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17th International conference on the Simulation of Adaptive Behavior, SAB 2024

ISBN: (纸本)9783031715327

The proceedings contain 26 papers. The special focus in this conference is on Simulation of Adaptive Behavior. The topics include: Vector-Based Navigation Inspired by Directional Place Cells;a Behavior-Based Model of Foraging Nectarivorous Echolocating Bats;benefit of Varying Navigation Strategies in Robot Teams;no-brainer: Morphological Computation Driven Adaptive Behavior in Soft Robots;cuttleBot: Emulating Cuttlefish Behavior and Intelligence in a Novel Robot Design;the Emergence of a Complex Representation of Touch Through Interaction with a Robot;analyzing Multi-robot Leader-Follower Formations in Obstacle-Laden Environments;spatio-Temporal Dynamics of Social Contagion in Bio-inspired Interaction Networks;behavioural Contagion in Human and Artificial Multi-agent Systems: A Computational modeling Approach;transient Milling Dynamics in Collective Motion with Visual Occlusions;extended Swarming with Embodied Neural Computation for Human control over Swarms;bio-Inspired Agent-Based Model for Collective Shepherding;DaNCES: A Framework for data-inspired Agent-Based Models of Collective Escape;the Role of Energy Constraints on the Evolution of Predictive Behavior;influence of the Costs of Acquisition of Private and Social Information on Animal Dispersal;integrated Information in Genetically Evolved Braitenberg Vehicles;neural Chaotic Dynamics for Adaptive Exploration control of an Autonomous Flying Robot;non-instructed Motor Skill Learning in Monkeys: Insights from Deep Reinforcement Learning Models;memory-Feedback controllers for Lifelong Sensorimotor Learning in Humanoid Robots;extracting Principles of Exploration Strategies with a Complex Ecological Task;the Cost of Behavioral Flexibility: Reversal Learning Driven by a Spiking Neural Network;"Value" Emerges from Imperfect Memory;the Role of Theory of Mind in Finding Predator-Prey Nash Equilibria;nonverbal Immediacy analysis in Education: A Multimodal Computational Model.

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2nd International conference on Smart Electrical Grid and Renewable Energy, SEGRE 2024

2nd International Conference on Smart Electrical Grid and Re...

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2nd International conference on Smart Electrical Grid and Renewable Energy, SEGRE 2024

ISBN: (纸本)9789819624553

The proceedings contain 76 papers. The special focus in this conference is on Smart Electrical Grid and Renewable Energy. The topics include: Point Cloud Denoising Method Based on Improved PointCleanNet;dynamic Dual-Strategy Update of Offline-to-Online Reinforcement Learning for Optimal Energy System Scheduling;knowledge Extraction Method of Electric Power Equipment Defects Based on Transfer Learning;a Secondary Bidding Strategy for the Charging Station Day-Ahead Market Based on the Evaluation of the Dispatchable Capacity of Electric Vehicles;primary Frequency Modulation control Strategy of Energy Storage System Based on State of Charge;building Integrated Energy System Expansion Planning Considering Reward-Punishment Ladder Carbon Mechanism and Load Uncertainty;Simulation analysis and Verification of Four-Air-Gap Current Transducer Based on TMR;optimal control of Large Number of Air Conditioners Driven by Dynamic Electric Carbon Emission Factor;application of a Bi-level Optimization Model for Energy Storage Capacity Allocation in Distribution Network with Renewable Energy Integration;power Load Disaggregation Method Based on Sparse Constraint;optimal Primary Frequency Support Demand Dispatch for Multiple Wind Turbines Considering Loss of Captured Wind Energy;promoting Eco-Friendly Power and Traffic Network Operations Using Machine Learning Techniques;Advanced-data Analytics for Household Power Consumption Forecasting Using CNN-LSTM Hybrid Network;research on the Interruption Performance of Environmentally Friendly 126 kV Circuit Breaker;research on Identification of Electricity Theft in Regional Power Grid Under New Energy Scenario;nonlinear modeling for One Pipe with Multiple Units Under Gird Connected of Pumped Storage Stations;a Methodology for the Evaluation of the Power Supply Capability of Subway Traction Power Supply System.

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Expressiveness of the LSAWfP Workflow Language Compared to BPMN 17th

Expressiveness of the LSAWfP Workflow Language Compared to...

