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Improving Supplier Evaluation Model using Ensemble Method-Machine Learning for Food Industry

Procedia computer science 2023年 227卷 307-315页

作者： Muhammad Asrol Sofyan Wahyudi Suharjito Christian Harito Ditdit N Utama Muhammad Syafrudin Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Department of Artificial Intelligence Sejong University Seoul 05006 South Korea

Supplier evaluation has a crucial role in maintaining efficiency in the food industry supply chain. Machine learning approaches can be employed to formulate models aimed at analyzing and evaluating supplier performance. Previous research has successfully designed decision tree and neural network models for assessing suppliers in the food industry with accuracies of 84.2% and 92.8% separately. Recognizing the opportunity to improve the model's performance, this study aims to advancing the machine learning models accuracy for analyzing and evaluating suppliers in the food industry. Two main models are proposed to enhance model accuracy: ensemble methods and support vector machine. This research has successfully designed a supplier evaluation model and demonstrated that the ensemble method - gradient boosting model outperforms other ensemble methods and support vector machine which is achieved a accuracy of 93.6% on a cross-validation dataset. The development of a dashboard is required to implement the supplier evaluation model using machine learning, facilitating decision-makers in evaluating and controlling supplier performance.

关键词： Ensemble Method Food Machine Learning Supply chain Supplier evaluation

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Diabetes Classification Using Support Vector Machine: Binary Classification Model 4

Diabetes Classification Using Support Vector Machine: Binary...

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4th International Conference on Information and Communications Technology, ICOIACT 2021

作者： Rabiha, Suciana Ghadati Wibowo, Antoni Lukas Heryadi, Yaya Computer Science Department BINUS Graduate Program-Doctor of Computer Science. Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University 11480 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University 11480 Indonesia

ISBN: (纸本)9781665433945

One of the health problems that require special attention is diabetes, besides the growth of this disease infection is increasing in various circles ranging from children, adults, men, women and the elderly. So to detect diabetes at an early stage, machine learning algorithms are the right choice to help detect indications of diabetes. This paper presents experimental results related to the exploration of the use of a Super Vector Machine (SVM) for data classification of Diabetes. The model used in this experiment is the Binary Classification Model. Based on the test results, the highest accuracy is obtained by using a total of 700 lines of data, which is 75,7143%. The Best estimated feasible point in Box Constraint is 272.29 with a KernelScale area of 72.627. © 2021 IEEE

关键词： Face recognition

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DEEP LEARNING-BASED CHANNEL ESTIMATION FOR RIS-ASSISTED UAV-ENABLED WIRELESS COMMUNICATIONS SYSTEM

DEEP LEARNING-BASED CHANNEL ESTIMATION FOR RIS-ASSISTED UAV-...

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2023 International Conference on Innovation, Communication and engineering, ICICE 2023

作者： Tesfaw, Belayneh A. Lin, Hsin-Piao Tarekegn, Getaneh B. Juang, Rong-Terng Jeng, Shiann-Shiun Department of International Graduate Program in Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Institute of Aerospace and System Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Electrical and Computer Engineering National Yang-Ming Chiao Tung University Hsinchu30010 Taiwan Department of Electrical Engineering National Dong Hwa University Hualien Taiwan

ISBN: (纸本)9781839539954

Wireless communication via unmanned aerial vehicles (UAVs) has drawn a great deal of attention due to its flexibility in establishing line-of-sight (LoS) communications. However, in complex urban and dynamic environments, the movement of UAVs can be blocked by trees and high-rise buildings that obstruct directional paths. This problem can be effectively addressed by employing reconfigurable intelligent surfaces (RIS). To achieve this goal, accurate channel estimation in RIS-assisted UAV-enabled wireless communications is crucial. This paper proposes an accurate channel estimation model using long short-term memory (LSTM) for a multi-user RIS-assisted UAV-enabled wireless communications system. Simulation results showed that LSTM can effectively improve the channel estimation performance of RIS-assisted UAV-enabled wireless communications. © The Institution of engineering & Technology 2023.

关键词： Channel estimation

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RSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing

arXiv

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arXiv 2024年

作者： Barahimi, Borna Singh, Hakam Tabassum, Hina Waqar, Omer Omer, Mohammad Electrical Engineering and Computer Science Department York University Canada School of Computing University of the Fraser Valley BC Canada Cognitive Systems Corp. Waterloo Canada

WiFi-enabled Internet-of-Things (IoT) devices are evolving from mere communication devices to sensing instruments, leveraging Channel State Information (CSI) extraction capabilities. Nevertheless, resource-constrained IoT devices and the intricacies of deep neural networks necessitate transmitting CSI to cloud servers for sensing. Although feasible, this leads to considerable communication overhead. In this context, this paper develops a novel Real-time Sensing and Compression Network (RSCNet) which enables sensing with compressed CSI;thereby reducing the communication overheads. RSCNet facilitates optimization across CSI windows composed of a few CSI frames. Once transmitted to cloud servers, it employs Long Short-Term Memory (LSTM) units to harness data from prior windows, thus bolstering both the sensing accuracy and CSI reconstruction. RSCNet adeptly balances the trade-off between CSI compression and sensing precision, thus streamlining real-time cloud-based WiFi sensing with reduced communication costs. Numerical findings demonstrate the gains of RSCNet over the existing benchmarks like SenseFi, showcasing a sensing accuracy of 97.4% with minimal CSI reconstruction error. Numerical results also show a computational analysis of the proposed RSCNet as a function of the number of CSI frames. © 2024, CC BY.

