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Media Power Measuring via Emotional Contagion

Journal of Social Computing 2024年第1期5卷 15-35页

作者： Xue Lin Hong Huang Zongya Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China School of Journalism Huazhong University of Science and TechnologyWuhan 430074China

Media power,the impact that media have on public opinion and perspectives,plays a significant role in maintaining internal stability,exerting external influence,and shaping international dynamics for nations/***,prior research has primarily concentrated on news content and reporting time,resulting in limitations in evaluating media *** more accurately assess media power,we use news content,news reporting time,and news emotion simultaneously to explore the emotional contagion between *** use emotional contagion to measure the mutual influence between media and regard the media with greater impact as having stronger media *** propose a framework called Measuring Media Power via Emotional Contagion(MMPEC)to capture emotional contagion among media,enabling a more accurate assessment of media power at the media and national/regional *** also interprets experimental results through correlation and causality analyses,ensuring *** analyses confirm the higher accuracy of MMPEC compared to other baseline models,as demonstrated in the context of COVID-19-related news,yielding compelling and interesting insights.

关键词： explainability media power emotional contagion

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Multi-classifier approach to detect Alzheimer's Disease based on Handwriting Analysis 3

Multi-classifier approach to detect Alzheimer's Disease base...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Dadhania, Jatan K. Chaudhuri, Abhilasha Sardar Vallabhbhai National Institute of Technology Department of Computer Science and Engineering Surat India

ISBN: (纸本)9798350384598

Alzheimer's disease (AD) is a degenerative brain condition that predominantly impacts older individuals, resulting in a gradual decline in cognitive function and memory. Effective management and treatment of AD rely heavily on early detection. In this study, we propose a machine learning (ML) model for predicting AD using a dataset of handwriting tasks performed by test subjects with varying degrees of cognitive impairment. Handwriting analysis has shown potential as a low-cost, non-invasive method for early AD identification. These data were used to train various ML classifiers to classify individuals as either AD or non-AD. Baseline evaluations were conducted using multiple classifiers on the entire feature set and on individual tasks. The insights gained from these evaluations were then utilized to create a more optimized model combining the strength of multiple classifiers with the help of the TOPSIS method. The optimized model achieved a promising accuracy of 97% on the test set, demonstrating its potential for early AD detection. Our findings suggest that handwriting analysis, coupled with ML techniques, holds significant promise as a tool for early AD prediction and warrants further investigation. © 2024 IEEE.

关键词： Adversarial machine learning

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A Novel Framework for Securing Information in Cyber Security System using Authentication, Intrusion Detection and Deep Learning-based Risk Prediction Tasks 5

A Novel Framework for Securing Information in Cyber Security...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Naik, R. Lakshman Jain, Sourabh Department of Computer Science and Engineering Indian Institute of Information Technology Haryana Sonepat India

ISBN: (纸本)9798350361155

The cyber security has become a hot research topic nowadays. The term cyber security is frequently employed replaceable with the term data security. In recent days, each system faces the cyber security threat virtually and the model framework should have the capacity to combine the security features and work as the system's integral elements. Various works have been conducted to discover the answer to the evaluation challenge of cyber security and numerous architectures have been designed. However, the techniques face distinct issues though it is functioning well initially at the development time. Hence, an efficient and advanced cyber security framework is implemented in this paper to prevent cyber attacks using deep learning. Initially, to predict the risks in cyber security devices, the necessary data is fetched from the available resources. The gathered data is forwarded to the authentication operation and then the authenticated data is given to the intrusion detection stage. After that, the intrusion-detected data is subjected to the risk prediction stage, where the Weighted Recurrent Neural Network (WRNN) is supported to forecast the risk level of the network. Here, the Dung Beetle Optimizer (DBO) is utilized to tune the weights optimally in WRNN. This authentication, intrusion detection, and risk prediction operations have enriched the security of the cyber networks. Lastly, the efficacy and the security of the implemented model are analyzed with its related models by utilizing several performance metrics. © 2024 IEEE.

