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

共 20256 条记录，以下是781-790 订阅

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Research on Storage and Query Optimization Algorithm of Distributed database for Big data 3

Research on Storage and Query Optimization Algorithm of Dist...

引用

3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Liu, Lixia College of Information Engineering Engineering University of PAP Shaanxi Xi'an710086 China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519032

This paper deeply discusses the storage and query optimization algorithm of distributed database for big data. Firstly, the importance of distributed database storage optimization is analyzed, and key technologies such as column storage technology, data backup and consistent hash algorithm are introduced. Then, the distributed database query optimization algorithm is mainly studied, and an innovative algorithm combining semi-join, direct join and hash partition equijoin strategy is proposed, and the query plan is dynamically adjusted by introducing an intelligent prediction model to maximize the query performance. The experimental results show that the algorithm has significantly improved the storage efficiency and query performance, which proves its effectiveness and application prospect in the big data environment. Future research will focus on further improving the adaptability and generalization ability of the algorithm to cope with more complex data scenarios and query requirements. © 2024 IEEE.

关键词： Structured Query Language

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Understanding Cybercrime: A Study of Malware Analysis, Phishing Attacks, and Sentiment Trends 3

Understanding Cybercrime: A Study of Malware Analysis, Phish...

引用

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

作者： Kumaran, U. Sreeraag, G. Narendran, Advik Meena, V.P. Gupta, Sushil Kumar Appaji, Abhishek Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bangalore India National Institute of Technology Department of Electrical Engineering Jharkhand Jamshedpur831014 India Bms College of Engineering Department of Medical Electronics Engineering Bangalore India

ISBN: (纸本)9798331515911

With the rapid evolution of computer systems and technologies, individuals face an increasing threat of cybercrime. This research explores the changing landscape of cybercrime by focusing on three key areas: malware analysis, phishing attack analysis, and sentiment analysis. The study aims to identify malware strategies for evading detection and analyze the prevalence of spyware by examining diverse malware samples. Additionally, trend analysis highlights the rising complexity and frequency of phishing attacks, with a notable shift toward targeting reputable platforms. Sentiment analysis of media headlines surrounding data breaches uncovers a paradox: while concerns about cybersecurity remain, there is a growing confidence in the ability to manage cybersecurity crises. These findings provide insights into emerging threats and evolving public perceptions, offering a comprehensive understanding of the current cyber- crime landscape. © 2025 IEEE.

关键词： Phishing

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Analyzing the Predictive Power of Solar Photovoltaics Using Three Machine learning Models 3

Analyzing the Predictive Power of Solar Photovoltaics Using ...

引用

3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Patel, Aadyasha Swathika, O. V. Gnana School of Electrical Engineering Vellore Institute of Technology Chennai India School of Electrical Engineering Vellore Institute of Technology Centre for Smart Grid Technologies Chennai India

ISBN: (纸本)9798350354379

The meteorological data corresponding to Vellore Institute of Technology, Chennai is downloaded by pinpointing the institute's latitude and longitude in the National Solar Radiation data Base viewer. The photovoltaic power generated by the panel is calculated using the downloaded meteorological parameters due to unavailability of power generation data. The collected data is pre-processed, relevant features for power prediction are selected, split into training and testing datasets. Three machine learning models - namely, Cubic Support Vector Machine, Bagged Trees and Rational Quadratic Gaussian Process Regression are trained to forecast the power generated from a photovoltaic panel. The models were evaluated using error metrices. The Coefficient of Determination, Root Mean Square Error and the Mean Average Error of all the three models was found during validation phase. The testing phase showed that the latter model is the better out of the three machine learning models under consideration. © 2024 IEEE.

关键词： Mean square error

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An Effective Crop Recommendation using Ensemble Classifier 3

An Effective Crop Recommendation using Ensemble Classifier

引用

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

作者： Kandula, Ashok Reddy Vadapalli, Jai Srinivas Mannava, Srinivasa Sai Shanmukha Modugumudi, Sai Seetharam Mohammad, Irfan Dept. of Artificial Intelligence & Data Science Seshadri Rao Gudlavalleru Engineering College Gudlavalleru India

ISBN: (纸本)9798331527549

Modern expectations for efficiency and sustainability are sometimes not addressed by the existing approaches in the continually changing environment of agricultural activities. In order to fully address these difficulties, research investigates how precision farming practices may be integrated while utilizing technological improvements. Study attempts to estimate optimal crop choices by assessing important characteristics including area, rainfall, temperature, and district-specific data, giving farmers the power to make well-informed and data-driven decisions. Precision farming lowers risks related to climatic unpredictability, increases output, and minimizes resource waste. By using the strategy, encourage resilience and innovation in the agricultural industry while bridging the gap between current farming practices and the sustainable agriculture of the future. Employing advanced algorithms including LR, Ridge Regression, DTs, and classifiers, achieved a remarkable 96% accuracy rate. Additionally, the proposed system offers real-time alerts via email or SMS, delivering timely advice and actionable insights to enhance agricultural sustainability and productivity. © 2025 IEEE.

