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arXiv 2024年

作者： Anandh, Thivin Ghose, Divij Jain, Himanshu Sunkad, Pratham Ganesan, Sashikumaar John, Volker Department of Computational and Data Sciences Indian Institute of Science Karnataka Bangalore India Department of Mechanical Engineering Indian Institute of Technology Tamil Nadu Madras India Weierstrass Institute for Applied Analysis and Stochastics Berlin Germany Department of Mathematics and Computer Science Freie University Berlin Germany

This paper proposes and studies two extensions of applying hp-variational physics-informed neural networks, more precisely the FastVPINNs framework, to convection-dominated convection-diffusion-reaction problems. First, a term in the spirit of a SUPG stabilization is included in the loss functional and a network architecture is proposed that predicts spatially varying stabilization parameters. Having observed that the selection of the indicator function in hard-constrained Dirichlet boundary conditions has a big impact on the accuracy of the computed solutions, the second novelty is the proposal of a network architecture that learns good parameters for a class of indicator functions. Numerical studies show that both proposals lead to noticeably more accurate results than approaches that can be found in the literature. © 2024, CC BY-NC-SA.

关键词： Stabilization

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Simultaneous Super-Resolution and Denoising on MRI via Conditional Stochastic Normalizing Flow

Simultaneous Super-Resolution and Denoising on MRI via Condi...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Zhenhong Wang, Xingce Wu, Zhongke Zhu, Yi-Cheng Frangi, Alejandro F. Beijing Normal University School of Artificial Intelligence Beijing China Peking Union Medical College Hospital Department of Neurology Beijing China Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences School of Health Sciences The Department of Computer Science School of Engineering The University of Manchester United Kingdom Department of Cardiovascular Sciences The Department of Electrical Engineering Ku Leuven Leuven Belgium Lan Turing Institute London United Kingdom

ISBN: (纸本)9798350337488

Magnetic resonance imaging (MRI) scans often suffer from noise and low-resolution (LR), which affect the diagnosis and treatment results obtained for patients. LR images and noise come together with MRI, and the existing methods solve image super-resolution (SR) reconstruction and denoising tasks in a step-by-step manner, which influences the overall real distribution of the MRI data. In this paper, we present a simultaneous SR and denoising algorithm based on a stochastic normalizing flow (SNF), named the MR image SR and denoising model based on an SNF (SRDSNF). SRDSNF adds the encoded information of the input image as the conditional information to each reverse step of the stochastic normalizing flow, which realizes a consistent description of the spatial distribution between the reconstruction result and the input image. We introduce rangenull space decomposition and subsequence sampling strategies to enhance the consistency of the input and output data and increase the generation speed of the model. Simultaneous SR and denoising tasks experiment is carried out using the BrainWeb and NFBS datasets. The experimental results show that good SR and denoising results are obtained with fewer sampling steps, these results are consistent with the ground truths, and the structural similarity and peak signal-to-noise ratio of the results are also higher than those of the comparison methods. The proposed method demonstrates potential clinical promise. © 2023 IEEE.

关键词： Denoising Diffusion model MR image Stochastic normalizing flow Super-resolution

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Random Security Generators with Enhanced Cryptography for Cybersecurity in Financial Supply Chains

Random Security Generators with Enhanced Cryptography for Cy...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Latha Thammareddi Venkateswara Rao Anumolu Kulasekhara Reddy Kotte Bhagath Chandra Chowdari Marella K Arun Kumar Jatin Bisht Project Management Operations India ERP Solutions-Information Technology India SLRI Solutions INC India Department of Financial Services Insights & Data India Big Data Architect India Department of Computer Science & Engineering Bipin Tripathi Kumaon Institute of Technology Almora India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Blockchains, like Ethereum, depend on a distributed system of collaborative servers referred to as inspectors or mine workers to validate transactions and generate novel legal blocks. In today's electronic world, it is critical to secure financial supply chains (FSCs) from complex cyber-attacks. This study combines Quantized Random Security Generators (QRSGs) with Artificial Intelligence (AI) to improve cybersecurity in FSC. QRSGs use quantum physics to produce random outcomes, which is critical for establishing safe cryptography credentials and resolving the flaws of regular predictable RSGs. This study found that the QRSG-based approach outperformed traditional RSG methods. The QRSG demonstrated strong entropy and uncertainty, transferring both NIST SP800-22 and Diehard examinations at much greater levels. The platform's real-time safety features were improved by the AI element's outstanding performance measures in identifying security breaches. More rapid decoding and encryption times, reduced delay, and increased resilience to brute-force, quantum, and prediction assaults were all demonstrated by the coupled QRSG and AI methods. This study emphasizes how crucial it is to use AI and QRSGs to safeguard FSCs. The combined platform provides a complete option to reduce cyber-attacks by offering strong cryptography security and real-time attack identification. These results demonstrate how QRSG and AI innovations can completely transform cybersecurity in the banking industry, safeguarding vital financial systems and guaranteeing the privacy and reliability of financial payments.

