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

共 20691 条记录，以下是401-410 订阅

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Brain Region Boundary Recommendation System Based on Deep learning 3

Brain Region Boundary Recommendation System Based on Deep Le...

引用

3rd international conference on Image Processing and Media Computing (ICIPMC)

作者： Liu, Yu Zheng, Yunfa Li, Yuanyuan Wu, Jun Lai, Chuxiao Tian, Hao Hao, Zhilang Qu, Lei Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei Peoples R China China Elect Technol Grp Corp Res Inst 38 Hefei Peoples R China Aero Photogrammetry & Remote Sensing Bur China Co Xian Peoples R China Southeast Univ SEU Allen Joint Ctr Inst Brain & Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350386660;9798350386677

Mapping the brain is a crucial task in brain science research. However, manual brain mapping requires highly specialized skills and consumes significant time and resources to process large-scale data. This paper proposes an automated brain region boundary recommendation system to address the challenge. The system can automatically recommend subsequent monkey brain slices based on the baseline brain map provided by the researcher. The system utilizes deep metric learning to extract features from image patches. This approach can extract richer and more discriminative feature information compared to traditional methods, resulting in improved system recommendation accuracy. The recommendation results demonstrate the system's high accuracy in automated brain mapping and its ability to significantly reduce researchers' workload.

关键词： boundary feature descriptor recommendation

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Lightweight Botnet Detection for IoT: Autoencoder-Based Approach Using Packet Length 3

Lightweight Botnet Detection for IoT: Autoencoder-Based Appr...

引用

3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Aljufri, Muhammad Arifuddin Ahmad, Tohari Aidiel, Muhammad D'layla, Adifa Widyadhani Chanda Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia

ISBN: (纸本)9798331523893

The growing prevalence of botnet attacks poses a significant threat, particularly as the demand for IoT devices continues to rise. This underscores the need for effective botnet detection techniques tailored for IoT environments. Machine learning and Deep learning are often used to tackle this problem with either one showing there are trade-offs between detection and computational performances. To address these challenges, this paper investigates the use of packet length as a key feature for botnet detection, leveraging an autoencoder model. This approach involves feature engineering of packet length and employs an autoencoder to identify anomalous network behavior, making it an efficient and effective solution for detecting Mirai botnet. This method ensures a lightweight detection framework, making it suitable for deployment on resource-constrained IoT devices while maintaining high detection with Precision at 99.64% and F1 Scores at 98,17% respectively tested on the MedBiot dataset. © 2025 IEEE.

关键词： Deep learning

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Real-time Tweets Analysis using Machine learning and Bigdata 3

Real-time Tweets Analysis using Machine Learning and Bigdata

引用

3rd IEEE North Karnataka Subsection Flagship international conference, NKCon 2024

作者： Reddy, P Nandieswar Sai Aswath, S. Alapati, Rithvika Radha, D. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350364569

Twitter's exponential increase in data has transformed it into a rich source for machine learning research, unveiling patterns of opinions and behaviors. This research introduces an automated pipeline built on Kafka, adept at handling large volumes of real-time Twitter data. Utilizing the Twitter API, tweets bearing the hashtag #justice are retrieved and efficiently processed via Kafka. PySpark facilitates data analysis by enabling aggregation, Jaccard similarity computation, and k-means clustering. Preprocessing with Python libraries and TF-IDF enhances analysis accuracy. Jaccard similarity aids in comparing tweets across socio-economic categories. Aggregated data is stored in MongoDB for further examination. Additionally, Streamlit offers visualization tools for effortless exploration of Twitter discourse trends. Thus, this project exemplifies the potential of big data and its impact on investigating social justice discourse in the digital age. © 2024 IEEE.

关键词： Tweets

来源：评论

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Smart Oral Health Monitor 3

Smart Oral Health Monitor

引用

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

作者： Kamkar, Spandana Anilkumar Sanjay, Srushti Vaishnavi, P.S. Karthik, S. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331515911

Oral health is vital to overall well-being but is often overlooked due to inefficient monitoring tools and delayed diagnosis. This project presents a smart handheld device featuring a miniaturized camera for detecting oral issues like plaque, cavities, teeth discoloration and gum diseases in real-time. Using image processing and deep learning algorithms, the device provides precise feedback and actionable insights. Paired with a mobile application offering detailed analysis and recommendations, it empowers users to adopt proactive oral hygiene practices to prevent teeth disorder. © 2025 IEEE.

