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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Shirish Dattatreya Kulkarni Ramakrishnan Raman Kruti Kamal Sutaria Nilamadhab Mishra S. Sujatha V. Nandagopal Symbiosis Law School (SLS) Pune & Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Vadodara Gujarat India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electrical and Electornics Engineering School of Engineering Mohan Babu University Tirupathi Andrapradesh India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

Personalized suggestions may enhance the use of online food shopping, an already popular and handy service. But data sparsity and scalability problems restrict the current recommendation systems rely on user-based collaborative filtering algorithms. Smart grocery delivery with personalized suggestions is proposed in this system using item-based collaborative filtering algorithms and Internet of Things (IoT) technologies. In the system, smart appliances like fridges and scales track food supplies and consumption, and then, according to the user's preferences, they automatically place purchases on internet platforms. It makes use of item-based collaborative filtering algorithms to examine other users' evaluations and purchase histories in order to provide the user with relevant and varied product recommendations. The method is tested on a real-world dataset and contrasted with collaborative filtering methods rely on user input. Techniques outperform the competition in terms of suggestion accuracy, variety, and coverage while simultaneously decreasing delivery time and costs. By implementing our strategy, not only can online grocery shopping be made more enjoyable for users, but it can also spur the growth of e-commerce and the IoT.

关键词： Collaborative filtering Scalability Smart homes Medical services Market research Internet of Things Electronic commerce

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Textile-Based Thermally Driven Actuators for Soft Robotic Mechanotherapy Applications

Textile-Based Thermally Driven Actuators for Soft Robotic Me...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Mehmet Fatih Ç elebi Zeynep Beler Elif Dede Emine Dilara Koç ak Ö zgü r Atalay Aslı Tunç ay Atalay Veysel Gazi Department of Electrical and Electronics Engineering Department of Mechatronics Engineering Faculty of Technology Institute of Pure and Applied Sciences Marmara University Istanbul Turkey Department of Polymer Science and Technology Institute of Pure and Applied Sciences Marmara University Istanbul Turkey Department of Metallurgy and Materials The University of Birmingham Birmingham United Kingdom Department of Textile Engineering Faculty of Technology Marmara University Istanbul Turkey Faculty of Textile Technology and Design Istanbul TechnicalDepartment of Textile EngineeringIstanbulTurkey Veysel Gazi is with the Department of Control and Automation Engi-neering Faculty of Electrical and Electronics Engineering Yildiz Technical University Istanbul Turkey

Rigid-body industrial robotic manipulators and pneumatic compression systems are widely used in mechano-therapeutic applications. However, rigid and bulky components reduce the wearability of the system in mobile environments. The current trend in wearable robotics tends to build such devices using textiles/silicone materials thanks to their lightness, compliancy, and comfort properties. In this study, thermally powered soft fluidic actuators that utilize liquid/gas phase transition property of low boiling point liquids are investigated. Proposed actuators generate desired pressure level and have great potential to free mechano-therapy devices from such bulky components. The developed structure incorporates textile-based heaters, thin film actuator shells, and low boiling point liquids. Here, two low boiling liquids with different boiling temperature are mixed successfully in order to obtain desired boiling point. Results a revealed that actuators constructed with PE/PA membrane did not show any deterioration even after 500 cycle actuation. In order to control the system, instantaneous temperature of the textile-based heater on the actuator device is used as feedback for the on-off controller.

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Applying a Machine Learning Framework to Improve the Efficacy of Network Intrusion Detection Systems

Applying a Machine Learning Framework to Improve the Efficac...

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International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)

作者： Abdullah Anil Kumar Kadim A. Jabbar Mohammed Yousif Oudah Abdul Azeem Khan Rajan Verma University Institute of Computing Chandigarh University Punjab India Department of CS & Engineering SRM University Lucknow India College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Scientific Research Center Al-Ayen University Thi-Qar Iraq Faculty of Islamic Technology Negara Darussalam Brunei Division of R&D Lovely Professional University Phagwara KKR and KSR Institute of Technology and Sciences Guntur Andhra Pradesh

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Intrusion Detection Systems (IDS) play a vital role in spotting aberrant traffic and analyzing large amounts of data, especially during Distributed Denial of Service (DDoS) assaults. To ensure an IDS operates at peak performance, a reliable traffic classification approach is required. The objective of this research is to identify the most effective machine learning technique for enhancing system accuracy from the three available options: Support Vector Machine (SVM), Decision Jungle (DJ), and Random Forest (RF). Reducing the number of false alarms while increasing the number of correct ones is the goal. The primary objectives of this research are to create a machine learning model for a network intrusion detection system and to evaluate the performance of three algorithms in detecting suspicious network activities. As a standard for assessing IDSs, the study uses the Intrusion Detection Evaluation Dataset in conjunction with the KDD approach. The SVM outperforms DJ (96.50%) and RF (96.76%) with an average accuracy of 98.18%. Similarly, SVM surpasses RF with a precision of 97.96% and DJ with a precision of 97.82%, achieving an average precision of 98.74%. RF outperforms SVM (95.63%) and DJ (95.77%) in terms of average recall, with the highest value of 97.62%. Ultimately, the SVM algorithm has the highest level of performance in the system for detecting intrusions.

