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Enhancing Privacy Through Time Aggregation of Load Profiles in Energy Management 8

Enhancing Privacy Through Time Aggregation of Load Profiles ...

引用

8th IEEE International Energy Conference, ENERGYCON 2024

作者： Varenhorst, Ivo A.M. Hoogsteen, Gerwin Gerards, Marco E.T. Hurink, Johann L. dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (纸本)9798350382150

Demand side management (DSM) applications rely on the exchange of load profiles to effectively manage the operation of energy systems. However, sharing detailed energy profile data raises substantial privacy concerns, such as potential misuse of personal information. To mitigate these concerns, we investigate the potential of time aggregation (TA), which involves merging multiple samples of a profile into a single value representing multiple intervals. TA reduces data exchange and computational requirements in energy management and helps preserve user privacy by reducing granularity of user data. We show that, for an effective implementation of TA in energy management, it is important to make the right choice of TA method. We compare and evaluate seven different TA methods. Furthermore, we perform TA across various time frames using an optimization based DSM approach. Our findings reveal that if we aggregate load profiles from 15 minutes to 4 hours, we obtain both enhanced privacy and a 21% decrease in the required number of iterations with the investigated DSM method, albeit at the cost of a 15% decrease in objective value performance. Based on this, we conclude that depending on the application needs, TA with a carefully selected aggregation method has the potential to bring value to energy management, even when aggregating to a considerable extent. ©2024 IEEE.

关键词： Energy management

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Analyzing the Impact of Air Quality on UV Index using Real-Time Weather Data from Major Cities Across the World 2

Analyzing the Impact of Air Quality on UV Index using Real-T...

引用

2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Middha, Parth Basim Muitaba Andrabi, Syed Gupta, Tanushree Singhal, Abhishek Amity University Uttar Pradesh Dept. of Computer Science & Engineering Noida India

ISBN: (纸本)9798331519056

Ultraviolet radiation from sunlight has both beneficial and harmful effects on human health, with the UV index serving as a crucial metric for assessing potential harm. Excessive exposure can lead to adverse effects like skin cancers, cataracts, and immune suppression. Air quality influences the UV index by affecting the scattering and absorption of UV radiation, with particulate matter and pollutants altering the amount of radiation reaching the Earth's surface. Recognizing the dynamic relationship between air quality and the UV index is vital for understanding their combined impact on human health and the environment. This work utilizes the real-time weather information from prominent cities across the world, capturing diverse climatic conditions and air quality variations. With a particular emphasis on UV index prediction, the study focuses on employing a comparative analysis of various regression models, including linear regression, decision trees, support vector machines, logistic regression, random forest, XGBoost, LightGBM, gradient boosting and neural networks. The comparative analysis aims to identify the most proficient model for capturing the intricate relationships between chosen meteorological variables, including temperature, wind degree, precipitation, and others, in relation to the UV index. This paper explores the impact of air quality, temperature, wind degree, precipitation, and other relevant factors on UV index variations, offering insights into the complex interplay between meteorological parameters and ultraviolet radiation. The findings of this research hold potential implications for public health, as understanding the factors influencing UV index can contribute to enhanced sun exposure risk assessments. Furthermore, the comparative analysis of regression models provides valuable guidance for future studies aiming to predict UV index variations based on real-time weather data, thereby contributing to the development of more accurate and regi

关键词： Air quality

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Prediction Model for Suicidal Behavior Disorder Risk Analysis by Correlating Cyber and Real World Data 4

Prediction Model for Suicidal Behavior Disorder Risk Analysi...

引用

4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Neelakantam, Gone Ecole Centrale School of Engineering Mahindra University Dept. Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350387063

In the era of internet of things (IoT), people are attentive to express their non verbal behavior on cyber world such as social media by using smart phone, laptop or tablet. People are more active in sharing their daily activities status such as current activity the person is into and personal life situations such as achievement, problem, stress and even any hazardous desire (i.e. suicide). In contrast with real world such as smart home environment, people generally spent their non verbal behavior in term of Activity Daily Livings (ADLs) in associating with smart home sensors. Based on non verbal and verbal behaviors, data are generated from both Cyber and Real world, which are big in volume and variety. However, there is lack of investigating Cyber and Real world data especially in analyzing the risk of committing suicide, considering suicide is a big issues and threats in the society. Therefore, it motivate us to propose prediction model to determine high and low risk of committing suicide by combining two separate approaches such as Activity Recognition (AR) for real world and Sentiment Analysis (SA) for cyber world. © 2024 IEEE.

关键词： Risk assessment

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Cardiac Disease Diagnosis from Echocardiogram using Random Forest Classifier 5

Cardiac Disease Diagnosis from Echocardiogram using Random F...

