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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Santhosh Chowhan Arun Gupta Department of Data Analytics and Mathematical Sciences Jain (deemed to be) University Bangalore India Department of Mechanical Engineering Teerthanker Mahaveer University Uttar Pradesh India

This paper provides a detailed exposition of the concept of ontology, focusing on the processes and methodologies involved in its construction. The utilization of ontologies has played a significant role in the advancement of the Semantic Web. It is an expansion of the existing World Wide Web, characterized by the provision of well-defined meanings to information. This facilitates the translation of unstructured data into meaningful representations, thereby enabling effective collaboration between humans and computers. Ontology can be described as a compilation of a group of concepts, along with their corresponding definitions and interconnections, which are structured hierarchically and referred to as Taxonomy. Several ontology development tools are accessible, such as Hozo, DOML, and AltovaSemantic Works. Protégé, a commonly utilized ontology development editor, has been employed to define ontology concepts. In addition, it provides support for multiple ontology representation languages, such as OWL. The present study demonstrates the process of ontology development through the utilization of Protégé 3.4.1, with a specific focus on the domain of automobiles. A case study is presented to exemplify the application of this methodology.

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Tuning Hyperparameters of Machine Learning Methods for Afan Oromo Hate Speech Text Detection for Social Media 4

Tuning Hyperparameters of Machine Learning Methods for Afan ...

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4th International Conference on Computing and Communications Technologies, ICCCT 2021

作者： Defersha, Naol Bakala Kekeba, Kula Kaliyaperumal, Karthikeyan College of Electrical and Mechanical Engineering Centre of Excellence for HPC Big Data Analytics Addis Ababa Science and Technology Ethiopia Centre of Excellence for HPC and Big Data Analytics College of Electrical and Mechanical Engineering Addis Ababa Science and Technology Ethiopia Ambo University Institute of Technology Department of Information Technology Ambo Ethiopia

ISBN: (纸本)9781665414470

With the rapidly growing penetration of social media networks in linguistically diverse and multicultural developing nations like Ethiopia, the conversations of online users have increasingly become more casual and multilingual. The emergency of hate speech text system. To this end, various automated hate speech detection and classification systems have been developed for resource-rich languages such as English and French even though online users are using many other languages on different social media platforms. Afan Oromo is one natural language used by social media users to express feelings, emotions and share messages. Hence, there is an urgent need for the development of an intelligent system that can automatically detect and classify hate speech, especially for resource-scarce indigenous Ethiopian languages like Afan Oromo. This work is about the identification of hate speech text from comments and posts generated in resource scary poor language Afan Oromo. We prepared first hate speech text detection dataset of Afan Oromo that containing comments and posts from social media. Then, n-gram and TF-IDF feature selection approaches were employed to select features. After the important feature selected Natural language processing tasks applied on the dataset. We applied six machine learning classifiers from default and tuned parameters to detect hate speech text posts and comments. The experiment show that Support Vector Machine outperform 92% values of F-measure than classifiers Afan Oromo hate speech text detection dataset. This Afan Oromo hate speech text dataset publicly available on https://***/for further research. © 2021 IEEE.

关键词： Social networking (online)

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Evolutionary Direction Learning With Multivariate Gaussian Probabilistic Model for Multiobjective Optimization

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IEEE Transactions on Evolutionary Computation 2025年

作者： Wang, Xianpeng Zhang, Jingchuan Tang, Lixin Liu, Yaxue Ministry of Education National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China

In recent years, utilizing data from the evolutionary process of multiobjective evolutionary algorithms (MOEAs) to learn knowledge and guide evolutionary search has become a popular research topic. However, existing knowledge learning (KL) frameworks often suffer from the low quality of collected datasets and the inefficiency of model construction, which significantly limits their effectiveness. To address this issue, this paper proposes a novel evolutionary direction learning (EDL) framework, which aims to learn the evolutionary direction (ED) knowledge for each objective to enhance the population generation of MOEAs. The proposed EDL incorporates an effective data collection method based on objective improvement to generate high-quality datasets, based on which a multivariate Gaussian probabilistic model is employed to learn ED knowledge for each objective through a data fusion modeling approach. Besides, a knowledge assignment method is designed to select the most suitable ED knowledge to guide the evolution of solutions. Experimental results on both synthetic and real-world problems demonstrate that the proposed EDL framework can accelerate the convergence of MOEAs and significantly improve their performance. A comparison of the proposed EDL with three state-of-the-art KL frameworks indicates that EDL is a highly competitive learning framework, achieving superior performance with larger datasets and impressive efficiency. © 1997-2012 IEEE.

关键词： Contrastive Learning

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Wind Sensitivity of Electric Vehicle Energy Consumption and Influence on Range Prediction and Optimal Vehicle Routes

Wind Sensitivity of Electric Vehicle Energy Consumption and ...

