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Clustering Data Log CITA Blockchain Performance Monitoring with Mean Shift Method

Clustering Data Log CITA Blockchain Performance Monitoring w...

引用

2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Ubaya, Huda Stiawan, Deris Suprapto, Bhakti Yudho Perdana, Muhammad Iqbal Universitas Sriwijaya Faculty of Computer Science Dept. of Computer Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Engineering Dept. of Electrical Engineering Palembang Indonesia

ISBN: (纸本)9798350307306

CITA is a high-performance enterprise blockchain system with six microservices (RPC, Auth, Consensus, Chain, Executor, and Network) that exchange information through Message Bus. There is a dataset in the form of performance logs from CITA in the format of CPU, Memory, Network, PReads, PWrite, and VMem taken in real-time using the Performance Monitoring Framework for Blockchain. This framework has the advantage of lower overhead, more details, and better scalability than other performance monitoring approaches, but there is no clustering feature in the monitoring results. Thus, it is necessary to include a clustering feature so that users know the performance directly without the need to analyze it more deeply. This research uses the Mean Shift algorithm, which does not require the number of clusters for the initial process but only determines the maximum distance of centroid displacement through calculation. The number of clusters estimated is 2 clusters, where CITA produces 7 cluster samples with cluster evaluation results with the highest average silhouette score from CITA, which is 0.555 in the Network-Received Network-Sent feature pair. So, it is known that the Mean Shift clustering algorithm produces goodness of fit results from clustering on the CITA Blockchain performance log, which means that the clusters are well separated and can be clearly distinguished. © 2023 IEEE.

关键词： Blockchain

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A Framework to Find Single Language Version Using Pattern Analysis in Mixed Script Queries 2

A Framework to Find Single Language Version Using Pattern An...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Chaudhary, Anu Shekhar, Shashi Gla University Dept. of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350371055

Pattern analysis for converting a complete mixed code into a single language after determining confidence levels is a systematic approach crucial for applications requiring language uniformity. The process begins by tokenizing the mixed code into individual words or segments and employing a language identification algorithm to assign potential languages with associated confidence scores to each segment. Establishment of a confidence threshold allows for the reliable selection of dominant languages within the mixed code. Segmentation based on confidence levels and the application of a majority language rule ensures the prioritization of the most reliable language segments. In cases where confidence falls below the threshold or ambiguity arises, a fallback mechanism can be implemented, offering options like defaulting to a predefined language or flagging for manual review. The conversion strategy is then formulated based on the identified dominant language, utilizing tools such as machine translation services or language-specific processing libraries. The final output is a unified, single-language version of the mixed code, providing consistency and reliability in diverse linguistic contexts. The bulk of the words are Hindi, it has been noted after handing to the model. This is the reason these mixed codes need to be translated into majority language script. Make a table of replacements to swap out the unclear acronym with the matching term. There are 79 words in all, and 1 of those words are erroneous;thus, the correctness of the work is (78 / 79) × 100=98.73 %, and the mistake rate is (1/79)× 100=1.27%. © 2024 IEEE.

关键词： Dominant language Pattern analysis Segmentation Single language version

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Real-Time Evaluation of Human Inertial properties in Immersive Simulators for Moth type Boats 6

Real-Time Evaluation of Human Inertial properties in Immersi...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Todesca, Davide Sergenti, Carol Giulietti, Nicola Carnevale, Marco Dept. Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (纸本)9798350394634

The aim of this work is to present a system designed for real-time markerless evaluation of human inertial properties, to be used in a Human In The Loop (HITL) simulator of a Moth class sailboat. The method is based on human body pose estimation algorithm used for 3D skeleton reconstruction from a multi-camera acquisition. The exploitation of a markerless system enables ease of use compared to marker-based systems, and reduces signal’s disturbances compared to Inertia Measurement Units (IMUs) for body pose reconstruction. The use of multi-camera setup and the integration of YOLOv8-pose model results with image triangulation techniques reduce occlusion occurrence, measurements drift and complexity. The 3D pose obtained is then used to estimate the Center of Mass (CoM) position and the Tensor of Inertia (ToI) of the skipper. The system allows for a real-time computation of the inertial properties of the skipper, thus enabling a real-time update of the mass matrix of the numerical model running the human-in-the loop simulator. This study shows the results achievable by the system in terms of inertia estimation for different skipper’s poses, and compares the cases in which the skipper is modelled as a point mass with real-time position updates with the case in which the full inertia tensor of the skipper is considered. Results show how in contexts like those of Moth class sailboat simulators the possibility to take into account and to vary in real-time the tensor of inertia of the sailor increases the accuracy achievable in the dynamic simulation. © 2018 IEEE.

