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.

The relationship between individual characteristics and practice behaviour within an adaptive arithmetic learning program

Background: Although research on mathematics learning programs has taken off in recent years, little is known about how different person characteristics are related to practice behaviour with such programs. When implementing a mathematics learning program in the classroom, it might be important to know whether students with specific characteristics need more encouragement to make use of this learning opportunity. Objectives: Using a supply‐use model, we analysed the predictive power of students' cognitive characteristics (prior mathematics knowledge), personality traits (conscientiousness), motivational‐affective characteristics (mathematics self‐concept and mathematics anxiety), and family background characteristics (socioeconomic status and migration background) for practising with an adaptive arithmetic learning program. Methods: We used longitudinal data from 203 fifth graders from seven non‐academic‐track schools in Germany. Practice behaviour, measured by completed tasks in the learning program, was recorded weekly for every student over a period of 22 weeks. Results and Conclusions: The results of our multilevel analyses showed that mathematics anxious students practised less with the program. We did not find any relationship with the other characteristics. Takeaways: Our results suggest that mathematics anxious students need more encouragement when practising with a mathematics learning program;otherwise, they do not get the chance to benefit from the use. Lay Description: What is already known about this topic: The use of mathematics learning programmes in mathematics education has recently intensified.It is important that students practice with such learning programmes regularly over a longer period of time in order for them to achieve learning success.Students differ in their mathematics learning behaviour. What this paper adds: Little is known about how person characteristics are related to practice behaviour with mathematics learning programmes.Students may differ in their use of a mathematics learning programme, which is why cognitive characteristics, personality traits, motivational‐affective characteristics, and family background characteristics may affect students' practice behaviour. Implications for practice: Mathematics anxious students practiced less with a mathematics learning program, and need more encouragement to benefit equally from the implementation in school.Teachers should keep in mind that after the initial enthusiasm, practice with a programme may decrease over time, especially after school holidays.

The comparative perceived effectiveness of simulation versus case study learning methods in the company training of digital marketers

Marketers must adapt to the challenges created by the COVID-19 pandemic, which accelerated innovation and changes across the world, but specifically in the digital marketing industry.Consumer demand and purchasing behaviors have changed fundamentally, and these current trends are affecting how marketers utilize digital marketing. As a result, firms must rely on innovation in marketing strategies for survival. The new expectations from consumers result in marketers determining what learning method for their staff offers a higher retention and implementation advantage to stay abreast of changes in their industry. This study analyzedemployees' preferred learning methods in the digital marketing sphere. This study adds to the body of knowledge on determining the retention and implementation of two learning methods:simulation training and case study learning. These two learning methods in relation to marketing professionals led to the generation of recommendations for employers to improve learning retention for their employees. To this end, a primary research question was identified: How does the selection of a learning method for marketing professionals improve the employees' retention and implementation of new material taught? Other related research areas were also explored,namely, whether age, experience, or gender impact the preferred learning material and whether different marketing categories resonate with different learning methods, resulting in more productive results. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

How student-led wards can build skills and confidence: Nursing students on collaborative learning programmes make decisions about care and gain clinical experience

When Beth Dennis first set foot on a hospital ward as a Birmingham City University nursing student she felt underprepared. COVID-19 had disrupted everything, including face-to-face learning and time to practise clinical skills. Many of her student peers had worked previously in healthcare, but Ms Dennis had entered nursing straight from school. 'I basically knew nothing,' she says. But by her second year, with more experience, she felt she could offer help to others starting out who felt as anxious as she had. 'So me and a few other nursing students decided to run sessions to ease nerves about placements,' she says.

A DEEP LEARNING APPROACH LUNG SEGMENTATION AND PNEUMONIA DETECTION FROM X-RAYS

Lung segmentation is a process of detection and identification of lung cancer and pneumonia with the help of image processing techniques. Deep learning algorithms can be incorporated to build the computer-aided diagnosis (CAD) system for detecting or recognizing broad objects like acute respiratory distress syndrome (ARDS), Tuberculosis, Pneumonia, Lung cancer, Covid, and several other respiratory diseases. This paper presents pneumonia detection from lung segmentation using deep learning methods on chest radiography. Chest X-ray is the most useful technique among other existing techniques, due to its lesser cost. The main drawback of a chest x-ray is that it cannot detect all problems in the chest. Thus, implementing convolutional neural networks (CNN) to perform lung segmentation and to obtain correct results. The 'lost' regions of the lungs are reconstructed by an automatic segmentation method from raw images of chest X-ray. © 2022 IEEE.

