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International Conference on Smart Systems and Inventive Technology (ICSSIT)

作者： Vijay Abrol Priyanka Singh R Subhashini A.Sasi Kumar Kamlesh Singh B. Raja Mannar Department of Computer Science Uttam Group of Institutions Agra Uttar Pradesh India Department of Technical Education Sgsj Government Polytechnic Bulandshahr Uttar Pradesh India Department of Information Technology Sona College of Technology Salem Tamil Nadu India Department of Cloud Technology & Data Science (Mentor-IT – inurture Education Solutions Pvt LtdBangalore) Institute of Engineering & Technology Srinivas UniversitySrinivas NagarMukka SurathkalMangalore Dakshina Kannada District Karnataka State India Department of Computer Science and Engineering Graphic Era Hill UniversityDehradun Campus Dehradun India Department of Commerce Sri Venkateswara University Tirupati Andhra Pradesh India

Bankruptcy prediction and credit risk study are amongstthe most important problems in the domain of financial and accounting decision making. Evolving an effectual Classification Rule Induction (CRD) structure for bankruptcy prediction and credit risk examination at suitable time is vital to avoid the business communities being bankrupt. Classical statistical approaches and Artificial Intelligence (AD) play an important role in bankruptcy prediction and credit risks. Most previous research works can be executed on quantitative approaches but some studies are presented on qualitative approaches for improvising the efficiency of bankruptcy prediction techniques. This study focuses on the development of Bankruptcy Prediction using Emperor Penguin optimizer with Deep Learning (BP-EPODL) model on Qualitative dataset. The projected BP-EPODL technique identifies the existence of bankruptcy or not. Here, the BP-EPODL technique exploits EPO algorithm for the choice of optimum features. For bankruptcy prediction, the BP-EPODL technique applies Long Short Term Memory (LS TM) model. At last, Adam optimizer is applied as a hyperparameter tuning technique for the IS TM model. The experimental validation of the BP-EPODL technique is tested on benchmark qualitative dataset. The obtained values inferred that the BP-EPODLtechnique has shown effectual results over other models.

关键词： Deep learning Decision making Predictive models Benchmark testing Prediction algorithms Artificial intelligence Bankruptcy

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Reinforcement Learning Infused MAC for Adaptive Connectivity

Reinforcement Learning Infused MAC for Adaptive Connectivity

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IEEE Conference on Wireless Communications and Networking

作者： Dinesh Kumar Sah Ali Nauman Muhammad Ali Jamshed Korhan Cengiz Nikola Ivković Vedran Uroš Division of Networked and Embedded Systems Research Group Data Communication Mälardalens University Västerås Sweden Department of Information and Communication Engineering Yeungnam University Gyeongbuk Republic of Korea College of Science and Engineering University of Glasgow United Kingdom Department of Computer Engineering Istinye University Istanbul Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove Czech Republic Faculty of Organization and Informatics University of Zagreb Varaždin Croatia Polytechnic “Marko Marulić” in Knin Knin Croatia

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The beginning of cellular communication (next-gen, such as 5G and 6G) promises an extreme leap in connectivity, introducing intelligent, adaptive solutions that integrate communication, artificial intelligence, and emerging technologies. Our approach combines reinforcement learning with Medium Access Control (MAC) protocols to dynamically optimize resource allocation and enhance network performance. In this work, we explore the integration of the adaptive frame size adjusting approach similar to the IEEE 802.1CB to ensure the efficient handling of seamless redundancy. The proposed solutions are validated through simulation, ensuring robustness and real-world applicability. Results indicate significant improvements in redundancy rate detection and delay in the network. This work contributes to achieving intelligent, adaptive, and seamless connectivity in the next generation of communication systems.

