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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Rekha, V. Shiny, R.M. Ramya, J. Sakthivel, B. Pratheep, V.G. Kumar, Ashok AgurchandManmull Jain College Department of Bachelors's in Computer Application Chennai India Velammal Institute of Technology Department of Artificial Intelligence and Data Science Thiruvallur India St. Joseph's College of Engineering Department of Computer Science Engineering Chennai India Vivekanandha College of Engineering for Women Department of Electronics and Communication Engineering Namakkal India Kongu Engineering College Department of Mechatronics Engineering Perundurai India Banasthali Vidyapith Department of Computer Science Banasthali India

ISBN: (纸本)9798350347456

Rice is the most widely grown crop in Asia. In Asia, 60% of the population relies on rice, China, Indonesia especially India consumes the majority of it. Food is the single most direct way for an individual to become infected with any ailment. Predicting the quality of rice is a crucial aspect as it is one of the most consumed foods in Asia. In this study, the quality of the rice is predicted using a Deep Learning (DL) model to predict the quality more precisely. The dataset required for this study is collected from the Kaggle website composed of a total of 5000 images of 5 different rice varieties. The collected raw dataset is then pre-processed using several methods to make them compatible with the DL model. The pre-proceed images are then subjected to the prediction of the quality of the rice using the Convolutional Neural Network (CNN), a DL model. Performance metrics such as accuracy, loss, and time are calculated to validate the precision of the model. The experimental result showed that the model has the highest accuracy of about 98.6% when compared to the other existing models. The model demonstrated a very minimal loss percentage of only about 1.24% and the model predicts the result in about 25 microseconds. The proposed DL model can be utilized for better and more objectively evaluate the quality of the rice at trading points inside the rice marketing chain due to its fairly high precision. © 2023 IEEE.

关键词： Convolutional neural networks

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Violence Detection System Using Resnet 5

Violence Detection System Using Resnet

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5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Shripriya, C. Akshaya, J. Sowmya, R. Poonkodi, M. Bachelors in Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Chennai India SRM Institute of Science and Technology Dept. of Computer Science and Engineering Vadapalani Chennai India

ISBN: (纸本)9781665435246

In recent years, human motion analysis has become the center of video processing, especially in motion detection and security surveillance. When performing specific tasks, it is always a tool to minimize human resources. However, earlier methods of detection often descriptors are extracted around spatiotemporal attention points or statistical features of motion regions are extracted, resulting in limited ability to effectively detect violence in videos. This paper is intended to use a Pre-trained Convolutional Neural Network Model (ResNet), where the video stream is sent to the ResNet together with a single shot detector (S S D) to detect violence and objects in the video. In addition, if an intense incident is identified, the relevant departments will be notified. This proposed methodology would be beneficial in monitoring scenarios such as detention center asylum. Also, on mobile phones with cameras. © 2021 IEEE.

关键词： Object detection

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Impact of Robotic Process Automation on Enterprise Resource Planning Systems

Impact of Robotic Process Automation on Enterprise Resource ...

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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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Evolutionary algorithm for graph coloring problem

arXiv

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arXiv 2021年

作者： Islam, Robiul Pramanik, Arup Kumar Bachelors of Science in Computer Science and Engineering East West University

The graph coloring problem (GCP) is one of the most studied NP-HARD problems in computer science. Given a graph, the task is to assign a color to all vertices such that no vertices sharing an edge receive the same color and that the number of used colors, is minimal. Different heuristic, meta-heuristic, machine learning and hybrid solution methods have been applied to obtain the solution. To solve this problem we use mutation of evolutionary algorithm. For this purpose we introduce binary encoding for Graph Coloring Problem. This binary encoding help us for mutation, evaluate, immune system and merge color easily and also reduce coloring dynamically. In the traditional evolutionary algorithm (EA) for graph coloring, k-coloring approach is used and the EA is run repeatedly until the lowest possible is reached. In our paper, we start with the theoretical upper bound of chromatic number, that is, maximum out-degree + 1 and in the process of evolution some of the colors are made unused to dynamically reduce the number of color in every generation. We test few standard DIMACS benchmark and compare resent paper. Maximum results are same as expected chromatic color and few data sets are larger than expected chromatic number. © 2021, CC BY.

