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2025 International Conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Krishnakumar, K. Gurrapu, Omprakash Reshma, P. Krishnammal, P. Muthu Khan, Huma Qamar Amane, Ninad Saveetha School of Engineering Chennai India Volvo Trucks North America NC United States Dhanalakshmi Srinivasan College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India VIT-AP University Amaravati India Joginpally B R Engineering College Department of Computer Science and Engineering Hyderabad India Bms Institute of Technology and Management Department of Ai&ml Bangalore India

ISBN: (纸本)9798331538538

Diagnostic imaging is an important technique which helps in diagnosing several diseases at the initial stage in order to take necessary treatments. Nevertheless, conventional techniques tend to have issues with regard to precision, speed, and quantitatively assessing multidimensional medical information. In this paper, we reveal the potential of deep convolutional networks (DCNs) in the field of medical image diagnostic. It means that, with the help of deep learning algorithms DCNs can learn meaningful features of medical images and improve diagnostic accuracy and speed. The near proposed technique involves the use of deep convolutional network model to analyse medical images for diagnosis of diseases such as cancer, heart diseases and neurological disorders. The obtained experimental results demonstrate that the presented model has the highest accuracy of 96.5% with very high sensitivity and specificity coefficients compared to other methods. Further, the interpretability of the proposed DCN model and different aspects of it are discussed to support clinical validation. Through this research, the effectiveness of deep learning in revolutionizing the diagnosis for medical imaging diagnostics while early disease detection and treatment is established. © 2025 IEEE.

关键词： Neuroimaging

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iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations

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BMC Biology 2025年第1期23卷 1-14页

作者： Wei, Hang Hou, Jialu Liu, Yumeng Shaytan, Alexey K. Liu, Bin Wu, Hao School of Computer Science and Technology Xidian University Shaanxi Xi’an 710126 China SMBU-MSU-BIT Joint Laboratory On Bioinformatics and Engineering Biology Shenzhen MSU-BIT University Guangdong Shenzhen 518172 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen 518118 China Department of Biology Lomonosov Moscow State University Moscow 119234 Russian Federation International Laboratory of Bioinformatics AI and Digital Sciences Institute Faculty of Computer Science HSE University Moscow 109028 Russian Federation Zhongguancun Academy Beijing 100094 China

Background: Exploring piRNA-disease associations can help discover candidate diagnostic or prognostic biomarkers and therapeutic targets. Several computational methods have been presented for identifying associations between piRNAs and diseases. However, the existing methods encounter challenges such as over-smoothing in feature learning and overlooking specific local proximity relationships, resulting in limited representation of piRNA-disease pairs and insufficient detection of association patterns. Results: In this study, we propose a novel computational method called iPiDA-LGE for piRNA-disease association identification. iPiDA-LGE comprises two graph convolutional neural network modules based on local and global piRNA-disease graphs, aimed at capturing specific and general features of piRNA-disease pairs. Additionally, it integrates their refined and macroscopic inferences to derive the final prediction result. Conclusions: The experimental results show that iPiDA-LGE effectively leverages the advantages of both local and global graph learning, thereby achieving more discriminative pair representation and superior predictive performance. © The Author(s) 2025.

关键词： Graph ensemble learning Local context graph piRNA-disease association identification

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Application of Machine Learning Algorithms for Customer Segmentation in E-commerce Management

Application of Machine Learning Algorithms for Customer Segm...