关键词： 6G mobile communication Adaptive systems Redundancy Reinforcement learning Media Access Protocol Throughput Resource management

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Entanglement and coherence in pure and doped Posner molecules

arXiv

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arXiv 2023年

作者： Adams, Betony Sinayskiy, Ilya Agarwal, Shivang Petruccione, Francesco Quantum Research Group University of KwaZulu-Natal South Africa National Institute for Theoretical and Computational Sciences South Africa The Guy Foundation Dorset United Kingdom The Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States School of Data Science and Computational Thinking Department of Physics Stellenbosch University South Africa

The potential role of spin in biological systems is a primary topic in quantum biology. However, much of this research focuses on electron spin. A recent hypothesis suggests that nuclear spin may be better suited to biological processes, being less sensitive to decoherence. The hypothesis details how phosphorus nuclei might be prepared in a spin entangled state, how this entanglement is protected by assembly into calcium phosphate (Posner) molecules, and how this entanglement might modulate calcium ion production and concomitant neural activation. In this paper we investigate the robustness of quantum effects such as coherence and entanglement in Posner molecules. We investigate how these effects are directly dependent on specific parameters such as spin-spin coupling strengths and Posner molecule symmetry. We also investigate how lithium isotope doped Posner molecules differentially modulate quantum resources such as coherence and entanglement and whether this is a viable explanation for lithium's mechanism of action in bipolar disease. Finally we illustrate how entanglement might possibly be preserved through exploitation of the biological environment. Copyright © 2023, The Authors. All rights reserved.

关键词： Molecules

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Complex-valued neural networks to speed-up MR Thermometry during Hyperthermia using Fourier PD and PDUNet

arXiv

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arXiv 2023年

作者： Khatun, Rupali Chatterjee, Soumick Bert, Christoph Wadepohl, Martin Ott, Oliver J. Semrau, Sabine Fietkau, Rainer Nürnberger, Andreas Gaipl, Udo S. Frey, Benjamin Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexnder-Universität Erlangen-Nürnberg Erlangen Germany Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexnder-Universität Erlangen-Nürnberg Erlangen Germany Comprehensive Cancer Centre Erlangen-EMN Erlangen Germany Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Dr Sennewald Medizintechnik GmbH Munich Germany

Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment for distinct solid tumour entities. In HT, tumour tissue is exogenously heated to temperatures between 39 and 43 °C for 60 minutes. Temperature monitoring can be performed non-invasively using dynamic magnetic resonance imaging (MRI). However, the slow nature of MRI leads to motion artefacts in the images due to the movements of patients during image acquisition. By discarding parts of the data, the speed of the acquisition can be increased - known as undersampling. However, due to the invalidation of the Nyquist criterion, the acquired images might be blurry and can also produce aliasing artefacts. The aim of this work was, therefore, to reconstruct highly undersampled MR thermometry acquisitions with better resolution and with fewer artefacts compared to conventional methods. The use of deep learning in the medical field has emerged in recent times, and various studies have shown that deep learning has the potential to solve inverse problems such as MR image reconstruction. However, most of the published work only focuses on the magnitude images, while the phase images are ignored, which are fundamental requirements for MR thermometry. This work, for the first time, presents deep learning-based solutions for reconstructing undersampled MR thermometry data. Two different deep learning models have been employed here, the Fourier Primal-Dual network and the Fourier Primal-Dual UNet, to reconstruct highly undersampled complex images of MR thermometry. MR images of 44 patients with different sarcoma types who received HT treatment in combination with radiotherapy and/or chemotherapy were used in this study. The method reduced the temperature difference between the undersampled MRIs and the fully sampled MRIs from 1.3 °C to 0.6 °C in full volume and 0.49 °C to 0.06 °C in the tumour region for an acceleration factor of 10. Copyright © 2023, The Authors. All rights res

关键词： Chemotherapy

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RAST: A Reward Augmented Model for Fine-Grained Sentiment Transfer 10th

RAST: A Reward Augmented Model for Fine-Grained Sentiment Tr...

