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Hitanshi Jain ◽  
Sai Teja Miyapuram ◽  
Sree Ranga Reddy ◽  

A fire accident can be caused by many hazards, such as a propane tank, a defective product, a vehicle crash, or poor workplace safety. Because accidents involving fire are often unexpected and sudden, there isn’t a standard legal process for dealing with them, other than filing a negligence or workers compensation claim. This project aims to detect and monitor Fire Accident incidents well in advance and alert the surroundings to minimize the losses. This is an integration of IoT and Deep Learning Technologies, where sensors are used to collect the relevant data under the supervision of a controller unit. The controller unit collects and sends this data to a cloud database, from where the data for the Deep Learning model is fetched. This data is then used for making some insights and further predictive analytics. From the insights, many variables were found to be one of the reasons for a fire accident to take place. We considered the information about variables like Flame sensor, Temperature, Heat Index, GPS coordinates, Smoke, Type of Gases, Date, and Time for feature set generation and fed the model to a deep neural network for making future predictions. Comparing to existing conventional methods, this proposed method is different in terms of integrating deep learning with IoT. This method of approach will predict the chance of accidents priorly by classification of data.

S. T. Kokhan ◽  
N. I. Vinogradova ◽  
Yu. V. Sarudeykina

The global online transition has become a real challenge to the traditional form of education, which has prompted universities to reconsider the system of providing education services. The authors conducted a sociological study based on the investigation of the relationships of the academic teaching staff, their assessment of the effectiveness of the measures taken by the universities in organizing the transition to a distance learning (DL) format of education. Lecturers of regional universities of Russia, Kyrgyzstan and Mongolia took part in the study. The results of the study made it possible to identify the most organized and optimistic category of lecturers of Mongolian universities and focused attention on the main problems in the implementation of DL at all universities. The normalization of the epidemiological situation in the future, the restoration of the economic level of development of each country will enable the universities to define their approaches to the use of distance learning technologies (DLT) and their role in the traditional face-to-face education system in accordance with the needs of the students and the peculiarities of social life.

2021 ◽  
pp. 1-18
Seyed Reza Shahamiri ◽  
Fadi Thabtah ◽  
Neda Abdelhamid

BACKGROUND: Autistic Spectrum Disorder (ASD) is a neurodevelopment condition that is normally linked with substantial healthcare costs. Typical ASD screening techniques are time consuming, so the early detection of ASD could reduce such costs and help limit the development of the condition. OBJECTIVE: We propose an automated approach to detect autistic traits that replaces the scoring function used in current ASD screening with a more intelligent and less subjective approach. METHODS: The proposed approach employs deep neural networks (DNNs) to detect hidden patterns from previously labelled cases and controls, then applies the knowledge derived to classify the individual being screened. Specificity, sensitivity, and accuracy of the proposed approach are evaluated using ten-fold cross-validation. A comparative analysis has also been conducted to compare the DNNs’ performance with other prominent machine learning algorithms. RESULTS: Results indicate that deep learning technologies can be embedded within existing ASD screening to assist the stakeholders in the early identification of ASD traits. CONCLUSION: The proposed system will facilitate access to needed support for the social, physical, and educational well-being of the patient and family by making ASD screening more intelligent and accurate.

2021 ◽  
Dominic C Henri ◽  
Stuart Nattrass ◽  
Katharine E Hubbard ◽  
Lesley J Morrell ◽  
Graham Scott

In order to maintain pace with rising expectations to provide an ‘excellent learning environment’, higher education institutions across the world are turning to learner analytics to help allocate resources efficiently. However, the exponential increase of digital learning technologies has resulted in learner analytics sharing the same practical and ethical concerns as ‘big data’ in the wider context. This study provides an important ‘proof-of-concept’ that learner analytics is better served by data from theory driven course design, and not more data. We explore the potential of learner analytics combined with course design that incorporates regular, automated, low-stakes assignments to provide ‘checkpoints’ of student engagement. We show for a cohort of 424 foundation year students that attainment is best predicted by ‘checkpoint’ submission and not by a host of demographic and behavioural variables that have previously been identified as ‘early-warning indicators’. To conclude, we identify how the practice of integrating learner analytics and course design can help us better align our practice with ethical use of data guidelines for learner analytics and the recommendations from the literature.

