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Simulation Winter Conference

作者： Christopher W.H. Davis Antonie J. Jetter Philippe J. Giabbanelli Microsoft Portland OR USA Dept. of Engineering & Technology Management Portland State University Portland OR USA Dept. of Computer Science & Software Engineering Miami University Oxford OH USA

A predictive simulation is built on a conceptual model (e.g., to identify relevant constructs and relationships) and serves to estimate the potential effects of ‘what-if’ scenarios. Developing the conceptual model and plausible scenarios has long been a time-consuming activity, often involving the manual processes of identifying and engaging with experts, then performing desk research, and finally crafting a compelling narrative about the potential futures captured as scenarios. Automation could speed-up these activities, particularly through text mining. We performed the first review on automation for simulation scenario building. Starting with 420 articles published between 1995 and 2022, we reduced them to 11 relevant works. We examined them through four research questions concerning data collection, extraction of individual elements, connecting elements of insight and (degree of automation of) scenario generation. Our review identifies opportunities to guide this growing research area by emphasizing consistency and transparency in the choice of datasets or methods.

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Intelligent Model for Optimizing Gantt Chart in the Planning Stage

Intelligent Model for Optimizing Gantt Chart in the Planning...

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International Conference on Information Technology (ICIT)

作者： Noor Bdair Wael Alzyadat Shadi AlZubi Dept of Software engineering Faculty of Science and IT Alzaytoonh University of Jordan Dept. of of Computer Science Faculty of Science and IT Alzaytoonh University of Jordan

Scheduling tasks under resource constraints is essential for project success, and this research focuses on scheduling tasks efficiently to minimize completion time. To tackle this problem, two optimization techniques are employed: Linear programming (LP) and particle swarm optimization (PSO). PSO significantly reduces the duration, leading to a reduction in project duration. But the LP algorithm does not improve the project duration. Using suitable algorithms to maximize project efficiency and success is essential for successful project management. This research provides valuable information for project managers and decision makers, highlighting the importance of utilizing suitable algorithms to maximize project efficiency and success.

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A New Configurable SC-based Dual Source Inverter Topology with Improved Reliability

A New Configurable SC-based Dual Source Inverter Topology wi...

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Energy Technologies for Future Grids (ETFG), IEEE International Conference on

作者： Mohammad Anas Anees Marif Daula Siddique Saad Mekhilef Marizan Binti Mubin Mehdi Seyedmahmoudian Alex Stojcevski Dept. of Electrical Engineering University of Malaya Malaysia Dept. of Electrical and Computer Engineering National University of Singapore Singapore School of Software and Electrical Engineering. Swinburne University of Tech. Australia

This paper presents a novel fifteen-level two-source inverter topology that is reliable against source outages. The topology has two DC sources, a total of ten switches, and four DC-link capacitors. Level-shifted PWM is used to generate the required number of levels. Simulations are performed for normal operation as well as for the source outage conditions. The proposed topology is able to generate multi-level output waveform satisfactorily even at the advent of source outages. The simulation results for the source outages are presented for both static and dynamic conditions along with the THD measurements.

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Kubernetes Cluster Disaster Recovery Using AWS

Kubernetes Cluster Disaster Recovery Using AWS

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Simran Bhavsar Apoorva Agrawal Tanvi Ropalkar Pranjali Kamdi Aparna Hajare Shilpa Deshpande Rahul Rathi Devendra Garg Dept. of Computer Engineering Cummins College of Engineering for Women Pune India Veritas Software Technologies India Private Limited Pune India

As applications in IT have been spanning multiple containers across servers, kubernetes is being used extensively for automating software deployment, scaling and management. Kubernetes eases the container tasks and also other activities like horizontal scaling and canary development. To run cloud-native applications, kubernetes has become the de facto standard for container orchestration, regardless of the underlying platform. This makes it very crucial to have a disaster recovery plan for kubernetes environments. This paper proposes a system for backup and restore of a kubernetes cluster using the snapshot method along with the Public Key Infrastructure (PKI) certificates of a cluster. It also focuses on automating the backup and restore operations using microservices. Furthermore, it makes use of Amazon Web Services (AWS) tools for the reliability, scalability and the automation of backup and restore operations. The user can run the proposed microservices to backup and restore the cluster from any specific timestamp before the disaster. The experimental results demonstrate the effective performance of the proposed method in terms of efficiently restoring the kubernetes cluster in optimal time.

