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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

共 7159 条记录，以下是471-480 订阅

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Development and Validation of an iPad-based Serious Game for Emotion Recognition and Attention Tracking towards Early Identification of Autism 11

Development and Validation of an iPad-based Serious Game for...

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11th international conference on Affective Computing and intelligent Interaction (ACIIW)

作者： Piazzalunga, Chiara Molino, Pierpaolo Giangregorio, Chiara Fontolan, Stefania Termine, Cristiano Ferrante, Simona Politecn Milan DEIB Milan Italy Univ Insubria Dept Med & Surg Varese Italy

ISBN: (纸本)9798350327458

the diagnosis of Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) can be challenging due to limited accessibility and subjective assessments. Autistic individuals often present difficulties in emotional regulation, emotion recognition and imitation, and in maintaining focus. Emotional expressions and attention are thus hallmarks of ASD and ADHD and can be analyzed to identify these conditions. In this study, we developed and validated a serious game that integrates emotion recognition and attention tracking as a novel tool for identification of ASD and ADHD. Leveraging the TrueDepth camera capabilities, our game provides a cost-effective and user-friendly alternative to current face-tracking technologies. We compared the accuracy of emotion recognition using Euclidean distance with calibrated reference expressions and a calibration-free system based on a machine learning model using Random Forest. We also identified children at risk of ADHD using the Bells test and constructed a machine learning model, utilizing Support Vector Machine and Leave-One-Out Cross Validation, trained on attention data and game data to predict this risk. Our game was tested on 20 adults to validate the emotion recognition system, and then on 17 children of the primary school to assess usability and test the constructed models. the emotion recognition system achieved an accuracy of 0.78 for adults and 0.45 for children, while the machine learning model predicted seven emotions in children with an accuracy of 0.50, suggesting the potential for eliminating the need for calibration. the model also obtained good results in predicting valence and arousal values. the attention model showed excellent validation scores (accuracy: 0.94), indicating the possibility of extending it to a larger cohort. the System Usability Score was excellent (85.0), and children found the game enjoyable, making it a promising tool for ASD and ADHD identification.

关键词： emotion attention autism ADHD serious game machine learning

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Identification of Uterine Fibroids in Medical Pictures Employing Deep Neural Networks 4

Identification of Uterine Fibroids in Medical Pictures Emplo...

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4th international conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Mohanty, Arpeeta Pattnayak, Parthasarathi Mallick, Pradeep Kumar Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350349856

Uterine fibroids, commonly known as leiomyomas, are prevalent non-cancerous pelvic tumors in fertile women. these growths, comprising muscle and fibrous tissue, vary in size. Often asymptomatic, fibroids can be challenging to detect through CT scans and ultrasounds if small in size. Due to its high specificity and sensitivity, as well as its affordability and greater accessibility compared to CT and MRI exams, ultrasonography is currently the initial form of imaging used for the clinical diagnosis of uterine fibroids. the primary issue is misinterpretation of big and subplasmic fibroids, as well as pelvic and adnexal tumours. In order to do this, an automated method that utilises deep learning classification models - namely, VGG 16 - for identifying the presence of fibroids in the uterus has been proposed in this study. Our approach has demonstrated a 97.5% accuracy rate which is the best model in predicting the uterine fibrosis class based on imaging data as compared with earlier studies. © 2024 IEEE.

关键词： Ultrasonography

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Survey on Path Loss Estimation of 5G mmWave using Deep learning 11

Survey on Path Loss Estimation of 5G mmWave using Deep Learn...

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11th international conference on ICT for Smart Society, ICISS 2024

作者： Afifah, Renata Nur Zakia, Irma School of Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350389654

this paper reviews various current path loss prediction models for mmWave-based 5G networks in dense urban areas, with a particular focus on deep learning techniques such as convolutional neural networks (CNN). this study explores how deep learning approaches, especially those utilizing satellite imagery, aim to improve the accuracy of path loss predictions. the results of literature analysis show that integrating distance information into CNN models can significantly improve prediction accuracy compared to models that only use image satelite. this paper highlights the strengths and weaknesses of various approaches, and proposes a simple CNN-based model that combines satellite imagery with distance information, which is able to provide more accurate and efficient path loss predictions in the context of 5G mmWave networks. this conclusion shows that the addition of distance information can improve prediction models that were previously only based on satellite imagery, improving prediction trendlines that are more in line with real conditions. these results provide valuable insights for further research and practical applications in 5G network planning and optimization. © 2024 IEEE.

