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Deepfakes - Generating Synthetic Images, and Detecting Artificially Generated Fake Visuals Using Deep Learning 14

Deepfakes - Generating Synthetic Images, and Detecting Artif...

引用

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

作者： Mishra, Ayushi Bharwaj, Aadi Yadav, Aditya Kumar Batra, Khushi Mishra, Nidhi Amity University Noida Dept. of Computer Science and Engineering Noida201313 India

ISBN: (纸本)9798350344837

The abstract delves into the groundbreaking realm of DeepFakes, a revolutionary synthesis of deep learning and synthetic media. Central to DeepFake generation is the utilization of Generative Adversarial Networks (GANs), particularly the innovative mechanisms of face reenactment involving DCGANs (Deep Convolutional GANs) and Autoencoders. These technologies empower a generator to transform random noise into hyper-realistic visuals, capturing intricate details such as facial expressions and lighting conditions through latent space interpolation. The adversarial interplay between generator and discriminator continuously refines the authenticity of the generated content. While DeepFakes unlocks creative possibilities for artists and filmmakers, the abstract underscores the ethical concerns surrounding misinformation, privacy breaches, and trust erosion in digital media. The discourse navigates the delicate balance between creative freedom and responsible use, highlighting how DeepFakes, with their roots in advanced deep learning techniques, redefine our perception of synthetic media, challenging notions of reality in our increasingly digital world. © 2024 IEEE.

关键词： Digital storage

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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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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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Blockchain-Aided Privacy Preserving Framework for Federated Learning 2

Blockchain-Aided Privacy Preserving Framework for Federated ...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Jain, Chitresha Chaudhari, Payal Pandit Deendayal Enerygy University Dept. of Computer Science and Engineering Gandhinagar India

ISBN: (纸本)9798350306415

Blockchain has shown tremendous growth in the past few years because of its decentralized and immutable architecture, which makes transactions transparent, making it a feasible choice for authentication and security purposes. Federated learning is a collaborative model training method that can benefit from Blockchain Technology. It is vulnerable to a single point of failure while performing aggregation in a centralized server architecture. Blockchain-aided Federated Learning can be proposed for enhancing the security and privacy of user data together with removing a single aggregator. It also supports a mechanism of giving rewards to users in the form of cryptocurrency to encourage users to follow the protocol. This paper introduces a framework named Blockchain-Aided privacy preserving framework for Federated Learning (BPPF) that helps in model training on the user side with added security and privacy. © 2024 IEEE.

关键词： Blockchain Differential Privacy Federated Learning Security

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Applications and Approaches of Sentiment Analysis: A Current Review 6

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

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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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Efficient Compressed Sensing for Real-Time Electrocardiogram Acquisition on Low-Power Medical Devices

Efficient Compressed Sensing for Real-Time Electrocardiogram...

引用

2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Terrell, Mitchell Weissman, Jon University of Minnesota Dept. of Computer Science and Engineering MinneapolisMN United States

ISBN: (纸本)9798350351552

Low cost wearable and implantable cardiac monitoring devices (WCM & ICM), combined with increasingly accurate disease and arrhythmia detection algorithms have proven effective to help slow the impact of cardiovascular disease, the worlds leading cause of death in 2022 [1]. Improvements to server-side detection algorithms along with hardware limitations of these devices such as slow processor speeds and minimal battery life has led to a desire to offload data from the devices for later analysis. Paired with limited storage capacity, this has led to a push to decrease the necessary storage for electrocardiogram (EKG) signals without sacrificing disease detection accuracy and device longevity. A promising recent innovation in EKG compression has come from Compressed Sensing (CS), which exposes inherent sparseness in the signal to selectively sample for eventual server-side reconstruction. Many CS approaches have been implemented on WCM devices which demonstrate a high compression ratio (CR) and accurate signal reconstruction, but quickly become impractical for the stricter hardware constraints of ICM devices due to the increased computation on the device and slow reconstruction time. In this paper we propose a CS approach known as Tailored Sensing (TS) which combines on-device prior knowledge, a custom transform basis, and optimal sense location selection to achieve improved CR and reconstruction accuracy while eliminating on-device computational burden and slow reconstruction time. Our approach offers equivalent or better signal reconstruction at previously unfathomable CRs due to our novel signal segmentation scheme. Additionally, our approach boasts a zero overhead on-device sensing strategy and a 93 % reduction in signal reconstruction time. © 2024 IEEE.

