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Improving the Resource Allocation in IoT Systems Based on the Integration of Reinforcement Learning and Rule-Based Approaches in Multi-agent Systems 8

Improving the Resource Allocation in IoT Systems Based on th...

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8th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2024

作者： Yarahmadi, Hossein Navidi, Hamidreza Challenger, Moharram University of Antwerp Department of Computer Science Belgium Islamic Azad University Department of Computer Engineering Borujerd Branch Borujerd Iran Shahed University Department of Mathematics and Computer Science Tehran Iran University of Antwerp Flanders Make Strategic Research Center Department of Computer Science Flanders Belgium

ISBN: (纸本)9798350374674

In recent years, the spread of Multi-agent Systems (MAS) and Reinforcement Learning (RL) as a subdomain of Machine Learning (ML) has affected many fields, such as Cyber-Physical Systems (CPS) and the Internet of Things (IoT). There are many Multi-agent Reinforcement Learning (MARL) applications in CPS and IoT, including energy management, smart transportation systems, job scheduling, etc. Due to the fact that the RL and MARL are slow processes, it is necessary to improve and accelerate them. In this study, we review some applications of MAS in the CPS and IoT. To improve the decision-making in MAS, we apply a combination of learning and reasoning (rule-based). We tested the performance of our proposed method in smart homes for 4 distinct tasks, including Face Detection and Window Blind Control, Finger Touch and Gate Control, Weather Check and Thermostat Control, and Temperature Check and Start Fan to assess its efficacy. We applied our method to the job scheduling problem using two parameters: process unit and bandwidth. The results reveal that the proposed approach performed better than the bankruptcy methods based on these criteria. © 2024 IEEE.

关键词： Multi agent systems

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Exploring the fixed point theory and numerical modeling of fish harvesting system with Allee effect

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Modeling Earth Systems and Environment 2025年第4期11卷 1-18页

作者： Yasin, Muhammad Waqas Akhtar, Mobeen Ahmed, Nauman Akgül, Ali Al-Mdallal, Qasem Department of mathematics University of Narowal Narowal Pakistan Department of Mathematics and Statistics The University of Lahore Lahore Pakistan Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Mathematics Art and Science Faculty Siirt University Siirt 56100 Turkey Department of Mathematics Mathematics Research Center Near East University Near East Boulevard Nicosia 99138 Turkey Department of Computer Engineering Biruni University Istanbul Topkapi 34010 Turkey Department of Mathematical Sciences UAE University P.O. Box 17551 Al Ain United Arab Emirates

Fish harvesting has a major role in nutritive food that is easily accessible for human nourishment. In this article, a reaction-diffusion fish harvesting model with the Allee effect is analyzed. The study of population models is a need of this hour because by using precautionary measures, mankind can handle the issue of food better. The basic mathematical properties are studied such as equilibrium analysis, stability, and consistency of this model. The Implicit finite difference and backward Euler methods are used for the computational results of the underlying model. The linear analysis of both schemes is derived and schemes are unconditionally stable. By using the Taylor series consistency of both schemes is proved. The positivity of the Implicit finite difference scheme is proved by using the induction technique. A test problem has been used for the numerical results. For the various values of the parameters, the simulations are drawn. The dynamical properties of continuous models, like positivity, are absent from the simulations produced by the backward Euler scheme. Implicit finite difference scheme preserves the dynamical properties of the model such as positivity, consistency, and stability. Simulations of the test problem prove the effectiveness of the Implicit finite difference scheme. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Allee effect Analysis Backward Euler scheme Holling type II Implicit finite difference scheme Reaction-diffusion model Simulations

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GEOMETRY-DRIVEN DESIGN OF MORPHABLE SURFACE STRUCTURES USING TOPOLOGY OPTIMIZATION AND CIRCLE PACKING

GEOMETRY-DRIVEN DESIGN OF MORPHABLE SURFACE STRUCTURES USING...

