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Application of Artificial Intelligence in the Supply Chain: A Systematic Literature Review 6th

Application of Artificial Intelligence in the Supply Chain: ...

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6th international conference on Big Data Cloud and Internet of things, BDIoT 2022

作者： Kriouich, Mohamed Sarir, Hicham Mahboub, Oussama Advanced Science and Technology Team ENSA Tetouan Abdelmalek Esaadi University Tetouan Morocco Information System and Software Engineering ENSA Tetouan Abdelmalek Esaadi University Tetouan Morocco

this research presents a systematic literature review of60;using artificial intelligence (AI) in the supply chain (SC). We examine the works done so far in relation to AI research, which still needs to be investiga... 详细信息

ISBN: (纸本)9783031283864

this research presents a systematic literature review of using artificial intelligence (AI) in the supply chain (SC). We examine the works done so far in relation to AI research, which still needs to be investigated in light of the potential benefits of implementing AI in supply chain management. Dynamic supply chain processes require artificial intelligence techniques capable of dealing with their increasing complexity. Artificial intelligence techniques such as machine learning, Deep learning, natural language processing, expert systems, knowledge representation, Fuzzy logic and models, social intelligence, Artificial neural networks, Support vector machines, Gaussian models, robotics, Ant colony, and computer vision can transform and optimize the supply chain. In order to classify the study material, we looked at 50 research papers from the Scopus database that were published between 2002 and 2022 and assigned each one a grade based on two fundamental structural factors are as follows: AI techniques and sector of application. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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LQR-based predictive Energy-saving Control for Commercial Vehicles Based On Slope Information 6

LQR-based Predictive Energy-saving Control for Commercial Ve...

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6th CAA international conference on Vehicular Control and Intelligence, CVCI 2022

作者： Ren, Xianfeng Lv, Xianyong Zhang, Lubing Chen, Liping Chu, Hongqing Gao, Bingzhao Electronic Control Research Institute Weichai Power Co. Ltd. Weifang China School of Automotive Studies Tongji University Shanghai China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

To effectively improve the driving economy of commercial vehicles by integrating intelligent control technology, this paper develops a predictive energy saving control system based on Linear Quadratic Regulator (LQR) using road slope information. the system realizes the co-optimization of the longitudinal speed and gear position of vehicles. We build a simulation model in a commercial software of MATLAB/Simulink to verify the performance of the proposed LQR algorithm, and the simulations for the proposed controller and ProportionalIntegralDerivative (PID) controller are carried out under the virtual and real road conditions. the experimental results show that the proposed LQR control strategy can achieve a fuel saving rate of 2% to 5% compared to the baseline method. © 2022 IEEE.

关键词： Commercial vehicles

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Multi-model Fusion Solution for IEEE UV 2022 'Vision Meets Algae' Object Detection Challenge 6

Multi-model Fusion Solution for IEEE UV 2022 'Vision Meets A...

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6th IEEE international conference on Universal Village, UV 2022

作者： Peng, Xiaoxiao Wang, Yueyi Chen, Dayu Tian, Yuchen Huang, Keyu Zheng, Jianfeng Robot Technology Co. Ltd Department of Automatic Driving Software Engineering Shanghai China King's College London Department of Informatics London United Kingdom Didi Chuxing Department of Algorithm Strategy Hangzhou China Institute of Advanced Technology University of Science and Technology of China Anhui China Koto Communication Technology Co. Ltd Department of Algorithm Strategy Xiamen China

ISBN: (纸本)9781665474771

this report summarizes the fourth-place solution of the 'Vision Meets Algae' object detection challenge held on IEEE UV'2022 focuses on object detection in marine biology images obtained through the microscope. First, we experimented with a large number of backbones and necks to improve mAP by enhancing the model structure. then, we designed and tested a variety of data augmentation schemes based on algal characteristics from a data perspective. Finally, with multiple models ensembled adopted, our methods achieve 57.579% mAP on the test set. © 2022 IEEE.

关键词： Algae

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Choosing the Discipline of Restoring Computer Systems with Acceptable Degradation with Consolidation of Node Resources Saved After Failures 6

Choosing the Discipline of Restoring Computer Systems with A...

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6th international Scientific conference on Information, Control, and Communication Technologies, ICCT 2022

作者： Bogatyrev, Vladimir A. Bogatyrev, Stanislav V. Bogatyrev, Anatoly V. Security Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Saint Petersburg Russia Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia Yadro Cloud Storage Development Center Saint Petersburg Russia

ISBN: (数字)9781665493673

ISBN: (纸本)9781665493673

An approach to substantiating the choice of a discipline for the maintenance of a redundant computer system, with the possible use of node resources saved after failures, is considered. the choice is aimed at improving the reliability and profitability of the system, taking into account the operational costs of restoring nodes. models of reliability of systems with service disciplines are proposed, providing both the possibility of immediate recovery of nodes after failures, and allowing degradation of the system when using node resources stored after failures in it. the models take into account the conditions of the admissibility or inadmissibility of the loss of information accumulated during the operation of the system. the operating costs are determined, taking into account the costs of restoring nodes for the system maintenance disciplines under consideration © 2022 IEEE.

