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arXiv 2024年

作者： He, Ri Wang, Hua Deng, Fenglin Gao, Yuxiang Zhang, Binwen Shi, Yubai Li, Run-Wei Zhong, Zhicheng Key Laboratory of Magnetic Materials Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China Center for Quantum Matter School of Physics Zhejiang University Hangzhou310058 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Fujian Key Laboratory of Functional Marine Sensing Materials Center for Advanced Marine Materials and Smart Sensors College of Material and Chemical Engineering Minjiang University Fuzhou350108 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China School of Future Technology University of Chinese Academy of Sciences Beijing100049 China School of Artificial Intelligence and Data Science University of Science and Technology of China Suzhou215123 China

Two-dimensional sliding ferroelectrics, with their unique stacking degrees of freedom, offer a different approach to manipulate polarization by interlayer sliding. Bending sliding ferroelectrics inevitably leads to interlayer sliding motion, thus altering stacking orders and polarization properties. Here, by using machine-learning force field, we investigate the effects of bending deformation on geometries, stackings, energies, and polarizations in sliding ferroelectric bilayer h-BN and 3R-MoS2. We predict that bent ferroelectric bilayer forms irreversible kinks instead of arc when the bending angle exceeds a critical value. We demonstrate that the kinks originate from the competition between bending energy and interlayer van der Waals energy. The kink contains a ferroelectric domain wall that reverses the polarization, effectively inducing a flexoelectric effect. We term this phenomenon "sliding flexoelectricity" to distinguish it from conventional strain-gradient-induced flexoelectricity. Copyright © 2024, The Authors. All rights reserved.

关键词： Van der Waals forces

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IoT based on Smart Traffic Lights and Streetlight System

IoT based on Smart Traffic Lights and Streetlight System

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International Conference on Edge Computing and Applications (ICECAA)

作者： Shobana S Shakunthala M C. Vimala Josphine M. Theodore Kingslin S. Sivarajan Chairma Lakshmi K R Department of Information Technology R.M.K Engineering College Chennai India Department of Electronics and Communication Engineering R.M.D Engineering College Chennai Department of Artificial Intelligence and Data Science R.M.K Engineering College Chennai Department of Electronics and Communication Engineering R.M.K College of Engineering College & Technology Department of Electrical and Electronics Engineering Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Department of Electronics and Instrumentation Engineering R.M.K. Engineering College Chennai India

Energy wastage, traffic jams, and traffic monitoring are the three main problems the world's population is confronting. The traditional roadway management method is ineffective at controlling the flow of traffic when one lane has significantly more traffic than the others. Additionally, the present street light controller uses lights that are not turned off in the morning even though it is sufficiently light outside and that are turned on in the late afternoon before even the sun sets, which causes some power loss. This proposed system will offer an advanced smart traffic management system and intelligent street lighting system to bring a standardized approach to solving this issue. The enhanced smart traffic control system is accomplished by modifying the signal timings based on the comparison of the densities on all roads, i.e., when a specific lane has much more density compared to the other lanes, that specific lane is modified to a green signal for a specified length of time. By computerizing solar-powered street lights with IoT, energy wastage in the traditional street lighting system is prevented. The street lights are activated whenever an item moves, or they remain off otherwise. This proposed method reduces traffic issues, and energy wastage and improves the traffic monitoring process also.

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An Alzheimer's disease classification model using transfer learning Densenet with embedded healthcare decision support system

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Decision Analytics Journal 2023年 9卷

作者： Saleh, Ahmad Waleed Gupta, Gaurav Khan, Surbhi B. Alkhaldi, Nora A. Verma, Amit Yogananda School of Artificial Intelligence Computers and Data Sciences Shoolini University Solan HP 173229 India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Data Science School of Science Engineering and Environment University of Salford United Kingdom Department of Engineering and Environment University of Religions and Denominations Qom Iran Department of Computer Science College of Computer Science and Information Technology King Faisal University Al Ahsa Saudi Arabia University Centre for Research and Development Department of Computer Science Chandigarh University Gharuan Punjab Mohali India

Training a Convolutional Neural Network (CNN) from scratch is time-consuming and expensive. In this study, we propose implementing the DenseNet architecture for classification of AD in three classes. Our approach leverages transfer learning architectures as the base model and showcases superior performance on the MRI dataset compared to other techniques. We use a variety of methodologies to provide a thorough study of our model. We first create a performance baseline without data augmentation, addressing the difficulties in classifying Alzheimer's disease (AD) caused by high-dimensional MRI brain scans. The improved model performance obtained through data augmentation is then highlighted, demonstrating its effectiveness in handling sparse data and assisting in generalization. We also investigate the impact of omitting particular transformations and modifying dataset split ratios, providing more insights into the behavior of the model. Through comprehensive evaluation, we demonstrate that our proposed system model achieves an accuracy of 96.5% and an impressive AUC of 99%, surpassing previous methods. This study mainly highlights the effectiveness of DenseNet architecture, current study limitations and future recommendation. Moreover, incorporating a healthcare decision support system further aid in providing valuable insights for AD diagnosis and decision-making in clinical settings. © 2023 The Authors

关键词： Convolutional Neural Networks data augmentation Deep learning Healthcare decision support system Machine learning Transfer learning

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Mobile Application for Water Management and Monitoring System in Residential Building

Mobile Application for Water Management and Monitoring Syste...

