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Computational Geometry: Theory and Applications 2024年 122卷

作者： Bae, Sang Won Okamato, Yoshio Division of Artificial Intelligence and Computer Science Kyonggi University Korea Republic of Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Japan

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A Self-matching Training Method with Annotation Embedding Models for Ontology Subsumption Prediction

arXiv

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arXiv 2024年

作者： Shiraishi, Yukihiro Kaneiwa, Ken Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

Recently, ontology embeddings representing entities in a low-dimensional space have been proposed for ontology completion. However, the ontology embeddings for concept subsumption prediction do not address the difficulties of similar and isolated entities and fail to extract the global information of annotation axioms from an ontology. In this paper, we propose a self-matching training method for the two ontology embedding models: Inverted-index Matrix Embedding (InME) and Co-occurrence Matrix Embedding (CoME). The two embeddings capture the global and local information in annotation axioms by means of the occurring locations of each word in a set of axioms and the co-occurrences of words in each axiom. The self-matching training method increases the robustness of the concept subsumption prediction when predicted superclasses are similar to subclasses and are isolated to other entities in an ontology. Our evaluation experiments show that the self-matching training method with InME outperforms the existing ontology embeddings for the GO and FoodOn ontologies and that the method with the concatenation of CoME and OWL2Vec* outperforms them for the HeLiS ontology. Copyright © 2024, The Authors. All rights reserved.

关键词： Ontology

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Optimizing Power Allocation and Beamforming in mmWave-NOMA Downlink with Deep Reinforcement Learning

Optimizing Power Allocation and Beamforming in mmWave-NOMA D...

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Communications and Networking (MECOM), IEEE Middle East Conference on

作者： Abbas Akbarpour Kasgari Chrysostomos Chrysostomou Department of Electrical Engineering Computer Engineering and Informatics Frederick University and Frederick Research Center Nicosia Cyprus

ISBN: (数字)9798350376715

ISBN: (纸本)9798350376722

The high demand for data rates in the fifth generation (5G) and beyond of wireless communication can be met by the Non-Orthogonal Multiple Access (NOMA) approach in the millimeter-wave (mmWave) frequency band. Joint power allocation and beamforming for 5 G and beyond mmWave-NOMA systems are essential, which can be achieved through optimization approaches. To this end, we have employed a Deep Reinforcement Learning (DRL) approach for policy generation, leading to an optimized sum-rate for users. Unlike existing methods, our approach is not susceptible to channel impulse response (CIR) estimation errors. The actor-critic framework is utilized to measure the immediate reward and provide new actions to maximize the overall Q-value of the network. The immediate reward is defined based on the summation of the rates of two users, considering the minimum guaranteed rate for each user and the total consumed power as constraints. The simulation results demonstrate the superiority of the proposed approach compared to state-of-the-art counterparts, including Time-Division Multiple Access (TDMA) and NLOS-NOMA optimization strategies, in terms of sum-rate of users, while considering both perfect and imperfect channel state information (CSI) at the receiver. This work can also be beneficial for communications in vehicular networks, particularly in applications involving Collective Perception Messages (CPMs) dissemination for autonomous driving.

关键词： Wireless communication NOMA Time division multiple access Array signal processing 5G mobile communication Deep reinforcement learning Resource management Millimeter wave communication Channel impulse response Optimization

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Computational examination to reveal Kaempferol as the most potent active compound from Euphorbia hirta against breast cancer by targeting AKT1 and ERα

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Egyptian Journal of Basic and Applied Sciences 2023年第1期10卷 753-767页

作者： Widyananda, Muhammad Hermawan Fatchiyah, Fatchiyah Muflikhah, Lailil Ulfa, Siti Mariyah Widodo, Nashi Department of Biology Faculty of Mathematics and Natural Sciences Universitas Brawijaya Malang Indonesia Department of Informatics Engineering Faculty of computer science Universitas Brawijaya Malang Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Brawijaya Malang Indonesia

