Protecting one's appearance has recently emerged as a widespread fashion trend and can now be considered the norm. In the present day or in the future, a large number of individuals will be obliged to wear masks in order to protect not only themselves but also the people around as well as the surrounding area. Face recognition has emerged as an increasingly vital tool in the fight against global terrorism. As part of this work, we are developing an AI system that will be able to determine whether or not a person is concealing their identity by wearing a mask. It will be of assistance to us in preventing the virus from spreading across the environment. In order to construct this work, we require the assistance of Machine Learning (ML), deep learning (DL), and Neural Network (NN), all of which will assist us in realizing the purpose of this work. We needed jupyter notebook in order to complete this work, and we also needed to install numpy, opencv, tensorflow, and numpy as well as a learning tool. This strategy will assist us in identifying the individual who is concealing their identity by wearing a mask in the imageand in real life picture. Additionally, it is able to recognize and distinguish a moving mask or face. © 2022 IEEE.

Face Mask Detection Using Convolution Neural Network

Face recognition in the industry now is playing an important role in each sector. Each person has different type of features and face;therefore, each identity is unidentical. In this COVID outbreak, a major crisis has occurred due to which preventions are to be made. One such prevention is use of a face mask which is very much important. Nowadays, various firms and organizations are using facial recognition systems for their own general purpose. We all know that it has now been a crucial task to wear a mask every time, when we go somewhere. But as we know it is not possible to keep track of who wears a mask and who does not. We make the use of AI in our daily life. We achieve this with the help of a neural network system, which we train so that it can further describe people's features. Even though the original dataset was limited, the Convolutional Neural Network (CNN) model achieved exceptional accuracy utilizing the deep learning technique. With the use of a face mask detection dataset that contains both with and without face mask photographs, we are able to recognize faces in real-time from a live webcam stream using OpenCV. We will develop a COVID-19 face mask detection system using our dataset, along with Python, OpenCV, Tensor Flow, and Keras. © 2022 IEEE.

Mask Recognition through Deep Learning

A major part of computer vision is formed by Object detection. Most of the such tasks are done with efficient object detection. This paper aims to incorporate techniques for facial mask detection to achieve an accurate and efficient mask detection algorithm. The goal is to examine various deep learning algorithms to perform mask detection in this era of Covid. This paper aims on building an application based on facial mask recognition using different deep learning algorithms and compare the results to find out the most accurate algorithm. © 2022 IEEE.

Deep Network Analysis and Prediction of Ophthalmic Disorders

Everybody, around the globe, is aware that their kids, relatives, and family are suffering from the pandemic COVID-19. S everal people are still facing post-COVID-19 issues. During COVID-19's second wave, mucormycosis, sometimes known as "black fungus, " plagued people, especially those who had previously been infected with the virus. The clinical manifestations of mucormycosis are quite varied, the disease affects the skin, subcutaneous fatty tissue, and visceral organs such as the eyes and brain. This paper surveys the Mucormycosis-affected eye diseases due to post-COVID-19 complications and leverages the Machine learning model to differentiate it from other eye diseases. COVID-19-associated Mucormycosis carries a very high mortality rate and timely detection that can assist people in starting therapy at an early stage of the disease, increasing their chances of recovery. Though it was evaluated for a specific disease (COVID-19-associated mucormycosis) we ended up developing a framework that can detect other eye diseases. Thus, the goal of this research is to distinguish Mucormycosis from other eye diseases such as Bulging Eyes, Cataracts, Crossed Eyes, Glaucoma, and Uveitis. This study implies Deep learning techniques with a Convolutional Neural Network based on the TensorFlow and Keras model to detect and make use of computer vision to accurately classify eye diseases. We achieved a precision of 70% in this study by developing a webpage using the trained model for an eye diseases evaluation. © 2022 IEEE

Real-time Face mask Detection Using Deep Learning and MobileNet V2

COVID-19 has affected the whole world severely. Lockdowns and quarantines are imposed all over the world to prevent its spread. Hand sanitizers and face masks were made compulsory for individuals to apply for safety of their own and their society. This project will check the presence or the absence of masks on the face of a person. There could be more than a single person in the input provided, and the input could vary from images to GIFs to Livestreams. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Convolution Neural Network Based Covid -19 Face Mask Detection and Recognition