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ASME 2024 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2024

作者： Gao, Lingfeng Zhu, Xiaoping Tanaka, Masato Song, Yuyang Zhou, Yuqing Gu, Xianfeng David Chen, Shikui Department of Mechanical Engineering Denmark Department of Computer Science United States Department of Applied Mathematics & Statistics State University of New York Stony BrookNY11794 United States Toyota Research Institute of North America Ann ArborMI48105 United States

ISBN: (纸本)9780791888414

This paper presents a new computational framework for the co-optimization and co-control of morphable surface structures using topology optimization and circle-packing algorithms. The proposed approach integrates the design of optimized compliant components and the system-level control of the overall surface morphology. By representing the surface shape using circle packing and leveraging conformal mapping, the framework enables smooth deformation between 2D and 3D shapes while maintaining local geometry and global morphology. The morphing surface design problem is recast as designing circular compliant actuators using level-set topology optimization with displacements and stiffness objectives. The optimized component designs are then mapped back onto the circle packing representation for coordinated control of the surface morphology. This integrated approach ensures compatibility between local and global geometries and enables efficient actuation of the morphable surface. The effectiveness of the proposed framework is demonstrated through numerical examples and physical prototypes, showcasing its ability to design and control complex morphable surfaces with applications in various fields. The co-optimization and co-control capabilities of the framework are verified, highlighting its potential for realizing advanced morphable structures with optimized geometries and coordinated actuation. This integrated approach goes beyond conventional methods by considering both local component geometry and global system morphology and enabling coordinated control of the morphable surface. The general nature of our approach makes it applicable to a wide range of problems involving the design and control of morphable structures with complex, adaptive geometries. Copyright © 2024 by ASME.

关键词： Conformal mapping

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Modified Integrated Regulator Strategy for High Density Interleaved DC-DC Converter with Enhancement Conversion Ratio and Losses Mitigation 50

Modified Integrated Regulator Strategy for High Density Inte...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Karimi, Mohsen Farsijani, Mohammad Jamil, Mohsin Iranian Research Institute for Electrical Engineering Academic Center for Education Culture and Research Tehran Iran Department of Electrical and Computer Engineering and Applied Science Memorial University of Newfoundland Newfoundland St John’sNL Canada Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL United States

ISBN: (纸本)9781665464543

The high power DC-DC converters are widely used in industrial and renewable application due to variable dc source. For this purpose, the interleaved dual cascade DCDC converter (IDCC) is the most advantageous due to its and low ripple with high boosting factor and high potential efficiency. To optimize IDCC performance, an integrated regulator ensures equal current sharing between levels, with voltage cascading to boost the output voltage. The integrated regulator technique (IRT) is designed to control all regulators at each level to maintain an optimal switching period, thereby reducing switching dissipation and decreasing ripple. Its ability to handle high voltages conversion with a consistent low ripple output and modular structure enhance performance. It has demonstrated a 1.5% current ripple and up to 94% efficiency in high-power density applications. Simulation and experimental results of a 2 kW prototype validate the IRT's performance. © 2024 IEEE.

关键词： DC-DC converters

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Using Deep Learning and Object-Oriented Metrics to Identify Critical Components in Object-Oriented Systems 23

Using Deep Learning and Object-Oriented Metrics to Identify ...

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5th World Symposium on Software engineering, WSSE 2023

作者： Tete, Akpedje Touré, Fadel Badri, Mourad Software Engineering Research laboratory Department of Mathematics and Computer Science University of Quebec Trois-RivièresQC Canada

ISBN: (纸本)9798400708053

This paper aims at studying the ability of deep machine learning to predict software faults based on object-oriented metrics. This research investigated software faults from the perspective of fault-proneness, faults number and faults frequency, and used data collected from several versions of a Java open-source software system. This study relied on Chidamber and Kemerer suite of metrics as proxy to capture various software characteristics. In this study, the deep learning regression and classification results were compared to linear and logistic regressions. Auto-Encoders (AE) and Principal Components Analysis (PCA) have been used to reduce redundant information from the dataset. To evaluate the prediction ability of the models, this research used the inter-version validation strategy. The results showed that the models can achieve a significant average performance up to 89%. © 2023 Copyright held by the owner/author(s).