Breast cancer belongs to diseases with the highest mortality rates. Euphorbia hirta is an herbal plant with various pharmacological properties and is predicted to have good anti-breast cancer properties. This current study analyzed a potent compound from E. hirta as an anti-breast cancer candidate and predicted its mechanism. The Bioactive compounds in E. hirta were screened for drug-likeness, toxicity, membrane permeability, and bioactivity. Protein target prediction utilized four databases, followed by functional annotation analysis. Molecular docking employed AutoDock Vina, and molecular dynamic simulation used Yet Another Scientific Artificial Reality Application with parameters reflecting human cell physiology. Kaempferol was the potent compound in E. hirta that passed the screening and was predicted to have better anti-breast cancer activity than that of other compounds. Kaempferol targets AKT1 and ERα, the proteins correlating with breast cancer progression. The molecular docking results showed that Kaempferol bound to inhibitor sites on both AKT1 and ERα. In addition, Molecular dynamic analysis showed that the interaction between Kaempferol with AKT1 and ERα was stable, as seen from the RMSD, RMSF, hydrogen bonding, and MM/PBSA binding energy values. Therefore, Kaempferol was a bioactive compound from E. hirta with promising anti-breast cancer activities by targeting the AKT1 and ERα. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： AKT1 anticancer ERα Euphorbia hirta Kaempferol

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Towards Psycho-Cognitive Modelling of Mobile Text Entry Behaviour 24

Towards Psycho-Cognitive Modelling of Mobile Text Entry Beha...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Andreas Komninos Ioulia Simou John Garofalakis University of Patras Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece

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PREDICTING FROM A DIFFERENT PERSPECTIVE IN RE-RANKING MODEL FOR INDUCTIVE KNOWLEDGE GRAPH COMPLETION

arXiv

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arXiv 2024年

作者： Iwamoto, Yuki Kaneiwa, Ken Department of Computer Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

Rule-induction models have demonstrated great power in the inductive setting of knowledge graph completion. In this setting, the models are tested on a knowledge graph entirely composed of unseen entities. These models learn relation patterns as rules by utilizing subgraphs. Providing the same inputs with different rules leads to differences in the model’s predictions. In this paper, we focus on the behavior of such models. We propose a re-ranking-based model called ReDistLP (Re-ranking with a Distinct Model for Link Prediction). This model enhances the effectiveness of re-ranking by leveraging the difference in the predictions between the initial retriever and the re-ranker. ReDistLP outperforms the state-of-the-art methods in 2 out of 3 benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Comparison Between Bi-Directional LSTM And Transfer Learning in Correcting Typing Errors on Twitter Social Media Posts 11

Comparison Between Bi-Directional LSTM And Transfer Learning...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Lubis, Arif Ridho Prayudani, Santi Hamzah, Muhammad Luthfi Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia Universitas Islam Negeri Sultan Syarif Kasim Riau Faculty of Science and Technology Pekanbaru Indonesia

ISBN: (纸本)9798350305968

Typing errors are a behavior that often occurs in communication via short messages or posts on social media platforms. In communicating on social media, many individuals without realizing it often make typing errors that can affect the meaning of sentences. This research will apply a model that can check typing errors using Bi-Directional LSTM and transfer learning which results in the Bi-Directional LSTM model achieving an accuracy of 78% after being trained for 20 epochs with a batch size of 64. On the other hand, the transfer learning model achieves an accuracy of 80% with the same epoch and batch size settings. Furthermore, when the Bi-Directional LSTM model was set with 20 epochs and batch size 64, the accuracy increased to 83%, while the transfer learning model achieved 87% accuracy. Finally, with 20 epochs and a batch size of 64, the Bi-Directional LSTM model achieved 87% accuracy, while the transfer learning model achieved an impressive accuracy of 93%. © 2023 IEEE.

