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Impact of Robotic Process Automation on Enterprise Resource Planning Systems

Impact of Robotic Process Automation on Enterprise Resource ...

2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary technology Management, IC-RVITM 2023

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

ISBN: (纸本)9798350306088

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. © 2023 IEEE.

关键词： Integration

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Remote Sensing–Based Food Processing for Changing Climatic Conditions

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 484-495页

作者： Raj, Roop Gondi, Dedeepya Sai Nitnaware, Swati R. Banerjee, Sudipta Athithan, Senthil Arpita D, Gopinath Department of Economics HNBG Central University Uttarakhand Srinagar India Department of Artificial Intelligence and Machine Learning The University of Texas at Dallas Richardson TX United States Department of Electronics Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Maharashtra Pune India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science Kristu Jayanti College (Autonomous) Karnataka Bengaluru India

A product must pass quality control inspections before it can be shipped to customers. In terms of quality assurance, this is crucial. Having a high-quality product is more significant for customer happiness than supplying a larger number of the same thing. As the climatic conditions change, the shelf time also varies. Quality can also be understood as the sum of all the factors that go into making something that the target audience values highly. There has been a dramatic uptick in recent years in the usage of the application of remote sensing and advanced image processing to enhance the appearance of fruits and further commodities. This is because these tools are especially helpful in overcoming the places where people’s eyesight misses the mark. This points out that the use of AI and imaging technology can replace overdue and unreliable techniques of quality assurance in manufacturing with quicker and more objective procedures. This article uses remote sensing and picture segmentation to describe how to analyze and rate food for different climatic conditions. It can tell the difference between several fruit varieties and also tell if a specific piece of fruit has gone bad. Gaussian elimination is first used to clean up the images by removing any visible noise. The images are then subjected to histogram equalization, which improves their overall quality. To carry out the segmentation process, the K-means clustering method is used. Then, photographs of fruits are classified using a number of strategies for machines to learn, including KNN, SVM, and C4.5 formulas to analyze the condition of a fruit and report back on its wholesomeness. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Agriculture artificial intelligence Food Processing Remote sensing technology

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Advanced Data Extraction and Predictive Analytics for Hemodialyzers Using AI and OCR

Advanced Data Extraction and Predictive Analytics for Hemodi...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Espin Shalo S P Jeeshan R J S Gunasekar Pius Agbulu G Sairam V A Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Tamilnadu Chennai Department of Computer Science and Design Rajalakshmi Engineering College Thandalam Tamilnadu Chennai Department of Mechatronics Engineering Chinhoyi University of Technology Zimbabwe Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Pune Maharashtra India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

The emphasis on medical technology has catalyzed increasing technology adoption in healthcare automation to manage patient data. Hemodialysis is a life-saving procedure in kidney disease requiring constant observation of such parameters as displayed on a hemodialysis machine. Paperbased data recording in these environments is tedious, timeconsuming, and susceptible to human errors mainly due to stress. This work proposes a method for extracting data from displays of a hemodialysis machine based on images from a mobile camera. With image analysis and Optical Character Recognition (OCR) technologies, the data, including temperature and conductivity, extracts and translates the values into JavaScript Object Notation (JSON). The algorithm has been developed to focus on plural machine models and not be limited to a specific format and screen sizes frequently used in clinical practice. In addition, the system uses artificial intelligence in predictive modelling to identify patterns and possible health issues and ensure professionals address them at the right time. The outcomes of the comparative analysis show that the mentioned scheme is more effective than the conventional paper-to-computer data managenent approach.

关键词： Temperature distribution Telemedicine Optical character recognition Medical services Conductivity Recording Data mining Predictive analytics artificial intelligence Stress

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Enhancing Insect Image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction

Enhancing Insect Image Recognition with Sand Cat Swarm Optim...

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International Conference on Advances in Information technology (ICAIT)

作者： C A Yogaraja C Priyanka Priyadharshini. SP J.Jagan Babu S. Rajagopal S. Poorani Computer Science and Engineering Ramco Institute of Technology Rajapalayam School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore Campus Department of Artificial Intelligence and Machine Learning (AML) St. Joseph's College of Engineering OMR Chennai Department of ECE R.M.D Engineering college Kavaraipettai Department of IT National Engineering College Kovilppati Computer Technology-UG Kongu Engineering College Erode

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Insect image recognition (IIR) is a specified field in machine learning (ML) and computer vision that efforts to automatically recognise and detection of insect species utilizing visual data attained from images. Leveraging deep learning (DL) techniques, convolutional neural network (CNN), and image processing enables exact and effective classification of a huge range of insect species. IIR has many applications that range from biodiversity observation and pest control in farming in order to entomological study and disease vector identification. The unique procedure of insect detection, it permits experts, entomologists, and ecologists to attain valuable insights, make knowledgeable decisions, and update challenges that contain insect detection which contribute to more real conservation efforts, ecological research, and agriculture management. This work contains a framework of Insect Image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction (IIR-SCSODFR) model. The developed IIR-SCSODFR method incorporates a complete procedure that starts with Gaussian filtering-based image pre-processing, improving image quality and decreasing noise in order to offer a clear basis for precise analysis. Deep feature extraction is carried out to capture intricate visual designs essential to numerous insect species, using advanced models for inclusive insect characterization. For exact insect detection, Long Short-Term Memory (LSTM) systems are employed, proficient in demonstrating time-based needs in image sequences. Besides, model leverages SCSO technique for parameter tuning, adjusting model's performance to adjust unique features of a dataset. The proposed IIR-SCSODFR model signifies an important leap forward in IIR, and provides a forceful and precise solution for automated identification of various insect species.