关键词： Electronic health record

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Enhancing Dyslexia Detection: A Robust Machine learning Approach 3

Enhancing Dyslexia Detection: A Robust Machine Learning Appr...

引用

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

作者： Manimaran, V. Shanthi, N. Aravindhraj, N. Rakesh, K. Revanth, K. Saran, S. Department of Computer Science and Engineering Kongu Engineering College Erode India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Dyslexia, recognized as a common learning disability, adversely affects reading, spelling, and word decoding capabilities, frequently resulting in considerable academic and social obstacles if not identified at an early stage. Conventional diagnostic approaches, encompassing neuroimaging and standardized assessments, prove to be effective yet are characterized by high costs and limited scalability. In response to this challenge, the present study introduces a hybrid classification model that integrates Random Forest and XGBoost methodologies to improve dyslexia detection through binary classification. The dataset employed comprises both numerical and categorical variables, such as "Gender" and "Native Language," which were processed via one-hot encoding to facilitate the transformation of categorical information. The Synthetic Minority Oversampling Technique (SMOTE) was utilized to mitigate class imbalance, while Principal Component Analysis (PCA) was implemented to diminish dimensionality, thereby enhancing model performance. Preliminary assessments of individual classifiers indicated that Random Forest surpassed both Decision Tree and Neural Network (MLPClassifier), achieving an accuracy of 95.31%, an F1-score of 0.95, and precision metrics of 0.94 for class 1 and 0.97 for class 0. Expanding upon these findings, a hybrid model that merges Random Forest and XGBoost through the application of a VotingClassifier was constructed. This hybrid framework attained an elevated accuracy of 96.23%, accompanied by consistently robust precision, recall, and F1-scores of 0.96. The results underscore the efficacy of the hybrid methodology in offering a scalable and dependable solution for the early detection of dyslexia within both clinical and educational contexts. © 2025 IEEE.

关键词： Adversarial machine learning

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Efficient Sentiment Analysis on IMDb Movie Reviews with Synonym Augmentation and Global Average Pooling 3

Efficient Sentiment Analysis on IMDb Movie Reviews with Syno...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Krishna, Aaqil Raj Vardhini, Aluru S Singh, Rimjhim Padam Kanchan, Sneha Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Universiti Tunku AbdulRahman Department of Internet Engineering & Computer Science Malaysia

ISBN: (纸本)9798350372120

With the advancement in the field of automated recommendations, efficient sentiment analysis of reviews has become immensely important. This study presents the application of the Global Average Pooling (GAP) model in efficient sentiment analysis that has traditionally been dominated by models such as Recurrent Neural Networks. Focusing on IMDb movie reviews this paper showcases the effectiveness of the GAP model in handling text data in extracting features and reducing dimensionality. Through this approach, we observed improvements in classification accuracy while using an efficient model. Our findings suggest that the GAP model could serve as an alternative to conventional sentiment analysis techniques. Furthermore, our research indicates benefits from exploring the integration of GAP, with deep learning methods to further enhance performance. © 2024 IEEE.

关键词： Sentiment analysis

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Ensemble learning for Enhanced Mango Leaf Disease Classification: A Deep learning Approach 3

Ensemble Learning for Enhanced Mango Leaf Disease Classifica...

引用

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

作者： Neog, Protyush P. Kaur, Navjot Department of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798331527549

