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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： S. Chanti P. Harika V. Venkata Vinay P. Guna Sri Charan Sk. Ajad Assistant Professor Computer Science and Engineering GMR Institute of Technology Andhra Pradesh India

With the increasing prevalence of digital documents in various domains, the demand for efficient and accurate question-answering (QA) systems has grown significantly. Traditional QA models primarily focus on text-based content, but the complex structure and visual elements present in documents pose unique challenges. This project explores the integration of LayoutLMv3, an advanced language and vision model, to elevate document question answering to the next level. The key role of LayoutLMv3 in document question answering (QA) lies in its ability to handle complex documents that contain not only textual information but also intricate visual elements like tables, images, and graphs. Traditional QA models typically focus on processing plain text and may struggle to comprehend the spatial arrangement and structure of documents. However, documents often contain vital information in their layout, which, if overlooked, can lead to inaccurate or incomplete answers. LayoutLMv3 overcomes this limitation by integrating both language and vision components into a single model. Through comprehensive tests on a variety of document datasets, this study shows the efficiency of DocumentQA. This study also analyzes the LayoutLMv3's interpretability and how it affects the way questions are answered. Through visualization techniques, various insights are gained on how the model analyses the document's layout, text, and interactions to generate accurate answers. The implications of this research extend beyond document QA, as LayoutLMv3's unique fusion of language and vision has broader applications in information retrieval, natural language processing, and document understanding tasks. The performance of LayoutLMv3 over LayoutLmv2 is also compared in this work showing efficient results.

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Performance Prediction of Students Using Machine Learning Algorithms 2nd

Performance Prediction of Students Using Machine Learning Al...

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2nd Conference of Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2020

作者： Gadde, Sai Sudha Anand, Dama Sasidhar Babu, N. Pujitha, B.V. Sai Reethi, M. Pradeep Ghantasala, G.S. Assistant Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Student Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Vijayawada India

ISBN: (纸本)9789811902956

To predict the accurate future performance of the students, we consider the data like student’s past and present academic records. It is very important to complete student’s academics in a successful way. The problem is to identify the students whose performance is poor in their courses. So according to their performance and capability there will be a chance to take some remedial actions. There are many literatures on predicting the grades and improving their accuracy. In this paper, we predict the final grades using naive Bayes algorithm and linear regression to predict the final performance of students so that we can get more accurate values. It issues a forecast for every student exclusively, when the expected exactness of the prediction is adequate. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Linear regression Naive Bayes algorithm Performance prediction

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An efficacy analysis of data encryption architecture for cloud platform

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Procedia computer science 2023年 218卷 989-1002页

作者： Sheenam Malhotra Williamjeet Singh Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Punjabi University Patiala Punjab India Assistant Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Punjabi University Patiala Punjab India

In recent times, cloud computing is being utilized largely for storage and information sharing purposes in several established commercial segments, particularly those where online businesses are prevalent, such as Google, Amazon, etc. Cloud system presents several benefits to users in terms of easy operations, low implementation, and maintenance expenses. However, significant risks are encountered in the data security procedures of cloud systems. Although the area is frequently being analyzed and reformed, the concern of cloud data protection and user reliability remains under uncertainty due to growing cyber-attack schemes as well as cloud storage system errors. To deal with this risk and contribute to the endeavor of providing optimal data security solutions in cloud data storage and retrieval system, this paper proposes a Symmetric Searchable Encryption influenced Machine Learning based cloud data encryption and retrieval model. The proposed model enhances data security and employs an effective keyword ranking approach by using an Artificial Neural Network. The comparative assessment of the proposed model against multiclass SVM and Naïve Bayes has established the better operational potentiality of the model. The effectiveness of the proposed work is justified by the association between high TPR and low FPR. Further, a low CCR of 0.6973 adds up to the success of the proposed work.