关键词： Social networking (online)

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Performance Evaluation of Oversampling Methods on Deep Learning-Based Skin Cancer Classification

Performance Evaluation of Oversampling Methods on Deep Learn...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Supriyanto, Catur Salam, Abu Zeniarja, Junta Wijaya, Adi Universitas Dian Nuswantoro Bachelor Program in Informatics Engineering Faculty of Computer Science Semarang Indonesia Universitas Indonesia Maju Department of Health Information Management Jakarta Indonesia

ISBN: (纸本)9798350339215

The field of dermatology faces considerable challenges when it comes to early detection of skin cancer. Our study focused on using different datasets, including original data, augmented data, and SMOTE oversampled data, to identify skin cancer. Our dataset consisted of images of skin lesions from the MNIST Skin Cancer dataset (HAM 10000), including samples of both cancerous and benign cases in the dataset. We employed data augmentation to expand the dataset's size and increase the diversity of skin lesion features. Furthermore, to tackle class imbalance in the dataset, we applied the SMOTE oversampling technique to generate synthetic samples for the under-represented group. With the original, augmented, and SMOTE oversampled datasets, we trained a Convolutional Neural Network (CNN) model. The performance of the model was evaluated using accuracy, recall, precision, and F1-score. The comparison between the results obtained from the original data, augmented data, and SMOTE oversampling data clearly revealed distinctions in performance. Our findings clearly demonstrate that employing data augmentation and SMOTE oversampling can significantly enhance the efficacy of skin cancer detection. © 2023 IEEE.

关键词： Neural network models

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Aspect Level Songs Rating Based Upon Reviews in English

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computers, Materials & Continua 2023年第2期74卷 2589-2605页

作者： Muhammad Aasim Qureshi Muhammad Asif Saira Anwar Umar Shaukat Atta-ur-Rahman Muhammad Adnan Khan Amir Mosavi Department of Computer Science Bahria University Lahore54000Pakistan Lahore Institute of Science and Technology Lahore 54792Pakistan Department of Multidisciplinary Engineering Texas A&M UniversityCollege Station77843USA Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam 31441Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

With the advancements in internet facilities,people are more inclined towards the use of online *** service providers shelve their items for *** users post their feedbacks,reviews,ratings,*** the use of the *** enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these *** Analysis(SA)is a technique that performs such decision *** research targets the ranking and rating through sentiment analysis of these reviews,on different *** a case study,Songs are opted to design and test the decision *** aspects of songs namely music,lyrics,song,voice and video are *** the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a *** machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are *** performed the best with 74.99%*** are validated using K-Fold.

关键词： Machine learning natural language processing songs reviews:sentiment analysis songs rating aspect level sentiment analysis reviews analysis text classification music

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Integration of Large Language Models into Higher Education: A Perspective from Learners 25

Integration of Large Language Models into Higher Education: ...

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25th International Symposium on computers in Education, SIIE 2023

作者： Zdravkova, Katerina Dalipi, Fisnik Ahlgren, Fredrik Ss. Cyril and Methodius University Faculty of Computer Science and Engineering Skopje Macedonia Linnaeus University Faculty of Technology Department of Informatics Kalmar Sweden Linnaeus University Faculty of Technology Department of Computer Science and Media Technology Kalmar Sweden

ISBN: (纸本)9798350329315

Large language models (LLMs) are being criticized for copyright infringement, inadvertent bias in training data, a danger to human innovation, the possibility of distributing incorrect or misleading information, and prejudice. Due to their popularity among students, the introduction of many comparable apps, and the inability to resist unfair and fraudulent student usage, their educational use needs to be adapted and harmonized. The incorporation of LLMs should be defined not only by pedagogues and educational institutions, but also by students who will actively utilize them to learn and prepare assignments. In order to find out what students from two universities think and suggest about LLMs use in education, they were asked to give their contribution by answering the survey that was conducted at the beginning of the spring semester of academic 2022/23. Their feedback was quantitatively and qualitatively analyzed, showing in a better light what students think about LLMs and how and why they would use them. Based on the analysis, the authors propose an original strategy for integrating LLMs into education. The proposed approach is also adapted for those students who are not interested in using LLMs and for those who prefer the hybrid mode by combining their own research with LLMs generated recommendations. The authors expect that by implementing the proposed strategy, schools will benefit from a better education in which research, creativity, academic honesty, recognition of false information, and the ability to improve knowledge will prevail. © 2023 IEEE.

关键词： Students

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