关键词： Forecasting

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A Hybrid Approach to Validate and Retrieve Fingerprint Image for Authenticating Database Sensitive Information Access and to Verify Some of Topological Multi-Granular Rough Sets Properties on Authenticated Database Fields 2

A Hybrid Approach to Validate and Retrieve Fingerprint Image...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Raghavan, R. John Singh, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Content based image retrieval has already begun to turn into an intriguing and exceptionally needed field to meet with the client prerequisite. Fingerprint approvals likewise have become a fascinating field to principally uphold the confirmation framework. Notwithstanding foundations regulating guaranteed about information straightforwardly, affiliations are also obliging them to examine the fingerprint endorsement. Despite establishments overseeing ensured about data, directly associations are similarly obliging them to research the fingerprint check. Organizations and foundations both are typically utilizing the idea of fingerprint for the participation and presence reason. Nevertheless, establishments presently managing secured information as like institutes and additionally going along with them to investigate the fingerprint authentication. More than this usual action there are some particular applications where the authentication would be considerable to help the application than using a traditional pattern of password. We proposed a fingerprint authentication system for the scientific based industry database access system. We removed the noise if any found in the input fingerprint using anisotropic Gauss filtering. The noise free fingerprint image is not further passed to the binarization phase as the need of binarization and some form of the drawback of binarization is replaced with a thinning algorithm. The thinned fingerprint image is followed by the matching process and once it is matched, then only the sensitive and highly confidential information are allowed to access from the database. The testing is done using OpenCV and FVC 2004 dataset to assess the projected retrieval method. Further the matched fingerprints are used to prove some of the internal structure properties of multi-granular rough sets (MGRS) on authenticated database fields. The accuracy obtained in this hybrid approach is found to be more satisfactory and hence the security towards acce

关键词： Database systems

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Sentiment-aware drug recommendations with a focus on symptom-condition mapping

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International Journal of Information technology (Singapore) 2024年第8期16卷 5195-5212页

作者： Anbazhagan, E. Sophiya, E. Prasanna Kumar, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology

The adoption of digital health records and the rise of online medical forums resulted in massive volumes of unstructured healthcare data. Most of the data used by traditional drug recommendation systems is obtained from patient Electronic Health Records (EHR) and subjective feedback and experiences included in patient evaluations. Nevertheless, the current systems based on sentiment analysis fail consider Symptom based diagnosis whereas researches that proposes Graph models doesn’t not include patient satisfaction and Health History as some has specific needs. To address the draw backs of existing drug recommendation systems, this study suggests a novel approach that combines symptom-disease mapping with sentiment analysis of patient reviews. The primary objective of the research is to utilize machine learning classifiers to make symptom-based predictions about probable medical conditions as Phase I. Then, before being fed into sequence network and machine learning models, patient reviews that are relevant to the predicted condition are filtered as Phase II. This method generates probabilities for suggesting certain drugs by evaluating sentiments and incorporating review ratings. With a Performance score of Ensemble Model up to 99.25% in Phase I and accuracy of 99.45% for sentiment analyser in Phase II. The performance of the model was evaluated based on accuracy, Receiver Operating Characteristic Curve (ROC)-Area Under Curve (AUC) score, sensitivity, selectivity. The proposed system helps in recommending the optimal drug for any type of symptom samples which is available in database. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Disease Drug recommendation system Electronic Health Records Personalized healthcare Sentiment analysis Symptom

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Facial Attendance Tracker using Enhanced Mean Local Binary Pattern 2

Facial Attendance Tracker using Enhanced Mean Local Binary P...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Charanya, R. Jayasurya, Arnold David Krithika, L.B. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350384277

Facial Attendance Tracker is a mobile application where a student can fetch his/her attendance by scanning his/her face from his/her own mobile. The faculty will project the Dynamic QR code when he/she wants to take the attendance. The students have to login into the mobile application using the login credentials and scan the dynamic QR code provided by the faculty. Once, the QR code is scanned successfully, it will redirect to the camera where the student can fetch his/her face. The system comprises of acquiring the image, followed by sending the image to the database, detection of face, pre-processing, extraction of features, classifying the features and post- processing stage. To detect the face from the image, Viola-Jones algorithm has been used and Enhanced Mean Local Binary Pattern (EMLBP) algorithm is used to extract features as it outperforms the other algorithms in a huge margin. © 2024 IEEE.

