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International Conference on science technology engineering and Management (ICONSTEM)

作者： M. Tamilselvi R Rajeshwari Chandra Kala. R N. Nagaraju Sasirekha. D Dept of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Department of MCA S.A. Engineering College Chennai Artificial Intelligence and Data Science VeltechMultitech Dr Rangarajan Dr Sakunthala Engineering College Chennai Department of Mechanical Engineering Annamacharya Institute of Technology & Sciences Rajampet Andhrapradesh Department of Electronics and Communication Engineering Rajalakshmi Engineering College Chennai

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In the realm of traffic management and urban planning, accurate prediction of vehicle counts is crucial for optimizing transportation systems and ensuring efficient traffic flow. Traditional prediction models often rely on historical data and mathematical algorithms, which may lack the ability to adapt to dynamic real-world conditions. This paper proposes a novel approach that integrates sensor data with Explainable artificial intelligence (XAI) principles to enhance the accuracy and interpretability of vehicle count predictions. The proposed system utilizes a network of sensors deployed across key traffic points to collect real-time data on vehicle movements, including speed, volume, and direction. This sensor data is then fed into a machine learning model, specifically designed to predict vehicle counts based on various environmental and contextual factors. Unlike conventional black-box models, the proposed system leverages XAI techniques to provide transparent insights into the decision-making process, enabling stakeholders to understand and trust the predictions. Key components of the XAI-based prediction model include feature importance analysis, model-agnostic interpretability methods, and interactive visualization tools. By identifying the most influential features and explaining the rationale behind predictions, the system empowers transportation planners and policymakers to make informed decisions regarding traffic management strategies and infrastructure investments. By enhancing both accuracy and interpretability, the proposed system paves the way for more effective traffic management strategies and ultimately improves the overall quality of urban mobility.

关键词： Analytical models Accuracy Explainable AI Heuristic algorithms Urban planning Transportation Predictive models

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Insurance Contract for High Renewable Energy Integration

arXiv

引用

arXiv 2022年

作者： Zhao, Dongwei Wang, Hao Huang, Jianwei Lin, Xiaojun The MIT Energy Initiative Massachusetts Institute of Technology United States The Department of Data Science and Artificial Intelligence Monash University Australia The School of Science and Engineering The Chinese University of Hong Kong Shenzhen and Shenzhen Institute of Artificial Intelligence and Robotics for Society China The Elmore Family School of Electrical and Computer Engineering Purdue University United States

The increasing penetration of renewable energy poses significant challenges to power grid reliability. There have been increasing interests in utilizing financial tools, such as insurance, to help end-users hedge the potential risk of lost load due to renewable energy variability. With insurance, a user pays a premium fee to the utility, so that he will get compensated in case his demand is not fully satisfied. A proper insurance design needs to resolve the following two challenges: (i) users’ reliability preference is private information;and (ii) the insurance design is tightly coupled with the renewable energy investment decision. To address these challenges, we adopt the contract theory to elicit users’ private reliability preferences, and we study how the utility can jointly optimize the insurance contract and the planning of renewable energy. A key analytical challenge is that the joint optimization of the insurance design and the planning of renewables is non-convex. We resolve this difficulty by revealing important structural properties of the optimal solution, using the help of two benchmark problems: the no-insurance benchmark and the social-optimum benchmark. Compared with the no-insurance benchmark, we prove that the social cost and users’ total energy cost are always no larger under the optimal contract. Simulation results show that the largest benefit of the insurance contract is achieved at a medium electricity-bill price together with a low type heterogeneity and a high renewable uncertainty. © 2022, CC BY.

关键词： Insurance

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XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Adversarial Network with Explainable AI System for NIR-II Fluorescence Molecular Imaging 22

XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Ad...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Fu, Lidan Lu, Binchun Li, Lingbing Shi, Xiaojing Tian, Jie Hu, Zhenhua Cas Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Precision Instrument Tsinghua University Beijing China Interventional Radiology Department Chinese Pla General Hospital Beijing China Beijing Advanced Innovation Center for Big Data-based Precision Medicine School of Engineering Medicine Beihang University Beijing China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an China National Key Laboratory of Kidney Diseases Beijing China

