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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61-70 订阅

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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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2024 international conference on advances in data Engineering and intelligent computing systems, ADICS 2024

作者： Pradeepa, M. Sumathi, V. Ponnusamy, Sivakumar Suneetha, G. Singh, Gurjeet Kruthika, P. School Of Computer Science Engineering And Information Systems Vellore Institute Of Technology Tamil Nadu Vellore632 014 India Sri Sai Ram Engineering College Department Of Mathematics West Tambaram Tamil Nadu Chennai44 India Ksr College Of Engineering Department Of Computer Science And Engineering Namakkal TamilNadu Tiruchengode India Koneru Lakshmaiah Education Foundation Department Of Cse Andhra Pradesh Vaddeswaram India Ies College Of Technology Ies University Bhopal Department Of Mechanical Engineering Madhya Pradesh India Sona College Of Technology Department Of Information Technology Tamil Nadu Salem India

ISBN: (纸本)9798350364828

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. © 2024 IEEE.

关键词： Forecasting

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Application of open data and integer linear programming to real-time generation of one-stroke traveling route for collecting and distributing for food loss reduction 12

Application of open data and integer linear programming to r...

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Joint 12th international conference on Soft computing and intelligent systems / 23rd international Symposium on Advanced intelligent systems (SCIS and ISIS)

作者： Kaneko, Itaru Yuda, Emi Okada, Hitoshi Tohoku Univ Ctr Data Driven Sci & Artificial Intelligence Sendai Miyagi Japan Natl Inst Informat Informat & Soc Res Div Chiyoda Ku Tokyo Japan

We believe that alleviating poverty and reducing food loss can contribute to the SDGs [1], and we have designed PoC for real time generation of one-stroke routes for real-time collection and distribution of food loss.... 详细信息

ISBN: (纸本)9781665499248

关键词： SDGs food loss integer programing open data traveling salesman

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Approximate Solution using Machine Learning and Evolutionary Algorithm for Large-Scale data-driven Chance Constrained Problems 12

Approximate Solution using Machine Learning and Evolutionary...

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Joint 12th international conference on Soft computing and intelligent systems / 23rd international Symposium on Advanced intelligent systems (SCIS and ISIS)

作者： Tagawa, Kiyoharu Kindai Univ Fac Informat 3-4-1 Kowakae Higashiosaka Osaka 5778502 Japan

ISBN: (纸本)9781665499248

By using a large-scale data set, Chance-Constraint Problem (CCP) is formulated as a deterministic optimization problem called Deterministic data-driven CCP (DDCP). DDCP not only determines the feasibility of a solution by whether or not it satisfies the constraints at a specified sufficiency level, but also classifies all given data into two classes. Then an approximate solution method for DDCP is proposed by combining a Support Vector Machine (SVM) and an Evolutionary Algorithm (EA). In order to select a training set for the SVM, a stratified sampling method using principal component analysis is employed. Finally, the performance of the proposed approximate solution method is evaluated through a real-world application, namely, the base-station location problem which is formulated as DDCP.

关键词： Chance-constraint problem big data support vector machine training set selection evolutionary algorithm

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Research on Smart Leisure Venue Management systems driven by the Internet of Things 1

Research on Smart Leisure Venue Management Systems Driven by...

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1st international conference on intelligent Manufacturing and Cloud computing, ICIMCC 2024

作者： Zhang, Lu Institute of Physical Education South China University of Technology Guangdong Guangzhou510641 China

ISBN: (纸本)9781643685779

The rapid development of Internet of Things (IoT) technology has provided new opportunities for constructing smart leisure venue management systems. This paper proposes an IoT-based intelligent leisure venue management system, employing a layered architectural design that includes data acquisition, data analysis, intelligent decision-making, and application service layers. The data acquisition layer collects environmental and user behavior data through distributed sensor networks and RFID technology. The data analysis layer utilizes big data processing techniques to achieve real-time data fusion and cleansing. The intelligent decision-making layer optimizes venue operations using reinforcement learning algorithms, while the application service layer offers personalized services and visual interfaces to users and managers. Experimental results demonstrate that the system excels in data acquisition accuracy, real-time processing capability, visitor flow prediction accuracy, and energy consumption optimization, effectively enhancing venue operational efficiency and user satisfaction. The conclusion highlights that the system has room for improvement in handling peak period visitor statistics. Future work will focus on optimizing algorithms and enhancing system scalability to further improve overall performance. © 2025 The Authors.

关键词： data collection

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Using Dynamic data driven Cyberinfrastructure for Next Generation Disaster Intelligence 4th

Using Dynamic Data Driven Cyberinfrastructure for Next Gener...

