A360 Bot Framework: Empowering Smart Robotic Process Automation Solutions

This research paper explores the significance of the “A360 Bot Framework” in Automation 360 (A360) platform. A360 is Automation Anywhere’s cloud-based automation platform designed to make business processes more efficient. It’s known for its user-friendly interface, which allows both technical and non-technical users to use it effectively. Automation 360 is versatile, offering a range of tools to automate tasks, manage complex workflows, and integrate various applications. It empowers users to create customized solutions for their specific needs. Being cloud-based it ensures scalability, security, and real-time updates, making it a top choice in the fast-paced digital world. As demand for A360 rises, having a structured way to develop bots becomes crucial. The paper introduces the “A360 Bot Framework” as a guiding approach for bot developments. This framework ensures consistency and scalability, especially when working with both professional developers and non-technical users. It outlines key elements like setting up work folders, managing logs, dealing with errors, and ensuring secure bot execution. Ultimately, the “A360 Bot Framework” is presented as a foundational structure that enhances consistency, resiliency, and development efficiency. By following predefined practices and templates, bot developers can mitigate risks and streamline debugging processes. This framework accelerates the bot development lifecycle, allowing developers to focus on specific functionalities and value-added features. The research paper aims to provide insights into the benefits of adopting the A360 Bot Framework and its potential to revolutionize A360 bot development practices, leading to more efficient and effective automation solutions.

How Does Robotics Process Automation (RPA) Affect Project Management Practices

The main purpose of this literature review is to understand the effectiveness and impacts of Robotics Process Automation (RPA) on the practices of Project Management (PM). For attaining the purposes of the research study, an extensive literature review was conducted, which helped in gaining a theoretical understanding. These findings were then justified with the help of current and relevant secondary sources in the analysis section. The findings suggested that RPA is quite advantageous for implementing in varied aspects of a business, especially in the field of project management. However, it can pose several challenges, which need to be taken into due consideration by the organizations during execution such as the capabilities of the employees and the abilities of the existing systems to incorporate automation, among others. It was ultimately concluded that RPA is highly advantageous for project management teams but its effective implementation is the key to success.

Maximizing the Efficiency of Automation Solutions with Automation 360: Approaches for Developing Subtasks and Retry Framework

I present a solution that explores the use of A360 subtasks as a comparable concept to functions in programming. By leveraging subtasks as reusable and maintainable functions, users can efficiently develop customized high-quality automation solutions. Additionally, the paper introduces the retry framework, which allows for the automatic retrying of subtasks in the event of system or unknown exceptions. This framework enhances efficiency and reduces the manual effort required to retrigger bots. The A360 Subtask and Retry Framework templates provide valuable assistance to both professional and citizen developers, improving code quality, maintainability, and the overall efficiency and resiliency of automation solutions.

数字化转型中RPA技术与配网抢修工单监控的融合应用

机器人流程自动化技术可以将人机交互的业务进行脚本化设计,通过逐一执行自动访问、执行、记录等模块化的功能,高效解决重复、复杂的流程,能够节约人工成本并可靠的完成既定任务。对RPA(Robotic process automation)技术进行了研究,分析其自动化执行工作流程的工作原理,针对配网抢修工单监控业务进行RPA开发,设计一套可定时触发轮询监控工单状态的RPA脚本。通过推广应用验证了所设计的配网工单监控RPA机器人的可行性,为进一步推广研究RPA技术提供参考。

Early Detection of Heartbeat from Multimodal Data Using RPA Learning with KDNN-SAE

Heartbeat detection stays central to cardiovascular an electrocardiogram(ECG)is used to help with disease diagnosis and management.Existing Convolutional Neural Network(CNN)-based methods suffer from the less generalization problem thus;the effectiveness and robustness of the traditional heartbeat detector methods cannot be guaranteed.In contrast,this work proposes a heartbeat detector Krill based Deep Neural Network Stacked Auto Encoders(KDNN-SAE)that computes the disease before the exact heart rate by combining features from multiple ECG Signals.Heartbeats are classified independently and multiple signals are fused to estimate life threatening conditions earlier without any error in classification of heart beat.This work contained Training and testing stages,in the preparation part at first the Adaptive Filter Enthalpy-based Empirical Mode Decomposition(EMD)is utilized to eliminate the motion artifact in the signal.At that point,the robotic process automation(RPA)learning part extracts the effective features are extracted,and normalized the value of the feature then estimated utilizing the RPA loss function.At last KDNN-SAE prepared training for the data stored in the dataset.In the subsequent stage,input signal compute motion artifact and RPA Learning the evaluation part determines the detection of Heartbeat.So early diagnosis of heart failures is an essential factor.The results of the experiments show that our proposed method has a high score outcome of 0.9997.Comparable to the CIF,which reaches 0.9990.The CNN and Artificial Neural Network(ANN)had less score 0.95115 and 0.90147.

Hybrid Approach to Document Anomaly Detection:An Application to Facilitate RPA in Title Insurance

Anomaly detection(AD)is an important aspect of various domains and title insurance(TI)is no exception.Robotic process automation(RPA)is taking over manual tasks in TI business processes,but it has its limitations without the support of artificial intelligence(AI)and machine learning(ML).With increasing data dimensionality and in composite population scenarios,the complexity of detecting anomalies increases and AD in automated document management systems(ADMS)is the least explored domain.Deep learning,being the fastest maturing technology can be combined along with traditional anomaly detectors to facilitate and improve the RPAs in TI.We present a hybrid model for AD,using autoencoders(AE)and a one-class support vector machine(OSVM).In the present study,OSVM receives input features representing real-time documents from the TI business,orchestrated and with dimensions reduced by AE.The results obtained from multiple experiments are comparable with traditional methods and within a business acceptable range,regarding accuracy and performance.