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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Zhi Liu, Yan Zhong, Sheng-Hua Chen, Gong Yang, Yu Cao, Jiannong The Hong Kong Polytechnic University Department of Computing 999077 Hong Kong Shenzhen University College of Computer Science and Software Engineering Guangdong Shenzhen518052 China The Education University of Hong Kong Centre for Learning Teaching and Technology 999077 Hong Kong

ISBN: (纸本)157735897X

Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is yet time-consuming. This paper proposes a novel framework, collaborative knowledge minigraph agents (CKMAs), to automate scholarly literature reviews. A novel prompt-based algorithm, the knowledge minigraph construction agent (KMCA), is designed to identify relations between concepts from academic literature and automatically constructs knowledge minigraphs. By leveraging the capabilities of large language models on constructed knowledge minigraphs, the multiple path summarization agent (MPSA) efficiently organizes concepts and relations from different viewpoints to generate literature review paragraphs. We evaluate CKMAs on three benchmark datasets. Experimental results show the effectiveness of the proposed method, further revealing promising applications of LLMs in scientific research. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge engineering

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Chi-Square and Cuckoo Search Based Feature Selection for Heart Disease Prediction

Chi-Square and Cuckoo Search Based Feature Selection for Hea...

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science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Bolanle Adejumo Elizabeth Ali Muhammad Usman Ahmad MohdAziz Hussein Wallace Ossai Ebinum Timothy Umar Maigari Ali Usman Abdullahi Jalo Abubakar Ibrahim Mohammed Joda Usman Jacob Philips Muhammad Dawaki Department of Computer Sciences Federal College of Education (Technical) Gombe Nigeria Deanship of E-Learning and Distance Education Umm Al-Qura University Makkah Saudi Arabia Department of Computer Engineering College of Engineering Kaduna Polytechnic Kaduna Nigeria Faculty of Computer Science and Information Technology Bayero University Kano Nigeria Department of Computer Sciences Gombe State University Gombe Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

In many countries across the globe, heart disease is the main root of death. It’s only that it has a lot of features that have been validated by numerous sources and incorporate distinct diagnostic techniques. Thus, physicians find it difficult to correctly identify the features that contribute to heart disease for each patient. Besides, most of these features are redundant and irrelevant that create noise towards the description of the target class. Nowadays, machine learning approaches are being used to aid physicians to correctly identify heart disease from a patient. In this study, the chi-square statistic is applied to rank the attributes in the heart disease data set while the cuckoo search optimization algorithm selects the most useful features that contribute significantly to heart disease diagnosis. The selected features are trained using the k-nearest neighbour (KNN) along with support vector machine (SVM). The investigational outcomes displayed that the features are minimized to the lowest level, mainly on the high dimensional data sets. Therefore, contributed positively to the heart disease diagnosis system. SVM outpace KNN in terms of accuracy after the proposed feature selection. On the other hand, KNN performed slightly better than SVM without feature selection on the majority of data sets. The proposed method performed better than other existing approaches especially correlation-based FS with PSO together with fuzzy analytic hierarchy process-based feed-forward neural network, modified differential evolution, on both percentage accuracy and number of chosen features (feature size).

关键词： Heart Support vector machines Accuracy Nearest neighbor methods Feature extraction Classification algorithms Medical diagnosis

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Balancing exploration and exploitation phases in whale optimization algorithm: an insightful and empirical analysis

arXiv

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arXiv 2023年

作者： Ahmed, Aram M. Rashid, Tarik A. Hassan, Bryar A. Majidpour, Jaffer Noori, Kaniaw A. Rahman, Chnoor Maheadeen Abdalla, Mohmad Hussein Qader, Shko M. Tayfor, Noor Mohammed, Naufel B. Department of Information Technology College of Science and Technology University of Human Development Sulaimani Iraq Department of Information Technology Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani46001 Iraq Department of Computer Science and Engineering School of Science and Engineering University of Kurdistan Hewler Erbil Iraq Department of Computer Science College of Science Charmo University Chamchamal Sulaimani46023 Iraq Department of Computer Science University of Raparin Sulaimani Iraq Database Technology Department Technical College of Informatics Sulaimani Polytechnic University Sulaimani Iraq Department of Computer Science Kurdistan Technical Institute Sulaimani Iraq

Agents of any metaheuristic algorithms are moving in two modes, namely exploration and exploitation. Obtaining robust results in any algorithm is strongly dependent on how to balance between these two modes. Whale optimization algorithm as a robust and well recognized metaheuristic algorithm in the literature, has proposed a novel scheme to achieve this balance. It has also shown superior results on a wide range of applications. Moreover, in the previous chapter, an equitable and fair performance evaluation of the algorithm was provided. However, to this point, only comparison of the final results is considered, which does not explain how these results are obtained. Therefore, this chapter attempts to empirically analyze the WOA algorithm in terms of the local and global search capabilities i.e. the ratio of exploration and exploitation phases. To achieve this objective, the dimension-wise diversity measurement is employed, which, at various stages of the optimization process, statistically evaluates the population's convergence and diversity. © 2023, CC BY.