关键词： Prediction models

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India's Stake in Emerging Technology - A Review 18

India's Stake in Emerging Technology - A Review

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Singh, Sugandha China Ramu, S. Lalit, Ruchika Sgt University Department of Computer Science and Engineering Gurugram India Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Hyderabad India University School of Automation and Robotics Ggsipu East Campus New Delhi India

ISBN: (纸本)9789380544519

Emerging technologies of today are altering the way we think about the built environment. In the meantime, many technologies have undergone revolutionary changes, and India, an expanding nation, offers opportunities for development in several new areas. the goal of the study is to show how organizations need to adapt their perspective and create new strategies for emerging technologies such as artificial intelligence (AI), machine learning (ML), big data, quantum, and space exploration in light of the swift advancements in technology. this paper presents that although India has come across a lot of advancements in this era of emerging technology, certain challenges have been faced in aspects like adapting, feasibility, and approval which brought many changes across the country, which in turn is impacting the pace to transform the country towards Industry 4.0 and Industry 5.0. therefore a study is presented to understand the impact of emerging technologies for countries coming up as Industry 4.0 and Industry 5.0. this paper emphasizes on for India's initiatives for emerging technologies to come up as VIKSIT BHARAT @2047. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Artificial Intelligence Big data Fabrication Hypersonic platforms Industry 4.0 and Industry 5.0 Quantum computing

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Machine learning Algorithms for Load Forecasting Based on Big data 11

Machine Learning Algorithms for Load Forecasting Based on Bi...

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11th international conference on ENERGY and ENVIRONMENT, CIEM 2023

作者： Tiboaca-Ciupageanu, Marius Eugen Costinas, Sorina Ion, Georgiana Stan, Andrei Energy Generation and Use Department Power Engineering Faculty National University of Science and Technology POLITEHNICA Bucharest Bucharest Romania

ISBN: (纸本)9798350340785

—Big data’s importance in the context of modern Power Systems’ transition is rapidly increasing. thus, the research interest towards techniques to explore and extract the most relevant information is in a continuous expansion. In this framework, Machine learning algorithms are the most suitable approach towards retrieving valuable knowledge in a heterogeneous and ever more complex data environment. Considering the high rate and velocity of data production in smart Power Systems, Machine learning implementation is mandatory. Load forecasting is a hot topic due to its high fluctuation and economic impact considering the increasing and variable Renewable Energy penetration. this paper presents a comparison between the statistical load forecast and two Machine learning algorithms based on multiple types of errors. It highlighted the necessity of refining the algorithms and enhancing the available data using Feature engineering to improve the performance. Nevertheless, Machine learning algorithms are the only approach feasible for the ever- increasing data pool with heterogeneous characteristics. ©2023 IEEE.

关键词： Decision trees

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Operation and Maintenance Method in Electric data Acquisition System Based on Topology Identification and intelligent Meter Localization 11

Operation and Maintenance Method in Electric Data Acquisitio...

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11th China international conference on Electricity Distribution, CICED 2024

作者： Wang, Shuai Zhang, Sijian Lin, Zhenyuan Wu, Jiaming Zhou, Lai China Southern Power Grid Technology Co. Ltd Guangdong Provincial Key Laboratory of New Technology for Smart Grid Guangzhou China Intelligent vehicle college Guangzhou Panyu Polytechnic Guangzhou China

ISBN: (纸本)9798350368345

the electric data acquisition system composed of massive meters is the underlying foundation for the perception of the operating environment of the novel power system, and acquisition system's operational efficiency directly affects the operational efficiency of the novel power system. At present, the electric data acquisition system encounters two major problems, namely the poor communication effect between the low-voltage substation concentrator and the meter, and the difficulty in intelligent meter operation and maintenance localization. therefore, this article proposes an operation and maintenance method in electric data acquisition system based on topology identification and intelligent meter localization. this article designs experiments based on advanced measurement infrastructure (AMI) to verify the proposed method. the experimental results show that the proposed method can effectively solve the two major problems in the operation and maintenance of the electric data acquisition system. © 2024 IEEE.

关键词： Identification Localization Low-voltage Power Grid Topology

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Agriculture Innovations: Broccoli Leaf Diseases Classification through Federated learning CNN's 11

Agriculture Innovations: Broccoli Leaf Diseases Classificati...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Chattopadhyay, Saumitra Verma, Aditya Kukreja, Vinay Mehta, Shiva Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350357

the study concentrates on applying federated learning with convolutional neural networks (CNN) to enhance the accuracy of diagnosing and classifying broccoli leaf diseases in agriculture. this paper provides details of a distributed machine-learning model applied to four agricultural customers to collaboratively analyze plant pathology, focusing on four severity levels of leaf disease. the experiment was based on federated learning, which integrated the local data prophecies into a powerful global model. CNN was the core analysis of the model for classifying the diseases by images. the research found that the federated CNN model achieved very high accuracy, ranging from 92.76% to 97.56% for the customers and recall from 92.83% to 97.55%. the F1 scores, which can control accuracy and recall, varied from 92.78% to 97.55% and were always high. the support values, which denoted how many actual instances of each class were, revealed a significant data representation ranging from 391 to 721.75 instances. Consistency showed exceptional model accuracy for all customers, never dropping below 96%. the validity of a model integrating macro, micro, and weighted averages was verified by thorough research that covered local and global data patterns. the macro averages fluctuated between 92.79% and 97.55%, which shows the consistency of the model in every category. the weighted averages, considering class imbalances, recorded high values varying from 92.85% to 97.54%, indicating the model's capability to deal with varied class distributions. the micro averages were very close to the weighted averages, with the range being 92.84% to 97.54%, and thus, the model provided a consistent performance in all categories. © 2024 IEEE.

