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检索条件"任意字段=International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022"

共 558 条记录，以下是211-220 订阅

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Enhancing the Bert Model for Qur'anic Question Answering System

Enhancing the Bert Model for Qur'anic Question Answering Sys...

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intelligent computing and Next Generation Networks（ICNGN), international conference on

作者： Ebtihal Alarabi Issmail Ellabib Computer Engineering Department University of Tripoli Tripoli Libya

ISBN: (数字)9798331529222

ISBN: (纸本)9798331529239

Machine Reading Comprehension is a rapidly evolving field that aims to bridge the gap between human-like understanding and machine text processing. With advancements in deep learning and the development of sophisticated models, MRC systems are becoming increasingly capable of understanding context and providing accurate answers. driven by the increasing demand for advanced question-answering (QA) systems on the Holy Qur'an, the inaugural Qur'an Question Answering shared task (Qur'an QA 2022 OSACT 2022), the second edition in Arabic NLP 2023. The Qur'an QA 2023 encompasses two essential sub-tasks: Passage Retrieval (PR) and Machine Reading Comprehension (MRC). The shared task aims to enhance the capabilities of QA systems and facilitate deeper engagement with the Qur'an. This research paper focuses on developing an improved BERT model, implemented using the KNIME platform, and tailored for Arabic. We aim to utilize this model for Qur'anic Question Answering, resulting in an Area under the Curve (AUC) of 0.5 and an accuracy rate of 0.997.

关键词： Deep learning Adaptation models Accuracy Computational modeling Soft sensors Question answering (information retrieval) data models Next generation networking Text processing Context modeling

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data Scientist Job Change Prediction Using Machine Learning Classification Techniques 1

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2nd international conference on Ubiquitous computing and intelligent Information systems (ICUIS)

作者： Kyalkond, Sameer A. Sanjay, V. Manikanta Athreya, H. Manoj Aithal, Sudhanva Suresh Rajashekar, Vishal Kushal, B. H. JSS Acad Tech Educ Bengaluru India BNM Inst Technol Bengaluru India

ISBN: (数字)9789811925412

ISBN: (纸本)9789811925412;9789811925405

In this ever-expanding world, each and every industry is highly competitive. Recently, the Artificial Intelligence [AI] technologies are influencing every facet of data science domain. This technology driven-competitive world creates a lot of difficulties for people working in the data science sector by developing a notion of abandoning the data scientist profession. On the other hand, the human expectations aren't matching reality, followed by No Clear Benchmarking in Salary Pay-Outs, Mapping a data Scientist's Role to Business Goals, and finally, there is a lack of upskilling for data Science Professionals. These are the causes for the job change in the individuals and as the competition increases, there is always a substitute, henceforth the job security also falls in this competitive market. Different machine learning methods are used to figure out or to forecast the job change for the employees in the firm. This can be observed by the HR and give additional rewards for employee since the employee is an important asset to the company and its development. The synergy of deep learning approaches, machine learning and ensemble methods are utilized. This research work attempts to perform a comparison on various classification approaches such as logistic regression, random forest, KNN and support vector machine in order to demonstrate which method performs the best to offer an insight to the general community.

关键词： Classification techniques data scientist job change Machine Learning Prediction Random Forest

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Employing a Machine Learning-Based Approach for Enhanced Big data Analysis in Supply Chain Management

Employing a Machine Learning-Based Approach for Enhanced Big...

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advances in data Engineering and intelligent computing systems (ADICS), international conference on

作者： M. Pradeepa V. Sumathi Sivakumar Ponnusamy G Suneetha Gurjeet Singh Kruthika P School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Mathematics Sri Sai Ram Engineering College West Tambaram Chennai-44 Tamil Nadu India Department of Computer Science and Engineering KSR College of Engineering Tiruchengode Namakkal TamilNadu India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Mechanical Engineering IES College of Technology & IES University Bhopal Madhya Pradesh India Department of Information Technology Sona College of Technology Salem Tamil Nadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