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4th international conference on Dynamic data driven Applications systems, DDDAS 2022

作者： Altintas, Ilkay University of California San Diego La JollaCA92093 United States

ISBN: (纸本)9783031526695

Wildfires and related disasters are increasing globally, making highly destructive megafires a part of our lives more frequently. A common observation across these large events is that fire behavior is changing, making applied datadriven fire research more important and time critical. Significant improvements towards modeling wildland fires and the dynamics of fire related environmental hazards and socio-economic impacts can be made through intelligent integration of modern data and computing technologies with techniques for data management, machine learning and artificial intelligence. However, there are many challenges and opportunities in integration of the scientific discoveries and datadriven methods for hazards with the advances in technology and computing in a way that provides and enables different modalities of sensing and computing. The WIFIRE cyberinfrastructure took the first steps to tackle this problem with a goal to create an integrated infrastructure, data and visualization services, and workflows for wildfire mitigation, monitoring, simulation, and response. Today, WIFIRE provides an end-to-end management infrastructure from the data sensing and collection to artificial intelligence and dynamic data-driven modeling efforts using a continuum of computing methods that integrate edge, cloud, and high-performance computing. Through this cyberinfrastructure, the WIFIRE project provides data driven knowledge for a wide range of public and private sector users, enabling scientific, municipal, and educational use. This paper summarizes the talk reviewing our recent work on building this dynamic data driven cyberinfrastructure and impactful application solution architectures that showcase integration of a variety of existing technologies and collaborative expertise. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Disasters

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Text Semantic Analysis and intelligent Interaction in Parallel Dispatching systems for Technical Station 4

Text Semantic Analysis and Intelligent Interaction in Parall...

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4th IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Jiao, Yuantao Wang, Jian Li, Runmei Xiong, Gang Chen, Shichao Beijing Jiaotong University School of Automation and Intelligence Beijing China Institute of Automation Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Beijing China Cloud Computing Center Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Dongguan China Chinese Academy of Sciences State Key Laboratory for Multimodal Artificial Intelligence Systems Beijing China

ISBN: (纸本)9798350349252

Technical station dispatching system plays an important role in cargo operation, but due to the large number of dispatching systems and complex operation, dispatchers rely on manual experience to complete the task, and many scenarios cannot be applied in the real system. Therefore, this paper utilizes the data-driven form to establish a parallel dispatching system for technical stations, semantically analyzes text data rich in dispatcher experience, completes the Computational Experiments on the textual semantic data in the artificial dispatching system, and synchronizes the Parallel Execution of data interaction with the real dispatching system. © 2024 IEEE.

关键词： parallel intelligence railway dispatching system technical station

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BAA: A Novel Decentralized Authorization System for Privacy-Sensitive Medical data 22

BAA: A Novel Decentralized Authorization System for Privacy-...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Zha, Cong Wu, Yulei Jiang, Zexun Zhao, Wenqian Yin, Hao Tsinghua Univ Network & Big Data Technol R&D Ctr Beijing Peoples R China Exeter Univ Dept Comp Sci Fac Environm Exeter Devon England Commun Univ China Sch Data Sci & Intelligent Media Beijing Peoples R China Univ North Texas Dept Informat Sci Denton TX 76203 USA

ISBN: (纸本)9798350381993;9798350382006

data authorization is the basis for the orderly sharing of medical data. Most of the applied decentralized authorization mechanisms rely on blockchain, but face the problems of privacy leakage and low efficiency. To solve these problems, we design a policy-driven decentralized authorization system named BAA, which protects user's behavior privacy in medical data sharing and improves efficiency of both on-chain and off-chain. In order to achieve these goals, BAA uses authorization tokens to represent permissions, protects privacy of the authorization process through hiding user behaviors, realizes batch data accessing by proposing a two-tier Merkle tree, and saves authorization data in a two-tier blockchain to improve on-chain efficiency. Extensive experimental results show that the overhead of cryptographic operations in BAA is acceptable compared to that in traditional authorization systems. In addition, throughput and latency of each operation in BAA can meet the efficiency needs of medical data authorization. The results also show that the preset authorization in blockchain is effective for reducing data user's waiting time and improving the efficiency of authorization.

