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Comparative Study on Extractive Summarization Using Sentence Ranking Algorithm and Text Ranking Algorithm

Comparative Study on Extractive Summarization Using Sentence...

引用

2023 International Conference on Power, Instrumentation, Control and Computing, PICC 2023

作者： Majeed, Mansoora Kala, M.T. Government Engineering College Dept. of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350334463

It is possible to create an accurate and succinct summary using a method called automatic text summarising. Before creating the necessary summary sentences, the machine learning algorithms may be trained to understand documents and recognise the portions that transmit crucial facts and information. Abstractive text summa- rization and Extractive text summarization are its two methods. An extractive text summarization refers to the process of removing significant text from a text document or source manuscript. Deep learning techniques are used to train an abstractive text summarizer, which then generates new phrases and keywords based on the content of the original text. The process of acquiring and assimilation of the knowledge from numerous sources takes time. Automatic text summary is used to speed up the process of finding and consuming pertinent information. An extractive text summarising technique chooses crucial phrases, sentences, and other parts of the original text and concatenates them into a compressed form. The extractive text summarization produces grammatically correct sentences. The project is to develop a model that demonstrates extractive text summarization using sentence ranking and text rank algorithm. It shows that the text rank algorithm does a remarkable performance to extractive text summarization. Using a sentence ranking algorithm, extractive summaries are created that rate sentences according to their weights. In order to create a high-quality summary of the input document and store the summary as audio, the input document's highly scored sentences are extracted. A non-supervised, extractive text summarising method is called TextRank. While calculating the value of words and phrases in a document, the TextRank algorithm looks at how they relate to one another. According on how important the words are in each phrase, it then assigns a score to each one. The summary is formed using the algorithm's choice of the most crucial phrases. © 2023 IE

关键词： Deep learning

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Smart City Surveillance: Survey on Current Status and Future Trends 14

Smart City Surveillance: Survey on Current Status and Future...

引用

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Neyaz, Hera Paul, Hrituparna Srivastava, Anima Singh, Shiksha Dept. of Computer Science and Engineering Shambhunath Institute of Engineering and Technology India

This paper presents a comprehensive survey on surveillance and security aspects of a smart city. Multiple dimensions of a smart city surveillance system have been discussed. The current trends and methodologies are thoroughly researched and presented. The algorithms of novel methods are reviewed along with analytics and methodologies. Impact of XAI in the smart city surveillance is discussed. Advantages and disadvantages of current infrastructures have been included in the broad literature survey. Lastly, the challenges and future trends are conferred to. © Grenze Scientific Society, 2023.

关键词： Smart city

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Exploring the Impact of Robotics in STEM Education Activities and Competitive Challenges 2

Exploring the Impact of Robotics in STEM Education Activitie...

引用

2nd International Conference on Cybernetics and Innovations, ICCI 2023

作者： Farooq, Adil Zukhraf, Syeda Zillay Nain Maryam, Hafsa Attard, Conrad Kamal, Mohsin University of Cyprus Dept. of Electrical and Computer Engineering Cyprus National University of Computer and Emerging Sciences Dept. of Electrical Engineering Pakistan University of Malta Dept. of Computer Information Systems Msida Malta

ISBN: (纸本)9798350336573

As robotics and technology become increasingly integrated into education and daily life, it is important to recognize their essential role. Even young children have access to electronic toys and gadgets, which can facilitate their learning and interaction. This study highlights the significance of STEM (science, technology, engineering, and mathematics) education at the elementary school level, emphasizing how university students from a variety of disciplines can design and construct robots capable of carrying out challenging tasks for humanitarian purposes. The study also emphasizes the need to encourage women's participation in robotics to showcase their skills and innovative ideas. Furthermore, the study demonstrates how robotics can effectively be used in both STEM education and competitive challenges, showcasing its effectiveness in teaching and promoting technological advancement. By encouraging early engagement in STEM education and promoting diversity in the field of robotics, we can empower future generations to lead the way in creating innovative solutions to global challenges. © 2023 IEEE.

关键词： Intelligent systems

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A Deep Learning Based Image Processing Technique for Early Lung Cancer Prediction

A Deep Learning Based Image Processing Technique for Early L...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent systems, ICETSIS 2024

作者： Tasnim, Nowshin Noor, Kazi Rifah Islam, Mursalina Huda, Mohammad Nurul Sarker, Iqbal H. Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chittagong4349 Bangladesh United International University Dept. of Computer Science & Engineering Dhaka1212 Bangladesh Edith Cowan University PerthWA6027 Australia

ISBN: (纸本)9798350372229

Lung cancer is the primary cause of cancer mor-tality all over the world due to the increase of tobacco consumption, and industrialization in developing nations. As the early-stage diagnosis can reduce the mortality rate significantly, early detection with the availability of high-tech Medical facilities is highly necessary. In this research, we used deep learning (DL) methods initially on patient's 1190 CT scan images from the Kaggle IQ-OTH lung cancer dataset, and after significant image preprocessing steps we found augmented images including normal, malignant, and benign cases to identify high-risk in-dividuals to detect lung cancer and also predict the malignancy and thus, taking early actions to prevent long-term consequences. A thorough performance comparison between several classifiers, including the conventional CNN, Resnet50, and InceptionV3, has been presented. Here, affine transformation, gaussian noise, and other rigorous image preprocessing techniques are used. The contribution obtained a 98% validation accuracy while reducing the model's complexity with the previous preprocessing stage. The comparison method shows that the suggested preprocessing method yields a higher F1 score value of 97%, validating our suggested methodology. © 2024 IEEE.

