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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Jiang, Guanwang Jia, Ziye He, Lijun Dong, Chao Wu, Qihui Han, Zhu Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Northwestern Polytechnical University School of Software Xi'an710072 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9798350351255

With the rapid increment of multiple users for data offloading and computation, it is challenging to guarantee the quality of service (QoS) in remote areas. To deal with the challenge, it is promising to combine aerial access networks (AANs) with multi-access edge computing (MEC) equipments to provide computation services with high QoS. However, as for uncertain data sizes of tasks, it is intractable to optimize the offloading decisions and the aerial resources. Hence, in this paper, we consider the AAN to provide MEC services for uncertain tasks. Specifically, we construct the uncertainty sets based on historical data to characterize the possible probability distribution of the uncertain tasks. Then, based on the constructed uncertainty sets, we formulate a distributionally robust optimization problem to minimize the system delay. Next, we relax the problem and reformulate it into a linear programming problem. Accordingly, we design a MEC-based distributionally robust latency optimization algorithm. Finally, simulation results reveal that the proposed algorithm achieves a superior balance between reducing system latency and minimizing energy consumption, as compared to other benchmark mechanisms in the existing literature. © 2024 IEEE.

关键词： Computation offloading

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A Node Type and Logs Combination-based Recommendations for Business Process Modeling

A Node Type and Logs Combination-based Recommendations for B...

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IEEE International Conference on web Services (ICWS)

作者： Yanpan Pei Yuanyuan Zhou Qingyuan Wang Sheng He Yishuang Ning Zhijun Ding Department of Computer Science and Technology Tongji University Shanghai China Department of Information Management and Information System Shanghai University Shanghai China Kingdee Research Kingdee International Software Group Co. Ltd Shenzhen China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

In today’s ever-changing business environment, enterprises face numerous challenges, including rapid changes in customer needs, intense market competition, and swift technological advancements. Consequently, business process modeling has emerged as a pivotal strategy to tackle these challenges, optimize operations, and enhance efficiency. Traditionally, manual modeling required participants to possess domain knowledge and modeling skills, rendering the process highly complex and prone to errors. To address this, business process recommendation techniques have been introduced to effectively assist business process modeling by leveraging node information from business process repositories. However, existing research on process recommendation often overlooks crucial factors such as nodetype information and fails to integrate actual execution log data, resulting in suboptimal recommendation results. In this study, we consider the node-type information contained in the process to design the edge expansion model and combine it with the log information generated during the actual execution of the process to calculate the confidence level comprehensively and as the basis for the recommendation to conduct offline mining, the results of mining to assist business process analysts in completing the process modeling. Furthermore, we perform a comparative analysis between our proposed algorithm and mainstream algorithms in process recommendation, utilizing a real dataset. The results demonstrate the superiority of our method in terms of precision and branch structure recommendation.

关键词： Analytical models Process modeling web services Manuals Rendering (computer graphics) Data mining Faces Business

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Utilizing Genetic Algorithms for Detecting Congenital Heart Defects

Utilizing Genetic Algorithms for Detecting Congenital Heart ...

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E-mobility, Power Control and Smart systems (ICEMPS), International Conference on

作者： Sachin C. Patil Balaram Yadav Kasula Vazeer Ali Mohammed Ketan Gupta T. Thamaraimanalan Senior Software Engineer USA Dept of Information Technology University Of The Cumberlands Williamsburg KY USA Dept. of Computer & Information System Lewis University Illinois USA Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu INDIA

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

Genetic Algorithms have been more and more employed to stumble on congenital heart defects in the last variety of years. Gasoline is a computational technique that uses evolutionary concepts to optimize answers to complex problems. This study proposes the use of Genetic Algorithms as a novel approach for detecting Congenital Heart Defects a common and potentially life-threatening condition affecting newborns. By incorporating genetic principles and simulations, GAs have proven to be effective in solving complex medical diagnosis problems. The study aims to optimize the classification process by employing evolutionary techniques to identify key features and prioritize them based on their importance in diagnosing CHDs. Results from the GA model showed a significant improvement in accuracy and speed compared to existing methods, making it a promising tool for early detection and intervention of CHDs in newborns. But gas is more appropriate for this assignment because of its potential to pick out extra subtle variations between ordinary and abnormal cardiovascular anatomy. Gas has additionally been established to be promising within the identity and quantification of CHDs.

关键词： Heart Pediatrics Accuracy Power control Prediction algorithms Genetics Market research

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A Topic-Aware Graph-Based Neural Network for User Interest Summarization and Item Recommendation in Social Media 28th

A Topic-Aware Graph-Based Neural Network for User Interest ...

