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

共 20250 条记录，以下是421-430 订阅

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Deep Seizure: An Enhanced System for Detecting Epileptic Seizure in Real-time based on EEG Signals Utilizing Sliding Window-based Deep learning Approach 3

Deep Seizure: An Enhanced System for Detecting Epileptic Sei...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mohideen, Akbar Badhusha Jhansi Lakshmi, D. Gopi Reddy, B. Jagadeesh, C.H. Kowsik, G. Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (纸本)9798331527549

Research explores epilepsy detection in real-time scenarios using a deep-learning framework with an innovative sliding window design approach. The primary challenge is that it is quite difficult to detect epileptic seizures in real-time using EEG signals accurately. Traditional methods are time-consuming and depend upon manual analysis of EEG data. In addition, variability in the seizure patterns and the presence of noise in EEG recordings can be complicated during the classification process. To overcome the problem, we will proceed with the following: The EEG signals will be taken as input, and with the help of CNN, the classification between seizure and non-seizure patients will be differentiated. The framework enhances the CNN implementation on the EEG recordings to evaluate the activity. The sliding window design will be helpful to make sure that the classes will be balanced within the training samples for improved accuracy. The outcome will be the enhancement of classification accuracy, and clinical decision-making, and the potential for future advancements in epilepsy management. © 2025 IEEE.

关键词： Decision making

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A Novel Framework Leveraging Adam Optimization Techniques, Coupled with Mask R-CNN Deep learning Mechanisms for Cervical Cancer Detection System 3

A Novel Framework Leveraging Adam Optimization Techniques, C...

引用

3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Saranya, G. Sujatha, C. Ssm Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624 002 India Ssm Institute of Engineering and Technology Department of Computer Science Engineering Dindigul624 002 India

ISBN: (纸本)9798350375190

Cervical cancer remains a significant public health concern, with early detection crucial for successful treatment. Deep learning techniques offer promising avenues for automated cancer diagnosis, and this work explores the potential of the Mask R-CNN model with Adam optimization for cervical cancer detection in colposcopy images. This study aims to develop and evaluate a deep learning system based on Mask R-CNN and Adam optimization for automated detection and segmentation of cervical lesions in colposcopy images. The proposed system utilizes Mask R-CNN, a deep convolutional neural network (CNN) is capable of both object detection and instance segmentation. Pre-trained on the COCO dataset, the model is fine-tuned on a labeled colposcopy image dataset for cervical lesion detection and segmentation. Adam optimization, an efficient gradient descent algorithm, is used to maximize the training process of the model. High accuracy in recognizing and differentiating cervical lesions in colposcopy images is anticipated from the system. The system should demonstrate its ability to perform effectively on unseen data and handle variations in image quality. The proposed system's performance is evaluated on a separate test dataset using standard metrics like accuracy, sensitivity, specificity, and precision for both lesion detection and segmentation tasks. Additionally, the system's generalizability and robustness to variations in image quality are assessed. This novel approach has the potential to significantly contribute to the field of cervical cancer diagnosis by providing an automated, accurate, and efficient system for early detection, ultimately leading to better patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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Comprehensive Review of Electric Vehicle Energy Demand Forecasting Techniques 3

Comprehensive Review of Electric Vehicle Energy Demand Forec...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Elavarasi, A. Barith Ahamed, J. Mohamed Ismail, M. Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This study tackles a significant problem in predicting the demand for electric vehicle charging in order to design charging infrastructure, manage energy efficiently, and maintain grid stability. A novel system that focuses on important temporal patterns and features is created by fusing LSTM networks with attention processes to improve predictive performance. Effective preprocessing ensures that the incoming data is of good quality, which helps the LSTMs identify temporal correlations and the attention mechanism to highlight important *** outperforms conventional methods like ARIMA and linear regression by using metrics like Mean Absolute Error (MAE) and R-squared (R2). A scalable and precise real-time energy management solution for sustainable transportation infrastructure is offered by this hybrid approach. © 2025 IEEE.

关键词： Demand response

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Detecting Malicious Threats On Mobile Applications Using Machine learning Approaches 3

Detecting Malicious Threats On Mobile Applications Using Mac...

