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distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation 31

Distance-Adaptive Quaternion Knowledge Graph Embedding with ...

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31st international conference on Computational Linguistics, COLING 2025

作者： Wang, Weihua Liang, Qiuyu Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet through either semantic matching or geometric distance scoring functions. However, it appears that semantic matching diminishes the separability of entities, while the distance scoring function weakens the semantics of entities. To address this issue, we propose a novel quaternion knowledge graph embedding model. Our model combines semantic matching with the geometric distance of entities to better measure the plausibility of triplets. Specifically, in the quaternion space, we perform a right rotation on head entity and a reverse rotation on tail entity to learn rich semantic features. We then utilize distance-adaptive translations to learn geometric distance between entities. Furthermore, we provide mathematical proofs to demonstrate our model can handle complex logical relationships. Extensive experimental results and analyses show our model significantly outperforms previous models on well-known knowledge graph completion benchmark datasets. Our code is available at https://***/llqy123/DaBR. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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An Effective Method to Detect DHCP Starvation Attack using Port Scanning

An Effective Method to Detect DHCP Starvation Attack using P...

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2023 international conference on Next-Generation Computing, IoT and Machine learning, NCIM 2023

作者： Jony, Asaduzzaman Miah, Abu Saleh Musa Islam, Muhammad Nazrul Dept. of Computer Science and Engineering Dhaka Bangladesh The University of Aizu Dept. of Computer Science and Engineering Aizuwakamatsu Japan Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Nowadays, the DHCP starvation attack is a crucial attack that denied the services for the legitimate network user where the attacker mainly occupies all the IP addresses in the DHCP server pool. Most of the existing studies proposed ICMP (Internet Control Message Protocol) and the ARP (Address Resolution Protocol) based techniques to detect the DHCP starvation attack. However, the ICMP-based detection is ineffective if the client has an active host-based firewall that blocks the ICMP ECHO REQUEST, while the ARP-based detection does not work for DHCP networks separated by a DHCP relay agent and works only within the same or local network. Therefore, the objective of this research is to propose an effective technique to detect DHCP starvation attack detection through port scanning to address the limitations of ICMP and ARP-based detection methods. To attain this objective, this study first highlights the limitations of existing detection methods demonstrating insightful facts. Secondly, the study proposed a port scan-based DHCP starvation attack detection method for local and remote (relay) DHCP networks. Finally, the effectiveness and validation of the proposed techniques were demonstrated using the Multivendor Network Emulation Software (EVE-NG). The study showed that the proposed port scanning-based technique could accurately detect the DHCP starvation attack while overcoming the limitations of the ICMP and ARP techniques. © 2023 IEEE.

关键词： Network security

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Self-Assessment Chatbot for COVID-19 prognosis using Deep learning-based Natural Language Processing (NLP)

Self-Assessment Chatbot for COVID-19 prognosis using Deep Le...

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2023 international conference on Science, engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Thwala, E.K.I. Adegun, A.A. Adigun, M.O. University of Zululand Department of Computer Science Kwadlangezwa South Africa

ISBN: (纸本)9798350324785

Chatbots are computer programs that communicate with humans using natural language processing. Conversational programming is the most crucial part of the development of a chatbot. Existing chatbots are based on simple rule-based techniques. They are therefore limited in terms of functionality, context, and coherence, with narrow domains. Some of these systems also lack flexibility and do not emulate actual human conversation. The available chatbots use machine learning algorithms to answer questions and have structured conversations. However, they lack flexibility and do not emulate actual human conversation. The study proposes a chatbot that will help users self-diagnose symptoms related to COVID-19. Even today, the creation of a good chatbot model remains a significant challenge in Natural Language Processing (NLP) using Machine learning (ML). This study suggests a newly developed method of creating a chatbot by using deep learning: The Recurrent Neural Network (RNN) will be used to model sequence data since it is modeled after the functionality of the human brain. Based on the classification of the available response classes, this study outputs both the chatbot response and the probability value. The system achieves a response time of 0.3 seconds and an accuracy of 95.35%. The study suggests a newly developed method of creating a chatbot by using deep learning with multiple processing layers, the deep learning approach provides better learning for chatbots to make intelligent decisions. Deep learning-based NLP chatbots result in better text classification accuracy with the ability to understand sarcasm, humor, and conversations better. © 2023 IEEE.

