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Using Machine Learning Algorithms to Increase the Supplier Selection Process Efficiency in Supply Chain 4.0 1

International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2022

作者： Abouloifa, Houria Bahaj, Mohamed Mathematics and Computer Science Department Faculty of Sciences and Techniques Lab L.M.I.E.T Hassan Ist University Settat Morocco

ISBN: (数字)9783031263842

ISBN: (纸本)9783031263835

Supply Chain in one of the processes that took an important part of the attention given to Industry 4.0 because of its information flows which are dispersed in different directions. To that purpose, many new technologies have seen the day to improve the flows between the different links of the Supply Chain: Machine learning is being introduced into the manufacturing environment, which initiated a fourth industrial revolution. As such, this paper explore the application of machine learning algorithm to the supplier selection process based on past supplier orders and choices. The proposed model will enable firms to predict the good suppliers from the bad ones using supervised learning algorithms. To this purpose, we start by using three supervised machine learning tasks: AdaBoost, SVM and KNN. AdaBoost shows the most reliable results with an accuracy of 98% and an F1-score of 86%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Industry 4.0

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Fraud Detection in Supply Chain 4.0: A Machine Learning Model 1

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International Conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2022

作者： Abouloifa, Houria Bahaj, Mohamed Mathematics and Computer Science Department Faculty of Sciences and Techniques Lab L.M.I.E.T Hassan Ist University Settat Morocco

ISBN: (数字)9783031352454

ISBN: (纸本)9783031352447

The fourth industrial revolution has begun with the introduction of artificial intelligence into the manufacturing environment. As a result, flow management have accelerated and worldwide Supply chain have become more automated than ever. In this context, Supply Chain financial risks have increased which requires companies to be more vigilant about fraud detection in their interactions with the different stakeholders. In this paper, we establish a machine learning model to predict fraudulent operations between the different links of Supply Chain. Three models are deployed to that purpose: Random forest, K-Nearest Neighbor and logistic regression. The most optimal model is then upgraded by using grid search cross validation. Results shows that the forecasting model is more efficient when cross validation is employed with a score of 97,7%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Supply chains

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An Enhancement Algorithm for Mobility over Fog Computing 3

An Enhancement Algorithm for Mobility over Fog Computing

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3rd IEEE International Conference on Electronic Engineering, ICEEM 2023

作者： Hussien, Doaa Abo El-Sisi, Ashraf El-Bahnasawy, Nirmeen A. Menoufia University Faculty of Electronic Eng. Computer Science & Eng. Dept. El-Menoufia Egypt Menoufia University Faculty of Computers and Information Computer Science Department El-Menoufia Egypt

ISBN: (纸本)9798350323511

Due to the mobility of users and end devices, providing continuous service in Fog Computing (FC) is a difficult challenge. With fog-assisted IoT healthcare frameworks, dealing with user mobility becomes even more complex because low latency in mobility support and fog node locating is a critical requirement. In this work, we offer a fog computing architecture with hand-over strategy that is tailored to user mobility and that reduces latency for location-sensitive operations and service stability with great data broadcast quality. In our design, it is no longer necessary to either complete a work or send it to the cloud;instead, it is necessary to connect with other FNs to continue with a request for a new job. This reduces average network delay by 30%-40% and increases end-to-end communication by 35%-45%. © 2023 IEEE.

关键词： Fog computing

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A Comparative Study of Machine Learning Models for Plant Disease Identification 19th

A Comparative Study of Machine Learning Models for Plant D...