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Mobility, Operations, Services and Technologies (MOST), IEEE International Conference on

作者： Trung Bach Tran Ilya Kolmanovsky Erik Biberstein Omar Makke Marina Tharayil Oleg Gusikhin Department of Aerospace Engineering The University of Michigan Ann Arbor Michigan USA Global Data Insight & Analytics Ford Motor Company Dearborn Michigan USA

The energy consumption of electric vehicles (EVs) depends on multiple factors. As it affects vehicle range, it must be accurately predicted. After a summary of the relevant literature, this paper focuses on the sensitivity of energy consumption to wind velocity and wind direction. The outcomes from model-based sensitivity analysis of the wind effects on energy consumption and the range of EVs over real-world routes are presented. data sources available for online and offline wind velocity and wind direction determination are discussed. Recognizing the interplay between range prediction and the route chosen, we consider a Markov Decision Process (MDP) based framework for battery-charge and travel-time aware route planning that accounts for the impact of the wind on optimal routing decisions including stops at the charging stations. Finally, we propose a framework that includes wind in the operation of EVs, which consists of learning the impact of wind, incorporating wind forecasting into range and energy prediction, and using that prediction to perform optimal routing.

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ANN-based methods for solving partial differential equations: a survey

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Arab Journal of Basic and Applied Sciences 2022年第1期29卷 233-248页

作者： Pratama, Danang A. Bakar, Maharani A. Ismail, N.B. Mashuri, M. Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Terengganu Malaysia Special Interest Group Modelling and Data Analytics Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Terengganu Malaysia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Jenderal Soedirman Purowkerto Indonesia

Traditionally, partial differential equation (PDE) problems are solved numerically through a discretization process. Iterative methods are then used to determine the algebraic system generated by this process. Recently, scientists have emerged artificial neural networks (ANNs), which solve PDE problems without a discretization process. Therefore, in view of the interest in developing ANN in solving PDEs, scientists investigated the variations of ANN which perform better than the classical discretization approaches. In this study, we discussed three methods for solving PDEs effectively, namely Pydens, NeuroDiffEq and Nangs methods. Pydens is the modified Deep Galerkin method (DGM) on the part of the approximate functions of PDEs. Then, NeuroDiffEq is the ANN model based on the trial analytical solution (TAS). Lastly, Nangs is the ANN-based method which uses the grid points for the training data. We compared the numerical results by solving the PDEs in terms of the accuracy and efficiency of the three methods. The results showed that NeuroDiffeq and Nangs have better performance in solving high-dimensional PDEs than the Pydens, while Pydens is only suitable for low-dimensional problems. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the University of Bahrain.

关键词： 68-XX 68W25 Artificial neural networks discretization Nangs module Neuroiffeq method partial differential equations Pydens method

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Towards Seamless Connectivity: Implementing 6G Communication Technologies in Next-Generation Networks 3

Towards Seamless Connectivity: Implementing 6G Communication...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Kalaiselvi, K. Sankar, R. Supriya, S. Kaushik, G. Swamy, Hemanth Devika, M. Saveetha College of Liberal Arts and Sciences SIMATS Department of Data Analytics Tamilnadu Chennai India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India MLR Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Motorola Solutions United States Dhanalakshmi College of Engineering Department of AI& DS Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

Advanced wireless networks are essential given the increase in demand for smart services enabled by the Internet of Everything (IoE). Despite the fact that 5G networks could enable Internet of Everything (IoE)-based services, they can't meet all of the requirements needed to create intelligent applications. In order to overcome the shortcomings of the current infrastructure, there is an increasing demand to investigate the potential of 6G wireless communication technologies. Complex network optimization problems can be resolved by integrating artificial intelligence into sixth generation networks. An increasing user base and support for large-scale data-driven applications are essential given the fast evolving 6G landscape. In addition to highlighting network needs, critical supporting technologies, and a comparison of 5G and 6G networks, this study looks at recent advancements and trends in 6G technology. The report also explores new approaches to 6G connectivity, such as edge computing, backscatter communications, AI-driven IoT networks, and holographic beamforming, all of which are meant to support intelligent communities. In order to achieve the goal of 6Gbased IoT networks, it concludes by outlining potential research avenues. © 2024 IEEE.

关键词： Internet of Everything

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A Decision-Dependent Chance-Constrained Planning Model for Distribution Networks Under Extreme Weather Events

A Decision-Dependent Chance-Constrained Planning Model for D...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Zhou, Anping Sun, Hongbo Kitamura, Shoichi Nikovski, Daniel Dept. of Electrical and Computer Engineering Southern Methodist University DallasTX75205 United States Data Analytics Group Mitsubishi Electric Research Laboratories CambridgeMA02139 United States Advanced Technology R&D Center Mitsubishi Electric Corporation Amagasaki661-8661 Japan

ISBN: (纸本)9798350396782

Extreme weather events have posed tremendous challenges to the operation of distribution networks. In this paper, we propose a decision-dependent chance-constrained model for the optimal planning of diesel generators, renewable distributed generations (RDGs), energy storage systems, and switches under contingency. A promising moment-based ambiguity set that incorporates the information of decision variables is employed to depict the uncertainty arising from RDGs. By leveraging effective approximation methods such as the Bonferroni approximation method to handle the considered joint chance constraints, the proposed model is transformed into a tractable mixed-integer second-order conic programming problem, which means it can easily be implemented. Numerical experiments are put forward on the IEEE 33-bus test system to validate the effectiveness of the developed approach. © 2023 IEEE.

