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Impact of Robotic Process Automation on Enterprise Resource Planning Systems

Impact of Robotic Process Automation on Enterprise Resource ...

Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM), International Conference on th

作者： Dileep Kumar Yendluri Jainath Ponnala Ramya Thatikonda M. Kempanna Reshmi Tatikonda A. Bhuvanesh Masters in Computer Science University of the Cumberlands USA Bachelors in ECE Jawaharlal Technological University Hyderbad India PhD in Information Technology University of the Cumberlands Williamsburg USA Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Bengaluru Karnataka India Masters in Computer Information Systems University of the Central Missouri Warrensburg USA Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India

ERP systems are merging their powers to generate a dynamic collaboration. By examining the integration between RPA (Robotic Process Automation) and ERP (Enterprise Resource Planning) systems, we can identify their shared potential for enhancing process flow. Combining automation and ERP integration makes RPA a powerful tool for process management across multiple industries. The paper examines the basics of RPA and its ERP integration, highlighting the advantages of enhanced productivity and fewer errors. Addressing security, compliance, and change management issues simultaneously. In addition to top-down and bottom-up methods, process analysis and selecting suitable processes are covered. Practical examples offer insightful observations on this integration’s success. The union of RPA and ERP creates a powerful combination that can elevate operational effectiveness, facilitate strategic insights, and reshape the workplace.

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Design and Implementation of Signal Processing for 2D Scanning Radar with Multi-Reflection Detection Using 60 GHz NRD Guide Pulse Radar

Design and Implementation of Signal Processing for 2D Scanni...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Thethi, H. Pal Reddy, N. V. Uma Sengar, R.S. Dashyam, Preethi Mounika, T. Abedi, Tamam Ali Abd Ulabbas Sharma, Geetika Lovely Professional University Phagwara India New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India Gl Bajaj Group of Institutions Department of Electrical & Electronics Engineering Mathura India Griet Department of Information Technology Bachupally Telangana Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India

ISBN: (纸本)9798331527495

The design and implementation of a signal processing system for a two dimensional (2D) electric scanning radar using multi-reflection detection to identify short range object using 60 GHz NRD (Non Radiative Dielectric) guide pulse radar is presented in this research paper. Designed to increase detection accuracy and precision by processing multiple reflections of the transmitted signals in order to recognize objects at short distances, the radar system is the one. The signal processing algorithms are adapted for MPTS handling, and the system takes advantage of the high frequency properties of the 60 GHz NRD guide. Hardware design, simulation results, and performance evaluation regarding the detection accuracy, range resolution, and processing latency, are also discussed. However, applications like autonomous driving, indoor navigation, and industrial automation require short range, high precision radar system, and this approach offers a big advantage. © 2025 IEEE.

关键词： Radar signal processing

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An Investigation of Smart Detection for Small Lung Tumor with Tumor Pattern Recognition Algorithm

An Investigation of Smart Detection for Small Lung Tumor wit...

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artificial intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： G. Hariharan P. Prasanth P. Arthi Devarani T. Sajana Indhumathi C Ashok Kumar Deportment of Artificial Intelligence and Machine Learning Malla Reddy University Hyderabad Telangana India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering R. M K College of Engineering and Technology Thiruvallur Tamil Nadu India Department of Artificial Intelligence and Data Science KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Business Systems R.M. K. Engineering College Kavaraipettai Tamil Nadu Department of Computer Science BanasthaliVidyapith Rajasthan India

The Small-cell lung tumor is the prime public concern, resulting in increased mortality. Various therapeutic approaches have made progress in the handling of small-cell lung tumor. It's considered the backbone of cytotoxic chemotherapy treatment. However, clinical research shows that because small-cell lung tumor is chemically approachable, high response rates among small-cell lung tumorinfecters are generally not translated into cure. Therefore, it is high time to make progress in small-cell lung tumor research because every success in clinical trials is fought, and every failure is a lesson. A recent study of small-cell lung tumor has improved the prognosis of patients. Some notable research areas have provided new options for patients by conducting multiple clinical trials. In this paper smart edge detection based small lung tumor identification method was proposed. This model operates with the pattern recognition algorithm model. This algorithm smartly finds the edges of the tumor cells and provides the edge results. This will helpful for the doctors to identify the tumor part exactly.

关键词： Chemotherapy Image edge detection Lung cancer Medical services Clinical trials Knowledge discovery Concurrent engineering

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Intelligent Plant Leaf Disease Detection Using Generative Adversarial Networks: a Case-study of Cassava Leaves

Open Agriculture Journal

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Open Agriculture Journal 2024年 18卷

作者： Lokesh, Gururaj Harinahalli Chandregowda, Soundarya Bidare Vishwanath, Janhavi Ravi, Vinayakumar Ravi, Pradeep Mazroa, Alanoud Al Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Artificial Intelligence and Machine Learning Alva’s Institute of Engineering and Technology Mangalore India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysuru India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Science and Engineering GSSS Institute of Engineering and Technology for Women Karnataka Mysuru India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia

Background: Cassava leaf disease detection is a major problem since it is very difficult to identify the disease in naked-eye observation and even experts such as agricultural scientists can fail in this task. The approach we use in this paper has the potential to overcome this problem. Materials and Methods: In this, we propose an approach based on artificial intelligence for leaf disease detection using deep learning with generative adversarial networks (GAN). Our experimental study used a dataset including 12880 cassava leaf pictures generated using CycleGAN showing five major disease classes. In order to avoid overfitting, a GAN architecture is proposed for data augmentation using two networks, i.e., a Generator and a Discriminator. The generator is trained to generate similar data samples as the original data Results: The proposed approach achieved an accuracy of 99.51% for the classification of healthy or unhealthy leaf images, which outperformed existing methods. Discussion: The discriminator is trained to distinguish between the unique and generated sample records, as actual or fake. To classify cassava images into five categories of diseases, a combination of machine learning models has been trained on original and generated images. The proposed approach showed better accuracy compared to the existing methods. Conclusion: The proposed deep learning-based method can be used as a tool for early disease diagnosis in cassava leaf disease detection and classification. © 2024 The Author(s). Published by Bentham Open.

关键词： Cassava CNN CycleGAN Deep learning Deep learning Plant leaf disease

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Cross-modal communication technology: A survey

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Fundamental Research 2024年

作者： Wei, Xin Wu, Dan Zhou, Liang Guizani, Mohsen School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing 210003 China College of Communications Engineering Army Engineering University of PLA Nanjing 210007 China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi 999041 United Arab Emirates

In the 5G era and beyond, multi-modal services that integrate audio, visual, and haptic signals are expected to become dominant applications. To support multi-modal services, the concept of cross-modal communications, which involves collaborative audio-visual and haptic interactions, has emerged. Despite significant research about cross-modal communication technology being conducted, a comprehensive literature review on this topic is lacking. To fill this gap, this paper presents a detailed survey on cross-modal communication technology. First, it provides a highly summarized description of representative research attempts in audio-visual and haptic communications, which serve as the foundation for cross-modal communications. Then, it delves into various aspects of cross-modal communications, including architectural, cross-modal coding, cross-modal transmission, cross-modal signal reconstruction, the essence of semantics, and prototype systems. Finally, it discusses conclusions and future research directions. This paper is expected to promote the theoretical research and practical applications of cross-modal communications. © 2023

关键词： Audio-visual Cross-modal communications Haptic Multi-modal service Semantics

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Toward Understanding and Harnessing the Effect of Image Transformation in Adversarial Detection

SSRN

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SSRN 2024年

作者： Liu, Hui Zhao, Bo Peng, Yuefeng Li, Weidong Liu, Peng School of Computer Science Central China Normal University Wuhan430079 China Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan430079 China School of Cyber Science and Engineering Wuhan University Wuhan430072 China Wuhan University China College of Information Sciences and Technology Pennsylvania State University PA16801 United States

Deep neural networks (DNNs) are threatened by adversarial examples. Adversarial detection, which distinguishes adversarial images from benign images, is fundamental for robust DNN-based services. Image transformation is one of the most effective approaches to detecting adversarial examples. During the last few years, a variety of image transformations have been studied and discussed to design reliable adversarial detectors. Despite many well-designed adversarial detectors based on image transformation have achieved good performance, the complex design conceals the effect of image transformation on adversarial detection. In this paper, we systematically synthesize the recent progress in adversarial detection via image transformations with a novel classification method. Then, we conduct extensive experiments to test the detection performance of image transformations against state-of-the-art adversarial attacks. Furthermore, we reveal that each individual transformation is not capable of detecting adversarial examples robustly and propose a DNN-based approach referred to as \emph{AdvJudge}, which combines scores of 9 image transformations. Without knowing which individual scores are misleading or not misleading, \emph{AdvJudge} can make the correct judgment and achieve a significant improvement in the detection rate. Finally, we utilize an explainable AI tool to show the contribution of each image transformation to adversarial detection. Experimental results show that \emph{AdvJudge} achieves a significant improvement in detection accuracy compared to all individual transformation detectors, and outperforms existing state-of-the-art adversarial detectors on the large-scale ImageNet dataset. \emph{AdvJudge} can be easily integrated into deployed image classifier as a plug-in robustness-enhancing module without compromising accuracy. © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Heterogeneous Cognitive learning Particle Swarm Optimization for Large-Scale Optimization Problems

SSRN

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SSRN 2022年

作者： Zhang, En Nie, Zihao Yang, Qiang Wang, Yiqiao Liu, Dong Jeon, Sang-Woon Zhang, Jun College of Computer and Information Engineering Henan Normal University Xinxiang China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang China School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China School of Management Beijing University of Chinese Medicine Beijing China Department of Electrical and Electronic Engineering Hanyang University Ansan Korea Republic of