Croup disease classification using VGG19 and ResNet50 transfer learning method

Detection of any disease in the early stage can save a life. There are many medical imaging modalities like MRI, FMRI, ultrasound, CT, and X-ray used in the detection of disease. In the last decades, neural network-based methods are effective in detecting and classifying the disease based on abnormalities present in the medical images. Acute laryngotracheobronchitis (croup) is one of the common diseases seen in children among the 0.5-3 years age group which infects the respiratory system that can cause the larynx, trachea, and bronchi. Prior detection can lower the risk of spreading and can be treated accurately by a pediatrician. Commonly this infection can be diagnosed though physical examination. But due to the similarity of Covid-19 symptoms urges the physicians to get accurate detection of this disease using X-ray and CT images of the infant's chest and throat. The proposed work aims to develop a croup diagnose system (CDS) which identify the Croup infection through post anterior (PA) view of pediatric X-ray using deep learning algorithm. We used the well-known transfer learning algorithm VGG19 and ResNet50. Data augmentation being adapted for reducing the overfitting and to improve the quantity of image samples. We show that the proposed transfer learning based CDS method can be used to classify the X-ray images into two classes namely, croup and normal. The experiment results confirm that VGG19 performs better than ResNet50 with promising classification accuracy (90.91%.). The results show that the proposed CDS models can be used for more pediatric medical image classification problem. © 2024 World Scientific Publishing Company.

Levels of anxiety and depression of students and responses to online learning methods during the COVID-19 pandemic

Introduction: The COVID-19 pandemic has affected various aspects of human life, including education. Educational institutions, including medical faculty, are trying to use the online learning approach to continue teaching and learning. Although it has many advantages due to its flexibility, there are also some disadvantages. Previous studies have shown that online learning is significantly related to student psychological disorders such as stress, anxiety to depression, thereby reducing social skills and competence in medical students. This study aims to determine the level of anxiety and depression in students of the Faculty of Medicine, Universitas Udayana Medical Education Study Program, during the COVID-19 pandemic and responses to the online learning system. Methods: This study used a cross-sectional design. The questionnaire was compiled using the Indonesian version of the Health and Anxiety Depression Scale (HADS) and distributed online. Data were analyzed using the SPSS ver.23 program by performing the chi-square test, with a p <0.05, considered statistically significant. Results: Based on demographic characteristics, it was found that female students got more anxiety symptoms than male students (74.8% vs. 25.2%, p=0.009), and preclinical students experienced more depression than clinical students (95.2% vs. 4.8%, p=0.037). Students who experience symptoms of anxiety and depression, as well as normal, are dominated by poor learning responses (66.3% vs. 33.7%). Study programs, family income, place of residence, and semester level do not influence anxiety and depression in students. Conclusion: The COVID-19 pandemic situation has led to poor learning responses in medical students, both those experiencing symptoms of anxiety/depression and students without symptoms (normal). © 2023, Sanglah General Hospital. All rights reserved.

Differences in stress levels between junior high school students and college students during online learning

The article discusses research which analyzed the differences in stress levels between junior high school students and college students during online learning during the COVID-19 pandemic in Indonesia. It discusses body change caused by stressors, the influence of sex on an individual's stress level, the potential of stress experienced by students to cause signs and symptoms of health problems, and factors influencing stress in male and female students.

