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Bilateral Contract for Load Frequency and Renewable Energy Sources Using Advanced Controller

computers, Materials & Continua 2022年第11期73卷 3165-3180页

作者： Krishan Arora Gyanendra Prasad Joshi Mahmoud Ragab Muhyaddin Rawa Ahmad H.Milyani Romany F.Mansour Eunmok Yang School of Electronics and Electrical Engineering Lovely Professional UniversityPhagwara144001PunjabIndia School of Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia King Abdullah City for Atomic and Renewable Energy(K.A.CARE) RiyadhSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoul02707Korea

Reestablishment in power system brings in significant transformation in the power sector by extinguishing the possession of sound consolidated ***,the collaboration of various manufacturing agencies,autonomous power manufacturers,and buyers have created complex installation *** regular active load and inefficiency of best measures among varied associates is a huge *** sudden load deviation will give rise to immediate amendment in frequency and tie-line power *** is essential to deal with every zone’s frequency and tie-line power within permitted confines followed by fluctuations within the ***,it can be proficient by implementing Load Frequency Control under the Bilateral case,stabilizing the power and frequency distinction within the interrelated power *** the net deviation in multiple areas is possible by minimizing the unbalance of Bilateral Contracts with the help of proportional integral and advanced controllers like Harris Hawks *** proposed the advanced controller Harris Hawk optimizer-based model and validated it on a test *** experiment results show that the delay time is 0.0029 s and the settling time of 20.86 s *** model can also be leveraged to examine the decision boundaries of the Bilateral case.

关键词： Bilateral contract load frequency control optimization harris hawks optimizer

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Impurity explicit optimal Machine learning and statistical perfect construction for air quality prediction

Impurity explicit optimal Machine learning and statistical p...

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Networking and Communications (ICNWC), International Conference on

作者： Nareshkumar R R. Shalinikumari S. Sri Nandhini Kowsalya Vijay Anand P Ajmal Mohamed S. Gayathri Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Architecture Bharath Institute of Higher Education and Research Selaiyur Chennai Tamilnadu India Department of Information Technology Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Tamilnadu India Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Department of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy Tamilnadu India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

The air quality index (AQI) is a metric used to report air quality levels. There has been a substantial rise in the level of air pollution in Indian cities. Multiple methodologies exist for formulating a mathematical equation to ascertain the air quality index. Multiple studies have established a correlation between exposure to air pollution and negative health effects in the general population. The objective of this study is to determine the optimal approach for predicting AQI in order to aid in climate regulation. Nine separate Machine learning (ML) algorithms have been employed in the suggested study to ascertain the AQI. The use of the SMOTE algorithm in the datasets in ML resulted in improved accuracy when comparing the outcomes. Furthermore, all of the implementations have been thoroughly documented using graphs and metrics, which clearly illustrate the differences in outcomes and aid in identifying the specific factors that led to the improvement in accuracy.

关键词： Measurement Machine learning algorithms Urban areas Sociology Machine learning Predictive models Air pollution

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

关键词： biomedical data mining biomedical data computer vision artificial intelligence artificial intelligence ai integrating biological knowledge models disease screeningto quantitative biology conference

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Building Intelligent Automated Real-Time data Analysis Systems Through Machine Learning

Building Intelligent Automated Real-Time Data Analysis Syste...

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International Conference on Futuristic Technologies (INCOFT)

作者： Sahil Rathee M. Vigenesh Kala Priydarshini Vijaykumar R Shweta Singh Jyoti Seth Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India

This paper explores the capacity of using gadget-get-to-know-based facts evaluation structures to build intelligent, automated real-time statistics analysis systems. It examines the demanding situations present points evaluation systems pose and proposes a unique remedy. The proposed method leverages supervised and unsupervised devices and learning strategies to enable deep information on complex datasets. It then makes use of predictive skills to make clever selections with the aid of comparing input facts about databases. ultimately, the paper describes a case to demonstrate the effectiveness of this proposed machine and outlines implications for comparable implementations in the enterprise. This work is precious for information scientists and commercial enterprise decision-makers, as it outlines an ability to deploy reliable and accurate records analysis structures.

