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Balance Relation-Aware Attention Embedding Model for Knowledge Graph Completion 6

Balance Relation-Aware Attention Embedding Model for Knowled...

引用

6th IEEE Pune Section International Conference, PuneCon 2023

作者： Khobragade, Anish Ghumbre, Shashikant Pachghare, Vinod Coep Technological University Dept. of Computer Science and Engineering Pune India Government College of Engineering and Research Avasari Khurd Dept. of Computer Science and Engineering Pune India

ISBN: (纸本)9798350324204

Predicting missing links has great significance in knowledge graph completion because it helps improve the accuracy and completeness of the information in the graph. The recent models employ an attention mechanism to capture the graph network. Previous attention-based models treat similar relation semantics for associated pairs of entities, but relations might have similar names but represent different semantics. Thus, models fail to capture the semantic dissimilarity of relations involved within the triplet entity pair. This paper proposes a new graph attention model capable of effectively learning embeddings by combining relationship features with triplet entity embeddings to balance relationship contributions while capturing meaningful graph structure. The model incorporates relational compositional embeddings to determine distinct attention for neighboring nodes during computation and update the entity embedding. This novel approach outperforms the previously established models on the benchmark dataset. © 2023 IEEE.

关键词： Knowledge graph

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Stock Market Prediction Using Genetic Algorithm Assisted LSTM-CNN Hybrid Model 14

Stock Market Prediction Using Genetic Algorithm Assisted LST...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Nikhil, Shreyas Sah, Rahul Kumar Kumar Parki, Santosh Tamang, Til Bikram Somashekhara Reddy, D. Mahesh, T.R. Faculty of Engineering and Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335095

Stock price prediction has always been a tough task for all the stakeholders involved. This paper focusses on four different models, namely LSTM, CNN, LSTM-CNN, and Genetic Algorithm-Assisted LSTM-CNN (GA-LSTM-CNN) for stock price prediction. The day-to-day stock prices of a certain company for a period of more than 10 years make up the dataset utilized in this study. Root mean square error (RMSE) has been utilized as performance metric for each model. Outcomes of our experiment show that the GA-LSTM-CNN model performed better than other models with an RMSE value of 0.0064 upon training and an RMSE value of 0.03 upon testing. The GA-LSTM-CNN model combines the strengths of LSTM as well as CNN models with the help of a genetic algorithm, which helps in optimizing the hyperparameters. According to the results, the GA-LSTM-CNN model can be very helpful for the stakeholders in predicting future stock values. © 2023 IEEE.

关键词： Genetic algorithms

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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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Leveraging Network Vulnerability Detection using Improved Import Vector Machine and Cuckoo Search based Grey Wolf Optimizer 1

Leveraging Network Vulnerability Detection using Improved Im...

引用

1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Preethi, P. Vasudevan, I. Saravanan, S. Krishna Prakash, R. Devendhiran, A. Kongunadu College of Engineering and Technology Dept. of Computer Science and Engineering Trichy India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India Kl University Dept. of Computer Science and Engineering Vijayawada India Sona College of Technology Dept. of Information Technology Salem India Vivekanandha College of Engineering for Women Dept. of Computer Science and Technology Namakkal India

ISBN: (纸本)9798350328042

In the current interconnected and susceptible digital environment, the demand for reliable network attack prediction systems has reached a critical level. This research paper presents a novel approach for network intrusion detection utilizing the Improved Import Vector Machine (IVM) classification algorithm combined with feature selection using Cuckoo Search based Grey Wolf Optimization (CS-GWO). The aim is to improve the accuracy and effectiveness of intrusion detection systems by selecting the most relevant features and employing a powerful classification algorithm. The proposed methodology begins with data preprocessing, ensuring the dataset is appropriately cleaned and normalized. Subsequently, the CS-GWO algorithm is applied for feature selection, leveraging the strengths of both Cuckoo Search and Grey Wolf Optimization. This hybrid approach efficiently explores the search space to identify the optimal feature subset that contributes to accurate intrusion detection. Once the feature selection process is completed, the IVM classification algorithm is employed to classify network instances into intrusion or normal categories. The IVM algorithm is chosen for its ability to handle high-dimensional data and its robustness in dealing with imbalanced datasets commonly encountered in network intrusion detection. To evaluate the performance of the proposed approach, extensive experiments are conducted using benchmark datasets. Various evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of the system in detecting network intrusions. The results demonstrate that the combination of IVM classification and CS-GWO feature selection significantly improves the accuracy and efficiency of network intrusion detection. The selected features obtained through CS-GWO contribute to enhanced classification performance, enabling the system to accurately identify and respond to potential intrusions in real-time scenarios. © 2023 IE

关键词： Network security

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Automated Attendance Management System 5

Automated Attendance Management System

引用

5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Goyal, Pranshu Nigam, Vishwas Khan, Shahnaw A.J. Singh, Shashi Kant Galgotias College Of Engineering And Technology Dept. Of Computer Science And Engineering India

ISBN: (纸本)9798350330861

the manual process of recording and managing attendance in educational institutions and organizations is often time-consuming, prone to errors, and inefficient. To address these challenges, there is a growing need for automated attendance management systems. This research paper presents the design and implementation of an Automated Attendance Management System (AAMS) using modern technologies. The AAMS utilizes various technologies such as biometric recognition, RFID (Radio Frequency Identification), and computer vision to accurately and efficiently track attendance. The system aims to simplify the attendance recording process for both students and instructors, reduce administrative burden, and improve overall efficiency. The proposed AAMS incorporates biometric recognition techniques, such as fingerprint scanning or facial recognition, to identify and verify the identity of individuals. © 2023 IEEE.

关键词： Efficiency

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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...

引用

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

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

引用

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

Distributionally Robust Clustered Federated Learning: A Case...

引用

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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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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Screening and Ranking resume's using Stacked Model 1

Screening and Ranking resume's using Stacked Model

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Kavitha, D. Padmavathi, B. Sushanth, V. Yashwanth, P. Yuvaraj, R. Easwari Engineering College Dept. of Computer Science & Engineering TN India

ISBN: (纸本)9798350348057

Finding the top candidates for a position is the aim of the resume screening process. The application must make use of machine learning methodologies as well as natural language processing to rate candidates in real time. Natural language processing (NLP) and machine learning methods are used to rate resumes. The output would be the resume of a top candidate, with resumes and job descriptions serving as the input. The output's results are obtained instantly. String matching, Cosine Similarity, and TF-IDF will all be done with Mon. Existing systems are straightforward and efficient, but they lack precision, efficiency, and processing capacity. © 2023 IEEE.

关键词： Natural language processing systems

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