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

共 20248 条记录，以下是31-40 订阅

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A Review on Oversampling Techniques for the Treatment of Class Imbalance Problem 3

A Review on Oversampling Techniques for the Treatment of Cla...

引用

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

作者： Anand, Apurv Biswas, Saroj Kr. Majumdar, Sounak Thounaojam, Dalton Meitei Department of Computer Science and Engineering National Institute of Technology Silchar Silchar India

ISBN: (纸本)9798331527549

The Class Imbalance Problem (CIP) is a critical challenge in machine learning, particularly in applications such as medical diagnosis and fraud detection, where minority classes are underrepresented but crucial. This research addresses the issue by introducing a novel classification of oversampling techniques into 11 distinct categories, with a unique distinction between duplication and derivation mechanisms. Through an in-depth review of each category, this study identifies specific challenges such as overfitting and noise in imbalanced datasets and demonstrates how these categorized strategies effectively enhance model performance. The outcomes highlight practical solutions to improving classification accuracy and robustness in imbalanced scenarios. © 2025 IEEE.

关键词： Contrastive learning

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Hive Destruction Protocol: An automated Defense System Utilizing YOLO Human Recognition and Real-Time GPS Tracking 3

Hive Destruction Protocol: An Automated Defense System Utili...

引用

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

作者： Vyshak, T.M. Gaayana, G.R. Sarala, T. Dept. of Electronics and Communication Engineering Bnm Institute of Technology Affiliated to Vtu Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

The Hive Destruction Protocol (HDP) introduces a cutting-edge automated border defense system that enhances security operations through advanced human recognition using YOLO, precise GPS tracking, and coordinated drone responses. Soldiers are equipped with camera-integrated spectacles that facilitate the rapid identification of enemy targets via visual cues and hand gestures. Upon confirming a target, the system transmits accurate GPS coordinates in real time to autonomous drones, which are deployed to neutralize threats from multiple angles, creating confusion and minimizing detection risk. To ensure reliability and real-time processing in dynamic environments, HDP employs optimized communication protocols for secure data exchange between ground personnel and drones, allowing for seamless coordination among multiple units, each capable of independently engaging identified threats based on shared GPS data. The integration of machine learning algorithms improves the system even more flexible to changing field conditions, improving detection accuracy and robustness under various environmental constraints. Overall, the Hive Destruction Protocol represents a forward-thinking solution for border defense, leveraging artificial intelligence and automation to provide proactive and responsive capabilities in critical security applications. © 2025 IEEE.

关键词： Target drones

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Dragon Fruit Maturity Detection and Quality Grading with Scalable Deep Neural Networks 3

Dragon Fruit Maturity Detection and Quality Grading with Sca...

引用

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

作者： Aishwarya, N. Devisowjanya, P. Shah, Deshana Vikas Niharika, Raavi Department of ECE Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798331527549

The paper addresses the critical need for an automated system to accurately determine the ideal harvest time for dragon fruit. It effectively differentiates between fresh and defective fruit, as well as mature and immature fruit, to maintain high-quality standards. The study presents an advanced approach for detecting the maturity and freshness of dragon fruit using pre-trained deep learning models, including VGG16 (a lightweight model), MobileNetV3 (a moderate-weight model), and EfficientNetB0 (a heavy-weight model). Each model utilizes three different activation functions: Exponential Linear Unit (ELU), Rectified Linear Unit (ReLU), and Scaled Exponential Linear Unit (SELU). The features extracted by these models, using the best activation function, were then employed to train five different machine learning classifiers: Support Vector Machine (SVM), Decision Tree, Random Forest Classifier, AdaBoost, and CatBoost. Experimental results indicate that for maturity detection, the combination of EfficientNetB0 with the SVM Classifier yields the most favorable results. For quality grading, the optimal outcomes are achieved with the MobileNetV3-SVM combination. This hybrid approach achieves a precision and recall of 99%, along with 99% accuracy for both maturity and freshness detection, thus facilitating the assessment of dragon fruit. © 2025 IEEE.