关键词： Technological innovation Uncertainty Supply chains Transforms Real-time systems Generators Power capacitors Blockchains Artificial intelligence Cyberattack

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ProAI: Proactive Multi-Agent Conversational AI with Structured Knowledge Base for Psychiatric Diagnosis

arXiv

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arXiv 2025年

作者： Wu, Yuqi Wan, Guangya Li, Jingjing Zhao, Shengming Ma, Lingfeng Ye, Tianyi Pop, Ion Zhang, Yanbo Chen, Jie Zhang, Mike Department of Electrical and Computer Engineering University of Alberta Canada School of Data Science University of Virginia United States McIntire School of Commerce University of Virginia United States Department of Psychiatry University of Alberta Canada Academy for Engineering Fudan University China

Most LLM-driven conversational AI systems operate reactively, responding to user prompts without guiding the interaction. However, many real-world applications—such as psychiatric diagnosis, consulting, and interviews—require AI to take a proactive role, asking the right questions and steering conversations toward specific objectives. Using mental health differential diagnosis as an application context, we introduce ProAI, a goal-oriented, proactive conversational AI framework. ProAI integrates structured knowledge-guided memory, multi-agent proactive reasoning, and a multi-faceted evaluation strategy, enabling LLMs to engage in clinician-style diagnostic reasoning rather than simple response generation. Through simulated patient interactions, user experience assessment, and professional clinical validation, we demonstrate that ProAI achieves up to 83.3% accuracy in mental disorder differential diagnosis while maintaining professional and empathetic interaction standards. These results highlight the potential for more reliable, adaptive, and goal-driven AI diagnostic assistants, advancing LLMs beyond reactive dialogue systems. © 2025, CC BY-NC-SA.

关键词： Chatbots

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Suicidal Tweet Detection Using Ensemble Learning: A Multi-Model Approach for Early Intervention

Suicidal Tweet Detection Using Ensemble Learning: A Multi-Mo...

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IEEE International Conference on engineering Education: Innovative Practices and Future Trends (AICERA)

作者： Arjun Sharma Shantanu Neema Computer Science and Engineering Department Chandigarh University Mohali Punjab India Senior Data Scientist Syntelli Solutions Inc. Charlotte United States

With the growth of social media platforms, there is a greater need to address pressing problems, such as the identification of suicide material to enable prompt and effective intervention. In this study, we give a thorough investigation of a suicidal tweet detection system that makes use of the advantages of ensemble learning methods, particularly the BaggingClassifier and GradientBoostingClassifier, together with conventional classifiers BernoulliNB and GaussianNB. data preparation, model selection, training, assessment, ethical issues, and interpretability are all parts of what we do. The outcomes of our investigation demonstrate the potential of ensemble learning to improve classifier performance. While the GradientBoostingClassifier and BaggingClassifier consistently performed well across a range of measures, the BernoulliNB and GaussianNB models presented distinctive trade-offs between recall and accuracy. Our study highlights the vital value of user privacy, model interpretability, and ethical deployment. In the future, we see the inclusion of new features, partnerships with mental health specialists, real-world implementation to assess system effectiveness and ethical consequences, as well as continuing system adaption to changing linguistic and social media trends.

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Correction to: A Systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications (Archives of Computational Methods in engineering, (2024), 31, 1, (291-311), 10.1007/s11831-023-09978-x)

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Archives of Computational Methods in engineering 2024年 1-1页

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Punjab Phagwara144411 India Department of Computer Science Government Bikram College of Commerce Punjab Patiala147001 India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan Mohali140413 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Division of Research and Development Lovely Professional University Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Tamil Nadu Rajaplayam India

Typesetter overlooked corrections regarding affiliations of author Rakesh Kumar and needs to be correctly read as only affiliated to: department of Mathematics, Lovely Professional University, Phagwara, Punjab 144411, India. The original article has been corrected. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2024.

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IKGN: Intention-aware Knowledge Graph Network for POI Recommendation

IKGN: Intention-aware Knowledge Graph Network for POI Recomm...

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IEEE International Conference on data Mining (ICDM)

作者： Xiaoyu Zhu Chenyang Bu Bingbing Dong Shengwei Ji Yi He Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology China School of Artificial Intelligence and Big Data Hefei University China Department of Computer Science Old Dominion University USA Research Center for Knowledge Engineering Zhejiang Lab China

Point-of-Interest (POI) recommendation, pivotal for guiding users to their next interested locale, grapples with the persistent challenge of data sparsity. Whereas knowledge graphs (KGs) have emerged as a favored tool to mitigate the issue, existing KG-based methods tend to overlook two crucial elements: the intention steering users’ location choices and the high-order topological structure within the KG. In this paper, we craft an Intention-aware Knowledge Graph (IKG) that harmonizes users’ visit histories, movement trajectories, and location categories to model user intentions. Building upon IKG, our novel Intention-aware Knowledge Graph Network (IKGN) delves deeper into the POI recommendation by weighing and propagating node embeddings through an attention mechanism, capturing the unique locational intent of each user. A sequential model like GRU is then employed to ensure a comprehensive representation of users’ short- and long-term location preferences. An empirical study on two real-world datasets validates the effectiveness of our proposed IKGN, with it markedly outshining seven benchmark rival models in both Recall and NDCG metrics. The code of IKGN is available at https://***/Jungle123456/IKGN.

