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

共 7123 条记录，以下是251-260 订阅

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Analyzing the Usability of an E-learning Application Using the Heuristics Evaluation Method 11

Analyzing the Usability of an E-Learning Application Using t...

引用

11th international conference on Information Technology, Computer and Electrical engineering, ICITACEE 2024

作者： then, Sarinthar Julius Surja, Stephanie Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350389289

the rise of E-learning, especially during the COVID-19 pandemic, has highlighted the importance of evaluating its usability to improve the user experience and interface design of educational platforms. this study focuses on GreatNusa, an E-learning platform by Bina Nusantara University. It uses heuristic evaluation principles to identify and address usability issues in its mobile application. the study adopts a quantitative-descriptive approach with data obtained from an online questionnaire completed by 473 respondents, predominantly undergraduate and diploma students. the analysis is based on 33 indicators obtained from previous research and aligned with the 10 heuristic evaluation principles, focusing on usability components such as Learnability, Efficiency, Memorability, Errors, and Satisfaction. the data is analyzed using SMARTPLS with SEM PLS for structural modeling. Results indicate that the variables;Efficiency, Memorability, and Satisfaction significantly affect usability while Learnability and Error do not show a significant impact. Overall, despite minor usability issues identified through this study, the design of GreatNusa's application is well-received, suggesting that it meets established usability standards and offers a satisfactory user experience. the findings provide actionable insights for enhancing the application's UI/UX, to ensure an even more user-friendly experience. © 2024 IEEE.

关键词： Contrastive learning

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Next-Gen Energy Management: IoT Gateway for Smart Monitoring and Appliance Control 6

Next-Gen Energy Management: IoT Gateway for Smart Monitoring...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Kabra, Sohum Nadagouda, Prithvi Nikam, Yash Kavedia, Manoj S. Thadomal Shahani Engineering College Mumbai India Thadomal Shahani Engineering College Mumbai India

ISBN: (纸本)9798331522667

this research study proposes an IoT-based solution for intelligent energy management, designed to monitor and optimize power consumption in both residential and industrial environments. the system will integrate an AI meter for real-time tracking of voltage, current, and power usage, alongside environmental sensors that measure parameters like temperature, humidity, light intensity, and motion. these data inputs are processed by an IoT gateway based on an ESP32 microcontroller and Raspberry pi, which will handle the data locally or transmits it to a cloud-based or edge machine learning model. the machine learning model analyzes real-time sensor data combined with historical consumption patterns to predict and implement optimal appliance control strategies. the system aims to reduce power consumption by up to 20-25%, contributing to energy efficiency without compromising operational performance and delays. Appliances are controlled remotely using IR Blasters and relays, allowing for dynamic, real-time adjustments based on environmental factors. the solution is scalable, making it applicable for both household energy management and large-scale warehouse automation. By utilizing predictive analytics and intelligent control, the system optimizes energy usage, offering a sustainable, automated solution for modern smart homes and industries. this integration of IoT and machine learning provides a significant step toward more efficient and sustainable energy management practices. © 2025 IEEE.

关键词： Gateways (computer networks)

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Optimizing Vehicle Coordination at Multi-lane Intersections Using Traffic Control Algorithms 25th

Optimizing Vehicle Coordination at Multi-lane Intersections ...

引用

25th international conference on intelligent data engineering and automated learning

作者： Gonzalez, Cesar L. Delgado, Santiago L. Alberola, Juan Miguel Nino, Luis F. Julian, Vicente Univ Politecn Valencia Valencian Res Inst Artificial Intelligence VRAIN Camino Vera S-N Valencia 46022 Spain Univ Nacl Colombia Lab Invest Sistemas Inteligentes LISI Ingn Sistemas Ind Carrera 45 26-85 Bogota 111321 Colombia Univ ECCI Grp Invest SIGCIENTY Carrera 10 19-64 Bogota 110311 Colombia

ISBN: (纸本)9783031777370;9783031777387

intelligent Transportation Systems (ITS) encompass several strategies aimed at ensuring safe and efficient vehicle-to-vehicle interactions on roads. these strategies involve the implementation of advanced technologies, including intelligent traffic lights, wireless (V2X) communication systems, visual recognition systems, and algorithms. In this research, we introduce the implementation of a distributed algorithm tailored for managing highly complex road infrastructure, particularly intersections in urban areas with multiple lanes. Our evaluation of the system's performance involves applying a type of density distribution called "Biased" and comparing it against a traditional traffic control system (traffic lights - "Green Wave"). the inclusion of this type of density distribution aims to align our approach with real-world traffic conditions, specifically the variation in densities across different lanes. this comprehensive assessment allows for a more thorough evaluation of system performance.