关键词： Fault tolerance

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Generic Recommendation System for Business Process Modeling 6th

Generic Recommendation System for Business Process Modeling

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6th international conference on Innovative Computing and Communication, ICICC 2023

作者： Shreya, J.L. Saini, Anu Kumari, Sunita Jain, Astha Department of Computer Science and Engineering Dr. SPM International Institute of Information Technology Naya Raipur India Department of Computer Science and Engineering G. B. Pant DSEU Okhla-1 Campus DSEU New Delhi India Tata Consultancy Services Limited Uttar Pradesh Noida India

ISBN: (纸本)9789819930098

Process modeling helps in the comprehension and reorganization of corporate activity. Manual process modeling takes longer time and is liable for errors. We propose to build and provide a supporting system for people who manage the creation and application of business process models for their organizations. Any person intending to build and get recommendations for business process models can utilize the proposed recommender system, depending upon the kind of organization the person is working for. the core functions of the recommender system are to recommend fragments of a business process model as well as in-built process models with the benefit of creating, storing, and utilizing them for multiple purposes. the recommendations are based on a number of factors such as the number of people who prefer a particular model along with the models’ ratings. Here, we use the basic principle of collaborative filtering. this recommendation system provides a user-friendly interface, with a lucid structure of query interface to help users surf easily. the recommendation system also avoids the risk of misusing users’ personal details since it does not use cookies. Users can freely utilize this recommendation system even if they are novice. this extended work takes into account all of the specifications of a business process model for a range of industries, such as education, software, food, medical, automotive, banking, and so on, thus developing a generic system. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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Separation of Concerns in an Edge-Based Compartmental Modeling Framework

Separation of Concerns in an Edge-Based Compartmental Modeli...

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16th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2023

作者： Guifo Fodjo, Yvan Lacmou Zeutouo, Jerry Bowong, Samuel CNRS UMR 7606 LIP6 Sorbonne Université Paris France URIFIA Université de Dschang Dschang Cameroon Inria Avalon LIP École Normale Supérieure de Lyon University of Lyon France IRD UMI 209 UMMISCO Bondy France Département de Mathématiques Université de Douala Douala Cameroon

A well-known framework with strong potential for epidemic prediction and the ability to incorporate realistic contact structures is edge-based compartmental modeling (EBCM). However, models built from this framework lead to a multiplication of ordinary differential equations and many parameters to be estimated, which make the models complex and difficult to extend or to reuse. the Kendrick approach has shown promising results in generalizing compartmental models to take into account aspects of contact networks while preserving the separation of concerns, thus allowing to define modular, extensible and reusable models. But this generalization of compartmental models to contact network aspects is still limited to a few contact networks. In this paper, we present an attempt to extend Kendrick’s approach from an approximation of EBCM models to further support aspects of contact networks, thereby improving the predictive quality of models with significant heterogeneity in contact structure, while maintaining the simplicity of compartmental models. this extension consists of an integration of the basic reproductive number R0 into the compartmental SIR framework. this attempted is validated using Miller’s mass action and the approximation of EBCM configuration model. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Reusability

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Towards Realistic, Applicable and Feasible Configuration-Aware Performance Modeling

Towards Realistic, Applicable and Feasible Configuration-Awa...

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international conference on software engineering - Companion, ICSE, Companion

作者： Yuanjie Xia University of Waterloo Waterloo Canada

ISBN: (数字)9798331536831

ISBN: (纸本)9798331536848

In modern software systems, configurability has become essential for optimizing performance across varying scenarios and user demands. However, predicting performance in configurable software remains challenging due to the complex interplay between configuration settings and workload characteristics. Existing performance models lack applicability and ignore the combined influence of configurations and workloads, limiting their applicability in dynamic environments. Additionally, current methods focus on either configurations or workloads in isolation, leaving the interactions between the two insufficiently explored. We conclude these challenges into three parts, which are realistic, applicable and feasible for the performance modeling, this thesis addresses the challenges through four projects. We conduct an empirical study to understand the complex relationships between configurations, workloads, and performance outcomes. Additionally, we develop a systematic sampling method to enhance the applicability and accuracy of configuration performance models, allowing models to learn from historical data actively. To further improve performance prediction, we propose a hybrid modeling approach that integrates configuration and workload variations, thereby increasing model simplicity and precision. Finally, we explore applying large language models (LLMs) to streamline the modeling process, embedding LLM insights into traditional methods to reduce the cost and complexity of performance modeling. these contributions aim to create robust performance models that better support software configuration and workload management, enhancing system reliability and efficiency.