关键词： Color

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Rice quality Prediction using Convolution Neural Network

Rice quality Prediction using Convolution Neural Network

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： V. Rekha R. M. Shiny J. Ramya B. Sakthivel V. G. Pratheep Ashok Kumar Department of Bachelors's in Computer Application AgurchandManmull Jain College Chennai India Department of Artificial Intelligence and Data Science Velammal Institute of Technology Thiruvallur India Department of Computer Science Engineering St. Joseph's College of Engineering Chennai India Department of Electronics and Communication Engineering Vivekanandha College of Engineering for Women Tamakkal India Department of Mechatronics Engineering Kongu Engineering College Perundurai India Department of Computer Science Banasthali Vidyapith Banasthali India

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Violence Detection System Using Resnet

Violence Detection System Using Resnet

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

作者： C Shripriya J Akshaya R Sowmya M Poonkodi Bachelors in Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Chennai India SRM Institute of Science and Technology Vadapalani Chennai

ISBN: (纸本)9781665435253

关键词： Systematics Surveillance Streaming media Feature extraction Cameras Motion detection Spatiotemporal phenomena

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Impact of Robotic Process Automation on Enterprise Resource Planning Systems

Impact of Robotic Process Automation on Enterprise Resource ...

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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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Role of RPA & AI in Optimizing Network Field Services

Role of RPA & AI in Optimizing Network Field Services

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

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

The paper investigates incorporating and implementing RPA and AI technologies within NFS to improve efficiency and boost service quality. Robotic Process Automation enables the streamlining of repetitive processes. It simplifies work processes and releases personnel for more critical projects. On the contrary, AI strengthens NFS providers via data-informed decision-making. Additionally, it facilitates anticipatory maintenance and preemptive network administration. The document explores particular RPA and cognitive computing instances, including AI-powered network issue resolution and AI-driven preventive maintenance for the network hardware. Furthermore, it emphasizes the advantages and constraints of adopting robotic process automation and artificial intelligence within the NFS framework. It considers aspects including data handling, growth potential, and moral implications. Prospects for the future related to RPA and AI within the NFS domain are also analyzed. It foresees self-governing network management, sophisticated predictive analytics, and merging with IoT and edge computing technologies. The article highlights the revolutionary capability of robotic process automation and artificial intelligence in transforming the field service sector, aiming for increased efficiency, reliability, and a customer-centric approach. It emphasizes embracing these technologies to keep up in the dynamic business sector.

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Naturalization of text by the insertion of pauses and filler words

arXiv

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arXiv 2020年

作者： Sharma, Richa Shah, Parth Vipul Joshi, Ashwini M. Bachelors in Technology Computer Science and Engineering PES University Bangalore India PES University 100 Feet Road BSK III Stage Bangalore560085 India Computer Science and Engineering PES University Bangalore India

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Real-Time Agriculture Yield Monitor System (AYMS) Using Deep Feedforward (DFF) Neural Network

SSRN

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SSRN 2020年

作者： C S, Manoj Athreya H R, Mohith Gowda K A, Amogh Babu Ravishankar, R. Software Engineer Bangalore India Bachelors of Engineering in Computer Science PES College of Engineering Mandya India Implementation Engineer Data Science Enthusiast Bangalore India Department of IS&E Adichunchanagiri Institute of Technology Chikmagalur India

Agriculture stands as the backbone of our Nation, by Contributing 7% of the total Indian Economy. The drivers of agriculture are facing a huge problem in predicting the yield in different varieties of soil. Currently, the use of sophisticated technologies in the field of agriculture is underdeveloped when compared to other sectors over the past few decades. Self-mortality rates of farmers considerably increasing from the past four-five years. This is mainly due to the debt overhead usually caused by low yield. Crops yield decline considerably due to unpredictable weather, environmental changes, and diseases. This can say that agricultural landowners fear to use new technologies and tend to follow the age-old tradition of farming. The sphere of computing with its rigorous learning capabilities is inevitable to find a novel solution for agriculture-related issues. In this paper, this issue is addressed and have come up with an improvised idea to help farmers get a better yield for their crops. Deep learning has been used to predict the yield. This technology is made handy for every farmer to learn and use it effectively via a simple IoT device installed in their fields and a smartphone application. This improvised system has been named as Agriculture Yield Monitor System (AYMS). This has proved to be more efficient and beneficial to farmers. © 2020, The Authors. All rights reserved.

关键词： Internet of things

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