关键词： Cloud Computing Symmetric Searchable Encryption Machine Learning

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Optimized Prognostic Models for Oral Cancer Survival using Feature Selection Methods

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Procedia computer science 2024年 235卷 1832-1840页

作者： Deepali Neelam Goel Padmavati University Institute of Engineering and Technology Panjab University Chandigarh 160014 India Assistant Professor Department of Computer Science Guru Nanak College Budhlada 151502 India Department of Computer Science Punjab Engineering College (deemed to be university) Chandigarh 160012 India

Accurately predicting survival time holds significant importance in various medical and scientific domains. This study proposes a novel survival prediction model for oral cancer using multi-omics data including clinical, DNA methylation, copy number alteration, and mRNA data. The proposed model incorporates four feature selection methods, namely LASSO, Elastic Net, Random Forest Regressor, and Recursive Feature Elimination (RFE), to improve the accuracy. The findings reveal that RFE outperforms the other methods, achieving the highest C-Index of 0.944. This is a significant improvement over previous models, which have a c-index of 0.694 using only clinical data and 0.916 using multi-omics data. In terms of precision, the Random Forest Regressor is the most precise with an MSE of 18.37, followed by RFE of 21.61. Notably, both RFE and Random Forest Regressor outperformed LASSO and Elastic Net in predictive accuracy. Additionally, the results highlight the consistency of RFE, as indicated by its low Median Absolute Error (MedAE) of 2.86. Random Forest Regressor also performed well in this regard, with a MedAE of 4.05. In contrast, Elastic Net exhibited a higher MedAE of 4.55, while LASSO showed more variability, with a MedAE of 8.58. These findings offer valuable insights into survival analysis and feature selection, helping researchers and practitioners select appropriate approaches for survival time prediction, and improving accuracy and reliability in critical applications.

关键词： Oral Cancer Survival Time Feature Selection Accuracy

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Improving Robotic Arms Through Natural Language Processing, computer Vision, and Edge Computing

Improving Robotic Arms Through Natural Language Processing, ...

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Mechatronics, Control and Robotics (ICMCR), International Conference on

作者： Pascal Sikorski Kaleb Yu Lucy Billadeau Flavio Esposito Hadi AliAkbarpour Madi Babaias Undergraduate Research Assistant Computer Science Department Saint Louis University Saint Louis USA Undergraduate Research Assistant Aerospace & Mechanical Engineering Department Saint Louis University Saint Louis USA Associate Professor Computer Science Department Saint Louis University Saint Louis USA Assistant Professor Computer Science Department Saint Louis University Saint Louis USA CBL Assistant Professor of Robotics Aerospace & Mechanical Engineering Department Saint Louis University Saint Louis USA

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

This paper introduces a prototype for a new approach to assistive robotics, integrating edge computing with Natural Language Processing (NLP) and computer vision to enhance the interaction between humans and robotic systems. Our proof of concept demonstrates the feasibility of using large language models (LLMs) and vision systems in tandem for interpreting and executing complex commands conveyed through natural language. This integration aims to improve the intuitiveness and accessibility of assistive robotic systems, making them more adaptable to the nuanced needs of users with disabilities. By leveraging the capabilities of edge computing, our system has the potential to minimize latency and support offline capability, enhancing the autonomy and responsiveness of assistive robots. Experimental results from our implementation on a robotic arm show promising outcomes in terms of accurate intent interpretation and object manipulation based on verbal commands. This research lays the groundwork for future developments in assistive robotics, focusing on creating highly responsive, user-centric systems that can significantly improve the quality of life for individuals with disabilities. For video demonstrations and source code, please refer to: https://***/EnhancedArmEdgeNLP.

