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.

Implications of the Improvement in Atmospheric Fine Particles: A Case Study of COVID-19 Pandemic in Northern Taiwan

The outbreak of COVID-19 pandemic in northern Taiwan led to the implementation of Level 3 alert measures during 2021 and thereby impacted the air quality significantly, which provided an unprecedented opportunity to better understand the control strategies on air pollutants in the future. This study investigated the variations in sources, chemical characteristics and human health risks of PM2.5 comprehensively. The PM2.5 mass concentrations decreased from pre-alert to Level 3 alert by 49.4%, and the inorganic ions, i.e., NH4+, NO3- and SO42-, dropped even more by 71%, 90% and 52%, respectively. Nonetheless, organic matter (OM) and elemental carbon (EC) simply decreased by 36% and 13%, which caused the chemical composition of PM2.5 to change so that the carbonaceous matter in PM2.5 dominated instead of the inorganic ions. Correlation-based hierarchical clustering analysis further showed that PM2.5 was clustered with carbonaceous matter during the Level 3 alert, while that clustered with inorganic ions during both pre-alert and post-alert periods. Moreover, 6 sources of PM2.5 were identified by positive matrix factorization (PMF), in which secondary nitrate (i.e., aging traffic aerosols) exhibited the most significant decrease and yet primary traffic-related emissions, dominated by carbonaceous matter, changed insignificantly. This implied that secondary traffic-related aerosols could be easily controlled when traffic volume declined, while primary traffic source needs more efforts in the future, especially for the reduction of carbonaceous matter. Therefore, cleaner energy for vehicles is still needed. Assessments of both carcinogenic risk and non-carcinogenic risk induced by the trace elements in PM2.5 showed insignificant decrease, which can be attributed to the factories that did not shut down during Level 3 alert. This study serves as a metric to underpin the mitigation strategies of air pollution in the future and highlights the importance of carbonaceous matter for the reduction in PM2.5.

Readiness of Sub-Districts in Palu City, Central Sulawesi Province, Indonesia, to Face the Covid-19 Pandemic

Indonesia confirmed its first case of COVID-19 on 2 March 2020. Several provinces in Indonesia, including Central Sulawesi, experienced local transmission of the virus. This study aimed to explore the readiness and understanding of the sub-district government in handling COVID-19. This study was conducted from April to June 2020 using a cross-sectional design. Interviews were performed by distributing a closed questionnaire to the heads or secretaries of sub-districts in Palu City. The parameters of this study were the COVID-19 alert village indicators issued by the Ministry of Home Affairs. The results show that all sub-districts in Palu City have formed the COVID-19 Alert Task Force, which socialized clean and healthy living as well as sterilized public and social facilities. Forty-five (97.82% ) sub-districts had provided information about the nearest public health centers (Puskesmas) or clinics where residents receive medical treatment when getting sick. Meanwhile, 39 (84.8%) sub-districts had created and activated WhatsApp groups to handle COVID-19. In general, sub-districts in Palu City have been ready to face the COVID-1 9 pandemic, as they have carried out COVID-19 prevention and handling activities following the guidelines.

Remotely Controlled Real-Time Health Surveillance System of COVID-19 Patients

This work focuses on the development of a portable physiological monitoring framework that can continuously monitor the patient's heartbeat, oxygen levels, temperature, ECG measurement, blood pressure, and other fundamental patient's data. As a result of this, the workload and the chances of being infected by COVID-19 of the health workers will be reduced and an efficient patient monitoring system can be maintained. In this paper, an IoT based continuous monitoring system has been developed to monitor all COVID-19 patient conditions and store patient data in the cloud server using Wi-Fi Module-based remote communication. In this monitoring system, data stored on IoT platform can be accessed by an authorized individual and ailments can be examined by the doctors from a distance based on the values obtained. If a patient's physical condition deteriorates, the doctor will immediately receive the emergency alert notification. This model proposed in this research work would be extremely important in dealing with the Corona epidemic around the world. © 2022 IEEE.

