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Risk Management in Robotic Process Automation 2

Risk Management in Robotic Process Automation

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Yendluri, Dileep Kumar Thatikonda, Ramya Thatikonda, Reshmi Ponnala, Jainath Kempanna, M. Bhuvanesh, A. University of the Cumberlands Department of Computer Science Williamsburg United States University of the Cumberlands Department of Information Technology Williamsburg United States University of Central Missouri Department of Computer Information Systems Williamsburg United States Jawaharlal Technological University Department of Electronics and Communication Engineering Hyderbad India Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Bangalore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350360523

The paper investigates the nuances of RPA risk management, detailing the difficulties, tendencies, and approaches organizations must adopt to integrate RPA into their operations successfully. From automation to optimization, RPA's impact on industry workflow is revolutionary. However, RPA introduces a spectrum of technical, operational, business, and compliance risks alongside its benefits. Amidst the intricate landscape of RPA, this paper sheds light on key risks. It offers practical strategies for tackling them, enabling organizations to confidently unlock automation's transformative power. Trends like advanced analytics fusion, AI-driven risk analysis, and blockchain utilization will likely influence future RPA risk management. In addition, the paper discusses evolving challenges, including ethical issues, breakneck technology evolution, and digital security dangers. Using an in-depth examination of RPA risk management, this paper prepares organizations for the ever-changing automation environment with informed decisions and proactive measures. © 2024 IEEE.

关键词： Risk assessment

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Comparison of *** and Spring Boot in Web Development 15

Comparison of *** and Spring Boot in Web Development

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15th International Conference on electronics, computers and Artificial Intelligence, ECAI 2023

作者： Novac, Ovidiu Constantin Ghiurau, David Novac, Mihaela Cornelia Gordan, Cornelia Emilia Oproescu, Mihai Bujdoso, Gyongyi Mihalca, Vlad Ovidiu University of Oradea Electrical Engineering and Information Technology Faculty Department of Computers and Information Technology Oradea Romania University of Oradea Management in Information Technology Electrical Engineering and Information Technology Faculty Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Electrical Engineering Oradea Romania University of Oradea Electrical Engineering and Information Technology Faculty Department of Electronics and Telecommunications Oradea Romania University of Pitesti Faculty of Electronics Communication and Computer Science Department of Electronics Computers and Electrical Engineering Pitesti Romania University of Debrecen Faculty of Informatics Department of Computer Science and Library and Information Science Debrecen Hungary School of Engineering Sciences University of Oradea Electrical Engineering and Information Technology Faculty Oradea Romania

ISBN: (纸本)9798350321388

In this paper we present two types of server implementation, we will look at the advantages and disadvantages of each technology. *** server application, built with ***, a single threaded server which will treat the requests in an asynchronous manner, with a non-blocking principle, whilst Spring Boot is a more robust and more common approach of a server technology with the benefits of having a strongly typed architecture and multiple threads available. These types of application will behave the same, making almost impossible to tell the difference between a *** and a Spring Boot web server. © 2023 IEEE.

关键词： Node.Js non-blocking single-multi threaded Spring Boot web server

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Advancing Rose Disease Diagnosis: A Deep Learning Framework with EfficientNet-B7 8

Advancing Rose Disease Diagnosis: A Deep Learning Framework ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Poornima, S. Divya, R. Krishnan, R. Santhana Jegadeesan, S. Yamini, G. Rajkumar, G. Vinoth Sri Ramakrishna College of Arts and Science for Women Department of Computer Science Coimbatore India Psna College of Engineering and Technology Department of Information Technology Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Information Technology Madurai India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of English Chennai India J.P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India

ISBN: (纸本)9798350376425

This study introduces an advanced deep learning framework for diagnosing rose diseases using EfficientNet B7, a high-performance model pre-trained on the ImageNet dataset. The research aims to automate the identification of four common rose diseases: Powdery Mildew, Black Spot, Downy Mildew, and Rust by applying transfer learning with EfficientNet B7. This approach is evaluated using two specialized datasets, the Rose Disease Dataset and the Rose Leaves Disease Detection Dataset. The framework's effectiveness is assessed by comparing its performance against other prominent deep learning models, including DenseNet, ResNet, and VGGNet. Performance metrics such as Accuracy, Precision, Recall, and F1 Score are used for this comparison. The results demonstrate that EfficientNet B7 outperforms DenseNet, ResNet, and VGGNet in terms of accuracy and reliability in disease classification. EfficientNet B7's efficiency and superior handling of complex feature representations contribute to its enhanced performance, making it a robust tool for rose disease diagnosis. This advanced framework offers significant improvements over existing models in terms of diagnostic accuracy and computational efficiency, providing a valuable resource for early detection and management of rose diseases. The results underscore the potential of leveraging state-of-the-art deep learning models to enhance agricultural practices and support more effective plant health management strategies. © 2024 IEEE.