关键词： Visualization Image recognition Insects Biological system modeling Noise Feature extraction Image sequences

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A Review on the Methods of Targeted Drug Delivery Using Nanotechnology

A Review on the Methods of Targeted Drug Delivery Using Nano...

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engineering and technology (PICET), Parul International Conference on

作者： Mohan Warbhe Prateek Verma Swapnil Gundewar Palash Gourshettiwar Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institude of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institude of Higher Education & Research (DU) Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institude of Higher Education & Research (DU) Wardha Maharashtra India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

The use of nanotechnology for targeted drug delivery is a rapidly growing field. Nanotechnology, with its manipulation of materials at the nanoscale, offers novel solutions across various fields, prominently in medicine. Nanotechnology enables precise drug administration to specific sites in targeted drug delivery, ensuring optimal efficacy while minimizing side effects. This approach encompasses passive and active targeting strategies, leveraging biological mechanisms and ligand-mediated interactions for enhanced drug delivery. The current study reviews the techniques of targeted drug delivery using nanotechnology, focusing on various methods of targeted drug delivery, their applications, and strategies for using nanotechnology. The key processes include drug loading, release, and overcoming biological barriers. Nanotechnology also finds applications in imaging, therapeutics, and drug development, promising personalized and precise medical interventions. Although several challenges, such as scalability, drug loading, and biological barriers, persist, the future scope remains promising, with personalized medicine, precision targeting, biomimetic nanoparticles, and advanced imaging integration poised to revolutionize healthcare.

关键词： Drugs Nanoparticles Targeted drug delivery Reviews Precision medicine Biomimetics Loading Medical services Nanotechnology Biomedical imaging

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Integrated IoT-Based Smart Home Security System: Real-Time Hazard Detection and Alerts Using Multi-Sensor Fusion

Integrated IoT-Based Smart Home Security System: Real-Time H...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Sushma, A.M.S.V. Aswini Priyanka, A. Lakshmi, A.N.G. Khatri, Narendra Sharma, Harish S R K R Engineering College Department of Electrical and Electronics Engineering Andhra Pradesh Bhimavaram India S R K R Engineering College Department of Computer Science and Design Engineering Andhra Pradesh Bhimavaram India Sir C.R. Reddy College Engineering Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram India Manipal Acacdemy of Higher Education Department of Mechatronics Manipal Institute of Technology Manipal India Manipal University Jaipur School of Computer Science and Engineering Department of Artificial Intelligence and Machine Learning Jaipur India

ISBN: (纸本)9798350390049

This paper presents an innovative IoT-based smart home security system that integrates multiple sensors to detect various hazards and provide real-time alerts. Unlike traditional single-function systems, this solution combines motion detection via a PIR sensor, gas leak detection using an MQ-2 sensor, and fire detection through an IR flame sensor into a single, affordable package. The Arduino UNO serves as the central controller, interfaced with a GSM module for instantaneous SMS notifications and supported by an on-site buzzer alarm. Extensive testing in diverse environments demonstrates the system's exceptional performance, detecting intrusions in under 2 seconds and reporting threats with approximately 89% accuracy, while minimizing false alarms. The integration of smart power management techniques reduces energy consumption compared to conventional systems. This approach not only enhances home security but also offers a scalable and easily deployable solution for both residential and commercial settings. This work addresses the urgent need for an affordable, multi-functional smart home security system and provides a robust framework for future IoT-driven safety solutions. © 2024 IEEE.

关键词： Smart homes

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Innovative Healthcare Advancements: Harnessing artificial and Human intelligence for Bionic Solutions

Innovative Healthcare Advancements: Harnessing Artificial an...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Sapkal, Samruddhi Jadhav, Sulaxan Mallikarjun, P. Shamim, Rejuwan Ul Islam, Atowar Bamane, Kalyan D y Patil International University Artificial Intelligence and Machine Learning Pune Akurdi411044 India D y Patil International University Pune Akurdi411044 India Malla Reddy Engineering College Jntuh Department of Electrical and Electronic Engineering Hyderabad India Maharishi University of Information Technology School of Data Science Noida India University of Science and Technology Meghalaya Department of Computer Science and Electronics Ri-Bhoi Techno city Kiling Road Meghalaya Baridua793101 India D y Patil College of Engineering Department of Information Technology Pune Akurdi India