The coronavirus is devastating global health. Ac- cording to WHO guidelines, wearing a mask and keeping a 6-foot distance between people can help to prevent the spread of COVID 19. As a condition of the international COVID-19 outbreak, protective equipment, the most vital of which is a face mask, is required. Wearing a face mask in public is a good way to be safe. This project seeks to develop a real-time, GUI- based face detection and identification system using machine learning. Tensor Flow, Keras, Scikit-learn, and Open CV are used to develop a Convolutional Neural Network (CNN) model to make the technique as accurate as possible. Principal Component Analysis (PCA) and the HAAR Cascade Algorithm are two components of the proposed methodology. If the person in front of the camera is wearing a mask, the classification algorithm's result will be displayed by a green rectangle overlaid around the region of the face;otherwise, it will be represented by a red rectangle superimposed around the area of the face. © 2022 IEEE.

Face Mask Detection using an Automated Hybrid Deep Learning Method in the COVID Scenario

Currently, in light of the health catastrophe due to the COVID-19 which has been unfolding all over the world. Wearing a defensive mask has ended up a substitute normal. Face recognition technology is most commonly implemented for surveillance and other applications. Traditional machine learning classifiers as well as deep transfer learning classifiers have been used to accomplish the face mask detection mechanism. In this paper, two hybrid deep learning models MobileNetV2-SVM and MobilNetV2-KNN has been proposed for the task of face mask detection. The models involve two processes: feature extraction and classification. For initialization, the MobileNetV2 pre-trained weights from ImageNet were employed, and during training, data augmentation and resampling were applied. By integrating the model with an SVM classifier and a KNN classifier, the model is further refined, creating hybrid models that are effective in terms of processing. The Kaggle dataset of 45000 images (22582 images are masked and 23423 images that are unmasked) of the proposed model/system is trained using MobilenetV2 and classified using SVM and K-NN algorithm in different models. Various machine learning frameworks were used like pandas, TensorFlow, Keras and NumPy. The accuracy achieved by the SVM model is 98.17% and 95.22% accuracy are achieved by using the K-NN classifier. © 2022 IEEE.

Application of Machine Learning and Deep Learning in Forecasting Covid Daily Death Counts

Time series modeling and forecasting has fundamental importance to a wide range of applications. While classical time series models dominated the forecasting field for years, their applications have been limited to single time series data at low frequency such as monthly, quarterly or annually. The goal of this paper is to build multi-series time series models to forecast future daily death counts for each county in the state of Pennsylvania. The data used in this paper include JHU daily death counts and confirmed cases and CDC vaccination rates from 1/22/2020 to 1/7/2022 at the county level for Pennsylvania. Both machine learning (Extreme Gradient Boosted Tree XGBoost) and deep learning (Keras Slim Residual Neural Network Regressor, Keras) algorithms were explored and time series modeling related steps such as feature engineering, data partition and project setup are discussed in detail. In addition, four metrics were calculated to evaluate the algorithms' performance. The comparison with a baseline time series model indicated that machine learning and deep learning algorithms did improve forecasting accuracy significantly and Keras has slightly better performance than XGBoost. Finally, the Keras model was utilized to forecast daily death counts for 60 days after 1/7/2022, i.e., 1/8/2022 to 3/8/2022. Based on the model forecasts, daily death counts should gradually ease off by mid-February which has been validated by the subsequent observations. () © 2022 IEEE.

Face Masked and Unmasked Humans Detection and Tracking in Video Surveillance

Due to the spread of COVID-19, people wearing face masks became a regular occurrence worldwide. Moreover, there are nations where covering one's face is done for religious or cultural reasons, or even wear face masks for convenience. However, current face detection and tracking systems are hindered by face masks as the full facial features are no longer visible and therefore became less effective. In this paper, it is proposed to improve current face detection and long-term tracking technology by extracting the facial features of the top regions of the face, taking into account the eye, eyebrow, and forehead. The methodology contains two models, the face detector and the long-term object tracker. The face detection model uses a joint dataset from ISL-UFMD and MaskedFace-Net. The dataset is used to train a Keras sequential model. The object detection model uses pre-trained YOLOv4 weights and DeepSORT to identify people and uses the tracking-by-detection method to perform long-term tracking throughout the surveillance video. The final face detection model results show a testing accuracy of 93.33% and a loss of 26.92%, which are up to par and comparable with other state-of-the-art models. © 2022 IEEE.

