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4th International conference on IoT Based Control Networks and intelligent Systems, ICICNIS 2023

4th International Conference on IoT Based Control Networks a...

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4th International conference on IoT Based Control Networks and intelligent Systems, ICICNIS 2023

ISBN: (纸本)9789819965854

the proceedings contain 52 papers. the special focus in this conference is on IoT Based Control Networks and intelligent Systems. the topics include: Development of IoT-Based Vehicle Speed Infringement and Alcohol Consumption Detection System;phonocardiogram Identification Using Mel Frequency and Gammatone Cepstral Coefficients and an Ensemble learning Classifier;Automatic Conversion of Image Design into HTML and CSS;Customizing Arduino LMiC Library through LEAN and Scrum to Support LoRaWAN v1.1 Specification for Developing IoT Prototypes;prevention of Wormhole Attack Using Mobile Secure Neighbour Discovery Protocol in Wireless Sensor Networks;Comparison of Feature Extraction Methods Between MFCC, BFCC, and GFCC with SVM Classifier for Parkinson’s Disease Diagnosis;a Comprehensive Study on Artificial Intelligence-Based Face Recognition Technologies;design of IoT-Based Smart Wearable Device for Human Safety;Detection of Artery/Vein in Retinal Images Using CNN and GCN for Diagnosis of Hypertensive Retinopathy;vehicle Information Management System Using Hyperledger Fabric;An Evolutionary optimization Based on Clustering Algorithm to Enhance VANET Communication Services;visual Sentiment Analysis: An Analysis of Emotions in Video and Audio;design and Functional Implementation of Green Data Center;patient Pulse Rate and Oxygen Level Monitoring System Using IoT;ioT-Based Solution for Monitoring Gas Emission in Sewage Treatment Plant to Prevent Human Health Hazards;Evaluation of the Capabilities of LDPC Codes for Network Applications in the 802.11ax Standard;parallel optimization Technique to Improve the Performance of Lightweight Intrusion Detection Systems;Enhancement in Securing Open Source SDN Controller Against DDoS Attack;proposal of a General Model for Creation of Anomaly Detection Systems in IoT Infrastructures;green IoT-Based Automated Door Hydroponics Farming System.

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Research on the Configuration optimization of the Storage System with Liftable Shuttles 4

Research on the Configuration Optimization of the Storage Sy...

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4th International conference on Mechatronics Technology and intelligent Manufacturing, ICMTIM 2023

作者： Tao, Guanghong Lin, Shuqiang Jiang, Chunying Shenyang Aerospace University Shenyang China

ISBN: (纸本)9798350314434

According to the 'cross-layer and cross-column' operation mode of the liftable shuttle, combined with the composition characteristics of the storage system, a multi-objective model for the configuration optimization of the lifting shuttles storage system is designed, which is with the objective functions including the construction and operating costs and the average throughput time, and the decision-making variables including the number of shelf layers, columns, rows, the number of shuttle vehicles, and the shuttle operation parameters. Based on a fast nondominated multi-objective optimization algorithm with elite retention strategy, the optimal solution set Pareto frontier is obtained. the research conclusion of decision-making variables parameters provides an effective reference for enterprises to make decision of planning layout and equipment selection of the actual different storage systems. © 2023 IEEE.

关键词： Multiobjective optimization

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A Machine learning Model for Predicting the Shear Resistance of RC Beams Strengthened with EBR-CFRP Systems 4th

A Machine Learning Model for Predicting the Shear Resistance...

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4th FIB International conference on Concrete Sustainability

作者： Mohammadi, Amirhossein Oliveira Barros, Joaquim Antonio Sena-Cruz, Jose Univ Minho Dept Civil Engn P-4800058 Guimaraes Portugal

ISBN: (纸本)9783031806742;9783031806728;9783031806711

the prediction of shear resistance in reinforced concrete (RC) beams, which are strengthened using externally bonded reinforcements (EBR), poses a significant challenge due to the complex nature of shear-resisting mechanisms and their complex interaction. Conventional models have been shown to have limited reliability and produce weak predictions. However, recent advancements in machine learning techniques and artificial intelligence have led to the development of several models in various studies that aim to predict the contribution of EBR-FRP to the shear resistance of RC beams. this study makes an attempt to enhance the predictive performance of the models by considering various factors. First, a comprehensive review is conducted on previous ML models, with a discussion on their strengths and weaknesses. the limitations of these models are addressed. Furthermore, potential approaches to improve the model predictive performance are discussed, and a new model is proposed.

关键词： Shear Strengthening Reinforced concrete beams Externally bonded reinforcements CFRP Machine learning XGBoost

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Parameter optimization Clustering Federated learning Algorithm Based on Data Heterogeneity 4

Parameter Optimization Clustering Federated Learning Algorit...