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2024 International Conference on science, Technology, Engineering and Management, ICSTEM 2024

作者： Rajyalaxmi, M. Vijai, C. Srivastava, Kingshuk Kalyan, NallaBala Pravallika, B. Dutt, Amit School of Business SR University Telangana Warangal India Department of Commerce and Business Administration Vel Tech Rangarajan Dr.SagunthalaR&D Institute of Science and Technology Chennai62 India Department of Data Science UPES Uttarakhand Dehradun India Karakambadi Road Andhra Pradesh Tirupati India Institute of Aeronautical Engineering Dundigal Telangana Hyderabad India Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350376913

Client segmentation is an essential component of e-commerce operations since it enables more effective marketing and a more satisfying experience for customers. Within the context of e-commerce platforms, this research investigates the functioning of machine literacy algorithms for effective client segmentation. Through the utilization of clustering methods and predictive models, such as k-means clustering, hierarchical clustering, and decision trees, our objective is to categorize guests according to their shopping behavior, demographics, and preferences. In addition, we investigate the incorporation of sophisticated machine literacy methods, such as ensemble styles and deep literacy infrastructures, to improve the delicateness and resilience of client segmentation models. We illustrate the efficacy of machine learningusing empirical evaluation and case studies. This enables us to relate different client components and adapt marketing strategies to fit the individual requirements of each client. In the context of e-commerce enterprises, our findings highlight the value of machine literacy-driven client segmentation in terms of optimizing marketing sweats, enhancing client retention, and maximizing profit. © 2024 IEEE.

关键词： Electronic commerce

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USV-AUV Collaboration Framework for Underwater Tasks under Extreme Sea Conditions

USV-AUV Collaboration Framework for Underwater Tasks under E...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jingzehua Xu Guanwen Xie Xinqi Wang Yimian Ding Shuai Zhang Tsinghua Shenzhen International Graduate School Tsinghua University China College of Information Science & Electronic Engineering Zhejiang University China Department of Data Science New Jersey Institute of Technology USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Autonomous underwater vehicles (AUVs) are valuable for ocean exploration due to their flexibility and ability to carry communication and detection units. Nevertheless, AUVs alone often face challenges in harsh and extreme sea conditions. This study introduces a unmanned surface vehicle (USV)–AUV collaboration framework, which includes high-precision multi-AUV positioning using USV path planning via Fisher information matrix optimization and reinforcement learning for multi-AUV cooperative tasks. Applied to a multi-AUV underwater data collection task scenario, extensive simulations validate the framework’s feasibility and superior performance, highlighting exceptional coordination and robustness under extreme sea conditions. To accelerate relevant research in this field, we have made simulation code (demo version) available as open-source 1 .

关键词： Training Codes Collaboration data collection Signal processing Robustness Path planning Speech processing Optimization Surface treatment

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Bounding the number of reticulation events for displaying multiple trees in a phylogenetic network

arXiv

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arXiv 2024年

作者： Wu, Yufeng Zhang, Louxin School of Computing University of Connecticut StorrsCT06269 United States Department of Mathematics Center for Data Science and Machine Learning National University of Singapore Singapore119076 Singapore

Reconstructing a parsimonious phylogenetic network that displays multiple phylogenetic trees is an important problem in phylogenetics, where the complexity of the inferred networks is measured by reticulation numbers. The reticulation number for a set of trees is defined as the minimum number of reticulations in a phylogenetic network that displays those trees. A mathematical problem is bounding the reticulation number for multiple trees over a fixed number of taxa. While this problem has been extensively studied for two trees, much less is known about the upper bounds on the reticulation numbers for three or more arbitrary trees. In this paper, we present a few non-trivial upper bounds on reticulation numbers for three or more *** Codes 5C30 © 2024, CC BY.

关键词： Abiotic

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Blockchain-based data Sharing Framework for Malaysia Government Aid Management System

Blockchain-based Data Sharing Framework for Malaysia Governm...

