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7th International conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Devi, v.Anusuya Pradeepaj, T.Sam Achyuth, N.Sai Nagarajan, R. Raj, R.Rahul Krishna, B.Navneetha School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankovil India

ISBN: (纸本)9798350397253

Stress in the workplace has a major impact on people's lives. IT industries are pushing the boundaries by introducing cutting-edge technologies and products but the high levels of stress among IT professionals remains as a concerning issue. Research has shown that stress can have an adverse effect on cognitive performance and physical health. This research study presents a novel neural network based wearable physiological IoT system to detect the stress and emotion levels of individual IT professionals in working environments. The proposed system is composed of a wearable device, mobile application, and neural network. The wearable device is equipped with three physiological sensors, namely heart rate, skin temperature, and electromyography (EMG) sensor. The mobile application collects the sensed data from the wearable device and then sends it to the neural network for analysis. The neural network is designed to detect the individual's stress and emotion levels based on the sensed physiological data. In addition, the mobile application provides the user with visual feedback and can be used to set goals and track progress. The proposed system is evaluated through a user study in an IT professional work environment. The results show that the system has the capability to detect the individual's stress and emotion levels with an accuracy rate of 91.6%. In order to minimize the potential risks of stress and its related effects, it is important to recognize these emotions and take appropriate steps to alleviate them. This work presents a system that can detect stress using facial analysis, a pulse oximeter, and temperature sensors. A camera is used to capture the person's front view. The proposed system utilizes a Convolutional Neural Network (CNN) Algorithm to analyze an individual's facial expression in an image frame and determine their stress level. A prototype was developed to detect if someone is under stress based on their heart rate and blood pressure variations.

关键词： Internet of things

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An Automatic Method to Prevent and Classify Cyber Bullying Incidents Using Machine Learning Approach 8

An Automatic Method to Prevent and Classify Cyber Bullying I...

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8th IEEE International conference on Science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Libina, M. Sasipriya, G. Rajasekar, v. Ifet College of Engineering Dept of Information Technology India

ISBN: (纸本)9798350347791

Deep learning models for CyberBullying detection on social media, as a relatively new field of research and application, need the identification, investigation, and analysis of a broad range of human-based expressions. Natural language processing encounters a categorization challenge when trying to identify whether a remark, post, message, or picture represents bullying. A thorough investigation of the meaning of words is also required. Previous attempts to identify CyberBullying focused mostly on manual feature extraction approaches. These tactics not only take a lot of time and effort, but also often misread the intended meaning of a communication. This removes the need for any further feature extraction approaches. Deep learning was used to identify CyberBullying in social media data that included both textual and visual aspects. Because of the large volume and diversity of user-generated information on current social media platforms, detecting CyberBullying in real time has become more challenging. The rapid transmission of information makes real-time regulation of online speech problematic. CyberBullying is a widespread issue that may take many forms. To stop CyberBullying, researchers used deep learning models to analyse social media content, modality, and language. This research found that embeddings with deep learning architectures accelerate representation learning and feature selection compared to typical machine learning methods. © 2023 IEEE.

关键词： Feature extraction

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Enhancing the Perception of Color Casting and Contrasting in Underwater Image using Fusion Methods

Enhancing the Perception of Color Casting and Contrasting in...

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IEEE International conference on System, Computation, Automation and Networking (ICSCAN)

作者： v. Rajesh S. Radhika Dept of ECE Sathyabama Institute of Science & Technology Manakula Vinayagar Institute of Technology Puducherry School of Electrical & Electronics Sathyabama Institute of Science &Technology Tamilnadu

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

A thorough method for improving color casting and contrast perception in underwater images is described, coupled with a compression and reconstruction technique to maintain the enhanced visual quality of the images while reducing storage and transmission costs. The suggested approach combines color correction and image enhancement techniques, followed by a robust compression algorithm leverages the underlying statistical properties of the enhanced image. The reconstruction process subsequently recovers the high-quality image using a decompression technique. Both subjective and objective image quality measures are used to assess the effectiveness of the suggested strategy, showing notable gains in visual perception and compression efficiency. The outcomes show that the suggested strategy has the potential to enable more efficient underwater data analysis and interpretation.

关键词： visualization Casting Image coding Image color analysis Image storage Time complexity Image reconstruction Image enhancement visual perception videos

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Fraud Detection in Financial Transaction Using GNN

Fraud Detection in Financial Transaction Using GNN

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visual Analytics and data visualization (ICvADv), International conference on

作者： v. Sivakumar Akash Kumar verma Piyush Kumar Nitesh Saw v. Hari Krishnan Information Technology Kalasalingam Academy of Research and Education Virudhunagar India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