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17th African conference on Research in Computer Science and Applied Mathematics, CARI 2024

作者： Nguedia Momo, Marionne Daniela Zekeng Ndadji, Milliam Maxime Tchoupé Tchendji, Maurice Tonle Noumbo, Franck Bruno Department of Mathematics and Computer Science University of Dschang PO Box 67 Dschang Cameroon Nairobi Kenya

ISBN: (纸本)9783031882258

Business processes modeling typically faces challenges in the integrated representation of processes’ lifecycle, informational, and organizational models. Despite the plethora of process modeling languages (workflow languages), studies on their expressiveness fail to identify a consensus on the languages and techniques to use. Consequently, some languages like LSAWfP, brimming with new techniques, continue to be proposed. LSAWfP relies on attributed context-free grammars to offer another perspective on process modeling with the goal of their decentralized execution. Preliminary studies on the expressiveness of LSAWfP demonstrate its support for process modeling cases generally supported by commercial solutions. However, these studies are insufficient as they only explore a small facet of LSAWfP’s expressiveness. This paper delves deeper into the expressiveness of LSAWfP by comparing it to the BPMN language, a domain standard. The comparative analysis between these two languages in this paper is conducted along three dimensions: control flow, data integration, and organizational structure. To leverage the theoretical studies in this paper, a foundation for a tool converting LSAWfP specifications into BPMN models is also proposed. The results of all these efforts further demonstrate that LSAWfP is a solution with significant operational and commercial potential. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： BPMN Business process Management Expressiveness LSAWfP process modeling

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Exploring Pricing, Trading, and Protection in the Big data Market

Exploring Pricing, Trading, and Protection in the Big Data M...

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2025 International conference on Intelligent control, Computing and Communications, IC3 2025

作者： Hassan, Muntather M. Ramupriya, G. Gokul, D. Muthubalaji, S. Bharathi, Palathirthapu Suganya, C. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Sethu Institute of Technology Department of Electrical Communication Engineering Pulloor Tamil Nadu Kariapatti626115 India Saveetha Medical College and Hospital Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Cmr College of Engineering & Technology Department of Eee Telangana Hydrabad India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India M.Kumarasamy College of Engineering Mba department Karur India

ISBN: (纸本)9798331527495

Big data is regarded as the secret to releasing the following massive surges of economic development. In light of a variety of new apps and platforms that are integrated into our everyday routines, such as smartphones and tablets, social access networks, and Website of Things-based automated devices, the quantity of data being gathered worldwide has been growing exponentially (smart grid, smart transportation, smart cities, etc.). How to effectively use the data has become a crucial problem due to the overall rise of data. This necessitates the creation of a big data market that facilitates effective data sharing. data proprietors and users can interact with each other, share, and increase the usefulness of data by moving it into the smartphone industry as a sort of product. The creation of a marketing server and going to trade ploys that maximize the people's benefits of income to buy while maintaining high - quality and anonymity, as well as measures to prevent the decided to trade data from it being resold in order to preserve the software's value, are barriers that must be overcome in allowing such a lead to customer satisfaction for data making trades. In this article, we perform a thorough analysis of the data lifetime and data collection. To be more precise, we first investigate a broad range of payment systems, classify them into various categories, and carry out a thorough analysis of the benefits and drawbacks of these modeling techniques. The creation of data sharing systems and methods that enable effective, safe, and private information data trading is the next area of emphasis. We examine information protection identification systems and managing digital rights systems in our final section. data security issues throughout the lifetime of data sharing are described using computer cryptography, timestamping, and other techniques. © 2025 IEEE.

关键词： User experience

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data Extraction Techniques for Analysing Agriculture Soil Factors

Data Extraction Techniques for Analysing Agriculture Soil Fa...

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2025 International conference on Intelligent control, Computing and Communications, IC3 2025

作者： Alabdeli, Haider Srinivas, Varanasi Kumari, M. Sirin Keerthana, S. Selvakumar, S. Kaliappan, S. College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Telangana Hyderabad India Cmr College of Engineering & Technology Department of Ece Telanagana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Sethu Institute of Technology Department of Electrical Communication Engineering Pulloor Tamil Nadu Kariapatti626115 India Lovely Professional University Division of Research and Development Jalandhar - Delhi G.T.Road Punjab Phagwara144411 India

ISBN: (纸本)9798331527495

An essential task in supplying the world's food requirement is soil analysis. It is the backbone of agriculture, particularly in developing nations like India, so if data mining methods are used to the fields, specifically to the soils, the pledge-making scenario may be altered, improving cultivations in the process. A significant portion of the decisions made about the majority of problems pertaining to the sector of agriculture include soil analysis. In addition to highlighting several data mining methods and the corresponding work done by various authors in the context of soil analysis, the primary emphasis of this study is on the function that data mining plays in soil analysis in the agricultural area. These data mining methods are quite modern in the soil analysis domain. The study's advancedness lies in its use of sophisticated data mining tools to better understand the characteristics of the soil, its nutrient content, and possible production results. Improving soil management techniques is the primary goal, since it has a direct impact on the agricultural system's sustainability and production. This research makes use of extensive data from soil samples collected from various agricultural locations. Consequently, a number of data mining methods, including cluster analysis, principal component analysis (PCA), and decision trees, will be used in the present study to discover patterns and relationships between crop performance and soil composition. Important soil characteristics, such as pH, the amount of organic matter, nitrogen, and potassium, all affect crop health and productivity. The objective of this study is to identify' soil clusters,' or groups of related prevalent conditions that either support or constrain agricultural output. Additionally, PCA gave me insight into the relevance of soil characteristics that have an impact on crop performance, making them very helpful for focused interventions aimed at improving the soil. It constructed the decision