Smart Embedded Framework of Real-Time Pollution Monitoring and Alert System

The sustainability and progress of humanity depend on a clean, pollution-free environment, which is essential for good health and hygiene. Huge indoor auditorium does not have proper ventilation for air flow so when the auditorium is crowded the carbon di-oxide is emitted and it stays there for many days this may be a chance to spreading of COVID-19 and other infectious diseases. Without proper ventilation virus may present in the indoor auditorium. In the proposed system, emissions are detected by air, noise, and dust sensors. If the signal limit is exceeded, a warning is given to the authorities via an Android application and WiFi, and data is stored in cloud networks. In this active system, CO2 sensor, noise sensor, dust sensor, Microcontroller and an exhaust fan are used. This ESP-32 based system is developed in Arduino Integrated Development Environment (Aurdino IDE) to monitor air, dust and noise pollution in an indoor auditorium to prevent unwanted health problems related to noise and dust. More importantly, using IoT Android Application is developed in Embedded C, which continuously records the variation in levels of 3 parameters mentioned above in cloud and display in Android screen. Also, it sends an alert message to the users if the level of parameters exceeds the minimum and maximum threshold values with more accuracy and sensitivity. Accuracy and sensitivity of this products are noted which is very high for various input values. © 2022 IEEE.

A Comprehensive Alert System Based on Social Distancing for Cautioning People Amidst the COVID-19 Pandemic Using Deep Neural Network Models

The World Health Organization (WHO) has suggested a successful social distancing strategy for reducing the COVID-19 virus spread in public places. All governments and national health bodies have mandated a 2-m physical distance between malls, schools, and congested areas. The existing algorithms proposed and developed for object detection are Simple Online and Real-time Tracking (SORT) and Convolutional Neural Networks (CNN). The YOLOv3 algorithm is used because YOLOv3 is an efficient and powerful real-time object detection algorithm in comparison with several other object detection algorithms. Video surveillance cameras are being used to implement this system. A model will be trained against the most comprehensive datasets, such as the COCO datasets, for this purpose. As a result, high-risk zones, or areas where virus spread is most likely, are identified. This may support authorities in enhancing the setup of a public space according to the precautionary measures to reduce hazardous zones. The developed framework is a comprehensive and precise solution for object detection that can be used in a variety of fields such as autonomous vehicles and human action recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Covid-19 crowd detection and alert system using image processing

In this paper, we aim to help in identifying the people that are violating social distancing norms set by the government (necessary during the COVID-19 pandemic in public places), by providing an efficient real-time deep learning-based framework to automate the process of monitoring the social distancing via object detection and tracking approaches. Our system is divided into two subsystems: one that deals with crowd detection and control, and the other that sends information to the police authorities. Our system technologies, including as IoT, image processing, web cams, BLE, OpenCV, and Cloud, are being considered for inclusion in the proposed framework. The image processing is divided into two sections, the first of which is the extraction of frames from real-time movies, and the second of which is the processing of the frame to determine the number of individuals in the crowd. Even in a crowd, dissemination may be restricted if people adhere to social distancing standards. As a result, the image processing model primarily targets the number of people who do not adhere to social distancing norms and stand too close together. © 2023 IEEE.

The acceptability of overdose alert and response technologies: introducing the TPOM-ODART framework.

