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Retraction Note: Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction

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Neural Computing and Applications 2024年第1期37卷 513-513页

作者： El-Rashidy, Nora ElSayed, Nesma E. El-Ghamry, Amir Talaat, Fatma M. Machine Learning and Information Retrieval Department Faculty of Artificial Intelligence Kafrelsheiksh University Kafrelsheiksh Egypt Computer Science Department Faculty of Computers and Information Mansoura University Mansoura Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Damietta University Damietta Egypt School of Engineering and Computer Science University of Hertfordshire Egypt Hosted By Global Academic Foundation Cairo Egypt Faculty of Computer Science and Engineering New Mansoura University New Mansoura Egypt

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Towards self-driving laboratories: The central role of density functional theory in the AI age

arXiv

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arXiv 2023年

作者： Huang, Bing von Rudorff, Guido Falk Anatole von Lilienfeld, O. University of Vienna Faculty of Physics Kolingasse 14-16 WienAT1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Heinrich-Plett-Straße 40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Department of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Density functional theory (DFT) plays a pivotal role for the chemical and materials science due to its relatively high predictive power, applicability, versatility and computational efficiency. We review recent progress in machine learning model developments which has relied heavily on density functional theory for synthetic data generation and for the design of model architectures. The general relevance of these developments is placed in some broader context for the chemical and materials sciences. Resulting in DFT based machine learning models with high efficiency, accuracy, scalability, and transferability (EAST), recent progress indicates probable ways for the routine use of successful experimental planning software within self-driving laboratories. © 2023, CC0.

关键词： Density functional theory

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A BERT-Based Pre-Training Model for Solving Math Application Problems

A BERT-Based Pre-Training Model for Solving Math Application...

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

作者： Yuhao Jia Pingheng Wang Zhen Zhang Chi Cheng Zhifei Li Xinguo Yu Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China School of Sports Engineering and Information Technology Wuhan Sports University Wuhan China School of Information Management Wuhan University Wuhan China National Engineering Research Center for E-Learning Central China Normal University Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China

ISBN: (纸本)9798350345636

Solving the math application problem is hot research in intelligence education. An increasing number of research scholars are using pre-trained models to tackle machine solution problems. Noteworthily, the semantic relationships required in the machine solution task are for describing math problems, while those of the BERT model with pre-training weights are of general significance, which will cause a mismatched word vector representation. To solve this problem, we proposed a self-supervised pre-training method based on loss priority. We use the input data from the downstream task datasets to fine-tune the existing BERT model so that the dynamic word vector it obtained can better match the downstream tasks. And the size of the loss value of each data batch in each round of training will be recorded to decide which data should be trained in the next round, so that the model has a faster convergence speed. Furthermore, considering that in large-scale mathematics application problems, some problems have almost the same forms of solution. We proposed a machine solution model training algorithm based on the analogy of the same problem type. Extensive experiments on two well-known datasets show the superiority of our proposed algorithms compared to other state-of-the-art algorithms.

关键词： Training Bit error rate Semantics Mathematical models Data models Task analysis Convergence

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Towards a Unified Benchmark and Framework for Deep learning-Based Prediction of Nuclear Magnetic Resonance Chemical Shifts

arXiv

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arXiv 2024年

作者： Xu, Fanjie Guo, Wentao Wang, Feng Yao, Lin Wang, Hongshuai Tang, Fujie Gao, Zhifeng Zhang, Linfeng Weinan, E. Tian, Zhong-Qun Cheng, Jun State Key Laboratory of Physical Chemistry of Solid Surface iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China DP Technology Beijing100080 China Department of Chemistry University of California Davis95616 United States Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University Xiamen361005 China Xiamen361005 China AI for Science Institute Beijing100080 China Center for Machine Learning Research Peking University Beijing100871 China School of Mathematical Sciences Peking University Beijing100871 China Institute of Artificial Intelligence Xiamen University Xiamen361005 China