关键词： Recurrent neural networks

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Crop Recommender System Based on Ensemble Classifiers

Crop Recommender System Based on Ensemble Classifiers

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2023 international conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Vuyyala, Voshma Reddy Kona, Michael Sadgun Rao Pusuluri, Sai Bhargavi Variganji, Swetha Nenavathu, Bhavani PVP Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350396485

Farmers are facing problems because they are unable to manage cultivation because of bad weather conditions and uneven rainfall. Thus, to reduce the problems of farmers, the latest technologies are introduced such as machine learning to implement crop recommendation systems. A wide range of classification techniques are used, and a specific model is selected based on their accuracy levels. By using feature selection techniques, the raw data is converted into a dataset which is useful for efficiently training the model with relevant data. Reducing redundant data and utilizing just the aspects that are significantly relevant in deciding the model's final output will improve the model's accuracy. The findings show that, compared to other classifiers, the ensemble approach delivers better prediction with a 99.54% accuracy rate. document is a ''live'' template and already defines the components of your paper [title, text, heads, etc.] in its style sheet. © 2023 IEEE.

关键词： Recommender systems

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Twitter Sentiment Analysis using Machine learning Techniques: A Case Study of ChatGPT 1

Twitter Sentiment Analysis using Machine Learning Techniques...

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1st IEEE international conference on Contemporary Computing and Communications, InC4 2023

作者： Kumari, Rajani Kumar, Sandeep Bangalore Off-Campus Center of ICFAI Foundation for Higher Education Bangalore India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335774

ChatGPT is a powerful AI bot developed by OpenAI. This technology has the potential to generate a humanlike response. ChatGPT is a pre-trained system capable of generating chat and understanding human speech. This paper identified the responses of ChatGPT users through related tweets with the help of natural language processing and machine learning techniques. This paper uses textBlob, VADER and human annotation to find the sentiment of each tweet;countvectorizer is used for feature extraction and different machine learning algorithms to classify them into different classes. LeXmo is used to identify the various sentiment analyses, and it is observed that positive and trust emotions are higher than other sentiments. SVM with 10-fold cross-validation shows better results than other techniques. © 2023 IEEE.

关键词： Sentiment analysis

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Leveraging Transfer learning for Improved Diagnostics in Dermatology 5

Leveraging Transfer Learning for Improved Diagnostics in Der...

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5th international conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Singh, Kiran Deep Saini, G.L. Singh, Prabh Deep Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Computing and Intelligent Systems Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9798400709418

The significance of leveraging transfer learning in dermatology lies in its potential to enhance early disease detection and diagnosis, ultimately leading to improved patient outcomes and more efficient healthcare delivery. This innovative approach offers a promising avenue for dermatologists to harness the power of AI in their practice, augmenting their expertise and expanding the horizons of skin disease diagnostics. This paper applies transfer learning to healthcare for Improved Diagnostics in Dermatology. It explores the core principles and methodologies of transfer learning, unveiling its applications across various domains, from computer vision to natural language processing. The discussion emphasizes the advantages of transfer learning over conventional methods, shedding light on its ability to mitigate data scarcity, expedite training, and enhance model generalization. Moreover, the dialogue delves into the steps involved in implementing transfer learning, offering practical guidance on data preparation, model adaptation, and rigorous evaluation, specifically referencing utilizing the Deep learning Toolbox in MATLAB. Transfer learning transforms medical diagnoses sustainably, which finishes the discourse with a captivating dermatological case study that shows how skillfully implemented may revolutionize early illness detection, diagnosis, and patient care, demonstrating its enormous influence on healthcare and smart infrastructure. © 2023 ACM.