关键词： Support vector machines

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Transforming Rice Disease Detection: VGG16 CNN for automated Leaf Disease Classification 3

Transforming Rice Disease Detection: VGG16 CNN for Automated...

引用

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

作者： Ankushavali, Md. Sharma, Jatin School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

About half of the world's population depends on rice as a basic food;but many leaf diseases—including Tungro, Brown Spot, Blast, and Bacterial Blight—threatens its output. Early and accurate diagnosis of these diseases is essential for good crop management since often-manual inspection methods are slow and prone to error. This work uses the VGG16 convolutional neural network (CNN) model to examine automatic categorization of rice leaf diseases. Created from the dataset were training, validation, and testing sets to assess model performance. After training, the model showed remarkable overall accuracy 95% great precision, recall, and F1-scores over all disease categories. The VGG16 model seems to be a useful instrument for improving control and detection of rice leaf disease. Quick diagnosis of disease symptoms allows this automated approach to significantly improve crop management strategies, reduce farmer economic losses, and therefore guarantee food security. The study highlights the opportunities of deep learning technology in agricultural applications, therefore providing the route for more continuous and efficient disease detection methods in rice farming. © 2025 IEEE.

关键词： Convolutional neural networks

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Research on the intelligent Question-Answering Model of Electric Power Knowledge Based on Deep learning 3

Research on the Intelligent Question-Answering Model of Elec...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Fu, Hanchen Xu, Jingjing Kang, Naidi Cao, Shilong State Grid Liaoning Marketing Service Center Liaoning Shenyang110168 China

ISBN: (纸本)9798350380989

In the context of digital economy era, power marketing data information is called the barometer of economic operation stability, which to a large extent represents the development trend and situation of social economy[l]. In recent years, with the continuous improvement of the attention of power marketing data, the opening and sharing of power marketing data has become one of the hot social topics[2]. In view of the shortcomings of the current knowledge base intelligent interaction, we put forward intelligent knowledge base model based on deep learning. By joining the BiGRU layer it can be extracted from the BERT output vector sequence up and down the correlation information, real-time update problem dynamic processing scheme of information improves the knowledge base, and accelerates the information transmission and sharing[3]. © 2024 IEEE.

关键词： BERT deep learning model repository

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Advances in Deep learning for Tuberculosis Detection in Chest X-Rays: A Review of Diagnostic Accuracy 3

Advances in Deep Learning for Tuberculosis Detection in Ches...

引用

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

作者： Jayakumar, K. Muthupandi, G. Dhayanidhi, R. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

Tuberculosis (TB) is one of the leading causes of deaths globally, mainly in low- and middle-income countries. Early and accurate detection is crucial for effective treatment and disease control. In this paper, models like CNNs-VGG16, ResNet, and DenseNet, to name a few, are taken into an exhaustive study. Optimization techniques, including transfer learning and ensemble methods, which can increase accuracy in the challenging diverse clinical settings, will be presented. Equitable healthcare outcome is achieved in terms of future research directions which include representative datasets and interpretability of the model. © 2025 IEEE.

关键词： Disease control

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Position Estimation for Mobile Robots using Machine learning 3

Position Estimation for Mobile Robots using Machine Learning

引用

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

作者： Varma, Kallepalli Rahul Sreenivasan, Medha Srivani, N. Bhattacharya, Shourjyo Glace Varghese, T. Kochuvila, Sreeja Dept. of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Robot localization is a critical challenge present in autonomous robots to estimate their position within the environment, ensuring accurate navigation and task execution. This research project compares the position estimation capabilities of advanced machine learning methods Gradient Boosting and K-Nearest Neighbors. In this work Machine learning (ML) models are built using a dataset from *** that tracked Received Signal Strength Indicator (RSSI) readings and tested them against strong performance measurements. This work findings show GB provides better results than KNN because it reaches an R2 score of 0.9341. These insights are applicable in various fields, including autonomous vehicles, industrial automation, service robotics, and multi-robot coordination, guiding the selection of appropriate algorithms for specific localization challenges. © 2025 IEEE.

