Journal of Big Data Technology and Business Analytics (e-ISSN: 2583-7834)

Enhancing Employee Productivity through Sentiment Analysis: A Focus on Workplace Well-being and Satisfaction

2023-07-12T15:10:31+0530

This research paper thoroughly examines the critical relationship between employee satisfaction, well-being, and productivity, with a primary emphasis on the utilization of sentiment analysis techniques to assess employee satisfaction within the workplace. To assist employers in creating a supportive work environment, this study develops a model that incorporates modified average word vector models to effectively classify employee feedback as positive, negative, or neutral, providing actionable insights for areas of improvement. The model proposed in this research paper serves as a valuable instrument for organizations striving to understand employee sentiment and identify potential concerns. Through comprehensive research and meticulous analysis, this study underscores the crucial importance of prioritizing employee well-being in the workplace. It highlights the role of advanced sentiment analysis techniques in capturing and comprehending the sentiments expressed by employees. By harnessing the power of technology, organizations can gain deep insights into the factors influencing employee satisfaction and take appropriate actions to address them. The research findings reveal a strong correlation between employee well-being, job satisfaction, and productivity. By diligently monitoring employee well-being and proactively addressing their concerns, organizations can cultivate a positive work culture that promotes job satisfaction, leading to improved performance and overall organizational success. It showcases the potential of sentiment analysis as a valuable tool for assessing employee satisfaction and guiding decision-making processes. By fostering a work environment that focuses on employee well-being, organizations can create a nurturing atmosphere that enhances job satisfaction, boosts productivity, and ultimately benefits both employees and the organization as a whole.

Cloud Computing Platforms and Technologies

2023-10-31T15:56:34+0530

Cloud computing is radically changing how
and when people work people work by
allocating, managing, and consuming
computer, storage, and networking resources,
and making them available to users
worldwide. Because of its strong processing
and storage, as well as high availability,
security, quick access and adaption,
consistent scalability and compatibility, cloud
computing is the most popular requirement
for today's rapidly increasing business world,
cost and time effectiveness. For each
business, when a client, company, or trade
embraces an expanding cloud environment,
they can choose the best Infrastructure,
platform, software, and network resources.
We begin this paper by developing a
thorough taxonomy for explaining the
architecture of Computing in the cloud. This
taxonomy allows for an analysis of the various
current cloud computing services provided by
various companies. Amazon, Google,
Microsoft, Sun, and Force.com are among the
corporations engaging in initiatives around
the world. The survey results are then used to
show the parallels and variations in cloud
computing architectural strategies.

Using Apple's ResearchKit and CareKit Frameworks for Explainable Artificial Intelligence Healthcare

2023-11-29T12:42:08+0530

Artificial intelligence (AI) has been a topic of
discussion in the healthcare industry for a
long time. The potential of AI to revolutionize
healthcare and medicine is significant.
However, some major obstacles to
implementing AI systems in the healthcare
industry exist. One such obstacle is more
transparency around how these systems work.
If AI systems are not transparent, it becomes
difficult for clinicians and patients to trust
them. To address this issue, Apple's opensource ResearchKit and CareKit frameworks
offer opportunities to develop explainable AI
tools that provide human-readable
explanations alongside recommendations or
predictions. These frameworks allow
developers to create AI systems that provide a
rationale for their outputs, indicate levels of
confidence, and allow for the evaluation of
fairness and bias. This article examines the
capabilities of ResearchKit and CareKit to
create transparent, interpretable AI systems.
Developing trustworthy and easily
understandable AI systems with these
frameworks can bridge the gap between
technologists and clinical end-users. However,
achieving this will require extensive
validation studies and close collaboration
between technologists and clinical end-users.
Some outstanding issues around privacy and
data sharing need to be addressed. By
addressing these issues, we can develop
trustworthy AI systems that clinicians and
patients can easily understand and act upon.
To sum up, by utilizing ResearchKit and
CareKit, we can create reliable AI systems in
the healthcare sector. Although certain
obstacles must be tackled, we can overcome
them by collaborating and guaranteeing that
the AI systems we design are transparent,
interpretable, and trustworthy

Application of Reinforcement Learning and Predictive Modeling for Smart Energy

2023-11-29T13:14:04+0530

The pursuit of smart energy solutions has
become paramount in addressing global
energy challenges, sustainability, and
efficiency. This research paper explores the
application of Reinforcement Learning (RL)
and Predictive Modeling in the domain of
Smart Energy. Smart Energy encompasses a
diverse range of systems, including smart
grids, smart buildings, and renewable energy
integration, where advanced data-driven
techniques are indispensable. The paper
begins by reviewing relevant literature on
smart energy and the capabilities of RL and
predictive modeling in energy management. It
provides a comprehensive understanding of
the challenges and opportunities presented by
smart energy systems. Reinforcement
Learning is investigated as a powerful tool for
optimizing energy consumption, enhancing
grid management, and enabling demand
response mechanisms. Various RL
algorithms, including Q-learning, Deep QNetworks (DQN), and Proximal Policy
Optimization (PPO), are discussed in the
context of their applications within smart
energy. Predictive Modeling is examined as a
vital component for forecasting energy
demand, renewable energy generation, and
energy prices. The paper delves into different
predictive modeling techniques such as time
series forecasting, regression models, and
neural networks, showcasing their relevance
and effectiveness in the energy domain.
Furthermore, the paper explores the
integration of RL and Predictive Modeling,
emphasizing the synergistic benefits derived
from their combined application. It suggests
future research directions and enhancements
to further advance the field. In conclusion, the
application of Reinforcement Learning and
Predictive Modeling emerges as a critical
approach to addressing the complex energy
management requirements of smart energy
systems. The findings of this research have
profound implications for energy
stakeholders, policymakers, and consumers,
paving the way for enhanced sustainability,
cost savings, and reduced environmental
impact in the evolving landscape of smart
energy.

Diabetes Prediction Using Machine Learning

2023-12-13T16:37:52+0530

“Diabetes is a widespread chronic condition that affects millions of people throughout the world”. Diabetes risk identification and prediction are critical in preventing its onset and effectively managing the condition. In this study, we employ machine learning techniques to develop a predictive model for diabetes risk assessment. We leverage a comprehensive dataset of patient information, including demographic, clinical, and lifestyle factors, to train and evaluate our model. Our results demonstrate the potential of machine learning in accurately predicting diabetes
risk, thus enabling timely interventions and Healthcare strategies that are tailored to the individual. This study adds to the increasing field of digital health and data-driven approaches to combating the diabetes epidemic. Utilizing a dataset comprising various health parameters and medical history, we develop predictive models to assess the likelihood of diabetes onset. Our research aims to enhance early diagnosis and intervention for diabetes, potentially reducing its impact on public health. We assess the performance of several machine learning algorithms and present promising results, demonstrating the potential for accurate and timely diabetes prediction.