关键词： Internet of things

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3D Facial Reconstruction Applied to Medical Cosmetology

3D Facial Reconstruction Applied to Medical Cosmetology

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IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability (ECBIOS)

作者： Sheng-Hsien Hsieh Ming-Yi Lin Ching-Han Chen Program of Artificial Intelligence and Mechatronics National Pingtung University of Science and Technology Pingtung Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Department of Computer Science and Engineering National Central University Taoyuan Taiwan

We present a high-accuracy 3D facial reconstruction system with the following features: real-time 3D facial reconstruction using exposure synchronization multi-camera, feature alignment to quantify facial differences, a software system based on a naked eye 3D display, and medical records for a 3D face database. The proposed system is novel and practical, and the algorithm and the hardware/software architecture improve the current non-quantitative communication status in medical aesthetics. The system achieves preoperative expectations, real-time recording and feedback during surgery, and postoperative tracking analysis, thus making doctor-patient communication more efficient. The facial data database records personal and quantitative beauty data and its changes, providing accurate medical and aesthetic treatment analysis for individuals. In addition, the collected data of different genders, ages, injection sites, and dosages contribute to the development of more accurate medical materials. With artificial intelligence and cloud architecture, we provide reliable data for medical aesthetics customers, helping them understand their medical aesthetic needs. Doctors also provide stable medical care quality to consumers through cloud data, and medical aesthetic consultants do not need to exaggerate medical effects excessively. They can inform customers of specific differences before and after surgery through precise data and provide visual communication and customer expansion to improve consultation conversion rates.

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An algorithm for conditional-fault local diagnosis of multiprocessor systems under the MM⁎ model

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Theoretical computer science 2024年 987卷

作者： Lv, Yali Lin, Cheng-Kuan Hsu, D. Frank Fan, Jianxi School of Information Technology Henan University of Chinese Medicine Zhengzhou China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taoyuan Taiwan Department of Computer and Information Science Fordham University New York United States School of Computer Science and Technology Soochow University Suzhou China

Diagnosis is a crucial subject for maintaining the reliability of multiprocessor systems. Under the MM⁎ diagnosis model, Sengupta and Dahbura proposed a polynomial-time algorithm with time complexity O(N5) to diagnose a system with N processors. In this paper, we propose a (α,β)-trees combination S(u,X,α,β) and give an algorithm to identify the fault or fault-free status of each processor for conditional local diagnosis under the MM⁎ model. According to our results, a connected network with a (α,β)-trees combination S(u,X,α,β) for a node u is conditionally locally (α+2β−3)-diagnosable at node u and the time complexity of our algorithm to diagnose u is O(α2β+αβ2). As an application, we show that our algorithm can identify the status of each node of n-dimensional star graph Sn if the faulty node number does not exceed 3n−8. Compared with existing algorithms, our algorithm allows more faulty nodes in a multiprocessor system. © 2024 Elsevier B.V.

关键词： Computational complexity

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Enhancement of Channel Capacity in 5G Ultra Dense Network-UDN 2

Enhancement of Channel Capacity in 5G Ultra Dense Network-UD...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Rastogi, Ritesh Shanthi, T. Kumar Naik, Pramod Sheela, M. Sahaya Mishra, A.K. Nandhini, T.J. Delhi India Paavai Engineering College Department of Ece Chennai India Dayananda Sagar University Departmeny of Computer Science Engineering Bangalore India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Dhofar University Math Unit Foundation Program Salalah Oman Chennai India

ISBN: (纸本)9798350347579

A 5G ultra dense network architecture makes use of a high density of micro cell base stations to provide increased coverage, capacity, and performance for 5G communication systems. This is accomplished by the utilization of a very densely packed network. A 5G ultradense network is another name for this particular category of network. Ultradense networks are built to overcome the limitations of ordinary cellular networks. This is accomplished by increasing the number of base stations that are located inside a given area. This is performed by reducing the coverage area provided by each base station, which ultimately leads to an increase in the capacity of the entire network. The maximum data rate or capacity that may be obtained through a certain wireless channel is referred to as the 'channel capacity' in a 5G ultradense network, when the phrase 'channel capacity' is used. One possibility is that this represents the maximum quantity of data that can be transmitted across the channel. These criteria, which represent the capabilities of the channel, can be used to evaluate the performance of channels and equipment on 5G networks. This article presents for further research a model for improving the channel capacity of the 5G ultradense network. The model can be found at the end of the article. This change will result in an increase in both the usefulness of the channel and the amount of bandwidth that it consumes;both of these aspects will improve. The fact that it uses a very small quantity of energy and power in compared to other methods is the major trait that sets it apart from other methods. This technology is superior to others in terms of both the amount of data it can communicate and the amount of electricity it consumes in the process. © 2023 IEEE.