ISBN: (纸本)9798350373783

According to initial data, individuals who have been diagnosed with type 2 diabetes (T2DM) appear to be at a more chances of evolving breast cancer compared to those who have not received a T2DM diagnosis. The primary goal of the research was to estimate the efficacy of three dissimilar methods in forecasting the probability of breast cancer in T2DM patients with diverse attributes. To achieve this objective, a danger expectation model was created for breast cancer in individuals with T2DM using the primary *** ensure a comprehensive analysis, we also gathered information on potential factors that may predict the growth of breast cancer. As the population sample size was restricted, we utilized Synthetic Minority Oversampling technology to amplify the quantity of accessible data. Random assignment of data points to training or test sets was conducted at a ratio of roughly 39 to 1. Three distinct models, specifically artificial Neural Network (ANN), Logistic Regression (LR), and Random Forest (RF), were assessed for effectiveness using a range of performance criteria, including as F1 score, area under the receiver operation characteristic curves, recalled, and correctness. (AUC). AUC values for these models were as follows: LR had an AUC of 0.834, ANN had an AUC of 0.865, and RF had an AUC of 0.959, with RF having the greatest AUC. According to our study, the Random Forest model outperformed the LR and ANN models in correctly estimating the incidence of breast cancer in people with T2DM. © 2024 IEEE.

关键词： Diseases

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Churn Prediction in Financial Risk Management using Deep learning Techniques 2

Churn Prediction in Financial Risk Management using Deep Lea...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Kumar, V Sunil Sowmyashree, S. Pareek, Piyush Kumar Khanna, Ashish Costa De Albuquerque, Victor Hugo Gupta, Deepak Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Computer Science & Engineering East West Institute of Technology Bengaluru India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Cearά Fortaleza Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India Federal University of Cearά Department of Teleinformatics Engineering Brazil

ISBN: (纸本)9781665493963

It is essential to develop an efficient Client Relationship Model in order to make use of the estimations about customer turnover (CRM). Finding and breaking down the facts surrounding the business information understanding is necessary for client produce prediction, which should be made possible effectively by altering the strategies used for business insight. The tools that are provided by company insight allow for the historical, current, and prospective perspectives of business activity to be anticipated and dissected. It is possible to deal with it in an efficient way by providing the information mining approaches that choose the data from the created informative index that is the most helpful. Fundamental leadership should be possible to achieve in a fruitful way if one has this information. The actions that consumers have taken in the recent past are going to be analyzed, and an accurate forecast is going to be formed about those customers who are likely going to get unhappy in the near future based on the results of that prediction. The problem is that deciding on a certain period to investigate the client's activities and also establishing on a specific day and age for each of the clients won't be appropriate6. This is due to the fact that the problem is caused by the fact that deciding on a certain period to investigate the client's activities will take too long. The challenge lies just in this aspect. How to decide on a certain amount of time to spend researching the activities of the customer. For instance, when planning a model, 100 customers are used, 70 of those customers are considered 'active,' which indicates that they are still conducting business with the organisation, and the remaining 30 customers are considered 'agitate,' which indicates that they have severed their ties with the organisation. The primary purpose of the work that has been suggested is to carry out exploratory research and management as a multicriteria basic leadership issue, a

关键词： Forecasting

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AI-Powered Advertisement Design: A PIL-Based Approach for Quality and Performance Analysis

AI-Powered Advertisement Design: A PIL-Based Approach for Qu...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Harshitha Chilupuri G. Ramesh J. Praveen Venkataramaiah Gude Dept. of Artificial Intelligence and Machine Learning Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Dept. of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Electrical and Electronics Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India GP Technologies LLC U.S.A.

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

The paper presents the development of a Data Science solution that applies generative AI models in order to create high-quality images to be used in visual marketing and advertising. Using the Python library PIL (Pillow) along with machine learning frameworks, the solution combines predefined product images with the selected backgrounds, enhancing image synthesis techniques for more appealing and contextually relevant visuals. This leads to improved efficiency and effectiveness in the creation of content by automatically placing product images onto the background images used in marketing campaigns. The approach demonstrated a 98.06% SSIM (Structural similarity Index) score, indicating significant visual quality improvement. Initial results suggest a 15% enhancement in visual appeal compared to traditional image creation methods.

关键词： Visualization Image transformation Generative AI Data science Libraries Performance analysis Indexes Advertising Optimization Python

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AES Algorithm for the Next Generation of 5G Network Encryption Standards 2

AES Algorithm for the Next Generation of 5G Network Encrypti...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Manojkumar, T. Tamilarasi, K. Mahalakshmi, S. Vinayagam, P. Mahanthesha, U. Mohanraj, S. Karpagam Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Excel Engineering College Department of Electronics and Communication Engineering Tamilnadu Namakkal India R.M.K. Engineering College Department of Computer Science and Design Tamilnadu Chennai India Saveetha Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Bnm Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350315905

The Advanced Encryption Standard (AES) symmetric key encryption algorithm is the industry standard for 5G communication encryption. technology advancements nowadays are mostly aimed at improving the security, speed, and efficiency of processors. The main expansion phase is made easier by the study's pipeline structure. This method simplifies the process of creating subkeys in AES encryption for use in 5G communication networks. There is a 37% increase in throughput between the simulated and implemented versions of the original design on FPGA Virtex4 XC4VLX200, and this difference may be attributed to the reduction in propagation latency while producing subkeys. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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