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2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023

作者： Azmi, Azman Yahya, Farashazillah Moung, Ervin Gubin Sallehudin, Hasimi Utomo, Rio Guntur Azman, Nur Afrina Universiti Malaysia Sabah Advanced Machine Intelligence Research Lab Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Universiti Malaysia Sabah Data Technologies and Application Research Lab Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Universiti Kebangsaan Malaysia Centre for Software Technology and Management Faculty of Information Science and Technology Malaysia Telkom University School of Computing Bandung Indonesia Foundation Study and Language Institute University of Reading Malaysia Johor Iskandar Puteri Malaysia

ISBN: (纸本)9798350310689

The Covid-19 pandemic has impacted the world economy, and Malaysia is no exception. Due to the closure of businesses, the rate of unemployment has been recorded at an all-time high. The Malaysian Government through its agencies is assisting citizens through an online platform for incentives and an aid-giving platform. The current architecture of data sharing is using centralized, organization-oriented authentication and is not immutable for audit capability. In addition, the blockchain network is still a proof of concept in the digital government ecosystem. This paper studies the current aid data-sharing ecosystem and analyzes the potential of having a state government consortium blockchain framework that will be used in the Government Network Infrastructure for decentralized, transparent data sharing with audit capability. Several works involving Blockchain adoption based on domain, Blockchain consortium architecture and Government applications using blockchain are being presented. The need for a framework in implementing decentralized data sharing using consortium blockchain architecture for multi-agencies data sharing focusing on Malaysia Government Aid Management is discussed in this paper to provide the opportunity for the research domain. The findings in this paper will be used for future work on the requirement of a proposed government multi-layer consortium blockchain framework for data sharing in the Malaysian Government specifically for the Johore State Government ecosystem. © 2023 IEEE.

关键词： Blockchain

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Agriculture 4.0 and Beyond: Evaluating Cyber Threat Intelligence Sources and Techniques in Smart Farming Ecosystems

SSRN

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SSRN 2024年

作者： Thanh, Hang Bui Aboutorab, Hamed Mahboubi, Arash Gao, Yansong Sultan, Nazatul Haque Chauhan, Aufeef Parvez, Mohammad Zavid Bewong, Michael Islam, Rafiqul Islam, Zahid Camtepe, Seyit A. Gauravaram, Praveen Singh, Dineshkumar Babar, Muhammad Ali Yan, Shihao School of Computing and Mathematics Charles Sturt University Australia CSIRO’s Data61 Australia The University of Adelaide Australia Tata Consultancy Services Australia School of Science Security Research Institute Edith Cowan University Australia

The digitisation of agriculture, integral to Agriculture 4.0, has brought significant benefits while simultaneously escalating cybersecurity risks. With the rapid adoption of smart farming technologies and infrastructure, the agricultural sector has become an attractive target for cyberattacks. This paper presents a systematic literature review that assesses the applicability of existing Cyber Threat Intelligence (CTI) techniques within Smart Farming Infrastructures (SFIs). We develop a comprehensive taxonomy of CTI techniques and sources, specifically tailored for the SFI context, addressing the unique cyber threat challenges in this domain. A crucial finding of our review is the identified necessity for a virtual Chief Information Security Officer (vCISO) in smart agriculture. While the concept of a vCISO is not yet established in the agricultural sector, our study highlights its potential significance. The implementation of a vCISO could play a pivotal role in enhancing cybersecurity measures by offering strategic guidance, developing robust security protocols, and facilitating real-time threat analysis and response strategies. This approach is critical for safeguarding the food supply chain against the evolving landscape of cyber threats. Our research underscores the importance of integrating a vCISO framework into smart farming practices as a vital step towards strengthening cybersecurity. This is essential for protecting the agriculture sector in the era of digital transformation, ensuring the resilience and sustainability of the food supply chain against emerging cyber risks. © 2024, The Authors. All rights reserved.

关键词： Cybersecurity

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Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated Cases 30th

Can We Transfer Noise Patterns? A Multi-environment Spectru...