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10th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2021

作者： Hu, Xiaoxuan Zhang, Hengtong Zhao, Wayne Xin Li, Yaliang Gao, Jing Wen, Ji-Rong School of Information Renmin University of China Beijing China School of Electrical and Computer Engineering Purdue University West Lafayette United States Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science and Engineering University at Buffalo Buffalo United States Alibaba Group Hangzhou China

ISBN: (纸本)9783030884796

In this paper, we propose a novel model RAST (Reward Augmented Sentiment Transfer) for fine-grained sentiment transfer. Existing methods usually suffer from two major drawbacks, i.e., blurre d sentiment distinction and unsatisfactory content preservation. Considering the above issues, we design two kinds of rewards to better control sentiment and content. Specially, we develop a pairwise comparative discriminator that enforces to generate sentences with clear distinctions for different sentiment intensities. Moreover, we utilize an effective sampling strategy to obtain pseudo-parallel sentences with minor changes on the input sentence to enhance content preservation. Experiments on a benchmark dataset show that the proposed model outperforms several competitive approaches. © 2021, Springer Nature Switzerland AG.

关键词： computers

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ARCH-COMP21 Category Report: Continuous and Hybrid Systems with Nonlinear Dynamics 8th

ARCH-COMP21 Category Report: Continuous and Hybrid Systems w...

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8th International Workshop on Applied Verification of Continuous and Hybrid Systems, ARCH 2021

作者： Geretti, Luca Sandretto, Julien Alexandre Dit Althoff, Matthias Benet, Luis Chapoutot, Alexandre Collins, Pieter Duggirala, Parasara Sridhar Forets, Marcelo Kim, Edward Linares, Uziel Sanders, David P. Schilling, Christian Wetzlinger, Mark Department of Computer Science University of Verona Verona Italy ENSTA Paris Institut Polytechnique de Paris Palaiseau France Technische Universität München Munich Germany Mexico Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Department of Computer Science University of North Carolina at Chapel Hill United States Universidad de la República Montevideo Uruguay Mexico City Mexico Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States University of Konstanz Konstanz Germany Aalborg University Aalborg Denmark

We present the results of a friendly competition for formal verification of continuous and hybrid systems with nonlinear continuous dynamics. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2021. This year, 5 tools Ariadne, CORA, DynIbex, JuliaReach and Kaa (in alphabetic order) participated. These tools are applied to solve reachability analysis problems on five benchmark problems, two of them featuring hybrid dynamics. We do not rank the tools based on the results, but show the current status and discover the potential advantages of different tools. © 2021, EasyChair. All rights reserved.

关键词： Hybrid systems

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Multi-Dimensional Research Software Categorization

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Computing in science and engineering 2025年

作者： Hasselbring, Wilhelm Druskat, Stephan Bernoth, Jan Betker, Philine Felderer, Michael Ferenz, Stephan Hermann, Ben Lamprecht, Anna-Lena Linxweiler, Jan Prat, Arnau Rumpe, Bernhard Schoening-Stierand, Katrin Yang, Shinhyung Kiel University Kiel24098 Germany Berlin12489 Germany University of Potsdam Potsdam14476 Germany Helmholtz Centre for Infection Research Department for Epidemiology Brunswick Germany University of Cologne Cologne51147 Germany Carl von Ossietzky Universität Oldenburg Department of Computer Science Oldenburg26129 Germany Secure Software Engineering Group TU Dortmund Dortmund44227 Germany TU Braunschweig Braunschweig38106 Germany Braunschweig38108 Germany RWTH Aachen University Germany University of Hamburg Hub of Computing and Data Science Hamburg22761 Germany

Research software has been categorized in different contexts to serve different goals. We start with a look at what research software is, before we discuss the purpose of research software categories. We propose a multi-dimensional categorization of research software. We present a template for characterizing such categories. As selected dimensions, we present our proposed role-based, readiness-based, developer-based, and dissemination-based categories. Since our work has been inspired by various previous efforts to categorize research software, we discuss them as related works. We characterize all these categories via the previously introduced template, to enable a systematic comparison. We report on the multi-dimensional categorization of selected research software examples. © 1999-2011 IEEE.