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19th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Mara, Măcelaru Pop, Petrică Barata, José Department of Mathematics and Computer Science North University Center at Baia Mare Technical University of Cluj-Napoca Cluj-Napoca Romania Electrical and Computer Engineering Department School of Sciences and Technology NOVA University of Lisbon Lisbon Portugal

ISBN: (纸本)9783031750090

Early identification of plant diseases plays an important role in reducing the financial losses for farmers and enhancing the productivity and quality of crops. The present work proposes five machine learning (ML) algorithms: K-Nearest Neighbors, Random Forest, Iterative Dichotomizer 3, Adaptive Boosting and Logistic Regression for plant disease identification. In order to test the proposed ML algorithms, we used a large data set containing 74798 images mainly from PlantVillage dataset. The achieved experimental results show that the proposed ML techniques can be used to reliably identify various plant diseases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Improving Imbalanced Data Classification Through Integrated Data-Level and Algorithm-Level Techniques 6

Improving Imbalanced Data Classification Through Integrated ...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： El Hajjami, Salma Amelal, Hicham El-Hajjami, Abdelkarim Kaushik, Keshav Khan, Inam Ullah Ibn Zohr University Faculty of Science LabSIV Department of Computer Science Agadir Morocco INP-ENSEEIHT University of Toulouse Department of Computer Science and Telecommunications France School of Computer Sciences University of Petroleum and Energy Studies Uttarakhand Dehradun India Department of Electronic Engineering Isra University Pakistan

ISBN: (纸本)9798350304480

Imbalanced class distribution is common issue in machine learning and data mining. It affects various applications like fraud detection, medical diagnosis, and network intrusion detection. The mentioned problem occurs when the majority class greatly outnumbers the minority class, hindering learning systems designed for balanced distribution. Furthermore, presence of overlapping instances between two classes exacerbates problem. This paper introduces a novel method that integrates ensemble and cost-sensitive techniques at algorithm-level with OSBNR approach at data-level to address this challenge. Furtherly, suggested technique is compared with conventional resampling methods, in conjunction with three ensemble algorithms and three cost-sensitive algorithms, to determine optimal combination. Simulation presents OSBNR is having optimal results in terms of processing time and evaluation metrics like G-mean, AUC-ROC, TPR-Recall, precision, Fl-score and FPR. PyOSBNR is simulated as python index package which can easily handle class imbalance in the presence of behavioral noise. © 2023 IEEE.

关键词： Cost-sensitive methods Ensemble methods Fraud detection Imbalanced class distribution Machine learning OSBNR PyOSBNR

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LAORA: Location-Aware Orientation Adjustment for MIMO Magnetic Wireless Charging System 21

LAORA: Location-Aware Orientation Adjustment for MIMO Magnet...

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21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Kong, Lingchang Wang, Xinyu Zhou, Hao Zhou, Fengyu Jiang, Shenyao Zhu, Peide Song, Qi Liu, Zhi LINKE Lab School of Computer Science and Technology University of Science and Technology of China China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China China Department of Computer Science City University of Hong Kong Hong Kong School of Mathematics and Statistics Fujian Normal University Fuzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo182-8585 Japan

ISBN: (纸本)9798331519186

Wireless power transfer (WPT) systems using magnetic resonant coupling (MRC) have made significant progress recently, leading to various optimization methods in scenarios involving multiple-input multiple-output (MIMO) to improve charging performance. Adjusting the coil orientation of the power transmitter (TX) is a simple but effective method due to the directional nature of magnetic field distribution, but existing approaches often require unnecessary coil rotations. In this study, we introduce a Location-Aware Orientation Adjustment algorithm, known as LAORA, to address these inefficiencies. LAORA focuses on solving the problems of charging devices (RX) localization and location-based optimization. We begin by introducing the concept of Equivalent Impedance Distribution Image (EIDI) and transform the RX localization problem into a combined matching process involving EIDI. In addition, we establish a dynamic simulation framework to predict charging performance using RX-related knowledge, enabling us to obtain optimal TX orientations through reinforcement learning without needing to rotate on mechanical devices physically. We then implement the prototype and conduct extensive experiments. The results show that, compared to other existing orientation adjustment methods, LAORA achieves an average improvement of 186% while reducing the mechanical rotations by 83.3%. © 2024 IEEE.

