Multiple Linear Regression and Deep Learning in Body Temperature Detection and Mask Detection

This study aims to determine the activities of fish traders in the new normal era at Wowoli Market and the profits obtained by fish traders in the new normal era at Wowoli Market, Toari District, Kolaka Regency. This research was conducted in December 2021 during the Covid-19 pandemic. Determination of the sample in this study using the census method amounted to 15 fish traders. This study uses data collection methods, namely through literature study, observation, interviews, and documentation. The data analysis used is descriptive qualitative analysis and profit analysis. The result showed that the activities of fish traders in the normal era at Wowoli Market, Toari Districk, Kolaka Regency included preparation before tranding, activities while at the market, and after tranding the same as activities in the normal era. Overall activities of fish traders in the new normal er were carried out by implementing health protocols. Like wering a mask. When the new normal, traders activities are starting to recover where the implementation of health protocols is no longer significantly implemented, such as washing hands in running water using gloves using a hand sanitaizer, checking body temperature and keeping a safe distance. The average profit level of tranding profits in the normal er ad the normal era is different. In the normal era profits Rp1.342.999/month. These profits are influenced by an increase in the number of visits by community buyers, while in the normal era, profits for fish traders amounted to Rp1.159.999/month with factors affecting the reduction in community visit due to the Covid-19 pandemic.

Introduction: Compliance with health protocols in the community including the Diabetic Vulnerable Group is one step to prevent the transmission of COVID-19, but the decline in physical and psychosocial functions in the Diabetic Vulnerable Group can be an obstacle for the Diabetic Vulnerable Group in implementing health protocols. This study aims to explore the experience of the Diabetic Vulnerable Group in implementing health protocols during the COVID-19 pandemic.
 Methods: A qualitative research conducted in Jatiduwur, Jombang, East Java, by conducting In-Depth interviews on 10 Diabetic Vulnerable Groups as main informants and 2 supporting informants, Ponkesdes Nurses and Midwives.
 Results: There are 3 themes that can be concluded from this study, namely 1) Description of various experiences of the Diabetic Vulnerable Group in implementing health protocols (uncomfortable using masks, uncomfortable washing hands frequently, keeping distance is not a problem and often forgetting to apply health protocols); 2) Motivation of the Diabetic Vulnerable Group in implementing health protocols (for health, so that the pandemic will pass quickly and government regulations compliance) and 3) Support for the Diabetic Vulnerable Group in implementing health protocols (family support and social support).
 Conclusion: The various experiences felt by the Diabetic Vulnerable Group in implementing health protocols are an illustration of a decrease in physical and psychosocial functions but the Diabetic Vulnerable Group remains motivated because they follow government recommendations and there is good support from families, from fellow Diabetic Vulnerable Groups and from other parties Village government.

The continuous increase in confirmed cases of COVID-19, especially in children aged 6–18, has prompted the government to provide advice to the public on how to implement health protocols and have clean and healthy living habits, especially in terms of implementing health protocols. One of the approaches that can be taken to provide knowledge to children is by communicating using storytelling. By storytelling or telling stories about implementing health protocols, children can better accept and digest what is given. The purpose of this study was to analyze the effect of communication using the storytelling method on knowledge of implementing school-aged children's health protocols at SD Kalipang 4 Grogol, Kediri district. The research design used was the pre-experiment using the one-group pretest-post-test design method. The population of this study was school-age children at SD Kalipang 4 Grogol, Kediri Regency, with a total of 26 students. The independent variable used is storytelling on the application of the health protocol, and the dependent variable is knowledge of the implementation of the health protocol. The sampling technique used is total sampling. By using a knowledge questionnaire about implementing health protocols. Data analysis used the Wilcoxon statistical test to analyze the effect of communication using the storytelling method on knowledge of implementing health protocols. Conclusion Communication using the storytelling method affects school-age children's knowledge about implementing health protocols at SD Kalipang 4 Grogol, Kediri, with a significance value of 0.000 (p=0.05)

Along with the development of pandemic situation which entered the new normal era and successful vaccination of COVID-19, community activities in Bali's tourism centers began to gradually recover, including Kedonganan Beach. However, the risk of COVID-19 transmission remains high due to congested community activities and decreased of health protocols. The solutions and methods offered in this community service are COVID-19 and health protocols refreshment, health assistance and counseling, digital payment socialization, and the provision of hand sanitizers and masks to prevent COVID-19. The implementation of this PKM activity has been going well, seen from the attendance and enthusiasm of the participants, and there has been an increase in participants' knowledge and attitudes regarding the prevention of COVID-19. Through this activity, participants are expected to be an example for the community and keep discipline in implementing health protocols.