引用

3rd international conference on Communication, Control, and intelligent Systems, CCIS 2024

作者： Padmavathi, A. Yasaswy, K. Rahul, K.Jayesh Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331528201

In Today scenario , Android is one of the most frequently used mobile operatings systems, thus it is a priority for advanced threat actors and hackers. Malicious code is often found in Android applications to which security solutions have a problem of identifying the threats. Today's Android malware is sophisticated enough that it can avoid most ***, machine learning is more efficient in stopping a large number of diverse contemporary threats to Android devices. These machine learning methods essentially first rely on the detection of sequential patterns of malicious behaviors, and secondly, distinguish between existing and new types of threats that have previously exhibited typical and atypical behavior. Our proposed approach builds on Ensemble learning where the methods include Support Vector Machines, Decision Trees, Logistic regression and XGBoost to bolster the detection abilities of the model. This methodology serves to enhance the specific model performance on identifying known malware. © 2024 IEEE.

关键词： Adversarial machine learning

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intelligent Computing System for Breast Cancer Identification utilizing U-Net for Digital Image Segmentation 3

Intelligent Computing System for Breast Cancer Identificatio...

引用

3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Goli, Ranadheer Reddy Jadala, Vijaya Chandra Jamalpur, Bhavana Department of Computer Science and Artificial Intelligene School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798331527549

As per WHO calculation of deaths, globally due to Breast Cancer is around one million. Breast cancer can be cured by early detection, in which abnormal breast cells proliferate uncontrollably, leading to tumor formation. Early detection of breast cancer may reduce deaths, as per World Health Organization records there are about one million deaths are caused by 2024 among them only 0.5 percent are of men. The different methodologies adopted in medical image processing related to breast cancer detection methods include mammography. We concentrated on medical image segmentation which helps to get visual information about breast tissue. This paper presents an intelligent computing system designed to support medical image processing through utilizing U-Net methodology for segmentation as part of deep learning methodologies. As part of Experimental Analysis, a BUSI dataset is used for the implementation of Deep learning methodologies. A graphic analysis based on medical images, followed by an analysis of the results. This intelligent Computing System based on artificial intelligence helps radiologists, medical professionals, and doctors with early and simplified breast cancer diagnosis. © 2025 IEEE.

关键词： Breast Cancer intelligent System Segmentation U-Net

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Portable data Collection Unit 3

Portable Data Collection Unit

引用

3rd international conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Guo, Zhenyan Zhu, Yongcan Bing, Kaiyang He, Jiacheng Xi'an Polytechnic University College of Electronics and Information Xi'an China

ISBN: (纸本)9798350352658

This study aims to address the challenge of inconsistent and difficult-to-unify archiving and analysis of data from live detection and preventive testing of electrical equipment in the intelligent operation and maintenance system of the power grid. To tackle this issue, we propose a portable data aggregation unit. This unit enables seamless integration of live detection and preventive testing instruments, and possesses the capability to extract data from various instruments, regardless of whether they have interfaces or not. Through communication interfaces and protocols of existing instrument devices, the unit collects device data, while for devices without interfaces, deep learning and machine learning algorithms are employed for image differentiation to achieve data recognition and communication. Consequently, collected device data can be transmitted in real-time to a platform server, resolving the problem of devices without interfaces being inaccessible and facilitating online data processing. Additionally, the collected data is utilized for transformer fault diagnosis under multiple feature data conditions. In summary, our research brings substantial improvements to the intelligent operation and maintenance system of the power grid, addressing issues of data recording and analysis inconsistency, and providing crucial support for fault prediction and health management of electrical equipment. © 2024 IEEE.

关键词： Instrument testing

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Implementing ML and DL Based Controllers for Improving Efficiency in EV Charging Application 3

Implementing ML and DL Based Controllers for Improving Effic...