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Data-Driven Decision Management: Multi-Modal Analytics in a Health Case Study 19

Data-Driven Decision Management: Multi-Modal Analytics in a ...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Agomuo, Okechukwu Clement Khan, Zohaib Otuomasirichi, Agomuo Ijeoma Uzoma, Agomuo Kingsley Muzamal, Junaid Hussain Department of Operations and Information Systems University of Utah Salt Lake City United States Dept. of Software Engineering FAST NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Fast School of Computing Fast NUCES Lahore Pakistan

ISBN: (纸本)9798331507817

In an era where data is abundant yet underutilized, organizations across various sectors face challenges in extracting actionable insights from complex, multi-modal datasets. To address these challenges, this study proposes a versatile data analytics framework designed to enhance predictive accuracy and support informed decision-making. The framework integrates structured and unstructured data, leveraging advanced Transformer architectures to capture temporal dynamics and complex interactions within the data. In a healthcare use case, the framework demonstrated its effectiveness by achieving an Accuracy of 0.854, an F1-Score of 0.895, and an AUC-ROC of 0.930, significantly outperforming traditional models. By incorporating SHAP for interpretability, the framework ensures that its predictions are both accurate and transparent, making it a valuable tool for decision-makers. While healthcare is one application, the framework’s flexibility allows it to be adapted to a wide range of industries. Future work will focus on validating the framework across different sectors and integrating it into operational workflows to optimize decision-making and outcomes. © 2025 IEEE.

关键词： Electronic health record

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Joint Energy and Computation Workload Management for Geo-distributed Data Centers

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Wang, Ran Wu, Rixin Liu, Linfeng Yi, Changyan Zhu, Kun Wang, Ping Niyato, Dusit Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science and Technology Nanjing China York University Dept. of Electrical Engineering and Computer Science Lassonde School of Engineering Canada Nanyang Technological University School of Computer Science and Engineering Singapore

The increasing demands of data computation and storage for cloud-based services motivate the development and deployment of large-scale data centers (DCs). The energy demand of these devices is rising rapidly and becoming a noticeable challenge for current power networks. The smart grid (SG) is deemed as the future power system paradigm enabling more affordable and sustainable energy supply, which can effectively relieve the load pressure from DCs. Moreover, with growing concerns regarding harmful emissions due to combustion of fossil fuels, the exploitation of renewable energy sources (RES) has attracted extensive attention, which can benefit SGs and DCs, as well as society at large. However, the geo-distributed property of DCs and SGs and the uncertain nature of RES production pose severe challenges to the optimal management of computation and energy resources in such a tripartite coupling system. Focusing on these issues, a joint energy and computation workload management framework is proposed for enabling a sustainable DC paradigm with distributed RES. Specifically, a three-layer game is formulated to model the iterations among entities including the energy market, data center operators (DCOs), and SGs. The market includes a certain amount of RES that must be dispatched. The SG offers the DCO an electricity selling price while simultaneously importing RES from the market at a buying price in order to maximize the benefit. The DCO allocates the workload to different DCs, aiming to minimize the costs of energy consumption and carbon emissions. The interactive processes between different entities are further decomposed into two coupling Stackelberg games. We obtain the equilibrium state of the game and prove its uniqueness and optimality. Simulation experiments are conducted to evaluate the performance of the joint energy and computation workload management scheme and show its superiority over counterparts in utilizing renewable energy and reducing emissions. Furthe

关键词： Smart power grids

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Machine Learning in Agriculture and Crop Management: A Comprehensive Review

Machine Learning in Agriculture and Crop Management: A Compr...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Rachna Rathore Archana Ganesh Said Preeti Jnagra Haritha Murari Saloni Bansal Harshita Singh Dept. of Mathematics Jiwaji University Gwalior India Dept. of Computer Engineering AISSMS’s Institute Of Information Technology Pune India Dept. of Biotechnology Lingayas Vidyapeeth Haryana India Dept. of Software Engineer Spark Infotech Inc Phoenix Arizona USA Dept. of Computer Eng. & Applications GLA University Mathura India Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Machine learning (ML) has transformed agriculture and crop management, revolutionizing modern farming techniques. By leveraging ML, farmers can accurately monitor and analyze agricultural data, gaining valuable insights into crop health, soil conditions, weather patterns, and pest infestations. Through the analysis of satellite imagery, sensor data, and historical records, ML models can predict optimal planting times, forecast yields, and identify potential disease outbreaks, enabling more efficient resource management. These innovations promote precision agriculture, reduce waste, and support sustainability. This paper explores the latest developments in ML applications for agriculture, emphasizing their potential to enhance food security, minimize environmental impact, and boost farm profitability through data-driven decision-making.