BACKGROUND: Opioids were implicated in approximately 88,000 fatal overdoses (OD) globally. However, in principle all opioid OD are reversible with the timely administration of naloxone hydrochloride. Despite the widespread availability of naloxone among people who use opioids (PWUO), many who suffer fatal OD use alone, without others present to administer the reversal agent. Recognising this key aspect of the challenge calls for innovations, a number of technological approaches have emerged which aim to connect OD victims with naloxone. However, the acceptability of OD response technologies to PWUO is of key concern. METHODS: Drawing on the Technology People Organisations Macroenvironment (TPOM) framework, this study sought to integrate acceptability-related findings in this space with primary research data from PWUO, affected family members and service providers to understand the factors involved in harm reduction technology acceptability. A qualitative study using a focus group methodology was conducted. The participant groups were people with lived experience of problem opioid use, affected family members and service providers. Data analysis followed a multi-stage approach to thematic analysis and utilised both inductive and deductive methods. RESULTS: Thirty individuals participated in one of six focus groups between November 2021 and September 2022. The analysis generated six major themes, three of which are reported in this article-selected for their close relevance to PWUO and their importance to developers of digital technologies for this group. 'Trust-in technologies, systems and people' was a major theme and was closely linked to data security, privacy and confidentiality. 'Balancing harm reduction, safety and ambivalence' reflects the delicate balance technological solutions must achieve to be acceptable to PWUO. Lastly, 'readiness-a double bind' encapsulates the perception shared across participant groups, that those at the highest risk, may be the least able to engage with interventions. CONCLUSION: Effective digital strategies to prevent fatal OD must be sensitive to the complex relationships between technological, social/human, organisational and wider macroenvironmental factors which can enable or impede intervention delivery. Trust, readiness and performance are central to technology acceptability for PWUO. An augmented TPOM was developed (the TPOM-ODART).

Pandemic pangs and fangs: Romania's public safety and civil liberties in the COVID-19 era

This chapter discusses Romania's COVID-19 journey through the lens of the trade-off between health security and civil liberties. It argues that the Romanian Government's response to the pandemic was less than perfect, partly because of the inadequate legal framework on emergency situations, and partly because of political crises and clashes that plagued Romania since the outbreak of the pandemic. The measures - implemented during an initial two-month State of Emergency, followed by an ongoing State of Alert - ranged from mandatory facemask and social distancing, to obligatory quarantine, to restrictions of movement, to total lockdown. In Romania, only laws can limit civil rights and liberties;Emergency Orders, Government Decisions, or Ministerial Orders cannot restrict these freedoms. Romania implemented long-lasting and very restrictive anti-COVID measures, which constrained such human rights and liberties as freedom of association, freedom of movement, right of education, and freedom/right to vote. © 2023 John Wiley & Sons, Inc.

Smart Energy Meter with Cloud Connectivity

The main tools for allowing customers to communicate openly and transparently with the company and other stakeholders are their electricity bills. However, last year due to pandemics, some residents petitioned the Madras high court claiming TANGEDCO's technique to measure power was arbitrary and unreasonable during the lockdown that COVID provoked. This was done amid complaints about excessive electricity bills. When compared to bills from other states, TANGEDCO's white meter card for energy bills is found to provide insufficient information on use and rates, according to a survey by the certain associations. Power bills urgently need to be redesigned to include comprehensive billing details and accurate assessments of electricity consumption from closed homes or homes in restricted zones. [4] We proposed and designed a Smart Home Energy Meter Monitoring System to solve this crisis. It consists of three systems. First system: Customized built energy meter with LCD. [6] Second system: Wi-Fi module with the Microcontroller (ATMega328P) and an alert system. Third system: Database (MySQL database). [12] The quantity of power used by the device is measured by an energy meter user and every two months, the final reading of the power consumption is taken by the micro controller where the electricity bill calculation program has been pre-programmed to give the value of power consumed during the two months and amount to be paid by the user and [4] It will be shown on the Energy Meter's LCD. The micro controller with a built-in Wi-Fi module (ESP8266) will send these displayed data to the service provider's database. [2] An alert system has been added to counteract the hefty usage and electricity bills to create awareness to the consumer about the slab-wise tariffs increase in the per-unit cost data that has been set by TANGEDCO. [10] The alert has been set in a way that the consumer receives a message for every 200 unit usage of power. The third system is a database created using MySQL database to transport the data to the service provider. © 2022 IEEE.