关键词： CT scans deep learning healthcare analytics image processing lung cancer prediction

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A Critical Analysis of Object Tracking Using Radial Function Networks

A Critical Analysis of Object Tracking Using Radial Function...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Mishra, Amit Kumar Pandey, Neeraj Kumar Diwakar, Manoj Bahadure, Nilesh Bhaskarrao Dumka, Ankur Patni, Jagdish Chandra Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Science & Engineering Dehradun India Symbiosis Institute of Technology Dept. of CSE Nagpur Campus Pune India Women Institute of Technology Dept. of Computer Science & Engineering Dehradun India Alliance University Alliance School of Advance Computing Dept. of CSE Karnataka Bengaluru India

ISBN: (纸本)9798350394412

A important method in several fields, including driverless cars, medical imaging, human-computer interaction, and investigation systems, is object tracking. Because of their flexibility to changes in object presence and ambient factors, as well as their capacity to estimated complex, non-linear functions, radial networks have become a potent tool for object stalking in recent years. The article conditions for the analysis of the radical working networking where the algorithm is a tracking segment that aims in moving the object. Object recognition is one of the most basic and centralized factor analyses in determining outputs having relevant values and a strong research background. Radial functioning is a phenomenon that has gained rapid training and desirable properties for improving the presentation of networking sites. radial networks, a form of artificial neural network, utilize radial basis functions as initiation functions. Because of this characteristic, RBF networks are especially well-suited for applications like object tracking that call for high accuracy in function approximation and pattern recognition. We explore the design of RBF networks in detail, emphasizing the functions of the input layer, output layer, and hidden layer using radial basis functions. We examine the mathematical foundations that allow RBF networks to represent intricate connections in the data, emphasizing how well they can generalize and interpolate. © 2024 IEEE.

关键词： Radial basis function networks

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DeepStock Forecast: Unveiling Market Movements through Advanced Deep Learning Models 4

DeepStock Forecast: Unveiling Market Movements through Advan...

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4th International Conference on Emerging systems and Intelligent Computing, ESIC 2024

作者： Das, Smruti Rekha Mishra, Debahuti Lenka, Arundhati Shaw, Kailash Gitam Deemed to Be University Dept. Computer Science and Engineering Andhra Pradesh Visakhapatnam India Siksha 'o' Anusandhan Deemed to Be University Odisha Dept. Computer Science and Engineering Odisha India Dept. of Aiml Symbiosis Institute of Technology Pune India

ISBN: (纸本)9798350349856

This study uses deep neural network (DNN) methodologies, including auto encoder, deep belief network (DBN), and backpropagation neural network (BPNN), to predict stock prices over 15 and 30 days. Utilizing BSE Sensex and NSE Sensex datasets with diverse technical indicators, the research highlights DNN's superior training accuracy, measured by minimal mean squared error (MSE). The study's key contribution is applying DNN to predict stock market volatility. While acknowledging the limitation of relying solely on training error, the study incorporates additional measures like RMSE, MAPE, MAE, and ARV during testing. Notably, DNN's MAPE results fall within a narrow range (0.0221 to 0.0255), indicating minimal error deviation. In a comprehensive evaluation across all datasets, DNN consistently outperforms DBN and BPNN in both training and testing performance. © 2024 IEEE.

关键词： Deep neural networks

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Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare 63

Distributionally Robust Clustered Federated Learning: A Case...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Konti, Xenia Riess, Hans Giannopoulos, Manos Shen, Yi Pencina, Michael J. Economou-Zavlanos, Nicoleta J Zavlanos, Michael M. Duke University School of Medicine United States Duke University Dept. of Computer Science United States Duke University Dept. of Electrical and Computer Engineering United States Duke University Dept. of Mechanical Engineering & Materials Science United States

ISBN: (纸本)9798350316339

In this paper, we address the challenge of heterogeneous data distributions in cross-silo federated learning by introducing a novel algorithm, which we term Cross-silo Robust Clustered Federated Learning (CS-RCFL). Our approach leverages the Wasserstein distance to construct ambiguity sets around each client's empirical distribution that capture possible distribution shifts in the local data, enabling evaluation of worst-case model performance. We then propose a model-agnostic integer fractional program to determine the optimal distributionally robust clustering of clients into coalitions so that possible biases in the local models caused by statistically heterogeneous client datasets are avoided, and analyze our method for linear and logistic regression models. Finally, we discuss a federated learning protocol that ensures the privacy of client distributions, a critical consideration, for instance, when clients are healthcare institutions. We evaluate our algorithm on synthetic and real-world healthcare data. © 2024 IEEE.