关键词： Autonomous driving Distributed intersection management Multiple lanes intelligent transportation system Density distribution

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Federated Vehicular Tailgating Detection Utilizing Distance and Speed data Analytics for intelligent Transport System 10

Federated Vehicular Tailgating Detection Utilizing Distance ...

引用

10th international conference on Communication and Signal Processing, ICCSP 2024

作者： Velumail, Subbulakshmi Chaudhari, Shilpa Shashikant Sai Kiran, T.S. Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350353068

Connected and automated Vehicles (CAVs) are designed with sensors, cameras, and other computing devices to collect data to support cooperative intelligent Transportation Systems (ITS). Many potential applications offered by CAVs exist for researchers and road safety stakeholders. Vehicle tailgating is one such application to analyze risky driving behavior. Reliable detection of such behavior is hard and expensive. this paper focuses on addressing the tailgating issue in CAVs using the dataset generated using Simulation of Urban Mobility (SUMO) for Indian roads. the proposed model of this study uses the Relative speed and Euclidean distance of the preceding and rear vehicle as parameters on the distributed Federated learning (FL) framework to incorporate security for sensitive vehicular data. We verified our prediction model in the TensorFlow Federated framework, and our results show an accuracy of 96 percent. © 2024 IEEE.

关键词： Vehicles

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RoadGen4Twins: A Modular Approach for Generating Multi-Purpose Geometric-Semantic Models for Digital Twins of Roads 19

RoadGen4Twins: A Modular Approach for Generating Multi-Purpo...

引用

19th 3D GeoInfo conference / 31st EG-ICE international Workshop on intelligent Computing in engineering

作者： Crampen, David Hein, Marcel Blankenbach, Joerg Rhein Westfal TH Aachen Geodet Inst Aachen Germany

the development of novel and robust digital methods to support the maintenance of existing road infrastructure requires a large amount of harmonized data. Especially in the context of automated modelling having a large amount of matching data from different perspectives enables disruptive, new use cases that might largely impact the efficiency in maintenance of the built environment. Unfortunately, such data compositions are tedious to collect in real world applications, due to many influential factors, leading to deviations between multiple data sources and the sheer complexity in the process of creating a digital model. However, for deep learning applications, a large amount of carefully annotated data is necessary for robust estimations. In this contribution, we tackle this problem by presenting a novel procedural modelling and model configuration approach for generating homogeneous data combinations to step towards direct parameter estimation for machine learning approaches utilizing point clouds of roads and end-to-end model generation of digital road models.

关键词： Digital Twins Road Infrastructure Automation Building Information Modelling Artificial Intelligence Scan-to-BIM Scan-to-Twin

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Machine learning Methods in Non-Invasive Detection of Iron Deficiency 11

Machine Learning Methods in Non-Invasive Detection of Iron D...

引用

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

作者： Singh, Juhi Saxena, Kushal Yadav, Vishu Sisodia, Luv Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

Anaemia is a condition in which there is a lack of red blood cells in the bloodstream. the goal of this study is to develop a model for predicting anaemia in human beings using Complete Blood Count results. Diagnosing anaemia usually involves examining the colour distribution of the palpebral conjunctiva;a blood test is not necessary for this process. Nevertheless, since individual colour perception varies, we try to mimic the physical inspection of the palpebral conjunctiva to identify anaemia. this way computers may automatically identify anaemia patients in a consolidated manner for a screening procedure. the approach employed in this study is separated into three phases: gathering the datasets, which is the eye (conjunctiva), pre-processing the picture, which includes segmentation of formican & palpebral area. We make use of a thresholding decision method that is based on the high hue rate (HHR) feature, which is taken from the HIS colour space. As such, diagnosing anaemia with low-cost, non-invasive techniques remains a difficulty. One can determine whether a patient has anaemia by looking at the pallor of their palms, fingers, nail beds and conjunctiva. In order to overcome the aforementioned issues, this paper presents a unique intelligent system that helps the automated detection of anaemia and is built on machine learning. As a result, we suggest an algorithm that adopts a more thorough methodology. It includes a wide range of features, two based on colour and one texture. To improve accuracy and dependability, it also used a more advanced classifier, like an artificial neural network (ANN) or support vector machine (SVM). Even though it takes more time and processing resources, this approach priorities precision and resilience, making it appropriate for applications where precise categorization is required. © 2024 IEEE.

关键词： Support vector machines

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Management of Multiple Microgrids Considering Soft Loads Based on Federated Reinforcement learning 11

Management of Multiple Microgrids Considering Soft Loads Bas...

