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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Li, Shenghan Wang, Jinyan Zeng, Huachang Li, Jun Wei, Yuecen Li, Xianxian Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining Security Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China School of Computer and Information Technology Anhui Vocational and Technical College Hefei China

ISBN: (纸本)9798350337020

Social recommendation utilizing social relations as auxiliary information can alleviate data sparsity issues of collaborative filtering methods. However, existing social recommendation methods usually focus on the shallow relations of user-item interactions and social networks to mine potential user preferences, which fail to provide a fine-grained consideration of the inhomogeneous influence and the changes in user interest or item attractiveness over time in high-order interactions of multiple relation types. To address these issues, we propose a novel method named Dynamic Social Recommendation with High-matching Inhomogeneous Relations (DSRHIR) to reveal the influence of inhomogeneity in dynamic interactions on social recommendation from multiple perspectives. Specifically, we first model the short-term dynamic and long-term static representations of both users with social relations and similar relations, and items with correlations. Then, we combine their dynamic and static representations during the interaction and the information from the high-order neighbors of these three relations to obtain a graph-augmented representation. Finally, the graph-augmented representation is used to modulate high-matching user interest and item attractiveness. Extensive experiments on two real-world datasets demonstrate the effectiveness of DSRHIR. © 2023 IEEE.

关键词： graph neural network inhomogeneous relations Recommender systems social networks

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Bird Species Recognition by Integrating EfficientNet-B0 Deep Learning Architecture and Broad Learning System

Bird Species Recognition by Integrating EfficientNet-B0 Deep...

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2024 China Automation Congress, CAC 2024

作者： Fan, Xing-Rong Chen, Hui-Ting Deng, Yu-He Wan, Hui Cai, Zong-Rong Peng, Qiu-Ju Engineering Research Centre for Waste Oil Recovery Technology and Equipment Ministry of Education Chongqing Technology and Business University Chongqing China Chongqing Key Laboratory of Intelligent Perception and Block Chain Technology Chongqing Technology and Business University Chongqing China School of Artificial Intelligence School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China

ISBN: (纸本)9798350368604

Accurate identification and classification of bird species are crucial for advancing research and conservation efforts, enhancing our understanding of population dynamics, and implementing targeted conservation strategies. Despite its importance, this task is challenging due to the wide variation in bird appearances and environmental noise, which can lead to poor generalization and increased complexity. To address these challenges, this paper proposes a novel bird species recognition method, EN-BLS, which integrates the EfficientNet-B0 deep learning architecture with a broad learning system. EfficientNet-B0 is used as the backbone network for feature extraction from bird images, with the final feature map layer serving as input for the broad learning system. The resulting hybrid model, EN-BLS, is designed for bird species classification. The model's performance is evaluated using the Birds 525 Species dataset, the latest publicly available benchmark dataset with the highest number of categories collected from diverse real-world environments. Experimental results show that EN-BLS achieves a recognition accuracy of 93.79% with lower computational complexity compared to existing methods, demonstrating its effectiveness for bird species diversity monitoring and conservation efforts. © 2024 IEEE.

关键词： Federated learning

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A new online field feature selection algorithm based on streaming data

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1365-1377页

作者： Zhang, Zhenjiang Song, Fuxing Zhang, Peng Chao, Han-Chieh Zhao, Yingsi School of Software Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Economics and Management Beijing Jiaotong University Beijing100044 China

The rapid development of Internet technology derived out a massive network text data. Therefore, how to classify the massive text data efficiently has important theoretical significance and application value. In order to acquire accurate classification results, the process has been divided into two parts. In terms of text representation, this paper proposes an online field feature selection algorithm (OFFS algorithm) based on streaming data, which solves the problems of low efficiency and memory consumption of traditional feature selection algorithms. With improvements in the vector space model, the new algorithm can select the real-time feature of the data and quickly generate text vector. In the aspect of classifier design, an OFFS-BP neural network text classifier based on BP neutral network and OFFS algorithm is designed. It adapts to the distributed parallel computing, reduces the training time and balances the computation efficiency and classification accuracy. Finally based on the Spark platform, the OFFS-BP neural network classifier is implemented. The experimental results show that the OFFS-BP neural network classifier is more suitable for big data environment with less computation time and higher classification efficiency. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Neural networks