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Photonic matrix multiplication lights up photonicaccelerator and beyond

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Light(science & Applications) 2022年第2期11卷 158-178页

作者： Hailong Zhou Jianji Dong Junwei Cheng Wenchan Dong Chaoran Huang Yichen Shen Qiming Zhang Min Gu Chao Qian Hongsheng Chen Zhichao Ruan Xinliang Zhang Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Department of Electronic Engineering The Chinese University of Hong KongShatinHong KongChina Lightelligence Hangzhou 311121China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical InstrumentationZJUHangzhou Global Scientific and Technological Innovation CenterZJU-UIUC InstituteZhejiang UniversityHangzhou 310027China Interdisciplinary Center of Quantum Information State Key Laboratory of Modern Optical Instrumentationand Zhejiang Province Key Laboratory of Quantum Technology and DeviceDepartment of PhysicsZhejiang UniversityHangzhou 310027China

Matrix computation,as a fundamental building block of information processing in science and technology,contributes most of the computational overheads in modern signal processing and artificial intelligence *** accelerators are designed to accelerate specific categories of computing in the optical domain,especially matrix multiplication,to address the growing demand for computing resources and *** matrix multiplication has much potential to expand the domain of telecommunication,and artificial intelligence benefiting from its superior *** research in photonic matrix multiplication has flourished and may provide opportunities to develop applications that are unachievable at present by conventional electronic *** this review,we first introduce the methods of photonic matrix multiplication,mainly including the plane light conversion method,Mach–Zehnder interferometer method and wavelength division multiplexing *** also summarize the developmental milestones of photonic matrix multiplication and the related ***,we review their detailed advances in applications to optical signal processing and artificial neural networks in recent ***,we comment on the challenges and perspectives of photonic matrix multiplication and photonic acceleration.

关键词： interferometer beyond matrix

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Structure-Aware DropEdge Towards Deep Graph Convolutional Networks

arXiv

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arXiv 2023年

作者： Han, Jiaqi Huang, Wenbing Rong, Yu Xu, Tingyang Sun, Fuchun Huang, Junzhou Department of Computer Science and Technology Tsinghua University Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Tencent AI Lab Shenzhen China Department of Computer Science and Engineering The University of Texas Arlington United States

It has been discovered that Graph Convolutional Networks (GCNs) encounter a remarkable drop in performance when multiple layers are piled up. The main factor that accounts for why deep GCNs fail lies in over-smoothing, which isolates the network output from the input with the increase of network depth, weakening expressivity and trainability. In this paper, we start by investigating refined measures upon DropEdge—an existing simple yet effective technique to relieve over-smoothing. We term our method as DropEdge++ for its two structure-aware samplers in contrast to DropEdge: layer-dependent sampler and feature-dependent sampler. Regarding the layer-dependent sampler, we interestingly find that increasingly sampling edges from the bottom layer yields superior performance than the decreasing counterpart as well as DropEdge. We theoretically reveal this phenomenon with Mean-Edge-Number (MEN), a metric closely related to over-smoothing. For the feature-dependent sampler, we associate the edge sampling probability with the feature similarity of node pairs, and prove that it further correlates the convergence subspace of the output layer with the input features. Extensive experiments on several node classification benchmarks, including both full- and semi- supervised tasks, illustrate the efficacy of DropEdge++ and its compatibility with a variety of backbones by achieving generally better performance over DropEdge and the no-drop version. Copyright © 2023, The Authors. All rights reserved.

关键词： Drops

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An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series

arXiv

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arXiv 2024年

作者： Huang, Qiang Meng, Chuizheng Cao, Defu Huang, Biwei Chang, Yi Liu, Yan School of Artificial Intelligence Jilin University Jilin Changchun China International Center of Future Science Jilin University Jilin Changchun China Department of Computer Science University of Southern California California Los Angeles United States Halicioğlu Data Science Institute University of California San Diego San DiegoCA United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Jilin Changchun China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Time series analysis

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Hybrid Movie Recommendation System using Neural Collaborative Filtering with Gradient Decent and Association Rule Mining

Hybrid Movie Recommendation System using Neural Collaborativ...

