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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Duran, Ege Ozturk, Cemalettin O'Sullivan, Barry SFI Centre for Research Training in AI School of Computer Science IT University College Cork Ireland Department of Process Energy & Transport Engineering Munster Technological University Cork Ireland Insight SFI Research Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

Advances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenges in efficiently planning and scheduling shared resources. Therefore, there is a need for scalable and quick solutions for practical applications. Shared manufacturing systems share characteristics with parallel machine scheduling, allowing for the application of advancements from this domain. This research focuses on Multi-Agent Parallel Machine Scheduling (MAPMS) in shared manufacturing, specifically addressing scenarios involving two parallel machines and distinct agents managing exclusive, set of non-overlapping orders. The study introduces a novel multi-objective mixed integer programming (MIP) model for order scheduling across multiple facilities, accounting for sequence-dependent setup times between orders. It also proposes a new heuristic method designed for industrial use. Benchmark instances demonstrate the practicality of both the MIP model and heuristic, contributing valuable insights into MAPMS challenges in shared manufacturing environments. © 2025 The Authors.

关键词： Mixed-integer linear programming

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： A. Dharshan Purushottam Kumar S. Ravimaran U Srinivasulu Reddy Department of Artificial Intelligence and Data Science Saranathan College of Engineering Trichy India CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems.

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Seeing the light: Hyperspectral retinal imaging as a surrogate marker for early stage Alzheimer's disease

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ACTA OPHTHALMOLOGICA 2025年第S284期103卷

作者： Ghesquiere, Michiel Christinaki, Eirini Van Eijgen, Jan Vandenbulcke, Mathieu Vandenberghe, Rik De Vos, Maarten Stalmans, Ingeborg Department of Neurosciences – Research Group Ophthalmology KU Leuven Leuven Belgium Department of Neurosciences – Research Group Psychiatry KU Leuven Leuven Belgium Department of Neurosciences – Research Group Experimental Neurology KU Leuven Leuven Department of Electrical Engineering (ESAT) – Dynamical Systems Signal Processing and Data Analytics (STADIUS) KU Leuven Leuven Belgium

Aims/Purpose: Alzheimer's disease (AD), the leading cause of dementia, affects millions, underscoring the need for early diagnostic methods. The retina, which can exhibit AD-related pathological processes, offers a promising diagnostic site. This study aims to utilize Hyperspectral Retinal Imaging (HSRI) to differentiate between positive (+) and negative (-) PET-amyloid beta (Aβ) status. Within this cohort, a subset is Aβ+ while remaining cognitively healthy, indicating preclinical amyloid accumulation. Unlike most research targeting later symptomatic stages, our approach could facilitate interventions before clinically apparent neurodegeneration occurs. Methods: data was used from 37 Aβ- and 25 Aβ+ individuals of which 8 with mild cognitive impairment (MCI). HSRI were captured using an easy-to-use hyperspectral snapshot camera, covering a spectral range of 460–630 nm. Images were taken from primary, inferior, and superior views of the retina, with further regions of interest (ROI) defined in the primary view. After preprocessing, these spectra were used to train machine learning classifiers [1]. Results: The best classifier performance was observed using XGBoost with the superior spectra (AUC = 0.72), and using a Linear Discriminant Analysis classifier in superior quadrants (S1 AUC = 0.74, S2 AUC = 0.68). Lower performance in other ROI underscored the variability in amyloid deposition across the retina, correlating with previous findings that retinal Aβ deposits in AD patients are frequently concentrated in the superior quadrants. Conclusions: We were able to achieve similar performance of previous research in AD patients [1], but in a preclinical cohort of cognitively normal participants up to MCI patients. Consistent with the literature, the superior retinal regions seem most discriminative for Aβ status. Further exploration of the specificity of the HSRI signal will pave the way for early AD detection, potentially improving outcomes through earlier therapeutic i

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Synchronous Parallel Heuristics for Solving the Joint Order Batching and Picker Routing Problem 13

Synchronous Parallel Heuristics for Solving the Joint Order ...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Tran, Son Thai Almeida, Rui Jorge Defryn, Christof Van Nieuwenhuyse, Inneke School of Business and Economics Maastricth University Dept. of Quantitative Economics Maastricht Netherlands School of Business and Economics Maastricth University Dept. of Data Analytics and Digitization Maastricht Netherlands University of Antwerp Faculty of Business and Economics Dept. of Engineering Management Antwerp Belgium FlandersMake@UHasselt Data Science Institute Hasselt University Faculty of Sciences Hasselt Belgium

ISBN: (纸本)9798350308365

The joint order batching and picker routing problem is an important problem for improving warehouse efficiency. The goal is to minimize the total distance travel of picking customer orders. It is NP-hard, indicating that exact solutions are intractable for large instances. Many solution methods provided in the current literature use local search to solve the problem sequentially, often including a complex searching procedure for the order batching problem (OBP) followed by a simple heuristic for the picker routing problem (PRP). In this paper, we propose three heuristics that jointly solves the OBP and the PRP: two of these are combinations of variable neighborhood search, simulated annealing, and tabu search, while the third one uses guided local search. We discuss how the search procedure of the proposed algorithms can be parallelized, and benchmark them against state-of-the-art heuristics using well-studied instances. The results show a reduction of 3.06% to 7.63% in the geometric mean of the total travel distance across 64 instances. Increasing the number of threads in parallelization leads to diminishing returns in reducing running time and has no statistical effect on travel distance. In contrast, increasing the chunk size results in a linear increase in running time for all proposed algorithms, and improves the travel distance obtained when the batch size is 75 items. © 2024 IEEE.

