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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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A Novel Approach for Single-Shot Target Recognition by Matching Graph of Feature Pixels 2

A Novel Approach for Single-Shot Target Recognition by Match...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Mitra, Baldivya Deshmukh, Maroti Kumar, Abhimanyu Dept. of Computer Science & Engineering National Institute of Technology Uttarakhand 246174 India

Image feature extraction and matching constitute fundamental steps in computer vision and image analysis, enabling various applications ranging from object recognition to scene reconstruction. Object recognition is one of the major applications of artificial intelligence. Since last two decades, different techniques for object recognition were given by researchers from across the world. This paper presents a novel approach for automatic target recognition in autonomous weapons by matching connected graph of feature pixels. The Moving and Stationary Target Acquisition and Recognition (MSTAR) is taken for experimental purpose. The Proposed model has three stages for a target recognition;Image Denoising, Feature extraction & graph generation, and feature matching. Firstly, a sample image of targets taken from the MSTAR is taken as an input. Further, input sample is de-noised and features are extracted using Speeded-Up Robust Features (SURF) algorithm. Thereafter, a graph is created using feature pixels and this complete process is repeated with test image. Further, both graphs are compared using a proposed feature graph matching algorithm which results an recognition accuracy score. Proposed model is tested over the MSTAR dataset and it resulted in varying accuracy at different feature matching thresholds. © 2024 Elsevier B.V.. All rights reserved.

关键词： Synthetic aperture radar

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Enhancing Image Translation with Conditional Generative Adversarial Networks 3

Enhancing Image Translation with Conditional Generative Adve...

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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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Applications and Approaches of Sentiment Analysis: A Current Review 6

Applications and Approaches of Sentiment Analysis: A Current...

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Sandra, G. Silas, Salaja Rajsingh, Elijah Blessing Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350381689

Limitless amount of data is loaded in every moment in social media platforms in the forms of audio, videos, texts, and photos. They are rich sources of information, which has immense value. Hence extraction of information from them is inevitable. Many methods are employed for this and the popular among them to mine the information is sentiment analysis. To materialize the objectives of this study, a systematic review of articles published from 2013 to 2023 was done. Our study reviewed the relevance of sentiment analysis in social media by probing the different approaches followed, the platforms used and its applications, as its main objectives. Its results show that majority of studies applied machine learning approach or hybrid method for executing sentiment analysis. © 2024 IEEE.

关键词： applications approaches categories dataset sentiment analysis

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Automatic Detection of Hypernasality in Cleft Palate Speech Using Phase Feature 14

Automatic Detection of Hypernasality in Cleft Palate Speech ...

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Hashreta, Kanuri Naga Venkata Sai Lalitha, Duggaraju Sri Lahari, Maddula Lalitha Sri, Pittala Deepthi Dubey, Akhilesh Kumar Koneru Lakshmaiah Education Foundation Dept. of Computer Science and Engineering Guntur India

ISBN: (纸本)9798350344837

The detection of hypernasality, a speech disorder common in children with cleft palate, typically involves analyzing vowels within speech stimuli. An essential acoustic cue for hypernasality detection is the presence of a nasal peak near the first formant (F1). Traditionally, a two-step process is employed, where vowel selection from stimuli is initially performed through manual annotation, followed by automatic feature extraction and classification. This work introduces a novel method for hypernasality detection, automating the entire process, including vowel region selection, feature extraction, and classification. The method leverages vowel onset points for the automatic identification of the vowel region. Subsequently, a modified group delay cepstral coefficient feature is extracted from this automatically selected vowel region. The feature is extracted from modified group delay spectrum, offering enhanced resolution to distinguish closely spaced nasal peaks and the F1, thus improving the capture of nasality evidence. The proposed method demonstrates promising results when applied to hypernasality detection using a support vector machine classifier with the accuracy of 83.50% for /a/ vowel and 93.21% for /i/ vowel. © 2024 IEEE.

