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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

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Performance prediction of densely deployed WLANs using Graph Isomorphism Networks 3

Performance prediction of densely deployed WLANs using Graph...

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3rd international conference on Electronics Technology and Artificial Intelligence, ETAI 2024

作者： Mohan, Rajasekar Navali, Anish Dhruva Chaitanya, G. Gagandeep, H.S. Patil, Neelan S. PES University Bangalore India

ISBN: (纸本)9781510683143

As the usage of wireless devices has increased exponentially, deployments of IEEE 802.11 WLANs has been significantly increasing and hence is bound to cause higher interference and reduced throughputs, particularly in dense-deployments. Though a network simulator like Komondor captures comprehensive deployment details of WLANs to provide accurate performance metrics, the high compute time and the concomitant complexity of such simulators cannot be disregarded. Deep learning techniques prove to be the ideal alternative to be able to quickly compute the throughput of a WLAN deployment with reduced compute time and complexity. In this paper we implement a Graph Isomorphism Network (GIN) for predicting the throughputs of BSSs in dense deployments. The regression model is trained on the data obtained from the Komondor network simulator for a several comprehensive set of deployments. As the topology of the deployments mimic a graph network, the capability of GIN is explored for mapping the deployments and to accurately predict the throughput of a densely deployed WLAN. © 2024 SPIE.

关键词： Deep learning

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Posture Correction Device Based on IMU Sensors and CNN 3rd

Posture Correction Device Based on IMU Sensors and CNN

引用

3rd international conference on Innovation in engineering, ICIE 2024

作者： Ionica, Darius Ioan, Gota Dan Stan, Ovidiu Technical University of Cluj-Napoca Cluj-Napoca400027 Romania

ISBN: (纸本)9783031615740

In our daily lives, good posture is important to prevent complications and promote good health. This paper introduces an innovative approach that integrates wearable technology with Convolutional Neural Networks (CNNs) to address the general issue of body posture. The proposed system comprises a lightweight wearable device embedded with sensors, continuously monitoring and capturing real-time posture data. Leveraging deep learning algorithms, a powerful CNN processes this data to accurately classify and identify various posture patterns. Unlike traditional methods, our approach offers personalized and immediate feedback through an intuitive mobile app. Users receive real-time posture assessments, visualizations, and corrective recommendations, empowering them to proactively improve their posture habits. This not only enhances posture correction but also fosters a sustainable approach to musculoskeletal health. The proposed method contributes to a more proactive approach to preventive healthcare, significantly improving musculoskeletal health and enhancing the quality of life. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Prediction of Irregular Bangladesh-EU Migration Trends Using Machine learning Techniques 3

Prediction of Irregular Bangladesh-EU Migration Trends Using...

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3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Islam, Md. Shariful Rahim, Md Abdur Podder, Nitun Kumar Hossain, Md. Najmul Hossain, Md Imran Pabna University of Scence and Technology Dept. of Computer Science and Engineering Pabna Bangladesh Pabna University of Scence and Technology Dept. of Electrical Electronic and Communication Engineering Pabna Bangladesh Pabna University of Scence and Technology Dept. of Inormation and Communication Engineering Pabna Bangladesh

ISBN: (纸本)9798350388282

Remittances are important economic contributors to a countrya's economy. It has increased national savings, lowered foreign exchange limits, and helped balance payments and development budgets. Migrant workers are the main source of these remittance revenues. Many workers worldwide leave their homes and move to different parts of the world for overseas employment. These workers are authorized and have documentation of their country of origin and final destination. Besides, many workers have migrated illegally globally, particularly to European Union (EU) countries via marine routes. However, illegal immigration leads to a loss of national revenue due to non-payment of taxes and social security contributions and problems with living circumstances and legal issues. Therefore, this paper aims to present a time series forecast of irregular migrants in EU countries from Bangladesh over the next five years. We used five methods to analyze this forecasting: ARIMA (an autoregressive integrated moving average), XGBoost Series, Decision Tree, Feed Forward Neural Network, and CatBoost Regressor. In addition, we used data from the European Border and Coast Guard Agency to perform the time series forecast. A field intervention is carried out to assess the gender, age, education, socioeconomic level, and skill profile of migrant workers. Moreover, a few case studies of migrant workers and a survey were conducted to investigate the underlying causes of irregular migration. Finally, this study explains the underlying cause of irregular migration and its repercussions. It can provide some ideas for decreasing the loss of human life and property by reducing irregular migration to help take essential actions in the home and destination countries. © 2024 IEEE.

关键词： Forecasting

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Compensation method for metal defect detection based on deep learning and ultrasonic wave 3

Compensation method for metal defect detection based on deep...