关键词： Optimization

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A Model Based On Multiple Intermediate Entity For Cross-Domain Authentication In Public Key Infrastructure and Blockchain System

A Model Based On Multiple Intermediate Entity For Cross-Doma...

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International Conference on Electronics, Communications and Information technology (CECIT)

作者： Mengcheng Ma Shanshan Wang Yuehong Li Network and Cloud Computing Department Guangdong Polytechnic of Water Resouces and Electric Engineering Guangzhou China Information Technology Department Guangdong Industry Polytechnic Guangzhou China College of Computer Science and Technology Harbin Engineering University Harbin China

In the cross-domain authentication architecture, the current method is based on public key infrastructure (PKI) and blockchain to enhance the security of data. However, most models need to be improved about the efficiency, robustness and security in the authentication phase, and in the revocation phase the most solutions cannot meet the real-time revocation or waste storage on-chain space. To solve the problem, this paper proposes a model based on multiple intermediate entities. Simulation experiments show that the model can improves the efficiency of cross-domain authentication, improves the robustness of the system, resists single-point attacks, meet the real-time in revocation, and saves space on the chain.

关键词： Data security Authentication Public key Resists Aerospace electronics Robustness Real-time systems

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Explicitly Guided Difficulty-Controllable Visual Question Generation 39

Explicitly Guided Difficulty-Controllable Visual Question Ge...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xie, Jiayuan Cheng, Mengqiu Zhang, Xinting Cai, Yi Hu, Guimin Xie, Mengying Li, Qing Department of Computing The Hong Kong Polytechnic University Hong Kong School of Software Engineering South China University of Technology Guangzhou China Department of Computer Science University of Copenhagen Denmark Department of Mathematics The University of Hong Kong Hong Kong College of Computer Science Chongqing University China Guangdong Neusoft University Foshan China

ISBN: (纸本)157735897X

Visual question generation (VQG) aims to generate questions from images automatically. While existing studies primarily focus on the quality of generated questions, such as fluency and relevance, the difficulty of the questions is also a crucial factor in assessing their quality. Question difficulty directly impacts the effectiveness of VQG systems in applications like education and human-computer interaction, where appropriately challenging questions can stimulate learning interest and improve interaction experiences. However, accurately defining and controlling question difficulty is a challenging task due to its multidimensional and subjective nature. In this paper, we propose a new definition of the difficulty of questions, i.e., being positively correlated with the number of reasoning steps required to answer a question. For our definition, we construct a corresponding dataset and propose a benchmark as a foundation for future research. Our benchmark is designed to progressively increase the reasoning steps involved in generating questions. Specifically, we first extract the relationships among objects in the image to form a reasoning chain, then gradually increase the difficulty by rewriting the generated question to include more reasoning sub-chains. Experimental results on our constructed dataset show that our benchmark significantly outperforms existing baselines in controlling the reasoning chains of generated questions, producing questions with varying difficulty levels. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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BowNetCrypt: Sleek Vaulted Messenger Secured by Bowerbird-Driven Finite Neural Messaging with Dual Elliptic Protection

BowNetCrypt: Sleek Vaulted Messenger Secured by Bowerbird-Dr...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Raghu Dhumpati Ankita Nainwal G. Sunitha Rekha Harshal Patil Vipul Vekariya Syed Noeman Taqui Department of Computer Science and Engineering Bahrain Polytechnic Isa Town Isa Town Bahrain Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering (Cyber Security) CVR College of Engineering Hyderabad Telangana India School of Computer Science & Engineering IILM University Greater Noida India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Post Limda Waghodia Gujarat India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Private message is an innate feature of digital communication and is inherent in maintaining the privacy and confidentiality of the information exchanged in interpersonal and business-to-business relationships. Due to new and more complex cyber threats, there is a need for efficient processing and stronger protection through encryption more than ever before. Conventional cryptographic approaches are prevalent and commonly applied, but they have issues with providing adequate security and carrying out the task with low computational requirements, which poses a problem for devices with limited processing power such as smartphones and IoT devices. However, problems arise with future messaging platforms that include higher latency, scalability, and the changes of different network conditions, which are important for continuous communication on message platforms. Some of the previous efforts include BLIND, LAPEP, PEGA, and SCNN-Bi-LSTM, which have brought improvements addressing some of these challenges. Our research introduces BowNetCrypt: Elegant Vaulted Messenger Secured by Bowerbird-Driven Finite Neural Messaging with Dual Elliptic Protection to address such hindrances. BowNetCrypt also uses the Finite Element Neural Fusion Network (FE-Net) system for better adaptability and accuracy in the private message. The Bowerbird Optimization (BowBO) algorithm is used to minimize the computational complexity as well as enhance the overall network performance. Furthermore, the Lightweight Dual Elliptic Security Protocol (LwDESP) is employed to ensure security and strong encryption without consuming a lot of resources. This method outperforms existing systems by achieving key performance metrics such as 99.2% encryption strength, a 98.7% reduction in latency, and a 97.9% increase in scalability. Such improvements offer BowNetCrypt as one of the most efficient and dependable techniques for implementing secure private messaging within advanced communication networks.