<div class="WordSection1"><div><table cellspacing="0" cellpadding="0" align="left"><tbody><tr><td align="left" valign="top"><p><em>The community has an important role in breaking the chain of transmission of COVID-19 so as not to cause new sources of transmission/clusters in places where the movement of people, the interaction between humans, and large gatherings of people occurs. The community must be able to carry out activities again in the COVID-19 pandemic situation by adapting to new habits that are healthier, cleaner, and more obedient, which are implemented by all components in the community and empower all existing resources. The community's role in breaking the chain of transmission of COVID-19 (the risk of contracting and transmitting it) must be done by implementing health protocols.</em><em> </em><em> To provide an overview of the behavior and response of the community to implement health protocols by the Covid-19 Education Team in public places during the New Normal era in the city of Dumai.</em><em> </em><em> This study is research with a qualitative approach. The data used in this study is to use observation which sees and directly observes people's behavior in the field and also takes some data online due to situations that require social distancing. This research was carried out in several public places, including the task stations of the Dumai City Covid-19 Education Team, which began in June-July 2020. The research subjects were the sources of data observed by the research problem.</em><em> </em><em> In general, the behavior and response of the community to the implementation of health protocols during the New Normal period in public places by the Covid-19 Education Team could have been better. Moreover, several factors cause this, namely education, knowledge, and the community's economy. As well as developing issues, public confidence decreases about the dangers of the Covid-19 virus. The educational activities provided will only be meaningful if there is compliance and mutual community support. </em><em></em></p></td></tr></tbody></table></div></div>

The dynamic modulus (|E*|) of hot-mix asphalt (HMA) is a crucial mechanistic characteristic essential in defining the strain response of asphalt concrete (AC) mixtures under varying loading rates and temperatures. This paper aims to conduct a comprehensive investigation of classical machine learning (ML) and deep learning (DL) algorithms as applied to the prediction of |E*| and compare their performance with renowned |E*| regression models (Witczak NCHRP 1-37A, Witczak NCHRP 1-40D, and Hirsch). Eight state-of-the-art ML and DL algorithms are attempted with diverse structures, including multiple linear regression (MLR), decision trees (DT), support vector regression (SVR), ensemble trees (ET), Gaussian process regression (GPR), artificial neural networks (ANN), recurrent neural networks (RNN), and convolutional neural networks (CNN). A comprehensive database was assembled, incorporating 50 AC mixtures, of which 25 were from the Kingdom of Saudi Arabia and 25 were from the state of Idaho, USA. This database encompasses an extensive dataset of 3,720 |E*| measurements, associated with thirteen input features representing the proposed AC mixtures’ aggregate gradations, binder characteristics, and volumetric properties. This pioneering study surpasses existing research by examining various algorithms to predict |E*| on the same dataset, applying them with different structures and individual optimization to achieve optimal performance. The developed models are evaluated based on multi-stage assessment criteria, including the accuracy and complexity performance measures and rationality based on a sensitivity analysis. The multi-stage comparative analysis results reveal that the bagging ETs, GPR with exponential kernel, and DT record the highest prediction accuracy; however, only the bagging ETs yield the highest accuracy, lowest training and testing complexity, and rational trends throughout the sensitivity analysis. The research outcome has the potential to provide pavement engineers with advanced tools for predicting |E*| and, therefore, optimizing pavement designs and rehabilitations. Doi: 10.28991/CEJ-2025-011-01-06 Full Text: PDF