关键词： computer vision Mechatronics Source coding Machine vision Large language models Prototypes Focusing Manipulators Natural language processing Edge computing

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Generative AI in Agri: Sustainability in Smart Precision Farming Yield Prediction Mapping System Based on GIS Using Deep Learning and GPS

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Procedia computer science 2025年 252卷 365-380页

作者： Senthil G. A S.U. Suganthi L. Prinslin R. Selvi R. Prabha Associate Professor Department of Information Technology Agni College of Technology Chennai India Associate Professor Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Assistant Professor Department of Computer Science and Engineering Agni College of Technology Chennai India Assistant Professor Department of Computing Technologies SRM Institute of Science and Technology kattankulathur Chennai India Associate Professor Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based system that enables the use of deep learning, Global Positioning System (GPS), and Geographic Information System (GIS) technologies to create a highly intelligent smart agricultural precision farming system. Its goal is to monitoring crop health and reduce disease risk, which will lead to improved resource utilization and environmentally sustainability techniques. The proposed framework addresses the urgent need for consistency in agricultural practices, especially as global agriculture deals with pressures from climate change, resource shortages, and increasing demand for food. Traditional agricultural methods for predicting and optimizing crop yields due to increasing factors affecting crop performance Not enough generative AI, especially the use of deep learning models, supports agricultural research in many cases, allowing patterns to be identified and future results to be predicted accurately. The integration of GPS and GIS allows for more accurate mapping, real-time analysis, and effective decision-making. Weather forecasting variability, resource constraints, and demand for more food are isolated from environmental influences using deep learning models, especially Artificial Neural Networks (ANN). By using large data sets, including historical crop yield performance, soil properties, and weather conditions, the system provides highly accurate crop forecasts. Generative Adversarial Networks (GANs) and You Only Look Once (YOLO) hybrid model is playing a key role in generating crop yield and growth potential under different conditions, adjusting model accuracy over time, and this combination of ANN, GANs and YOLO optimization algorithms ensures that the system continuously enhances its predictive accuracy and overall effectiveness. The proposed gene

关键词： Generative AI Deep Learning Agriculture Smart Farming Crop Health Yield Prediction Global Positioning System Geographic Information System Artificial Neural Networks Generative Adversarial Networks Sustainability You Only Look Once (YOLO)

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A Comprehensive Review on Detection of Dacrocytes from Blood Smear Images using ML Algorithms

A Comprehensive Review on Detection of Dacrocytes from Blood...

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International Conference on Recent Trends in Advance Computing (ICRTAC)

作者： D. Kavitha B. Padmavathi Shakthi Priya S Sanjana N Sidesh S Department of Computer Science and Engineering Assistant Professor of Easwari Engineering College Chennai India Department of Computer Science and Engineering Student of Easwari Engineering College Chennai India

A nation’s capacity to develop is greatly impacted by the prevalence of anemia, a major public health issue. Despite this, it is a sickness that many developing countries choose to overlook. A million people die each year from iron deficiency, which is the underlying cause of anemia in at least half of those cases. Approximately 75% of these fatalities take place in Southeast Asia and Africa. One of the leading causes of death on a global scale is iron deficiency anemia (IDA). The use of machine learning techniques for the prediction of anemia was the focus of this work. Light Gradient Boosting Machines, Logistic Regression, Gaussian Naive Bayes, Support Vector Machines, and K-Nearest Neighbors were the five machine learning models studied in the study. The voting classifier method is used to merge various models in order to improve the accuracy of predictions. The need of accurate sickness forecasting by medical practitioners is highlighted by the study. Being able to predict when anemia may occur is crucial for effective prevention and treatment of the condition. This study proves that specific machine learning algorithms can improve disease prevention and treatment by predicting the occurrence of anemia. Using advanced algorithms and data processing techniques can help doctors make better predictions and decisions, which in turn improves patient outcomes. According to the results, the vote classifier has a 99.95% accuracy rate.