关键词： Digital storage

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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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Using ChatGPT in Undergraduate computer science and Software engineering Courses: A Students' Perspective 54

Using ChatGPT in Undergraduate Computer Science and Software...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Andersen-Kiel, Noah Linos, Panagiotis Butler University Computer Science and Software Engineering Dept. IndianapolisIN United States

ISBN: (纸本)9798350351507

This innovative practice full paper presents an empirical study aimed at evaluating the potential of ChatGPT, an advanced AI-driven chatbot, as a supplementary educational tool in undergraduate computer science and Software engineering (CSSE) courses. The study, initiated in the summer of 2023, focused on assessing ChatGPT's capabilities in generating accurate and complete computer code, identifying and rectifying code defects (bugs), and its scalability in handling larger programs. To achieve this, we conducted a series of experiments with ChatGPT. In one experiment, we introduced bugs into small programs from introductory CSSE courses. ChatGPT was tasked with detecting these defects and providing recommendations for fixing them. We evaluated ChatGPT's effectiveness in bug detection, the quality of its recommendations, and the completeness of the proposed solutions. We sought answers to questions such as whether ChatGPT found all injected defects, provided appropriate recommendations, and delivered high-quality solutions based on criteria like code completeness, size, complexity, and readability. In another experiment, ChatGPT was asked to generate code for assignments from previous CSSE courses, including Intro to computer science and Programming in C++, Intro to Python Programming, and Object-Oriented Programming and Data Structures using Java. We assessed the generated code's correctness and quality in comparison to student-written code. Similarly, in a third experiment, we evaluated ChatGPT's ability to generate larger programs using requirement specifications from an upper-division CSSE course on Agile Software engineering. Analyzing both qualitative and quantitative data from these experiments during the summer, we determined that ChatGPT showed promise as an educational tool. Consequently, we developed a plan to integrate ChatGPT into select CSSE courses for the fall semester of 2023. Specifically, ChatGPT was integrated into two of our introductory CSSE cou

关键词： Object oriented programming

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Quora Question Sincerity Detection Using BERT-Based Framework 14

Quora Question Sincerity Detection Using BERT-Based Framewor...

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

作者： Arora, Drishti Aggarwal, Garima Asif, Farhan Amity University Dept. of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350344837

In the dynamic landscape of online knowledge exchange, platforms such as Quora have become vital arenas for diverse discussions and information dissemination. However, distinguishing sincere from insincere questions poses a challenge, impacting user experience and content credibility. The research seeks to utilize BERT-based frameworks to classify Quora questions, with a specific focus on discerning between sincere and insincere inquiries. The study underscores the significance of automated insincere content identification in fostering a trustworthy and constructive online environment on Quora, thereby contributing to the improvement of online interaction quality. The research delves into a comprehensive methodology, including data preprocessing, model training, fine-tuning, and performance evaluation. Extensive experimentation demonstrates that the proposed BERT-based approach achieves an impressive F1-score of 96.1%, outperforming existing methods. The contribution of the study lies in its potential to enhance online interactions by automating the identification of insincere content, fostering a conducive environment for meaningful conversations. © 2024 IEEE.

关键词： Information dissemination

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Notes Application using Android Studio and Cloud-based Data Management 7

Notes Application using Android Studio and Cloud-based Data ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Dodda, Ratnam Yanala, Maneesh Reddy Pavishetti, Srija Cvr College of Engineering Dept. of Computer Science And Information Technology Hyderabad India

ISBN: (纸本)9798350359299

In today's rapidly advancing technological land-scape, the widespread adoption of mobile applications has become a defining feature of our digital age. This research study presents a on the development of a mobile notes application using Android Studio, with the primary goal of offering users a versatile platform for creating and managing notes. The proposed notes application is designed with a simple and user-friendly interface, facilitating effortless note creation, editing, and deletion. Moreover, it has features like voice-to-text for making notes, text-to-speech for reading the notes aloud, note sharing for easily sharing ideas with others and image insertion for storing the photocopy of the written notes or diagrams based on user interest. This research work uses Android Studio as the integrated development environment (IDE) and utilizes the Java programming language for the app's core functionalities. Furthermore, it incorporates Firebase Authentication to ensure secure user account management and the Firestore for Database for seamless storage and retrieval of notes. This research study provides an essential tool for students, professionals, and anyone seeking a convenient means to keep track of vital information. By combining mobile application development tools and cloud-based data management like Firebase, the application simplifies how information is managed, aligning with the way we live in the digital age. © 2024 IEEE.

关键词： Authentication

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