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Cognitive Analysis of Medical Decision-Making: An Extended MULTIMOORA-Based Multigranulation Probabilistic Model with Evidential Reasoning

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Cognitive Computation 2024年第6期16卷 3149-3167页

作者： Bai, Wenhui Zhang, Chao Zhai, Yanhui Sangaiah, Arun Kumar Wang, Baoli Li, Wentao School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia University Centre for Research and Development Chandigarh University Punjab Mohali140413 India School of Mathematics and Information Technology Yuncheng University Shanxi Yuncheng044000 China College of Artificial Intelligence Southwest University Chongqing400715 China

Cognitive computation has leveraged the capabilities of computer algorithms, rendering it an exceptionally efficient approach for addressing multi-attribute group decision-making (MAGDM) problems. Due to the stability of MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) and the capability of evidential reasoning (ER) to combine information from multiple sources, the technique of multigranulation probabilistic rough sets (MG PRSs) holds great promise for solving MAGDM problems. Thus, a new and stable method for MAGDM is proposed. Initially, three forms of multigranulation Pythagorean fuzzy probabilistic rough sets (MG PF PRSs) are constructed using MULTIMOORA approaches. Next, the hierarchical clustering method is employed to cluster similar decision information and consolidate the decision-makers’ preferences. Representatives are chosen from each category to simplify information fusion calculations and reduce complexity by reducing the model’s dimensionality. Following that, the rankings obtained from the three methods are fused using ER. Ultimately, the validity of our method is revealed via a case analysis on chickenpox cases from the UCI data set by employing cognitive analysis. The paper outlines a method for MAGDM that provides significant advantages. Specifically, the use of MULTIMOORA improves the stability of decision results, while the incorporation of ER reduces the overall uncertainty of entire decision processes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Granular computing

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Overview of CCL24-Eval Task1: Chinese Frame Semantic Parsing Evaluation 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Yang, Peiyuan Li, Juncai Yan, Zhichao Su, Xuefeng Li, Ru School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Shanxi University Shanxi Taiyuan030006 China School of Modern Logistics Shanxi Vocational University of Engineering Science and Technology Shanxi Jinzhong030609 China

ISBN: (纸本)9780000000002

Chinese Frame-semantic Parsing (CFSP) aims to extract fine-grained frame-semantic structures from texts, which can provide fine-grained semantic information for natural language understanding models to enhance their abilities of semantic representations. Based on the CCL-23 CFSP evaluation task, we introduce construction grammar to expand the targets, as basic units activating frames in texts, from word-style to construction-style, and publish a more challenging CFSP evaluation task in CCL-2024. The evaluation dataset consists of 22,000 annotated examples involving nearly 695 frames. The evaluation task is divided into three subtasks: frame identification, argument identification, and role identification, involving two tracks: close track and open track. The evaluation task has attracted wide attention from both industry and academia, with a total of 1988 participating teams. As for the evaluation results, the team from China University of Petroleum won the first place in the closed track with the final score of 71.34, while the team frome Suzhou University won the first place in the open track with the final socre of 48.77. In this article, we reports the key information about the evaluation task, including key concepts, evaluation dataset, top-3 results and corresponding methods. More information about this task can be found on the website of the CCL-2024 CFSP evaluation task 1 © 2024 China National Conference on Computational Linguistics.