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30th International Conference on Neural Information Processing, ICONIP 2023

作者： Du, Haiwen Ju, Zheng An, Yu Du, Honghui Zhu, Dongjie Tian, Zhaoshuo Lawlor, Aonghus Dong, Ruihai School of Astronautics Harbin Institute of Technology Harbin China Insight Centre for Data Analytics Dublin Ireland School of Computer Science and Technology Harbin Institute of Technology Weihai China

ISBN: (纸本)9789819981830

Spectrum analysis systems in online water quality testing are designed to detect types and concentrations of pollutants and enable regulatory agencies to respond promptly to pollution incidents. However, spectral data-based testing devices suffer from complex noise patterns when deployed in non-laboratory environments. To make the analysis model applicable to more environments, we propose a noise patterns transferring model, which takes the spectrum of standard water samples in different environments as cases and learns the differences in their noise patterns, thus enabling noise patterns to transfer to unknown samples. Unfortunately, the inevitable sample-level baseline noise makes the model unable to obtain the paired data that only differ in dataset-level environmental noise. To address the problem, we generate a sample-to-sample case-base to exclude the interference of sample-level noise on dataset-level noise learning, enhancing the system’s learning performance. Experiments on spectral data with different background noises demonstrate the good noise-transferring ability of the proposed method against baseline systems ranging from wavelet denoising, deep neural networks, and generative models. From this research, we posit that our method can enhance the performance of DL models by generating high-quality cases. The source code is made publicly available online at https://***/Magnomic/CNST. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Spectrum analysis

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SHELLEY: Exploring Learning-Based Network Alignment on Biological data

SHELLEY: Exploring Learning-Based Network Alignment on Biolo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： De Luca, Riccardo Petti, Manuela Guzzi, Pietro H. Tieri, Paolo Sapienza University of Rome Program in Data Science Rome Italy Sapienza University of Rome Diag Dept. Comp. Contr. Manag. Eng. Rome Italy Magna Graecia University Dept. Umg Catanzaro Italy Cnr National Research Council Iac Institute for Applied Computing Rome Italy

ISBN: (纸本)9798350386226

Global network alignment is the computational problem of determining the similarity between nodes of different networks to establish a one-to-one correspondence between them. It has important applications in the biological field, particularly for discovering similar roles between the elements of different systems or for transferring knowledge from a well-studied system to another. In this paper, we present SHELLEY, a tool that facilitates the development, testing, and combination of learning-based network alignment algorithms by providing a set of modules that allow for the recreation and combination of both representation learning methods (RLMs) and deep matching methods (DMMs). We then present a case study in which we apply this tool to a protein-protein interaction network (PPI), demonstrating how the representation phase of RLMs is crucial for model robustness against *** code of SHELLEY is available at: https://***/rickydeluca/shelley © 2024 IEEE.

关键词： Contrastive Learning

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SpatioTemporal Learning for Human Pose Estimation in Sparsely-Labeled Videos

arXiv

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arXiv 2025年

作者： Jiao, Yingying Wang, Zhigang Wu, Sifan Fan, Shaojing Liu, Zhenguang Xu, Zhuoyue Wu, Zheqi College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Zhejiang Gongshang University China School of Computing National University of Singapore Singapore The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Human pose estimation in videos remains a challenge, largely due to the reliance on extensive manual annotation of large datasets, which is expensive and labor-intensive. Furthermore, existing approaches often struggle to capture long-range temporal dependencies and overlook the complementary relationship between temporal pose heatmaps and visual features. To address these limitations, we introduce STDPose, a novel framework that enhances human pose estimation by learning spatiotemporal dynamics in sparsely-labeled videos. STDPose incorporates two key innovations: 1) A novel Dynamic-Aware Mask to capture long-range motion context, allowing for a nuanced understanding of pose changes. 2) A system for encoding and aggregating spatiotemporal representations and motion dynamics to effectively model spatiotemporal relationships, improving the accuracy and robustness of pose estimation. STDPose establishes a new performance benchmark for both video pose propagation (i.e., propagating pose annotations from labeled frames to unlabeled frames) and pose estimation tasks, across three large-scale evaluation datasets. Additionally, utilizing pseudo-labels generated by pose propagation, STDPose achieves competitive performance with only 26.7% labeled data. Copyright © 2025, The Authors. All rights reserved.

关键词： Large datasets

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