关键词： Energy efficiency

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Multi-Robot Local Motion Planning Using Dynamic Optimization Fabrics

arXiv

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arXiv 2023年

作者： Bakker, Saray Knoedler, Luzia Spahn, Max Böhmer, Wendelin Alonso-Mora, Javier The department of Cognitive Robotics Delft University of Technology Netherlands The department of Electrical Engineering Mathematics & Computer Science Delft University of Technology Netherlands

In this paper, we address the problem of real-time motion planning for multiple robotic manipulators that operate in close proximity. We build upon the concept of dynamic fabrics and extend them to multi-robot systems, referred to as Multi-Robot Dynamic Fabrics (MRDF). This geometric method enables a very high planning frequency for high-dimensional systems at the expense of being reactive and prone to deadlocks. To detect and resolve deadlocks, we propose Rollout Fabrics where MRDF are forward simulated in a decentralized manner. We validate the methods in simulated close-proximity pick- and-place scenarios with multiple manipulators, showing high success rates and real-time performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Multipurpose robots

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Unlocking the potential of RNN and CNN models for accurate rehabilitation exercise classification on multi-datasets

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Multimedia Tools and Applications 2025年第3期84卷 1261-1301页

作者： Zaher, Moamen Ghoneim, Amr S. Abdelhamid, Laila Atia, Ayman Software Engineering Program Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Computer Science Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Information System Department Faculty of Computers & Artificial Intelligence—Helwan University Cairo Ain Helwan11795 Egypt Cairo Ain Helwan11795 Egypt Giza October12451 Egypt

Physical rehabilitation is crucial in healthcare, facilitating recovery from injuries or illnesses and improving overall health. However, a notable global challenge stems from the shortage of professional physiotherapists, particularly acute in some developing countries, where the ratio can be as low as one physiotherapist per 100,000 individuals. To address these challenges and elevate patient care, the field of physical rehabilitation is progressively integrating computer Vision and Human Activity Recognition (HAR) techniques. Numerous research efforts aim to explore methodologies that assist in rehabilitation exercises and evaluate patient movements, which is crucial as incorrect exercises can potentially worsen conditions. This study investigates applying various deep-learning models for classifying exercises using the benchmark KIMORE and UI-PRMD datasets. Employing Bi-LSTM, LSTM, CNN, and CNN-LSTM, alongside a Random Search for architectural design and Hyper-parameter tuning, our investigation reveals the (CNN) model as the top performer. After applying cross-validation, the technique achieves remarkable mean testing accuracy rates of 93.08% on the KIMORE dataset and 99.7% on the UI-PRMD dataset. This marks a slight improvement of 0.75% and 0.1%, respectively, compared to previous techniques. In addition, expanding beyond exercise classification, this study explores the KIMORE dataset’s utility for disease identification, where the (CNN) model consistently demonstrates an outstanding accuracy of 89.87%, indicating its promising role in both exercises and disease identification within the context of physical rehabilitation. © The Author(s) 2024.

关键词： Classification (of information)

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Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance 8

Updating Clinical Risk Stratification Models Using Rank-Base...

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8th Machine Learning for Healthcare Conference, MLHC 2023

作者： Ötles, Erkin Denton, Brian T. Wiens, Jenna Medical Scientist Training Program University of Michigan Ann ArborMI United States Department of Industrial and Operations Engineering University of Michigan Ann ArborMI United States Division of Computer Science and Engineering University of Michigan Ann ArborMI United States

As data shift or new data become available, updating clinical machine learning models may be necessary to maintain or improve performance over time. However, updating a model can introduce compatibility issues when the behavior of the updated model does not align with user expectations, resulting in poor user-model team performance. Existing compatibility measures depend on model decision thresholds, limiting their applicability in settings where models are used to generate rankings based on estimated risk. To address this limitation, we propose a novel rank-based compatibility measure, CR, and a new loss function that aims to optimize discriminative performance while encouraging good compatibility. Applied to a case study in mortality risk stratification leveraging data from MIMIC, our approach yields more compatible models while maintaining discriminative performance compared to existing model selection techniques, with an increase in CR of 0.019 (95% confidence interval: 0.005, 0.035). This work provides new tools to analyze and update risk stratification models used in clinical care. © 2023 E. Ötles., B.T. Denton & J. Wiens.

关键词： Risk assessment

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