关键词： Sustainable agriculture

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Padel Two-Dimensional Tracking Extraction from Monocular Video Recordings 25th

Padel Two-Dimensional Tracking Extraction from Monocular Vid...

引用

25th international conference on intelligent data engineering and automated learning

作者： Novillo, Alvaro Acena, Victor Lancho, Carmen Cuesta, Marina De Diego, Isaac Martin Rey Juan Carlos Univ Data Sci Lab C Tulipan S-N Mostoles 28933 Spain

ISBN: (纸本)9783031777301;9783031777318

This study introduces a novel framework for the automatic two-dimensional tracking of padel games using monocular recordings. By integrating advanced Computer Vision and Deep learning techniques, our algorithm detects and tracks players, the court, and the ball. Through homography, we accurately project detected player positions onto a two-dimensional court, enabling comprehensive tracking throughout the game. We tested the proposed algorithm using amateur video recordings of padel games found in literature. This approach remains user-friendly, cost-effective, and adaptable to various camera angles and lighting conditions. This makes it accessible to both amateur and professional players and coaches, providing a valuable tool for performance analysis. Additionally, the proposed framework holds potential for adaptation to other sports with minimal modifications, further broadening its applicability.

关键词： Computer Vision Padel Deep learning Court Detection Homography Applied Intelligence Tracking data

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Enhancing Efficiency in Multicore Processor 3

Enhancing Efficiency in Multicore Processor

引用

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

作者： Balachandra, M. Jayanth Naidu, P.J. Rakesh, H.R. Sangli, Mihir Hegwad, Jaideep A. Radha, R.C. B.M.S. College of Engineering Dept. Electronics and Communication Bangalore India

ISBN: (纸本)9798331515911

Traditional multi-core processor designs face challenges related to underutilized resources during program execution, particularly when cores encounter halts, such as when waiting for subroutines or lengthy tasks. This underutilization can result in reduced system efficiency and responsiveness. To address this problem, our concept incorporates dynamic load balancing, multi-threading, and a 5-stage pipelining approach, allowing a second core to work concurrently with the first core during execution halts. This integrated approach optimizes resource utilization, minimizes idle times, and enhances the overall execution time of programs. Mechanisms for task scheduling, synchronization, and communication are essential for effective implementation, ensuring seamless collaboration between cores without conflicts or data inconsistencies. © 2025 IEEE.

关键词： Subroutines

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Advancements in Sensor Technology and Computer Vision for Enhanced Road Safety and Anomaly Detection using Machine learning Approach 3

Advancements in Sensor Technology and Computer Vision for En...

引用

3rd international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2023

作者： Jesi, V. Elizabeth Jenitha Mary, L. Saranya, S. Poonguzhali, C. Mohanaprakash, T.A. Saranya, K. School of Computing College of Engineeering Srm Institute of Science and Technology Department of Networking and Communications Kattankulatur Chennai India Dhanalakshmi College of Engineering Department of Cse Chennai India Rajalakshmi Institute of Technology Department of Aids Chennai India S A Engineering College Department of Ece Chennai India Panimalar Engineeering College Department of Cse Chennai India Panimalar Engineeering College Department of Aids Chennai India

ISBN: (纸本)9798350306989

Development of sensor technology has significantly increased recently. To combat the high rates of fatalities and expenditures connected with injuries caused by traffic, sensor technology and computer vision have achieved considerable advancements. To improve road safety, an intelligent system has been created with the aim of spotting anomalies in the road and providing driving support. Utilizing a combination of accelerometer sensors, vibration motion sensors, gyroscopic sensors, and GPS technology, this system is smoothly integrated into cars. A comprehensive and evidence-based systematic review focused on Computer Vision applications for automated Road Defect and Anomaly Detection is still lacking, despite the fact that prior studies and applications of computer vision have concentrated on particular aspects of road hazards. The system can identify several types of road irregularities based on the current location of the vehicle by using machine learning techniques to examine sensor data. Additionally, it gathers and compares data from other vehicles at the same GPS locations to confirm the presence of road problems like speed bumps or potholes. The GPS application provides access to a cloud-based repository where all the acquired data is safely kept. The technology notifies drivers ahead of time, advising them to slow down as they approach GPS spots that have previously been identified as having road abnormalities. The results of this poll could provide the scientific community with insightful information that would help improve traffic conditions and safety in general. experience. © 2023 IEEE.

