检索结果-内蒙古大学图书馆

Wireless Networks 2023年第1期30卷 593-593页

作者： Dakshinamoorthy, Prabakar Vaitilingam, Saminadan Sundar, Ramesh Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Electronics and Communication Engineering Puducherry Technological University Puducherry India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Dispatching and Control Information Freshness-Aware Federated learning for Simplified Power IoT

学校读者我要写书评

暂无评论

Dispatching and Control Information Freshness-Aware Federate...

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Zehan Jia Ziqi Yu Haijun Liao Zhao Wang Zhenyu Zhou Xiaoyan Wang Guoqing He Shahid Mumtaz Mohsen Guizani Hebei Key Laboratory of Power Internet of Things Technology North China Electric Power University Beijing Baoding Hebei China Graduate School of Science and Engineering Ibaraki University Ibaraki-ken Japan State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China Instituto de Telecomunicações Aveiro Portugal Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE

ISBN: (纸本)9781665435413

Dispatching and control information freshness conducts an important impact on the training accuracy of distributed energy dispatching and control model. Poor information freshness will increase the loss function of the training model, and reduce the reliability and economy of dispatching and control. Simplified power internet of things can provide plug-and-play and multi- mode fusion communication support, but it still faces challenges of the coupling of model training and data transmission as well as the difficulty in guaranteeing dispatching and control information freshness. In this paper, a semi-distributed federated learning- based framework for dispatching and control model training decision-making is proposed, and a dispatChing and control informAtion fReshness-aware batch size Optimization aLgorithm (CAROL) is presented. CAROL leverages deep Q network and dispatching and control information freshness awareness to learn the batch size optimization strategy. CAROL can minimize model loss function while guaranteeing long-term dispatching and control information freshness constraints. Compared with existing feder- ated learning algorithms, CAROL achieves superior performance in global loss function and information freshness.

关键词： Training Computational modeling Simulation Decision making Minimization Dispatching Data models

From quantum alchemy to Hammett's equation: Covalent bonding from atomic energy partitioning

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Sahre, Michael J. von Rudorff, Guido Falk von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 14-16 Vienna1090 Austria Währinger Str. 42 Vienna1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

We present an intuitive and general analytical approximation estimating the energy of covalent single and double bonds between participating atoms in terms of their respective nuclear charges with just three parameters, [EAB ≈ a − bZAZB + c(ZA7/3 + ZB7/3)]. The functional form of our expression models an alchemical atomic energy decomposition between participating atoms A and B. After calibration, reasonably accurate bond energy estimates are obtained for hydrogen-saturated diatomics composed of p-block elements coming from the same row 2 ≤ n ≤ 4 in the periodic table. Corresponding changes in bond energies due to substitution of atom B by C can be obtained via simple formulas. While being of different functional form and origin, our model is as simple and accurate as Pauling’s well-known electronegativity model. Analysis indicates that the model’s response in covalent bonding to variation in nuclear charge is near-linear—which is consistent with Hammett’s equation. © 2022, CC BY.

关键词： Atoms

Encrypted machine learning of molecular quantum properties

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Weinreich, Jan von Rudorff, Guido Falk von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria University of Vienna Vienna Doctoral School in Physics Boltzmanngasse 5 Vienna1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Large machine learning models with improved predictions have become widely available in the chemical sciences. Unfortunately, these models do not protect the privacy necessary within commercial settings, prohibiting the use of potentially extremely valuable data by others. Encrypting the prediction process can solve this problem by double-blind model evaluation and prohibits the extraction of training or query data. Unfortunately, contemporary ML models based on fully homomorphic encryption or federated learning are either too expensive for practical use or have to trade higher speed for weaker security. We have implemented secure and computationally feasible encrypted machine learning models using oblivious transfer enabling and secure predictions of molecular quantum properties across chemical compound space. However, we find that encrypted predictions using kernel ridge regression models are a million times more expensive than without encryption. This demonstrates a dire need for a compact machine learning model architecture, including molecular representation and kernel matrix size, that minimizes model evaluation costs. © 2022, CC BY.