引用

11th international Forum on Electrical engineering and Automation, IFEEA 2024

作者： Mu, Zixin Tang, Lei Zhou, Chen Li, Ling Long, Yang Li, Dong Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9798331516611

As the energy transition advances, multi-microgrid systems are being widely applied due to their advantages in effectively accommodating clean energy and achieving zero-carbon power supply within certain areas. However, due to the instability and intermittency of renewable energy units within these systems, intelligent regulation of the output of various units in multi-microgrid systems has become an urgent issue to address. Reinforcement learning (RL), benefiting from its strength in making decisions through interaction with the environment, has emerged as one of the effective solutions to this problem. Yet, the generalization performance of the model places demands on training data and diversity. To this end, a reinforcement learning model based on a federated learning framework is proposed, taking into account soft loads on the user side and power losses within the network. After modeling the multi-microgrid system, the specific training process can be briefly summarized as follows: first, mixed strategies are employed at each local end to train the RL models within individual microgrids;then, parameters required for model updates are transmitted to a global server for federated learning;subsequently, the models of each microgrid are updated based on the global server model. Finally, simulations and analyses are conducted on a small-scale multi-microgrid example composed of three microgrids, verifying the effectiveness and superiority of the algorithm, as well as its enhancement of market economic benefits. © 2024 IEEE.

关键词： Federated learning

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Alzheimer's Disease Detection using Deep learning Algorithm 18

Alzheimer's Disease Detection using Deep Learning Algorithm

引用

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

作者： Banu, J. Faritha Kingsly Stephen, R. Aditya, N. Dhanush Raaghav, L.C. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai Ramapuram India

ISBN: (纸本)9789380544519

Alzheimer's disease is a neurological disorder. Research on early detection and classification using machine learning techniques has become essential in recent years. this paper focuses on developing a Convolutional Neural Network for early-stage Alzheimer's disease detection from brain MRI scans. Aim to create a highly accurate and robust model with sensitivity and specificity for identifying Alzheimer's, mild cognitive impairment, and healthy individuals. the research utilizes the OASIS Alzheimer's image dataset as the input data set. CNN-based preprocessing, feature extraction, extensive training, fine-tuning, data augmentation, and cross-validation on these datasets ensure the model's reliability. the proposed prediction model achieves the precision values for each class ranging from 0.209 for mild demented to 0.255 for moderate demented. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Alzheimer's disease Brain MRI scans CNN data augmentation Early detection

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A Perspective Analysis of Optical Biosensors in Machine learning Applications 18

A Perspective Analysis of Optical Biosensors in Machine Lear...

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

作者： Kumar, B. Naveen Panduranga Rao, M.V. Sharan, Preeta Varun, K.R. Kallam, Suresh Kumaresan, M. Faculty of Engineering and Technology Department of Computer Science and Engineering Bengaluru India The Oxford College of Engineering Department of Electronics and Communication Engineering Bengaluru India The Oxford College of Engineering Department of Mechanical Engineering Bengaluru India

ISBN: (纸本)9789380544519

Optical biosensors, a novel invention at the intersection of biotechnology and optics, are revolutionizing the field of biomedical diagnostics. Optical biosensors act by identifying variations in light's wavelength, phase, polarization, or intensity caused by interactions between a sensing element and the target analyte. Optical biosensors outperform other sensor types in sensing applications due to their quick, accurate, and extremely sensitive results. When evaluating optical sensors, total power, core power, efficient area, and indices are the most crucial variables to consider. Recent years have seen notable advancements in machine learning (ML), a branch of artificial intelligence (AI). this review investigates the complementary nature of optical biosensors and machine learning methods. Here, the potential utility of ML for optical biosensors is explored in detail. the advantages and drawbacks of the most popular machine learning methods are listed based on an analysis of the optical biosensing data. It highlights the possible effects of machine learning on improving the sensitivity, selectivity, and adaptability of optical biosensing devices, and it summarizes current advancements in the field. Finally, the study considers the challenges and limitations that associated with current optical biosensing techniques. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning optical biosensor photonic crystal surface plasmon resonance surface-enhanced Raman scattering

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EVAL IQ: intelligent Educational Assessment and Recommendation Platform 4

EVAL IQ: Intelligent Educational Assessment and Recommendati...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Vandana, C.P. Poornachandra, Poornachandra Shetty, Rohan New Horizon College of Engineering Department of Information Science and Engineering Bangalore India

ISBN: (纸本)9798331518981

EVAL IQ is an innovative educational assessment platform designed to simplify the evaluation process for both educators and learners. It offers separate portals tailored to each group's needs. Teachers can manage student data, create, and organize courses, design questions, and assess performance using automated scoring and in-depth analytics. the platform also includes a feedback system to facilitate continuous improvement. For students, EVAL IQ provides an easy-to-use interface for completing assessments, meeting deadlines, and providing feedback. Built with modern technology, EVAL IQ ensures efficient, transparent, and effective educational assessments, enhancing the overall teaching and learning experience. © 2024 IEEE.

关键词： Contrastive learning

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