关键词： Inductive power transmission

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Using an Epic Finale in mathematics Classes

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PRIMUS 2022年第1期32卷 31-44页

作者： Koss, Lorelei Department of Mathematics and Computer Science Dickinson College Carlisle PA United States

In a recent article, Crider recommends ending a course with a memorable learning experience, called an epic finale, instead of a final exam. Here, we give the details of epic finales given in four mathematics courses: Discrete mathematics, Information and Coding Theory, Real Analysis, and Complex Analysis. We describe how to reconfigure a course to allow for this type of closing activity and give suggestions for how to create an epic finale. © 2020 Taylor & Francis Group, LLC.

关键词： complex analysis discrete mathematics Final exam real analysis

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Mitigating congestion in multi-agent traffic signal control: an efficient self-attention proximal policy optimization approach

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International Journal of Information Technology (Singapore) 2024年第4期16卷 2273-2282页

作者： Chergui, Oussama Sayad, Lamri Laboratory of Signals and Systems Analysis Faculty of Technology University of M′sila PO Box 166 Ichebilia M′sila 28000 Algeria Faculty of Mathematics and Computer Science University of M′sila PO Box 166 Ichebilia M′sila 28000 Algeria Laboratory of Informatics and its Applications of M′sila (LIAM) Faculty of Mathematics and Computer Science University of M′sila PO Box 166 Ichebilia M′sila 28000 Algeria

Traffic congestion is a persistent problem that effects cities worldwide, necessitating innovative solutions. This paper presents a novel traffic light control system using multi-agent proximal policy optimization with self-attention. Our approach outperforms traditional methods by 30% in reducing waiting times in high-traffic demand scenarios. By utilizing transfer learning and encoding mechanisms for dynamic input size adaptation, our approach enables scalability to larger networks without the need for costly training. This study underscores the potential of our approach as a dependable solution for addressing large-scale traffic congestion challenges. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Deep reinforcement learning Multi-agent Proximal policy optimisation Self attention Traffic lights control Transfer learning

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Preventing Pressure Oscillations Does Not Fix Local Linear Stability Issues of Entropy-Based Split-Form High-Order Schemes

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Communications on Applied mathematics and Computation 2022年第3期4卷 880-903页

作者： Hendrik Ranocha Gregor J.Gassner Applied Mathematics Münster University of Münster48149 MünsterGermany Computer Electrical and Mathematical Science and Engineering(CEMSE)Division King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Department of Mathematics and Computer Science Center for Data and Simulation ScienceUniversity of CologneCologneGermany

Recently,it was discovered that the entropy-conserving/dissipative high-order split-form discontinuous Galerkin discretizations have robustness issues when trying to solve the sim-ple density wave propagation example for the compressible Euler *** issue is related to missing local linear stability,i.e.,the stability of the discretization towards per-turbations added to a stable base *** is strongly related to an anti-diffusion mech-anism,that is inherent in entropy-conserving two-point fluxes,which are a key ingredi-ent for the high-order discontinuous Galerkin *** this paper,we investigate if pressure equilibrium preservation is a remedy to these recently found local linear stability issues of entropy-conservative/dissipative high-order split-form discontinuous Galerkin methods for the compressible Euler *** equilibrium preservation describes the property of a discretization to keep pressure and velocity constant for pure density wave *** present the full theoretical derivation,analysis,and show corresponding numerical results to underline our *** addition,we characterize numerical fluxes for the Euler equations that are entropy-conservative,kinetic-energy-preserving,pressure-equilibrium-preserving,and have a density flux that does not depend on the *** source code to reproduce all numerical experiments presented in this article is available online(https://***/10.5281/zenodo.4054366).

关键词： Entropy conservation Kinetic energy preservation Pressure equilibrium preservation Compressible Euler equations Local linear stability Summation-by-parts

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Bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment 9th

Bioinformatics exploration into potential mechanisms of Cala...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Kenneth, Christian Nirwantono, Rudi Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco-derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy. © 2024 The Authors.

关键词： Lung cancer

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