A Review based Investigation of Exploratory analysis in AI and Machine Learning for a Variety of Applications

In recent years, the number of production settings that make use of machine learning (ML) and other types of AI has grown significantly. The research presents a comprehensive review of where machine learning (ML) applications stand in industrial contexts at present. The development of smart mining tools has allowed for the generation, collection, and exchange of data in near-real time. This is why there is so much interest in machine learning (ML) studies in the mining industry. Additionally, this study provided a thorough evaluation of data sciences and ML's applications in a variety of petroleum engineering and geosciences domains, such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, with a focus on the rapidly developing area of unconventional reservoirs. Future directions for data science and ML in the oil and gas industry are discussed, and the properties of ML that are necessary to enhance prediction are analysed. This study provides a detailed comparison of various ML techniques that can be used in the oil and gas industry. New possibilities for analysing and predicting medical data have emerged thanks to the development of artificial intelligence and machine learning, which were covered in this article. Multiple recent studies have shown that AI and ML can be used to fight the COVID-19 pandemic. This article's goal is to offer reviewers with an overview of recent studies that have made use of AI and ML in a variety of contexts. © 2022 The authors.

A Survey of Machine Learning Approaches for Detecting Depression Using Smartphone Data

Depression is one of the most common mental health issues worldwide and has only become more widespread after the emergence of the Covid-19 pandemic. Although depression can be treated through various methods, it often goes undiagnosed and therefore untreated, forcing individuals to go through life with a condition that is nothing short of debilitating. With mobile phones being an integral part of people's lives, they can provide valuable information about a person's habits and behaviors, which can then be used to detect depressive tendencies. This paper provides a review of several studies conducted in recent years on the possibility of using machine learning and smartphone data to detect depression. © 2022 IEEE.

The Detrimental Clinical Associations of Anxiety and Depression with Difficult Asthma Outcomes

Difficult asthma describes asthma in which comorbidities, inadequate treatment, suboptimal inhaler technique and/or poor adherence impede good asthma control. The association of anxiety and depression with difficult asthma outcomes (exacerbations, hospital admissions, asthma control, etc.) is unclear. This study assessed the clinical associations of anxiety and depression with difficult asthma outcomes in patients with a specialist diagnosis of difficult asthma. Using real-world data, we retrospectively phenotyped patients from the Wessex Asthma Cohort of Difficult Asthma (N = 441) using clinical diagnoses of anxiety and depression against those without anxiety or depression (controls). Additionally, we stratified patients by severity of psychological distress using the Hospital Anxiety and Depression Scale (HADS). We found that depression and/or anxiety were reported in 43.1% of subjects and were associated with worse disease-related questionnaire scores. Each psychological comorbidity group showed differential associations with difficult asthma outcomes. Anxiety alone (7.9%) was associated with dysfunctional breathing and more hospitalisations [anxiety, median (IQR): 0 (2) vs. controls: 0 (0)], while depression alone (11.6%) was associated with obesity and obstructive sleep apnoea. The dual anxiety and depression group (23.6%) displayed multimorbidity, worse asthma outcomes, female predominance and earlier asthma onset. Worse HADS-A scores in patients with anxiety were associated with worse subjective outcomes (questionnaire scores), while worse HADS-D scores in patients with depression were associated with worse objective (ICU admissions and maintenance oral corticosteroid requirements) and subjective outcomes. In conclusion, anxiety and depression are common in difficult asthma but exert differential detrimental effects. Difficult asthma patients with dual anxiety and depression experience worse asthma outcomes alongside worse measures of psychological distress. There is a severity-gradient association of HADS scores with worse difficult asthma outcomes. Collectively, our findings highlight the need for holistic, multidisciplinary approaches that promote early identification and management of anxiety and depression in difficult asthma patients.

An observational study to determine the clinical course of asymptomatic and mildly symptomatic COVID-19-positive patients admitted to a tertiary care centre in Puducherry

Background: Severe acute respiratory syndrome Coronavirus2 (SARSCoV2) disease (COVID-19) has spread nationwide including union territory of Puducherry. Methods: Consecutive asymptomatic or mildly symptomatic COVID-19 patients admitted to the COVID-19 ward were included in the study. Demographic details, following of social norms, contact-exposure history, presence of co-morbidities, vital parameters, clinical symptoms and signs, development of new symptoms, progression and outcome of study patients are reported. Results: Six hundred and forty two patients were included for final analysis. Most of symptomatic patients did not use face mask (87%) and did not follow social distancing (84.1%) or hand hygiene (91.3%). Out of mildly symptomatic patients, 12 become moderately or severely symptomatic and were shifted to intensive care unit. All these patients were male, aged more than 50 years with co-morbidities. Conclusions: Wearing face mask, social distancing and hand hygiene can decrease disease severity. Male patients with co-morbidities and old age are at higher risk of progression to moderate or severe COVID-19 infection. [ FROM AUTHOR] Copyright of Journal of Clinical & Scientific Research is the property of Sri Venkateswara Institute of Medical Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Can Non-Humanoid Social Robots Reduce Workload of Special Educators : An Online and In-Premises Field Study

Although Socially Assistive Robotics have been used in Autism Spectrum Disorder (ASD) interventions, such studies often exclude Special Educators (SEs) and often use expensive humanoid robots. In this paper, we investigate whether non-humanoid toy robots can act as teaching aids in ASD Education, in particular, can they reduce the workload of SEs. We target two most common yet divergent problems from Individualized Education Plans (IEPs) of ASD children - communication and gross motor skills. We present results from three studies a) toy robot Cozmo assists SEs in verbal lessons in school premises, b) mini drone Tello helps SEs in exercise lessons in school premises, and c) Cozmo, SEs, and ASD children connect remotely, as mandated due to the Covid-19 pandemic, for verbal lessons. All three studies showed improvement in learning outcomes and reduction in prompts from the SEs, denoting reduced workload. The effect of a robot's virtual presence in online ASD interventions has not been studied before. However, our results show that children spent more time on lessons in online intervention with Cozmo, suggesting that using robots should also be considered when designing online interventions. Furthermore, the roles of Cozmo were analyzed, and we found children showed increased spontaneous interaction when Cozmo acts as a Co-Instructor. Thus, preliminary results indicate toy robots, as opposed to expensive humanoids, may have significant potential in aiding SEs in Autism education.