关键词： Principal component analysis

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Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm

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Multimedia Tools and Applications 2025年第12期84卷 10071-10094页

作者： Al Saaidah, Adeeb Abualhaj, Mosleh M. Shambour, Qusai Y. Abu-Shareha, Ahmad Adel Abualigah, Laith Al-Khatib, Sumaya N. Alraba’nah, Yousef H Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Software Engineering Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon College of Engineering Yuan Ze University Taoyuan32003 Taiwan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm, utilizing tailored parameter settings and the Firefly Optimization Algorithm (FOA). The study leverages the MalMem-2022 dataset to assess the efficacy of KNN and KNN with FOA in malware detection. The impact of parameter tuning and feature selection on classification is elucidated by comparing the performance of both approaches. Encouragingly, the results reveal promising advancements in one of the multiclass classification scenarios when employing KNN with FOA, yielding noteworthy enhancements in Accuracy, Recall, Precision, Matthews Correlation Coefficient, TNR, and F1-score by 2.65%, 2.65%, 2.47%, 3.59%, 0.17%, and 2.61%, respectively. These findings accentuate the significance of optimizing KNN’s parameters and implementing FOA for feature selection, culminating in heightened accuracy in malware detection. The symbiotic fusion of KNN and FOA exhibits remarkable effectiveness in augmenting the classification model’s performance, thus offering valuable insights for bolstering cybersecurity measures. Adopting ML algorithms, particularly KNN with specific parameter settings and FOA, presents a promising avenue for enhancing malware detection capabilities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

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Comparative Analysis of Learning Models for Solving Natural Language Processing Tasks

Comparative Analysis of Learning Models for Solving Natural ...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Khabibrakhmanova, Alsu Alekseev, Ilya Tazieva, Ramilya Kazan State Power Engineering University Department of Information Technologies and Intelligent Systems Kazan Russia Kazan National Research Technological University Department of Informatics and Applied Mathematics Kazan Russia

ISBN: (纸本)9798350395044

The paper contains an analytical review of methods for solving problems of semantically coherent text processing, search and selection of learning models for solving text processing problems, comparison of the obtained results, summarising the results. The learning models provided in Hugging Face and Scikit-Learn library for solving text generation, text tone detection and text classification tasks were selected for the study. As a result of the study, a comparative analysis of learning models for solving natural language processing tasks was carried out. Various machine learning methods such as SGDClassifier, KNeighborsClassifier, MultinomialNB, LogisticRegression, Decision TreeClassifier, RandomForestClassifier, support vector method and others were considered. © 2024 IEEE.

关键词： Text processing

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Optimization of Artificial Neural Network Modelling for Improving Diabetic Diagnosis: A Promising Approach for Early Detection

Optimization of Artificial Neural Network Modelling for Impr...

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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Thumu, Muni Balaji Balajiraja, N. Yousoof, Muhammed Selvaraju, Sivamani PG and Research Department of Computer Science Pudukkottai India Pudukkottai India Dhofar University Salalah Oman College of Engineering and Technology Univarsity of Technology and Applied Sciences-Salalah Engineering Department Salalah Oman

ISBN: (纸本)9798331540067

Diabetes impacts food-to-energy conversion and is chronic. Diabetics store glucose in their blood instead of using it for energy, causing major health issues. The WHO reports that diabetes is rising worldwide, and early detection and treatment are crucial to preventing complications. Artificial intelligence (AI) and machine learning (ML) techniques have the potential to revolutionize the diagnosis of diabetes by making it possible to identify people at risk earlier and diagnose it more accurately. This work introduces a supervised learning approach in the artificial neural network (ANN) to improve the accuracy of diabetes diagnosis. Eight input variables from the National Institute of Diabetes and Digestive and Kidney Diseases dataset were used, including 2000 patients with 65.8% healthy and 34.2% diabetic. Altering the feedforward backpropagation neural network architecture improved the ANN's accuracy. Changes were made to hidden layers (1 to 3) and neurons (20 to 100) using R2 and MSE. The ANN model suited the dataset well with R2 and MSE of 99.1% and 7.38× 10-6 in the optimal architecture. These results imply that the suggested method might quickly detect diabetes. © 2024 IEEE.