关键词： Cameras

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Weather Forecasting Web application 4

Weather Forecasting Web application

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Gowrav, N. Dhanush, V. Chavan, Chandrashekhar Pomu Pes University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331518981

Weather Refers to the Atmosphere state of certain area and time in terms of temperature, clouds, winds, dry state, sun and rain. The most important geophysical phenomenon that affects us is weather. Weather fluctuates greatly and is impacted by a number of elements including cloudiness, climate, and other variables. The primary goal of the project is to develop an app that can deliver weather forecasts for each city on the planet. We wish to use the ReactJS java script technology to create a web application. © 2024 IEEE.

关键词： Java programming language

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Harnessing Transformative Potential of UAV-Based AI-Powered Data Analysis for Emission Monitoring 6

Harnessing Transformative Potential of UAV-Based AI-Powered ...

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6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Namdev, Mayank Dholaria, Jay Rathi, Meet Manipal University Dept. of Computer Science Engineering Jaipur India

ISBN: (纸本)9798331516796

With the growing pressure to reduce environmental pollution, the combination of Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence (AI) offers a viable path forward for modernizing emission monitoring. This research investigates the revolutionary potential of UAV-based AI-powered data analysis for emissions monitoring, with a particular emphasis on the comparison of XGBoost and CatBoost algorithms. Real-time monitoring, identification, and analysis of pollutants can now be done with remarkable accuracy and efficiency thanks to UAVs outfitted with modern sensors and AI algorithms. This novel methodology has significant advantages over typical monitoring approaches, including increased spatial coverage, lower costs, and higher data quality. Furthermore, AI integration provides predictive analytics and anomaly identification, allowing for proactive environmental risk mitigation interventions. Despite its enormous promise, challenges such as regulatory frameworks, privacy concerns, and technological restrictions must be solved before widespread usage. Overall, the combination of UAVs and AI, as well as the comparison of XGBoost and CatBoost, hold enormous promise for revolutionizing emission monitoring by providing a scalable and versatile solution to protect environmental quality and human health. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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An Approach for XAI Visualizations for Explainability of Alzheimer's Detection 2

An Approach for XAI Visualizations for Explainability of Alz...

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Khanapur, Shraddha Bharadwaj, Chirag B. Bhardwaj, Raghav Nayak, Jyothi S. BMS College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350367461

In the field of medical image analysis, the integration of machine learning (ML) models has significantly enhanced the process of identifying and diagnosing Alzheimer's disease. However, the opacity of these models, often termed as "black boxes,"raises concerns regarding their interpretability and reliability in clinical settings. Explainable artificial intelligence (XAI) techniques have emerged as crucial tools to address these challenges by clarifying the reasoning behind model outputs. This paper offers an extensive review of literature concerning the employment of Explainable Artificial Intelligence (XAI) methodologies, such as SHAP, LIME, GradCAM, DeepLIFT, Saliency Maps, and LRP to augment the comprehensibility of ML models applied in the detection of Alzheimer's disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Design and Development of Expense App

Design and Development of Expense App

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Girdhar, Gunit Kumar, Sachin Bhardwaj, Anuj Sharma, Megha Chandigarh University Dept. of Computer Science & Engineering Mohali India

ISBN: (纸本)9798350388800

With the help of Expense Tracker, you can easily and intuitively manage your revenue and costs while keeping track of your everyday, weekly, monthly, and annual spending. Users may choose the kind of expenditure, add location, upload more data, choose the amount of expense, and more. Additionally, the relational database will save this data. Users have the option to see and sort spending weekly, monthly, or annually. We may track the spending and minimize the amount of human computations for their costs by employing this. This allows a user to enter their income to get an estimate of their daily costs, and the results are saved for each individual user. Individuals who frequently go on excursions or to the theatre with friends might use this tracker to keep tabs on their spending. They will find it simple to split the bill using this tracker. This will show graphs according to the chosen perspective. Additionally, a user may write in this TR his monthly income or monthly expenditure cap. An integrated feature set is offered by this tracker system to assist you in managing your spending and cash flow. © 2024 IEEE.