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shruthi, T. Srimathi, P. Janani, S. St. Joseph's College of Engineering Dept. of Computer Science Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Cardiovascular diseases (CVDs) represent a leading global health issue, necessitating early detection and accurate diagnosis for effective treatment. Electrocardiography (ECG) serves as an essential tool that provides valuable insights into the heart's electrical activity, aiding in the identification of potential abnormalities. This project leverages machine learning techniques to enhance the diagnostic potential of ECG data, paving the way for improved cardiovascular care through precision medicine. The methodology focuses on training machine learning models to analyze ECG signals and classify heartbeats into categories that reflect various cardiac conditions. A com- prehensive dataset of approximately 20,000 ECG recordings, encompassing a wide range of heart rhythms and anomalies, establishes a robust foundation for model training. The Decision Tree classifier achieves an outstanding accuracy of 97%, demonstrating the ability to detect subtle patterns in ECG data. Furthermore, the Random Forest classifier showcases strong performance in distinguishing between different cardiac rhythms. Key metrics, such as precision, recall, and F1-score, provide a deeper understanding of the classifier's diagnostic effectiveness. The confusion matrix serves as a visualization tool to pinpoint areas where model adjustments are necessary, further enhancing accuracy. These promising results highlight the potential of machine learning in transforming cardiac health monitoring and improving diagnostic processes, ultimately contributing to better patient care and outcomes. By harnessing advanced algorithms, this project illustrates the immense possibilities for integrating machine learning into modern healthcare systems. © 2024 IEEE.

关键词： Electrocardiography

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Water Region Extraction in Aerial Images Using Deep Learning

Water Region Extraction in Aerial Images Using Deep Learning

引用

2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Pandey, Nikita Kumar, Saumya Yadav, Satish Kumar Srivastava, Shashwat Singh, Shomil Gupta, Deepak Pranveer Singh Institute of Technology Dept. of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350369175

Water bodies are essential component of Earth's surface and accurate water region mapping is crucial for disaster management and environmental monitoring. Existing methods to map water bodies relies on traditional modelling methods that need extensive manual calibration struggle with scalability and lack advanced pattern recognition. This paper evaluates the effectiveness of deep learning-based semantic segmentation models, DeepLabV3+ and U-Net, using aerial images resized to $\mathbf{2 5 6 x} 256$ pixels in mapping water regions in aerial images. DeepLabV3+, with a ResNet50 backbone, and U-Net are trained on a comprehensive flood-affected dataset with preprocessing techniques such as resizing and augmentation. Model performance is assessed using metrics like IoU, F1 score, accuracy, precision, and recall. The results indicate that DeepLabV3+ outperforms U-Net, showing promise for automating water region mapping and improving flood surveys and urban planning. © 2024 IEEE.

关键词： Semantic Segmentation

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Minimization of data centers in survivable dynamic SDM-EONs 14

Minimization of data centers in survivable dynamic SDM-EONs

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Chandra, Sourabh Mondal, Khokan Calcutta Institute of Technology Dept. of Computer Science & Engineering Howrah India Techno India University Dept. of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350335095

Energy consumption due to massive use of data center (DC) is increasing rapidly. The need to improve the energy efficiency of such DC aims to develop an effective DC-placement algorithm. Again, a sudden disaster in the network can affect both fiber path as well as the DC nodes resulting in fiber cuts and huge data loss. Space division multiplexing-based elastic optical network (SDM-EON), being more efficient than the traditional elastic optical network (EON) due to the inclusion of multiple cores in a single fiber link, tries to maximize the utilization of resources by reducing blocking probability. This paper presents a novel DC-placement algorithm in dynamic DC-based SDM-EONs which focuses on enhancing energy efficiency in the network, thereby ensuring the survivability of the network against any sudden disaster. Rigorous simulation analysis proves the efficacy of the proposed algorithm over other existing algorithms. © 2023 IEEE.

关键词： Energy efficiency

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Enhancing Image Translation with Conditional Generative Adversarial Networks 3

Enhancing Image Translation with Conditional Generative Adve...

引用

3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Verma, Shobhit Arora, Srishti Kayat, Tanvi Goel, Anurag Delhi Technological University Dept. of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350391565

This article examines the advantages of engaging in conflictual discourse as a means to address and transform image-related issues in a revolutionary manner. Unlike earlier approaches that necessitated human customization for particular applications, Conditional Generative Adversarial Networks (cGANs) automatically produce image outputs and negative impacts. Picture painting, object reconstruction from edge maps, and picture combining from a label map are just a few activities that may be executed using a unified framework. The notion has been tested and proven to work in various ways it doesn't need to be compared to other standards in any way to be implemented or embraced, many people on the internet, including artists, have played around with Pix2pix and its related apps since it was first released, making it more flexible and easier to use. As a major step forward in traditional image interpretation, this study eliminates the need for human map generation. So, there's no requirement for overlapping sessions devoted to particular objectives the community can get satisfactory answers for various image interpretation issues in this way. © 2024 IEEE.