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4th International Symposium on Computer Engineering and intelligent Communications, ISCEIC 2023

作者： Miao, Jing Wang, Yanan Cheng, Xiaodong Yang, Yanjun Inner Mongolia University College of Electronic Information Engineering Hohhot China

ISBN: (纸本)9798350306323

Federated learning, as a new privacy-preserving distributed learning paradigm, can break data silos and train better models. In the case of client heterogeneity, the significant difference in data distribution among different clients makes the model convergence speed significantly lower, which leads to data drift. To address the impact of data heterogeneity in federated learning, a density-based federated clustering method, FedDec, is pro-posed. FedDec firstly divides the hyperparameters of the overall clients into different clusters by using the extreme value clustering method to ensure that the clients within each cluster have a high degree of similarity, and at the same time safeguard the data privacy and security. the parameter optimization is performed for each client to improve the accuracy of the client model before going to participate in model aggregation. And it is compared with some classical algorithms on the public datasets MNSIT and CFAR-10, and the accuracy is improved on both the MNIST dataset and CFAR-10 dataset. © 2023 IEEE.

关键词： Genetic algorithms

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Adaboost Optimized by Sinh Cosh Algorithm for Prediction of Software Defects 4th

Adaboost Optimized by Sinh Cosh Algorithm for Prediction of ...

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4th International conference on intelligent Systems and Pattern Recognition-ISPR

作者： Villoth, Saramma John Villoth, John Philipose Zivkovic, Tamara Zivkovic, Miodrag Jovanovic, Luka Bacanin, Nebojsa Mani, Joseph Univ Technol & Appl Sci Nizwa Oman Singidunum Univ Belgrade Serbia Univ Belgrade Sch Elect Engn Belgrade Serbia Modern Coll Business & Sci Muscat Oman

ISBN: (纸本)9783031821523;9783031821530

Defect detection in software development represents a vital activity that relies on efficient and precise techniques to assure the quality of the software. this manuscript tries to explore the possibilities of the Adaboost model optimized by a modified sinh cosh metaheuristics algorithm for accurate and efficient detection of defects. As modern development projects are dynamic and with tight deadlines, the capability of Adaboost classifier to adopt intricate patterns and metrics in the software code may help in identifying problematic modules, thus allowing focused testing in limited available time. As software development industry recently recognized the significance of the proactive testing, this manuscript suggests an approach where machine learning model was effectively integrated in a framework that helps in identifying error-prone modules. the suggested optimized Adaboost classifier has shown very promising performance with respect to the precision and accuracy of identifying faulty modules based on the software metrics, making it a potentially crucial tool that could be applied in the modern software development practice.

关键词： Software testing defects identification Adaboost machine learning optimization methods swarm intelligence sinh cosh algorithm

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Optimising Energy Performance of buildings through Digital Twins and Machine learning: Lessons learnt and future directions 4

Optimising Energy Performance of buildings through Digital T...

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4th International conference on Applied Artificial Intelligence (ICAPAI)

作者： Renganayagalu, Sathiya Kumar Bodal, Terje Bryntesen, Tom-Robert Kvalvik, Petter Inst Energy Technol Dept Virtual & Augmented Real Halden Norway Inst Energy Technol Dept Appl Data Sci Halden Norway Inst Energy Technol Dept Business Dev Halden Norway

ISBN: (纸本)9798350349764;9798350349771

In recent years, the energy efficiency of buildings has received increasing attention due to climate change mitigation goals, and higher energy costs. this paper explores the integration of 3D models, IoT sensors, Digital Twins (DT), data-driven modeling, and Artificial Intelligence (AI), particularly Machine learning (ML) algorithms, to enhance energy performance prediction and optimisation in existing buildings. By leveraging real-time data from IoT sensors, DTs provide a comprehensive digital representation of buildings, facilitating intelligent monitoring and control for enhanced energy efficiency and occupant comfort. this paper presents the development and application of a data-driven DT for an office building in Norway, focusing on energy performance prediction. through a case study, specific outcomes and insights are gathered regarding the feasibility and benefits of this approach, together with its inherent limitations. the results highlight that significant advancements in energy efficiency could be achieved through predictive modeling and intelligent control strategies. In future, adaptation of these technologies requires addressing key challenges and advancing methodologies for broader implementation. By identifying and addressing these challenges, the integration of IoT sensors, DTs, and AI holds considerable scope for optimising building energy performance and advancing sustainability objectives.

关键词： AI Artificial Neural Network energy efficiency case study

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intelligent Multi Objective Resource Allocation Method Based on D3QN 4

Intelligent Multi Objective Resource Allocation Method Based...