关键词： Forecasting

Arabic handwritten character recognition based on convolution neural networks and support vector machine

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Shams El Den, Mahmoud Yassien Elsonbaty, Amira A. ElSawy, Wael Z. Department of Machine Learning and Information Retrieval Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt Department of Electronic and Communication Higher Institute of Engineering and Technology New Damietta Egypt Department of Business Information System Faculty of Management Technology and Information System PortSaid University 42511 Egypt

Recognition of Arabic characters is essential for natural language processing and computer vision fields. The need to recognize and classify the handwritten Arabic letters and characters are essentially required. In this paper, we present an algorithm for recognizing Arabic letters and characters based on using deep convolution neural networks (DCNN) and support vector machine (SVM). This paper addresses the problem of recognizing the Arabic handwritten characters by determining the similarity between the input templates and the pre-stored templates using both fully connected DCNN and dropout SVM. Furthermore, this paper determines the correct classification rate (CRR) depends on the accuracy of the corrected classified templates, of the recognized handwritten Arabic characters. Moreover, we determine the error classification rate (ECR). The experimental results of this work indicate the ability of the proposed algorithm to recognize, identify, and verify the input handwritten Arabic characters. Furthermore, the proposed system determines similar Arabic characters using a clustering algorithm based on the K-means clustering approach to handle the problem of multi-stroke in Arabic characters. The comparative evaluation is stated and the system accuracy reached 95.07% CRR with 4.93% ECR compared with the state of the art. Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

Optimizing large-scale hyperparameters via automated learning algorithm

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Gu, Bin Liu, Guodong Zhang, Yanfu Geng, Xiang Huang, Heng Department of machine learning Mohamed bin ZayedUniversity of Artificial Intelligence United Arab Emirates JD Finance America Corporation Mountain ViewCA94043 United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States School of Computer & Software Nanjing University of Information Science & Technology Nanjing China

Modern machine learning algorithms usually involve tuning multiple (from one to thousands) hyperparameters which play a pivotal role in terms of model generalizability. Black-box optimization and gradient-based algorithms are two dominant approaches to hyperparameter optimization while they have totally distinct advantages. How to design a new hyperparameter optimization technique inheriting all benefits from both approaches is still an open problem. To address this challenging problem, in this paper, we propose a new hyperparameter optimization method with zeroth-order hyper-gradients (HOZOG). Specifically, we first exactly formulate hyperparameter optimization as an A-based constrained optimization problem, where A is a black-box optimization algorithm (such as deep neural network). Then, we use the average zeroth-order hyper-gradients to update hyperparameters. We provide the feasibility analysis of using HOZOG to achieve hyperparameter optimization. Finally, the experimental results on three representative hyperparameter (the size is from 1 to 1250) optimization tasks demonstrate the benefits of HOZOG in terms of simplicity, scalability, flexibility, effectiveness and efficiency compared with the state-of-the-art hyperparameter optimization methods. © 2021, CC0.

关键词： Deep neural networks

OLSR+: A new routing method based on fuzzy logic in flying ad-hoc networks (FANETs)

学校读者我要写书评

暂无评论

Vehicular Communications 2022年 36卷

作者： Rahmani, Amir Masoud Ali, Saqib Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Javaheri, Danial Lalbakhsh, Pooia Hassan Ahmed, Omed Hosseinzadeh, Mehdi Lee, Sang-Woong Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Chosun University Gwangju 61452 South Korea Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Clayton 3800 VIC Australia Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Flying ad-hoc networks (FANETs) have many applications in military, industrial and agricultural areas. Due to specific features of FANETs, such as high-speed nodes, low density of nodes in the network, and rapid changes in the topology, most routing protocols designed for mobile ad hoc networks (MANETs) or vehicular ad hoc networks (VANETs) are not compatible with FANETs. In this paper, we propose a fuzzy logic-based routing approach called OLSR+ for FANETs. In this scheme, we seek to improve the optimized link state routing protocol (OLSR) so that it can efficiently be used in FANETs. OLSR+ includes four main phases: 1) Discovering neighboring nodes. In this phase, we propose a new and efficient technique for estimating the lifetime of the link between two unmanned ariel vehicles (UAVs) based on the link quality, distance, relative velocity, and movement direction. 2) Selecting multipoint relays (MPRs). In this phase, we present a fuzzy mechanism for selecting a set of MPR nodes. According to this mechanism, when a node has higher residual energy, higher link lifetime, and more neighborhood degree compared to others, it achieves more fitness to be selected as MPR. 3) Discovering the network topology. In this phase, we modify the format of the topology control (TC) message and add two fields, including route energy and route lifetime to this message. 4) Calculating the routing table. In OLSR+, we consider two parameters, including route energy and route lifetime, for establishing stable paths. Finally, we simulate OLSR+ using NS3 and compare its performance with two methods, namely greedy optimized link state routing (G-OLSR) and optimized link state routing (OLSR). The simulation results show that OLSR+ successfully reduces delay compared to G-OLSR and OLSR. In addition, it has higher packet delivery rate and throughput than others. Also, it improves energy consumption in the network. However, OLSR+ has more routing overhead than G-OLSR. © 2022 Elsevier Inc.