关键词： Contrastive Learning

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A Novel Edge-Assisted IoT-ML-Based Smart Healthcare Framework for COVID-19

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computer Modeling in engineering & Sciences 2023年第12期137卷 2529-2565页

作者： Mahmood Hussain Mir Sanjay Jamwal Ummer Iqbal Abolfazl Mehbodniya Julian Webber Umar Hafiz Khan Department of Computer Sciences Baba Ghulam Shah Badshah UniversityRajouriJammu&KashmirIndia Department of Computer Sciences National Institute of Electronics and Information TechnologySrinagarJammu&KashmirIndia Department of Electronics and Communication Engineering Kuwait College of Science and TechnologyKuwait City20185145Kuwait Department of Geriatric Medicine Sher-i-Kashmir Institute of Medical SciencesSrinagarJammu&KashmirIndia

The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID-19 since its *** Internet of Things(IoT)along with other technologies like Machine Learning can revolutionize the traditional healthcare *** of reactive healthcare systems,IoT technology combined with machine learning and edge computing can deliver proactive and preventive healthcare *** this study,a novel healthcare edge-assisted framework has been proposed to detect and prognosticate the COVID-19 suspects in the initial phases to stop the transmission of coronavirus *** proposed framework is based on edge computing to provide personalized healthcare facilities with minimal latency,short response time,and optimal energy *** this paper,the COVID-19 primary novel dataset has been used for experimental purposes employing various classification-based machine learning *** proposed models were validated using kcross-validation to ensure the consistency of *** on the experimental results,our proposed models have recorded good accuracies with highest of 97.767%by Support Vector *** to the findings of experiments,the proposed conceptual model will aid in the early detection and prediction of COVID-19 suspects,as well as continuous monitoring of the patient in order to provide emergency care in case of medical volatile situation.

关键词： COVID-19 edge computing framework Internet of Things(IoT) machine learning(ML) network symptoms

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Importance of Search Engine Optimization in Digitalization - A Comprehensive Analysis

Importance of Search Engine Optimization in Digitalization -...

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Tatikonda, Reshmi Ponnala, Jainath Tatikonda, Ramya Yendluri, Dileep Kumar Kempanna, M. Bhuvanesh, A. University of the Central Missouri Department of Computer Information Systems Warrensburg United States Jawaharlal Technological University Department of Electronics and Communication Engineering Hyderabad India University of the Cumberlands Department of Information Technology Williamsburg United States University of the Cumberlands Department of Computer Science Williamsburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Bangalore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350388800

This article examines the complex realm of search engine optimization and its importance in the era of digitalization. With an emphasis on different facets of search engine optimization, including optimizing on-page elements, off-page optimization, technology optimization, and optimizing content, this document thoroughly comprehends how enterprises can boost their online appearance and generate natural website visitors. Moreover, it explores the complexities of search engine optimization algorithms and advancements and reviews the available resources and methods for enhancement. Additionally, it covers recommended approaches and plans for the successful implementation of SEO. This article further explores upcoming patterns within search engine optimization, including enhancing voice search and incorporating AI and ML. Furthermore, it discusses potential hurdles and potential openings in SEO's future. Lastly, the study provides suggestions to explore further and improve optimizing search engines. It includes regional SEO, UX optimization, sound search, and socially conscious SEO practices. © 2024 IEEE.