关键词： Measurement Privacy Protocols Scalability Neural networks Finite element analysis Encryption Security Protection Optimization

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EPRD: Exploiting prior knowledge for evidence-providing automatic rumor detection

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Neurocomputing 2024年 563卷

作者： Li, Jiawen Li, Ronghui Ni, Shiwen Kao, Hung-Yu Naval Architecture and Shipping College Guangdong Ocean University Guangdong Zhanjiang524000 China Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province Guangdong Zhanjiang524000 China Intelligent Knowledge Management Lab National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the prevalence of social media platforms, rumors have been a serious social problem. Notably, existing rumor detection methods simply provide detection labels while ignoring their explanation. However, illustrating the reasons why a suspicious statement is a rumor is essential. To address this realistic scenario, we propose a novel Evidence-Providing Rumor Detection model called EPRD. EPRD incorporates a wide variety of information from both prior knowledge sources and current comments. It also learns bilaterally friendly representations for interpretable rumor detection. EPRD first retrieves evidence from prior knowledge sources and checks the relationship between the given statement and its evidence. Our model then constructs two heterogeneous graph objects to simulate the propagation layout of the current comments and evidence relationships. Finally, EPRD integrates the GraphSAGE component and attention mechanism to detect rumors. To the best of our knowledge, we propose the first model that incorporates prior knowledge to verify rumors and boost credibility. Experiments on two real-world Twitter datasets demonstrate that EPRD consistently exhibits the best rumor detection performance. Moreover, EPRD outperforms other baselines in the early rumor detection task. © 2023

关键词： Graph neural networks

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Frequency-Domain Identification By Basis Pursuit 20

Frequency-Domain Identification By Basis Pursuit

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Proceedings of the 2020 4th International Conference on Electronic Information technology and computer engineering

作者： Shuang Li Computer Engineering Technical College (Artificial Intelligence College) Guangdong Polytechnic of Science and Technology (GDPST) Zhuhai Guang Dong China

ISBN: (纸本)9781450387811

In this paper, we propose a new adaptive method for frequency-domain identification problem of discrete LTI systems. It is based on a dictionary that is consisting of normalized reproducing kernels. We prove that the singular values of the matrix generated by this dictionary converge to zero rapidly and this makes it quite efficient in representing the original systems with only a few elements. Three examples are presented to illustrate the idea.

关键词： Basis Pursuit System Identification Frequency-Domain

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CNN-based Transfer Learning for Covid-19 Diagnosis

CNN-based Transfer Learning for Covid-19 Diagnosis

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2021 International Conference on Information technology, ICIT 2021

作者： Ahmed, Zanear Sh. Surameery, Nigar M. Shafiq Rashid, Rasber Dh. Salih, Shadman Q. Abdulla, Hawre Kh. Erbil Technology College Erbil Polytechnic University Information Technology Department Kurdistan Region Erbil Iraq College of Engineering University of Garmian Civil Engineering Department Sulaimani Kurdistan Region Kalar Iraq College of Science Salahaddin University Computer Science and IT Department Kurdistan Region Erbil Iraq Technical College of Informatics Sulaimani Polytechnic University Database Technology Department Kurdistan Region Sulaimani Iraq

ISBN: (纸本)9781665428705

CNN-based transfer learning method plays a significant role in the detection of various objects such as cars, dogs, motorcycles, face and human detection in nighttime images by using visible light camera sensors. This method mainly depends on the images captured by cameras in order to detect the mentioned objects in a variety of environments based on convolutional neural networks (CNNs). In this study, we utilized the same method to detect coronavirus phenomena by using chest X-ray images that have been collected from three different open-source datasets with the aim of rapid detection of the infected patients and speed up the diagnostic process. We used one of the deep learning architectures in a Transfer Learning mode and modified its final layers to adapt to the number of classes in our investigation. The deep learning architecture that we used for the purpose of COVID-19 detection from X-ray images is a CNN designed to detect human in nighttime. We also modified the CNN architecture in three different scenarios named (Model 1, Model 2 and Model 3) in order to improve the classification results. Compared to model one and two, the result improved in model three and the number of misclassified cases reduced particularly in detecting Abnormal and COVID-19 cases. Although our CNN-based method shows high performance in COVID-19 detection, CNN decisions should not to be taken into consideration until clinical tests confirms symptoms of the infected patients. © 2021 IEEE.

关键词： COVID-19

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Mixture of Knowledge Minigraph Agents for Literature Review Generation

arXiv

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arXiv 2024年

作者： Zhang, Zhi Liu, Yan Zhong, Sheng-Hua Chen, Gong Yang, Yu Cao, Jiannong The Hong Kong Polytechnic University the Department of Computing 999077 Hong Kong Shenzhen University the College of Computer Science and Software Engineering Guangdong Shenzhen518052 China The Education University of Hong Kong Centre for Learning Teaching and Technology 999077 Hong Kong

关键词： Reviews

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