This study uses machine learning models as potent analytical tools to look into the issue of inventory cost and profit prediction in the automobile industry. Throughout a ten-year dataset from 2012 to 2023, the study carefully analyses several cost components and profit data, illuminating the complex relationships between these elements. To provide insights into inventory dynamics, the Convolutional Neural Network (CNN), k-nearest Neighbours (KNN), and Support Vector Machine (SVM) models are rigorously trained and tested. An impressive feat is achieved by the CNN model, which achieves a phenomenal accuracy of 98.7 % , followed by KNN at 96.7 % and SVM at 93.4%. Additional proof of the models' propensity to make accurate predictions comes from their precision, recall, F1 score, and confusion matrices. The research provides data-driven insights that players in the dynamic vehicle industry can use to improve inventory management procedures and financial results. This work serves as an example of how machine learning may fundamentally alter how people make decisions in this difficult domain.

关键词： Support vector machines Industries Costs Profitability Machine learning Predictive models Inventory management

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Mobile computing Based Security Analysis of IoT Devices

Mobile Computing Based Security Analysis of IoT Devices

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international conference on advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2022

作者： Valte, Nikita Gautam, Borkar Department of Information Technology Ramrao Adik Institute of Technology Navi Mumbai India D Y Patil Deemed to be University Navi Mumbai India

ISBN: (纸本)9789819959730

The Internet of Things is among the most advanced technologies. The Internet of Things (IoT) connects intelligent items to offer a variety of features. IoT refers to the internet-based interconnection of numerous devices, resulting in no human intervention. IoT components are connected by networks, which may be wired or wireless, incorporating sensors, the cloud, wifi, Bluetooth, and other technologies. A significant amount of sensitive data is being transferred by these devices across the network. That makes it simpler for people to go about their daily lives;therefore it's critical to make sure technology is safeguarded as much as is practical. Information in the IoT world may malfunction as a result of various risks and attacks. As a result, strong security systems are required to manage diverse kinds of attacks and security challenges. In the future, every electronic device will develop into a smart device that can connect to handheld equipment. An IoT security is a huge worry because the bulk of IoT nodes are battery-powered and utilizes very little electricity. Identification and authentication present the greatest protection and security challenges in the IoT. This study identifies the security issues and challenges with IoT devices. Then, by offering prospective security measures and solutions, mobile computing has been leveraged to address these problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Internet of things

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Hybrid Estimation of Distribution Based on Knowledge Transfer for Flexible Job-shop Scheduling Problem 4

Hybrid Estimation of Distribution Based on Knowledge Transfe...

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4th international conference on data-driven Optimization of Complex systems, DOCS 2022

作者： Cao, Lulu Jiang, Min Feng, Liang Lin, Qiuzhen Pan, Renhu Tan, Kay Chen Xiamen University School of Informatics Fujian China Chongqing University College of Computer Science Chongqing China Shenzhen University College of Computer Science and Software Engineering Guangdong China Fujian Longking CO.LTD. Fujian China Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

In this work, we introduce a new method called G-EDA for solving flexible job-shop scheduling problems (FJSP). Based on the investigation of current works, it can be found that the solution of FJSP mainly focuses on the intelligent optimization algorithm, such as the genetic algorithm, which obtains new population through crossover and mutation. When searching individuals, this algorithm does not fully use the knowledge hidden in the excellent individuals in the previous generation population, and its performance is poor when facing high-dimensional problems. This paper hopes to find a method to find the distribution law of high-quality individuals in the previous generation population and transfer the knowledge contained in the previous generation population to the next generation to improve the performance of the algorithm. In this paper, we propose a hybrid estimation of distribution algorithm using population grouping mechanism(G-EDA). We conduct numerical simulations based on two sets of international standard examples. And we compare G-EDA with some existing advanced algorithms. The results show that G-EDA is effective and practical in solving multi-objective FJSP. © 2022 IEEE.