关键词： Decision making

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Application of Ensemble Learning Based on High-Dimensional Features in Financial Big data 1

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2nd International conference on Artificial Intelligence Security and Privacy, AIS and P 2024

作者： Zhang, Yexin Li, Yunhao Zhang, Gaoming Ding, Ziyu Wu, Yaqi Peng, Yun Institute of Artificial Intelligence Guangzhou University Guangzhou510006 China Guangdong Provincial Key Laboratory of Blockchain Security Guangzhou510006 China

ISBN: (数字)9789819611485

ISBN: (纸本)9789819611478

Currently, most of the research on Internet financial models is based on traditional machine learning models, which often fail to adequately capture the complex features and potential non-linear interactions within financial data. In addition, the training process of the models is usually complex and time-consuming, making it difficult to meet the demand for rapid processing and analysis of financial data. To address these issues, this paper proposes a new approach that combines high-dimensional feature engineering with ensemble learning. Unlike most existing feature engineering methods, this paper specifically focuses on the high-dimensional features of financial data, and thoroughly investigates how to efficiently construct and select more useful features to fully explore the potential information of the data. We combine tree-based ensemble methods (e.g., LightGBM and XGBoost) with neural network models to construct a multi-level ensemble learning framework for complex financial data features. Experimental results show that the method not only generates high-quality and informative features, but also significantly enhances the robustness and prediction accuracy of the model, effectively solving the challenges for modeling and training complex financial data, especially when facing large-scale and multi-dimensional datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Boosting Cyberattack Detection Using Binary Metaheuristics With Deep Learning on Cyber-Physical System Environment

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IEEE ACCESS 2025年 13卷 11280-11294页

作者： Al Mazroa, Alanoud Albogamy, Fahad R. Khairi Ishak, Mohamad Mostafa, Samih M. Princess Nourah bint Abdulrahman Univ PNU Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia Taif Univ Turabah Univ Coll Comp Sci Program Taif 21944 Saudi Arabia Ajman Univ Coll Engn & Informat Technol Dept Elect & Comp Engn Ajman U Arab Emirates South Valley Univ Fac Comp & Informat Comp Sci Dept Qena 83523 Egypt New Assiut Technol Univ NATU Fac Ind & Energy Technol New Assiut City 71684 Egypt

The swift advancement of cyber-physical systems (CPSs) across sectors such as healthcare, transportation, critical infrastructure, and energy enhances the crucial requirement for robust cybersecurity measures to protect these systems from cyberattacks. The cyber-physical method is a hybrid of cyber and physical components, and a safety breach in the element is central to catastrophic consequences. Cyberattack recognition and mitigation techniques in CPSs include using numerous models like intrusion detection systems (IDSs), access control mechanisms, encryption, and firewalls. Cyberattack detection employing deep learning (DL) contains training neural networks to identify patterns indicative of malicious actions within system logs or network traffic, allowing positive classification and mitigation of cyber-attacks. By leveraging the integral ability of DL methods to learn complex representations, this technique enhances the accuracy and efficiency of detecting diverse and growing cyber-attacks. Thus, the study proposes an automated Cyberattack Detection using Binary Metaheuristics with Deep Learning (ACAD-BMDL) method in a CPS environment. The ACAD-BMDL method mainly focuses on enhancing security in the CPS environment via the cyberattack detection process. The ACAD-BMDL method uses Z-score normalization to scale the input dataset. In addition, the binary grey wolf optimizer (BGWO) model is utilized to choose an optimal feature subset. Moreover, the Enhanced Elman Spike Neural Network (EESNN) model detects cyber-attacks. Furthermore, the Archimedes Optimization Algorithm (AOA) model is employed to select the optimum hyperparameter for the EESNN model. The empirical analysis of the ACAD-BMDL technique is performed on a benchmark dataset. The experimental validation of the ACAD-BMDL technique portrayed a superior accuracy value of 99.12% and 99.36% under NSLKDD2015 and CICIDS2017 datasets in the CPS environment.