关键词： artificial intelligence (AI) Flying ad hoc networks (FANETs) Fuzzy logic Routing Unmanned ariel vehicles (UAVs)

Multiple texts as a limiting factor in online learning: quantifying (dis-)similarities of knowledge networks across languages

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Mehler, Alexander Hemati, Wahed Welke, Pascal Konca, Maxim Uslu, Tolga Text-Technology Lab Institute of Computer Science Faculty of Computer Science and Mathematics Goethe University Frankfurt Frankfurt Germany Machine Learning and Artificial Intelligence Lab Institute for Computer Science University of Bonn Bonn Germany

We test the hypothesis that the extent to which one obtains information on a given topic through Wikipedia depends on the language in which it is consulted. Controlling the size factor, we investigate this hypothesis for a number of 25 subject areas. Since Wikipedia is a central part of the web-based information landscape, this indicates a language-related, linguistic bias. The article therefore deals with the question of whether Wikipedia exhibits this kind of linguistic relativity or not. From the perspective of educational science, the article develops a computational model of the information landscape from which multiple texts are drawn as typical input of web-based reading. For this purpose, it develops a hybrid model of intra- and intertextual similarity of different parts of the information landscape and tests this model on the example of 35 languages and corresponding Wikipedias. In this way the article builds a bridge between reading research, educational science, Wikipedia research and computational *** Codes 68T50 (Primary) 68T30, 91F20 (Secondary). Copyright © 2020, The Authors. All rights reserved.

关键词： Relativity

Corrections to “Innovations in Stroke Identification: A machine learning-Based Diagnostic Model Using Neuroimages”

学校读者我要写书评

暂无评论

IEEE Access 2024年 12卷 116085-116085页

作者： Muhammad Asim Saleem Ashir Javeed Wasan Akarathanawat Aurauma Chutinet Nijasri Charnnarong Suwanwela Widhyakorn Asdornwised Surachai Chaitusaney Sunchai Deelertpaiboon Wattanasak Srisiri Watit Benjapolakul Pasu Kaewplung Department of Electrical Engineering Faculty of Engineering Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Chulalongkorn University Bangkok Thailand Aging Research Center Karolinska Institutet Stockholm Sweden Department of Medicine Faculty of Medicine Division of Neurology Chulalongkorn University Bangkok Thailand Chulalongkorn Stroke Center King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand Chula Neuroscience Center King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand

Presents corrections to the paper, Innovations in Stroke Identification: A machine learning-Based Diagnostic Model Using Neuroimages.

关键词： Technological innovation

Advancements in machine learning for recommender systems: A focus on NNMFC and particle swarm optimization techniques

学校读者我要写书评

暂无评论

AIP Conference Proceedings 2024年第1期3193卷

作者： S. Prema M. Varalatchoumy G. Nirmaladevi S. Vijayakumar S. Kayalvili M. Rajendiran R. Premanand V. Vijayan Department of Information Technology Mahendra Engineering College Namakkal Tamil Nadu 637503 India Department of Artificial Intelligence and Machine Learning Cambridge Institute of Technology Bengaluru Karnataka 560036 India Department of Computer Science Engineering Sri Sairam Institute of Technology Chennai Tamil Nadu 600044 India Department of Mathematics R.M.K. Engineering College Kavarapettai Tamil Nadu 601206 India Department of Artificial Intelligence Kongu Engineering College (Autonomous) Erode Tamil Nadu 638060 India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu 600123 India Department of Humanities and Science (Physics) Sri Sai Ram Engineering College Chennai Tamil Nadu 600044 India Department of Mechanical Engineering K.Ramakrishnan College of Technology Trichy Tamilnadu India

Through the use of interest models, the Recommender System assists users in discovering content that is relevant to them. In order to make product suggestions based on past purchases, content-based recommender systems do not require user ratings. These systems are the subject of this study. Now these systems can profile products and customers using machine learning. Together with Non-Negative Matrix Factorization Clustering (NNMFC), we present a new approach to collaborative learning for online video sites. The research utilizes a sliding window clustering approach that relies on Particle Swarm Optimization (PSO) and gradient descent. We utilized three approaches to assess the model’s performance: sliding window PSO (SWPSO), sliding window gradient descent and gradient descent. The Root Mean Square Error (RMSE) was calculated for each. Outperforming current state-of-the-art methods like UPCSim, K-Mean, and Collaborative Filtering, the suggested work’s result analysis attained the lowest RMSE of 1.02. With a significant improvement of 10.2% over previous techniques, the Sliding Window PSO was shown to be the most effective.

关键词：