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International Conference on Futuristic Technologies (INCOFT)

作者： Nalla Akhila Aswadhati Sirisha P. Dass P M Dinesh T Sujatha Amit Kamalakar Gaikwad Computer Science and Engineering Aditya Engineering College Surampalem Jawaharlal Nehru technological university Kakinada Kakinada East Godavari district Information Technology (IT) Department Vignans institute of information technology (A) Duvvada India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Department of ECE Sona College of Technology Salem Department of Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore Department of Computer Science G.H.Raisoni University Amravati Maharashtra

Water scarcity is one of the major threats to human society. Though there are many reasons for water scarcity like water pollution, drought and global warming, overuse of domestic water is one of the major causes of water scarcity. More than one billion people are deprived of clean drinking water and water for other purposes. One of the main solutions that were found to this problem is rainwater harvesting. Many countries encouraged the citizens to collect the rainwater for further uses. Though the rainwater is preserved, another issue named water over usage comes into existence. Most of the time, the water supplied for domestic reasons may undergo several preprocessing like filtration, osmosis, etc. to ensure the cleanliness of the water. When this water is wasted for petty reasons like washing dishes and watering plants, it may lead to the shortage of freshwater for necessary needs like cattle feeding, food production, farming, etc. Many researchers and scientists have found a lot of techniques and processes to minimize the usage of fresh water in households. This project aims the development of a mobile application that constantly monitors the water usage by a house in a community. There will be a prefixed value for the amount of water each house can use. If the water usage exceeds the prefixed value, the inmates of the house are asked to pay a certain fine amount which is calculated by the number of extra liters used and the prefixed fine amount for each liter. If the fine amount was not paid, the water supply to that particular house will be blocked. All of the above-mentioned processes are monitored by this application. Hardware components like multiple sensors, adaptors, motors are used for this purpose. The whole working of the hardware is controlled by a microcontroller namely Arduino Uno. This application helps in the control of the over usage of water in residential buildings. This can be equipped in apartments, community houses, quarters, etc. to constan

关键词： Microcontrollers Buildings Production Water pollution Hardware Osmosis Sensors

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HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN

arXiv

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arXiv 2023年

作者： Islam, Md Shofiqul Hasan, Khondokar Fida Sultana, Sunjida Uddin, Shahadat Lio', Pietro Quinn, Julian M.W. Moni, Mohammad Ali Faculty of Computing Universiti Malaysia Pahang Gambang Pahang Kuantan26300 Malaysia Lebuhraya Tun Razak Gambang Pahang Kuantan26300 Malaysia 2 George Street Brisbane4000 Australia Department of Computer Science and Engineering Islamic University Kushtia7600 Bangladesh School of Project Management Faculty of Engineering The University of Sydney Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Bone Research Group The Garvan Institute of Medical Research DarlinghurstNSW Australia Artificial Intelligence & Data Science School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Deep learning-based models have achieved significant success in detecting cardiac arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the performance of such models, we have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditional dilated convolutional neural network (CNN) models disregard the correlation between contexts and gradient dispersion. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. As a result of incorporating both local and global feature information and an attention mechanism, the model's performance for prediction is improved. By combining the fusion features with a dilated CNN and a hierarchical attention mechanism, the trained HARDC model showed significantly improved classification results and interpretability of feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score normalization, filtering, denoising, and segmentation are used to prepare the raw data for analysis. CGAN (Conditional Generative Adversarial Network) is then used to generate synthetic signals from the processed data. The experimental results demonstrate that the proposed HARDC model significantly outperforms other existing models, achieving an accuracy of 99.60%, F1 score of 98.21%, a precision of 97.66%, and recall of 99.60% using MIT-BIH generated ECG. In addition, this approach substantially reduces run time when using dilated CNN compared to normal convolution. Overall, this hybrid model demonstrates an innovative and cost-effective strategy for ECG signal compression and high-performance ECG recognition. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise. © 2023, CC BY.