A Novel Machine Learning-assisted Pairs Trading Approach for Trading Risk Reduction

The cryptocurrency market has been growing rapidly in recent years. The volume of transactions and the number of participants in the cryptocurrency market makes it huge enough that we cannot ignore it. At the same time, the global stock market has also reached a new height in the past two years. However, due to the COVID epidemic and other political and economic-related factors in the last two years, the uncertainty in the capital market remains high, and short-term large fluctuations occur frequently;thus, many investors have suffered substantial losses. Pairs trading, an advanced statistical arbitrage method, is believed to hedge the risk and profit off the market regardless of market condition. Amongst the vast literature on pairs trading, there have been investors trading a pair of cryptocurrencies or a pair of stocks using machine learning or empirical methods. This research probes the boundary of utilizing machine learning methods to do pairs trading with one stock asset and another cryptocurrency. Briefly, we built an assets pool with both stocks and cryptocurrencies to find the best trading pair. In addition, we applied mainstream machine learning models to the trading strategy. We finally evaluated the accuracy of the proposed method in prediction and compared their returns based on the actual U.S. Stock and Cryptocurrency Market data. The test results show that our method outperforms other state-of-the-art methods. © 2022 IEEE.

A Survey on Yogic Posture Recognition

Yoga has been a great form of physical activity and one of the promising applications in personal health care. Several studies prove that yoga is used as one of the physical treatments for cancer, musculoskeletal disorder, depression, Parkinson's disease, and respiratory heart diseases. In yoga, the body should be mechanically aligned with some effort on the muscles, ligaments, and joints for optimal posture. Postural-based yoga increases flexibility, energy, overall brain activity and reduces stress, blood pressure, and back pain. Body Postural Alignment is a very important aspect while performing yogic asanas. Many yogic asanas including uttanasana, kurmasana, ustrasana, and dhanurasana, require bending forward or backward, and if the asanas are performed incorrectly, strain in the joints, ligaments, and backbone can result, which can cause problems with the hip joints. Hence it is vital to monitor the correct yoga poses while performing different asanas. Yoga posture prediction and automatic movement analysis are now possible because of advancements in computer vision algorithms and sensors. This research investigates a thorough analysis of yoga posture identification systems using computer vision, machine learning, and deep learning techniques.

Comparison of Different Models in Predicting COVID-19 Severity Based on Chest X-Ray Scans

The global outbreak of COVID-19 has resulted in a surge in patients in hospitals and intensive care units. This unprecedented demand for medical resources has severely burdened healthcare systems. Chest X-Ray (CXR) images can be used by hospitals and small clinics to predict COVID-19 severity to maximize efficiency and allot medical resources to patients with severe COVID-19. This research compares the accuracies of four convolutional neural network models in predicting COVID-19 severity using chest X-Rays images. The CNN models include VGG-16, ResNet 50, Xception, and a custom CNN model. Through the comparison, VGG-16 had the highest COVID-19 severity prediction accuracy of all four models, with 95.56% testing accuracy and 88.33% validation accuracy. Using a machine learning method, disease progression can be tracked more accurately and help prioritize patients to ensure effective and timely treatment. © 2022 IEEE.

Performance of Machine Learning Methods Using Tweets

Public opinions shared in common platforms like Twitter, Facebook, Instagram, etc. act as the sources of information for experts. Transportation and analysis of such data is very important and difficult due to data regulations and its structure. The pre-processing approaches and word-based dictionaries are used to understand the unprocessed data and make possible the opinions/tweets to be analyzed. Machine learning algorithms learn from past experience and use a variety of statistical, probabilistic and optimization algorithms to detect useful patterns from unstructured data sets. Our study aims to compare the performance of classification algorithms to predict individuals with COVID-19(+ ) or COVID-19(−) using the emotions among the tweets by text mining procedures. Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Trees (DT), Random Forest (RF), Artificial Neural Networks (ANN), Gradient Boost (GBM) and XGradient algorithms were used to extract the accuracy of model performance of each model for the detection and identification of the disease related to the COVID-19 virus, which has been on the agenda recently. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Transfer Learning Method for Covid-19 and Pneumonia Diagnosis Based on Chest Radiograph Classification