关键词： Genetic algorithms

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Simulating Scenarios to Evaluate data Filtering Techniques for Mobile Users 1

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20th international conference on advances in Mobile computing and Multimedia Intelligence (MoMM)

作者： Ilarri, Sergio Trillo-Lado, Raquel Arraez, Angel Piedrafita, Alejandro Univ Zaragoza I3A Zaragoza 50018 Spain Univ Zaragoza Zaragoza 50018 Spain

ISBN: (数字)9783031204364

ISBN: (纸本)9783031204357;9783031204364

Citizens are nowadays being flooded with huge amounts of information, which will keep growing as the physical spaces become more intelligent, with the proliferation of sensors (e.g., pollution sensors, traffic sensors, etc.), mobile apps, and information services of different types (e.g., malls providing offers and other kinds of information to nearby customers). To actually become resilient modern citizens, people need to be able to handle all this highly-dynamic information and act upon it by taking suitable decisions. In this context, the development of suitable data management techniques to help citizens in their daily life plays a major role. Motivated by this, we focus on the design of novel data management techniques for mobile users (pedestrians) and for drivers, which are two key areas in the daily life of citizens. More specifically, we consider the problem of recommending relevant items to pedestrians (e.g., tourists) and the challenges of drivers when they try to find an available parking space. As evaluating data management strategies in a real environment in a large-scale is very challenging, in this paper we propose suitable simulation approaches that facilitate the evaluation task. Through simulations, we obtain some initial experimental results that show the additional difficulties that appear when we want to satisfy additional constraints such as the desire to minimize the risk of virus spread in a COVID-19 scenario.

关键词： data management Mobile computing Recommender systems Parking spaces

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Research on Big data Platform for Railway Transport Station 7

Research on Big Data Platform for Railway Transport Station

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2022 7th international conference on Information Science, Computer Technology and Transportation, ISCTT 2022

作者： Xu, Chunjie Li, Jun Zhang, Yawei Chen, Ruifeng Li, Chao Yang, Guoyuan Lv, Xiaojun Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing100081 China Depart of Electromechanical Equipment and System Beijing Jingwei Information Technology Co. Ltd Beijing100081 China

ISBN: (纸本)9783800760565

In view of the status that current systems for railway transport station meanly focus on functional integration, partial information sharing and business-based data mining, which can’t meet the requirements of scientific decision and intelligent execution. The architecture of big data platform for railway transport station is proposed in this paper. Firstly, the online real-time data collection technology based on Internet of Things is adopted, the operating parameters of the systems and equipment is collected by the Internet of Things gateway. Using StreamSets component, data visualization ETL is realized, and the data quality and reliability is improved. Secondly, based on the unified data flow management, the data collection, integration and transparent access are realized, and the processing logic global scheduling for the platform is implemented. Finally, the data development models by the visual studio suite are constructed rapidly. And the platform supports secondary development for railway transport station business. It realizes efficient and low-cost data asset construction and data analysis application. And based on data-driven it provides technical support for quality improvement and business innovation. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Internet of things

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3rd international conference on Smart IoT systems: Innovations and computing, SSIC 2021

3rd International Conference on Smart IoT Systems: Innovatio...

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3rd international conference on Smart IoT systems: Innovations and computing, SSIC 2021

