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Cyclical Training Framework with Graph Feature Optimization for Knowledge Graph Reasoning

computers, Materials & Continua 2025年第5期83卷 1951-1971页

作者： Xiaotong Han Yunqi Jiang Haitao Wang Yuan Tian School of Artificial Intelligence Jilin UniversityChangchun130012China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOEChangchun130012China College of Computer Science and Technology Jilin UniversityChangchun130012China

Knowledge graphs(KGs),which organize real-world knowledge in triples,often suffer from issues of *** address this,multi-hop knowledge graph reasoning(KGR)methods have been proposed for interpretable knowledge graph *** primary approaches to KGR can be broadly classified into two categories:reinforcement learning(RL)-based methods and sequence-to-sequence(seq2seq)-based *** each method has its own distinct advantages,they also come with inherent *** leverage the strengths of each method while addressing their weaknesses,we propose a cyclical training method that alternates for several loops between the seq2seq training phase and the policy-based RL training phase using a transformer ***,a multimodal data encoding(MDE)module is introduced to improve the representation of entities and relations in *** module treats entities and relations as distinct modalities,processing each with a dedicated network specialized for its respective *** then combines the representations of entities and relations in a dynamic and fine-grained manner using a gating *** experimental results from the knowledge graph completion task highlight the effectiveness of the proposed *** five benchmark datasets,our framework achieves an average improvement of 1.7%in the Hits@1 metric and a 0.8%average increase in the Mean Reciprocal Rank(MRR)compared to other strong baseline ***,the maximum improvement in Hits@1 exceeds 4%,further demonstrating the effectiveness of the proposed approach.

关键词： Knowledge graph reinforcement learning transformer

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A Secure and Efficient Framework for Storing Medical Data using Blockchain and Cloud Servers 8

A Secure and Efficient Framework for Storing Medical Data us...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Lakshmanan, M. Mala, G.S. Anandha Sivakumar, G. Divya Sundar, V.S. Kesavan, Rupa Ajoe Sweetlin Jeena, A. Dayananda Sagar University Department of Artificial Intelligence and Machine Learning Bangalore India Easwari Engineering College Anna Univeristy Department of Computer Science and Engineering Chennai India Gnanamani College of Technology Anna University Department of Artificial Intelligence and Machine Learning India Anna Univeristy Saveetha Engineering College Chennai India Sri Venkateswara College of Engineering Computer Science and Engineering Sriperumbudur India

ISBN: (纸本)9798350367904

The digitization of Electronic Health Records (EHRs) has brought a revolution in health care delivery and even practices in reporting key organizational HR processes. However, these advancements have their merits and demerits particularly in regard to data security, the speed of data retrieval, and how data transactions are performed. This paper presents a solution to this problem in the medical world. It presents a unique architecture that connects cloud servers with Blockchain technology for better and secure medical databases. Using Merkle trees to protect health information and advanced heuristic algorithms to optimize operations, the system deals with main issues of transaction time, retrieval rate, and data privacy. The method is tested on a realized medical database and shows that the performance indicators increased substantially. It would seem that Blockchain assures that data cannot be altered once uploaded, while cloud servers provide flexibility to users and allow faster data retrieval. Proposed system improves the transaction time by 30 percent and the retrieval rate by 40 percent compared with traditional methods and supports strategic management of EHR systems. © 2024 IEEE.

关键词： Electronic health record

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Transformer based Compressed Data Recovery for Efficient Data Collection in Wireless Sensor Networks

Transformer based Compressed Data Recovery for Efficient Dat...

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2024 IEEE Students Conference on Engineering and Systems, SCES 2024

作者： Balaji, V.S. Sekar, K. Devendar Rao, B. Dheepa, T. Rajalakshmi Engineering College Dept. of Artificial Intelligence and Machine Learning Chennai India Kongunadu College of Engineering and Technology Dept. of Computer Science and Engineering Tiruchirappalli India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374711

Data collection is challenging in wireless sensor networks (WSNs) since energy consumption remains a significant constraint. Although energy consumption has increased, most data collection methods incur excessive computation overhead. Compressive sensing is used to minimize the number of sensing samples with a suitable sparse basis. A novel Transformer neural network-based compressed data recovery (TCDR) method has been proposed for extracting temporal correlation structures from compressed signals and reconstructing them with high accuracy. The compressed signal is forwarded to the base station through multi-hop communication. The base station uses an attentionbased Transformer Neural Network (TNN) to recover the compressed signal. This model provides greater energy saving performance than the other baseline models considered for evaluation, which are 5.23 percent, 6.82 percent, 10.26 percent, and 11.47 percent, respectively. © 2024 IEEE.