关键词： Search engines

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A novel ontological-based trust aware hybrid key management scheme (OTAHKMS) to enhance network lifetime and energy usage in wireless sensor networks (WSNs)

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International Journal of information Technology (Singapore) 2024年第3期16卷 1429-1435页

作者： Barve, Amit Pallavi, R. Deepak, Shashikant Murugan, R. Yadav, Dhananjay Singh, Ashutosh Kr. Sharma, Meenakshi Shalini, S. Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Computer Science Engineering Presidency University Karnataka Bangalore India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Noida India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology UP Noida India Department of Education Sanskriti University UP Mathura India Department of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore India

Security, heterogeneity, and interoperability are the prominent concerns in wireless sensor networks (WSNs) with restricted amounts of resources such as energy, transmission range, and processing of packets. Moreover, secured and trusted communication among WSNs is required to enhance network lifetime and security, thereby reducing energy consumption in the network. Thus, the given paper presents an ontological-based trust aware hybrid key management scheme (OTAHKMS) for WSNs. The proposed approach is a two-fold approach, where the first phase involves the encryption process for maintaining data confidentiality and integrity, while the second phase involves the use of ontology for capturing trust relationships and representing contextual information among several nodes in the network. The performance of the proposed trusted approach is validated and compared with existing cited works based on evaluation metrics such as communication trust value (CTV), network throughput (NT), packet delivery ratio (PDR), and energy consumption (EC). The results show that the proposed ontological-based trusted approach efficiently outperforms existing protocols, thereby enhancing network lifetime and energy usage in WSNs. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Encryption Packets transmission Security Trust validation Wireless sensor networks (WSNs)

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The Pre Assumptions to be Made for the Design of Underwater System

The Pre Assumptions to be Made for the Design of Underwater ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Mamtha, G.N. Pandey, Pratik Alam, Intekhab Department of Electronics and Communication Engineering Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineeing & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

With significant bandwidth advantages over acoustics and radio-frequency technologies, submerged wireless optical communication (UWOC) has become a fascinating topic with special appeal for a variety of underwater applications. The possibility of attaining large data rates-tens of gigabits per second across considerable distances-has been shown by recent advancements, such as the establishment of testbeds and pre-aligned UWOC lines. This has opened the door for the construction of a worldwide interconnected 'Internet of Underwater Things' (IoUT). One of the suggested remedies is the implementation of a non-line-of-sight communication mode based on light scattering, which provides a different strategy from conventional line-of-sight techniques. This broad view, which takes into account both problems and solutions, emphasizes how important a role UWOC might play in promoting underwater communication and exploration. © 2023 IEEE.

关键词： Internet Of Underwater Things IoUT Light Scattering Non-Line-Of-Sight communication Optical communication Modes Optical Waveguide and High-Speed Data Transmission Underwater Wireless Optical communication UWOC

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A New Median Filter Circuit Design Based on Atomic Silicon Quantum-Dot for Digital Image Processing and IoT Applications

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IEEE Internet of Things Journal 2025年第13期12卷 23000-23007页

作者： Ahmadpour, Seyed-Sajad Avval, Danial Bakhshayeshi Navimipour, Nima Jafari Rasmi, Hadi Heidari, Arash Kassa, Sankit Misra, Neeraj Kumar Navin, Ahmad Habibizad Mosleh, Mohammad Hosseinzadeh, Mehdi Patidar, Mukesh Kadir Has University Department of Computer Engineering Istanbul Turkey Sakarya University Department of Information Systems Engineering Sakarya Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Yunlin64002 Taiwan Western Caspian University Research Center of High Technologies and Innovative Engineering Baku Azerbaijan Islamic Azad University Department of Computer Engineering Dez.C. Dezful Iran İstanbul Atlas University Faculty of Engineering and Natural Science Department of Computer Engineering Istanbul Turkey Symbiosis International Deemed University Symbiois Institute of Technology Pune India VIT-AP University School of Electronics Engineering Andhra Pradesh Amaravathi522237 India Islamic Azad University Department of Computer Engineering Tabriz Branch Tabriz*** Iran Islamic Azad University Department of Computer Engineering Dezful Branch Dezful Iran Duy Tan University Institute of Research and Development Da Nang Viet Nam Indore Institute of Science and Technology Department of Electronics and Communication Engineering Madhya Pradesh Indore453111 India