ISBN: (纸本)9789811628764

The proceedings contain 55 papers. The special focus in this conference is on Smart IoT systems: Innovations and computing. The topics include: Requirements in PLS, SSML and SRGS Standard—Hindi as a Case Study;Effectiveness Analysis of Different POS Tagging Techniques for Bangla Language;intelligent Surveillance systems for Smart Cities: A Systematic Literature Review;the Social and Ethical Implications of Smart Environments in the Age of Big data;Enhanced Energy-Efficient Routing for Wireless Sensor Network Using Extended Power-Efficient Gathering in Sensor Information systems (E-PEGASIS) Protocol;sentiment Analysis of Amazon Product Reviews Using Hybrid Rule-Based Approach;artificial Intelligence and Machine Learning Based: advances in Demand-Side Response of Renewable Energy-Integrated Smart Grid;design of IoT-Enabled Farmer-Centric Multi-Crop Irrigation Support System;intelligent Infrastructure and Transportation: A Case of Passenger Transportation System in Jaipur City of Rajasthan;conceptual Machine Learning Framework for Performance-Based Resource Allocation to the Institutions: A Hybrid Approach;Criticality Analysis for Safety–Critical systems Using FMEA;significance of Internet of Things (IoT) in Health Care with Trending Smart Application;greek Handwritten Character Recognition Using Inception V3;An Exploratory Analysis on the Unfold of Fake News During COVID-19 Pandemic;a Research on Fake News Detection Using Machine Learning Algorithm;an intelligent Model to Suggest Top Productive Seasonal Crops Based on User Location in the Context of Bangladesh;brightness Intensity-Based Transient Motion Prediction;state of the Art and Challenges in Blockchain Applications;swarm Intelligence Techniques for Automated Cryptanalysis of Classical Transposition Cipher: A Review;YOLOv4-Based Monitoring Model for COVID-19 Social Distancing Control;facial Expression Recognition System.

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TSPM-CNN Based Power System Transient Stability Assessment Using a New data Extraction Method 12

TSPM-CNN Based Power System Transient Stability Assessment U...

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12th IET international conference on advances in Power System Control, Operation and Management, APSCOM 2022

作者： Zhou, Jiaqi Bu, Siqi Department of Electrical Engineering The Hong Kong Polytechnic University Kowloon Hong Kong The Hong Kong Polytechnic University Shenzhen Research Institute Guangdong Shenzhen China Research Institute for Smart Energy The Hong Kong Polytechnic University Kowloon Hong Kong Policy Research Centre for Innovation and Technology The Hong Kong Polytechnic University Kowloon Hong Kong

ISBN: (纸本)9781839538513

The increasing deployment of Wide Area Measurement systems (WAMS) and Phasor Measurement Units (PMUs) in power grids has significantly improved the situational awareness capability of the system. Together with the increasing computing power of computers, data-driven Transient Stability Assessment (TSA) has become a very popular tool for system operator. In order to improve the accuracy of data-driven transient stability prediction, a new TSPM-CNN TSA method based on a new data extraction method is proposed in this paper. The method makes full use of the excellent feature self-extraction capability of convolutional neural network (CNN) in order to effectively extract the time-series information of individual features and the correlation information of different features, and effectively solve the problems of insufficient datasets and loss of time-series information in traditional data-driven TSA methods. Furthermore, this paper proposes a TSA method based on a combination of time-series prediction model (TSPM) and CNN. The method uses the features of the TSPM to predict the subsequent data of the PMU of the power system and combines it with CNN to predict the transient stability status of the power systems. The effectiveness and accuracy of the method have been successfully verified by simulation experiments in this paper. © 2022 The Institution of Engineering and Technology. All Rights Reserved.

关键词： Phasor measurement units

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data-driven Recommendation Model with Meta-learning Autoencoder for Algorithm Selection 3rd

Data-Driven Recommendation Model with Meta-learning Autoenco...

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3rd international conference on Neural computing for Advanced Applications, NCAA 2022

作者： Chu, Xianghua Pang, Yongsheng Wang, Jiayun Guo, Yuqiu Qu, Yuanju Wang, Yangpeng College of Management Shenzhen University Shenzhen China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen China The Mount School York United Kingdom

ISBN: (纸本)9789811961410

To improve the efficiency of problem-solving for complex optimization problems, meta-learning was applied in algorithm selection to choose the most appropriate algorithm recently. However, the common meta-learners are feature-sensitive, where the selection and extraction of meta-features impact the quality of algorithm recommendations. In this study, we propose a data-driven recommendation model to implement the intelligent algorithm selection based on deep meta-features. A new kind of supervised stacked Autoencoder, named meta-learning Autoencoder, has been designed to process the deep meta-feature which is suitable both for instance-based and model-based meta-learners. To evaluate the performance of the proposed model, experiments have been conducted on some benchmark problems. Experimental results show that the recommendation accuracy of the model achieves nearly 100% in the seen problems and more than 80% in the unseen problems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning systems

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