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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Annie Silviya, S.H. Kingsley, S. Agitha, W. Sriman, B. Priyadarshan, V.J. Mohana, V. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Easwari Engineering College Department of Computer Science and Engineering Chennai India P.B. College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350322842

The FOD-R Dataset is a collection of images that depict common types of foreign object debris (FOD) that can be found on runways or taxiways. The dataset has primarily been annotated using bounding boxes to facilitate object detection. However, the FOD-xR Dataset includes an additional feature - color codes - that provide information on the ease of removing the debris. Lightweight objects are marked in green, while heavy objects are marked in red. This centralized system simplifies coordination efforts, making it easier for organizations to manage FOD removal effectively. This, in turn, can have a positive impact on an organization's bottom line by minimizing delays and damage caused by FOD incidents. Overall, the FOD-R and FOD-xR Datasets are valuable resources for organizations looking to improve their FOD detection and removal processes. © 2023 IEEE.

关键词： Object detection

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On the Rique Number of Series-Parallel Graphs and Planar Bipartite Graphs 2nd

On the Rique Number of Series-Parallel Graphs and Planar ...

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Azgor, Sk Ruhul Rahman, Md. Saidur Graph Drawing and Information Visualization Laboratory Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9783031845420

The restricted input queue (rique) is a data structure which is recently introduced to study linear layout of graphs. The rique data structure is a special queue where insertions occur only at the head and removals occur at both the head and the tail. Considering a rique data structure the goal of a linear layout of a graph is to find a linear order of the vertices of the graph and a partition of its edges into pages such that the edges in each page follow the restriction of rique in the underlying order. The rique number of a graph is the smallest number of pages required for linear layout of the graph with rique data structure. A characterization of graphs for admitting a single page rique layout and some bounds on the rique number of complete and complete bipartite graphs are known. In this paper, we show that the rique number of series-parallel graphs as well as planar bipartite graphs is 2. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Directed graphs

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An Exploratory Assessment on Text Summarization 2

An Exploratory Assessment on Text Summarization

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Nishitha, P. Sarika, T. Vijayakumari, E.N. Vuyyuru, Asha Silparaj, M. Mohanty, Jnyana Ranjan Mlr Institute of Technology Department of Assistant Computer Science and Information Technology Hyderabad India Mlr Institute of Technology Department of Computer Science and Information Technology Hyderabad India Mlr Institute of Technology Department of Computer Science Engineering Hyderabad India Hyderabad India Hyderabad501510 India Kalinga Institute of Industrial Technology University India

ISBN: (纸本)9798350370188

Text summarization involves creating a short, concise summary that captures the salient points of the source text. We'll demonstrate a synthesis process based on the use of trainable machine learning algorithms with a collection of features taken straight from the source text. There are two groups of these features: statistics-also referred to as extractive summarization-based on how frequently different textual components occur and linguistics also known as abstractive summarization - based on morphological and syntactic analysis of documents. In this paper, we show how BERT can be usefully applied in text summarization and provide a common framework for abstraction models. We also demonstrate that a fine-tuning method can further improve the quality of the generated summaries. © 2024 IEEE.

关键词： Syntactics

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A Comprehensive Exploration of Noise Models, Blur Models, Filters and Restoration Techniques

A Comprehensive Exploration of Noise Models, Blur Models, Fi...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Malathy, S. Prasanna Devi, S. Rajasekar, V. Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani India

ISBN: (纸本)9798350394580

With an objective of restoring a clear image from a distorted version, image restoration is necessary for sophisticated image processing. It employs a mathematical model to address degradation during image acquisition. Noise plays a substantial part in the deterioration of images during their prisoner or transmission. To attack this issue, there exists a wide array of algorithms and filtering ways, each carrying its own set of hypotheticals, advantages, and limitations, all depending on previous knowledge of the noise. Image smoothing is a dynamic and massively retained process in replica processing. The ideal of the conventional deed is to present a compact overview of the most efficacious restoration miniatures attainable on the inquiry. In this study, we will explore and assess the pros and cons linked to different image restoration styles. © 2023 IEEE.

关键词： Blurring Techniques Degradation model Filters Noise Models Restoration Techniques

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An IoT Based Framework for Monitoring Power Consumption in Smart Grids

An IoT Based Framework for Monitoring Power Consumption in S...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Raghavendra, R. Chauhan, Nidhi Mittal, Gaurav Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Information Technology & M. Tech Integrated Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

The electrical power system grid in India, commonly referred to as the power grid, has been in operation for an extensive duration. During this time period, there were no significant advancements or alterations documented in the electricity grid infrastructure. The need for electrical power consumption is seeing a significant increase, but the current grid infrastructure is unable to meet these growing demands. In order to meet the demands of future power load, it is imperative to develop an enhanced system that possesses the qualities of reliability, security, intelligence, and efficiency. This study develops a power management system and explains its automation and functionality by utilising smart grid and IoT technologies. The research suggests a method for keeping an eye on consumer power use and controlling the use of electrical devices by putting in place a system that can identify voltage, current, and weather conditions in addition to alerting users in advance of possible problems with power usage. Future system improvements that are region-specific and intended to reduce loads and maximise system performance are made possible by the data collected. © 2024 IEEE.