引用

3rd IEEE international conference on Power Electronics, intelligent Control and Energy Systems, ICPEICES 2024

作者： Garg, Grisham Talan, Dhruv Himanshu Yadav, Gaurav Singh, Mukhtiar Delhi Technological University Dept. of Electrical Engineering New Delhi India

ISBN: (纸本)9798350387193

This paper analyzes the potential of various Machine learning and Deep learning algorithms for replacement of the traditional Proportional Integral (PI) Controller in a single-phase two-stage on-board Electric Vehicle (EV) charging system to improve its adaptability and efficiency. Traditional PI controllers, though reliable, often do not perform optimally under the dynamic demands of EV charging, leading to in-efficiencies. Our investigation aims to understand how these ML/DL methodologies can better handle the variable nature of EV charging through adaptability, accuracy, and efficiency in managing charging operations. Experimental results from both simulations and real-world data reveal the effectiveness of these ML/DL models in enhancing charging system performance over standard PI controllers. Decision Trees provide clear, rule- based control paths;Neural networks excel in modeling complex, non-linear relationships for real-time predictions;and Linear Regression offers a straightforward approach for linear charging dynamics. © 2024 IEEE.

关键词： Random forests

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Automatic Classification of Brain MRI slices into Glioma/Meningioma using Deep Transfer-learning 3

Automatic Classification of Brain MRI slices into Glioma/Men...

引用

3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Vickram, A.S. Mathan Muthu, C.M. Saveetha School of Engineering Department of Biosciences TN Chennai602105 India Simats Saveetha School of Engineering Department of Biosciences TN Chennai602105 India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Brain tumor (BT) is a harsh brain abnormality and early detection and treatment is essential to reduce the impact of the disease. This work proposed a Deep Transfer-learning (DT) approach to detect the Glioma/ Meningioma with a better accuracy. The stages of this work includes;image collection and modifying its dimension to 224x224 pixels, feature extraction using chosen DT-models, feature reduction and serial features integration to get the fused-features, and bi-level classification using SoftMax and verifying the merit using 5-fold cross validation. This work initially executed the detection using individual features and then the experiment is repeated using the fused-feature vector (FFV). The FFV based detection helps to achieve a detection accuracy >97% on the chosen task and this outcome confirms the clinical significance of the proposed technique. In this work, the detection is performed using only the axial-plane 2D MRI slides, and in future, it can be extended to clinical MRI examination task. © 2025 IEEE.

关键词： Image acquisition

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intelligent Maintenance Scheduling for 5G Networks with ECA-DenseNet-DQN 3

Intelligent Maintenance Scheduling for 5G Networks with ECA-...

引用

3rd international conference on intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Yang, Mingye Li, Zhen Xu, Chuang Li, Chao International School Beijing University of Posts and Telecommunications Beijing China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798331529222

This paper presents an intelligent maintenance scheduling model for 5G networks using a deep reinforcement learning approach, specifically ECA-DenseNet-DQN. The proposed model optimizes the allocation of maintenance personnel by considering both travel and task completion times, aiming to minimize total task duration. Unlike traditional scheduling methods that often disregard skill levels and workloads of personnel, the model integrates Efficient Channel Attention (ECA) mechanisms into DenseNet to enhance feature extraction capabilities, thereby improving scheduling decisions. Simulation experiments demonstrate the model’s superior performance in reducing task completion time and achieving faster convergence compared to existing algorithms. ©2024 IEEE.

关键词： Scheduled maintenance

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Smart Sign Language Decoder 25th

Smart Sign Language Decoder

引用

25th international conference on intelligent data engineering and automated learning

作者： Costescu, Constantin Marius Popescu, Paul Stefan Mihaescu, Marian Cristian Univ Craiova Fac Automat Comp Sci & Elect Craiova Romania

ISBN: (纸本)9783031777370;9783031777387

Sign language recognition and understanding are challenging tasks for many people who are not familiar with it, which limits communication between deaf-mute people and others. The system presented in this paper lowers the communication barrier, introducing an automatic translation layer that facilitates sign language understanding. The system uses a deep-learning model for sign language detection and a separate library for hand joint mapping. The application's architecture was designed to allow users to access the system from desktop and mobile devices. The model's results revealed an 82% accuracy, and after several tweaks on the activation function in our tests, we achieved perfect classification in our real word tests. The results of the system offered excellent accuracy, and its usability lowers the communication barrier between people, providing flexibility as the application is available for any device with a browser.

关键词： Sign Language Recognition deep learning dataset

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