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International Conference on Information Fusion

作者： Almir Antônio Monteiro Junior Diego Brandão Felipe Da Rocha Henriques Claudio M. De Farias Pedro Henrique González System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Computer Science Postgraduate Program Federal Center for Technological Education of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

This study addresses the Wireless Sensor Network Planning Problem with Multiple Sources/Destinations (WSNPMSD), an optimization challenge focused on reducing the sensor count within a network topology for a specified area, considering numerous sources and destinations. We introduce a hybrid strategy for tackling WSNP-MSD, particularly effective for large-scale scenarios, combining a Biased Random-key Genetic Algorithm with a Local Branching Technique. This methodology is justified by the limitations exact methods may encounter when the number of variables increases. Through computational experiments, we demonstrate the superiority of our proposed method over conventional exact methods in managing large instances of the WSNP-MSD.

关键词： Wireless communication Wireless sensor networks Network topology Planning Optimization Genetic algorithms

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On the Global Convergence of a General Class of Augmented Lagrangian Methods

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Journal of Optimization Theory and Applications 2025年第3期206卷 1-25页

作者： Birgin, Ernesto G. Haeser, Gabriel Maculan, Nelson Ramirez, Lennin Mallma Department of Computer Science Institute of Mathematics and Statistics University of São Paulo São Paulo Brazil Department of Applied Mathematics Institute of Mathematics and Statistics University of São Paulo São Paulo Brazil Systems Engineering and Computer Science Program-Applied Mathematics (COPPE/IM) Federal University of Rio de Janeiro Rio de Janeiro Brazil Systems Engineering and Computer Science Program (COPPE) Federal University of Rio de Janeiro Rio de Janeiro Brazil

In [E. G. Birgin, R. Castillo and J. M. Martínez, Computational Optimization and Applications 31, pp. 31–55, 2005], a general class of safeguarded augmented Lagrangian methods is introduced which includes a large number of different methods from the literature. Besides a numerical comparison including 65 different methods, primal-dual global convergence to a KKT point is shown under a (strong) regularity condition. In the present work, we generalize this framework by considering also classical/non-safeguarded Lagrange multipliers updates. This is done in order to give a rigorous theoretical study to the so-called hyperbolic augmented Lagrangian method, which is not safeguarded, while also including the classical Powell-Hestenes-Rockafellar augmented Lagrangian method. Our results are based on a weak regularity condition which does not require boundedness of the set of Lagrange multipliers. Somewhat surprisingly, in non-safeguarded methods, we show that the penalty parameter may be kept constant at every iteration even in the lack of convexity assumptions. Numerical experiments with all the problems in the Netlib and CUTEst collections are reported to compare and discuss the different approaches.

关键词： Nonlinear optimization Augmented Lagrangian methods Convergence Numerical experiments

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Facial Instance Learning for Video-based ASD Diagnosis

Facial Instance Learning for Video-based ASD Diagnosis

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Wenhao Duan Jing Li Gaoxiang Ouyang School of Computer Science and Engineering Tianjin University of Technology China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder marked by social interaction difficulties and repetitive, stereotyped behaviors. Previous studies have shown that facial expression recognition has been successfully applied to ASD classification. However, they typically designed a specific framework to explore predefined facial patterns in video data, which usually ignore subtle but potentially vital information embedded in facial movements. To address this limitation and fully leverage the richness of facial data, we design the facial instance learning (FIL) network, a video classification network based on weakly supervised learning. Firstly, the FIL network leverages multi-instance learning to efficiently analyze video sequences without relying on frame-by-frame annotations. Secondly, we employ the R3D18 backbone network to capture both short-term facial dynamics and spatial details of video sequences. Finally, we design the spatiotemporal aggregation module to learn long-term temporal relationships and spatial features. The experiments on the DERW dataset show that the performance of the FIL network is competitive in dynamic facial expression recognition and shows a surprising effect on ASD diagnosis, achieving the accuracy of 98.72%.

关键词： Autism Accuracy Mechatronics Face recognition Machine vision Video sequences Supervised learning Feature extraction Data models Spatiotemporal phenomena

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids 21

Integrating Noise Classification and Speech Enhancement Mode...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Hung, Ying-Hsiu Chang, Yen-Ching Lai, Shin-Chi Juang, Wen-Ho Sheu, Ming-Hwa Lee, Jeng-Dao National Yunlin University of Science and Technology Dept. Electronics Engineering Douliu Taiwan National Formosa University Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan National Formosa University Dept. Automation Engineering Smart Machinery and Intelligent Manufacturing Research Center Huwei Taiwan National Formosa University Dept. Computer Science and Information Engineering Huwei Taiwan National Formosa University Dept. Automation Engineering Huwei Taiwan

ISBN: (纸本)9798350377088

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm. © 2024 IEEE.

关键词： Generative adversarial networks

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Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using Random Forest, KNN, Decision Tree and Naïve Bayes 7

Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using ...