关键词： Local binary pattern

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Segmentation of Brain Tumor using Modified Linknet Architecture from MRI Images

Segmentation of Brain Tumor using Modified Linknet Architect...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Suraj Venkatesh Babu, G.S. Skanda Kumar, C.S. Kowshik, R.G. Ashok, K.N. S J C Institute of Technology Department of Computer Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

Segmentation of Cancerous cell is the most significant tasks in medical image processing. It is believed that early diagnosis of cancerous cell is essential for enhancing treatment options and raising patient survival rates. The manual segmentation is dependent on radiotherapist involvement and expertise. MRI scans are often speedy and an excellent diagnostics step for medical professionals than CT scans. As a result, in an emergency, doctors advise getting an MRI scan. However, there is a chance for inaccuracy because there is a lot of MRI data. This has made automatic cancerous cell segmentation a feasible process. Currently, ML methods are in use for segmentation. This research proposes segmentation of cancerous cell using modified LinkNet architecture from MRI images. The proposed modified LinkNet architecture incorporates advanced features such as attention mechanisms and residual connections to improve its ability to capture intricate details and subtle variations within brain images. The model is trained with a diverse and extensive dataset comprising various types and sizes of brain tumors, providing robust performance across different clinical scenarios. © 2024 IEEE.

关键词： Diseases

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Multi-scale Cross Aggregation Network for Camouflage Object Detection Based on Boundary Guidance 9

Multi-scale Cross Aggregation Network for Camouflage Object ...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Chen, Yuhong Dai, Meng Shanghai Institute of Technology School of Computer Science and Information Engineering Shang Hai China

ISBN: (纸本)9798350363043

Camouflaged object detection (COD) is challenging due to objects blending seamlessly with their surroundings. This task is crucial in medical imaging, surface defect detection, and surveillance. To address these challenges, we propose MABNet, a framework that aggregates multi-scale cross features and uses edge-guided localization. MABNet integrates multi-level contextual features and mines information from boundary pixels. Key components include the Multi-scale Cross Adaptive Aggregation Block (MCAB), the Edge-guidance Feature Block (EGB) for boundary exploration, and the Multi-scale Residual Block (MRB) for focusing on object structures and details. Extensive experiments on five benchmark datasets show that MABNet outperforms seven state-of-the-art methods, validating its effectiveness in accurately detecting camouflaged objects in various challenging scenarios. © 2024 IEEE.

关键词： Camouflage

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Fine-Tuning PID Constants Using Optimizers for an Autonomous Underwater Vehicle (AUV) 6

Fine-Tuning PID Constants Using Optimizers for an Autonomous...

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6th International Conference on Control and Robotics, ICCR 2024

作者： Gunturu, Shasank Shrote, Sumukh Sriraman, Harini Murugadass, Arthi School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India

ISBN: (纸本)9798331518158

This paper introduces a novel method to fine-tune the Proportional Integral Derivative (PID) controller for the MIRA Autonomous Underwater Vehicle (AUV), which secured the runners-up position at the TAC Challenge 2024. Traditional PID tuning, often relying on manual adjustments, falls short for the complex dynamics of AUVs. Our method combines gradient descent with advanced optimizers, Adam and Adagrad, to fine-tune PID parameters (Kp, Ki, Kd) across six degrees of freedom. A special cost function using the square root of squared errors weighted by time intervals enhances control accuracy and response efficiency. Through testing, the Adam optimizer demon-strated superior real-time performance compared to Adagrad, with its adaptive learning rate enabling quicker convergence and more reliable results in the dynamic underwater environment. Adam's handling of the AUV's changing needs contributed to stable and precise real-time docking during the competition, highlighting its effectiveness in improving control accuracy and robustness in challenging underwater conditions. © 2024 IEEE.

关键词： Cost functions

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A Robotic Arm Vehicle using Voice Recognition for Physically Challenged People 2

A Robotic Arm Vehicle using Voice Recognition for Physically...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Nannda, Jia Dora, Lokareddy Venkanna Gupta, Tanvi Chouhan, Aryan Verma, Anmol Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies India Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies Faridabad India Department of electrical and electronics engineering Manav Rachna International Institute of Research and Studies India

ISBN: (纸本)9798331541217

In Today's world, all the work is automated by robots. The Robots have become a significant part of today's world, improving many people's lives, especially those who are physically challenged. This research paper presents a unique method for helping people with physical disabilities: A robotic arm vehicle using voice recognition for physically challenged people. Two remarkable features of the robot are its design and its adaptability in handling objects accurately. The comparative research demonstrates improved performance, such as faster job completion and increased grasping efficiency, when compared to older methods. Additionally, by integrating AI, the robotic arm gains the flexibility needed to operate in real-world environments. This finding might lead to more autonomous and effective assistive technology for personal support and healthcare applications. © 2024 IEEE.

关键词： Robotic arms

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