关键词： Blockchain medical data authorization attribute-based encryption privacy protection

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Adaptive Segmentation on Extracting Textural and Fractal Patterns for Assessing Mangrove Dynamics Using Multi-spectral data 1

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2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Bhattacharjee, Anindita Das Chakravortty, Somdatta Institute of Engineering and Management West Bengal Saltlake Kolkata700091 India Maulana Abul Kalam Azad University of Technology Nadia West Bengal Haringhata741249 India

ISBN: (数字)9789819995189

ISBN: (纸本)9789819995172

This study primarily aims to develop and validate the Kurtosis-based Otsu segmentation technique (KOT), focusing on its effectiveness in separating object boundaries within complex and diverse image environments. Since the fractal dimension measures the complexity and intricateness of an object’s boundary or structure, precise boundary detection ensures the accuracy of the fractal dimension computation. This work elaborates on the investigation of fractal dimensions using Hausdorff approach in order to find self-similar patterns among the diverse mangrove communities of interest present in the individual clusters that make up the target region. The analysis shows that clusters 1, 2, and 3 and cluster 4 have high average Hausdorff fractal dimension values of 0.986 and 0.757, respectively. This suggests the presence of two separate mangrove communities and distinct self-similar patterns within these clusters. The analysis of multi-spectral imagery from the Landsat 8 Operational Land Imager (OLI) is at the heart of the investigation. The study area is located in the Saptamukhi Reserve Forest in the Sundarbans of West Bengal at roughly 21∘ 35′ 17.98″ N latitude and 88∘ 19′ 09.01″ E longitude. The study uses measures like Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) to assess the segmentation results. After segmenting Band1, the higher PSNR result of 193.31 dB indicates accurate spectral information preservation and decreased distortion. The execution time of 504.66 s and temporal complexity of O(nlogn) of the total KOT segmentation are faster than those of region growing and mean shift segmentation algorithms, pointing to a clear path for efficiently implementing this study for remote sensing-based applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Remote sensing

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Towards Improving Mathematics Learning Using the Deep Learning Models 1

Towards Improving Mathematics Learning Using the Deep Learni...

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1st IEEE international conference on advances in data-driven Analytics and intelligent systems, ADACIS 2023

作者： Lmati, Imane Khoulimi, Hind Hassan First University IR2M Laboratory Faculty of Sciences and Techniques Settat Morocco Hassan Ii University of Casablanca Applied Mathematics and Computing Laboratory Higher Normal School Morocco

ISBN: (纸本)9798350344158

Nowadays, the majority of intelligent tutoring systems (ITS) have significantly advanced in terms of capabilities and technologies. These systems have evolved to provide personalized and adaptive learning experiences for students. In mathematics, ITS can provide hints or messages to facilitate problem solving, but they do not provide feedback on educational concepts useful for resolving (such as theorems, definitions, etc.). Our methodology enables the generation of feedback in human learning environments. It makes it possible to predict the didactic concepts relevant to solving a given question Q(i) in an exercise based on existing or previously demonstrated data (Q(i-1), Q(i-2),...) using the Math-Bridge ontology. To extract knowledge from mathematical text, we use vectorization with SBERT models to calculate the semantic similarity between ontology concepts and exercises. We tested our approach on a set of exercises from mathematics teaching platforms and compared the number of concepts generated by our approach with those of experts. The results highlight the encouraging potential of our exercise solution method. © 2023 IEEE.

关键词： Ontology

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An Integrated Framework for Enhanced Learning Environments: IoT-driven Smart Classrooms with Multi-Layered Security Protocols and Adaptive Infrastructure

An Integrated Framework for Enhanced Learning Environments: ...

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2024 international conference on advances in computing, Communication, Electrical, and Smart systems, iCACCESS 2024

作者： Shayer, Khandokar Shadman Medul, Mahamudul Hasan Badoruzzaman, Md. Islam Shuvo, Jahidul Rabbu, Mohammad Mahmudul Haque, F.M. American International University-Bangladesh Department of Electrical & Electronic Engineering Dhaka Bangladesh International University of Business Agriculture and Technology Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350350289

The learning environment has experienced an evolution in this era of advanced instructional technologies. Smart classrooms are currently equipped with interactive devices and resources to enhance the effectiveness of learning, and this trend has evolved into an innovative classroom management technique. Conventional classes require a substantial workforce and an administrative team. In a conventional classroom setting, the level of security is generally either limited or completely absent. Considering the circumstances mentioned above, research has been conducted as a provisional resolution. Utilizing the Internet of Things, this research aims to develop a smart classroom that effectively handles real-time information and implements techniques for intelligent security and energy saving. The research utilized ESP32-CAM, a fingerprint sensor, a flame sensor, SIM8001, and an Arduino N ano. The prototype transmits live data to the website server. The hardware implementation results have been thoroughly assessed to verify the project's effective completion. © 2024 IEEE.

关键词： Websites

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