关键词： Deep learning

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Forecasting Cardiovascular Disease Using Deep learning 3

Forecasting Cardiovascular Disease Using Deep Learning

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3rd international conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Singh, Dalwinder Singh, Arun Rakhra, Manik Singh, Janpreet Kumar, Rakesh Lovely Professional University Department of Computer Science & Engineering Jalandhar Phagwara India Lovely Professional University Department of Mathematics Punjab Phagwara144411 India

ISBN: (纸本)9798350303360

Across the world most of the deaths are caused due to heart disease. To lower death rates, early heart disease identification and prevention are essential. In recent years, early detection and cardiac disease prediction have both benefited from the machine learning techniques application. With the use of the *** dataset, this study compares how well Support Vector Machine (SVM), Random Forest (RF) and Decision Tree (DT) do at forecasting heart disease. The findings show that Random Forest outperforms SVM and Decision Tree with 100% accuracy. © 2023 IEEE.

关键词： Support vector machines

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Using Deep Reinforcement learning to Solve a Navigation Problem for a Swarm Robotics System 24

Using Deep Reinforcement Learning to Solve a Navigation Prob...

引用

24th international Carpathian Control conference, ICCC 2023

作者： Iskandar, Alaa Rostum, Hussam M. Kovacs, Bela University of Miskolc Faculty of Mechanical Engineering and Informatics Miskolc Hungary University of Miskolc Institute of Automation and Info-Communication Miskolc Hungary

ISBN: (纸本)9798350310221

Five mobile robots in a swarm are trained in this paper using the deep reinforcement learning method to solve a navigation problem with two targets without prior knowledge of the environment. The Proximal Policy Optimization method trains E-puck mobile robots to avoid obstacles while completing tasks in the shortest distance for each robot. A Webots simulator is used to model the environment in three-dimension space. The suggested algorithm works with continuous states derived from eight infrared sensors and continuous action spaces that reflect the velocities of two motors for each robot in the swarm. Then the robot's behavior will be examined in light of two categories of rewards: Spares and Shaping rewards. If the environment's complexity is not decreased, proximal policy optimization with spare rewards will not be able to train every robot in the system how to accomplish its goal. Compared to the spare rewards technique, shaping rewards aid the robot in gaining experience from prior knowledge during the training process. This speeds up learning and aids navigation in more complex environments. © 2023 IEEE.

关键词： Mobile robots

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Machine learning-Based Prediction of Coronary Heart Disease 9

Machine Learning-Based Prediction of Coronary Heart Disease

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9th international conference on Control, Instrumentation and Automation, ICCIA 2023

作者： Shirazi, Badrosadat Nategholeslam Naghsh, Shiva Safavi, Ali Akbar Sharafkhaneh, Amir School of Electrical and Computer Engineering Shiraz University Shiraz Iran Baylor College of Medicine Department of Medicine HoustonTX United States

ISBN: (纸本)9798350366013

Coronary Heart Disease (CHD) remains a significant global health concern. This study utilizes the Sleep Heart Health Study (SHHS) dataset, encompassing demographic features, measurement features, and combination of these two categories, to develop a predictive model for CHD risk. The Multi-Layer Perceptron (MLP) neural network is employed to capture intricate relationships within the data. An accuracy mostly greater than 70% in CHD risk prediction is exhibited by the MLP model when categories of selected features are fed to it. This research emphasizes the importance of using readily available data for physicians and advanced machine learning technique to enhance early CHD risk prediction. These findings have the potential to improve personalized medicine and targeted interventions for CHD prevention and management. © 2023 IEEE.

关键词： Machine learning

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Investigating the Effects of Different L2 Caption Types on Young Learners' Incidental Vocabulary learning 23

Investigating the Effects of Different L2 Caption Types on Y...

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8th international conference on distance Education and learning, ICDEL 2023

作者： He, Fang General Teaching Department Nanning University China

ISBN: (纸本)9798400700422

Captioned videos plays an important role in incidental vocabulary learning and retention. However, to date there has been limited research on how they affected young learners' word acquisition. The present study examines the effectiveness of four captioning conditions, i.e., glossed captions, full captions, keyword captions, and no captioning, on incidental vocabulary learning and retention. A total of 125 young learners of English as a foreign language, divided into four groups, watched four videos and completed two vocabulary tests, i.e., form recognition and meaning recall, administered as pretest, post-test, and delayed test. The results showed that captioning types, particularly glossed captions, significantly affects incidental vocabulary learning and retention. And pedagogically relevant implications and limitations were discussed based on the results. © 2023 ACM.

关键词： computer programming

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