关键词： Technological innovation Systematics Reviews Weather forecasting Machine learning Predictive models Soil Satellite images Sustainable development Crop yield

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Ensemble Learning for Seated People Counting using WiFi Signals: Performance Study and Transferability Assessment

Ensemble Learning for Seated People Counting using WiFi Sign...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Bernaola, Jose Ramon Merino Sobron, Iker Del Ser, Javier Landa, Iratxe Eizmendi, Inaki Velez, Manuel Dept. of Communications Engineering Bilbao48013 Spain Dept. of Languages and Computer Systems Bilbao Spain Derio48170 Spain

ISBN: (纸本)9781665423908

The detection, location, and behavior recognition of human beings in different environments is not only a subject of a wide range of studies, but has also triggered the development of a large number of applications, including those which enhance sustainability and efficiency of infrastructures. For instance, the estimation of the occupancy could improve the energy management of a building. Due to human presence or movement over a particular area, the analysis of variations in wireless signal properties of already deployed wireless technology such as WiFi systems provides the information needed for Machine Learning models to accomplish the non-intrusive (device-free) detection and classification of different human activities. In this context, this work focuses on detecting seated people in an indoor scenario by using ensemble learning, a particular branch of Machine Learning models for supervised learning that hinges on combining the outputs of individual predictors. Furthermore, we evaluate the transferability of the knowledge modeled by ensemble learners. When trained in a particular frequency or channel, such models are used to classify data captured over another different frequency. Our experimental setup and discussed results reveal that while ensembles attain satisfactory levels of predictive accuracy predictions, their knowledge cannot be transferred among different frequencies. This conclusion opens an exciting future towards new means to perform effective knowledge transfer over the frequency domain. © 2021 IEEE.

关键词： Learning systems

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AI-Driven Forecasting Mechanism for Cardiovascular Diseases: A Hybrid Approach using MLP and K-NN Models 2

AI-Driven Forecasting Mechanism for Cardiovascular Diseases:...

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2nd International Conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2024

作者： Chaganti, Koushik Reddy Krishnamurty, Pokuri Venkataradha Kumar, Addala Hemantha Gowd, G. Sudha Balakrishna, Chinnala Naresh, P. University of the Cumberlands Dept. of Computer and Information Systems KY United States Audisankara College of Engineering and Technology Cse Department Andhra Pradesh Gudur India Srinivasa Ramanujan Institute Of Technology Cse Department Anantapuram India Cse Department Hyderabad India Dayananda Sagar University Dept of Cse Bangalore India

ISBN: (纸本)9798350368413

Cardiovascular disease (CVD) results in malfunctioning of the heart and also the related blood vessels and regularly primes to death or corporal paralysis. Initial and automated discovery of CVD is crucial for saving many of the common humanoid lives. While several Investigations have been carried out to achieve this goal, and there is still room for development in terms of performance and reliability. This new work provides one more step in this area, since it utilizes two reliable ML algorithms, 'multi-layer perceptron (MLP)' and 'K-nearest neighbour (K-NN)'to detect CVD using publicly available data from the University of engineering California Irvine source. The models' concerts are enhanced optimally and by eradicating the possible outliers and common existing attributes with values that are treated as null. They performed general Experimental and effective findings show that the MLP model outperforms the KNN model in terms of reporting accuracy (82.47%) and area below the curve (86.41%). As a result, the proposed MLP model is recommended for automated CVD identification. Furthermore, this approach may be used to identify new illnesses, and the suggested model's routine can be tested against other standard datasets. © 2024 IEEE.

关键词： Nearest neighbor search

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Transparent AI for Adaptive Fraud Detection 19

Transparent AI for Adaptive Fraud Detection

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Agomuo, Okechukwu Clement Uzoma, Agomuo Kingsley Khan, Zohaib Otuomasirichi, Agomuo Ijeoma Muzamal, Junaid Hussain Department of Operations and Information Systems University of Utah Salt Lake City United States Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Dept. of Software Engineering FAST NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Fast School of Computing Fast NUCES Lahore Pakistan

ISBN: (纸本)9798331507817

The rapid evolution of digital financial services has significantly increased the vulnerability of financial systems to fraudulent activities. Traditional machine learning models have offered substantial progress in detecting fraudulent transactions;however, they often lack adaptability and transparency, critical for dynamic financial environments and regulatory compliance. This paper introduces a novel approach that integrates Deep Reinforcement Learning (DRL) with Explainable Artificial Intelligence (XAI) to enhance fraud detection capabilities in cloud-based financial platforms. Our model, tested on the synthetic PaySim mobile money transaction dataset, leverages the adaptive nature of DRL to continuously learn and update its fraud detection strategies based on evolving transaction patterns. Concurrently, the integration of XAI provides clear, interpretable insights into the decision-making process, thus ensuring transparency and building trust. The proposed model demonstrates superior performance with an achieved accuracy of 99.5% and an F1-score of 99.0%, significantly outperforming traditional fraud detection systems. These results not only underscore the efficacy of our model in improving the accuracy of fraud detection but also highlight its capability in offering significant improvements in understanding the underlying reasons for each decision, thereby aiding compliance with financial regulations. The practical implications of this research highlight its potential to provide a robust, scalable, and transparent solution for real-time fraud detection, which is essential for safeguarding the integrity of modern financial systems. © 2025 IEEE.

关键词： Decentralized finance

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