Large-scale optimization problems (LSOPs) become increasingly common but complicated nowadays. Confronted with such sophisticated optimization problems, the optimization performance of existing optimizers deteriorates dramatically. To tackle this type of problems effectively, this paper proposes a heterogeneous cognitive learning particle swarm optimization (HCLPSO) algorithm. Specifically, during the optimization, particles in the swarm are partitioned into superior particles (SP) and inferior particles (IP) based on their fitness. Next, this paper treats the two categories of particles differently by designing a random elite cognitive learning strategy to evolve the inferior particles and a stochastic dominant cognitive learning strategy to evolve the superior ones. To further help HCLPSO search the vast space with proper compromise between intensification and diversification, this paper devises a dynamic swarm partition strategy to dynamically separate particles into the two categories. With this dynamic strategy, HCLPSO gradually switches from exploring the search space to exploiting the found optimal zones intensively. A series of experiments are conducted on the publicly acknowledged CEC2010 and CEC2013 LSOP suites to compare HCLPSO with several state-of-the-art large-scale optimizers. Experimental results show that HCLPSO is effective to tackle LSOPs, and attains highly competitive or even far better optimization performance than the compared state-of-the-art large-scale optimizers. © 2022, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Bi-level Doubly Variational learning for Energy-based Latent Variable Models

arXiv

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arXiv 2022年

作者： Kan, Ge Lü, Jinhu Wang, Tian Zhang, Baochang Zhu, Aichun Huang, Lei Guo, Guodong Snoussi, Hichem School of Automation Science and Electrical Engineering Institue of Artificial Intelligence Beihang University Beijing China School of Computer Science and Technology Nanjing Tech University Nanjing China Institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application Beijing China University of Technology of Troyes Troyes France

Energy-based latent variable models (EBLVMs) are more expressive than conventional energy-based models. However, its potential on visual tasks are limited by its training process based on maximum likelihood estimate that requires sampling from two intractable distributions. In this paper, we propose Bi-level doubly variational learning (BiDVL), which is based on a new bi-level optimization framework and two tractable variational distributions to facilitate learning EBLVMs. Particularly, we lead a decoupled EBLVM consisting of a marginal energy-based distribution and a structural posterior to handle the difficulties when learning deep EBLVMs on images. By choosing a symmetric KL divergence in the lower level of our framework, a compact BiDVL for visual tasks can be obtained. Our model achieves impressive image generation performance over related works. It also demonstrates the significant capacity of testing image reconstruction and out-of-distribution detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Vision

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Functional Linear Non-Gaussian Acyclic Model for Causal Discovery

arXiv

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arXiv 2024年

作者： Yang, Tian-Le Lee, Kuang-Yao Zhang, Kun Suzuki, Joe Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Osaka Toyonaka Japan Department of Statistics Operations and Data Science Temple University PhiladelphiaPA19122 United States Machine Learning Department Carnegie Mellon University Mohamed bin Zayed University of Artificial Intelligence PittsburghPA15213 United States Masdar city Abu Dhabi United Arab Emirates

In causal discovery, non-Gaussianity has been used to characterize the complete configuration of a Linear Non-Gaussian Acyclic Model (LiNGAM), encompassing both the causal ordering of variables and their respective connection strengths. However, LiNGAM can only deal with the finite-dimensional case. To expand this concept, we extend the notion of variables to encompass vectors and even functions, leading to the Functional Linear Non-Gaussian Acyclic Model (Func-LiNGAM). Our motivation stems from the desire to identify causal relationships in brain-effective connectivity tasks involving, for example, fMRI and EEG datasets. We demonstrate why the original LiNGAM fails to handle these inherently infinite-dimensional datasets and explain the availability of functional data analysis from both empirical and theoretical perspectives. We establish theoretical guarantees of the identifiability of the causal relationship among non-Gaussian random vectors and even random functions in infinite-dimensional Hilbert spaces. To address the issue of sparsity in discrete time points within intrinsic infinite-dimensional functional data, we propose optimizing the coordinates of the vectors using functional principal component analysis. Experimental results on synthetic data verify the ability of the proposed framework to identify causal relationships among multivariate functions using the observed samples. For real data, we focus on analyzing the brain connectivity patterns derived from fMRI data. © 2024, CC BY.

关键词： Vector spaces

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Design and Development of S-Shaped Antenna for Wireless Networks

Design and Development of S-Shaped Antenna for Wireless Netw...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Vanitha V P Kishor Kumar Y. Sreenivasulu Ruchira Rawat S. Ramesh Jogendra Kumar Department of Electronics and Communication Engineering AarupadaiVeedu Institute of Technology Vinayaka Missions Research Foundation (Deemed to be University) Chennai Tamil Nadu India Department of Electronics and Communications Engineering Ravindra College of Engineering for Women Kurnool Andhra Pradesh India Department of Electronics and Communications Engineering Sreenidhi Institute of Science and Technology Hyderabad Telangana India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand Department of Computational Intelligence Institute of Artificial Intelligence and Machine Learning Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

In this study, a linear array of S-shaped Microstrip patch antennas is created and examined. IE3D V15.2 software was used to design, study, and simulate the antenna. The ground plane is 40x70 mm, and the patch is 30x60 mm, we'll say. In only one band, the bandwidth has increased. Gain at 2.43271 GHz is 3.68152 dBi, whereas bandwidth at 1.8744 GHz is 63.05 percent. Substratum thickness of 1.6 mm, dielectric constant of 4.2, and loss tangent of 0.0013 are taken into account in this computation. This antenna is mostly used in wireless systems.

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