关键词： Support vector machines Radio frequency Accuracy Machine learning algorithms Network intrusion detection Vectors Classification algorithms Reliability Computer crime Standards

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Reinforcement Learning for Solving the Pricing Problem in Column Generation: Applications to Vehicle Routing

arXiv

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arXiv 2025年

作者： Abouelrous, Abdo Bliek, Laurens Gabor, Adriana F. Wu, Yaoxin Zhang, Yingqian Department of Information Systems Faculty of Industrial Engineering and Innovation Sciences Technical University Eindhoven Netherlands Department of Mathematics College of Computing and Mathematical Sciences Khalifa University United Arab Emirates

In this paper, we address the problem of Column Generation (CG) using Reinforcement Learning (RL). Specifically, we use a RL model based on the attention-mechanism architecture to find the columns with most negative reduced cost in the Pricing Problem (PP). Unlike previous Machine Learning (ML) applications for CG, our model deploys an end-to-end mechanism as it independently solves the pricing problem without the help of any heuristic. We consider a variant of Vehicle Routing Problem (VRP) as a case study for our method. Through a set of experiments where our method is compared against a Dynamic Programming (DP)-based heuristic for solving the PP, we show that our method solves the linear relaxation up to a reasonable objective gap within 9% in significantly shorter running times, up to over 300 times faster for instances with 100 customers. © 2025, CC BY.

关键词： Reinforcement learning

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Cloud-Powered Neural Networks for Sustainable Fishing Practices and Stock Management System

Cloud-Powered Neural Networks for Sustainable Fishing Practi...

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Sustainable Expert Systems (ICSES), International Conference on

作者： M. Xavier Suresh Prathik A V. Arun R. Venugopal T. R. GaneshBabu M. Muthulekshmi Department of Physics School of Advanced Sciences and Languages VIT Bhopal University Bhopal Madhya Pradesh India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Mathematics Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (DU) Salem Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Sustainable fishing methods and efficient stock management are crucial considering growing environmental concerns; fishing practices and effective stock management are imperative. This research presents a novel approach that uses neural networks (NN) hosted in the cloud to transform sustainable fishing and stock management. This method trains NN to make predictions by combining several data sources, such as satellite images, weather patterns, and catch records. These models can reliably predict fish stocks' dynamics, giving stakeholders the information needed to make informed decisions. Crucial for real-time processing and storage of massive information is the scalability and flexibility offered by cloud infrastructure. This framework provides the appropriate usage of resources while simultaneously optimizing fishing tactics, which helps protect marine ecosystems. It demonstrates that method promotes sustainable fishing via thorough case studies and simulations. It provides a flexible and scalable solution to the problems confronting marine biodiversity and the fishing industry by combining advanced technology with ecological principles. To ensure a more egalitarian and sustainable future for fisheries management on a global scale, method is a giant leap forward in balancing commercial interests with environmental protection. Integrating data, ensuring sensors are accurate, managing huge datasets, managing regulatory compliance, and encouraging stakeholders to work together for sustainable practices are all challenges.

关键词： Cloud computing Technological innovation Biological system modeling Decision making Artificial neural networks Predictive models Fisheries Real-time systems Stakeholders Resource management

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Stochastic thermodynamics of nanoscale friction

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Physical Review E 2021年第5期103卷 052104-052104页

作者： P. C. Torche P. Nicolini T. Polcar O. Hovorka Engineering and Physical Sciences University of Southampton Southampton SO17 1BJ United Kingdom Department of Control Engineering Faculty of Electrical Engineering Czech Technical University in Prague Technicka 2 Prague 6 16627 Czech Republic

Developing the thermodynamics of nanoscale friction is needed in a wide range of tribological applications, where the key objective is to optimally control the energy dissipation. Here we show that modern stochastic thermodynamics allows us to interpret the measurements obtained by friction force microscopy, which is the standard tool for investigating the frictional properties of materials, in terms of basic thermodynamics concepts such as fluctuating work and entropy. We show that this allows the identification of the heat produced during the friction process as an unambiguous measure of thermodynamic irreversibility. We have applied this procedure to quantify the heat produced during the frictional sliding in a broad velocity range, and we observe velocity-dependent scaling behavior, which is useful for interpreting the experimental outcomes.