As financial transactions increasingly shift to digital platforms, fraud has become a growing concern, impacting individuals and institutions alike. Conventional fraud detection systems, relying on rule-based or statistical approaches, often fall short of adapting to the sophisticated tactics fraudsters employ. This paper introduces a Graph Neural Network (GNN) model for detecting fraudulent transactions by capturing the intricate relationships between users, accounts, and transactional patterns. Unlike traditional machine learning algorithms, GNNs are inherently suited to handle graph-structured data, where entities are connected through various types of interactions that can reveal suspicious behavior. Transactions in fraud approach are presented as a graph where the nodes represent users or accounts, and edges represent the transactions themselves, encoded in the features of transaction amount, frequency, location, and time of day. This graph-based structure enables the GNN to take advantage of both local and global dependencies, thereby detecting otherwise hard-to-detect abnormal transaction patterns. For more accurate detection, we include additional features that capture certain behaviors that are specific indicators of fraud, such as repeated transactions within short intervals or location-based anomalies. proposed model is evaluated on a structured financial transaction dataset, and the results indicate significant improvements in accuracy, recall, and precision over traditional models, underscoring the efficacy of GNNs in fraud detection.

关键词： Time-frequency analysis Accuracy visual analytics QR codes Graph neural networks User experience Real-time systems Fraud Security Reliability

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A Semantic Approach for Big data exploration in Industry 4.0

arXiv

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arXiv 2024年

作者： Berges, Idoia Ramírez-Durán, víctor Julio Illarramendi, Arantza University of the Basque Country UPV/EHU Donostia - San Sebastián20018 Spain

The growing trends in automation, Internet of Things, big data and cloud computing technologies have led to the fourth industrial revolution (Industry 4.0), where it is possible to visualize and identify patterns and insights, which results in a better understanding of the data and can improve the manufacturing process. However, many times, the task of data exploration results difficult for manufacturing experts because they might be interested in analyzing also data that does not appear in pre-designed visualizations and therefore they must be assisted by Information Technology experts. In this paper, we present a proposal materialized in a semantic-based visual query system developed for a real Industry 4.0 scenario that allows domain experts to explore and visualize data in a friendly way. The main novelty of the system is the combined use that it makes of captured data that are semantically annotated first, and a 2D customized digital representation of a machine that is also linked with semantic descriptions. Those descriptions are expressed using terms of an ontology, where, among others, the sensors that are used to capture indicators about the performance of a machine that belongs to a Industry 4.0 scenario have been modeled. Moreover, this semantic description allows to: formulate queries at a higher level of abstraction, provide customized graphical visualizations of the results based on the format and nature of the data, and download enriched data enabling further types of analysis. © 2024, CC BY-NC-ND.

关键词： Ontology

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ANGLE-vARIANT 4D SEISMIC TIME-SHIFT analysis FOR vELOCITY CHANGE AND SUBSURFACE STRAIN ESTIMATION 83

ANGLE-VARIANT 4D SEISMIC TIME-SHIFT ANALYSIS FOR VELOCITY CH...

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83rd EAGE conference and Exhibition 2022

作者： Taweesintananon, K. Landrø, M. Narongsirikul, S. Folstad, P.G. Norwegian University of Science and Technology Norway PTT Exploration and Production Public Company Limited ConocoPhillips Norge

ISBN: (纸本)9781713859314

In a time-lapse (also called 4D) seismic analysis, the time-shift of a certain event is caused by the changes in the seismic velocity and the depth of the event. An interpretation of 4D time-shifts is normally simplified by neglecting displacement changes (strains) or assuming a linear relation of velocity and thickness strains. Here we go beyond these assumptions and propose a least-squares inversion method to simultaneously estimate the velocity and thickness strains from 4D angle stacks. We show, through examples from synthetic and field data, that the 4D velocity and thickness strains can be estimated simultaneously without an assumption of their linear relationship. Our method can be applied to any other 4D seismic data set in which angle-stack images are available. We see that our method has high potential in many other applications, because the velocity and thickness strains form a basis of other properties used in 4D seismic and geomechanics applications. © (2022) by the European Association of Geoscientists & Engineers (EAGE) All rights reserved

关键词： velocity

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Lunar terrain coverage analysis data delivery workflow

Lunar terrain coverage analysis data delivery workflow

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Accelerating Space Commerce, exploration, and New Discovery conference, ASCEND 2021

作者： Lee, Carlyn Lee, Charles H. Shaikh, Mohsin A. Michels, Dominik L. NASA Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States KAUST Core Labs Thuwal Saudi Arabia KAUST Visual Computing Center Thuwal Saudi Arabia

ISBN: (数字)9781624106125

ISBN: (纸本)9781624106125

In this work, we are developing a lunar terrain database to enable fast rendering of sun illumination and earth visibility for a proposed coverage analysis tool. This development will advance lunar mission design and formulation for current and future communications architectures, and will aid in lunar surface mission planning and communications/navigation operations. Our effort can be described in three steps: (1) we parallelize a brute force algorithm, which computes elevation masks from laser altimetry data acquired by the Lunar Reconnaissance Orbiter’s (LRO) Lunar Orbiter Laser Altimeter (LOLA);(2) we investigate parallel I/O methods to store terrain mask information from step (1) into a parallel file system;and (3) we finally deliver data to the terrain coverage analysis tool. © 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Orbits

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MT-EmotiEffNet for Multi-task Human Affective Behavior analysis and Learning from Synthetic data 17th

MT-EmotiEffNet for Multi-task Human Affective Behavior Anal...