The village government has the authority and policy in handling COVID-19. As the village frontline, we have a very important role in controlling COVID-19. Wantisari Village, as one of the villages in Lebak Regency, should take strategic steps in implementing health protocols and adapting new habits at the village level. However, the actualization of health protocols in Wantisari Village has not run optimally, it can be proven by the presence of nineteen positive cases in Wantisari Village. Based on this background, the researcher will discuss about the role of the Wantisari village government in implementing health protocols and how to enforce law in implementing health protocols? The research method used is empirical juridical with qualitative juridical data analysis sourced from primary data based on field research in wantisari village and primary legal materials as a form of literature study based on the provisions of the Lebak regent regulation number 28 of 2020. Based on the results of the study, the authority of the wantisari village government based on the regent's regulation number 28 of 2020 has carried out several efforts in implementing health protocols. This is evidenced by the existence of several village government programs such as the establishment of a village Covid-19 task force, a tough village program, regular routine raids, and various law enforcement. However, the lack of information distribution to all community groups makes the level of community compliance with health protocols very minimal. There needs to be optimization in law enforcement by the Wantisari village government. Community participation is very much needed in terms of implementing health protocols, the community needs to work together and help each other so that the Wantisari village is protected from COVID-19.

A comparison of multiple deep learning methods for predicting soil organic carbon in Southern Xinjiang, China

Comprehensive modelling of rotary desiccant wheel with different multiple regression and machine learning methods for balanced flow

Covid 19 is a new type of coronavirus that causes respiratory tract infections in humans ranging from cold coughs to more serious ones such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS) and is contagious. To stop the spread of covid 19, we must implement health protocols in our daily life. This study aims to determine the knowledge, attitudes, and behavior of health protocols from simple to Islamic concepts using qualitative methods with in-depth online interviews taken from 4 UMJ student informants. It was found that there were still students who still did not believe in the existence of Covid-19, knowing that the riskiest spread of covid came from droplets or sneezing, students mentioned 3M and 5M as the health protocols they applied, but the most effective way of prevention is to reduce mobilization in the community. outside the home, in terms of Islam students also know how to maintain cleanliness following religious teachings and pray that this pandemic will end soon. From this research, in implementing the health protocol, UMJ students have implemented it quite well and correctly. The knowledge, attitudes, and behavior of UMJ students in responding to the COVID-19 pandemics are good, starting with implementing 5M, always maintaining cleanliness, and from an Islamic point of view, they understand well.

Background Machine learning (ML) algorithm support vector machine (SVM) performed better than Thrombolysis in Myocardial Infarction (TIMI) score for ASIAN STEMI patients. However, Deep Learning (DL) effectiveness in the multiethnic ASIAN population has yet to be determined. DL has automatic learning of the feature from a given dataset without the need to conduct feature selection. However, the selected features by the algorithm is black box. Identifying features associated with mortality is essential to recognize characteristics of patients with high risk for better patient management. Purpose To develop a DL algorithm for in-hospital mortality in multiethnic STEMI patients using predictors identified from the SVM algorithm. To investigate DL performance constructed using predictors from SVM feature extraction and expert-recommended predictors. Methods We constructed four algorithms; a) DL and SVM algorithms with predictors identified from the SVM variable importance b) DL and SVM using predictors based on expert recommendation. We used registry data from the National Cardiovascular Disease Database of 11397 patient's. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were considered. The Area under the curve (AUC) is the performance evaluation metric. Algorithms were validated against the TIMI and tested using the same validation data. SVM variable importance with backward elimination was used to select and rank important variables. Results DL algorithms outperform SVM and TIMI on the validation dataset; i) DL with SVM selected predictors (15 predictors, AUC = 0.97), ii) DL with expert-recommended predictors (16 predictors, AUC = 0.96), iii) SVM with selected predictors (15 predictors, AUC = 0.92), iv) SVM with expert-recommended predictors (AUC = 0.89) and TIMI (AUC = 0.82). Common predictors across SVM feature selection, expert-recommendation and TIMI are: age, heart rate, Killip class, fasting blood glucose, systolic blood pressure, comorbid diseases and ST-elevation. SVM feature selection also identified diuretics, PCI and pharmacotherapy drugs as predictors that improve mortality prediction in STEMI patients. Our findings suggest that the TIMI score underestimates patients risk of mortality. DL algorithm using selected predictors classified 35% of nonsurvival patients as high risk (risk probabilities >50%) compared to only 12.7% nonsurvival patients by TIMI (score >5) (Figure below). Conclusions In the ASIAN population, patients with STEMI can be better classified using the DL algorithm compared to the ML and TIMI score. Combining ML feature selection with DL allows the identification of distinct factors in a unique ASIAN population for better mortality prediction than relying solely on an expert recommendation as it is a very subjective approach. Continuous validation on population-specific algorithms using DL and ML is needed before implementing in a real clinical setting. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1 TIMI performance on validation setDL performance on validation set