ISBN: (纸本)9798331520526

Fluorescence imaging in the second near-infrared window (NIR-II) facilitates the real-time optical contrast for in vivo biomedical imaging. However, the detection noise is an inevitable byproduct of the real-time imaging process. Additionally, fluorescent probes currently approved for clinical use remain limited within the wavelength of NIR-IIa (1000-1300 nm). NIR-IIb (1500-1700 nm) imaging has the potential to deliver higher resolution than NIR-IIa, but relies on toxic fluorescent probes not approved for human use. To support clinical high-precision imaging, we propose a NIR-II fluorescence enhanced imaging method based on perception-enhanced spectrum shift generative adversarial network with explainable AI systems (xAI-PSSGAN). The proposed method tackles the problem of limited unpaired data training, and generates NIR-IIb resembling images with enhanced resolution and reduced noise. Experimental results demonstrate that proposed method effectively improves the imaging quality and surpasses competing alternatives, which is of great significance to promote the high-quality NIR-II imaging in clinical practice. © 2025 IEEE.

关键词： Molecular imaging

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Early Cancer Detection by Plasmonic PCF Sensor

Early Cancer Detection by Plasmonic PCF Sensor

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2022 International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2022

作者： Abdelghaffar, M. Gamal, Yusuf Soliman, Wafaa Badr, Y. Hameed, Mohamed Farhat O. Obayya, S.S.A. Cairo University Faculty of Computers and Artificial Intelligence Giza Egypt Zewail City of Science and Technology October Gardens 6th of October City Center for Photonics and Smart Materials Giza12578 Egypt Cairo University Engineering Application of Laser Department Egypt Cairo University Laser Sciences and Interactions Departement Egypt

ISBN: (数字)9781665478991

ISBN: (纸本)9781665478991

Great research work has been studied for cancer detection due to its high death rate. In this paper, a novel design of photonic crystal fiber (PCF) biosensor based on surface plasmon resonance (SPR) is introduced and numerically analyzed for cancer cell detection. Full vectorial finite element method (FVFEM) is used throughout the numerical analysis of the reported structure. The reported PCF has a V-shaped surface that is coated by ZrN as a plasmonic material. A coupling occurs between the core guided mode and surface plasmon mode which depends on the studied analyte. The geometrical parameters are optimized to achieve high sensor sensitivity. The proposed biosensor achieves high optical sensitivity of 5600 nm/RIU, 5700 nm/RIU for the x- and y-polarized modes, respectively. The reported optical sensor can pave the road for efficient and simple technique for cancer detection instead of surgical and chemical techniques. © 2022 IEEE.

关键词： Numerical methods

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Exploring General intelligence via Gated Graph Transformer in Functional Connectivity Studies

arXiv

引用

arXiv 2024年

作者： Qu, Gang Orlichenko, Anton Wang, Junqi Zhang, Gemeng Xiao, Li Zhang, Aiying Ding, Zhengming Wang, Yu-Ping Department of Biomedical Engineering Tulane University United States Department of Radiology Cincinnati Children’s Hospital Medical Center United States Tulane University United States Department of Neurology Mayo Clinic United States School of Information Science and Technology University of Science and Technology of China China Institute of Artificial Intelligence School of Data Science University of Virginia United States Department of Computer Science Tulane University United States

Functional connectivity (FC) as derived from fMRI has emerged as a pivotal tool in elucidating the intricacies of various psychiatric disorders and delineating the neural pathways that underpin cognitive and behavioral dynamics inherent to the human brain. While Graph Neural Networks (GNNs) offer a structured approach to represent neuroimaging data, they are limited by their need for a predefined graph structure to depict associations between brain regions, a detail not solely provided by FCs. To bridge this gap, we introduce the Gated Graph Transformer (GGT) framework, designed to predict cognitive metrics based on FCs. Empirical validation on the Philadelphia Neurodevelopmental Cohort (PNC) underscores the superior predictive prowess of our model, further accentuating its potential in identifying pivotal neural connectivities that correlate with human cognitive processes. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Optimizing Forwarding Strategies in Sparsely Delay-Tolerant Networks with Short Contact Durations

Optimizing Forwarding Strategies in Sparsely Delay-Tolerant ...