关键词： Spatio-temporal data

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A Comparative Analysis of Rule-based and AI-Driven Systems for Improving Customer Satisfaction and Engagement in E-Commerce using Chatbots Powered by artificial intelligence 6

A Comparative Analysis of Rule-based and AI-Driven Systems f...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Deshmukh, Santoshi Gundewar, Swapnil Department of Artificial Intelligence and Data Science Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Wardha India

ISBN: (纸本)9798331522667

Chatbots: Chatbots, as robots, are the most recent and intelligent live chat communication devices. Furthermore, these days, it is commonly one of the customer service methods as it is also perfect for providing an array o f services, like personalized offers, individual communication, responsive customer support, etc. The interaction between banks and other financial institutions and their consumers has been entirely transformed due to the marriage of financial technology and artificial intelligence (AI). This scientific investigation deals with artificial intelligence (AI) technology for better communication among people;its special focus is on the effective use of chatbots and natural language processing (NLP) in banking support. Banks are increasingly turning their efforts to AI-driven chatbots as valuable means of enhancing customer support while channeling communication effectively. We are now at the point where human creativity and technology will combine and reimagine finance principles. © 2025 IEEE.

关键词： Customer satisfaction

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A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service Robotics 23

A Comparison of Prompt Engineering Techniques for Task Plann...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Bode, Jonas Pätzold, Bastian Memmesheimer, Raphael Behnke, Sven Computer Science Institute Vi - Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Autonomous Intelligent Systems group Germany

ISBN: (纸本)9798350373578

Recent advances in Large Language Models (LLMs) have been instrumental in autonomous robot control and human-robot interaction by leveraging their vast general knowledge and capabilities to understand and reason across a wide range of tasks and scenarios. Previous works have investigated various prompt engineering techniques for improving the performance of LLMs to accomplish tasks, while others have proposed methods that utilize LLMs to plan and execute tasks based on the available functionalities of a given robot platform. In this work, we consider both lines of research by comparing prompt engineering techniques and combinations thereof within the application of high-level task planning and execution in service robotics. We define a diverse set of tasks and a simple set of functionalities in simulation, and measure task completion accuracy and execution time for several state-of-the-art models. We make our code, including all prompts, available at https://***/AIS-Bonn/Prompt-Engineering. © 2024 IEEE.

关键词： Human robot interaction

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Harnessing NLP and Deep learning to Decode Disease-Gene Interactions in Lung Cancer 1

Harnessing NLP and Deep Learning to Decode Disease-Gene Inte...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Rani, K. Mary Sudha Prasad, V. Kamakshi Chaitanya Bharathi Institute of Technology Department of Artificial Intelligence and Machine Learning Hyderabad India Jawaharlal Nehru Technological University Hyderabad Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331518943

Lung cancer remains a critical global health challenge, necessitating advanced predictive models to enhance diagnostic and prognostic accuracy. This study employs natural language processing (NLP) and deep learning to advance personalized lung cancer prognosis and treatment response prediction. Using a synthetic dataset, our novel neural network model integrates clinical data with patient narratives to extract meaningful insights, correlating patient transcripts with existing datasets. The model interprets lung cancer histology types, gene sets, and genetic mutations, achieving 85% accuracy in predicting treatment responses, with a sensitivity of 0.88 and specificity of 0.83. By merging clinical and transcript-based data, this research provides clinicians with a reliable tool to support personalized medicine and informed decision-making. The results highlight the potential of AI-driven approaches to improve disease-gene association analysis, paving the way for innovative computational methodologies in lung cancer research. © 2024 IEEE.

关键词： Lung cancer

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Automated Cleaning Solutions: Robotics Innovation in Homecare 4

Automated Cleaning Solutions: Robotics Innovation in Homecar...

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Sujatha, E. Devi, S.Renuka Divya, M. Raju, D. Naveen Department of Computer Science and Engineering Chennai India Department of Artificial Intelligence and Machine Learning Chennai India Department of Computer Science and Engineering Chennai India R.M.K. Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350364729

A room cleaning robot, often known as a vacuum robot, is a clever appliance designed to simplify household cleaning tasks. It is equipped with sensors and brushes that allow it to navigate rooms, detect obstacles, and collect dirt and debris from floors. These robots can be programmed to operate autonomously, following predefined cleaning patterns or avoiding specific areas. They are also commonly controlled remotely through smartphone apps or voice commands, making them user- friendly and convenient. Room cleaning robots are known for their efficiency in reaching tight spaces and cleaning under furniture, which can be challenging for traditional vacuum cleaners. They often use rechargeable batteries, returning to their charging docks when the battery is low, ensuring uninterrupted cleaning They have become popular for their ability to maintain clean floors in homes and businesses without continuous human supervision. Additionally, their compact and sleek designs make them fit seamlessly into modern living spaces. As technology advances, these robots continue to evolve, offering improved cleaning performance and smarter features. © 2024 IEEE.

关键词： Vacuum cleaners

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Design Principles and a Software Reference Architecture for Big Data Question Answering Systems 25

Design Principles and a Software Reference Architecture for ...