Intelligent Face Mask and Body Temperature Detection System using Machine Learning Algorithm

Face mask and body temperature detection is necessary for current pandemic period. Detecting face mask and body temperature helps in decreasing or to avoid spreading of COVID cases especially in crowded areas. The main purpose of face mask recognition and temperature prediction system is to find whether a person is wearing a mask or not and to check the body temperature. With the help of deep neural network based Convolution Neural Network algorithm, face mask has been recognized. For body temperature, LM35 temperature sensor is used. This system undergoes data pre-processing, training, detecting face mask and temperature. By using MobileNet V2, Frontcascadexml file, tensor flow and keras software library the face mask is detected. Then, the result is send to the Arduino microcontroller and displays that the face mask is detected or not by using LED. If the mask is not weared by the person, buzzer will be alarmed. Similar procedure was carried out for monitoring the temperature of a person using LM35 temperature sensor. The main advantages of MobileNet V2 are higher performance, lesser network size and minimum number of parameter are required. © 2022 IEEE.

Face Mask Detection and Recognition System

As today a disease called COVID-19 is causing health crisis and deaths, it became most essential to wear a mask for protecting ourselves from Corona virus. Even in public areas, where is more rush we should wear mask as no virus can spread from person to person if any one of person from public is Corona positive. This paper introduces face mask detection that can be used by the authorities to make mitigation, evaluation, prevention, and action planning against COVID19. So basically in this project we are going to use Python, Keras, OpenCV alongwith MobileNet for this Face Mask Detection System. This includes some steps like data preprocessing, training and testing the model, run and view the accuracy and applying model in the camera. The inputs has provided here are 1000+ images of people with mask and without mask. First the data get processed and then by checking features of each image it will train all the models and the persons with and without mask get separated to two categories: with mask and without mask. If person is wearing mask with 90 or more percent of accuracy, then he will get added to with mask category and person not wearing mask get added to without mask category, so that we can permit with mask person to public areas. © 2022 IEEE.

Leveraging Convolutional Neural Networks for Face Mask Detection

COVID-19 has made face masks an imperative whenever an individual is going out in public. However, many people are remiss in fulfilling their duty to society. They are deviating from the lockdown norms and violating the regulatory measures set by the government. Such a situation only proliferates the spread of COVID-19 and makes it difficult to control it. In this paper, we use Convolutional Neural Networks (CNNs) to detect whether a person is wearing a face mask. This research uses TensorFlow and Keras to build a CNN which detects face masks with an accuracy of over 98% within 10 epochs. This algorithm will be a boon in places like malls or public areas where automated doors can be shut tight if the prospect trying to enter the store is not wearing a mask. Overall, this paper will help create products that can be used to safely break the COVID-19 chain. © 2022 IEEE.

Real-time Face mask Detection Using Deep Learning and MobileNet V2

COVID-19 has affected the whole world severely. Lockdowns and quarantines are imposed all over the world to prevent its spread. Hand sanitizers and face masks were made compulsory for individuals to apply for safety of their own and their society. This project will check the presence or the absence of masks on the face of a person. There could be more than a single person in the input provided, and the input could vary from images to GIFs to Livestreams. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Face-Mask Recognition and Detection Using Deep Learning

A methodical approach to curb the spread of this menacing disease, COVID-19 must be taken. 'CO' answer for corona, 'VI' denote virus, and 'D' represent disease. The absence of highly effective medicines and scarcity of vaccination make this disease more lethal and vicious;this makes it important for us to find a provisional yet efficacious way to cushion ourselves and that one love. Wearing masks can act like that cushion, it's truly a camouflage, acting as a Non-Pharmaceutical Intervention (NPI) proceeding that could be easily implemented without much capital investment. This thesis evaluates an efficient way of face mask detection that can be used by private or government authorities as a tool against COVID-19. This research aims to build a light weighted and user-friendly model that can be easily used in static or real-time face mask detection. This study of face mask detection is made possible using Deep Learning, Convolutional neural network algorithm, and MobileNetV2, a python open-source image-processing and classification model. Steps involved in designing and implementing the model are collecting and accessing the dataset, data processing and encoding, testing, and training data, accuracy prediction, and model implementation in a real-time project that can instantly detect and provide the desired output. The model can make 98% accurate production in real-time, distinguishing between individuals with a mask or without ma masks. Our proposed face mask detection model outperforms existing models in terms of accuracy and easy usability. © 2022 IEEE.