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Symbolic regression-based hybrid models for a manufacturing process

Symbolic regression-based hybrid models for a manufacturing ...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Seulki Han Debasish Mishra Krishna R. Pattipati George M. Bollas Department of Chemical & Biomolecular Engineering University of Connecticut Storrs CT USA Data Science & Information Systems Krea University Sri City Andhra Pradesh India Department of Electrical & Computer Engineering University of Connecticut Storrs CT USA

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Hybrid models are increasingly employed to simulate intricate physical processes by combining domain expertise embedded in physics-based models with process measurements-based data. The combination of domain knowledge and measurement data results in hybrid models that lead to high-accuracy decisions, extrapolation capabilities, and compliance with basic physical laws. Surrogate modeling, one aspect of hybrid modeling, involves a simplified model to capture the physics in a process, emphasizing an adaptable mathematical expression. This study applies hybrid modeling to address the intricate tool wear process in precision machining by developing a recursive model using symbolic regression.

关键词： Adaptation models Manufacturing processes Machining Predictive models Feature extraction Mathematical models data models Time measurement Information technology Physics

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Codebook Design and Performance Analysis for Wideband Beamforming in Terahertz Communications

arXiv

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arXiv 2024年

作者： Ning, Boyu Mei, Weidong Zhu, Lipeng Chen, Zhi Zhang, Rui National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

The codebook-based analog beamforming is appealing for future terahertz (THz) communications since it can generate high-gain directional beams with low-cost phase shifters via low-complexity beam training. However, conventional beamforming codebook design based on array response vectors for narrowband communications may suffer from severe performance loss in wideband systems due to the "beam squint" effect over frequency. To tackle this issue, we propose in this paper a new codebook design method for analog beamforming in wideband THz systems. In particular, to characterize the analog beamforming performance in wideband systems, we propose a new metric termed wideband beam gain, which is given by the minimum beamforming gain over the entire frequency band given a target angle. Based on this metric, a wideband analog beamforming codebook design problem is formulated for optimally balancing the beamforming gains in both the spatial and frequency domains, and the performance loss of conventional narrowband beamforming in wideband systems is analyzed. To solve the new wideband beamforming codebook design problem, we divide the spatial domain into orthogonal angular zones each associated with one beam, thereby decoupling the codebook design into a zone division sub-problem and a set of beamforming optimization sub-problems each for one zone. For the zone division sub-problem, we propose a bisection method to obtain the optimal boundaries for separating adjacent zones. While for each of the per-zone-based beamforming optimization sub-problems, we further propose an efficient augmented Lagrange method (ALM) to solve it. Numerical results demonstrate the performance superiority of our proposed codebook design for wideband analog beamforming to the narrowband beamforming codebook and also validate our performance analysis. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Motion Planning in Automated Vehicles Based on Hierarchical MPC

Motion Planning in Automated Vehicles Based on Hierarchical ...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Pooja Kapila Pankaj Kumar Rahul Negi Bhupender Kumar Bharti Akash Punhani Suraj Singh Bisht Department of Artifical Intelligence & Data Science IIMT College of Engineering Department of Statistics Amity University Kolkata India Department of ECE Tula's Institute Dehradun India Department of Mechanical Engineering VMSB UTU Campus Pithoragarh India Department of Computer Science KIET Group of Institutions Noida India Department of ECE Bipin Tripathi Kumaon Institute of Technology Dwarahat Almora India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

In this study, a thorough hierarchical control structure that supports autonomous decision-making that arises in autonomous systems and robots is proposed. Distinct state and decision/control sets are frequently used to describe high-level decision making in a conventional hierarchical control architecture. To keep up with proficiency, wellbeing, and solace, robotized vehicles generally depend serious areas of strength for on powerful movement arranging calculations. The capacity of Model Prescient Control (MPC) to oversee imperatives and upgrade directions progressively has made it a strong system for movement arranging. To approve the proposed strategy, this exploration utilizes MATLAB recreations to look at various levelled MPC for movement arranging in robotized vehicles. With the low-level regulator guaranteeing exact direction observing and the undeniable level organizer computing a worldwide direction, a two-layer progressive MPC structure is fabricated. The discoveries show how well the progressive MPC handles street ebb and flow, dynamic hindrances, and continuous computational impediments. It is demonstrated that the suggested autonomous decision-making scheme's recursive practicality and stability are ensured by carefully crafting its essential components. An intelligent vehicle's autonomous lane-changing system is developed using the suggested framework. Simulation demonstrates that it has a potential capacity to manage a variety of behaviours in complex and dynamic contexts. It is anticipated that the suggested framework will be useful for autonomous decision-making in a variety of robotics along with autonomous technologies where safety as well as optimality are important factors.

关键词： Analytical models Autonomous systems Computational modeling Decision making Turning Planning Trajectory Safety Vehicle dynamics Robots

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