引用

3rd international conference on Advanced Manufacturing Technology and Manufacturing Systems, ICAMTMS 2024

作者： Wu, Chunjie Ma, Zaisheng Ding, Yong Fu, Junlei Wu, Xinjia Zhejiang Fangyuan Test Group Co. Ltd. Hangzhou310018 China Zhejiang Fangyuan Metal Materials Test Co. Ltd. Hangzhou310018 China

ISBN: (纸本)9781510681804

Ultrasonic nondestructive testing plays a crucial role in evaluating the integrity of metal structures. When there are defects in the tested workpiece, there will be reflection, refraction, diffraction, and other behaviors of sound waves. By analyzing the ultrasonic echo signal, the location and depth of defects can be identified. In previous ultrasonic defect detection, due to changes in the ultrasonic probe, the identified defects may change, affecting the accuracy of defect discrimination. This paper proposes a method for detecting the depth of metal cracks based on deep learning. This method combines finite element models and experimental analysis data to compensate for the depth of metal cracks, improving the accuracy of defect identification. Finally, an ultrasonic experimental study on metal components was conducted to verify the effectiveness of this method. © 2024 SPIE.

关键词： Ultrasonic testing

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Crafting Customer Segmentation Strategies for Advanced Recommendation System 3

Crafting Customer Segmentation Strategies for Advanced Recom...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Seethalakshmi, K. Bhagyalakshmi, A. Anitha, G. Sheela, E. Sandeep, Varada Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Tami Nadu Chennai India S.A Engineering College Department of Computer Science and Business Systems Tamil Nadu Chennai India St Peter's Institute of Higher Education and Research Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350369083

With the static nature of recommender systems, it is crucial to take into account the intricate details of customer behaviour to better improve operating efficiencies. This paper is about original methods to augment recommendation frameworks by employing spectral clustering methods, to be more specific. Limited segmentation techniques now cannot catch up with the growing complexity and variety of customer conduct. To address this need, we are proposing a new way in which the spectral clustering technique, a supervised learning technique, can be applied. Revealing in-depth relationships inside customer data will enable an upgrade in the segmentation approach beyond the existing norms. Spectral clustering stands out as a really amazing tool for accomplishing extraordinarily precise customer segments, which help understand the tastes of users on a deeper level. Through discovering separate customer groups, we are aiming for a meaningful contribution in the field of recommendation systems. This project combines the effectiveness of spectral clustering with the convenience of a recommendation system that aims to provide a holistic, meaningful, and user-oriented approach to enhancing recommendation systems in the area of personalised recommendation. Our aim is to create recommendation systems that are not only attuned to the heterogeneity of customer preferences but also adopt new trends in user experience design beforehand. The segmentation is done with K-Means along with the proposed algorithm and the clusters are hence compared and Implementation results shows that spectral clustering is much more efficient than the existing K-Means algorithm © 2024 IEEE.

关键词： Sales

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Deep Convolutional Networks for Flower Species Recognition: A ResNet Study 2

Deep Convolutional Networks for Flower Species Recognition: ...

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2nd international conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Verma, Goldy Prasad, Chandan Mahajan, Shubham Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Pimpri-Chinchwad University Computer Science & Engineering Plot No. 44 49 50 Mohitewadi Rd Mohitewadi Sate Tal- Maval Maharashtra Pune412106 India Deemed to Be University Department Of Computer Science Christ Delhi-NCR India

ISBN: (纸本)9798350364699

Research on deep learning approaches of flower classification has been mostly focused on key implications for environmental monitoring, botany, and horticulture. Usually labour-intensive and prone to inaccuracies, conventional methods of floral identification rely largely on professional knowledge and manual judgment. Aiming high accuracy in identifying between five different flower classes: Lily, Lotus, Orchid, Sunflower, and Tulip, this work presents a solution using a revised fine-tuned ResNet model. Using a dataset obtained from Kaggle encompassing varied photos of these flowers, the model goes through a rigorous training procedure by means of data augmentation techniques including rotation, flipping, and scaling to boost its resilience and generalizing capacity. By splitting the dataset into training, validation, and test sets, stratified sampling ensures fair representation over all classes. Performance evaluation is done with accuracy, precision, recall, and F1-score;error analysis provides insight of misclassification tendencies. The improved fine-tuned ResNet model reveals the outstanding high-precision floral recognition ability of the model and achieves an incredible accuracy of 95%, thereby differentiating the flower classes. This work presents a significant response for exact and rapid floral classification and shows how well deep learning changes automated plant identification. © 2024 IEEE.

关键词： Deep learning

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Mental Workload Assessment in Human–Computer Interaction Multitasking Environment Based on Multimodal Physiological Signals 3rd

Mental Workload Assessment in Human–Computer Interaction Mu...