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4th International conference on Electronic Information Engineering and Computer Science, EIECS 2024

作者： Zhao, Zhiyuan Li, Qinfu Qin, Tao Tan, Qikun Ren, Zhonggang China Academy of Electronics and Information Technology China Electronic Technology Group Corporation Beijing China

ISBN: (纸本)9798331531409

the multi-objective resource allocation problem refers to how to allocate limited resources reasonably to multiple objectives under given conditions and environment, in order to obtain the best returns. this is an important and challenging problem in the field of command and control, as well as a complex combinatorial optimization problem that involves comprehensive consideration of various factors and constraints such as resource performance, target characteristics, operating environment, combat objectives, and other parameters. It has a high degree of nonlinearity, dynamism, and uncertainty, making it difficult to solve using traditional mathematical methods. Based on this, this article proposes a method of using Deep Q-Network (DQN) to solve the Dynamic Weapon Target Allocation (DWTA) problem. On the basis of reinforcement learning, a deep neural network is used to predict the Q-value function, and the Dueling method is used to improve the DQN algorithm by using a Double network for prediction. In the final simulation experiment, compared with the traditional DQN method, faster convergence speed and more accurate Q-value prediction were achieved. © 2024 IEEE.

关键词： Deep neural networks

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Federated learning for Resource Allocation in Maritime IoT Networks with Compressed Sensing 4

Federated Learning for Resource Allocation in Maritime IoT N...

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4th International conference on Communication Technology and Information Technology, ICCTIT 2024

作者： Liu, Jiwei Li, Baiyi Liu, Shutong Yang, Tingting Mao, Zhongyang Dalian Maritime University Navigation College Dalian China Naval Aviation University Department of Electronic and Information Engineering Yantai China

ISBN: (纸本)9798331528973

Maritime transportation and shipping are vital components of global economic development. In order to enhance shipping efficiency and ensure safety, a distributed machine learning architecture can be utilized to achieve it. Due to concerns about data privacy, many shipping data are not shared. Federated learning FL), as a form of distributed machine learning, offers the advantage of preserving privacy. the problem of communication efficiency and resource joint optimization in FL over communication networks has been studied, and proposed a communication resource allocation algorithm with compressed sensing. through experiments, we found that compared to the basic FL method with uniform resource allocation, the proposed RACS algorithm significantly improves the communication efficiency of wireless IoT network FL, as well as the learning performance in terms of training loss and testing accuracy. © 2024 IEEE.

关键词： Federated learning

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Artificial Intelligence - Machine learning Strategies for Crop Leaf Disease Detection 4

Artificial Intelligence - Machine Learning Strategies for Cr...

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4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Sarwade, Jayesh M. Gujar, Milind Shinde, Jagruti thombare, Prathmesh Rupnur, Prathmesh Arbad, Ganesh JSPM's Rajarshi Shahu College Of Engineering Department of Information Technology Pune India

ISBN: (纸本)9798331529635

this research study reviews the recent advancements and emerging trends in developments and new directions in machine learning (ML) and artificial intelligence (AI) for precision agriculture's identification and management of crop leaf diseases. the productivity of agriculture is seriously threatened by crop diseases and, traditional detection methods are often time-consuming, labour-intensive, and poses a threat to environment due to excessive chemical treatments. AI-ML approaches, particularly image processing and deep learning techniques, offer a more efficient and sustainable solution to the identified problem statement. this paper reviews various AI-ML models used for identifying plant diseases from leaf images, utilizing data from both public datasets and field conditions. It also delves into AI-ML applications in disease management, including optimized chemical spraying and disease spread forecasting, which help improve precision in treatment and reduce environmental impact. Additionally, this research study highlights the challenges faced by these AI-ML models, such as adapting to real-world farming conditions, and identifies promising areas for future research and development. these include improving model robustness in varying environments and making AI-ML technologies more accessible to farmers. © 2024 IEEE.

关键词： Artificial Intelligence (AI) Crop Disease Detection Deep learning Machine learning (ML) Precision Agriculture Sustainable Agriculture

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Optimizing Traffic Sign Detection with MobileNetV2: A Lightweight Deep learning Approach 4

Optimizing Traffic Sign Detection with MobileNetV2: A Lightw...

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4th International conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Shourie, Poonam Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350387490

the ability to recognize traffic signs is a prerequisite for both autonomous driving and intelligent transportation systems, and it is essential to the realization of sustainable cities and communities. By lowering the number of traffic accidents and fatalities, efficient and accurate traffic sign recognition improves road safety and urban mobility, promoting good health and well-being. this project uses deep learning methods, namely Convolutional Neural Networks (CNNs) and MobileNetV2 architecture, to classify Indian traffic signs into 85 distinct categories using a dataset from Kaggle. the collection of traffic signs features a range of signs that are commonly seen on Indian highways and that promote infrastructure, industry, and innovation through the application of transportation technology. the integration of MobileNetV2 in the CNN model offers high accuracy and possible implementation of real-time traffic deployment. Furthermore, the CNN model will support Climate Action;it can aid in the superior intelligent management in traffic matters while curbing car emissions through efficient flow of traffic. Deep learning models make a massive improvement of traffic sign recognition efficiency and accuracy since it is significant in the evolution of better road safety and navigation of self-driving vehicles. this technical innovation advances the sector of the autonomous car besides making cities more resilient, safe, inclusive, and sustainable. All things considered, this study brings out the importance of deep learning to the creation of intelligent transportation systems that support and promote safer and more sustainable urban lives. © 2024 IEEE.

关键词： Convolutional neural networks

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