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International Conference on Computing and Networking technology (ICCNT)

作者： Yadesa Bedasa Nemera Leta Tesfaye Jule S. Sivakumar J Pavithra A. Sampathkumar Ravi Rastogi Department of Information Technology Dambi Dollo Univeristy Ethiopia Department of Physics College of Natural and Computational Science Dambi Dollo University Dambi Dollo Ethiopia Department of CSE Nehru Institute of Engineering and Technology Coimbatore India Department of Artificial intelligence and data science Dhanalakshmi Srinivasan College of Engineering Coimbatore India Department of Computer Science Dambi Dollo Univeristy Ethiopia Department of CSE Koneru Lakshmaiah Education Foundation Guntur AP India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Delay-tolerant networks (DTNs) are specific settings that experience either instability or the absence of a complete path between the source and destination nodes. The contact lifetime between pairs of network nodes is constrained by the mobility of nodes and the restricted bandwidth of peer- to-peer wireless transmission. In sparsely delay-tolerant networks, the re-establishment of contact between pairs of nodes is significantly delayed beyond the duration of the initial contact. Hence, the need for careful thought in designing a buffer control forwarding policy arises due to the limited contact period. Although forwarding algorithms have been designed extensively, much research has not been done on forwarding when there is a short contact time. When the message exchange between the pairs of network nodes is not completed during the contract period, they must wait until they are within communication range once more. To tackle the issue, we have suggested an innovative forwarding policy in buffer management that considers the duration of interaction. To assess the effectiveness of the proposed effort, we utilized the Opportunistic Network Environment (ONE) networking simulator. The suggested approach is combined with the MaxProp routing protocol buffer control forwarding policy and evaluated using two distinct metrics: bundle delivery rate and average delivery delay. In addition, we evaluated the performance of the suggested work by considering the number of nodes, buffer size, and simulation time. Across all the trials conducted in this thesis, the simulation results consistently demonstrate that our suggested method beats the existing forwarding strategy.

关键词： Wireless communication Measurement Simulation Receivers Programming Routing protocols Product delivery Peer-to-peer computing Delays Contracts

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Legal-Onto Model for Efficient Land Law Updates in Vietnam 11th

Legal-Onto Model for Efficient Land Law Updates in Vietnam

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11th International Conference on Future data and Security engineering, FDSE 2024

作者： D. T. Do, Huy D. Nguyen, Hien T. Pham, Vuong Nguyen, Tri-Hai Faculty of Computer Science University of Information Technology Ho Chi Minh City Viet Nam Faculty of Mathematics and Computer Science University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Computer Science Department New Mexico State University Las Cruces United States Institute of Data Science and Artificial Intelligence Sai Gon University Ho Chi Minh City Viet Nam Faculty of Information Technology School of Technology Van Lang University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819604333

In the rapidly evolving economic and social landscape of Vietnam, land remains a pivotal resource, crucial for economic growth and social stability. The recent enactment of the Land Law 2024, which supersedes the 2013 legislation, necessitates continuous updates for land users, investors, and regulatory authorities to ensure compliance and optimize land utilization. Traditional legal information retrieval systems are constrained by keyword-based searches and pre-set responses, which are inadequate for addressing specific user queries or rapidly integrating new legislative updates. This paper presents an advanced technological solution employing the Legal-Onto model to represent and update legal knowledge. The Legal-Onto model, inspired by relational ontology and keyphrase graphs, captures the semantic content of legal documents. By employing natural language processing techniques, the system automatically compares old and new legal texts, identifying differences at the article, point, and clause levels. This enables efficient updates and provides a valuable tool for legal professionals. It demonstrates the potential of artificial intelligence in legal contexts, offering a more effective and accessible approach to legal information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Information Retrieval Intelligent System Knowledge-Based Systems Legal Document Ontology Vietnamese Land Law

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Transformer-based RGBT Tracking with Spatio-Temporal Information Fusion

引用

IEEE Sensors Journal 2025年

作者： Yuan, Di Zhang, Haiping Liu, Qiao Chang, Xiaojun He, Zhenyu Xidian University Guangzhou Institute of Technology Guangzhou 510555 China Chongqing Normal University National Center for Applied Mathematics Chongqing 401331 China Faculty of Engineering and Information Technology University of Technology Australian Artificial Intelligence Institute Sydney 2007 Australia Harbin Institute of Technology School of Computer Science and Technology Shenzhen 518055 China