关键词： Cyberattack Optimization Feature extraction data models Accuracy Security Neural networks Adaptation models Deep learning Computational modeling Cyber-physical systems deep learning cyberattack detection metaheuristics Archimedes optimization algorithm

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MLP-Based Adaptive Sampling and Optimization of Laser-Ion Acceleration with Ultra-Short Laser Pulses 15th

MLP-Based Adaptive Sampling and Optimization of Laser-Ion ...

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15th International conference on Parallel processing and Applied Mathematics, PPAM 2024

作者： Miethlinger, Thomas Bussmann, Michael Schramm, Ulrich Kluge, Thomas Helmholtz -Zentrum Dresden-Rossendorf Dresden01328 Germany Technische Universität Dresden Dresden01069 Germany

ISBN: (纸本)9783031857027

The field of laser-ion acceleration faces significant challenges in handling high-dimensional, computationally intensive problems, often constrained by budgets and available computational power. Reliably achieving high ion energies with current laser technologies through ultra-high intensity pulses in near-critical to overdense plasmas remains difficult, necessitating detailed, costly simulations to explore various acceleration mechanisms and optimize outcomes. Due to the high computational cost of these simulations, there is a need for precise, scalable, and efficient adaptive sampling methods that balance exploring new mechanisms while exploiting known parameter dependencies and that can operate in high-performance computing environments. In this work, we propose and investigate a scalable adaptive sampling approach that intrinsically supports parallel processing. We apply this method to data obtained from particle-in-cell simulations using multilayer perceptrons (MLPs), chosen for their flexibility in modeling complex dependencies. We benchmark our results against Bayesian optimization, and we highlight the limitations of MLPs when accounting for the intrinsic uncertainty present in the acceleration process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Budget control

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Wafer Defect Root Cause analysis with Partial Trajectory Regression DM: Big data Management and Machine Learning

Wafer Defect Root Cause Analysis with Partial Trajectory Reg...

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IEEE/SEMI conference and Workshop on Advanced Semiconductor Manufacturing

作者： Kohei Miyaguchi Masao Joko Rebekah Sheraw Tsuyoshi Idé IBM Research – Tokyo Tokyo Japan IBM Semiconductors Tokyo Japan IBM Semiconductors Albany NY USA IBM Semiconductors NY USA

ISBN: (数字)9798331531850

ISBN: (纸本)9798331531867

Identifying upstream processes responsible for wafer defects is challenging due to the combinatorial nature of process flows and the inherent variability in processing routes, which arises from factors such as rework operations and random process waiting times. This paper presents a novel framework for wafer defect root cause analysis, called Partial Trajectory Regression (PTR). The proposed framework is carefully designed to address the limitations of conventional vector-based regression models, particularly in handling variable-length processing routes that span a large number of heterogeneous physical processes. To compute the attribution score of each process given a detected high defect density on a specific wafer, we propose a new algorithm that compares two counterfactual outcomes derived from partial process trajectories. This is enabled by new representation learning methods, proc2vec and route2vec. We demonstrate the effectiveness of the proposed framework using real wafer history data from the NY CREATES fab in Albany.

关键词： Semiconductor device modeling Representation learning Root cause analysis Computational modeling Semiconductor device manufacture Big data Trajectory Random processes History Optimization

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Impact of Stem Cell Therapy in Leprosy Pathogenesis Through Mathematical Study: An Impulsive control Based Treatment Design 2nd

Impact of Stem Cell Therapy in Leprosy Pathogenesis Throug...

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2nd International conference on Mathematical analysis and Applications in modeling, ICMAAM 2023

作者： Mukherjee, Satyajit Ghosh, Tushar Ghosh, Salil Centre for Mathematical Biology and Ecology Department of Mathematics Jadavpur University Kolkata India

ISBN: (纸本)9789819791934

Leprosy, characterized by dermatological manifestations and peripheral nervous system impairment due to Mycobacterium leprae infection in Schwann cells, poses persistent challenges in treatment efficacy. Stem cell therapy (SCT), an innovative field in regenerative medicine, holds immense promise for addressing these challenges. Stem cells, distinguished by their unique ability to differentiate into specialized cell types and self-renew, offer a valuable resource for tissue regeneration and healing. In this context, we have built a nonlinear ODE-based four dimensional mathematical model. This research article primarily aims to investigate the dynamic changes and interrelations of the cellular populations within the system during the progression of leprosy. The main focus throughout the research article is to predict the dissemination process of the disease following the transplantation of SCT. Specifically, our goal is to comprehend the disease dynamics involving M. leprae and Schwann cells due to the incorporation of stem cells through an impulsive manner. All analytical outcomes have been verified through numerical simulations and compared with existing clinical findings, ensuring the reliability and applicability of our novel research findings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stem cells

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