Digital Image Processing (DIP) is the ability to manipulate digital photographs via algorithms for pattern detection, segmentation, enhancement, and noise reduction. In addition, the Internet of Things (IoT) acts as the eye and system for all DIP in various applications. It can possess a camera or another image sensor in order to capture real-time data from its environment. All vital data is processed by image processing in such a way that it recognizes the object, detects an anomaly, and automatically decides in real-time. In addition, in an IoT system, the median filter is the technique used for noise reduction by substituting the value of the pixel with the central value of the surrounding pixels. It provides speed and efficiency for quick analysis in all IoT systems. However, the images can get corrupted, especially in resource-constrained IoT devices with small cameras, because of random glitches. Moreover, using new quantum technology like atomic-scale silicon dangling bond (DB) logic circuits, which have advanced in fabrication and become a strong contender for field-coupled nano-computing, can solve previous problems in IoT systems. In this paper, we propose a unique quantum CSM based on two new proposed Mux and De-mux. The proposed CSM can be used for computational circuits like median filter circuits (MFC) in a wide range of digital circuits, specifically IoT devices. The proposed design is verified and validated using the powerful SiQAD tool. When comparing CSM to the newest designs, the suggested quantum circuit uses 85% less energy and takes up 61% less area. © 2014 IEEE.

关键词： Dangling bonds

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High-Performance Intelligent System for Real-Time Medical Image Using Deep Learning and Augmented Reality 4th

High-Performance Intelligent System for Real-Time Medical Im...

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4th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2023

作者： Senthil, G.A. Prabha, R. Rajesh Kanna, R. Umadevi Venkat, G. Deepa, R. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Computer Science and Engineering Vels Institute of Science and Technology and Advanced Studies Chennai India

ISBN: (纸本)9789819965854

Evolving new diseases demand the need for technology to identify the disease in an effective way. Medical imaging in the field of disease identification helps to identify the disease by scanning the human parts, thereby preventing the increased rate of deaths. Deep learning algorithms make it easier to identify and analyze disease efficiently through medical imaging. The high performance of these models is needed for the disease to be predicted with accurate results. The prediction rate of the disease can be increased by the efficient use of deep learning modules and algorithms. This research involves the use of deep learning models in identifying brain hemorrhage and retinopathy diseases through deep learning algorithms. The deep learning algorithms AlexNet and convolutional neural network (CNN) with the accuracy of 90% and 96%, respectively, are employed for the detection of brain hemorrhage, and ResNet-50 and CNN with accuracy of 70% and 92%, respectively, are used for the identification of retinopathy. The output of the model is displayed using augmented reality (AR), which makes it interactive for the user to analyze the results. The AR display is achieved using the unity engine along with the Vuforia package and using the barracuda package for importing the deep learning model into unity. Thus, by increasing the accuracy rate of the system, this research demonstrates the high performance of the intelligent system. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Augmented reality

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Deepfake Detection System Using a Hybrid Model 14

Deepfake Detection System Using a Hybrid Model

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14th International Conference on Computing communication and Networking Technologies, ICCCNT 2023

作者： Sudharson, S. Kokil, Priyanka Sasikala, D. Swamy, Penta Aravinda Vellore Institute of Technology Computer Science and Engineering Chennai India Indian Institute of Information Technology Design and Manufacturing Electronics and Communication Engineering Chennai600127 India Amrita School of Computing Chennai601103 India

ISBN: (纸本)9798350335095

There has been a rapid advancement in deep fake technology in recent years, which makes it increasingly difficult to detect fake images, especially those involving human faces, which have become difficult to detect. The traditional methods of detecting deep fake images are usually based on a human-crafted feature or a rule-based system, which can be easily beaten with simple cheating techniques. Intending to detect deepfake face images, we present a novel method that uses CapsNet and EfficientNet as feature extractors to detect deepfake face images. They are well suited to detect deepfake images that have undergone geometric transformations or variations as they can learn the spatial relationship between those features. Using EfficientNet, a powerful and efficient deep learning architecture, as a feature extractor, we extract high-level characteristics from an input image to create a more accurate image *** on our experiments,it was found that this method demonstrated the capability to detect various types of deep fake images with an accuracy rate of 99.64%, including those fabricated through face swapping and re-enacting. This method could be applied to a wide range of real-world applications, including in media forensics, law enforcement, and online media verification, so that it could be applied in many different contexts. © 2023 IEEE.

关键词： Deep learning

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