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Face Recognition from Partial Face Data and Fraud Detection

Face Recognition from Partial Face Data and Fraud Detection

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Thavisala Veneela Sonti Vivek Sai Yeturu Vamshidhar Reddy Vishnu Karthik Varma Kothapally dept. of Assistant professor/computer science and engineering B V RAJU INSTITUTE OF TECHNOLOGY Narsapur Medak Telangana India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Face recognition is a critical era with applications starting from safety to personalization. However, traditional face Recognition systems frequently conflict with partial face information, which can occur because of occlusions or picture excellent g troubles. I t consists of a novel technique to stand reputation using partial face statistics, specializing in fraud detection scenarios. This approach leverages deep getting to know strategies, mainly convolutional neural networks (CNNs), to extract functions from partial face snap shots. These features are then used to create a compact illustration of the face, which is robust to occlusions and noise. A similarity measure is employed to evaluate those representations, enabling the identification of individuals even from partial face records. In the context of fraud detection, it can be used to verify the identity of people based totally on partial face data captured from surveillance cameras or different sources. Experimental consequences on benchmark datasets show the effectiveness of the proposed approach, outperforming conventional face recognition procedures in eventualities with partial face information. It gives a promising solution for improving face recognition accuracy in challenging conditions, with capability packages in security, regulation enforcement, and personal identification systems. It also analyzes facial images with masks using Multi-Task Cascaded Convolutional Neural Networks (MTCNN). FaceNet algorithm is also used which adds more embeddings and verifications to face recognition. Support vector Machine (SVM) algorithm labels the data sets to produce a reliable prediction probability and along with that it also detects the frauds.

关键词： Support vector machines Accuracy Face recognition Surveillance Prediction algorithms Vectors Robustness Fraud Safety Convolutional neural networks

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Evaluating Generative Adversarial Networks for Virtual Contrast-Enhanced Kidney Segmentation using Res-UNet in Non-Contrast CT Images

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Multimedia Tools and Applications 2024年第18期84卷 1-24页

作者： Syamala, Maganti Chandrasekaran, Raja Balamurali, R. Rani, R. Hashmi, Arshad Kiran, Ajmeera Rajaram, A. Assistant Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur District Andhra Pradesh Vaddeswaram522502 India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Tamil Nadu Chennai India Centre for non linear systems Chennai institute of technology Chennai India Department of ECE Vemu Institute of Technology Tirupathi-chittoor Highway P. Kothakota Chittoor District AP Pakala India King Abdulaziz University P.O. Box 344 Rabigh21911 Saudi Arabia Department of Computer Science and Engineering MLR Institute of Technology Dundigal Telangana Hyderabad500043 India Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam611002 India

This study addresses the challenges of accurately segmenting kidneys in non-contrast CT images;where Convolutional neural networks (CNNs) perform best. The main goal is to investigate how well a Generative Adversarial Network (GAN) performs in producing virtual contrast-enhanced (vCECT) images from non-contrast sources. A GAN model is trained to generate simulated contrast-enhanced images that preserve pertinent information throughout translation by utilizing a dataset of 286 paired non-contrast and contrast-enhanced images. Following, 20 pairs of non-contrast and vCECT pictures are used to assess the performance of a unique segmentation model named Res-U-Net. The Res-U-Net architecture, which combines a U-Net design with residual connections, is optimized to efficiently extract detailed information from original and vCECT images. Image preprocessing methods like Hounsfield windowing with histogram equalization are used to improve the quality of the data before it is fed into the deep neural network. Dice similarity coefficient (DSC) is used to measure Res-U-Net's performance and acts as a benchmark against the most advanced kidney segmentation techniques. ResU-Net's performance is evaluated using the DSC. When compared to state-of-the-art methods for kidney segmentation recognition, the ResU-Net systems with intact connections achieved a DSC value of 0.97 percent for organs recognition and 0.83 percent for segmentation approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Generative AI in Agri: Sustainability in Smart Precision Farming Yield Prediction Mapping System Based on GIS Using Deep Learning and GPS 4

Generative AI in Agri: Sustainability in Smart Precision Far...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Senthil, G.A. Suganthi, S.U. Prinslin, L. Selvi, R. Prabha, R. Department of Information Technology Agni College of Technology Chennai India Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India Assistant Professor Department of Computing Technologies SRM Institute of Science and Technology kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

关键词： Generative adversarial networks

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