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7th IEEE International Conference on Information Technology and Digital Applications, ICITDA 2022

作者： Pohan, Husni Iskandar Suparta, Wayan Heryadi, Yaya Wibowo, Antoni Lukas Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta55281 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

Dengue Hemorrhagic Fever is an acute viral infectious disease caused by the dengue virus. Transmitted through the bite of Aedes Mosquitoes and divided into 4 severity. Severity 1 and 2 are characterized by a decrease in platelet levels below normal and there is a change in hemoconcentration, while Severity 3 and 4 are included in Dengue Shock Syndrome (DSS). The purpose of the study was to classify the severity of dengue, based on age, gender, platelets, body temperature, bleeding, and also doctor's recommendation. The advancement of information technology is the reason for the development of medical science, especially with artificial intelligence. Abundant medical data becomes a source that can be processed in improving the quality of medical information. The method chosen is Random Forest, kNN, Decision Tree and Naïve Bayes. The dataset used is the medical records of all DHF patients for 30 months in the period Jan 2019 to June 2021, at the Nadhifa Al Ghiffari Clinic in Bojongsoang District, Bandung Regency. © 2022 IEEE.

关键词： Decision trees

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning 9

The Effect of Data Augmentation on the Accuracy of Fish Spec...

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9th International Conference on Informatics and Computing, ICIC 2024

作者： Helmud, Ellya Widodo, Catur Edi Nurhayati, Oky Dwi Diponegoro University Semarang Indonesia Atma Luhur Institute of Science and Business Faculty of Information Technology Information Systems Study Program Pangkalpinang Indonesia Diponegoro University Faculty of Science and Mathematics Physics Department Semarang Indonesia Diponegoro University Faculty of Engineering Department of Computer Engineering Semarang Indonesia

ISBN: (纸本)9798331517601

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of 3010 fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of 95% with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of 94.56% with a loss value of 0.1794. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance. © 2024 IEEE.

关键词： Accuracy Convolutional Neural Network Data Augmentation Deep Learning Fish Species Classification

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Effective Tumor Annotation for Automated Diagnosis of Liver Cancer

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IEEE Journal of Translational engineering in Health and Medicine 2025年 13卷 251-260页

作者： Chuang, Yi-Hsuan Su, Ja-Hwung Lin, Tzu-Chieh Cheng, Hue-Xin Shen, Pin-Hao Ou, Jin-Ping Han, Ding-Hong Liao, Yi-Wen Lee, Yeong-Chyi Cheng, Yu-Fan Hong, Tzung-Pei Shu-Min Li, Katherine Lu, Yi Wang, Chih-Chi Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Liver Transplantation Program Departments of Diagnostic Radiology Niao-Sung District Kaohsiung 833401 Taiwan National University of Kaohsiung Department of Computer Science and Information Engineering Kaohsiung 811 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung 804 Taiwan National Kaohsiung University of Science and Technology Department of Intelligent Commerce Kaohsiung 824004 Taiwan Cheng Shiu University Department of Information Management Kaohsiung 833301 Taiwan Liver Transplantation Center Department of Surgery Kaohsiung Chang Gung Memorial Hospital Niao-Sung District Kaohsiung 833401 Taiwan

In recent years, visual cancer information retrieval using Artificial Intelligence has been shown to be effective in diagnosis and treatment. Especially for a modern liver-cancer diagnosis system, the automated tumor annotation plays a crucial role. So-called tumor annotation refers to tagging the tumor in Biomedical images by computer vision technologies such as Deep Learning. After annotation, the tumor information such as tumor location, tumor size and tumor characteristics can be output into a clinical report. To this end, this paper proposes an effective approach that includes tumor segmentation, tumor location, tumor measuring, and tumor recognition to achieve high-quality tumor annotation, thereby assisting radiologists in efficiently making accurate diagnosis reports. For tumor segmentation, a Multi-Residual Attention Unet is proposed to alleviate problems of vanishing gradient and information diversity. For tumor location, an effective Multi-SeResUnet is proposed to partition the liver into 8 couinaud segments. Based on the partitioned segments, the tumor is located accurately. For tumor recognition, an effective multi-labeling classifier is used to recognize the tumor characteristics by the visual tumor features. For tumor measuring, a regression model is proposed to measure the tumor size. To reveal the effectiveness of individual methods, each method was evaluated on real datasets. The experimental results reveal that the proposed methods are more promising than the state-of-the-art methods in tumor segmentation, tumor measuring, tumor localization and tumor recognition. Specifically, the average tumor size error and the annotation accuracy are 0.432 cm and 91.6%, respectively, which suggest potential for reducing radiologists’ workload. In summary, this paper proposes an effective tumor annotation for an automated diagnosis support system. Clinical and Translational Impact Statement—The proposed methods have been evaluated and shown to significantly imp

