Insights from large and medium firms in this paper provide an understanding on how manufacturing firms can enhance financial management processes using artificial intelligence enabled enterprise systems. These results highlight the impact of these systems in developing financial management prowess and contribute valuable knowledge to both industry practitioners and academia.
Predicting certain information from abstracts only via AI is a bold application of AI. The success of this project can significantly help biotech companies to market their products to potential customers. It can also broaden the application spectrum of AI.
The research advances automated platypus detection using state-of-the-art models, achieving high precision and efficiency, significantly reducing manual classification in ecological studies.
1. The paper identifies and outlines the security challenges
faced by high-risk industries, particularly in sectors such
as energy, healthcare, transportation, and manufacturing,
where human lives are at stake.
2. It proposes an innovative solution that integrates digital
twin technology and blockchain to address the identified
security challenges. Digital twin technology enables
real-time virtual representations of physical assets, while
blockchain ensures data integrity and immutability.
This paper gives an overview on wireless power transfer (WPT) for assistive mobility devices (AMD). Shows how WPT can significantly contribute in enhancement of AMD for better inclusivity of persons with mobility challenges in the society as a whole.
We are outlining the detailed process of retrofitting a legacy CNC machine to add connectivity and integrate it into the Industry 4.0 framework. This work offers significant benefits, especially for small and medium-sized companies. The outcome has the potential to enhance sustainability in industrial processes by focusing on the machine’s energy consumption.
We have developed a framework for integrating digital twins into older CNC machines, focusing on monitoring their electrical energy consumption. The digital twin’s virtual component utilizes discrete event simulation software in a digital manufacturing environment. Our goal with this proposal is to promote greater sustainability in manufacturing systems.
Human Activity Recognition (HAR) is a feature
of an automated system that recognizes human actions. Since
most people these days are health-conscious, people use their
smartphones or smartwatches to track their daily activities. This
helps them organize their schedules and lifestyles more effectively.
Recent advancements in Deep Learning (DL) performance have
mitigated certain issues related to HAR. Consequently, DL methods
are essential for improved competence and precision. This
paper provides a comparative study that utilizes state-of-the-art
Kolmogorov-Arnold Network (KAN) and Multi-layer Perceptron
(MLP) to classify human activities using biometrics data. The
Biometrics dataset, which includes 18 classes representing a
variety of activities, is used for HAR. For optimal outcomes, the
suggested algorithm is trained and tested using the TensorFlow
structure and a hyperparameter tuning technique. The outcomes
show that the KAN algorithm performs quite well in identifying
human activity with an accuracy of 72.64% and a loss rate of
0.9136. The experiment’s findings suggested that the KAN model
performs more effectively and accurately for human activity
identification.
This study delves into the spatial characteristics of housing prices within California, with a specific focus on San Francisco. We explore various models to predict housing prices based on different feature sets, including coordinate and non-coordinate attributes. Through extensive analysis, we find that incorporating geographic coordinates significantly enhances the predictive accuracy of housing prices. Utilizing a neural network model, we achieve nearly 100% accuracy in predicting the quartile of median housing prices, underscoring the complexity and influence of location-specific features. Finally, we utilized i-SLFN algorithm for developing a precise price prediction model and describing communities characteristics.