Healthcare ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1369
Afiq Izzudin A. Rahim ◽  
Mohd Ismail Ibrahim ◽  
Kamarul Imran Musa ◽  
Sook-Ling Chua ◽  
Najib Majdi Yaacob

Social media sites, dubbed patient online reviews (POR), have been proposed as new methods for assessing patient satisfaction and monitoring quality of care. However, the unstructured nature of POR data derived from social media creates a number of challenges. The objectives of this research were to identify service quality (SERVQUAL) dimensions automatically from hospital Facebook reviews using a machine learning classifier, and to examine their associations with patient dissatisfaction. From January 2017 to December 2019, empirical research was conducted in which POR were gathered from the official Facebook page of Malaysian public hospitals. To find SERVQUAL dimensions in POR, a machine learning topic classification utilising supervised learning was developed, and this study’s objective was established using logistic regression analysis. It was discovered that 73.5% of patients were satisfied with the public hospital service, whereas 26.5% were dissatisfied. SERVQUAL dimensions identified were 13.2% reviews of tangible, 68.9% of reliability, 6.8% of responsiveness, 19.5% of assurance, and 64.3% of empathy. After controlling for hospital variables, all SERVQUAL dimensions except tangible and assurance were shown to be significantly related with patient dissatisfaction (reliability, p < 0.001; responsiveness, p = 0.016; and empathy, p < 0.001). Rural hospitals had a higher probability of patient dissatisfaction (p < 0.001). Therefore, POR, assisted by machine learning technologies, provided a pragmatic and feasible way for capturing patient perceptions of care quality and supplementing conventional patient satisfaction surveys. The findings offer critical information that will assist healthcare authorities in capitalising on POR by monitoring and evaluating the quality of services in real time.

2021 ◽  
Vol 7 (3D) ◽  
pp. 521-532
Karine Aramyan ◽  
Viktor Krivopuskov

The purpose of the article is to analyze the main characteristics of digitization of the education system on the background of FIR and the transition of humanity to the Digital Age to formulate new tasks arising for education in this regard, to consider new resources for its development. In the course of research, the following will be used: comparative and comparative method; system approach and methods of analysis and synthesis; historical and logical methods, comparative analysis, content analysis. Justification of the expediency of personalized learning in the transition to digital education as leading learning technologies, the need to improve teaching methods and models of digital competencies as well as the system of additional professional education as an effective tool for solving problems in the labor market and employment. The results of the analysis allow integrating international and domestic developments in terms of understanding the socio-humanitarian aspects of the digitalization process, the existing approaches to the digitalization of education.

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2503
Minseon Cho ◽  
Donghyun Kang

Today, research trends clearly confirm the fact that machine learning technologies open up new opportunities in various computing environments, such as Internet of Things, mobile, and enterprise. Unfortunately, the prior efforts rarely focused on designing system-level input/output stacks (e.g., page cache, file system, block input/output, and storage devices). In this paper, we propose a new page replacement algorithm, called ML-CLOCK, that embeds single-layer perceptron neural network algorithms to enable an intelligent eviction policy. In addition, ML-CLOCK employs preference rules that consider the features of the underlying storage media (e.g., asymmetric read and write costs and efficient write patterns). For evaluation, we implemented a prototype of ML-CLOCK based on trace-driven simulation and compared it with the traditional four replacement algorithms and one flash-friendly algorithm. Our experimental results on the trace-driven environments clearly confirm that ML-CLOCK can improve the hit ratio by up to 72% and reduces the elapsed time by up to 2.16x compared with least frequently used replacement algorithms.

Lydia Robb

Abstract Introduction Burn-related injuries are a leading cause of morbidity across the globe. Accurate assessment and treatment have been demonstrated to reduce the morbidity and mortality. This essay explores the forms of artificial intelligence to be implemented the field of burns management to optimise the care we deliver in the National Health Service (NHS) in the UK. Methods Machine Learning methods which predict or classify are explored. This includes linear and logistic regression, artificial neural networks, deep learning, and decision tree analysis. Discussion Utilizing Machine Learning in burns care holds potential from prevention, burns assessment, predicting mortality and critical care monitoring to healing time. Establishing a regional or national Machine Learning group would be the first step towards the development of these essential technologies. Conclusion The implementation of machine learning technologies will require buy-in from the NHS health boards, with significant implications with cost of investment, implementation, employment of machine learning teams and provision of training to medical professionals.

2021 ◽  
Ibnu Rafi ◽  
Heri Retnawati ◽  
Ezi Apino ◽  
Munaya Nikma Rosyada

The Coronavirus disease (COVID-19) pandemic forces learning, including mathematics learning, to be carried out in online or distance mode. This situation is a challenge for teachers in facilitating mathematics learning because they are required to organize mathematics content and integrate it with certain learning technologies. In this article, we reviewed a total of 14 articles to describe how mathematics teacher in Indonesia facilitates online learning during the COVID-19 pandemic by focusing on types of technology used and reasons for choosing the technology, strategies used in integrating the technology with certain learning models or methods, ways of facilitating online discussion to construct knowledge, and assessments conducted as well as effects of the online learning facilitated by the teacher. Some implications for policy and practices are also provided in this article for improvement of online mathematics learning during the COVID-19 pandemic and may also after the pandemic is over in which it is possible to combine online and offline learning.

2021 ◽  
Vol 4 (4) ◽  
Randall Spain ◽  
Carlos Penilla ◽  
Elizabeth Ozer ◽  
Robert Taylor ◽  
Cathy Ringstaff ◽  

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