关键词： Electrocardiograms

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Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks

arXiv

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arXiv 2022年

作者： Zhao, Yu Wei, Shaopeng Guo, Yu Yang, Qing Chen, Xingyan Li, Qing Zhuang, Fuzhen Liu, Ji Kou, Gang Financial Intelligence and Financial Engineering Key Laboratory of Sichuan Province Department of Artificial Intelligence Southwestern University of Finance and Economics China School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Institute of Artificial Intelligence Beihang University Beijing100191 China SKLSDE School of Computer Science Beihang University Beijing100191 China Kuaishou Technology United States

Predicting the bankruptcy risk of small and medium-sized enterprises (SMEs) is an important step for financial institutions when making decisions about loans. Existing studies in both finance and AI research fields, however, tend to only consider either the intra-risk or contagion risk of enterprises, ignoring their interactions and combinatorial effects. This study for the first time considers both types of risk and their joint effects in bankruptcy prediction. Specifically, we first propose an enterprise intra-risk encoder based on statistically significant enterprise risk indicators taken from its basic business information as well as litigation information for its intra-risk learning. Then, we propose an enterprise contagion risk encoder based on enterprise relation information from an enterprise knowledge graph for its contagion risk embedding. In particular, the contagion risk encoder includes both the newly proposed Hyper-Graph Neural Networks (Hyper-GNNs) and Heterogeneous Graph Neural Networks (Heter-GNNs), which can model contagion risk in two different aspects, i.e. common risk factors based on hyperedges and direct diffusion risk from neighbors, respectively. Using these two types of encoders, we design a unified framework to simultaneously capture intra-risk and contagion risk for bankruptcy prediction. To evaluate the model, we collect real-world multi-sources data on SMEs and build a novel benchmark dataset called SMEsD. We provide open access to the dataset, which is expected to further promote research on financial risk analysis. Experiments on SMEsD against twelve state-of-the-art baselines demonstrate the effectiveness of the proposed model for bankruptcy prediction. © 2022, CC BY-NC-SA.

关键词： Risk assessment

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Exploring Nanoscale Perovskite Materials for Next‑Generation Photodetectors:A Comprehensive Review and Future Directions

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Nano-Micro Letters 2025年第2期17卷 46-108页

作者： Xin Li Sikandar Aftab Maria Mukhtar Fahmid Kabir Muhammad Farooq Khan Hosameldin Helmy Hegazy Erdi Akman State Key Laboratory of Pulsed Power Laser Technology National University of Defense TechnologyHefei 230037AnhuiPeople’s Republic of China Anhui Laboratory of Advanced Laser Technology Hefei 230037AnhuiPeople’s Republic of China Nanhu Laser Laboratory Changsha 410015HunanPeople’s Republic of China Department of Semiconductor Systems Engineering and Clean Energy Sejong UniversitySeoul 05006Republic of Korea Department of Artificial Intelligence and Robotics Sejong UniversitySeoul 05006Republic of Korea School of Engineering Science Simon Fraser UniversityBurnabyBC V5A 1S6Canada Department of Electrical Engineering Sejong UniversitySeoul 05006South Korea Department of Physics Faculty of ScienceKing Khalid UniversityP.O.Box 9004AbhaSaudi Arabia Central Labs King Khalid UniversityAlQura’aP.O.Box 96061413 AbhaSaudi Arabia Scientific and Technological Research and Application Center Karamanoglu Mehmetbey University70100 KaramanTurkey

The rapid advancement of nanotechnology has sparked much interest in applying nanoscale perovskite materials for photodetection *** materials are promising candidates for next-generation photodetectors(PDs)due to their unique optoelectronic properties and flexible synthesis *** review explores the approaches used in the development and use of optoelectronic devices made of different nanoscale perovskite architectures,including quantum dots,nanosheets,nanorods,nanowires,and *** a thorough analysis of recent literature,the review also addresses common issues like the mechanisms underlying the degradation of perovskite PDs and offers perspectives on potential solutions to improve stability and scalability that impede widespread *** addition,it highlights that photodetection encompasses the detection of light fields in dimensions other than light intensity and suggests potential avenues for future research to overcome these obstacles and fully realize the potential of nanoscale perovskite materials in state-of-the-art photodetection *** review provides a comprehensive overview of nanoscale perovskite PDs and guides future research efforts towards improved performance and wider applicability,making it a valuable resource for researchers.