Crowd Control, Planning, and Prediction Using Sentiment Analysis: An Alert System for City Authorities

Modern means of communication, economic crises, and political decisions play imperative roles in reshaping political and administrative systems throughout the world. Twitter, a micro-blogging website, has gained paramount importance in terms of public opinion-sharing. Manual intelligence of law enforcement agencies (i.e., in changing situations) cannot cope in real time. Thus, to address this problem, we built an alert system for government authorities in the province of Punjab, Pakistan. The alert system gathers real-time data from Twitter in English and Roman Urdu about forthcoming gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.). To determine public sentiment regarding upcoming anti-government gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.), the alert system determines the polarity of tweets. Using keywords, the system provides information for future gatherings by extracting the entities like date, time, and location from Twitter data obtained in real time. Our system was trained and tested with different machine learning (ML) algorithms, such as random forest (RF), decision tree (DT), support vector machine (SVM), multinomial naïve Bayes (MNB), and Gaussian naïve Bayes (GNB), along with two vectorization techniques, i.e., term frequency–inverse document frequency (TFIDF) and count vectorization. Moreover, this paper compares the accuracy results of sentiment analysis (SA) of Twitter data by applying supervised machine learning (ML) algorithms. In our research experiment, we used two data sets, i.e., a small data set of 1000 tweets and a large data set of 4000 tweets. Results showed that RF along with count vectorization performed best for the small data set with an accuracy of 82%;with the large data set, MNB along with count vectorization outperformed all other classifiers with an accuracy of 75%. Additionally, language models, e.g., bigram and trigram, were used to generate the word clouds of positive and negative words to visualize the most frequently used words.

Research and Design of an Emergency Supply Assurance Monitoring System in the Post-epidemic Context

At present, the COVID-19 epidemic is still ravaging the world, and the domestic epidemic is still recurring and continues to affect people's life and work. The research and design of an emergency supply assurance monitoring system in response to the epidemic and other emergencies, which provides the competent authorities with monitoring alert and trend data of supply, demand and price of essential goods market, is of great significance to stabilize people's basic essential goods materials. Based on the data of essential goods under epidemic, the system carries out the construction and application of monitoring and warning model and RNN-SARIMA hybrid model. Through the research and design of the system, monitoring and warning of abnormal fluctuations of essential goods and predicting price trends are realized. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Los viajes combinados y medidas contra el covid-19

La declaración del estado de alarma derivada de la pandemia del covid-19 ha supuesto, en el ámbito del contrato de viaje combinado, una sustancial alteración de los elementos del contrato: el objeto del mismo ha devenido imposible y la voluntad de las partes contratantes ha sido revocada, pues un gran número de viajeros no desean proseguir con la ejecución del viaje. A través del presente trabajo reflejaré cuáles son las situaciones ante las que se encuentra el viajero que ha visto frustrada la ejecución de su viaje combinado, así como las herramientas jurídicas y los derechos que le asisten. En este sentido, se describirán las consecuencias jurídicas que derivan del ejercicio del desistimiento, de la declaración de fuerza mayor y la pérdida de interés en el cumplimiento del contrato, los efectos de la imposibilidad sobrevenida y, por último, las concretas medidas adoptadas en virtud de los reales decretos leyes por los que se adoptan medidas complementarias para hacer frente al covid-19.Alternate :The announcement of the state of alert resulted by the covid-19 pandemic implied, inside the scope of package travel agreements, a significant alteration to the contract elements: its object has become imposible to fulfil and the will of the parties has been revoked due to a massive number of travellers who are not interested on continuing with the travel execution. By means of the present essay I will reveal which are the situations the traveller, whose package travel agreement has been frustrated, must face;as well as the legal tools and the rights that belong to the traveller. In this respect, legal consequences derived from the exercise of the right of withdrawal, the declaration of force majeure and loss of interest and, finally, the concrete measures adopted under the royal decree-laws concerning complementary measures to address covid-19 pandemic.

Multi-horizon predictive models for guiding extracorporeal resource allocation in critically ill COVID-19 patients.