关键词： Chance constraint contingency distribution network distributionally robust optimization renewable energy

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Corrosion inhibition of weldment by Nephelium lappaceum peel extract in 3.5% NaCl solution

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South African Journal of Chemical engineering 2022年 41卷 223-232页

作者： Gapsari, Femiana Setyarini, Putu H. Kurniawan, Fredy Ahnaf, Ahnaf Anwar, Moch.Syaiful Zuryati, Ulul Khairi Department of Mechanical Engineering Faculty of Engineering Brawijaya University MT Haryono 167 Malang 65145 Indonesia Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Arief Rahman Hakim Surabaya 60111 Indonesia Research Center for Metallurgy and Materials National Research and Innovation Agency Komplek Puspiptek Serpong Tangerang Selatan 15314 Indonesia Analytics Chemistry Laboratory Faculty of Mathematics and Natural Sciences Mataram University Majapahit 62 Mataram 83115 Indonesia

Nephelium lappaceum peel Extract (NPE) is effective to control carbon steel weldment corrosion in acidic *** study analyzes NPE performance (0 to 60 ppm) as corrosion inhibitor on weldment in 3.5% NaCl. Potentiodynamic polarization was used to investigate corrosion rate and inhibition performance. The results revealed that NPE has strong effect (83%) on heat affected zone (HAZ) and on base metal (BM) (62%) and on weld metal (WM) (71%). NPE acted as mixed type inhibitor on all weldment areas. On weldment areas, there was physisorption (WM and HAZ) following isothermal Langmuir, but on BM chemisorption might also happen. The results surface analysis, FTIR and UV Vis are in line, indicating that inhibition behavior happened due to the formation of adsorbed layer on the weldment surface. This study revealed that NPE had different performance in each region and solution temperature. © 2022 The Authors

关键词： Chemisorption Corrosion Inhibitor Physisorption

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Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential Recommendation

arXiv

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arXiv 2023年

作者： Peng, Bo Parthasarathy, Srinivasan Ning, Xia The Department of Computer Science and Engineering The Ohio State University ColumbusOH43210 United States The Department of Computer Science and Engineering The Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States The Department of Biomedical Informatics The Department of Computer Science and Engineering The Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

Learning effective recommendation models from sparse user interactions represents a fundamental challenge in developing sequential recommendation methods. Recently, pre-training-based methods have been developed to tackle this challenge. Though promising, in this paper, we show that existing methods suffer from the notorious negative transfer issue, where the model adapted from the pre-trained model results in worse performance compared to the model learned from scratch in the task of interest (i.e., target task). To address this issue, we develop a method, denoted as ANT, for transferable sequential recommendation. ANT mitigates negative transfer by 1) incorporating multi-modality item information, including item texts, images and prices, to effectively learn more transferable knowledge from related tasks (i.e., auxiliary tasks);and 2) better capturing task-specific knowledge in the target task using a re-learning-based adaptation strategy. We evaluate ANT against eight state-of-the-art baseline methods on five target tasks. Our experimental results demonstrate that ANT does not suffer from the negative transfer issue on any of the target tasks. The results also demonstrate that ANT substantially outperforms baseline methods in the target tasks with an improvement of as much as 15.2%. Our analysis highlights the superior effectiveness of our re-learning-based strategy compared to fine-tuning on the target tasks. © 2023, CC BY.

关键词： Recommender systems

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Incentivized Federated Learning and Unlearning

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IEEE Transactions on Mobile Computing 2025年

作者： Ding, Ningning Sun, Zhenyu Wei, Ermin Berry, Randall Data Science and Analytics Thrust Internet of Things Thrust Information Hub Guangzhou511453 China Northwestern University Department of Electrical and Computer Engineering EvanstonIL60208 United States Northwestern University Electrical and Computer Engineering Department Industrial Engineering and Management Sciences Department EvanstonIL60208 United States

To protect users' right to be forgotten in federated learning, federated unlearning aims at eliminating the impact of leaving users' data on the global learned model. The current research in federated unlearning mainly concentrates on developing effective and efficient unlearning techniques. However, the issue of incentivizing valuable users to remain engaged and preventing their data from being unlearned is still under-explored, yet important to the unlearned model performance. This paper focuses on the incentive issue and develops an incentive mechanism for federated learning and unlearning. We first characterize the leaving users' impact on the global model accuracy and the required communication rounds for unlearning. Building on these results, we propose a four-stage game to capture the interaction and information updates during the learning and unlearning process. A key contribution is to summarize users' multi-dimensional private information into one-dimensional metrics to guide the incentive design. Interestingly, we prove that allowing federated unlearning can result in reduced payoffs for both the server and users, compared to a scenario without unlearning. Numerical results demonstrate the necessity of unlearning incentives for retaining valuable leaving users, and also show that our proposed mechanisms decrease the server's cost by up to 53.91% compared to state-of-the-art benchmarks. © 2002-2012 IEEE.

关键词： federated learning federated unlearning incentive mechanism

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