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28th International Conference on Database systems for Advanced Applications, DASFAA 2023

作者： Chen, Junyang Fan, Ge Gong, Zhiguo Li, Xueliang Leung, Victor C. M. Wang, Mengzhu Yang, Ming College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen China Tencent Inc. Shenzhen China State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science University of Macau China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9783031306716

User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information. While the interested messages (e.g., tags or posts) from a single user are usually sparse becoming a bottleneck for existing methods, we propose a topic-aware graph-based neural interest summarization method (UGraphNet), enhancing user semantic mining by unearthing potential user relations and jointly learning the latent topic representations of posts that facilitates user interest learning. Experiments on two datasets collected from well-known social media platforms demonstrate the superior performance of our model in the tasks of user interest summarization and item recommendation. Further discussions also show that exploiting the latent topic representations and user relations are conductive to the user automatic language understanding. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphic methods

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Research on Restructuring Path of Children's Digital Publishing Supply and Demand Chain 2

Research on Restructuring Path of Children's Digital Publish...

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2nd International Conference on information Science and Education, ICISE-IE 2021

作者： Chen, Huan Liu, Ying Shan, Chan Zhang, Hequn School of Journalism and Communication of Minjiang University Fujian Fuzhou350108 China Department of Software Technology Xiamen Institute of Software Technology Fujian Xiamen China Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia College of Information Engineering Jimei University Xiamen361021 China

ISBN: (纸本)9781665438292

In the era of rapid development of intelligent technology, the children's digital publishing industry is facing a disruptive change, and this change may be realized through the reconstruction of the supply and demand chain. The value power of source content creation and the ability to gather users have been deconstructed. The increase in the amount of content requires platform institutions to have a more professional content operation system. The convenience of payment technology has greatly increased the digital content payment rate of users of children's digital products. A development model of the supply and demand chain of children's digital publishing with more innovative flexibility and enabling effects. © 2021 IEEE

关键词： Economics

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Computational network analysis of two popular skin cancers provides insights into the molecular mechanisms and reveals common therapeutic targets

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Heliyon 2025年第1期11卷 e41688页

作者： Mahmud, Md Sujan Paul, Bikash Kumar Hasan, Md. Rakibul Islam, K.M. Tanjida Mahmud, Imran Mahmud, Shahin Department of Software Engineering Daffodil International University Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Department of Biotechnology and Genetic Engineering Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Department of Computing and Information System (CIS) Daffodil International University Dhaka Bangladesh

Basal Cell Carcinoma (BCC) and Actinic Keratosis (AK) are prevalent skin conditions with significant health complications. The molecular mechanisms underlying these conditions and their potential shared pathways remain ambiguous despite their prevalence. Therefore, this study aims to elucidate the common molecular pathways and potential therapeutic targets for BCC and AK through comprehensive computational network analysis. Linkage analysis was performed to identify common liable genes between BCC and AK. Protein-protein interactions (PPIs), Topological properties, GO enrichment, pathway enrichment, and gene regulatory network analyses were also performed to reveal potential molecular mechanisms and pathways. Furthermore, we evaluated protein-drug interactions (PDIs) to identify potential therapeutic targets. Our analysis revealed 22 common genes between BCC and AK: TP53, EGFR, CDKN2A, MMP9, PTGS2, VDR, BCL2, MMP2, EZH2, TP63, FOXP3, MSH2, MMP14, FLG, MC1R, CDKN2B, TIMP3, TYR, SOX10, IRF4, KRT17, and NID1. PPI network analysis highlighted TP53 and EGFR as central hubs, validated using RNA-seq data. Co-expression and physical interaction analysis revealed a strong interplay between the common genes at the transcriptional and functional levels. GO analysis identified skin cancer-relevant terms: "skin development", "immune system development", and “response to radiation” as significantly enriched biological processes, while pathway enrichment analysis highlighted several cancer-related pathways enrichment. Gene regulatory network analysis revealed complex interactions between genes, miRNAs, and transcription factors, with TP53, BCL2, and EGFR playing central roles. PDI network analysis identified ibuprofen as a potential therapeutic agent targeting PTGS2 and BCL2, while other proteins VDR, MMP2, MMP9, and TYR showed interactions with multiple drugs. This computational analysis provides valuable insights into the shared molecular mechanisms of BCC and AK, revealing comm

关键词： Actinic keratosis Basal cell carcinoma Bioinformatics Drug repurposing Protein-drug interaction Protein-protein interaction Skin cancers

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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AI-Based Child Care Parental Control system

AI-Based Child Care Parental Control System

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Advancements in Computing (ICAC), International Conference on