关键词： Simulated annealing

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On Scalable Design for User-Centric Multi-Modal Shared E-Mobility Systems using MILP and Modified Dijkstra’s Algorithm

arXiv

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arXiv 2024年

作者： Shah, Maqsood Hussain Li, Ji Liu, Mingming Insight Centre of Data Analytics The School of Electronic Engineering Dublin City University Dublin Ireland Department of Mechanical Engineering University of Birmingham United Kingdom

In the rapidly evolving landscape of urban transportation, shared e-mobility services have emerged as a sustainable solution to meet growing demand for flexible, eco-friendly travel. However, the existing literature lacks a comprehensive multi-modal optimization framework with focus on user preferences and real-world constraints. This paper presents a multi-modal optimization framework for shared e-mobility, with a particular focus on e-mobility hubs (e-hubs) with micromobility. We propose and evaluate two approaches: a mixed-integer linear programming (MILP) solution, complemented by a heuristic graph reduction technique to manage computational complexity in scenarios with limited e-hubs, achieving a computational advantage of 93%, 72%, and 47% for 20, 50, and 100 e-hubs, respectively. Additionally, the modified Dijkstra’s algorithm offers a more scalable, real-time alternative for larger e-hub networks, with median execution times consistently around 53 ms, regardless of the number of e-hubs. Thorough experimental evaluation on real-world map and simulated traffic data of Dublin City Centre reveals that both methods seamlessly adapt to practical considerations and constraints such as multi-modality, user-preferences and state of charge for different e-mobility tools. While MILP offers greater flexibility for incorporating additional objectives and constraints, the modified Dijkstra’s algorithm is better suited for large-scale, real-time applications due to its computational efficiency. © 2024, CC BY.

关键词： Urban transportation

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Stability Analysis of Grid-Connected Microgrid Integrated with Biomass Plant

Stability Analysis of Grid-Connected Microgrid Integrated wi...

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IEEE General Meeting Power& Energy Society

作者： Namita Kumari Ankush Sharma Binh Tran Damminda Alahakoon Department of Electrical Engineering Indian Institute of Technology Kanpur India Centre for Data Analytics and Cognition Latrobe University Melbourne Australia

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

The Microgrid concept plays a pivotal role in ensuring a dependable power supply for a region, offering substantial benefits. A microgrid consists of various energy resources, energy storage, and power converters, which provide continuous power supply, with energy storage acting as a reliable backup. Generally, most of the existing microgrids comprise solar, diesel, biogas, and wind as energy resources with batteries as energy storage devices. The stable operation of a microgrid is one of the most important concerns, either in grid-connected mode or islanding mode. This paper addresses an existing practical issue of frequency instability within a microgrid system that incorporates a small biomass energy system while operating in the grid-connected mode. A practical microgrid developed as part of the joint Indo-US project was utilized for the analysis purpose. A component of this microgrid system comprises a 30 kW photovoltaic system, a 30 kW biomass energy system, and a 100 kWh battery energy storage unit situated in a village near the host institute. When connecting the microgrid to the main utility grid while the biomass plant was operational, the microgrid encountered frequency instability issues, preventing a successful connection to the utility grid. This research tackles the problem by employing a back-to-back converter in conjunction with a permanent magnet synchronous machine. Verification of the proposed solution was conducted through OPAL-RT simulations under various conditions.

关键词： Wind Wind energy Microgrids Wind farms Stability analysis Real-time systems Biomass Batteries Synchronization Reliability

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AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity

AdaFGL: A New Paradigm for Federated Node Classification wit...

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International Conference on data engineering