RGBT tracking (RGB-Thermal) usually uses an RGB tracker as the base model, and then uses the RGBT dataset to fully fine-tune the model. These methods ignore the differences in target features in the RGB domain and the TIR domain. At the same time, existing RGBT trackers match the spatial features of the initial template and the search image, ignoring the role of temporal information in RGBT tracking, resulting in the failure of the tracker to track in complex scenarios such as change in the appearance of the target and occlusion. To address the above problems, we propose a simple and efficient tracker called STTrack. The tracker adopts a symmetric dual-stream structure, which consists of several FT Transformer(Fine Tuning Transformer) Encoders, a prediction head, and an online update module. Specifically, the FT Transformer Encoder first adds some trainable parameters to the frozen pre-trained RGB-based tracker, transfers the feature extraction capability from the RGB domain to the TIR domain, and enhances the model’s perception of cross-modal data;secondly, the output features of the RGB and TIR modalities are fused and fed into the prediction head to obtain the target’s position;finally, the online update module obtains an online template with temporal information, which complements the spatial information provided by the initial template. The spatio-temporal information provided by the dual templates improves the RGBT tracker’s ability to locate targets in complex environments. Extensive quantitative and qualitative experiments demonstrate that our approach achieves state-of-the-art performance on four most popular RGBT benchmarks and runs at 32FPS in real time. © 2001-2012 IEEE.

关键词： Adapter Learning RGBT Tracking Spatio-Temporal Information Fusion Temporal Information

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An Energy-Efficient Event Reliability Protocol for Wireless Communication Networks

引用

Mobile Information Systems 2022年第1期2022卷

作者： Khan, Gulista Kumar Agarwal, Ambuj Khapre, Shailesh Arunkumar, G. Shankar, Achyut Khosravi, Mohammad R. Faculty of Engineering Teerthanker Mahaveer University Moradabad India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Data Science and Artificial Intelligence IIIT-Naya Raipur Chhattisgarh Raipur India School of Computer Science and Engineering VIT University Vellore India Department of Computer Science and Engineering Amity University Noida India Faculty of Engineering and Built Environment University of Johannesburg Johannesburg South Africa Department of Computer Engineering Persian Gulf University Bushehr Iran

Nowadays, WSN (wireless sensor networks) are used in every aspect of life. It consists of tiny sensor nodes that are capable of sensing, aggregating, dispensation, and sending data for end-user queries. Two major constraints in WSN are power consumption and reliability. As we know, power is limited per sensor node, so efficient utilization of power is needed all the way. Another constraint is reliability. Both constraints are in conflict with each other. If we try to increase reliability by sending the same packet on a different path, then it will increase power consumption. Many algorithms have been defined in the past to reduce power consumption and have reliable transmission. Basically, there are two types of reliability mechanisms: packet reliability and occurrence reliability. Package reliability explains about all packages must be reached at the destination. Event reliability ensures that at least one event should be successful in a specific region. This paper proposes an analysis model to evaluate the reliability of wireless sensor networks. In addition, this paper has proposed a reliability technique for events. The proposed algorithm has been simulated, analyzed, and compared with the already-published algorithms, namely BaseModel, SWIA, and ERP techniques. © 2022 Gulista Khan et al.

关键词： Electric power utilization

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Gradient and non-gradient-based first order reliability method for robust and accurate structural reliability analysis

引用

Procedia Structural Integrity 2023年 48卷 348-355页

作者： Behrooz Keshtegar Mohamed El Amine Ben Seghier José A.F.O. Correia Department of Civil Engineering Faculty of Engineering University of Zabol P.B. *** Zabol Iran Laboratory for Computational Mechanics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Vietnam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Vietnam INEGI Faculty of Engineering University of Porto Rua Dr. Roberto Frias 4200-465 Porto Portugal

The first order reliability method (FORM) may provide unstable and inaccurate results for gray/ highly nonlinear performance functions. In this study, FORM approaches based on gradient and non-gradient methods are compared for both accuracy and robustness of results in nonlinear/complex reliability problems. Two gradient based sensitivity vectors using steepest descent search direction as (HL-RF) and directional stability transformation method (DSTM) are compared to two non-gradient approaches of harmony search optimization (HS) and particle swarm optimization (PSO). A refined probabilistic model using FORM is used for non-gradient optimization methods. The accuracy and robustness of FORM-based gradient formulations and refined FORM-based non-gradient approaches are compared on nonlinear/complex performance functions. The comparative results demonstrate that the PSO-based refined FORM provides accurate and robust results compared to FORM using HL-RF and DSTM. In addition to that, PSO shows superior performances compared to HS and gradient-FORM in complex engineering structures applications.

关键词： Type your keywords here separated by semicolons

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