关键词： Federated learning

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The Rational Construction of a Wheeler DFA 35

The Rational Construction of a Wheeler DFA

引用

35th Annual Symposium on Combinatorial Pattern Matching, CPM 2024

作者： Manzini, Giovanni Policriti, Alberto Prezza, Nicola Riccardi, Brian Dept. of Computer Science University of Pisa Italy Dept. of Mathematics Computer Science and Physics University of Udine Italy Dept. of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Italy Dept. of Informatics Systems and Communication University of Milano-Bicocca Italy

ISBN: (纸本)9783959773263

Deterministic Finite Wheeler Automata are a natural generalisation to regular languages of the theory of compressed data structures originated by the introduction of the Burrows-Wheeler transform. Indeed, if we can find a Wheeler automaton recognizing a given language L, such automaton can be used to design time and space efficient algorithms for representing and searching L. In this paper we introduce an alternative representation of Deterministic Wheeler Automata by showing that a natural map between strings and rational numbers in Qr0, 1q can be extended to represent the automaton’s states as intervals in Qr0, 1q. Using this representation it emerges a natural relationship between automata properties and some properties of real numbers. In addition, such representation enables us to formulate problems related to automata in a numerical setting. Although at the moment the numerical approach does not lead to time efficient algorithms, we believe this new perspective deserves further consideration. As a further demonstration of the convenience of this new representation, we use it to provide a simple proof of an unexpected result on regular languages. More precisely, we compare the size of the smallest Wheeler automaton recognizing a given language L with respect to the size of the smallest automaton, possibly non-Wheeler, recognizing the same language. We show settings in which there can be an exponential gap between the two sizes, and we discuss the implications of this result on the problem of representing regular languages. © Giovanni Manzini, Alberto Policriti, Nicola Prezza, and Brian Riccardi.

关键词： Finite automata

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Investigation of Convolutional Neural Network with Motor Imagery-based Brain-computer Interface Decoding 14

Investigation of Convolutional Neural Network with Motor Ima...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Madhan, H.K. Bindushree, V. Dept. of Computer Science and Engineering Navkis College of Engineering Hassan India

In recent years, Brain-computer Interfaces (BCI) have become increasingly popular for assisting individuals with disabilities in restoring communication and mobility. The area of research focuses on utilizing convolutional neural networks (CNNs), specifically deeper architectures with high dropout, to decode brain signals and enable users to interact with the world more naturally, particularly through motor imagination. Motor imagination involves analyzing brain signals to decipher a person's intention to perform a motor task. CNNs are well-suited for BCI decoding due to their ability to learn intricate features from raw EEG data. However, the challenge lies in preventing overfitting in deeper CNN architectures, which have a larger number of parameters. To overcome this, the proposed approach involves a high truncation CNN architecture, a regularization technique that randomly turns off neurons during training to mitigate overfitting. © Grenze Scientific Society, 2023.

关键词： Brain computer interface

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UHI Web Application: Empowering Healthcare with Disease Prediction for Seamless Doctor Consultations 4

UHI Web Application: Empowering Healthcare with Disease Pred...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Vardhan, Nunna Harsha Rajesh, M. Vishal Matcha, V.R.S. Reddy, Sathi Ravi Shankar Chowdary, Verelli Nivas Dept. of Computer Science Engineering Amrita School of Engineering Bangalore India

ISBN: (纸本)9798350305258

The large amount of data generated by hospitals is helping machine learning algorithms to generate accurate solutions. This data includes information about treatment, diagnosis, and medications. This valuable information can be used to create automated solutions. The Indian government is planning to introduce a technology like the United Payment Interface (UPI) in the healthcare sector. This technology is called the United Health Interface (UHI). The UHI aims to consolidate various healthcare services, such as the storage of patients' health records, management of blood banks, provision of hospitals' information, doctors' details, bed availability, medical insurance services, and more. Our goal is to develop a similar system where all these services can be accessed through a single account. The UHI is an advanced technology that has revolutionized the healthcare industry. It offers numerous benefits to both patients and healthcare providers. One of the most significant advantages of UHI is the seamless sharing of patient information across different healthcare providers and institutions. This means that doctors, nurses, and other healthcare professionals can easily access patient data, regardless of their location, and make informed decisions regarding treatment. The online doctor appointment system has also revolutionized healthcare. It offers several advantages for patients and healthcare providers alike. These advantages include convenience, time savings, improved access to healthcare, reduced costs, and enhanced patient management. Therefore, our aim is to develop a web application that consolidates all healthcare services under one platform, significantly improving our lives. Here are some additional details about the UHI and the online doctor appointment system, The UHI is a network of open protocols that enable the interoperability of health services. This means that patients' health records can be shared securely and easily between different healthcare provi

关键词： Disease prediction Gradient Boosting Classifier Random Forest Symptoms Unified health interface

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