关键词： Entropy production Fluctuation theorems Friction Irreversible processes Nonequilibrium & irreversible thermodynamics Nonequilibrium statistical mechanics Stochastic processes Tribology Work theorems Atomic force microscopy Markovian processes Master equation Noncontact atomic force microscopy Stochastic processes & statistics

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An interpretability analysis of travel decision learning in cyber-physical-social-systems 1

An interpretability analysis of travel decision learning in ...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Guo, Miao Ye, Peijun Liu, Xudong Liu, Sheng Xiong, Gang Zhang, Liguo Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Beijing China Faculty of Information and Technology Beijing University of Technology Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Beijing China

In the few decades, the rise of Internet and social media has fundamentally changed the management, control, and operation modes of modern engineering and social systems like defense, energy, critical health care and ... 详细信息

ISBN: (纸本)9781665433372

关键词： Deep learning Social networking (online) Scalability Perturbation methods Neurons Transportation Transforms

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Fast calibration of agent-based model using mean-field approach 1

Fast calibration of agent-based model using mean-field appro...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Lu, Wanze Xiong, Gang Liu, Xudong Liu, Sheng Ye, Peijun Zhang, Liguo Beijing University of Technology Faculty of Information and Technology Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665433372

Calibration of simulation system is an important step to establish a reliable simulation system. In this paper, based on the existing mean field calibration theory, a new fast calibration method for complex urban traffic system based on mean field method is discussed by establishing the relationship between the macroscopic observation quantity and the proxy microscopic parameters. This method makes use of the machine learning method, and reduces the dimension of the state vector in the discrete time period, so that the dimension of the macroscopic state transition matrix is lower, and then reduces the computational complexity of the system calibration. Finally, the calibration results of the simulation system were verified by SUMO traffic simulation software. © 2021 IEEE.

关键词： Calibration

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Pilot Study on Using Hybrid–Cascade Filtering on Brain Signals for the control Purposes

Pilot Study on Using Hybrid–Cascade Filtering on Brain Sign...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Mariusz Pelc Dariusz Mikołajewski Edward Jacek Gorzelańczyk Anna Wieczorek Henryk Racheniuk Adam Sudoł Kayhan Latifzadeh Luis A. Leiva Aleksandra Kawala-Sterniuk Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland Faculty of Physical Education and Physiotherapy Opole University of Technology Opole Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Department of Computer Science University of Luxembourg Luxembourg Luxembourg

We present an initial study conducted on fNIRS signals using Hybrid-Cascade filters for the purpose of their quality improvement. Whilst many studies focus on filtering brain signals, so that their frequency domain properties would allow e.g. widely understood diagnostics, here we focus on the study of time-domain signal characteristics, which is relevant for potential control purposes. Taking into account various kinds of artifacts, we propose a novel cascade 1D Kalman filter to handle fNIRS signals.

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Cloud-Integrated Clean Fuel Generation for Solar-Hydrogen Production with Wireless Sensor Networks

Cloud-Integrated Clean Fuel Generation for Solar-Hydrogen Pr...

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International Conference on Inventive Computation Technologies (ICICT)

作者： N. Naveenkumar V. Sridevi R. Bharathi Nilamadhab Mishra S. Murugan S. Velmurugan Department of Computer Science and Engineering Muthayammal Engineering College (Autonomous) Rasipuram Namakkal Tamil Nadu India Electrical and Electronics Engineering Academy of Maritime Education and Training Deemed to be University Chennai Tamil Nadu India Faculty of Management SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

This research study proposes a novel system that integrates Wireless Sensor Networks (WSNs) with cloud-based platforms to efficiently generate renewable energy and produce clean fuel. By enhancing the safety, efficiency, and reliability of solar power and hydrogen generation, the system integrates optimization strategies, data analysis, and real-time monitoring. Critical parameters such as temperature, sun irradiance, electrolysis efficiency, and hydrogen purity are monitored using WSNs, with data transmitted and processed via cloud techniques to identify patterns, outliers, and areas for improvement. Proactive decision-making and rapid adjustments are facilitated through remote monitoring and control capabilities, leading to maximized energy production and clean fuel output. Security measures including encryption, access controls, and regular audits safeguard data privacy and integrity, while multiple sensor deployments, robust communication protocols, and a resilient cloud infrastructure ensure system reliability. This integrated approach offers an effective solution for advancing renewable energy generation and clean fuel production, contributing to environmental sustainability and a greener future.

关键词： Temperature sensors Wireless sensor networks Cloud computing Renewable energy sources Protocols Hydrogen Green products

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