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Review Article
Corporate Social Responsibility in the MedTech Industry, the Emergence of Artificial Intelligence in the ERA of COVID-19
Issue:
Volume 8, Issue 1, June 2024
Pages:
1-4
Received:
12 December 2023
Accepted:
26 December 2023
Published:
8 January 2024
Abstract: The medical technology industry has faced several unprecedented challenges due to COVID-19, including critical medical devices and supply chain shortages. We had to pivot rapidly to address the immediate need to provide lifesaving and sustaining devices, collaborate with competitors, and work collectively with regulators for emergency use authorization when the typical pathway was not viable. AI has provided an opportunity to use new technology in the MedTech space by offering ways to stay productive where human contact is not advisable. Artificial Intelligence has begun to be incorporated into all areas of our lives. The MedTech industry is responsible for improving the quality of a patient’s life through technological advancements. Whether it be predictive or early diagnosis, streamlined workflows, and the utilization of electronic health records, the MedTech industry has the opportunity to be a leader in the responsible use of AI. This article examines the impact of COVID-19, the emergence of artificial intelligence (AI), what we did, how we do it, and our collective corporate social responsibility and ethics to our stakeholders, employees, customers, and the communities in which we operate. Finally, the article examines future innovations such as AI and how it can be used in a socially responsible way.
Abstract: The medical technology industry has faced several unprecedented challenges due to COVID-19, including critical medical devices and supply chain shortages. We had to pivot rapidly to address the immediate need to provide lifesaving and sustaining devices, collaborate with competitors, and work collectively with regulators for emergency use authoriza...
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Research Article
Boosting Workplace Well-Being: A Novel Approach with a Mental Health Chatbot for Employee Engagement and Satisfaction
Sourav Banerjee*,
Ayushi Agarwal,
Promila Ghosh,
Ayush Kumar Bar
Issue:
Volume 8, Issue 1, June 2024
Pages:
5-12
Received:
18 December 2023
Accepted:
29 December 2023
Published:
11 January 2024
Abstract: Digital interventions for health, like chatbots, are being recognised as tools for addressing the growing mental health crisis. This study investigates the efficacy of AI-driven mental health interventions in the workplace, focusing on a chatbot designed to promote employee well-being. With an urgent need to address the rising mental health issues in corporate settings, the research delves into the current landscape of mental health support and evaluates the potential of technology-based solutions. A survey involving 25 HR professionals and Chief Human Resources Officers (CHROs) from various industries across the United States formed the backbone of the study. These participants, representing a significant portion of the corporate workforce, provided insights into the effectiveness, accessibility, and perceptions of mental health strategies in their organisations. Utilising a quantitative analysis method, specifically T-test hypothesis testing, the study aimed to understand attitudes towards AI-based mental health solutions and their actual implementation within organisations. It was found that while awareness of virtual AI coaches and therapists is relatively high, around 72%, only about 7% of respondents could accurately identify specific AI-driven mental health tools. Over 60% of participants preferred anonymity when discussing mental health issues, underscoring the sensitive nature of the topic. Despite around 84% indicating the presence of mental health support in organisations, at least 68% questioned its accessibility and comprehensiveness. The majority, about 83%, believed that AI-driven apps positively impact employee productivity, with at least 77% suggesting they could reduce attrition rates. However, concerns about data privacy and cultural acceptance within organisations were evident, with 52-64% of executives expressing reservations, respectively. The study illuminates a path forward, suggesting a nuanced understanding and strategic implementation of AI mental health solutions in the workplace. It underscores the need for comprehensive awareness programs, enhanced accessibility, and addressing privacy and cultural concerns, thereby paving the way for a more empathetic, efficient, and technology-integrated approach to employee mental health.
Abstract: Digital interventions for health, like chatbots, are being recognised as tools for addressing the growing mental health crisis. This study investigates the efficacy of AI-driven mental health interventions in the workplace, focusing on a chatbot designed to promote employee well-being. With an urgent need to address the rising mental health issues ...
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Research Article
Proteomics Data Classification Using Advanced Machine Learning Algorithm
Preethi Kolluru Ramanaiah*
Issue:
Volume 8, Issue 1, June 2024
Pages:
13-21
Received:
21 April 2024
Accepted:
3 May 2024
Published:
17 May 2024
Abstract: Proteomics, the study of proteins and their functions within biological systems, has become increasingly data-intensive, presenting both opportunities and challenges. This project addresses the need for advanced data analytics and data integrity in proteomics research. Leveraging the power of machine learning (ML) and blockchain technology, this attempt aims to transform proteomics research. This work encompasses three key objectives. First, collect, clean, and integrate proteomics data from diverse sources, ensuring data quality and consistency. Second, employ ML algorithms to analyze this data, revealing crucial insights, identifying proteins, and predicting their functions. Third, implement blockchain technology to safeguard the authenticity and integrity of the proteomics data, providing an auditable and tamper-proof record. Implemented a user-friendly web interface, facilitating collaboration among researchers and scientists by granting access to shared data and results. This study included various classification methods for the investigation of protein classification, namely, random forests, logistic regression, neural networks, support vector machines, and decision trees. In conclusion, the proposed work is poised to revolutionize proteomics research by enhancing data analytics capabilities and securing data integrity, thereby enabling scientists to make more informed and confident discoveries in this critical field.
Abstract: Proteomics, the study of proteins and their functions within biological systems, has become increasingly data-intensive, presenting both opportunities and challenges. This project addresses the need for advanced data analytics and data integrity in proteomics research. Leveraging the power of machine learning (ML) and blockchain technology, this at...
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Research Article
Deep Learning Social Filtering Model for Event Recommendation Services
David Ademola Oyemade*,
Linda Chioma Aworonye
Issue:
Volume 8, Issue 1, June 2024
Pages:
22-31
Received:
14 May 2024
Accepted:
29 May 2024
Published:
14 June 2024
Abstract: In the contemporary time, technology has made the determination and discovery of human preferences, priorities and personal inclinations possible through the use of recommender systems. Activities of users on the internet can be monitored, extracted, stored, analyzed and used by the recommender systems for suggesting future events to users on the web. This paper aims at developing and analyzing a model for event services recommendation for visitors to events. Event seekers, organizers and event service providers get notified, plan and book for upcoming events from their comfort zones without hassles of gallivanting nooks and crannies to enquire about prospective events. There is not any compelling need to interface with under-enthusiasts and intermediaries in the course of organizing, visiting and providing services for an event. However, it is obvious that massive amount of available information on the web exhibit frustrating attributes, hence it is increasingly a difficult task for users to find the content of interest; in other words, a huge chunk of information undiscovered on the network is left behind as “dark information”. In context, event service recommendation uses deep learning social filtering base techniques which adopt similarity computation measures with a bias for Pearson correlation coefficient, cosine similarity, and Euclidean similarity to recommend related and most relevant events/services to the targeted online audience. In this paper, the aim is to develop a deep learning model which integrates social filtering technique for enhancing the quality of event recommendation for users. A model based on the deep learning algorithm of multilayered perceptron and Neural Collaborative Filtering is proposed for event recommender services. The results from various simulations using meetup website dataset shows that the proposed model performs better than other techniques. The results yield 70% accuracy, 66% precision and 98% recall.
Abstract: In the contemporary time, technology has made the determination and discovery of human preferences, priorities and personal inclinations possible through the use of recommender systems. Activities of users on the internet can be monitored, extracted, stored, analyzed and used by the recommender systems for suggesting future events to users on the w...
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