关键词： Cost benefit analysis

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Integrating Deep Learning Techniques for Enhanced Cyclone Prediction and its Impact on Air Quality Index Utilizing Satellite Imagery

Integrating Deep Learning Techniques for Enhanced Cyclone Pr...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Vijayakumar, Sushruth Jamadhiar, Sonakshi Raj, Srujan M Mr, Vishnudeep Saritha, K. PES University Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350374711

Tropical cyclones (TCs greatly impact ecosystems and weather patterns, often leading to catastrophic outcomes this research explores dynamic relationship between the Air Quality Index (AQI) and Tropical Cyclones (TCs), examining changes in air quality during different stages of TCs. Focusing on Cyclone Biparjoy's impact on India's coastline in June 2023, we uncover its influence on air quality. The study shows good predictive performance by utilizing meteorological data, satellite observations, and machine learning algorithms such as CNN and ConvLSTM. Results show promising outcomes, with CNN achieving an RMSE of about 12.03 and an MAE of approximately 8.523 for TC intensity prediction, and ConvLSTM producing an RMSE of approximately 0.22 and a loss of around 0.04 for TC track prediction. Additionally, regression techniques using meteorological data from Tuetempo for AQI prediction were employed. The ExtraTrees Regression model exhibited strong predictive performance with an RMSE of approximately 19.627, while the Xgboost model had a slightly higher RMSE of around 21.72 our dataset included meteorological data from Tuetempo website for AQI forecasts and datasets like Hursat and INSAT 3D satellite photos for TC-related data, this work offers important insights into the dynamics of air quality during TC events. © 2024 IEEE.

关键词： Tropical cyclone

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TexAVi: Generating Stereoscopic VR Video Clips from Text Descriptions

TexAVi: Generating Stereoscopic VR Video Clips from Text Des...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Jayaraman, Shruti Bhavya, R. Srihari, Vriksha Mary Anita Rajam, V. College of Engineering Dept. of Computer Science and Engineering Guindy Chennai India

ISBN: (纸本)9798350376876

While generative models such as text-to-image, large language models and text-to-video have seen significant progress, the extension to text-to-virtual-reality remains largely unexplored, due to a deficit in training data and the complexity of achieving realistic dept. and motion in virtual environments. This paper proposes an approach to coalesce existing generative systems to form a stereoscopic virtual reality video from text. Carried out in three main stages, we start with a base text-to-image model that captures context from an input text. We then employ Stable Diffusion on the rudimentary image produced, to generate frames with enhanced realism and overall quality. These frames are processed with dept. estimation algorithms to create left-eye and right-eye views, which are stitched side-by-side to create an immersive viewing experience. Such systems would be highly beneficial in virtual reality production, since filming and scene building often require extensive hours of work and post-production effort. We utilize image evaluation techniques, specifically Fréchet Inception Distance and CLIP Score, to assess the visual quality of frames produced for the video. These quantitative measures establish the proficiency of the proposed method. Our work highlights the exciting possibilities of using natural language-driven graphics in fields like virtual reality simulations. © 2024 IEEE.

关键词： Virtual environments

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Cyberbullying Detection and Severity Classification Using Bi-LSTM 9

Cyberbullying Detection and Severity Classification Using Bi...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Devi, K. Nirmala Rajasekar, Vani Jayanthi, P. Balasubramani, Kavin Kandasamy, Kaviya Gowrisankar, Keerthana Kongu Engineering College Dept. of Computer Science Erode India

ISBN: (纸本)9798350377972

Cyberbullying has grown to be a significant issue in the internet age, affecting individuals of all ages, particularly teens. It involves using internet channels to harass, threaten, or disparage people, often with lingering emotional and psychological repercussions. In social media platforms cyber bullying is becoming more and more common, which has raised the need for efficient tools to identify and remove harmful content. Based on the frequency of objectionable words, a system was created for this research to categorize tweets into four groups: neither, low, medium, or high. A Kaggle dataset was first cleaned and prepared by pre-processing with Keras and Natural Language Toolkit (NLTK). The offending severity level of the tweets was determined using a fuzzy logic with a triangular membership function. Several models were trained using that categorized datasets like Bidirectional Long Short-Term Memory (Bi-LSTM), Bidirectional Encoder Representations from Transformers (BERT-Base) and DistilBERT. Among those models Bi-LSTM showed a high degree of accuracy with a precision score of 95.52%, recall of 95.48%, accuracy score of95.48%, and F1 score of 95.49%. © 2024 IEEE.

关键词： Deep learning

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