关键词： Anomaly detection

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automated Malware Classification Using Deep learning Neural Networks 3

Automated Malware Classification Using Deep Learning Neural ...

引用

3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Sharma, Anubhav Reddy, M. Udayapal Lathigara, Amit Verma, Ankit Ies University Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Ies College of Technology Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Computer Engineering Rk University India Ies College of Technology Department of Electronics & Communication Engineering Madhya Pradesh Bhopal462044 India

ISBN: (纸本)9798350343274

automated classification may significantly reduce the time it takes to discover and remove malware. This paper suggests a detailed method for automating the classification of malware using deep learning neural networks. The three main techniques at the core of the approach are: data preprocessing and feature extraction;neural network model development;training and assessment. data preprocessing includes cleaning and organizing the raw malware dataset as well as feature extraction using n-grams, opcode sequences, API call sequences, and byte-level n-grams. The collected features are then used to construct a deep learning neural network model including convolutional, recurrent, and fully connected layers. Once the model has been trained on a labeled dataset, performance measures like accuracy, precision, recall, F1 score, false positive rate, and false negative rate are used to evaluate the model's effectiveness. The new method, called 'DeepMal,' performs remarkably well when measured against traditional methods for finding malware. © 2023 IEEE.

关键词： Malware

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Modular Approaches to Complex Reasoning in Visual Question Answering Systems 3

Modular Approaches to Complex Reasoning in Visual Question A...

引用

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

作者： Bhosale, Akshay Wandre, Sujit Chavan, Sagar Waje, Ketan Pande, Sandeep MIT Academy of Engineering School of Computer Engineering Pune India MIT Academy of Engineering School of ENTC Engineering Pune India MIT Academy of Engineering School of Mechanical Engineering Pune India

ISBN: (纸本)9798331523893

Visual Question Answering is a challenging task requiring the integration of visual and textual information to answer questions about images. This research introduces a robust VQA system that combines advanced deep learning techniques, including Visual BERT, ViLT, cross-modal memory networks, memory-augmented attention mechanisms, and vision language pre-training models such as Flamingo and BLIP. These methods enable effective multimodal fusion, dynamic memory retrieval, and adaptation to novel question types using few-shot learning. Using the VQA v2.0 dataset, the system achieved 80% accuracy, outperforming traditional models such as LSTM-CNN and attention-only approaches. Enhanced BLEU scores and precision-recall metrics underscore its capability in handling complex, multi-step reasoning tasks. This work highlights significant improvements in VQA systems and establishes a scalable framework for advancing multimodal learning, paving the way for future research on larger datasets and real-world applications. © 2025 IEEE.

关键词： Question answering

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Exploratory data Analysis of Geolocation data using Machine learning 3

Exploratory Data Analysis of Geolocation Data using Machine ...

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

作者： Vinora, A. Afrin, S. Manoj, Lavina Roshni Alagu Gomathi, A. Department of Information Technology Velammal College of Engineering and Technology Tamilnadu Madurai India

ISBN: (纸本)9798331527549

Geographical location is the process of identifying an object or person's location with the help of technology. It uses various methods, such as GPS (Uses satellite signals to pinpoint a device’s location globally, with an accuracy of 5–10 meters outdoors) Wi-Fi location (Relies on nearby Wi-Fi signals to estimate location, achieving 5–30 meters’ accuracy, especially in urban and indoor settings) and IP positioning, to determine the latitude and length of a device or individual. Common uses of geolocation include navigation and location services, social media, electronic commerce, fleet management, and emergency services. The exploration analysis of geolocation data is an important aspect of gaining insights into the patterns and trends within the data. The problem arises when people move from one place to another and need to find the right place. The existing system includes hostels and apartments for rent, and it has purchased and sold options. It also does not recommend restaurants, gyms, etc. based on users' preferences. Previous research lacks the accuracy of the real recommendations. An algorithm called K- means clustering is used to classify certain data elements with certain properties. However, it has a drawback when the radii of two circular groups with the same average center diverge. Another is Hierarchical clustering. Actually, it is a very useful technique in exploratory analysis of geolocation data, since it discovers hierarchical relationships and structures within the spatial data set. The technique designs a hierarchy of clusters from bottom- up, which is known as agglomerative, or top-down, that is known as divisive. Agglomerative clustering is more commonly used. © 2025 IEEE.

关键词： data accuracy

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