关键词： Liver cancer diagnosis liver tumor location liver tumor measuring liver tumor recognition liver tumor segmentation

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Improving multi-view document clustering: leveraging multi-structure processor and hybrid ensemble clustering module 30

Improving multi-view document clustering: leveraging multi-s...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Bai, Ruina Bai, Qi Huang, Ruizhang Chen, Yanping Qin, Yongbin Text Computing & Cognitive Intelligence Engineering Research Center National Education Ministry College of Computer Science and Technology Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China School of Mathematical Sciences Zhejiang University Hangzhou310058 China

ISBN: (纸本)9782493814104

We introduce a multi-view document clustering model called DMsECN (Deep Multi-structure Ensemble Clustering Network), comprising a multi-structure processor and a hybrid ensemble clustering module. Unlike existing models, DMsECN distinguishes itself by creating a consensus structure from multiple clustering structures. The multi-structure processor comprises two stages, each contributing to the extraction of clustering structures that preserve both consistency and complementarity across multiple views. Representation learning extracts both view and view-fused representations from multi-views through the use of contrastive learning. Subsequently, multi-structure learning employs distinct view clustering guidance to generate the corresponding clustering structures. The hybrid ensemble clustering module merges two ensemble methods to amalgamate multiple structures, producing a consensus structure that guarantees both the separability and compactness of clusters within the clustering results. The attention-based ensemble primarily concentrates on learning the contribution weights of diverse clustering structures, while the similarity-based ensemble employs cluster assignment similarity and cluster classification dissimilarity to guide the refinement of the consensus structure. Experimental results demonstrate that DMsECN outperforms other models, achieving new state-of-the-art results on four multi-view document clustering datasets. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Structure (composition)

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The Scientific Progress and Prospects of Artificial Intelligence for Cancer Detection: A Bibliometric Analysis 24

The Scientific Progress and Prospects of Artificial Intellig...

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Maulana, Fairuz Iqbal Adi, Puput Dani Prasetyo Lestari, Dian Ariyanto, Sandy Vikki Purnomo, Agung Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia National Research and Innovation Agency Telecommunication Research Center Jakarta11480 Indonesia Universitas Airlangga Faculty of Medicine Surabaya60132 Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia Bina Nusantara University BINUS Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia

ISBN: (纸本)9798350313956

Using the Scopus database, this study aims to investigate the use of artificial intelligence for cancer detection in the last ten years from 2013 to 2022. The researchers used bibliometric analysis combined with VosViewer and Rstudio software quantification method for literature analysis. The results of the flushed articles show data such as publication year, journal, country, keywords, and authors, to the highest number of citations. Common keywords used by researchers are artificial intelligence, medical, and human. Researchers limited the findings through keywords such as in the last ten years, documents are journals, and English only so that 1868 articles were obtained. The results found that Harvard Medical School affiliation had the highest number of articles, with 101 articles, by subject area, Medicine had a proportion of 41.9% (n=1320). This bibliometric study will be useful for other researchers to examine the development of research on Artificial Intelligence for Cancer Detection in the last ten years. © 2023 IEEE.

关键词： Diseases

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Integration of Artificial Intelligence in Software Defined Networking Technology through Developing Machine Learning Algorithms 3

Integration of Artificial Intelligence in Software Defined N...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Naibaho, Lamhot Saxena, Pulkit Raj Sharma, Kuldeep Alfurhood, Badria Sulaiman Pallavi, L. Pant, Bhasker Faculty of Letters and Languages Christian University of Indonesia English Language Education Study Program Indonesia Teer Thanker Mahaveer University College of Computing Science and Information Technology Uttar Pradesh Moradabad India Lovely Professional University Department of Computer Science and Engineering Punjab Jalandhar Phagwara India College of Computer Information Sciences Princess Nourah Bint Abdulrahman University Department of Computer Sciences Saudi Arabia B v Raju Institute of Technology Cse Telangana Narsapur India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

A future networking design called "software-defined networking"combines network programmability with centralized administration (SDN). Network administration is currently handled by SDN, which, at the regulator, which acts as the cable network processor and divides the data plane into a control plane and a data aircraft. Recent breakthroughs in artificial intelligence (Al) have shown a stronger inclination for the science community to benefit from their ability to give learning capabilities and enhance Indicator. The author of this research provides a comprehensive analysis of initiatives underway to incorporate Al with SDN. The study concluded that the three primary Al thread where scientific research was centered were computer science, conceptual, and fuzzy reasoning systems. The authors of this paper explore the several fields in which these approaches may be used, their potential future applications, and the innovations made possible by the integration of AI-based methods into the SDN paradigm. By choosing the right SDN controller, any large organization may lower network complexity, implementation expenses, and maintenance costs. The focus of this article is software defined networking's benefits and downsides (SDN). Following is a basic explanation of artificial intelligence and a list of some of its most significant applications in SDN. © 2023 IEEE.

关键词： Software defined networking

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