关键词： Neural network models

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Image to Audio Conversion to Aid Visually Impaired People by CNN 4

Image to Audio Conversion to Aid Visually Impaired People by...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Sivaganesan, D. Venkateshwaran, M. Dhinesh, S.P. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Sri Krishna Arts and Science College Department of Computer Science Coimbatore India

ISBN: (纸本)9798350300093

This study suggests an innovative method for helping people who are blind or visually handicapped by turning visuals into sounds. In the proposed system, audio descriptions are produced in real-time together with significant features that are extracted from photos using deep learning algorithms. The proposed work is developed to be user-friendly, which includes a simple interface that aids blind individuals to easily capture and process images using a mobile device. A user research was undertaken to assess the efficiency of the suggested method, and the results were encouraging in terms of precision and usability. This initiative offers a promising technique to give people who are blind or visually impaired an alternate means of perceiving and interacting with their environment, therefore improving their quality of life. The suggested picture to audio converter system aims to overcome the drawbacks of current assistive devices that rely on braille or textual descriptions. Blind people can more easily interpret visual information that is necessary for daily life, such as recognising items, interpreting signs, or navigate unfamiliar situations, through offering audio descriptions of images. The system makes use of recent deep learning developments that have significantly improved picture identification as natural language processing. As a result, the suggested technique has the ability to offer audio descriptions that are more precise and comprehensive than current methods. This technology has the potential to be implemented into a variety of products, from cellphones to intelligent glasses, and could significantly improve the lives of people who are blind or visually impaired. © 2023 IEEE.

关键词： Convolutional neural networks

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Towards Underwater Sustainability with AIoT: Underwater Trash Management System Comprised of YOLOv8 with IoT-applied Segmentation 2

Towards Underwater Sustainability with AIoT: Underwater Tras...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Paneru, Biplov Poudel, Buddhi Sagar Shah, Krishna Bikram Paneru, Bishwash Chhetri, Sanjog Poudel, Yam Krishna Nepal Engineering College Pokhara University Department of Electronics and Communication Bhaktapur Nepal Nepal Engineering College Pokhara University Department of Computer Science and Engineering Bhaktapur Nepal Institute of Engineering Tribhuvan University Department of Applied Science & Chemical Engineering Lalitpur Nepal Nepal Research and Collaboration Center Department of Research and Development Kathmandu Nepal

ISBN: (纸本)9798331517984

Effective techniques for managing rubbish underwater are essential due to growing environmental concerns about contamination under the sea. In this research, we offer a novel method for efficient underwater trash detection and management that makes use of the Internet of Things (IoT) and the YOLOV8 object detection model. Using YOLOV8, the suggested system seeks to identify and categorize underwater garbage in real-time. It then uses IoT devices for segmentation and management. We outline the suggested system's design, talk about how YOLOV8 integrates with Internet of Things gadgets, and provide experimental results to validate the system's efficacy. Our findings suggest that the suggested strategy has great potential to advance efforts to manage undersea waste and manage pollution as well as segment using 6-DOF Robotic Arm. The IoT based system allows remote monitoring of trash density and location along with the raspberry-pi server-based video feed allows the users to suitably monitor aquatic conditions remotely. The fusion of IoT and deep learning on YOLO v8 model opens gateway for water sustainability and enhances further opportunities in employing robotic arm-based trash collection system. © 2024 IEEE.

关键词： Robotic arms

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