The study of structure-spectrum relationships is essential for spectral interpretation, impacting structural elucidation and material design. Predicting spectra from molecular structures is challenging due to their complex relationships. Herein, we introduce NMRNet, a deep learning framework using the SE(3) Transformer for atomic environment modeling, following a pre-training and fine-tuning paradigm. To support the evaluation of NMR chemical shift prediction models, we have established a comprehensive benchmark based on previous research and databases, covering diverse chemical systems. Applying NMRNet to these benchmark datasets, we achieve state-of-the-art performance in both liquid-state and solid-state NMR datasets, demonstrating its robustness and practical utility in real-world scenarios. This marks the first integration of solid and liquid state NMR within a unified model architecture, highlighting the need for domainspecific handling of different atomic environments. Our work sets a new standard for NMR prediction, advancing deep learning applications in analytical and structural chemistry. © 2024, CC BY.

关键词： Prediction models

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Intelligent FORecasting Model for Climate Variations (InFORM): An Urban Climate Case Study

Intelligent FORecasting Model for Climate Variations (InFORM...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Pralhad P. Teggi Santhi Natarajan Bharathi Malakreddy Computer Science and Engineering BMS Institute of Technology and Management Bangalore INDIA Artificial Intelligence and Machine Learning BMS Institute of Technology and Management Bangalore INDIA

ISBN: (数字)9789380544380

ISBN: (纸本)9781728129587

When perceived as a deeply connected socio-ecological system, Bangalore which is popularly addressed as the Silicon Valley of India faces the major challenge of integrating the systems of water, climate, energy and waste management. Climate changes have resulted in extreme variations in temperature, relative humidity and precipitation levels, thus altering the monsoon patterns in Bangalore over the last few years. The alarming increase in pollution levels results in decreased visibility considering increased traffic and emission. A complex and accurate climate modelling is essential to enable precise weather forecasting in this scenario. Through this paper, we present InFORM, an Intelligent Forecasting Model for Climatic Variations. InFORM presents an approach to forecast daily weather parameters like temperature, relative humidity and visibility using time series ARIMA model. The approach is built as a pipeline that makes forecasting predictions for multiple variables using a unique singular algorithm, which can be integrated to the complex climatic models to make them more accurate and precise.

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Gender Detection using Facial Features with Support Vector machine

Gender Detection using Facial Features with Support Vector M...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Jayaprada S. Hiremath Shantala S. Hiremath Sujith Kumar Manjunath S. Chincholi Shantakumar B. Patil Mrutyunjaya S. Hiremath Computer Science and Engineering Research Scholar at VTU Belgaum Karnataka India Specialist in Image Processing Tata Elxsi Limited Bangalore Karnataka India Principal Engineer (Artificial Intelligence / Machine learning) VVDN Technologies Pvt. Ltd. Bangalore Karnataka India Dept. of Image Processing eMath Technology Pvt. Ltd. Bangalore Karnataka India Computer Science and Engineering Sai Vidya Institute of Technology Bengaluru Karnataka India

ISBN: (纸本)9781665491129

Innovative security technologies have increased the need for accurate identification. Gender identification has been widespread use of image analysis in recent decades. The face is one of the most popular biometric features in image processing. In Image Processing and video surveillance, systems that automatically discern gender from facial photos are gaining prominence. This study discusses face traits and gender categorization. In this study, we created a technique with good runtime and efficiency to determine human gender using face photos. It uses characteristics taken from pre-processed face photographs of various ages. Support Vector machine (SVM) Classification was used to determine class thresholds. Our method classifies UTK-FACE gender with 91.63% accuracy.

