The proposed research aims to enhance ticket creation efficiency by utilising a chatbot embedded with automation capabilities, allowing tickets to be completed without human intervention. This will significantly improve overall processing time and workflow from both a customer service and technical efficiency perspective.
Abstract— MANET stands for Mobile Ad-hoc Network also
called wireless Ad-hoc Network or Ad-hoc Wireless Network. It is
a decentralized wireless network consisting of mobile devices
(nodes) that communicate with each other without relying on a
fixed infrastructure. MANET forms a highly dynamic
autonomous topology with the presence of one or multiple
different transceivers between modes. MANETs consist of a peer
to-peer, self-configuring and self-healing modes. Mobile Ad-hoc
Network has wide range of applications such as military and
defense operations, healthcare, sensor networks, wireless sensor
networks, Internet of Things (IoT) etc. In order to enhance the
routing efficiency and performance in Mobile Ad-Hoc Networks
(MANETs) this paper proposing different deep learning
techniques.
Keywords— Mobile Ad-Hoc Networks, Wireless Network
Topology, Wireless Communication, Deep Learning.
Abstract— MANET stands for Mobile Ad-hoc Network also
called wireless Ad-hoc Network or Ad-hoc Wireless Network. It is
a decentralized wireless network consisting of mobile devices
(nodes) that communicate with each other without relying on a
fixed infrastructure. MANET forms a highly dynamic
autonomous topology with the presence of one or multiple
different transceivers between modes. MANETs consist of a peer
to-peer, self-configuring and self-healing modes. Mobile Ad-hoc
Network has wide range of applications such as military and
defense operations, healthcare, sensor networks, wireless sensor
networks, Internet of Things (IoT) etc. In order to enhance the
routing efficiency and performance in Mobile Ad-Hoc Networks
(MANETs) this paper proposing different deep learning
techniques.
Keywords— Mobile Ad-Hoc Networks, Wireless Network
Topology, Wireless Communication, Deep Learning.
Brushless DC (BLDC) motors are widely utilized in various fields, including high-speed drives, artificial heart pumps, and electric vehicles, due to their superior torque, compact size, and enhanced efficiency. However, it is very difficult to obtain satisfactory control
performance for BLDC motors using conventional PID
controller, because of the difficulty in tuning the proper PID parameters.
In this paper, an optimal cascade PID controller is designed for
controlling the BLDC motor. The proposed cascade controller consist of an inner loop and an outer loop, each responsible for different aspects of the control process. The inner loop handles fast dynamics, namely current control, while the outer loop deals with slower dynamics, as speed control. This separation allows each loop to be optimized individually, resulting in improved overall system performance and stability. By quickly responding to disturbances in the inner loop, cascade controllers can effectively overcome oscillations and enhance the stability of the motor. Additionally, cascade controllers can better handle non-linearities and parameter variations, leading to more accurate and reliable control of the output. However, tuning PID controller gains is crucial for achieving optimal performance and stability in control systems, and metaheuristic algorithms offer significant benefits by efficiently searching for the best gain values, even in complex and high-dimensional parameter spaces. The proposed cascade PID controller’s gains are optimized using the Antlion Optimization (ALO) algorithm, a modern metaheuristic algorithm known for its effectiveness in constrained problems and diverse search spaces. This optimization enhances the controller’s robustness against disturbances, particularly supply voltage variations. To validate the system’s performance, Hardware-in-Loop (HIL) Typhon technology is employed, allowing real-time testing of the BDCM and controller under various conditions. This ensures the reliability and effectiveness of the system before actual implementation.
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.
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.
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.
The significance of the paper is to study how 5G networks respond and adapt to different application conditions.