关键词： Semantics

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The Performance Analysis of Daffier-Hellman Key Exchange Algorithm for Secure Transmission of Data in Cryptography Networks 3

The Performance Analysis of Daffier-Hellman Key Exchange Alg...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ahmad, Arsalan Sakhare, Nitin N. Mondal, Dipannita Vigenesh, M. Indira, G. Futane, Pravin R. Mangalayatan University Department of Management Aligarh India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Dr. D.Y. Patil College of Engineering and Innovation Artificial Intelligence and Data Science Department Talegaon India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India

ISBN: (纸本)9798350319125

This paper examines the overall performance of the Daffier-Hellman key trade algorithm for the relaxed transmission of data in cryptography networks. Two types of Daffier-Hellman key exchanges are considered: the traditional scheme without elliptic curves and the current model utilizing elliptic curves. The parameters and their respective performance of both schemes are compared. In addition, it is located that the elliptic curve-primarily based Daffier-Hellman set of rules offers better overall performance than the conventional one, with better security and a drastically shorter key-status quo time. An in-depth analysis of the velocity and protection of both algorithms is supplied. The consequences of the have look indicate that the elliptic curve model of DaffierHellman is the fine-acting set of rules for secure records switches in cryptography networks. Moreover, the paper additionally discusses the safety problems and challenges arising from implementing the Daffier-Hellman key trade algorithm and indicates some possible solutions. Ultimately, the paper also offers an end on the overall performance of DaffierHellman Key's alternate set of rules regarding pace, safety, and reliability. © 2023 IEEE.

关键词： algorithm consequences performance possible safety

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Intrusion Detection in Networks using Honey Badger Algorithm with improved Adaptive Neuro-Fuzzy Inference System

Intrusion Detection in Networks using Honey Badger Algorithm...

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2023 Al-Sadiq International Conference on Communication and information technology, AICCIT 2023

作者： Alrikabi, Ibrahim Riyadh Alazzawi, Murtadha A. Al-Khaleefa, Ahmed Al-Mosawi, Buraq T. Sh. Kareem, Hassanain Raheem Alsalamy, Ali Shahid Chamran University of Ahvaz Faculty of Engineering Department of Computer Engineering Ahvaz Iran Department of Computer Techniques Engineering Baghdad Iraq University of Misan Faculty of Education Department of Physics Maysan Iraq Imam Ja'afar Al-Sadiq University College of Information Technology Department of Computer Technical Engineering Al-Muthanna Iraq

ISBN: (纸本)9798350341881

An Intrusion detection system IDS is a key component of the security management infrastructure. The goal of IDS is to monitor the processes prevailing in a network and to analyze them for signs of any possible deviations. Because of the large volume of data, the network gets expansion with a false alarm rate of intrusion, and detection accuracy decreased. It is one of the main problems in unknown attacks on network experiences. The basic aim was to develop accuracy also reduce the false alarm rate (FAR). We will design an Adaptive Neuro-Fuzzy based system to identify the intrusion activities within a network effectively. The Honey Badger Algorithm with an improved Adaptive Neuro-Fuzzy Inference System (HBA-IANFIS) is used to address the problems presented. The IANFIS combines an artificial neural network and a fuzzy interference system. To enhance the performance of the IANFIS model, the Honey Badger Algorithm is used to optimize the IANFIS. The intrusion detection results based on the NSL-KDD dataset were better and more efficient than those models because the detection rate was 96.68%, and the FAR result was 0.438%. © 2023 IEEE.

关键词： Fuzzy inference

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Analysis of Traffic Patterns and Optimization of Routing for Cognitive Radio Networks 15

Analysis of Traffic Patterns and Optimization of Routing for...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Jayashankari, J. Bhushan, Bharat Rekha, M.M. Bagade, Jayashree V. Yuvaraj, K. Garg, Prateek Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Sciences Department of Chemistry and Biochemistry Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350370249