关键词： Support vector machines Wireless communication Training machine learning algorithms Face recognition Support vector machine classification Feature extraction

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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Teleoperation of Humanoid Robots: A Survey

arXiv

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arXiv 2023年

作者： Darvish, Kourosh Penco, Luigi Ramos, Joao Cisneros, Rafael Pratt, Jerry Yoshida, Eiichi Ivaldi, Serena Pucci, Daniele Artificial and Mechanical Intelligence Center for Robotics and Intelligent Systems Istituto Italiano di Tecnologia Genoa Italy Florida Institute for Human and Machine Cognition 40 S Alcaniz St PensacolaFL32502 United States Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign United States Department of Information Technology and Human Factors Tsukuba Japan Inria Loria Université de Lorraine CNRS Nancy France Department of Applied Electronics Faculty of Advanced Engineering Tokyo University of Science Japan

Teleoperation of humanoid robots enables the integration of the cognitive skills and domain expertise of humans with the physical capabilities of humanoid robots. The operational versatility of humanoid robots makes them the ideal platform for a wide range of applications when teleoperating in a remote environment. However, the complexity of humanoid robots imposes challenges for teleoperation, particularly in unstructured dynamic environments with limited communication. Many advancements have been achieved in the last decades in this area, but a comprehensive overview is still missing. This survey paper gives an extensive overview of humanoid robot teleoperation, presenting the general architecture of a teleoperation system and analyzing the different components. We also discuss different aspects of the topic, including technological and methodological advances, as well as potential applications. A web-based version of the paper can be found at https://***/. © 2023, CC BY.

关键词： Remote control

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An application review on internet of things and machine learning based smart agriculture

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AIP Conference Proceedings 2024年第1期3161卷

作者： G. Lavanya S. Vignesh K. Sundaramurthy S. Sivakumar Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India 602105 Department of Mechanical Engineering Saveetha Engineering College Chennai India 602105 Department of Mechanical Engineering Paavai Engineering College Namakkal India 637018 School of Engineering Asia Pacific University of Technology and Innovation Wilayah Persekutuan Kuala Lumpur Malaysia

Smart farming is an approach to governance which concentrates on equipping the farming sector with the necessary tools to employ cutting-edge technologies such as big data, the internet of things (IoT), and the use of cloud computing for observing, tracking, computerized, and evaluating processes. IoT intelligent farming technologies use cameras and automated monitoring and control systems to aid in farming field monitoring. As a result, farmers and associated companies may easily track field conditions virtually anywhere. machine learning (ML) approaches are widely accepted as viable replacements and supplements to commonly used modelling strategies in agriculture and other associated sectors. machine learning may help agriculture at every level, including handling soils, identification of illnesses, crop oversight, livestock administration, and furthermore. This review paper summarizes the importance on smart agriculture, components involved in the IoT, ML based smart agriculture and its recent trends.

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Joint Parking and Power Management for Electric Vehicle Edge Computing: A Bilevel Optimization Approach

Joint Parking and Power Management for Electric Vehicle Edge...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Xumin Huang Weifeng Zhong Jiangtian Nie Jiawen Kang Zehui Xiong Yuan Wu Mohsen Guizani School of Automation Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China School of Computer Science and Engineering Nanyang Technological University Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Computer and Information Science University of Macau Macau China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi UAE

With the vehicle-to-grid and computing capabilities, a parked electric vehicle (EV) has a dual role, namely being an energy prosumer as well as a computing node for accommodating computation-offloading services. This dual-role feature of EVs yields a new computing paradigm named Electric Vehicle Edge Computing (EVEC). To ease the implementation of EVEC, we propose a fine-grained EV management approach to jointly provide parking guidance for EVs and control their charging/discharging power in parking lots. We formulate a bilevel optimization problem where the top-level problem optimizes the matching between EVs and parking lots from the perspective of computation offloading, and the bottom-level problem optimizes the control of EV charging/discharging power from the view of power networks. We transform the bilevel optimization problem into a single-level form, which is a nonconvex mixed-integer nonlinear programming problem, and we further tackle it by linearization techniques. Finally, we provide numerical results to demonstrate the efficiency and effectiveness of our approach.

关键词： Wireless communication Vehicle-to-grid Systematics Power system management Computational modeling Transforms Relaxation methods

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