关键词： Diagnosis

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EfficientNetV2 and Attention Mechanisms for the automated detection of Cephalometric landmarks 6

EfficientNetV2 and Attention Mechanisms for the automated de...

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6th international conference on intelligent Systems and Computer Vision, ISCV 2024

作者： Tafala, Idriss Ben-Bouazza, Fatima-Ezzahraa Edder, Aymane Manchadi, Oumaima Et-Taoussi, Mehdi Jioudi, Bassma Mohammed VI University of Sciences and Health Clinical and Medical Sciences and Biomedical Engineering Laboratory CasablancaUM6SS Morocco Hassan 1er University Faculty of Science and Technology Morocco France

ISBN: (纸本)9798350350180

Artificial intelligence (AI) is becoming acknowledged for its significant potential to bring about transformation across various sectors, including dentistry. Cephalometry is crucial in craniofacial imaging for medical diagnostics, assisting in diagnosis, surgical planning, and growth research. Manual cephalometric analysis is laborious and error-prone, emphasizing the necessity for automated landmark identification techniques to improve efficiency and precision. this paper proposes a new method for completely automating the localization of cephalometric landmarks using the deep neural network EfficientNetV2 with attention mechanisms. the goal of our approach is to precisely identify specific points on lateral cephalograms, overcoming the challenge of having a small amount of data in the medical field. this research enhances diagnostic accuracy and treatment planning in dentistry by improving automated landmark detection in cephalometry, leading to better patient care and outcomes. By integrating cutting-edge deep learning models with craniofacial imaging, this research aligns with the broader goal of leveraging AI to improve patient care across various medical fields. © 2024 IEEE.

关键词： Patient treatment

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intelligent Reflecting Surface-Assisted Fresh data Collection in UAV Communications 11th

Intelligent Reflecting Surface-Assisted Fresh Data Collectio...

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11th international conference on Communications, Signal Processing, and Systems, CSPS 2022

作者： Huang, Hongli Liu, Juan Xie, Lingfu Ningbo University Ningbo315211 China

ISBN: (纸本)9789819923618

this paper considers employing intelligent reflecting Surface (IRS) in unmanned aerial vehicle (UAV) assisted wireless communications to ensure the freshness of the collected data in Internet of things (IoT). We aim to minimize the average Age of Information (AoI) of the sensor nodes (SNs) by jointly optimizing the UAV flight trajectory, the SNs’ scheduling and the IRS phase shift matrix. It is modeled as a Markovian Decision Process (MDP) problem. A deep reinforcement learning algorithm based on a Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed to learn and find the optimal UAV trajectory and scheduling of the SNs. For a scheduled transmission, the IRS is used based on the channel information to align the signal phase shifts. Simulation results show that IRS-assisted UAV data collection can significantly reduce the AoI of the SNs. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Novel Framework for Dengue Classification and Early Recovery using Machine learning Algorithms 18

Novel Framework for Dengue Classification and Early Recovery...

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18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Faritha Banu, J. Hariprasad, G. Archana, T. Srivatsan, Prahadees Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai Ramapuram India

ISBN: (纸本)9789380544519

Dengue fever is a fast-emerging public health hazard that primarily affects tropical and sub-tropical regions. Arthropods that feed on blood, such as fleas, parasites, and mosquitoes, are the carriers of this disease, which can infect humans and cause significant illness. Five serotypes of the dengue virus - DEN V1, DEN V2, DEN V3, DEN V4, and DEN V5 are spread by Aedes aegypti mosquitoes. Currently, this illness is spreading to many other countries. A delayed diagnosis combined with the wrong method of treatment increases the chance of death. Based on the severity of the infection, dengue is classified into Dengue Hemorrhagic Fever (DHF) or Dengue Shock Syndrome (DSS) which are categorized into febrile, critical, and recovery phases. A framework model is essential to analyze the severity of the infection and identify the phase of severity. Among various machine learning algorithms, this study uses Support Vector Machine and Random Forest for their proficiency in handling complex data. through rigorous feature engineering and data preprocessing, critical information for precise discrimination was extracted and examined for DHF phases. Experimental results demonstrate both models are competent, but SVM is more suitable for the proposed prediction model with an accuracy 95%, precision of 93%, recall of 94%, and F-measure metrics of 94%. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Dengue Dengue Haemorrhagic Fever Dengue Shock Syndrome Support Vector Machine and Random Forest

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