Deep learning has gained immense popularity in scientific computing, and its algorithms are widely used in complex problem-solving industries. Every deep learning algorithm use different types of neural networks to perform indented tasks. Deep learning (DL) algorithms have emerged from different machine learning and soft computing methodologies. Since then, a number of deep learning (DL) algorithms have been recently introduced in the scientific community and applied in various application fields. Today, the use of DLs has become indispensable due to their intelligence, effective learning, accuracy and reliability in model creation. However, a comprehensive list of DL algorithms has not yet been presented in the scientific literature. This article lists the most popular DL algorithms and their application areas. Deep learning uses ANN artificial neural networks to perform convoluted calculations on huge amounts of data. It is a type of machine learning based on the structure and function of the human brain. Deep learning algorithms train machines by learning from examples. Industries such as healthcare, e-commerce, entertainment and advertising often use deep learning.

The machine learning (ML) and Internet of things (IoT) technologies are increasingly focussed on decision tree classification algorithm. Its use is expanding through numerous fields, solving incredibly complex problems. DTCA is also being used in medical health data using computer-aided diagnosis to identify chronic kidney diseases like cancer and diabetes. Deep learning is a class of machine learning that utilizes neural networks to solve problems and learn unsupervised from unstructured or unlabelled data. The DL used to deep stacked auto-encoder and softmax classifier methods is applied for CKD. In this work, based an R Studio and Python Colab software using random forest, SVM, C5.0, decision tree classification algorithm, C4.5, ANN, neuro-fuzzy systems, classification and clustering, CNN, RNN, MLP is used to predict multiple machine and deep learning techniques, discover an early diagnosis of CKD patients. In this work, classify the chronic kidney disease various stages are identified.KeywordsDecision tree classification algorithmComputational decision support systemChronic kidney diseaseDeep and machine learning algorithmsIoT and CKD stages

The COVID-19 pandemic requires people to change their lifestyle, implement health protocols to avoid the Covid-19 virus. The reality on the ground is that there are still people who do not comply with the existing health protocols. The purpose of this activity is to reduce the risk of the community being exposed to COVID-19 by implementing health protocols and supporting government programs in an effort to prevent the transmission of COVID-19. The method of implementing this service is through questions and answers and education through the distribution of leaflets, masks and hand sanitisers to traders and buyers. Stages of activities include the stages of preparation, implementation and evaluation carried out with. Implement health protocols. The indicator for achieving this activity is increasing public understanding and awareness in complying with health protocols. Keywords: Community Compliance, Health Protocol and Covid-19

COVID-19 is an infectious disease that attacks the respiratory tract. Its spread is so fast that all countries in the world have contracted this disease. As of the time of writing, in the world there are an estimated 69 million positive cases. In Indonesia, in 2020 there were 605,243 confirmed cases. West Java occupied the 4th position in number of cases in Indonesia with 64,072 confirmed cases. Meanwhile, in the Garut Regency area, there were 2,641 confirmed cases from different sub-districts, one of which is Malangbong District. This study aimed to determine the relationship between the knowledge and attitudes of the community towards implementing health protocols in efforts to prevent COVID-19 in Sakawayana village. This research used a descriptive correlation method with a cross-sectional approach. 98 people in Sakawayana village (which has a population of 5,329 people) were recruited through purposive sampling. The results of this study indicated that 78 people (79.6%) had a good level of knowledge, 55 people (56.1%) had a positive attitude, and 78 people (79.6%) appropriately applied the health protocols. There was a relationship between knowledge and attitudes of the community towards implementing health protocols in efforts to prevent COVID-19 in Sakawayana village, with a p-value < 0.05. Keywords: attitudes, health protocols, knowledge