OBJECTIVE: Extracorporeal membrane oxygenation (ECMO) resource allocation tools are currently lacking. We developed machine learning (ML) models for predicting COVID-19 patients at risk of receiving ECMO to guide patient triage and resource allocation. MATERIAL AND METHODS: We included COVID-19 patients admitted to intensive care units for >24 h from March 2020 to October 2021, divided into training and testing development and testing-only holdout cohorts. We developed ECMO deployment timely prediction model ForecastECMO using Gradient Boosting Tree (GBT), with pre-ECMO prediction horizons from 0 to 48 h, compared to PaO2/FiO2 ratio, Sequential Organ Failure Assessment score, PREdiction of Survival on ECMO Therapy score, logistic regression, and 30 pre-selected clinical variables GBT Clinical GBT models, with area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics. RESULTS: ECMO prevalence was 2.89% and 1.73% in development and holdout cohorts. ForecastECMO had the best performance in both cohorts. At the 18-h prediction horizon, a potentially clinically actionable pre-ECMO window, ForecastECMO, had the highest AUROC (0.94 and 0.95) and AUPRC (0.54 and 0.37) in development and holdout cohorts in identifying ECMO patients without data 18 h prior to ECMO. DISCUSSION AND CONCLUSIONS: We developed a multi-horizon model, ForecastECMO, with high performance in identifying patients receiving ECMO at various prediction horizons. This model has potential to be used as early alert tool to guide ECMO resource allocation for COVID-19 patients. Future prospective multicenter validation would provide evidence for generalizability and real-world application of such models to improve patient outcomes.

Could Emergency Diseases Surveillance Systems Be Transitioned to Routine Surveillance Systems? A Proposed Transition Strategy for Early Warning, Alert, and Response Network.

In humanitarian emergencies, traditional disease surveillance systems either do not exist to begin with or come under stress due to a huge influx of internal or external migrants. However, cramped camps with an unreliable supply of safe water and weak sanitation systems are the ideal setting for major disease outbreaks of all kinds. The Early Warning, Alert and Response Network (EWARN) has been supported by the WHO since the late 1990s to ensure health system capacity to identify and control risks early before they become major epidemics. These systems have been proven to be an excellent asset in reducing morbidity and mortality in humanitarian crises around the world. However, there is also a global challenge of transitioning them back to a regular or national monitoring system in their respective countries. This article is the result of in-country consultations arranged by the Eastern Mediterranean office of the World Health Organization. In these consultations, the unique local conditions and limitations of different countries were discussed to identify a way forward for transitioning these emergency disease surveillance systems into regular systems. After these discussions, different options were presented which could be further modified according to local needs. As there has not been any documented evidence of a successful transition of any emergency surveillance system, it is difficult to discuss or determine the gold standard for transition. As with any public health program being practiced in the field, local decision-making with some broad guidelines will be the best approach available. This article provides these guidelines and practical steps which could be further modified according to country needs.

COVID Hotspot Alert System using Geo-Fencing Technique

Covid-19 is an extremely communicable disease. It becomes extremely hard to control once it begins to spread. One of the most important and effective steps to break the chain and keep healthy people from getting infected is social isolation/distancing. When an infected person comes into contact with a healthy person, that person becomes infected as well, and the chain reaction continues. To curb this, COVID alert system using geo-fencing is developed. This system uses a GPS module to create a Geo Fence around the infected area and the healthy area. The live/current GPS location/coordinate is compared with the hotspot co-ordinates. The GSM module with Sim800L will send an alert to healthy people when they come into contact with virus-infected areas. The device comes with a GPS, GSM module with Sim800L and an OLED which displays the alert message. The device can be fit into any public or private transport, so that the healthy person will be prevented from entering the hotspot zones unnecessarily, thereby blocking the virus spread. © 2022 IEEE.

Scarcity nationalism during COVID-19: Identifying the impact on trade costs.

During the COVID-19 pandemic, many countries used export and import policy as a tool to expand the availability of scarce critical medical products in the domestic market (scarcity nationalism). This paper assesses the direct and indirect (via trade in intermediates) increases in trade costs of critical medical goods resulting from these uncooperative policies. The results show that scarcity nationalism led to substantial increases in trade costs between February 2020 and December 2021 for most COVID-19 critical medical products, particularly garments (for example, face masks) and ventilators. The exception is vaccines, which saw a reduction in trade costs, which, however, was driven by the reduction in indirect trade costs for high-income countries, consistent with the view of a COVID-19 vaccine production club.