Cognitive radio networks (CRNs) had been developed to satisfy the demands of contemporary communiqué structures. CRNs are characterized by way of their adaptability to dynamically change their operational parameters and configurations for you to meet the converting wishes of customers. To do that, CRNs must be able to investigate the cutting-edge visitors styles to be able to optimize their routing based on the to be had resources inside the device. This paper offers a observe of visitors patterns and an optimization of routing for CRNs. We analyze the community site visitor's styles and use the patterns alongside the network's resource optimization methods to generate most advantageous routes on the way to maximize throughput and limit put off. Via simulations, we evaluate the overall performance of our proposed gadget and examine it to other existing routing procedures. The simulation outcomes display that our machine achieves an improvement of 7-14% in throughput. Additionally, our proposed gadget reduces the average cease-to-cease put off by way of 10-20%. These effects suggest that our proposed system is an effective technique to routing in CRNs. © 2024 IEEE.

关键词： Cognitive radio

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Development of a Calibration-Free Brain-computer Interface Utilizing Common Spatial Patterns and Artificial Neural Networks for EEG Signal Analysis 6

Development of a Calibration-Free Brain-Computer Interface U...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Cengiz, Korhan Shreyas, J. Udayaprasad, P.K. Gururaj, H.L. Kumar, Dilip S.M. College of Information Technology University of Fujairah United Arab Emirates Faculty of Organization and Informatics University of Zagreb Croatia Croatia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Artificial Intelligence & Machine Learning Global Academy of Technology Bengaluru India Dept. of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (纸本)9798350394634

A brain computer interface (BCI) system uses a technique known calibration, that takes 20 to 30 minutes to accomplish. For the objective of creating a reliable decoder, the calibration process is challenging and expensive. In order to address the drawbacks of the current system, a spectral-spatial technique has been suggested. The motor imagery (MI) data set, comprising 15 electroencephalography (EEG) signals and fourteen test subjects, is taken into consideration. The two modules are designed to extract characteristics and process data. An artificial neural network (ANN) is used to independently train and test the suggested spectral-spatial algorithm. Based on it, a variety of machine learning techniques, including random forest (RF), neural networks (NN), and XGboost, are used to classify the data, that is then sent to the hidden layer (Lth layer). The obtained results indicates 2% of improvement in comparison with existing methodology. © 2024 IEEE.

关键词： Electroencephalography

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NestedVGG: A Novel Deep Learning-based Architecture for Brain Tumour Diagnosis 3

NestedVGG: A Novel Deep Learning-based Architecture for Brai...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Maurya, Ritesh Thirwani, Parth Gopalakrishnan, T. Dutta, Malay Kishore Panchal, Soumyashree M Amity University Amity Centre for Artificial Intelligence Noida India Amity University Dept. of Artificial Intelligence Noida India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India

ISBN: (纸本)9798350384598

Brain tumors are sudden outbreak of cells in the brain or on its surface and are categorized into gliomas, meningiomas, and pituitary adenomas. Traditionally, tumors of the brain are categorized using a manual approach that includes visual examination by radiologists on Magnetic Resonance Imaging (MRI). This is arduous and subject to inter-expert variability. In view of the subtle and fine-grained compact variations that exist in MRI images of these three types of brain tumors, this paper proposes an automated brain tumor detection system using deep learning (DL) with MRI scans. This paper presents an automated system for brain tumor detection using DL on MRI scans. The "NestedVGG"model implements transfer learning(TL) with the outer architecture of "Outer VGG"for feature extraction and the inner architecture for fine-grained identification. This "Outer VGG"utilizes a pre-trained VGG16 model to acquire information about features from MRI images. While the inner "Inner VGG"can capture minute variations between tumor types to classify glioma, meningioma, no-tumour, and pituitary cases effectively. Publicly available MRI dataset consists of three classes of tumours were used for testing and this framework has achieves accuracy of 97.71% on the test set, thus outperforming the prior methods in this task. Moreover, it has maintained its high accuracy across various types of tumors, thus making the model flexible. This paper furthers brain tumor diagnosis through an effective architecture for brain tumor identification, which can lead to advanced diagnostic tools and better patient care. © 2024 IEEE.

关键词： Magnetic resonance imaging

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