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3rd international conference on Smart Manufacturing, Industrial and Logistics engineering, SMILE 2023 and the 7th Asian conference of Management Science and Applications, ACMSA 2023

作者： Yang, Chenjie Li, Kuntong Yang, Yuxin Xiao, Jingwen School of Management Tianjin University of Technology Tianjin300382 China Tianjin Intelligent Manufacturing and Intelligent Security Technology Innovation Think Tank Tianjin300384 China

ISBN: (纸本)9789819701933

Based on the second generation of Multi-Attribute Task Battery-II (MATB-II) experimental paradigm of concurrent multi-task, this paper collects the multi-modal physiological signals and subjective mental workload data of NASA-TLX scale during the task completion process of experimental objects, proposes a mental workload classification model based on multi-modal physiological signal feature analysis and pattern recognition, and compares the classification and recognition effects of different modal physiological signals and their combinations in three typical machine learning algorithms (Random forests, decision trees, and k-nearest neighbor models). The results show that, among the classification models based on single-modal physiological signals, the classification models based on skin electrical, electrocardiographic, and EEG signals increase in accuracy in turn;the classification models based on multi-modal physiological signals are generally better than the single-modal classification models;the random forest classification model based on the three modalities of EEG, ECG, and skin electrical physiological signals has the highest classification accuracy. In occupational settings, the interaction between perceived mental workload and physical health effects should be considered, as workers often face both physical and mental demands at the same time. Controlling the mental workload of operators within reasonable limits can reduce human error. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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Rnbformer: A High-Performance Roadside Noise Barriers Recognition Algorithm 24

Rnbformer: A High-Performance Roadside Noise Barriers Recogn...

引用

3rd Asia conference on Algorithms, Computing and Machine learning, CACML 2024

作者： Zhang, Zirui Wu, Jiaheng Huang, Heqiang Chen, Junzhou Hu, Hongyu Zhang, Ronghui Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-Sen Uniyersity Guangdong Guangzhou510275 China State Key Laboratory of Automotive Simulation and Control Jilin University Changchun Jilin Changchun130022 China

ISBN: (纸本)9798400716416

Traffic noise pollution is a significant source of urban pollution. Roadside noise barriers (RNBs) serve as a primary solution to urban traffic noise pollution, but achieving precise positioning of RNBs requires on-site inspections and manual labeling, which is time-consuming and labor-intensive. Deploying an RNB recognition algorithm on inspection vehicles equipped with cameras and positioning devices is an efficient approach to establishing a high-precision RNB positioning database. We propose a high-accuracy RNB recognition algorithm, RNBFormer. In the model design, we integrate both convolutional and transformer structures to enhance the model's global feature extraction capability while optimizing the representation of local features. Additionally, we incorporate a channel attention mechanism to provide more discriminative features at the channel level. We trained and tested our model on a publicly available dataset, achieving a classification accuracy of 98.93%. © 2024 ACM.

关键词： Convolution

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MQTT equipped Intrusion detection system for IoT devices based Machine learning Algorithms 3

MQTT equipped Intrusion detection system for IoT devices bas...

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3rd IEEE international conference on Computing, Communication, and intelligent Systems, ICCCIS 2022

作者： Sharma, Priya Sharma, Sanjay Kumar Dani, Diksha University School of Information and Communication Technology Gautam Buddha University Greater Noida India Indraprastha Engineering College Ghaziabad India

ISBN: (纸本)9781665462006

In the era of COVID19, the world has shifted to an online presence and is now forced to embrace the usage of digital technology in their daily lives. With the meteoric rise of internet-based devices, there is a requirement for a protocol for secure communication between these devices. Message Queueing Telemetry Transport (MQTT) is the standard protocol for IoT devices. The MQTT implementation with IDS have very prominent usability and has huge potential for increasing efficiency. Therefore, in this paper, an IDS has been proposed with MQTT as a protocol for IoT devices, using machine learning to improve the pattern recognition of the IDS. The proposed system has been tested with three machine learning algorithms, namely, and the results show that they are adequate for the MQTT protocol © 2022 IEEE.

关键词： Intrusion detection

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Multi-class Skin Lesion Classification Using intelligent Techniques 3rd

Multi-class Skin Lesion Classification Using Intelligent Tec...

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3rd international conference on Advanced Computing and intelligent Technologies, ICACIT 2023

作者： Ranjan, Vibhav Chaurasia, Kuldeep Singh, Jagendra School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9789819719600

This study explores the development and assessment of an advanced skin cancer classification system utilizing deep learning techniques for accurate discrimination among diverse skin cancer types. The primary aim is to construct an effective classification framework based on deep learning that has the capability of distinguishing melanoma, vascular lesions, melanocytic nevus, cutaneous fibromas, benign keratosis, and various carcinomas and skin moles. Evaluation of the system involves comprehensive analysis employing metrics like accuracy, recall, precision, and F1-score using HAM10000 dataset. We utilized pre-trained models, like EfficientNetB0, VGG19, MobileNet, and ResNet50V2 and analyzed the performance for unbalanced and manually balanced dataset for different hyperparameters. The AI-based automatic system serves as a crucial tool in aiding accurate skin cancer diagnosis. It was found that MobileNet and ResNet50V2 performed quite well as compared to other models after dataset augmentation achieving overall accuracy of 84% and average F1-score across classes of 84% for both MobileNet and ResNet50V2. While performance may vary based on image quality, dataset diversity, and population-specific factors, this research highlights the efficacy and potential of deep learning in advancing skin cancer diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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