关键词： Aerospace engineering Analytical models data collection data models data visualization Monitoring Numerical models Software Software development management Software engineering

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Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting

arXiv

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arXiv 2023年

作者： Jin, Ming Shi, Guangsi Li, Yuan-Fang Xiong, Bo Zhou, Tian Salim, Flora D. Zhao, Liang Wu, Lingfei Wen, Qingsong Pan, Shirui School of Information and Communication Technology Griffith University Gold Coast Australia Department of Data Science and AI Monash University Melbourne Australia International Max Plank Research School for Intelligent Systems The University of Stuttgart Stuttgart Germany Alibaba Group Hangzhou China School of Computer Science and Engineering University of New South Wales Sydney Australia Department of Computer Science Emory University Atlanta United States Anytime.AI New York United States Squirrel Ai Learning Bellevue United States

Time series forecasting has remained a focal point due to its vital applications in sectors such as energy management and transportation planning. Spectral-temporal graph neural network is a promising abstraction underlying most time series forecasting models that are based on graph neural networks (GNNs). However, more is needed to know about the underpinnings of this branch of methods. In this paper, we establish a theoretical framework that unravels the expressive power of spectral-temporal GNNs. Our results show that linear spectral-temporal GNNs are universal under mild assumptions, and their expressive power is bounded by our extended first-order Weisfeiler-Leman algorithm on discrete-time dynamic graphs. To make our findings useful in practice on valid instantiations, we discuss related constraints in detail and outline a theoretical blueprint for designing spatial and temporal modules in spectral domains. Building on these insights and to demonstrate how powerful spectral-temporal GNNs are based on our framework, we propose a simple instantiation named Temporal Graph Gegenbauer Convolution (TGGC), which significantly outperforms most existing models with only linear components and shows better model efficiency. Our findings pave the way for devising a broader array of provably expressive GNN-based models for time series. © 2023, CC BY.

关键词： Time series

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A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification

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science China(Technological sciences) 2021年第9期64卷 1863-1871页

作者： ZHANG Jin FENG Fan HAN TianYi DUAN Feng SUN Zhe CAIAFA Cesar F. SOLÉ-CASALS Jordi College of Computer Science Nankai UniversityTianjin 300350China Department of Artificial Intelligence Nankai UniversityTianjin 300350China Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKEN351-0198 SaitamaJapan Instituto Argentino de Radioastronomía-CCT La Plata CONICET/CIC-PBA/UNLP1894 V.ElisaArgentina Department of Psychiatry University of CambridgeCambridge CB20SZUnited Kingdom Data and Signal Processing Group University of Vic-Central University of Catalonia08500 VicCataloniaSpain

Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high-dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.

关键词： gifted children identification morphometric features tensor completion feature selection

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A Deep Neural Network based Multimodal Video Recognition System for Caring

A Deep Neural Network based Multimodal Video Recognition Sys...

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IEEE International Conference on Human-Machine Systems

作者： Chao Yan Jiahua Xu Bastian Klopfer Andreas Nürnberger Data & Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany

ISBN: (数字)9781728158716

ISBN: (纸本)9781728158723

Home caring usually refers to taking care of the elder, the young kids and the patients at home, and this depends on both caregivers and caring systems. The system which can provide sufficient and accurate information to doctors or caregivers without a delay will bring benefits for all the persons who need to be cared and allow doctor or care givers to take right action based on the information immediately. Available corresponding products in the market mainly are some smart home devices or some medical facilities based on electroencephalo-graph (EEG), electrocardiograph (ECG) or blood pressure check, however, these parameters cannot give doctors, caregivers or family members a direct feedback. To address the problem, this paper introduces a deep learning based design - a visual recognition system developed for clinical monitoring which can supervise both the emotions and gestures of patients at the same time and give responsible persons instant and direct feedback so that the right treatment will be taken by them. This product uses a Raspberry Pi computer with its camera as hardware and implementing several deep learning models to fulfill three main functions which are: Facial Recognition, Emotion Detection and Pose Estimation onto a portable device, which enhances the utilization of the application and this brings more possibilities. Compared to theavailable products, this application emphasizes monitoring per-sons via visual analysis and gives more direct feedback all thetime instead of traditional ways which are not always timely or practical.

关键词： Faces Computational modeling Machine learning Face recognition Pose estimation Cameras Medical services

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