Pneumonia has been a tough and dangerous human illness for a history-long time, notably since the COVID-19 pandemic outbreak. Many pathogens, including bacteria or viruses like COVID-19, can cause pneumonia, leading to inflammation in patients' alveoli. A corresponding symptom is the appearance of lung opacities, which are vague white clouds in the lungs' darkness in chest radiographs. Modern medicine has indicated that pneumonia-associated opacities are distinguishable and can be seen as fine-grained labels, which make it possible to use deep learning to classify chest radiographs as a supplementary aid for disease diagnosis and performing pre-screening. However, deep learning-based medical imaging solutions, including convolutional neural networks, often encounter a performance bottleneck when encountering a new disease due to the dataset's limited size or class imbalance. This study proposes a deep learning-based approach using transfer learning and weighted loss to overcome this problem. The contributions of it are three-fold. First, we propose an image classification model based on pre-trained Densely Connected Convolutional Networks using Weighted Cross Entropy. Second, we test the effect of masking non-lung regions on the classification performance of chest radiographs. Finally, we summarize a generic practical paradigm for medical image classification based on transfer learning. Using our method, we demonstrate that pre-training on the COVID-19 dataset effectively improves the model's performance on the non-COVID Pneumonia dataset. Overall, the proposed model achieves excellent performance with 95.75% testing accuracy on a multiclass classification for the COVID-19 dataset and 98.29% on a binary classification for the Pneumonia dataset. © 2023 IEEE.

Lung Disease Classification Using Deep Learning Models from Chest X-ray Images

In the very recent past, Infectious disease-related sickness has long posed a concern on a global scale. Each year, COVID-19, pneumonia, and tuberculosis cause a large number of deaths because they all affect the lungs. Early detection and diagnosis can increase the likelihood of receiving quality treatment in all circumstances. A low-cost, simple imaging approach called chest X-ray imaging enables to detection and screen lung abnormalities brought on by infectious diseases for example Covid-19, pneumonia, and tuberculosis. This paper provided a thorough analysis of current deep-learning methods for diagnosing Covid-19, pneumonia, and TB. According to the research papers reviewed, Deep Convolutional Neural Network is the most used deep learning method for identifying Covid-19, pneumonia, and TB from chest X-ray (CXR) images. We compared the proposed DNN to well-known DNNs like Efficient-NetB0, DenseNet169, and DenseNet201 in order to more accurately assess how well it performed. Our findings are equivalent to the state-of-the-art, and since the proposed CNN is lightweight, it may be employed for widespread screening in areas with limited resources. From three diverse publicly accessible datasets merged into one dataset, the suggested DNN generated the following precisions for that dataset: 99.15%, 98.89%, and 97.79% for EfficientNetB0, DenseNet169, and DenseNet201 respectively. The proposed network can help radiologists make quick and accurate diagnoses because it is effective at identifying COVID-19 and other lung contagious disorders utilizing chest X-ray images. This paper also gives young scientists a good insight into how to create CNN models that are highly efficient when used with medical images to identify diseases early. © 2023 IEEE.

Covid Wave Prediction using SARIMA Machine Learning Algorithm

A speedy and accurate diagnosis of COVID-19 is made possible by effective SARS-Co V -2 screening, which can also lessen the strain on health care systems. There have been built prediction models that assess the likelihood of infection by combining a number of parameters. These are intended to help medical professionals worldwide prioritize patients, particularly when there are few healthcare resources available. From a dataset of 51,831 tested people, out of which 4,769 were confirmed to have COVID-19 virus, a machine learning method was developed and trained. Records of the following week with 47,401 tested people, of which 3,624 were tested positive was also considered. Our method accurately predicted the COVID-19 test results using eight binary characteristics, including gender, age 60, known contact with an infected person, and the presence of five early clinical signs. © 2023 IEEE.

A Prototype for Interpretation of Sars-Cov-2 Tests Using Artificial Vision

Covid-19 has been declared a pandemic by the World Health Organization in March 2020, so science has been trying to help mitigate its effects from its various fields of study. Machine learning methods can play an important role in identifying test results that reveal whether an individual has the disease. This degree work presents a prototype based on computer vision and machine learning techniques to automatically detect SARS-CoV-2 serology tests. The goal of the prototype is to identify and classify the serology test cassette result by Immunoglobulin G and Immunoglobulin M indicators that are flagged after a test reaction time which is approximately 15 minutes. The results in the identification performed by the prototype are promising and ease its analysis, reducing the errors in the identification of the test and the interpretation of the results. The result is a prototype that allows to perform, simplify and improve the tasks of health professionals, which they must perform daily in the triage area. © 2022 IEEE.