关键词： Accuracy Costs Systematics Large language models software performance predictive models software systems Data models Complexity theory software engineering

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SupervisedImmuneNet: Training Artificial Immune Networks using a Supervised Learning Approach for Improved Multi-Class Classification 6

SupervisedImmuneNet: Training Artificial Immune Networks usi...

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6th international conference on Computational Intelligence and Intelligent Systems, CIIS 2023

作者： Sithungu, Siphesihle Philezwini Ehlers, Elizabeth Marie Academy of Computer Science and Software Engineering University of Johannesburg South Africa

ISBN: (纸本)9798400709067

the most common application of artificial immune networks (AINs) is on unsupervised learning tasks. this is due to the fact that AINs are inspired by the adaptive immune system, which consists of a network of antibodies that self-organises to form a memory of external antigens. the self-organising nature of AINs makes them a natural approach for solving problems involving learning and adapting to patterns or structures present in a dataset to form an abstract representation. Training AINs in this fashion means that the dataset need not have class labels because the typical aim of the learning process is not to perform classification. However, there have been attempts to use AINs for classification tasks by considering the resulting clusters of antibodies as representative of the classes present in a dataset. this has also been done when applying AINs to the task of recognising handwritten characters. However, in all the approaches found in the literature, the common method was to leave the task of discovering classes to the AINs. Doing so is contrary to how other models are trained to do classification tasks where data samples are provided along with their class labels to guide the learning process. therefore, this paper presents a novel supervised learning approach to training AINs for multi-class classification. the proposed approach was tested on the MNIST handwritten digits dataset and achieved a classification accuracy of 99.45%. © 2023 ACM.

关键词： Antibodies

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Generic, Stand-Alone IoT Solution for Cloud Connectivity in Electric Vehicles 6

Generic, Stand-Alone IoT Solution for Cloud Connectivity in ...

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6th international conference on Computing, Communication, Control and Automation, ICCUBEA 2022

作者： Deshmukh, Aditya Murali, Meera Jadhav, Shivraj Mandhana, Abhishek College of Engineering Pune Department of Electrical Engineering Pune India Global Software House Hella India Automotive Pvt. Ltd. Pune India Advance Engineering Hella India Automotive Pvt. Ltd. Pune India

ISBN: (纸本)9781665484510

the past decade has witnessed tremendous advancements in the field of electric vehicle, which in turn has resulted in the need for cloud connectivity in vehicles due to the numerous advantages provided by it like aid in autonomous driving, connected cars and real-time fault notifications. Currently, incorporation of Internet of things into automobiles is in its relative early stage and new solutions are coming up every day for connecting automobiles to the cloud. this paper presents a generic and scalable solution for incorporating IoT to establish cloud connectivity in real-time, within the dynamic environment of an automobile. A hardware prototype, which is a plug-and-play type IoT device, is developed which is comparatively 63% cheaper as compared to the existing models that are currently available in the market. the device can carry out data collection and logging for up-to 2 CAN buses, with data transmission to a cloud server made possible with the help of a GSM module, thus eliminating the need for Wi-Fi or ethernet and making the device a truly stand-alone system. © 2022 IEEE.

关键词： Internet of things

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Assessing Clinical Usefulness of Readmission Risk Prediction Model 6th

Assessing Clinical Usefulness of Readmission Risk Prediction...

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6th Kuala Lumpur international conference on Biomedical engineering, BioMed 2021

作者： Teo, Kareen Yong, Ching Wai Chuah, Joon Huang Hasikin‬, Khairunnisa Salim, Maheza Irna Mohd Hum, Yan Chai Lai, Khin Wee Faculty of Engineering Department of Biomedical Engineering Universiti Malaya Kuala Lumpur Malaysia Faculty of Engineering Department of Electrical Engineering Universiti Malaya Kuala Lumpur Malaysia Faculty of Engineering School of Biomedical Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Mechatronics and Biomedical Engineering Universiti Tunku Abdul Rahman Sungai Long43000 Malaysia

ISBN: (纸本)9783030907235

Readmission manifests signs of degraded quality of care and increased healthcare cost. Such adverse event may be attributed to premature discharge, unsuccessful treatments, or worsening comorbidities. predictive modeling provides useful information to identify patients at a higher risk for readmission for targeted interventions. though many studies have proposed readmission risk predictive models and validated their discriminative ability with performance metrics, few examined the net benefit realized by a predictive model. We compared traditional logistic regression against modern neural network to predict unplanned readmission. An added value of 7% on discriminative ability is observed for modern machine learning model compared to regression. A cost analysis is provided to assist physicians and hospital management for translating the theoretical value into real cost and resource allocation after model implementation. the neural network model is projected to contribute 15× more savings by reducing readmissions. Aside from constructing better performing models, the results of our study demonstrate the potential of a clinically helpful prediction tool in terms of strategies to reduce cost associated with readmission. © 2022, Springer Nature Switzerland AG.

关键词： Forecasting

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