Mangoes are highly consumed fruits especially in the tropical as well as subtropical zones of the earth. It is said that nearly half of the earth’s population consumes mangoes in some form or the other either as fresh mangoes or in processed form such as juice, dried mangos, or other cooked dishes. That said, this significant food crop has a number of limitations in its cultivation and the least of these is attack by pests. The agro-ecosystem of mango plantation includes several factors such as soil, insects, diseases, weather and many more which determine the well-being of the mango trees in that plantation. Of all these concerns, leaf diseases turn out to be the most offensive by causing losses in both the quantity and quality of mangoes produced. To mitigate these problems, research has been done and continues to be done towards identifying and classifying the mango leaf diseases with better yields and less losses thanks to processing techniques such as deep learning and image processing. Recently published papers focus on these modern methods and analyze the advantages of their applications for the diagnosis and treatment of mango plants. This paper proposes a novel framework that identifies the diseases in mango leaves. The approach demonstrates an impressive 99.94% testing accuracy employing ensemble learning and transfer learning techniques. It is superior to other popular deep learning-based models that include DenseNet121, ResNet50 and VGG19. The results of the study demonstrate the efficiency of the proposed approach as well as its superiority in important performance metrics such as precision and recall. This evolution provides a viable answer to the very pertinent challenge of disease detection in mango cultivation allowing for better management of crops and hence increased production. © 2025 IEEE.

关键词： Plant diseases

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Real-Time Object Detection from Surveillance using Deep learning 3

Real-Time Object Detection from Surveillance using Deep Lear...

引用

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

作者： Tanisha, A. Tanisha, N. Chaitra, M. Tejasri, P.R. Bnm Institute of Technology Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Object detection in surveillance systems leverages advanced deep learning techniques to enhance security measures through real-time analysis of dynamic video feeds. This project integrates the YOLOv5 model for detecting weapons in both pre-recorded videos and live camera feeds. The model, trained on a dataset with 4000 images labeled for handguns and knives, utilizes image preprocessing steps such as resizing, normalization, and augmentation to improve detection accuracy. Implementing the system with OpenCV and Tkinter, the application processes video streams, identifying potential threats with high precision rate of 0.85 and recall rate of 0.90. The integration of a graphical user interface ensures user-friendly operation for security personnel. Key outcomes demonstrate the efficacy of deep learning models in real-time object detection, significantly contributing to proactive surveillance and enhanced situational awareness. © 2025 IEEE.

关键词： Graphical user interfaces

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A New Federated learning Framework for Plant Leaf Disease Diagnosis 3

A New Federated Learning Framework for Plant Leaf Disease Di...

引用

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

作者： Singh, Aadarsh Kumar Chattopadhyay, Pratik Singh, Lokesh Pattern Recognition Lab Dept. of Cse Varanasi India Reinventbio.io Varanasi India

ISBN: (纸本)9798331528201

Several Deep learning-based approaches have recently been proposed for plant leaf disease detection. Traditional learning-based models necessitate centralizing large image datasets, requiring huge volumes of storage space. It appears that a better predictive model can be developed if the available data at multiple data sites can be effectively utilized using a collaborative learning strategy. Federated learning (FL) offers a decentralized alternative, enabling collaborative model training by allowing multiple participants to update a global model using their local data without sharing high volumes of raw image datasets. Our work presents an FL framework to train a Vision Transformer-based model with an improved Federated Averaging (FedLossAvg) algorithm. Experimental results demonstrate our model's capability to handle large, varied datasets. Using four clients, our global model achieved an F1-score of 95.62% and an accuracy of 99.89% on the PlantVillage data. © 2024 IEEE.

关键词： Federated learning

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An intelligent Disaster Management System: Integrating Technology for Effective Response and Recovery 3

An Intelligent Disaster Management System: Integrating Techn...

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3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Pandey, Nandini Rani, Priya Kundu, Sumana Dr. B. C. Roy Engineering College Department of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798331523893

This proposed Disaster Management System (DMS) is an advanced application aimed to improve disaster preparedness, response, and recovery efforts through real-time data integration, predictive analytics, and efficient communication. It leverages Artificial Intelligence to predict disasters like earthquakes and floods, providing early alerts and resource management to minimize impact. Key features include role-based access, geolocation tracking, incident reporting, and multilingual support. DMS facilitates coordination between government agencies, NGOs, and volunteers, ensuring rapid response and effective resource allocation. With future integration of IoT and blockchain technologies, the system aims to enhance transparency, optimize resource distribution, and support international disaster management efforts. By integrating real-time data, predictive analytics, and communication tools, this system supports efficient coordination during emergencies. © 2025 IEEE.

关键词： Resource allocation

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