关键词： Nanoscale perovskites Photodetectors Nanosheets Nanorods Nanowires Quantum dots Nanocrystals

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Generic ESD Generator Model using artificial Neural Network

Generic ESD Generator Model using Artificial Neural Network

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EMC/SI/PI and EMC Europe Symposium, IEEE International Joint

作者： Jawad Yousaf Kamran Javed Mohammed Ghazal Abu Dhabi University Abu Dhabi United Arab Emirates Saudi Data and Artificial Intelligence Authority (SDAIA) National Centre for Artificial Intelligence (NCAI) Riyadh Saudi Arabia Faculty of Computer Sciences and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi Pakistan

Different commercial ESD gun models, although complying with the standard ESD waveform requirements in terms of rise time and current values for standard Pellegrini target, produce different ESD waveforms. The variations of the ESD source and target impedance in real-time with the change in the gun model affects the ESD susceptibility compliance testing results and immunity analysis for the estimation of possible ESD failure in a product. This study presents, for the first time, a novel generic ESD generator model using artificial neural network (ANN) based deep learning techniques. The developed deep learning model incorporates the characteristics of the real-time generated ESD waveforms by various commonly used commercial ESD gun models with different target load impedances. The presented model could be used as a generic ESD source for fast ESD susceptibility and immunity testing’s at the design stage of a product using numerical or circuit-analysis-based tools.

关键词： Deep learning artificial neural networks Immunity testing Electrostatic discharges Generators Real-time systems Numerical models

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LFCKT: A Learning and Forgetting Convolutional Knowledge Tracking Model

LFCKT: A Learning and Forgetting Convolutional Knowledge Tra...

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Intelligent Education and Intelligent Research (IEIR), International Conference on

作者： Mengjuan Li Lei Niu Jinhua Zhao Yuchen Wang Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China Central China Normal University Wollongong Joint Institute Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China Central China Normal University Wollongong Joint Institute Central China Normal University Wuhan China School of Information and Electronic Engineering Zhejiang University of Science and Technology Zhejiang China

ISBN: (纸本)9798350345636

Personalized exercise recommendation is a key research direction of personalized learning. In personalized exercise recommendation, we recommend suitable exercises for students according to their knowledge mastery status to improve their learning efficiency. Therefore, the accuracy of predicting students’ knowledge state in personalized exercise recommendation affects the goodness of the exercise recommendation. In the process of students’ learning, learning behavior and forgetting behavior are intertwined, and students’ forgetting behavior has a great influence on the knowledge state. In order to accurately model students’ learning and forgetting, we propose a Learning and Forgetting Convolutional Knowledge Tracking model (LFCKT) that takes into account both learning and forgetting behaviors. The model takes into account three factors that affect knowledge forgetting, including the interval time of target knowledge interaction, the count of past target knowledge interaction and student’s state of knowledge. LFCKT model uses students’ answer results as indirect feedback of knowledge mastery in the process of knowledge tracking, and integrates individual personalized learning behavior and individual forgetting behavior. Through experiments on the real online education public dataset, LFCKT can better track students’ knowledge mastery status and has better predictive performance than current knowledge tracking models.

关键词： Knowledge engineering Target tracking Education Predictive models Real-time systems Behavioral sciences

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artificial intelligence for objective assessment of acrobatic movements: How to apply machine learning for identifying tumbling elements in cheer sports

arXiv

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arXiv 2025年

作者： Wesely, Sophia Hofer, Ella Curth, Robin Paryani, Shyam Mills, Nicole Ueberschär, Olaf Westermayr, Julia Leipzig University Faculty of Chemistry and Mineralogy Institute of Physical and Theoretical Chemistry Leipzig Germany Magdeburg-Stendal University of Applied Sciences Department of Engineering and Industrial Design Magdeburg Germany Center for Scalable Data Analytics and Artificial Intelligence Dresden Leipzig Germany University of North Florida Brooks College of Health JacksonvilleFL United States University of North Florida Athletics Department JacksonvilleFL United States Institute for Applied Training Science Leipzig Germany

Over the past four decades, cheerleading has evolved from a sideline activity at major sporting events into a professional, competitive sport with growing global popularity. Evaluating tumbling elements in cheerleading relies on both objective measures and subjective judgments, such as difficulty and execution quality. However, the complexity of tumbling—encompassing team synchronicity, ground interactions, choreography, and artistic expression—makes objective assessment challenging. artificial intelligence (AI) has revolutionized various scientific fields and industries through precise data-driven analyses, yet their application in acrobatic sports remains limited despite significant potential for enhancing performance evaluation and coaching. This study investigates the feasibility of using an AI-based approach with data from a single inertial measurement unit to accurately identify and objectively assess tumbling elements in standard cheerleading routines. A sample of 16 participants (13 females, 3 males) from a Division I collegiate cheerleading team wore a single inertial measurement unit at the dorsal pelvis. Over a 4-week seasonal preparation period, 1102 tumbling elements were recorded during regular practice sessions. Using triaxial accelerations and rotational speeds, various ML algorithms were employed to classify and evaluate the execution of tumbling manoeuvres. Results indicate that certain machine learning models can effectively identify different tumbling elements despite inter-individual variability and data noise, achieving high accuracy. These findings demonstrate the significant potential for integrating AI-driven assessments into cheerleading and other acrobatic sports, providing objective metrics that complement traditional judging methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Sports

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