作者： Xunkai Li Zhengyu Wu Wentao Zhang Henan Sun Rong-Hua Li Guoren Wang Beijing Institute of Technology Beijing China Peking University National Engineering Labratory for Big Data Analytics and Applications Beijing China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Recently, Federated Graph Learning (FGL) has attracted significant attention as a distributed framework based on graph neural networks, primarily due to its capability to break data silos. Existing FGL studies employ community split on the homophilous global graph by default to simulate federated semisupervised node classification settings. Such a strategy assumes the consistency of topology between the multi-client subgraphs and the global graph, where connected nodes are highly likely to possess similar feature distributions and the same label. However, in real-world implementations, the varying perspectives of local data engineering result in various subgraph topologies, posing unique heterogeneity challenges in FGL. Unlike the well-known label Non-independent identical distribution (Non-iid) problems in federated learning, FGL heterogeneity essentially reveals the topological divergence among multiple clients, namely homophily or heterophily. To simulate and handle this unique challenge, we introduce the concept of structure Non-iid split and then present a new paradigm called Adaptive Federated Graph Learning (AdaFGL), a decoupled two-step personalized approach. To begin with, AdaFGL employs standard multi-client federated collaborative training to acquire the federated knowledge extractor by aggregating uploaded models in the final round at the server. Then, each client conducts personalized training based on the local subgraph and the federated knowledge extractor. Extensive experiments on the 12 graph benchmark datasets validate the superior performance of AdaFGL over state-of-the-art baselines. Specifically, in terms of test accuracy, our proposed AdaFGL outperforms baselines by significant margins of 3.24 % and 5.57 % on community split and structure Non-iid split, respectively.

关键词： Training Network topology Federated learning Collaboration Benchmark testing data engineering Graph neural networks

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Cardiovascular Disease Prediction Using Machine Learning Approaches

Cardiovascular Disease Prediction Using Machine Learning App...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Islam, Taminul Vuyia, Adifa Hasan, Mahadi Rana, Md Masum Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh University of Tennessee Department of Data Analytics ChattanoogaTN United States University of South Dakota Department of Computer Science VermillionSD United States

ISBN: (纸本)9798350323917

As of the release of COVID-19, cardiovascular disease has surpassed all other causes of mortality among both sexes. In most cases, this condition is associated with atherosclerosis and the formation of blood clots. Heart disease, stroke, and other CVDs are major causes of mortality worldwide. Because even a small inaccuracy might lead to exhaustion or death, increased precision, perfection, and accuracy are required for diagnosing and predicting heart-related disorders. In this paper, data was collected from 1189 patients with some attributes related to heart disease and kept 80% data for training, and 20% data for testing and determining their accuracy using different models. In this study, enhanced preprocessing steps were used to increase the accuracy of cardiovascular disease prediction. It aids in determining whether a patient has heart disease and helps a doctor to determine whether or not a patient has cardiovascular disease. The applied models are Extreme Gradient Boosting, Random Forest, CART, Extra Tree Classifier, and Gradient Boosting Machine. This work compared the performance of several Machine Learning algorithms that make use of the accuracy of the metrics, F1-score, recall, and precision to demonstrate the validity of our findings. The Extreme Gradient Boosting model has achieved the best 91.9% accuracy in this research. © 2023 IEEE.

关键词： Forecasting

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NLP Systems for Intelligent Report Generation in Business

NLP Systems for Intelligent Report Generation in Business

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Al Khaldy Ameen Shaheen Department of Business Intelligence and Data Analytics University of Petra Amman Jordan Department of Software Engineering Al-Zaytoonah University Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

In this research, we explore the application of Natural Language Processing (NLP) in the automated generation of business intelligence reports, addressing the increasing demand for efficient data analysis and presentation. We argue that NLP technologies significantly enhance report generation by automating data interpretation and creating insightful narratives, which fosters informed decision-making and operational efficiency. The findings are divided into three main sections: the definition and evolution of NLP, the technological perspectives on its application in business intelligence, and a series of case studies from various industries demonstrating the transformative impact of NLP-driven solutions. By highlighting the connection between NLP advancements and enhanced reporting processes, we illustrate how businesses can leverage these technologies to improve accuracy and responsiveness in their operations. Ultimately, we emphasize the necessity for further research and the adoption of NLP to maintain competitiveness in a data-centric environment.

关键词： Industries Automation Accuracy Decision making Natural language processing Business intelligence Computational intelligence

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Optimizing Fintech Marketing: A Comparative Study of Logistic Regression and XGBoost

arXiv

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arXiv 2024年

作者： Attota, Dinesh Chowdary Yarmohammadtoosky, Sahar Department of Data Science & Analytics College of Computer Science and Software Engineering Kennesaw State University MariettaGA United States

As several studies have shown, predicting credit risk is still a major concern for the financial services industry and is receiving a lot of scholarly interest. This area of study is crucial because it aids financial organizations in determining the probability that borrowers would default, which has a direct bearing on lending choices and risk management tactics. Despite the progress made in this domain, there is still a substantial knowledge gap concerning consumer actions that take place prior to the filing of credit card applications. The objective of this study is to predict customer responses to mail campaigns and assess the likelihood of default among those who engage. This research employs advanced machine learning techniques, specifically logistic regression and XGBoost, to analyze consumer behavior and predict responses to direct mail campaigns. By integrating different data preprocessing strategies, including imputation and binning, we enhance the robustness and accuracy of our predictive models. The results indicate that XGBoost consistently outperforms logistic regression across various metrics, particularly in scenarios using categorical binning and custom imputation. These findings suggest that XGBoost is particularly effective in handling complex data structures and provides a strong predictive capability in assessing credit risk. Copyright © 2024, The Authors. All rights reserved.

关键词： Logistic regression

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