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Software, Systems and Information Technology (SSITCON), International Conference on

作者： J Cindhamani Rajkumar Pogaku M. P Sahana Myasar M. Adnan M. Suganya Department of Artificial Intelligence and Data Science Vel Tech Multi Tech Dr. Rangrajan Dr. Sakunthala Engineering College Avadi Chennai India School of Computer Science and Engineering Malla Reddy Engineering College for Women (UGC-Autonomous) Maisammaguda Hyderabad India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

In recent days, most of the people are entertained by watching movies and shows, so developing a recommendation system help people across the world in saving the time to search for high rated movies. Usually, movies are recommended based on the user interests in the different movie genres such as horror, drama, romantic and comedy. Previous researchers suggested various methods for movie recommendation using Content Based Filtering (CBF), Collaborative Filtering (CF) which failed to predict the non-linear relationship recommendation due to the availability of many genres. To overcome this challenge, we proposed hybrid model namely Neural Collaborative Filtering (NCF) with gradient decent and Association Rule Mining (ARM). Initially, MovieLens dataset is employed and then data cleaning data organization is incorporated in pre-processing. Further, the features are extracted through the Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA) to convert the text data into numerical data and increase the performance respectively. Finally, the developed model was measured using Root mean square error (RMSE), precision, recall, and F 1 score. Hence, the proposed hybrid recommendation model provides higher accuracy value when compared to Knowledge-aware Attentional Neural Network (KANN).

关键词： Collaborative filtering Semantics Neural networks Organizations Motion pictures Feature extraction Frequency conversion Numerical models Association rule learning Recommender systems

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Advances in Clustering Algorithms for Large-Scale data Processing with AI

Advances in Clustering Algorithms for Large-Scale Data Proce...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Sonal Sharma Vandana Singh Jeyalaxmi M Prasad B. Chaudhari Neeraj Das Avni Garg Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

Clustering is a widely used technique in statistics mining for exploring and reading records with AI. It is an unsupervised gaining knowledge of technique, which may be used for exploratory facts evaluation. Clustering primarily aims to group similar statistics factors in a given facts set. In recent years, diverse advances in clustering algorithms have been developed for massive-scale record processing with AI. Some of the maximum popular clustering algorithms are ok-means, hierarchical clustering, fuzzy clustering, and spectral clustering. This kind of algorithm was used with AI for massive-scale records processing. Okay-approach and hierarchical clustering algorithms were widely used in diverse information mining responsibilities. They permit one to discover and organize the maximum similar statistics points. Fuzzy clustering and spectral clustering algorithms have become famous recently because of their capability to duplicate human perception and questioning when clustering records. Lately, deep learning techniques have been used with clustering algorithms to enhance their overall performance and boom the accuracy of records evaluation. Deep mastering is a subset of machine mastering which allows computers to research from repeated exposure to big datasets robotically. It permits clustering algorithms to investigate big-scale facts faster and more as it should be. Clustering with AI is an effective method for processing and analyzing huge-scale records. Recent advances in clustering algorithms and deep mastering strategies have improved performance and accuracy. Moreover, these algorithms may be used in diverse applications, which include client segmentation, community evaluation, and anomaly detection.

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Chaotic Particle Swarm Optimization Based Shortest Path Routing in VANET

Chaotic Particle Swarm Optimization Based Shortest Path Rout...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： R. Ramya A. Yazhini T. Geetha R. Sahila Devi Kavin K. S. S. Dhanalakshmi Department of Nano Electronics Materials and Sensors ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of Computer Science & Engineering College of Engineering Anna University Chennai India Department of Artificial Intelligence and Data Science Agni College of Engineering and Technology Chennai India Department of Computer Science and Engineering Rohini College of Engineering and Technology Palkulam India Department of Electrical and Electronics Engineering Government College of Engineering Tirunelveli Tamilnadu India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam Erode India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

The present study examines the Vehicle Ad hoc Networks (VANETs) exhibit unexpected behavior due to their high mobility and random network structure. Including ad hoc networks, a variety of problems, including repeated connected breakdowns, flexibility, multi-hop transmission of information, and information loss, impact Transmission Control Protocols (TCP). To overcome this problem, this paper suggests employing zone-based routing in VANETs while taking mobility into account. The routing technique is implemented with a hybrid optimization strategy. The most significant swarm intelligence (SI) algorithm for solving optimization issues is the chaotic particle swarm optimization (CPSO) algorithm. Due to its simplicity, which involves just a few parameters, CPSO has grown to be considered one of the most frequently used optimization techniques. When resolving challenging optimization problems, the routing algorithms experience high overhead or incompatibilities. A CPSO based shortest path routing method is presented for VANET that offers a high-quality path for connection among nodes, improving the distribution of packets percentage and preventing end-to-end delay, in order to enhanced routing effectiveness and throughput in prevailing approaches. These achievements show that CPSO performs adequately and is experiencing significant progress.

关键词： TCP Protocols Vehicular ad hoc networks Spread spectrum communication Routing Throughput Search problems

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