A Decision Support System using Machine Learning -IoT to tackle the Covid-19 pandemic in Uttarakhand

Covid-19 spread is worldwide;India is now at the second place where this epidemic is spreading with high rate. The state of Uttarakhand, a hilly state of India also has a significant impact of Covid-19. This paper suggests that machine learning techniques with IOT can equipped the doctors, and lab technicians to deal with this pandemic. Here, we also design a prediction system to help the doctors so that they can keep the records of infected patients. We used IoT, machine learning and ensemble methods for healthcare to store infected patient's data in the cloud database, and enable doctors/others to screen patient's data about their disease. We developed a decision support system to detect the diseases quickly and the treatment can be initiated immediately. © 2022 IEEE.

A Comparison of COVID-19 Detection using Deep Learning Methods

The recognition of covid-19 is major confront in today's world, specified as sudden increase in spreading of the disease. Hence, identifying this infection in earlier phase facilitates medicinal fields such as doctors, nurses and lab reporters. This article introduces a novel deep learning technique especially Convolutional Neural Network (CNN) by analyzing features in chest input images. Moreover, this proposed Convolutional Neural Network detects the covid-19 disease under several layers and finally performs binary classification that categorizes input images into covid 19 and non-covid patients. Finally, comparisons had made among all models to predict which model diagnose the disease accurately. To evaluate the overall model performance in detection and classification of covid disease, metrics criterias precision, recall and F1-score are evaluated. Validation analysis were completed for quantify the outcomes via performance measures for each model. This proposed comparison attains maximum accuracy of 100% along with least loss as 0.04 that might diminish human inaccuracy in identification procedure. © 2023 IEEE.

Specific mining of COVID-19 reviews using deep learning methods

Sentiment classification is a valid measure to monitor public opinion on the COVID-19 epidemic. This study provides a significant basis for preventing the spread of adverse public opinion. Firstly, in epidemic texts, we use a convolutional neural network and bidirectional long short-term memory neural network BiLSTM model to classify and analyze the sentiment of the comment texts about the epidemic situation on Weibo. Secondly, embedded in the model layer to generate adversarial samples and extract semantics. Then, semantic information is weighted using the attention mechanism. Finally, the RMS optimizer is used to update the neural network weights iteratively. According to comparative experiments, the experimental results show that such four evaluation metrics as accuracy, precision, recall, and f1-score with our proposed model have obtained better classification performance. © 2022 IEEE.

Teaching and learning modalities for continuing professional development in the long-term care: A rapid synthesis review.

AIM: To describe the various teaching and learning modalities for the delivery of Continuing Professional Development activities for health care professionals in the long-term care sector. BACKGROUND: Continuing Professional Development is a key activity that organisations undertake to achieve effective workforce planning, recruitment, retention and upskilling strategies in long-term care settings. During the Covid-19 pandemic there was a rapid move to online modalities of Continuous Professional Development, but there is a paucity of evidence in relation to their effectiveness compared with face-to-face, or in-class learning. DESIGN: A rapid synthesis review. METHODS: MEDLINE, CINAHL and HEALTH BUSINESS ELITE databases were used to identify relevant articles that were published between 2016 and 2022. Original studies of any design investigating Continuing Professional Development activities, with or without a comparison between interventions or activities were included. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was followed. The Kirkpatrick model was adopted as a globally recognised method for evaluating training programmes. RESULTS: After a full text analysis, 34 papers were included in the review. Face to face was the most common method of delivery followed by online, while blended (a mix of face-to-face and online delivery) was the least common method used. The teaching modalities were not associated with specific learning contents, but were used for a range of content. Most studies obtained positive outcomes following implementation of the educational interventions. Kirkpatrick Level 4 (results) was the most commonly measured outcome. CONCLUSIONS: While blended learning was the least common method of delivery, it was found to be more beneficial for learners than face-to-face or online exclusively. There are now new spaces to learn and new technologies that allow us to 'reimagine' where, when and how we teach. This requires Continuing Professional Development providers to design and tailor their courses according to health professionals' learning needs and the clinical contexts where they work. We recommend that Continuing Professional Development providers involve employers when designing teaching and learning activities for Long Term Care workers, to decide which modalities enable effective knowledge translation.