关键词： Generative adversarial networks

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A Comparative Study of Deep Image Retrieval Models Leveraging Deep Features

A Comparative Study of Deep Image Retrieval Models Leveragin...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Jain, Ashish Varshney, Sudeep SET Sharda University Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350378092

This review investigates how deep learning methods can be utilized for efficient image retrieval based on content. Obtaining accurate images from vast digital collections poses significant challenges, motivating research in CBIR. The effectiveness of these methods varies depending on the dataset's type and size, with certain algorithms excelling with specific dataset characteristics. An extensive and well-structured review of successful image retrieval techniques is given in this paper. Our primary objective is to evaluate various deep learning models applied by researchers and compare their performance based on the outcome of evaluation matrices. These models encompass CNNs, DBNs, and other deep architectures tailored for image retrieval tasks. By synthesizing insights from this review, researchers can make informed decisions regarding model selection and potentially enhance retrieval performance by leveraging advanced deep learning features. The importance of deep features in image retrieval is the ability to capture complex visual patterns and semantic information that cannot be easily extracted by traditional handcrafted features. With the increasing volume of online images, Image retrieval using deep learning has become crucial for applications like object recognition, image retrieval, and image search engines. © 2024 IEEE.

关键词： Contrastive Learning

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A Comparative Analysis of Early Departure Buttons in Coordinated Control of EV Charging

A Comparative Analysis of Early Departure Buttons in Coordin...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Ozceylan, Baver Hoogsteen, Gerwin Hurink, Johann L. University of Twente Dept. of Electrical Engineering Mathematics and Computer Science Enschede Netherlands

ISBN: (纸本)9789531842976

The increasing adoption of electric vehicles (EVs) necessitates the efficient management of large EV parking facilities to prevent them from exceeding grid capacity and to improve the overall user experience. This paper introduces a data-driven approach for coordinated control of EV charging in an office parking facility, integrating Early Departure Buttons (EDB) into the system. These buttons provide a binary option for users to indicate an earlier departure than a predefined time. We employ the EDB data to improve departure time estimations and to address issues where EVs receive no or only very low energy. We utilize three datasets originating from different geographical locations. One dataset displays a user pattern where users leave shortly after completing their charging, unlike the other datasets which follow typical working hours. Our simulations show that the unique user pattern significantly increases fairness among users, and integrating EDBs improves fairness for the other datasets to levels similar to those of quick station turnover. © 2024 IEEE.

关键词： electric vehicle energy management smart grid user information

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Developing a Low-Cost Prosthetic Hand with Real-Time Machine Learning Capabilities 23

Developing a Low-Cost Prosthetic Hand with Real-Time Machine...

引用

8th International Conference on Sustainable Information engineering and Technology, SIET 2023

作者： Adani, M. Syakhisk Naufal Widasari, Edita Rosana Setiawan, Eko Dept. Computer Engineering Faculty of Computer Science Brawijaya University Indonesia

ISBN: (纸本)9798400708503

The global prevalence of arm amputations, particularly transradial or below-elbow amputations, presents a pressing challenge due to the high costs of commercially available prosthetic hands. This issue is particularly acute in developing countries, where financial constraints hinder access to functional prosthetic solutions. Addressing this gap, this research introduces an innovative approach that merges electromyography (EMG) signal recognition and machine learning to develop an affordable and versatile bionic below-elbow prosthetic hand. The integration of EMG signals for control, coupled with machine learning algorithms, enables accurate classification of hand movements, resulting in enhanced movement precision and a broader range of gestures. This solution overcomes limitations in existing prosthetic hands, offering advanced EMG-based control and comprehensive movement capabilities, empowering users to perform intricate tasks and regain functional autonomy. The study undertakes a comprehensive analysis of the developed system's performance, encompassing hardware, software, and machine learning components. Results show promising accuracy levels for movement classification using the K-Nearest Neighbors (KNN) machine learning method. Execution time variations are observed among subjects and gestures, influenced by factors such as subject sensitivity and gesture complexity. Energy consumption analysis reveals disparities in power usage among different movements, emphasizing the impact of movement complexity on power requirements. A cost analysis demonstrates the affordability of the prototype, positioning it as a cost-effective alternative to existing prosthetic hand options. Comparing this solution to other research and commercial prosthetic hands highlights its uniqueness, particularly its emphasis on affordability. © 2023 Owner/Author.

关键词： Nearest neighbor search

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