Journal of Advanced Research in Modeling and Simulation

Ml Meets EI: The Role of Machine Learning in Enhancing Emotional Intelligence

Neeru Narang — 2024-08-02

Emotions are a vital part of our organic make-up, and each morning they march into the workplace with us and impact our behavior. Our ultimate awareness is on Emotional Intelligence (EI) and we integrate it with data mining. Coping with employees' feelings with the usage of machine learning is one of the exceptional researches in modern global. Right here, we observe to what extent the personnel are aware of their very own self and discover the ideas and views of an individual regarding themselves and others. Without the right expertise about their persona, it will likely be very difficult for a person to control their personal feelings. This paper explores the intersection of Machine Learning (ML) and Emotional Intelligence (EI) and investigates the role of ML in enhancing EI. Emotional intelligence, which includes the ability to recognize, understand, and manage emotions in various aspects of human life. It provides a comprehensive understanding of the current state, challenges, and future prospects in this emerging field.

Justification of Mass Customization Practices in Textile Industry-An Empirical Investigation

Harmesh Lal — 2024-08-02

The aim of this investigation is to look at issues with mass customization in clothing merchandising. A questionnaire survey is used to look at people's expectations for mass personalizing goods, procedures, and places, all of which can have an impact on how successful a mass personalization programme is. The aim of this study was to look at the effect of mass personalization on competitive strategy in order to find a way to bridge the gap between the two strategies. The questionnaire provides a convenience sample of more than 100 respondents from the garment and clothing industries. The data was analyzed using analytical modeling, descriptive statistics, factor analysis, and regression utilizing SPSS software. Results indicated that identifying the correct product, process, and position measurements is critical for effective mass customization of clothing in the retail sector. Our findings address merchandising issues about customer participation in the design and fitting of clothing products in retail stores.

Student Grade Prediction in Education Sector Using Machine Learning

Arti — 2024-08-02

Great results are achieved by working more on weak zones rather than working on strong areas. Bringing the same picture in context of student exams, students will be able to achieve higher grades if they work on lagging areas. Hence, realization of student’s performing abilities is very important for both teacher and the student. One of the ways to do this is using student grade prediction. By using machine learning algorithms, we can predict how well the students are going to perform so that we can help the students whose grades are predicted low.

It has been observed that till now, almost all popular student grade prediction models have been made on foreign university’s datasets. This work proposes a student grade prediction model which uses Machine Learning and Deep Learning technologies for predicting Indian Student’s Marks. As India is a large country and it is impossible to sample students from the entire county, so the scope of the problem is narrowed down to the Computer Science Engineering students of popular colleges in Punjab (Ludhiana) area. A dataset of thousands of students has been formed by manual collection of information from the students via internet such as Google Forms and then has been processed for its data analysis, preceded by various machine and deep learning algorithms like Linear Regression, Ridge, Lasso, Elastic Nets, Random Forests, Gradient Boost, SVM, Deep Learning and Decision Trees.

Navigating Challenges in The Adoption of Industry 4.0: Overcoming Limitations in Manufacturing Industries

Gaurav Tejpal — 2024-08-02

The advent of the fourth industrial revolution, commonly denoted as Industry 4.0, is not devoid of challenges. This approach will explore various typical issues and constraints associated with the adoption and utilization of Industry 4.0 technology in the manufacturing sector. The fourth industrial revolution signifies a transformative period for manufacturing, prompting this study to investigate and scrutinize common problems and limitations linked to the implementation of Industry 4.0 technology in manufacturing. Identified constraints encompass financial considerations, the threat of security breaches, the upheaval of existing job structures, compatibility issues with current infrastructure, workforce skill gaps, concerns regarding data security, data quality, and standardization, as well as organizational culture and the challenges of change management. Additionally, regulatory and legal complexities are among the limitations highlighted. Successful implementation of Industry 4.0 technologies hinges on companies acknowledging and addressing these limitations, thereby enabling informed decision-making and appropriate actions. Stakeholders’ comprehension and overcoming of these limitations are crucial for the effective deployment of Industry 4.0 technologies in manufacturing, facilitating a complete realization of the potential embedded in this revolutionary paradigm.

Applications of Data Visualization and Storytelling in Various Fields

Aditya Pandey — 2024-06-21

In today's data-driven world, the ability to effectively communicate and interpret information has become increasingly crucial. Data visualization, the art of translating data into visual representations, plays a pivotal role in bridging the gap between raw data and actionable insights. However, mere data visualization alone is often insufficient to fully convey the underlying story embedded within the numbers. This is where storytelling comes into play. Storytelling is a powerful tool for engaging audiences, guiding their understanding, and eliciting emotions. It allows us to connect with information on a deeper level, making it more memorable and relatable. By incorporating storytelling into data visualization, we can transform dry data into engaging narratives that resonate with diverse audiences. This combined approach, known as data storytelling, offers a powerful tool for unlocking the hidden stories within data and sharing them in a way that resonates with diverse audiences.