作者： Udara Jayasekara Hansindu Maniyangama Kalhan Vithana Tharana Weerasinghe Janaka Wijekoon Rrubaa Panchendrarajan Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka Department of Computer System Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398106

Due to the prevalence of the COVID-19 epidemic around the globe, children were compelled to engage in remote learning through online platforms, hence mobile phone has become one of their predominant devices. Mobile device with Internet access offers a major outlet for education, entertainment, and social connection, but this combination can lead to several significant bad sequences such as online exploitation, harmful addictions, and other negative impacts of online social networking. To address harmful effects, parental controls are becoming more crucial, yet Sri Lankan parents are less aware of this. Consequently, this study proposes a parental control system to monitor their child’s activities. Android, Microsoft Azure, Java, Python, OpenCV, MySQL, and FastAPI are among the most prominent technologies utilized in the proposed application’s development. The suggested approach focuses primarily on the Sri Lankan context and aims to enhance parental digital literacy while safeguarding children from cyber threats. Yielded results showed the proposed mobile application for the identification of toxic words, drugs & alcohol content, game character images, and Instagram Sinhala comments severity as 94%, 95%, 97%, and 55% respectively in controlled experiments.

关键词： Pediatrics Social networking (online) Multimedia web sites Process control Control systems Real-time systems Mobile applications

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Development of Multi Objective Approach for Validation of PID Controller for Buck Converter

Development of Multi Objective Approach for Validation of PI...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Arun Kumar K Gopi Chand Vegineni C. Suresh Bhagath Chandra Chowdari Marella Sireesha Addanki Palak Chimwal Big Data Architect India Sr.Software Engineer Enrollment and Eligibility Team India Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Samayapuram Trichy Department of Financial Services Insights & Data Capgemini India Principal software developer System soft Technologies llc Information Technology India Department of Electrical Engineering Bipin Tripathi Kumaon Institute of Technology Dwarahat Almora India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Buck converters are a common converter of DCDC used for high energy conversion and power sources. These regulators are utilized in electronic gear to alter the result voltage because of changes in the heap current and information voltage. Therefore, in order to react to enquiries in real time, an efficient control system is required. In this work, an upgraded Sine Cosine Algorithm (ISCA) with proportional-integral-derivative (PID) controller is proposed for Mult objective optimization of a DC-DC buck converter. Through two distinct modifications, the limitations of the traditional SCA were therefore addressed, leading to the innovative use of nonlinear equations in conjunction with instrumental mechanisms to update the average location. By contrasting its response for frequency, response for increment, uncertainties of parametric, statistics of time-space indispensable error execution, objective capability, and resistance from aggravation with the previous methods-based PID for buck converters of DC-DC, the suggested ISCA-PID technique's robustness was *** results obtained later revealed that the suggested approach outperformed its algorithmic predecessors in terms of the DC-DC buck converter's remarkably improved transient responsiveness.

关键词： Buck converters Regulators Machine learning algorithms Uncertainty Heuristic algorithms Velocity control Power system stability Real-time systems Voltage control Optimization

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Multi-TransPathoNet: A transfer learning-based neural network for pathology image classification with multi-Task learning 3

Multi-TransPathoNet: A transfer learning-based neural networ...

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3rd International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Deng, Xin Li, Min Ren, Xiaojun Zheng, Xiuyan Liang, Fei He, Liang Zhu, Min School of Software Xinjiang University China School of Information Science and Engineering Xinjiang University China Xinjiang Key Laboratory of Clinical Genetic Testing and Biomedical Information China Xinjiang Clinical Research Center for Precision Medicine of Digestive System Tumor China Department of Pathology Karamay Central Hospital China Xinjiang Key Laboratory of Signal Detection and Processing China Dept. of Electron. Eng. and Beijing National Research Center for Information Science and Technology Tsinghua University China

ISBN: (纸本)9781510668522

Histopathological images are one of the common diagnostic tools used in the medical field. However, the complex cellular and tissue structures inside colorectal cancer histopathological images make it difficult for physicians to perform efficient cancer diagnosis and quantitative analysis. Therefore, this study proposed a multi-Task learning-based pathology image classification method Multi-TransPathoNet (A transfer learning-based neural network for pathology image classification with multi-Task learning, Multi-TransPathoNet) for detection and visualization output of colorectal cancer histopathology images, thus helping to improve medical efficiency. To verify the practical application value of this method, this study used the paracancerous, highly differentiated, and high-moderately differentiated histopathological images which are more difficult to distinguish in colorectal cancer as the dataset for classification test, and our method obtained 92% accuracy. Besides, we compared with the single-Task learning models Resnet50 and Vgg19 respectively, which also have good performance in image classification tasks, to comprehensively evaluate the proposed model's performance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computer aided diagnosis

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