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25th International Conference on Enterprise Information Systems, ICEIS 2023

作者： Moraes, Leonardo Mauro Pereira Jardim, Pedro Calciolari Aguiar, Cristina Dutra Department of Computer Science University of São Paulo São Carlos Brazil Machine Learning & Artificial Intelligence Sinch Stockholm Sweden

ISBN: (纸本)9789897586484

Companies continuously produce several documents containing valuable information for users. However, querying these documents is challenging, mainly because of the heterogeneity and volume of documents available. In this work, we investigate the challenge of developing a Big Data Question Answering system, i.e., a system that provides a unified, reliable, and accurate way to query documents through naturally asked questions. We define a set of design principles and introduce BigQA, the first software reference architecture to meet these design principles. The architecture consists of high-level layers and is independent of programming language, technology, querying and answering algorithms. BigQA was validated through a pharmaceutical case study managing over 18k documents from Wikipedia articles and FAQ about Coronavirus. The results demonstrated the applicability of BigQA to real-world applications. In addition, we conducted 27 experiments on three open-domain datasets and compared the recall results of the well-established BM25, TF-IDF, and Dense Passage Retriever algorithms to find the most appropriate generic querying algorithm. According to the experiments, BM25 provided the highest overall performance. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Big data

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Integration between Bioinformatics Algorithms and Neutrosophic Theory

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Neutrosophic Sets and Systems 2024年 66卷 34-54页

作者： Farag, Romany M. Shams, Mahmoud Y. Aldawody, Dalia A. Khalid, Huda E. El-Bakry, Hazem M. Salama, Ahmed A. Dept. of Math and Computer Science Faculty of Science Port Said Univ. Egypt Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Egypt Telafer University Telafer Iraq Dept. of Information Systems Faculty of Computer and Information Sciences Mansoura University Egypt Dept. of Math and Computer Science Faculty of Science Port Said University Egypt

This paper presents a neutrosophic inference model for bioinformatics. The model is used to develop a system for accurate comparisons of human nucleic acids, where the new nucleic acid is compared to a database of old nucleic acids. The comparisons are analyzed in terms of accuracy, certainty, uncertainty, neutrality, and bias. The proposed system achieves good results and provides a reliable standard for future comparisons. It highlights the potential of neutrosophic inference models in bioinformatics applications. Data mining and bioinformatics play a crucial role in computational biology, with applications in scientific research and industrial development. Biological analysts rely on specialized tools and algorithms to collect, store, categorize, and analyze large volumes of unstructured data. Data mining techniques are used to extract valuable information from this data, aiding in the development of new therapies and understanding genetic relationships between organisms. Recent advancements in bioinformatics include gene expression tools, Bio sequencing, and Bio databases, which facilitate the extraction and analysis of vital biological information. These technologies contribute to the analysis of big data, identification of key bioinformatics insights, and generation of new biological knowledge. Data collection, analysis, and interpretation in this field involves the use of modern technologies such as cloud computing, machine learning, and artificial intelligence, enabling more efficient and accurate results. Ultimately, data mining and bioinformatics enhance our understanding of genetic relationships, aid in developing new therapies, and improve healthcare outcomes. © (2024), (Neutrosophic Sets and Systems). All rights reserved.

关键词： Bioinformatics

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On the Equivalence of Maximum Reaction Time and Maximum Data Age for Cause-Effect Chains 35

On the Equivalence of Maximum Reaction Time and Maximum Data...

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35th Euromicro Conference on Real-Time Systems, ECRTS 2023

作者： Günzel, Mario Teper, Harun Chen, Kuan-Hsun von der Brüggen, Georg Chen, Jian-Jia Department of Computer Science TU Dortmund University Germany University of Twente Netherlands Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany

ISBN: (纸本)9783959772808

Real-time systems require a formal guarantee of timing-constraints, not only for individual tasks but also for data-propagation. The timing behavior of data-propagation paths in a given system is typically described by its maximum reaction time and its maximum data age. This paper shows that they are equivalent. To reach this conclusion, partitioned job chains are introduced, which consist of one immediate forward and one immediate backward job chain. Such partitioned job chains are proven to describe maximum reaction time and maximum data age in a universal manner. This universal description does not only show the equivalence of maximum reaction time and maximum data age, but can also be exploited to speed up the computation of such significantly. In particular, the speed-up for synthesized task sets based on automotive benchmarks can be up to 1600. Since only very few non-restrictive assumptions are made, the equivalence of maximum data age and maximum reaction time holds for almost any scheduling mechanism and even for tasks which do not adhere to the typical periodic or sporadic task model. This observation is supported by a simulation of a ROS2 navigation system. © Mario Günzel, Harun Teper, Kuan-Hsun Chen, Georg von der Brüggen, and Jian-Jia Chen.

关键词： Real time systems

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