关键词： Internet of things

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A Survey on Malware Classification using Deep Learning Techniques

A Survey on Malware Classification using Deep Learning Techn...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Vani, E. Prabhavathy, P. Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani India

ISBN: (纸本)9798350322309

Information technology is emerging at fast phase in present environment. The main concerns for a cyber-attack are Privacy and Security. New malwares are increasing at a very fast rate, according to studies. The war between the attackers and security concerns are needed to protect the system from dangerous and malicious software is a continuous process. Attackers are constantly attempting to avoid Malicious malware signatures that are generally being developed by antivirus vendors. Deep learning architectures are used in cyber security applications to examine the essential properties of sample. Also, emerging DL (Deep Learning) techniques and resources identifies the disadvantages in the current work that is used to represent an image of the current trends in the area, that is also used to provide insights and provide the researchers and developers with the best practices that work on similar issues. © 2023 IEEE.

关键词： Cybersecurity

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A deep learning model framework for diabetic retinopathy detection

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International Journal of Networking and Virtual Organisations 2022年第2期27卷 107-124页

作者： Padmapriya, M. Pasupathy, S. Sumathi, R. Punitha, V. Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Department of Computer Science and Engineering Annamalai University Tamilnadu Annamalai Nagar India Department of Information Technology Saranathan College of Engineering Tamilnadu India

Diabetic retinopathy (DR) is the typical diabetic eye issue and a main reason of blindness around the world. As per the International Diabetes Federation (IDF), the rates of diabetes would rise to 552 million by 2034. Breakthroughs in computer science techniques inclusive of artificial intelligence (AI) and deep learning (DL) have multiplied opportunities for early detection of DR. This indicates that the likelihood of a patient's healing will improve, and the risk of eyesight loss could be minimised in due course. A deep learning model (ResNet) for medical DR detection was examined in this article. The dataset of Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 was used to train and test the DL model. To demonstrate the vitality of the chosen ResNet model, performance measures and testing accuracy like recall, precision, and F1 score were determined. The modified ResNet model attained a testing accuracy of around 84% even with only a few dataset images. Also, training time and computational complexity were reduced with this simpler model. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Statistical tests

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Pneumonia Classification from CXR images

Pneumonia Classification from CXR images

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Senthil Murugan, K.R. Murugan, K. Aravintha Kumar, M. Deepalakshmi, R. Jesson Daniel, J. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350348910

Pneumonia is one of the most common and serious respiratory infections. It occurs due to inflammation of alveoli and air sacs filled with other substances like pus and mucus. It affects the lungs and makes it delicate to breathe. Colorful pathogens can cause pneumonia. Numerous algorithms have surfaced to describe pneumonia. In this paper, we will see one similar effective and accurate algorithm that detects pneumonia by using casketX-Ray images(CXR). This paper introduces an LSTM model that uses deep learning and Convolutional Neural Networks (CNNs) to detect pneumonia. The model is also suitable to separate bacterial and viral pneumonia. A similar bracket will be veritably helpful for croakers. The results of our work show that we're suitable to separate COVID pneumonia and bacterial pneumonia. As COVID-19 pneumonia is contagious, we can take preventive measures fluently with the proposed system. Our system provides a delicacy of 93.72 percent and perceptivity of 92.80 percent. The deep literacy model that we achieved made for better perfection. © 2023 IEEE.

关键词： SARS

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Web based Payment Tracking and Accounting Application 7

Web based Payment Tracking and Accounting Application

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Baibhav, Jayesh Ghatak, Jyotishman Mayan, J. Albert Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

The Payment Tracking System (PTS) is a sophisticated web-based platform designed for proficiently tracking and managing payments from various vendors. It serves as a centralized hub for consolidating payment requests from the accounts department to upper management, thereby ensuring timely delivery of supplier payments through the implementation of integrated best practices for managing operations and services. The system boasts advanced Transaction Management functionality such as Invoice Generation and Payment Reminders, providing unparalleled visibility, insight, isolation, and swift resolution of payment-related issues for any type of organization by delivering the necessary information in a timely manner to the user. In today's fast-paced business environment, managing invoicing and chasing payments from clients can be a challenging task. This innovative application offers a solution to this problem by providing businesses with the capability to track payments from clients and gain valuable insights into their earnings. The system not only facilitates the creation, sending, and receiving of invoices, but also generates automated follow-up reminders for overdue payments. By utilizing this application, clients can also avoid incurring late fees © 2023 IEEE.

关键词： Websites

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A Comprehensive Comparison of Machine Learning Algorithms for Breast Cancer Prediction 9

A Comprehensive Comparison of Machine Learning Algorithms fo...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Ebin, N.L. Jawahar Raja, P. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397376

The root cause of death for women is breast cancer. Early detection is essential for rectifying cancer. In this study, we explored the use of machine learning models for predicting breast cancer. We evaluated logistic regression, K-neighbors classifier, support vector machine, decision trees, random forest, and voting classifier using a dataset of breast cancer patients and clinical and demographic factors. Our results showed that the voting classifier had the highest accuracy, followed by the random forest and support vector machine. Age, family history, and breast density were essential factors influencing the development of breast cancer. These findings suggest that machine learning can be helpful in early detection and diagnosis and highlight the importance of considering these risk factors in screeningprograms. © 2023 IEEE.

关键词： Classification (of information)

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