Smart Crowd Monitoring System using Image Processing Technique During Covid-19

During the COVID-19 scenario, due to partial lockdown, people did not have permission to go out and buy items freely. Instead, they were given a specific time for purchasing goods. As a result, people were found in multitudes during these hours, without maintaining social distance. Managing this crowd to maintain social distance is a huge task for the government, and hence a system that will assist them in controlling the people is required. The You Only Look Once (YOLO) approach was used to detect the objects. Compared to other object detection methods, this technique has a lot of advantages. YOLO finds objects by applying convolutional networks to forecast bounding boxes and class probabilities for these boxes, and it does it much faster than the existing works. This paper develops a device using a Raspberry Pi-4 board that detects people who are in the frame of the camera, and if they are closer than the distance allocated in the device, an alarm will sound, informing them that they are breaking the rules, and the alert message will be sent to the nearby police station. In this way, the crowd can be managed in a pandemic situation. © 2022 IEEE.

Sirs and Septic Shock Alert Evaluation

INTRODUCTION: Prompt recognition of sepsis is imperative for timely treatment and has led to the creation of the CERNER St. Johns Sepsis (SJS) Surveillance Agent Algorithm. Patient specific values are analyzed to determine if criteria for a Systemic Inflammatory Response Syndrome (SIRS) or Septic Shock Alert is met. The SJS Surveillance Agent Algorithm has a positive predictive value of 64%. The study site modified the alert criteria in the acute care areas and the emergency department to reduce alert fatigue. The purpose of this evaluation was to assess whether the modified alert criteria accurately identifies patients with possible sepsis. METHOD(S): This is a single center, retrospective, cohort evaluation. A Cerner Sepsis Audit report of all SIRS and Septic Shock Alert patients admitted between July 1 and July 14, 2021 was used for analysis. Patients were screened at random and included once per admission, first occurrence only. Patients were excluded if they had a presumed or confirmed COVID-19 diagnosis during admission. The proportion of SIRS and Septic Shock Alerts that correlate to a sepsis diagnosis and the proportion of patients with a discharge diagnosis code of sepsis where no alert was generated were analyzed using descriptive statistics. RESULT(S): A total of 147 patients were screened for inclusion with 121 patients included in the final analysis. There were 105 patients who triggered a SIRS or Septic Shock Alert and 16 patients coded for sepsis with no alert generated. The modified SJS criteria resulted in a positive predictive value of 60% (63 sepsis diagnosis vs. 42 noninfectious diagnosis). A majority of alerts were generated by SIRS criteria versus Septic Shock criteria. The most common non-infectious diagnoses in patients who alerted without sepsis were hemorrhage, hypovolemia, and trauma. CONCLUSION(S): Modification of the SJS algorithm occurred in an effort to decrease alert fatigue and was found to be comparable in positive predictive value to the unmodified algorithm.

AI-Based COVID Alert System on Embedded Platform

Corona Virus Disease (COVID-19) has hit the world hard and almost every country has faced its consequences may be the population and number of people affected or economically. Crowd management is incredibly tough for big surroundings and continuous watching manually is troublesome to execute. Vaccinated people are also getting affected by the virus so it is advisable to take Public Health & Social Measures (PHSM) such as wearing a proper mask, sanitization and keeping social distancing in crowded places. The proposed paper presents a machine learning based real-time Covid alert and prevention system to ensure Covid appropriate behavior in public places and social gatherings. There are three modules under this system: (i) Real-time Face mask detection, where persons with masks, improper masks or no mask are detected and classified;(ii) Real-time people counting for ensuring a limit on public meetings and social gatherings and (iii) Real-time social distance monitoring. All these modules are integrated and deployed on embedded hardware, NVidia's Jetson Nano. The implementation results are presented and analysis of the detection is done in real-time on the edge-AI platform. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.