关键词： Support vector machines

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Elastic Blockchain Security Solution for Edge Computing Supporting Mobile IoT Devices

Elastic Blockchain Security Solution for Edge Computing Supp...

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2024 International Conference on Electrical and computer engineering Researches, ICECER 2024

作者： Almubark, Hameed Jedidi, Ahmed Al-Raweshidy, Hamed S. Brunel University London Dept. of Computer Science United Kingdom Ahlia University Dept. of Computer Engineering Manama Bahrain Brunel University of London Dept. of Electronic and Electrical Engineering United Kingdom

ISBN: (纸本)9798331539733

When integrated, blockchain technology and cloud computing (EC) may improve security and protect data. However, when data is sent to EC, the Internet of Things (IoT) device layer cannot guarantee data security. As a result, in the IoT ecosystem, end users and IoT devices are susceptible to assaults. Additionally, as the number of IoT devices rises and generates a large quantity of data, especially in mobile IoT devices, security concerns rise dramatically. High security and privacy standards are necessary for industrial Internet of Things (IIoT) applications in order to safeguard data and guarantee that IoT devices may seamlessly switch to another network. By utilising its decentralised structure, consensus process, and encryption features, blockchain technology can offer a solution. However, because IIoT devices have limited resources, the encryption procedure might be complicated. Actually, in order to secure communication between IIoT devices and EC, this research suggests a new security mechanism built on blockchain technology. Additionally, two security tiers will be used by the Elastic Blockchain Security Solution (EBC) to create a dynamic system for mobile IIoT devices. To evaluate the EBC solution's effectiveness, scalability, and performance, as well as its consensus method and encryption capabilities, it was put into practice and tested in a number of scenarios © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Novel Approach to Monitor Location of the Student in the Institution 15

Novel Approach to Monitor Location of the Student in the Ins...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kulkarni, R.N. Suma, Rochana Y. Sravanthi, B. Rakshitha, G. Krathika, T. Ballari Institute of Technology and Management Dept. of Computer Science & Engineering Ballari India

ISBN: (纸本)9798331300579

During the last few decades there is a tremendous growth in the information and communication technology wherein many organizations are making use of the hardware and software for various applications. Marking the attendance of a student in the school or college as well as the presence of the employee in the workplace is to be either captured or manually marked in the registers. Performing various analyses by taking the manual input is cumbersome because the number of students in the class or number of employees in the organizations are increasing. To overcome the difficulties of managing the things manually in this paper we are proposing an automated tool to capture the presence of the student in the specific location. This paper proposes a novel approach to mark the presence of a student in the college premises. The tool developed here is an Android Application (App) wherein the students can send the details to the App. Two modules are proposed here, one for the student and another for the Administrator. Through the student module, each student can login to the App and capture the self-image and send it to the Administrator Module. The captured image comprises the GPS details like Longitude, Latitude, Time, and date information. The information sent through the App determines the location of the student whether the student is in the college premise such as Class room, Laboratory and Library. The application which is managed by the teacher or the administrator or proctor will get the details about the student's image, GPS locations such as latitude, longitude, date and time. The mobile app verifies the GPS location and image of the student and compares it with the database. If the captured information matches with the appropriate GPS location then the students attendance is marked as present in that particular duration and appropriate files are updated. © Grenze Scientific Society, 2024.

关键词： Face recognition

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An Efficient Approach for Maintaining and Mining Frequent Episodes 6

An Efficient Approach for Maintaining and Mining Frequent Ep...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Yen, Show-Jane Lee, Yue-Shi Ming Chuan University Dept. of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350344349

Data mining technology plays a crucial role in the area of data analysis. Mining frequent episodes stands out as a pivotal task within this domain, enabling users to forecast future events based on present occurrences. Conventional methods for discovering frequent episodes typically follow a hierarchical approach, involving the generation of candidate episodes and subsequent scanning of sequence data to count their frequency, which can be quite time-consuming, as it necessitates repeated scans of the sequence data and the search for candidate episodes. In this paper, we introduce a novel approach for episode mining in a data stream. Our method distinguishes itself by scanning newly added data to update existing frequent episodes, all without the need for scanning the original data or searching for candidate episodes. The experiments also show that our approach is more efficient compared to other existing methods. © 2024 IEEE.