关键词： Gradient Boosting K-Nearest Neighbors Machine learning Navigation Robot localization

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Optimizing Symptom Prediction in Healthcare: An Integrative Machine learning Approach to Regression and Classification Models 3

Optimizing Symptom Prediction in Healthcare: An Integrative ...

引用

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

作者： Goddilla, Nagarjuna Reddy Sivasangari, A. Sathyabama Institute of Science and Technology Dept of Cse Chennai India

ISBN: (纸本)9798331515911

This research explores the application of combined machine learning techniques for predicting disease symptoms, aiming to support early diagnosis and improve healthcare outcomes. By analyzing both regression and classification models, the study evaluates their respective performance, strengths, and challenges in predicting symptoms. Using a comprehensive dataset with medical records, demographic data, and clinical variables, we developed predictive models to capture the complexities of healthcare data. To enhance accuracy and manage issues like data imbalance and feature diversity, ensemble methods and feature engineering were applied, with the Random Forest classifier achieving a leading accuracy of 91.84%. Additionally, the study emphasizes model interpretability, which is crucial for healthcare professionals to trust and adopt these predictive tools. The findings provide valuable guidance for healthcare professionals and data scientists in choosing machine learning approaches suited to specific medical applications. By bridging the roles of regression and classification models, this research offers a well-rounded perspective on the potential of predictive analytics in healthcare. © 2025 IEEE.

关键词： Adversarial machine learning

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Colorectal Cancer Detection based on Integrated Graph Convolutional Networks and Shift Window Transformer 3

Colorectal Cancer Detection based on Integrated Graph Convol...

引用

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

作者： Gayathri, M. Thamarai Selvi, D. Kalaiselvi, S. Santhi, S. Department of computer science and engineering National Engineering College Tamil Nadu Kovilpatti India Department of information technology National Engineering College Tamil Nadu Kovilpatti India

ISBN: (纸本)9798331527549

Advanced predictive analytics is required to improve early identification and patient outcomes for colorectal cancer (CRC), which is still a major worldwide health concern. Using deep learning and graph-based methods, this study suggests a new prediction framework that makes use of Graph Convolutional Networks (GCNs) and a Shifted Window Transformer architecture by means of thorough pre-processing, which includes graph creation and normalization, we establish a strong input structure for our model. To improve feature extraction from histopathology pictures, the Shifted Window Transformer—which is well-known for its effectiveness in capturing long-range dependencies—is incorporated. The model is able to utilize both individual cellular properties and their interrelations thanks to GCNs that learn from the graph structure. By automatically identifying colorectal cancer in its early stages, this prediction system seeks to do away with the requirement for human identification by medical professionals and allow for prompt treatment without awaiting a formal diagnosis. This work aims to improve early-stage (stage 1) detection using our suggested automated models because it is challenging to detect CRC in stage 0. This innovative strategy promises prompt interventions that will enhance patient care and clinical results. © 2025 IEEE.

关键词： Diseases

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Ensemble-based Machine learning Classification for Early Detection of Autism Spectrum Disorder 3

Ensemble-based Machine Learning Classification for Early Det...

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

作者： Subhash, Ambika Rani Ashwin Kumar, U.M. School of Computer Science and Engineering REVA University Bengaluru India Department of Information Science and Engineering BMS Institute of Technology and Management Bengaluru India

ISBN: (纸本)9798331527549

A complicated neurological illness, autism spectrum disorder (ASD) impacts social skills, communication, and other behavioral elements. Because the symptoms of ASD might be confused with those of other mental health conditions, diagnosing it can take a long time. The goal of this study is to develop a categorization model that can forecast the risk of ASD in toddlers, kids, teens, and adults. To improve speed, we used a variety of machine learning methods and ensembled them. Four publicly accessible non-clinical ASD screening datasets from Kaggle and the UCI library were used to assess the models. These datasets included 1,054 cases with 19 features for toddlers and different characteristics for older age groups. The classifiers' accuracy was almost 99%, according to the results. © 2025 IEEE.

关键词： Adversarial machine learning

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