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17th European conference on Computer vision, ECCv 2022

作者： Savchenko, Andrey v. HSE University Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia Sber AI Lab Moscow Russia

ISBN: (纸本)9783031250743

In this paper, we present the novel multi-task EfficientNet model and its usage in the 4th competition on Affective Behavior analysis in-the-wild (ABAW). This model is trained for simultaneous recognition of facial expressions and prediction of valence and arousal on static photos. The resulting MT-EmotiEffNet extracts visual features that are fed into simple feed-forward neural networks in the multi-task learning challenge. We obtain performance measure 1.3 on the validation set, which is significantly greater when compared to either performance of baseline (0.3) or existing models that are trained only on the s-Aff-Wild2 database. In the learning from synthetic data challenge, the quality of the original synthetic training set is increased by using the super-resolution techniques, such as Real-ESRGAN. Next, the MT-EmotiEffNet is fine-tuned on the new training set. The final prediction is a simple blending ensemble of pre-trained and fine-tuned MT-EmotiEffNets. Our average validation F1 score is 18% greater than the baseline model. As a result, our team took the first place in the learning from synthetic data task and the third place in the multi-task learning challenge. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blending

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Constructing Spaces and Times for Tactical analysis in Football

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2021年第4期27卷 2280-2297页

作者： Andrienko, Gennady Andrienko, Natalia Anzer, Gabriel Bauer, Pascal Budziak, Guido Fuchs, Georg Hecker, Dirk Weber, Hendrik Wrobel, Stefan Fraunhofer IAIS D-53757 St Augustin Germany City Univ London London EC1V 0HB England Sportec Solut GmbH D-50679 Cologne Germany DFB Akad D-60528 Frankfurt Germany TU Eindhoven NL-5612 AZ Eindhoven Netherlands DFL Deutsch Fussball Liga GmbH Frankfurt Germany Univ Bonn D-53113 Bonn Germany

A possible objective in analyzing trajectories of multiple simultaneously moving objects, such as football players during a game, is to extract and understand the general patterns of coordinated movement in different classes of situations as they develop. For achieving this objective, we propose an approach that includes a combination of query techniques for flexible selection of episodes of situation development, a method for dynamic aggregation of data from selected groups of episodes, and a data structure for representing the aggregates that enables their exploration and use in further analysis. The aggregation, which is meant to abstract general movement patterns, involves construction of new time-homomorphic reference systems owing to iterative application of aggregation operators to a sequence of data selections. As similar patterns may occur at different spatial locations, we also propose constructing new spatial reference systems for aligning and matching movements irrespective of their absolute locations. The approach was tested in application to tracking data from two Bundesliga games of the 2018/2019 season. It enabled detection of interesting and meaningful general patterns of team behaviors in three classes of situations defined by football experts. The experts found the approach and the underlying concepts worth implementing in tools for football analysts.

关键词： Sports Games Aggregates data mining data visualization Trajectory Companies visual analytics movement data coordinated movement sport analytics football soccer

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Air Monitoring with Cloud and IoT

Air Monitoring with Cloud and IoT

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2023 International conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Kalaivani, K. Subramanian, S. Swedha, G.K.S. vinoth, N. vishnu Priya, v. Sns College of Engineering Department of Computer Science and Design Coimbatore India Sns College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350333602

The air is an essential aspect of humans' daily lives as it helps in breathing. With the rise of air pollution, it is necessary to have an efficient air quality monitoring system that can detect the concentration of pollutants in the air. In this study, the design and implementation of air quality monitoring system is discussed which uses an MQ2 sensor and ESP32 microcontroller. The system detects the ppm levels in the air to classify whether it is fresh or poor air. The challenges include establishing reliable connectivity, visualizing data effectively, integrating multiple sensors, ensuring accurate measurements, and enabling real-time analysis. The ESP32 microcontroller addresses connectivity issues by providing built-in Wi-Fi and Bluetooth capabilities. The LCD display offers real-time visual feedback, enhancing data visualization. Integration of the DHT11 sensor for temperature and humidity measurement and the MQ2 sensor for gas detection enables comprehensive air quality monitoring. The ThingSpeak application acts as a cloud-based IoT platform, allowing data logging, remote monitoring, and real-time alerts and notifications. If the ppm levels are less than 1000, it is considered poor air, and a buzzer is triggered, along with an LCD I2C display that shows the levels using the I2C protocol. The system also uses a DHT11 sensor to detect temperature and humidity and passes all data to the cloud channel by using IoT, triggering a mail alert using Google sheet with an on and off button for ppm levels using Javascript extension and GPS. The system is designed to provide a real-time solution for monitoring air quality, making it an essential tool for public health and environmental protection. © 2023 IEEE.

关键词： Microcontrollers

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