关键词： Data mining

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Deep Learning Based Segmentation of Weed Images in crop fields using U-Net 3

Deep Learning Based Segmentation of Weed Images in crop fiel...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Naik, K. Jairam National Institute of Technology Raipur Dept. of Computer Science & Engineering Raipur India

ISBN: (纸本)9798350354171

Weeds are problems for crop yields because they exert harmful effects on the yields by competing with cultivated crops for necessary resources like nutrients, sunlight, and soil. As a result, continual attempts are being made to create safer and more effective herbicide formulations. For precision weeding and to reduce pesticide pollution, segmenting weeds in photos is extremely important. However, weeds and crops are comparable in the field setting, making it challenging to precisely segregate weed from intricate field photos. A deep learning-based system for segmenting weeds from photos was proposed in this paper. This algorithm can distinguish between weeds and crops and soil in photos. Utilizing a condensed U-net, this semantic segmentation method was created. An image augmentation strategy was suggested because image tagging for the semantic segmentation of weeds is challenging. The classification algorithms used in the modified U-Net are Support Vector Machine (SVM), Random Forrest (RF) and Decision Tree (DT). These were also used for predicting the weed part in a sample image before their segmentation. The threshold accuracy for segmenting the image using to be around 51.85% with a segmentation time of approximately 01.02 ms whereas our modified U-Netis obtained 91.03% accuracy and the segmentation time of 50.35ms. The results showed the modified U-Net successfully differentiated weeds from images with numerous other plants. This study seeks to demonstrate that the weed-associated image segmentation technique is effectively identifying and separating weeds in intricate field conditions. © 2024 IEEE.

关键词： Semantic Segmentation

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Smart Healthcare - IoT and Artificial Neural Network(ANN) 15

Smart Healthcare - IoT and Artificial Neural Network(ANN)

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sutar, Laxmikanta Chinara, Suchismita National Institute of Technology Rourkela Dept. of Computer Science and Engineering Rourkela India

ISBN: (纸本)9798350370249

With the use of the Internet of Medical Things (IoMT), the growing accessibility of wearable devices, and the continual reduction in the cost of sensor-based technologies and green computing, internet-connected sensors allow users to track, analyze, and share welfare information employing a cloud-based platform. Artificial Neural Networks (ANN) find extensive use across various applications, including healthcare systems, drawing inspiration from the biological concept of neurons. This study explores the prediction of heart attack risks using a combination of Internet of Things (IoT) sensors and artificial neural networks (ANN). Through the integration of IoT sensors such as the pulse sensor and LM35 temperature sensor, essential physiological data related to heart rate and body temperature was collected. An ANN model was developed, focusing on predictive capabilities using the sigmoid function, feedforward and backpropagation techniques, and random weight initialization. The investigation further utilized Keras with TensorFlow and optimized model parameters using Adam Optimization and Later with One Hot Encoding plus a Genetic Algorithm (GA). Additionally, the study involved training neural networks on higher-quality UCI datasets to evaluate performance. Scatterplots were generated to visualize relationships between variables. Results demonstrated promising performance improvements, with the model achieving close to 90% accuracy on the testing dataset. Encoding techniques were employed to handle categorical variables effectively, contributing to the model's robustness and generalization. Overall, this research offers valuable insights into leveraging IoT sensors and ANN for heart attack risk prediction. © 2024 IEEE.

关键词： Adam Optimisation ANN Back Propagation Dense Layers Feed Forwarding Fuzzy Genetic Algorithm HTTP IoT MQTT One Hot Encoding Protocols UCI Heart Data

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